Emergency treatment method and device based on artificial intelligence, electronic equipment and storage medium

Abstract

The invention provides an emergency treatment method and device based on artificial intelligence, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: processing physical condition information of a user by adopting an artificial intelligence model so as to generate emergency processing suggestions and emergency related questions for the user; in response to the received answer to the emergency related question, adopting an artificial intelligence model to analyze the answer and the physical condition information to determine disease information of the user; if the disease information of the user cannot be determined, continuing to generate emergency related questions for performing supplementary inquiry on the user by adopting an artificial intelligence model until the disease information of the user is determined; and generating a comprehensive disposal suggestion for the user. According to the method and the device, supplementary inquiry can be continuously performed on the user under the condition that the disease information cannot be determined, so that accurate disease information can be finally obtained, and comprehensive treatment suggestions are generated for emergency of the user.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to an emergency treatment method, device, electronic device and storage medium based on artificial intelligence. Background Art

[0002] According to the World Health Organization (WHO), millions of avoidable deaths occur every year, particularly in areas such as infectious diseases, heart disease and obstetric emergencies, many of which are caused by delayed treatment or lack of access to timely and appropriate medical interventions. These problems are particularly serious in low- and middle-income countries.

[0003] However, the human resources for high-quality medical care are always limited and often require high costs, which is also the main reason for delayed treatment. Summary of the invention

[0004] In view of this, the purpose of the present disclosure is to propose an emergency treatment method, device, electronic device and storage medium based on artificial intelligence, which can solve the existing problems in a targeted manner.

[0005] Based on the above-mentioned purposes, in the first aspect, the present disclosure proposes an emergency treatment method based on artificial intelligence, including: using an artificial intelligence model to process the user's physical condition information to generate emergency treatment suggestions and emergency-related questions for the user, the physical condition information including physical attribute information and health status information; in response to receiving answers to the emergency-related questions, using the artificial intelligence model to analyze the answers and physical condition information to determine the user's symptom information; if the user's symptom information cannot be determined, using the artificial intelligence model to continue to generate emergency-related questions for supplementary inquiries to the user, and receiving corresponding answers, analyzing all received answers and physical condition information until the user's symptom information is determined; using the artificial intelligence model to process the symptom information and emergency treatment suggestions, and generate comprehensive treatment suggestions for the user.

[0006] On the second aspect, an emergency treatment device based on artificial intelligence is also provided, wherein the processing unit is configured to adopt an artificial intelligence model to process the user's physical condition information to generate emergency treatment suggestions and emergency-related questions for the user, and the physical condition information includes physical attribute information and health status information; the analysis unit is configured to respond to receiving answers to the emergency-related questions and adopt the artificial intelligence model to analyze the answers and physical condition information to determine the user's symptom information; the generation unit is configured to adopt the artificial intelligence model to continue to generate emergency-related questions for supplementary inquiries to the user if the user's symptom information cannot be determined, and receive corresponding answers, and analyze all received answers and physical condition information until the user's symptom information is determined; the suggestion unit is configured to adopt the artificial intelligence model to process symptom information and emergency treatment suggestions and generate comprehensive treatment suggestions for the user.

[0007] According to a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0008] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement any method described in the first aspect.

[0009] In a fifth aspect, a computer program product is also provided, including a computer program, wherein the computer program is executed by a processor to implement any method described in the first aspect.

[0010] In general, the present disclosure has at least the following beneficial effects: when the symptom information cannot be determined, additional inquiries can be continued to the user to ensure that accurate symptom information can be ultimately obtained, thereby generating comprehensive treatment suggestions for the user's emergency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in accordance with the present disclosure and should not be regarded as limiting the scope of the present disclosure.

[0012] Figure 1 A flowchart of an emergency treatment method based on artificial intelligence according to an embodiment of the present disclosure is shown; Figure 2 A flowchart of artificial intelligence training of an emergency treatment method based on artificial intelligence according to an embodiment of the present disclosure is shown; Figure 3 Another flow chart of an emergency treatment method based on artificial intelligence according to an embodiment of the present disclosure is shown; Figure 4 A schematic diagram of an emergency treatment device based on artificial intelligence according to an embodiment of the present disclosure is shown; Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure is shown; Figure 6 A schematic diagram of a storage medium provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0013] The present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0014] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0015] Figure 1 The emergency treatment method based on artificial intelligence of the present disclosure is shown. In an embodiment of the present disclosure, the method includes: Step S101, using an artificial intelligence model to process the user's physical condition information to generate emergency treatment suggestions and emergency-related issues for the user, the physical condition information including physical attribute information and health status information.

[0016] In this embodiment, the execution subject of the emergency treatment method based on artificial intelligence can call the artificial intelligence model to process the user's physical condition information, obtain emergency treatment suggestions for the user and emergency-related questions to be asked to the user. Physical attribute information can include, for example, gender, height, weight, chronic disease history, etc. Health status information can include, for example, physical problems such as pain location, pain degree, etc.

[0017] Step S102, in response to receiving an answer to the emergency-related question, using the artificial intelligence model to analyze the answer and physical condition information to determine the user's disease information.

[0018] In this embodiment, if the user answers a question related to an emergency, the execution subject obtains a corresponding answer, and the artificial intelligence model can be used to analyze the answer and the physical condition information to analyze the user's symptom information. The symptom information can reflect the symptom of the user.

[0019] Step S103, if the user's disease information cannot be determined, the artificial intelligence model is used to continue to generate emergency-related questions for supplementary inquiries to the user, and receive corresponding answers, and analyze all received answers and physical condition information until the user's disease information is determined.

[0020] In this embodiment, when the user's symptom information cannot be determined, the user may be asked questions related to the emergency condition continuously until the user's symptom information can be determined, and then the asking of emergency-related questions will be stopped.

[0021] Step S104, using an artificial intelligence model to process symptom information and emergency treatment suggestions, and generate comprehensive treatment suggestions for the user.

[0022] In this embodiment, the above-mentioned execution entity can use an artificial intelligence model to process symptom information and emergency treatment suggestions to generate comprehensive treatment suggestions for users.

[0023] This embodiment can continue to make additional inquiries to the user when the symptom information cannot be determined, so as to ensure that accurate symptom information can be finally obtained, and then generate comprehensive treatment suggestions for the user's emergency.

[0024] In some optional implementations of any embodiment of the present disclosure, the determining the symptom information of the user includes: determining a symptom identifier of the symptom suffered by the user and a probability that the user suffers from the symptom; if the probability is greater than a preset probability threshold, determining the symptom identifier as the symptom information of the user. If the probability is less than or equal to the preset probability threshold, the symptom information of the user cannot be determined.

[0025] In some optional implementations of any embodiment of the present disclosure, the training step of the artificial intelligence model includes: step S201, obtaining a data set of the artificial intelligence model, the training samples in the data set including physical condition information, emergency-related questions, answers, emergency treatment suggestions, symptom information, comprehensive treatment suggestions, and corresponding body optimization probability, the body optimization probability including at least one of the following: the probability of reducing survival risk, the probability of reducing disease risk and the probability of reducing user psychological stress; step S202, using the body optimization probability and taking the body optimization as the training goal to train the artificial intelligence model to obtain the artificial intelligence model.

[0026] The training goal is to reduce at least one of the following: survival risk, disease risk, and user psychological stress, specifically, to increase the probability of reducing survival risk, to increase the probability of reducing disease risk, and to increase the probability of reducing user psychological stress.

[0027] In some optional implementations of any one of the embodiments of the present disclosure, the use of the artificial intelligence model to analyze the answers and physical condition information includes: using the artificial intelligence model to extract feature information for determining disease information from the answers and the physical condition information, and when the answers and the physical condition information both include at least one of images, text, and voice, the feature information includes at least one of image features, text features, and voice features; based on the feature information, generating emergency treatment suggestions and emergency-related questions for the user.

[0028] In some optional implementations of any embodiment of the present disclosure, the extracting characteristic information for determining symptom information from the answer and the physical condition information includes: when the answer is about the user's physical pain, extracting pain degree information, pain location information and pain time corresponding to the pain degree information from the answer and the physical condition information.

[0029] In some optional implementations of any embodiment of the present disclosure, the method further includes: if initial physical condition information input by a user is received, performing at least one of text processing on the text in the initial physical condition information: text cleaning, word segmentation, part-of-speech tagging and sentence analysis; and using the initial physical condition information after text processing as the physical condition information.

[0030] In some optional implementations of any embodiment of the present disclosure, obtaining the data set of the artificial intelligence model includes: constructing a data set for training the artificial intelligence model through audited medical data, patient symptom information, medical history information, diagnosis results, treatment amendments, and medical databases.

[0031] In some optional implementations of any embodiment of the present disclosure, the present disclosure develops an intelligent medical dialogue system based on large language model (LLM) technology, combined with medical data strictly reviewed by the director, deputy director, and attending physician of a tertiary hospital, to provide users with timely, accurate, and personalized medical advice, especially for timely treatment of emergency scenarios. Its core technologies include: Natural language understanding and generation driven by large language models: The system automatically analyzes the user's dialogue content through LLM technology, understands the patient's symptom description and intention, can effectively handle complex and ambiguous descriptions, and give an accurate consultation process in combination with the context.

[0032] Authoritative medical data reviewed by experts: LLM’s training data comes from medical materials compiled and reviewed by professional doctors from tertiary hospitals, ensuring that the recommendations generated by the system are based on the latest and authoritative medical knowledge and reducing misjudgments.

[0033] Real-time search and reasoning: LLM understands the user's intention in real time based on the user's input, and performs reasoning and search based on professional data, gradually asking questions and confirming the condition until the system locates the specific disease.

[0034] Multi-level medical advice: Based on the analysis results, the system can provide emergency treatment suggestions, specific medical action suggestions (such as immediate medical treatment or emergency measures), and subsequent professional medical advice.

[0035] Improve diagnostic accuracy: By combining the powerful natural language processing capabilities of the large language model with audited medical data, the system's ability to understand user input is greatly improved, and it can provide more accurate diagnosis and treatment suggestions, reducing misdiagnosis and missed diagnosis. Strong real-time and emergency response: The system can quickly respond to users' medical problems, especially in emergency situations, and provide emergency treatment suggestions to ensure that users can quickly take effective measures in emergency situations. Personalized medical services: The system gradually analyzes the content of the user's conversation and provides personalized suggestions to ensure that the most suitable medical guidance is given according to different health backgrounds, symptoms and needs. High credibility: The system's data has been reviewed by medical experts to ensure the authority and reliability of the advice content, and users can take action based on the system's advice with greater confidence.

[0036] like Figure 3 As shown, the present disclosure also provides an emergency treatment method based on artificial intelligence, comprising the following steps: Step S110, collecting medical information compiled and reviewed by doctors: In some embodiments, a dataset is prepared for algorithm model training, the dataset including medical information reviewed by a doctor, the patient's symptoms, medical history, diagnosis results, and treatment amendments to cover various diseases and symptoms, by constructing the dataset using a medical database, a medical image database, and a publicly available medical dataset.

[0037] Step S120: large language model learning and training and storing the training results: In some embodiments, a deep learning model or other machine learning algorithm model is trained by one of a convolutional neural network, a recurrent neural network, or a self-compiled encoder, and the processed information data is input into the trained model to output corresponding diagnosis and treatment recommendations.

[0038] Step S130, the user logs in to complete the information.

[0039] Step S140, user consultation (text, voice, picture): In some embodiments, after the user logs in in step S130, personal information, health information, and medical history information are completed, and the information obtained by asking the user in step S140 is combined, wherein useful information is extracted from voice and pictures using text recognition technology, and after preprocessing this information, the data set is subjected to text cleaning, word segmentation, part-of-speech tagging, sentence analysis, etc., to provide meaningful features, which are recorded in the user's personal information to facilitate diagnosis and treatment of diseases.

[0040] Step S150 The model makes inferences based on the user information (height, weight, chronic disease history, etc.) and the user consultation content: Use machine learning algorithms, such as word models and TF-IDF algorithms, to extract useful features from the literature, such as pain severity, pain location, pain severity and duration, etc. Use learning algorithms, such as decision trees and support vector machines, to train machine learning models suitable for disease diagnosis and treatment plans. Use the trained model to analyze the information provided, generate emergency treatment suggestions, and ask questions to the user to further confirm the condition.

[0041] Step 160 adds useful information in the conversation to the user information (for example, if the user mentions that he suffers from high blood pressure, but the existing user information does not include high blood pressure, high blood pressure is added to the user information): In some embodiments, useful information is extracted from the user's consultation information during the conversation, including voice and pictures, using text recognition technology. After pre-processing this information, the data set is subjected to text cleaning, word segmentation, part-of-speech tagging, sentence analysis, etc. to provide meaningful features, which are recorded in the user's personal information to facilitate the diagnosis and treatment of the disease.

[0042] Step S170 provides emergency response suggestions: In some embodiments, a machine learning algorithm is used to analyze the patient's personal information and consultation content, perform disease diagnosis and treatment recommendations, and give emergency response recommendations to reduce survival risks and user psychological pressure.

[0043] Step S180 asks questions to further confirm the condition; Step S190: User answers; In some embodiments, the patient's personal information and consultation content are analyzed through a machine learning algorithm. In step S180, after the user asks a question, the program will comprehensively analyze and infer the user's personal information, and then give an answer in S190.

[0044] Step S200 adds useful information in the conversation to the user information (for example, if the user mentions that he suffers from high blood pressure, but it is not included in the existing user information, high blood pressure is added to the user information).

[0045] Step S210 confirms the condition and gives action suggestions and guidance suggestions based on the medical information compiled and reviewed by the doctor.

[0046] The embodiment of the present disclosure provides an emergency treatment device based on artificial intelligence, which is used to execute the emergency treatment method based on artificial intelligence described in the above embodiment, such as Figure 4 As shown, the device includes: a processing unit 401, which is configured to use an artificial intelligence model to process the user's physical condition information to generate emergency treatment suggestions and emergency-related questions for the user, and the physical condition information includes physical attribute information and health status information; an analysis unit 402, which is configured to respond to receiving an answer to the emergency-related question and use the artificial intelligence model to analyze the answer and the physical condition information to determine the user's symptom information; a generation unit 403, which is configured to use the artificial intelligence model to continue to generate emergency-related questions for supplementary inquiries to the user if the user's symptom information cannot be determined, and receive corresponding answers, and analyze all received answers and physical condition information until the user's symptom information is determined; a suggestion unit 404, which is configured to use an artificial intelligence model to process symptom information and emergency treatment suggestions to generate comprehensive treatment suggestions for the user.

[0047] The artificial intelligence-based emergency treatment device provided by the above-mentioned embodiments of the present disclosure and the artificial intelligence-based emergency treatment method provided by the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0048] The embodiments of the present disclosure also provide an electronic device corresponding to the artificial intelligence-based emergency treatment method provided in the above embodiments, so as to execute the above artificial intelligence-based emergency treatment method. The embodiments of the present disclosure are not limited.

[0049] Please refer to Figure 5 , which shows a schematic diagram of an electronic device provided by some embodiments of the present disclosure. Figure 5 As shown, the electronic device 50 includes: a processor 500, a memory 501, a bus 502 and a communication interface 503, and the processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; the memory 501 stores a computer program that can be run on the processor 500, and the processor 500 executes the method provided in any of the aforementioned embodiments of the present disclosure when running the computer program.

[0050] The memory 501 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 503 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0051] The bus 502 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 501 is used to store programs, and the processor 500 executes the programs after receiving the execution instructions. The artificial intelligence-based emergency treatment method disclosed in any implementation of the aforementioned embodiment of the present disclosure may be applied to the processor 500, or implemented by the processor 500.

[0052] The processor 500 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 500 or the instruction in the form of software. The above processor 500 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 501, and the processor 500 reads the information in the memory 501 and completes the steps of the above method in combination with its hardware.

[0053] The electronic device provided by the embodiment of the present disclosure and the artificial intelligence-based emergency treatment method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented by them.

[0054] The present disclosure also provides a computer-readable storage medium corresponding to the artificial intelligence-based emergency treatment method provided in the above-mentioned embodiment, please refer to Figure 6 The computer-readable storage medium shown is a CD 60 on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the artificial intelligence-based emergency treatment method provided by any of the aforementioned embodiments.

[0055] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0056] The computer-readable storage medium provided by the above-mentioned embodiments of the present disclosure and the artificial intelligence-based emergency treatment method provided by the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0057] It should be noted that: In the above text, the term "comprises", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or 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 device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be noted that the scope of the method and device in the embodiments of the present disclosure is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0058] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the various embodiments of the present disclosure.

[0059] The embodiments of the present disclosure are described above in conjunction with the accompanying drawings, which are only specific implementation modes of the present disclosure. However, the present disclosure is not limited to the above-mentioned specific implementation modes. The above-mentioned specific implementation modes are merely illustrative and not restrictive. Under the guidance of the present disclosure, ordinary technicians in this field can also make many forms without departing from the purpose of the present disclosure and the scope of protection of the claims, all of which are within the protection of the present disclosure.

Claims

1. An emergency treatment method based on artificial intelligence, characterized in that: include: Using an artificial intelligence model to process the user's physical condition information to generate emergency treatment suggestions and emergency-related issues for the user, the physical condition information including physical attribute information and health status information; In response to receiving an answer to the emergency-related question, using the artificial intelligence model to analyze the answer and the physical condition information to determine the user's disease information; If the symptom information of the user cannot be determined, the artificial intelligence model is used to continue to generate emergency-related questions for supplementary inquiries to the user, and corresponding answers are received, and all received answers and physical condition information are analyzed until the symptom information of the user is determined; An artificial intelligence model is used to process symptom information and emergency treatment suggestions, and generate comprehensive treatment suggestions for the user.

2. The method according to claim 1, characterized in that The determining the symptom information of the user includes: Determine a disease identifier of the disease suffered by the user and a probability that the user suffers from the disease; If the probability is greater than a preset probability threshold, the symptom identifier is determined to be the symptom information of the user. If the probability is less than or equal to the preset probability threshold, the symptom information of the user cannot be determined.

3. The method according to claim 1, characterized in that The training steps of the artificial intelligence model include: Acquire a data set of the artificial intelligence model, wherein the training samples in the data set include physical condition information, emergency-related questions, answers, emergency treatment suggestions, symptom information, comprehensive treatment suggestions, and corresponding physical optimization probabilities, wherein the physical optimization probabilities include at least one of the following: a probability of reducing survival risk, a probability of reducing disease risk, and a probability of reducing user psychological stress; The probability of the body optimization is adopted, and the body optimization is taken as a training target to train the artificial intelligence model to obtain the artificial intelligence model.

4. The method according to claim 1, characterized in that: The artificial intelligence model is used to analyze the answers and physical condition information, including: Using the artificial intelligence model, extracting feature information for determining the symptom information from the answer and the physical condition information, wherein when both the answer and the physical condition information include at least one of an image, a text, and a voice, the feature information includes at least one of an image feature, a text feature, and a voice feature; Based on the characteristic information, emergency treatment suggestions and emergency-related issues are generated for the user.

5. The method according to claim 1, characterized in that The step of extracting characteristic information for determining symptom information from the answer and the physical condition information includes: In the case where the answer is for the user's physical pain, pain level information, pain site information, and pain time corresponding to the pain level information are extracted from the answer and the physical condition information.

6. The method according to claim 1, characterized in that The method further comprises: If the initial physical condition information input by the user is received, performing at least one of the following text processing steps on the text in the initial physical condition information: text cleaning, word segmentation, part-of-speech tagging, and sentence analysis; The initial physical condition information after text processing is used as the physical condition information.

7. The method according to claim 1, characterized in that The step of obtaining a data set of the artificial intelligence model comprises: A data set for training the artificial intelligence model is constructed through reviewed medical data, patient symptom information, medical history information, diagnosis results, treatment amendments, and medical databases.

8. An emergency treatment device based on artificial intelligence, characterized in that: include: a processing unit configured to process the user's physical condition information using an artificial intelligence model to generate emergency treatment suggestions and emergency-related questions for the user, wherein the physical condition information includes physical attribute information and health status information; an analysis unit configured to, in response to receiving an answer to the emergency-related question, analyze the answer and the physical condition information using the artificial intelligence model to determine the symptom information of the user; The generating unit is configured to, if the symptom information of the user cannot be determined, continue to generate emergency-related questions for supplementary inquiry to the user using an artificial intelligence model, receive corresponding answers, and analyze all received answers and physical condition information until the symptom information of the user is determined; The suggestion unit is configured to use an artificial intelligence model to process symptom information and emergency treatment suggestions to generate comprehensive treatment suggestions for the user.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor runs the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method according to any one of claims 1 to 7.

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