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

Abstract

The invention discloses an exercise prescription generation method and device, electronic equipment and a storage medium, and the method and device are applied to the electronic equipment and are used for generating targeted exercise prescriptions for patients suffering from different chronic diseases. The method specifically comprises the steps that disease description information input by a user is preprocessed, deconstruction information of the disease description information is obtained, and the deconstruction information comprises part or all of segmented words, part-of-speech tagging and syntactic analysis results; processing the deconstruction information based on a retrieval enhanced generation model and a plug-in knowledge base to obtain an exercise prescription; and feeding back the exercise prescription to the user to enable the user to implement health management based on an exercise scheme specified by the exercise prescription. The exercise prescription provided by the invention is based on the disease description information input by the user and the professional knowledge base in the field of chronic diseases, so that the exercise prescription has pertinence and scientificity, and can help the patient to recover to the greatest extent.

Description

Technical Field

[0001] The present application relates to the field of sports health technology, and more specifically, to a method, device, electronic device and storage medium for generating an exercise prescription. Background Art

[0002] Chronic diseases, also known as chronic non-communicable diseases (NCDs), are a general term for a class of diseases that have a long development process, cannot be prevented by vaccines, and are not easily completely cured by drug treatment. Chronic diseases usually include the following types: cardiovascular disease, metabolic disease, chronic respiratory disease, neurodegenerative disease, cancer, chronic kidney disease, etc. These chronic diseases are long-term and irreversible. Many chronic diseases cannot be completely cured by themselves, but they can be controlled through treatment and management.

[0003] Management refers to long-term disease management for patients, including drug treatment, lifestyle adjustment, etc. The management of chronic diseases usually requires comprehensive measures, including healthy diet, regular exercise, smoking cessation and alcohol restriction, regular physical examinations and a good mental state. The focus is on healthy diet and regular exercise. Regular exercise can greatly help the recovery of patients with chronic diseases, but different chronic diseases require different exercise plans, that is, targeted exercise plans can help patients recover to the greatest extent. Otherwise, it will not only fail to help patients recover, but may also bring adverse effects. Summary of the invention

[0004] In view of this, the present application provides an exercise prescription generation method, device, electronic device and storage medium for providing targeted exercise prescriptions for patients with specific chronic diseases to avoid.

[0005] In order to achieve the above objectives, the proposed solution is as follows:

[0006] A method for generating an exercise prescription, applied to an electronic device, is used to generate targeted exercise prescriptions for patients with different chronic diseases. The method comprises the following steps:

[0007] Preprocessing the disease description information input by the user to obtain deconstructed information of the disease description information, wherein the deconstructed information includes part or all of the word segmentation, part-of-speech tagging and syntactic analysis results;

[0008] Processing the deconstructed information based on a retrieval-enhanced generation model and an external knowledge base to obtain an exercise prescription;

[0009] The exercise prescription is fed back to the user so that the user can implement health management based on the exercise plan specified in the exercise prescription.

[0010] Optionally, the preprocessing of the disease description information input by the user comprises the steps of:

[0011] Receiving disease description information input by a user, wherein the disease description information is voice information, text information, image information or video information, and is used to describe the disease type, symptom description and / or physical condition;

[0012] The disease description information is formatted to obtain disease description information in a unified format.

[0013] Optionally, the deconstructed information is processed based on the retrieval-enhanced generation model and the plug-in knowledge base to obtain an exercise prescription, comprising the steps of:

[0014] Searching the deconstruction information from the plug-in knowledge base based on the retrieval mechanism of the retrieval-enhanced generation model to obtain professional knowledge related to the deconstruction information;

[0015] The professional knowledge is processed based on the generation mechanism of the retrieval enhanced generation model to obtain the exercise prescription.

[0016] Optionally, the following steps are also included:

[0017] The retrieval-enhanced generative model is optimized using a fine-tuning dataset to optimize the performance of the retrieval-enhanced generative model on a specific task.

[0018] Optionally, the following steps are also included:

[0019] Based on the exercise prescription and the specific information of the user, reasoning is performed to obtain and feed back a personalized exercise prescription to the user.

[0020] A device for generating an exercise prescription, applied to an electronic device, for generating targeted exercise prescriptions for patients with different chronic diseases, the generating device comprising:

[0021] A data preprocessing module is configured to preprocess the disease description information input by the user to obtain deconstructed information of the disease description information, wherein the deconstructed information includes part or all of the word segmentation, part-of-speech tagging and syntactic analysis results;

[0022] An information generation module is configured to process the deconstructed information based on a retrieval-enhanced generation model and an external knowledge base to obtain an exercise prescription;

[0023] The information output module is configured to feed back the exercise prescription to the user so that the user can implement health management based on the exercise plan specified in the exercise prescription.

[0024] Optionally, also include:

[0025] The model fine-tuning module is configured to optimize the retrieval-enhanced generative model using a fine-tuning dataset to optimize the performance of the retrieval-enhanced generative model on a specific task.

[0026] Optionally, also include:

[0027] The personalized reasoning module is configured to perform reasoning based on the exercise prescription and the user's specific information to obtain and feed back a personalized exercise prescription to the user.

[0028] An electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0029] The memory is used to store computer programs or instructions;

[0030] The processor is used to execute the computer program or instructions so that the electronic device can implement the method for generating an exercise prescription as described above.

[0031] 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 method for generating an exercise prescription as described above.

[0032] It can be seen from the above technical solutions that the present application discloses a method, device, electronic device and storage medium for generating an exercise prescription. The method and device are applied to electronic devices to generate targeted exercise prescriptions for patients with different chronic diseases. Specifically, the disease description information input by the user is preprocessed to obtain deconstruction information of the disease description information, and the deconstruction information includes part or all of the word segmentation, part-of-speech tagging and syntactic analysis results; the deconstruction information is processed based on the retrieval enhanced generation model and the plug-in knowledge base to obtain the exercise prescription; the exercise prescription is fed back to the user so that the user can implement health management based on the exercise plan specified in the exercise prescription. In view of the fact that the exercise prescription provided by the present application is based on the disease description information input by the user and the professional knowledge base in the field of chronic diseases, it is targeted and can help patients recover to the greatest extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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.

[0034] Figure 1A flowchart of a method for generating an exercise prescription according to an embodiment of the present application;

[0035] Figure 2 A schematic diagram of an interactive interface of an embodiment of the present application;

[0036] Figure 3 This is an example of a chronic disease-related professional knowledge base in an embodiment of the present application;

[0037] Figure 4 A flowchart of another method for generating an exercise prescription according to an embodiment of the present application;

[0038] Figure 5 A flowchart of another method for generating an exercise prescription according to an embodiment of the present application;

[0039] Figure 6 A block diagram of a device for generating an exercise prescription according to an embodiment of the present application;

[0040] Figure 7 A block diagram of another device for generating exercise prescription according to an embodiment of the present application;

[0041] Figure 8 A block diagram of another device for generating an exercise prescription according to an embodiment of the present application;

[0042] Fig. 9 A block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] 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.

[0044] Figure 1 This is a flowchart of a method for generating an exercise prescription according to an embodiment of the present application.

[0045] like Figure 1 As shown, the method for generating an exercise prescription provided in this embodiment is applied to an electronic device, and is used to generate targeted exercise prescriptions for patients with different chronic diseases. The electronic device can be understood as a computer, server, and cloud platform with data computing and information processing capabilities. The generation method specifically includes the following steps:

[0046] S1. Preprocess the disease description information input by the user to obtain deconstructed information.

[0047] When the user inputs the disease description information through a specific channel, such as a computer interactive interface or other electronic port, the disease description information is preprocessed to obtain deconstructed information, which includes but is not limited to part or all of the word segmentation, part-of-speech tagging and syntactic analysis results. The specific contents are as follows:

[0048] First, based on a specific path or electronic port, disease description information for input is received, and the disease description information can be voice information, text information, image information or video information, which is used to describe the disease type, symptom description or physical condition of the disease. Figure 2 shown.

[0049] Then, the disease description information is processed based on natural language processing tools or corresponding models to obtain the above-mentioned word segmentation, part-of-speech tagging and syntactic analysis results that can be recognized and processed by a certain model.

[0050] S2. Processing structural information based on retrieval-enhanced generation model and external knowledge base.

[0051] That is, the above deconstructed information is processed based on the pre-built retrieval enhanced generation model (RAG model) and the plug-in knowledge base to generate an exercise prescription that meets the requirements. The plug-in knowledge base can be a chronic disease knowledge base, an exercise therapy knowledge base, a chronic disease exercise therapy knowledge base or an exercise therapy knowledge base in other fields, such as Figure 3 As shown. The retrieval-enhanced generation model has a retrieval mechanism and a generation mechanism. The specific processing process of the structural information is as follows:

[0052] First, the search-enhanced generation model is called, and its search mechanism is used to search from the plug-in knowledge base according to the deconstruction information, thereby obtaining professional knowledge related to the structural information. The plug-in knowledge base can be a technical feature of the present application, and the search of the plug-in knowledge base can also be realized through a corresponding plug-in interface. In this way, the present application does not have the technical feature of the plug-in knowledge base, but has a plug-in interface.

[0053] Then, based on calling the retrieval-enhanced generation model, the professional knowledge is processed using its generation mechanism to obtain the exercise prescription.

[0054] S3. Send the obtained exercise prescription to the user.

[0055] The sending or feedback path may be a path for the user to input the disease description information, such as a port or interactive interface for the user to input the disease description information. By feeding back the exercise prescription to the user, the user can implement health management based on the exercise plan specified in the exercise prescription.

[0056] It can be seen from the above technical solution that this embodiment provides a method for generating an exercise prescription, which is applied to electronic devices and is used to generate targeted exercise prescriptions for patients with different chronic diseases. Specifically, the disease description information input by the user is preprocessed to obtain deconstruction information of the disease description information, and the deconstruction information includes part or all of the word segmentation, part-of-speech tagging and syntactic analysis results; the deconstruction information is processed based on the retrieval enhanced generation model and the plug-in knowledge base to obtain an exercise prescription; the exercise prescription is fed back to the user so that the user can implement health management based on the exercise plan specified in the exercise prescription. In view of the fact that the exercise prescription provided in this application is based on the disease description information input by the user and the professional knowledge base in the field of chronic diseases, it is targeted and can help patients recover to the greatest extent.

[0057] The model in this application complies with medical ethics and data protection regulations when processing information input by users. In particular, when processing sensitive medical information, personal information will be automatically filtered out for desensitization.

[0058] In a specific implementation of the present application, the following steps are also included: Figure 4 As shown:

[0059] S4. Optimize the retrieval-enhanced generative model using the fine-tuning dataset.

[0060] The RAG model is trained in a targeted manner using the fine-tuning dataset to optimize the model's performance on a specific task (personalized guidance of exercise therapy for chronic diseases). Prompt word engineering is used to guide and restrict the model's actions. Restrictions include but are not limited to "strictly follow medical guidelines, fully consider individual differences among patients, and give personalized professional advice; resolutely do not involve advice on the use of specific drugs to prevent misleading patients."

[0061] In addition, in another specific implementation of the present application, the following steps may also be included: Figure 5 As shown:

[0062] S5. Perform reasoning based on the exercise prescription and the user's specific information.

[0063] In order to make the exercise prescription more personalized, based on the exercise prescription, the specific information input by the user, such as age, gender and physical condition, can be processed based on the personalized reasoning engine to obtain a personalized exercise prescription, so that the user's exercise prescription is more targeted and the effect of the health management implemented is better.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] Figure 6 This is a block diagram of a device for generating an exercise prescription according to an embodiment of the present application.

[0069] like Figure 6As shown, the exercise prescription generation device provided in this embodiment is applied to an electronic device, and is used to generate targeted exercise prescriptions for patients with different chronic diseases. The electronic device can be understood as a computer, server and cloud platform with data computing and information processing capabilities. The generation device specifically includes a data processing module 10, an information generation module 20 and an information output module 30.

[0070] The data processing module is used to pre-process the disease description information input by the user to obtain deconstructed information.

[0071] When the user inputs the disease description information through a specific channel, such as a computer interactive interface or other electronic port, the disease description information is preprocessed to obtain deconstructed information, which includes but is not limited to part or all of the word segmentation, part-of-speech tagging and syntactic analysis results. The specific contents are as follows:

[0072] First, based on a specific path or electronic port, disease description information for input is received, and the disease description information can be voice information, text information, image information or video information, which is used to describe the disease type, symptom description or physical condition of the disease. Figure 2 shown.

[0073] Then, the disease description information is processed based on natural language processing tools or corresponding models to obtain the above-mentioned word segmentation, part-of-speech tagging and syntactic analysis results that can be recognized and processed by a certain model.

[0074] The information generation module is used to process structural information based on the retrieval enhanced generation model and the plug-in knowledge base.

[0075] That is, the above deconstructed information is processed based on the pre-built retrieval enhanced generation model (RAG model) and the plug-in knowledge base to generate an exercise prescription that meets the requirements. The plug-in knowledge base can be a chronic disease knowledge base, an exercise therapy knowledge base, a chronic disease exercise therapy knowledge base or an exercise therapy knowledge base in other fields, such as Figure 3 As shown. The retrieval-enhanced generation model has a retrieval mechanism and a generation mechanism. The specific processing process of the structural information is as follows:

[0076] First, the search-enhanced generation model is called, and its search mechanism is used to search from the plug-in knowledge base according to the deconstruction information, thereby obtaining professional knowledge related to the structural information. The plug-in knowledge base can be a technical feature of the present application, and the search of the plug-in knowledge base can also be realized through a corresponding plug-in interface. In this way, the present application does not have the technical feature of the plug-in knowledge base, but has a plug-in interface.

[0077] Then, based on calling the retrieval-enhanced generation model, the professional knowledge is processed using its generation mechanism to obtain the exercise prescription.

[0078] The information output module is used to send the obtained exercise prescription to the user.

[0079] The sending or feedback path may be a path for the user to input the disease description information, such as a port or interactive interface for the user to input the disease description information. By feeding back the exercise prescription to the user, the user can implement health management based on the exercise plan specified in the exercise prescription.

[0080] It can be seen from the above technical solution that this embodiment provides a device for generating an exercise prescription, which is applied to an electronic device and is used to generate targeted exercise prescriptions for patients with different chronic diseases. Specifically, the disease description information input by the user is preprocessed to obtain deconstruction information of the disease description information, and the deconstruction information includes part or all of the word segmentation, part-of-speech tagging and syntactic analysis results; the deconstruction information is processed based on the retrieval enhanced generation model and the plug-in knowledge base to obtain the exercise prescription; the exercise prescription is fed back to the user so that the user can implement health management based on the exercise plan specified in the exercise prescription. In view of the fact that the exercise prescription provided in this application is based on the disease description information input by the user and the professional knowledge base in the field of chronic diseases, it is targeted and can help patients recover to the greatest extent.

[0081] In a specific embodiment of the present application, a model fine-tuning module 40 is also included, such as Figure 7 As shown:

[0082] The model fine-tuning module is used to optimize the retrieval-enhanced generation model using the fine-tuning dataset.

[0083] The RAG model is trained in a targeted manner using the fine-tuning dataset to optimize the model's performance on a specific task (personalized guidance of exercise therapy for chronic diseases). Prompt word engineering is used to guide and restrict the model's actions. Restrictions include but are not limited to "strictly follow medical guidelines, fully consider individual differences among patients, and give personalized professional advice; resolutely do not involve advice on the use of specific drugs to prevent misleading patients."

[0084] In addition, in another specific implementation of the present application, a personality reasoning module 50 may also be included, such as Figure 8 As shown:

[0085] The personality reasoning module is used to perform reasoning based on exercise prescription and user's specific information.

[0086] In order to make the exercise prescription more personalized, based on the exercise prescription, the specific information input by the user, such as age, gender and physical condition, can be processed based on the personalized reasoning engine to obtain a personalized exercise prescription, so that the user's exercise prescription is more targeted and the effect of the health management implemented is better.

[0087] 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".

[0088] 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.

[0089] Fig. 9 A block diagram of an electronic device according to an embodiment of the present application.

[0090] Reference below Fig. 9 , 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.

[0091] The electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 902 or a program loaded from an input device 906 into a random access memory RAM 903. 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 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0092] 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 907 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 908 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 909. The communication device 909 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.

[0093] The present application also provides an embodiment of a computer-readable storage medium.

[0094] The computer-readable storage medium carries one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device pre-processes the disease description information input by the user to obtain deconstruction information of the disease description information, and the deconstruction information includes part or all of the segmentation, part-of-speech tagging and syntactic analysis results; the deconstruction information is processed based on the retrieval-enhanced generation model and the plug-in knowledge base to obtain an exercise prescription; and the exercise prescription is fed back to the user so that the user can implement health management based on the exercise plan specified in the exercise prescription. In view of the fact that the exercise prescription provided in this application is based on the disease description information input by the user and the professional knowledge base in the field of chronic diseases, it is targeted and can help patients recover to the greatest extent.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other 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.

[0099] 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.

[0100] 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 generating an exercise prescription, applied to an electronic device, for generating targeted exercise prescriptions for patients with different chronic diseases, characterized in that: The generation method comprises the steps of: Preprocessing the disease description information input by the user to obtain deconstructed information of the disease description information, wherein the deconstructed information includes part or all of the word segmentation, part-of-speech tagging and syntactic analysis results; Processing the deconstructed information based on a retrieval-enhanced generation model and an external knowledge base to obtain an exercise prescription; The exercise prescription is fed back to the user so that the user can implement health management based on the exercise plan specified in the exercise prescription.

2. The generation method according to claim 1, characterized in that: The preprocessing of the disease description information input by the user comprises the steps of: Receiving disease description information input by a user, wherein the disease description information is voice information, text information, image information or video information, and is used to describe the disease type, symptom description and / or physical condition; The disease description information is formatted to obtain disease description information in a unified format.

3. The generation method according to claim 1, characterized in that: The method processes the deconstructed information based on the retrieval-enhanced generation model and the plug-in knowledge base to obtain an exercise prescription, including the steps of: Searching the deconstruction information from the plug-in knowledge base based on the retrieval mechanism of the retrieval-enhanced generation model to obtain professional knowledge related to the deconstruction information; The professional knowledge is processed based on the generation mechanism of the retrieval enhanced generation model to obtain the exercise prescription.

4. The generation method according to any one of claims 1 to 3, characterized in that: Also includes the steps: The retrieval-enhanced generative model is optimized using a fine-tuning dataset to optimize the performance of the retrieval-enhanced generative model on a specific task.

5. The generation method according to any one of claims 1 to 3, characterized in that: Also includes the steps: Based on the exercise prescription and the specific information of the user, reasoning is performed to obtain and feed back a personalized exercise prescription to the user.

6. A device for generating exercise prescriptions, applied to electronic devices, for generating targeted exercise prescriptions for patients with different chronic diseases, characterized in that: The generating device comprises: A data preprocessing module is configured to preprocess the disease description information input by the user to obtain deconstructed information of the disease description information, wherein the deconstructed information includes part or all of the word segmentation, part-of-speech tagging and syntactic analysis results; An information generation module is configured to process the deconstructed information based on a retrieval-enhanced generation model and an external knowledge base to obtain an exercise prescription; The information output module is configured to feed back the exercise prescription to the user so that the user can implement health management based on the exercise plan specified in the exercise prescription.

7. The generating device according to claim 6, characterized in that Also includes: The model fine-tuning module is configured to optimize the retrieval-enhanced generative model using a fine-tuning dataset to optimize the performance of the retrieval-enhanced generative model on a specific task.

8. The generating device according to claim 6, characterized in that Also includes: The personalized reasoning module is configured to perform reasoning based on the exercise prescription and the user's specific information to obtain and feed back a personalized 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 method for generating an exercise prescription as described in any one of claims 1 to 5.

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 method for generating an exercise prescription as described in any one of claims 1 to 5.

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