First-aid system based on portable multifunctional first-aid kit

Abstract

The invention provides a first-aid system based on a portable multifunctional first-aid kit, which is characterized in that a receiving module receives symptom information input by a user through an interactive interface, outputs symptom description and identifies key symptom information; the conversion module analyzes and converts the key symptom information to generate a data form so as to select a first-aid scheme; the guiding module optimizes the taking-out sequence of the first-aid articles based on the selected first-aid scheme, adjusts voice prompts and generates an operation guide to guide the user to correctly use the first-aid articles; the communication module triggers remote communication connection, evaluates a key area of a first-aid site, and transmits information to an expert platform to obtain professional treatment suggestions; the statistics module records the number of first-aid appliances used in each first-aid process, updates the inventory state in the portable multifunctional first-aid kit and generates a use log; the generation module collects and processes key information of a first-aid site and generates a detailed first-aid report. The first-aid efficiency and the success rate are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of portable multifunctional first aid kits, and in particular to a first aid system based on a portable multifunctional first aid kit. Background Art

[0002] In emergency medical situations, timely and accurate first aid measures are essential. As an important emergency tool, portable multifunctional first aid kits are widely used in different scenarios such as homes, public places, transportation, and remote areas. Users need to quickly input symptom information related to emergency medical situations through the interactive interface, and the system should be able to quickly output symptom descriptions and identify key symptom information. In order to improve the efficiency and accuracy of first aid, the system must also have intelligent symptom classification and first aid plan selection capabilities to ensure personalized treatment according to specific circumstances. In addition, the first aid kit must be equipped with an intelligent distribution system and path planning algorithm to optimize the order in which first aid items are removed, and provide intuitive operating guides to guide users to use first aid items correctly. For situations beyond the scope of local processing, the system should also have remote communication capabilities, connect to the expert platform to obtain professional treatment advice, and generate detailed first aid reports for subsequent analysis.

[0003] Existing first aid methods usually rely on preset first aid manuals or simple automatic response systems. Users select the type of first aid through a simple button or menu on the first aid kit, and the system provides basic first aid guidance according to the preset process. Some advanced first aid kits may include some basic symptom classification functions, but these classifications are often based on a fixed rule base and lack flexibility and intelligence. For the management of first aid items, existing solutions mostly use fixed locations for storage, and users need to find the required items by themselves, which increases the operation time and difficulty. In complex situations or beyond the preset processing scope, the existing system usually does not have remote communication and professional support functions, and users can only rely on their own experience and limited information to handle them.

[0004] However, the existing technology has obvious deficiencies in terms of intelligence level, operational efficiency, remote support and data security. First, the intelligence level of the existing system is low. Most first aid kits only provide static first aid guides and cannot be dynamically adjusted according to the user's real-time symptom input, resulting in the first aid plan not being personalized and accurate enough. Secondly, the storage and retrieval order of first aid items has not been optimized, and users need to spend extra time to find and prepare the required items, delaying the precious first aid opportunity. Furthermore, when encountering complex situations or situations beyond the preset processing range, the existing system cannot immediately connect to the expert platform to obtain professional treatment advice, which limits the effectiveness and safety of first aid. Finally, the existing solution lacks an effective encrypted communication mechanism during information transmission, and there is a risk of information leakage, which affects patient privacy and data security. These problems not only reduce the success rate of first aid, but also increase the uncertainty and risk in the first aid process. A more advanced, efficient and secure solution is urgently needed to meet the needs of modern first aid. Summary of the invention

[0005] The embodiment of the present application provides a first aid system based on a portable multifunctional first aid kit, so as to solve the problems of low first aid efficiency and success rate in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a first aid system based on a portable multifunctional first aid kit, comprising:

[0007] A receiving module, for receiving an emergency medical condition, wherein a user inputs symptom information related to the emergency medical condition through an interactive interface on the portable multifunctional first aid kit, outputs a symptom description of the emergency medical condition, and identifies key symptom information from the symptom description;

[0008] a conversion module, for parsing and converting the key symptom information using the symptom classification model preset in the portable multifunctional first aid kit to obtain a structured data form, applying a multi-dimensional evaluation and weight distribution algorithm in combination with the data form to select a first aid plan from a plurality of preset first aid plans included in the symptom classification model;

[0009] A guidance module, which is used to optimize the order of taking out the first aid items in the first aid plan based on the first aid plan and the intelligent distribution system in the portable multifunctional first aid box, using a path planning algorithm to ensure that the first aid items required by the user are provided as quickly as possible, and to display and guide the correct use of the first aid items in the first aid plan according to the voice prompts adjusted by the augmented reality guidance module and the emotional calculation, and generate an operation guide;

[0010] A communication module, for triggering the establishment of a remote communication connection based on information of the first aid scene in combination with the operation guide when a situation beyond the scope of the first aid plan occurs at the first aid scene, and using image and video analysis algorithms to evaluate key areas from the situation at the first aid scene, and transmitting and analyzing the information of the key areas to an expert platform established by the remote communication connection through an encrypted communication protocol to obtain treatment suggestions;

[0011] A statistics module, used to count the number and usage of first aid supplies in the first aid plan, record the first aid supplies used in each first aid process, update the number of first aid supplies in the portable multifunctional first aid box, and generate a usage log;

[0012] A generating module is used to collect and process key information of the first aid scene based on the treatment suggestion and the usage log to generate a first aid report. Optionally, the conversion module includes:

[0013] An extraction unit is used to quantify and encode semantic features from the key symptom information based on a symptom classification model preset in the portable multifunctional first aid kit by using a deep learning semantic analysis network, and to extract medical entity features from the key symptom information in combination with entity relationships in a medical knowledge base based on a knowledge graph technology, and to perform high-dimensional quantification and encoding on the semantic features and the medical entity features to obtain a high-dimensional structured data form;

[0014] A construction unit is used to construct an evaluation index system based on the data form, and evaluate the relevance and applicability of the preset first aid plan in the symptom classification model based on the evaluation index system combined with a multi-dimensional evaluation algorithm of integrated learning, so as to calculate a matching score between the preset first aid plan and the key symptom information;

[0015] An updating unit, configured to set an initial weight value by applying a Bayesian optimization algorithm according to the matching score, and update the initial weight value by iteration, and search for an optimal weight combination in the updated weight values ​​by using a genetic algorithm, and perform a comprehensive scoring process on the preset first aid plan based on the optimal weight combination to obtain an optimal comprehensive scoring result;

[0016] The optimization unit is used to adopt a reinforcement learning strategy based on the comprehensive scoring result to establish a reinforcement learning framework based on the Markov decision process from the selection process of the preset first aid plan, and use a Q learning algorithm to optimize the selection strategy of the preset first aid plan within the reinforcement learning framework to improve the accuracy and efficiency of the selection of the preset first aid plan, and select the first aid plan with the highest comprehensive score from the multiple preset first aid plans as the most suitable first aid plan.

[0017] Optionally, the construction unit comprises:

[0018] A definition subunit is used to define and construct a multi-dimensional evaluation index system based on the high-dimensional structured data form, and set quantitative standards for each dimension in the evaluation index system according to medical standards and emergency needs to obtain a comprehensive evaluation index system, wherein the multiple dimensions include symptom type, severity, urgency, and medical history;

[0019] An evaluation subunit, used to evaluate the relevance and applicability of the preset first aid plan in the symptom classification model by using the dimensional standards of each dimension in the comprehensive evaluation index system in combination with the multi-dimensional evaluation algorithm of ensemble learning, so as to calculate the similarity or relevance score of the preset first aid plan in each dimension in the comprehensive evaluation index system, and obtain a preliminary dimension score;

[0020] The analysis subunit is used to perform a comprehensive comparison and analysis of the preset first aid plan and the key symptom information based on the preliminary dimension scores through an integrated learning algorithm, so as to summarize the similarity or correlation scores of each dimension in the comprehensive evaluation index system, and obtain a comprehensive matching score between the preset first aid plan and the key symptom information.

[0021] Optionally, the evaluation subunit is further used for:

[0022] Based on each dimension standard in the comprehensive evaluation index system, using the set quantitative standards, comparing the key symptom information with each preset first aid plan in the symptom classification model, and obtaining a preliminary score of each preset first aid plan in the symptom classification model in each dimension;

[0023] Applying a multi-dimensional evaluation algorithm of integrated learning to evaluate the preliminary score of each preset first aid plan in each dimension, and obtaining the preliminary dimension score after comprehensive evaluation through multiple machine learning models;

[0024] Based on the preliminary dimension scores after the comprehensive evaluation, the performance of the preset first aid plan in each dimension in the comprehensive evaluation index system is standardized to generate standardized dimension scores;

[0025] The standardized dimension scores are summarized to calculate the similarity or correlation scores of the preset first aid plan in each dimension in the comprehensive evaluation index system, and obtain the preliminary dimension scores between the preset first aid plan and the key symptom information.

[0026] Optionally, the updating unit is further used for:

[0027] Obtaining historical first aid cases, applying a Bayesian optimization algorithm, setting a preliminary weight configuration based on information provided by the matching score, and dynamically adjusting the initial weight value by combining the data of the historical first aid cases and user input feedback obtained from an interactive interface in the portable multifunctional first aid box using the Bayesian optimization algorithm;

[0028] The initial weight value is updated by using the iterative mechanism of the Bayesian optimization algorithm, and the selection and use feedback of the user on the preset first aid plan are collected and monitored through the interactive interface on the portable multifunctional first aid box, and the effectiveness of the selection and use feedback of the preset first aid plan is evaluated according to the actual effect after the first aid is completed, so as to obtain the weight value after multiple iterations of optimization;

[0029] Using a genetic algorithm to search for a preliminary weight combination in the weight values ​​after multiple iterations of optimization, based on the preliminary weight combination, through the selection, crossover and mutation operations of the genetic algorithm, the best performing weight combination is screened out, and the fitness of the weight combination is evaluated according to a comprehensive scoring function to obtain the optimal weight combination;

[0030] The preset first aid plan is scored comprehensively on the dimensions, the optimal weight combination is applied to the matching scores of each dimension in the evaluation index system, the comprehensive score of each preset first aid plan is calculated, and the optimal comprehensive score result is obtained.

[0031] Optionally, the searching for a preliminary weight combination in the weight values ​​by using a genetic algorithm, screening out the best performing weight combination based on the preliminary weight combination through selection, crossover and mutation operations of the genetic algorithm, and evaluating the fitness of the weight combination according to a comprehensive scoring function to obtain the optimal weight combination, includes:

[0032] Applying a selection operation of a genetic algorithm, using a comprehensive scoring function to evaluate the fitness of each of the preliminary weight combinations, so as to select weight combinations with higher fitness as candidates for the next generation population, and obtain high-quality candidate weight combinations;

[0033] Select two weight combinations from the candidate weight combinations as parents, exchange some weight values ​​of the two weights to generate offspring weight combinations, and generate a diversified new generation population based on the offspring weight combinations by using crossover operations and mutation operations;

[0034] According to the new generation population, performing a mutation operation of a genetic algorithm on the offspring weight combination to generate a mutated offspring weight combination;

[0035] A comprehensive scoring function is applied to evaluate the fitness of the mutated offspring weight combination, and the best performing weight combination is screened out through multiple rounds of evolutionary iterations. The optimal weight combination is obtained based on the fact that the best weight combination exhibits the highest fitness in the evaluation index system.

[0036] Optionally, the communication module further includes:

[0037] A monitoring subunit is used to monitor the situation at the emergency scene based on the intelligent monitoring system in the portable multifunctional first aid kit, and to establish contact with the expert platform in a timely manner to establish a remote communication connection when a situation beyond the scope of the first aid plan is detected at the emergency scene and professional help is needed;

[0038] An analysis subunit is used to identify and mark key areas of the first aid scene based on the first aid scene information obtained through the remote communication connection, in combination with the operation guide, using image and video analysis algorithms, and applying computer vision technology to obtain key area information;

[0039] The encryption subunit is used to securely transmit and optimize the key area information according to the encryption communication protocol combined with the compression algorithm to ensure that the expert platform quickly receives high-quality image and video data and generates a data packet for secure transmission;

[0040] The evaluation subunit is used for evaluating the situation at the emergency scene based on the securely transmitted data packet, and for professionals on the expert platform to use artificial intelligence-assisted diagnosis tools to determine the best treatment plan. Based on the treatment plan, the expert platform introduces a historical case library and a medical knowledge graph to generate detailed treatment recommendations.

[0041] Optionally, the analysis subunit is further used for:

[0042] Based on the intelligent monitoring system in the portable multifunctional first aid kit, through remote communication connection, when the first aid scene appears a situation beyond the scope of the first aid plan, the portable multifunctional first aid kit captures the picture of the first aid scene through the camera and sensor to obtain the image and video data of the first aid scene;

[0043] Comparing the image and video data of the first aid scene with the operation guide to determine the first aid links and item locations that need to be focused on, generating preliminary analysis results, and based on the preliminary analysis results, using image and video analysis algorithms and applying computer vision technology to identify and mark key areas of the first aid scene;

[0044] Based on the key areas, through multi-angle view analysis and time series data processing, the specific situation of the emergency scene is evaluated in detail, and a detailed dynamic evaluation report is generated. The dynamic evaluation report is converted into a structured data form, and the coordinates, type and attributes of each key area are recorded to obtain accurate key area information.

[0045] Optionally, the guidance module is further used to:

[0046] Based on the first aid plan, combined with the intelligent distribution system in the portable multifunctional first aid box, the first aid items in the portable multifunctional first aid box are located by using a three-dimensional spatial positioning algorithm, through visual sensors and laser radar technology, and the coordinates of each first aid item in the three-dimensional space are recorded. The first aid items are identified and classified by applying a deep learning model to obtain accurate location information of the first aid items;

[0047] According to the location information of the first aid items, a variety of path planning algorithms and machine learning prediction models are applied to calculate the optimal path from opening the portable multifunctional first aid box to taking out all the required first aid items, and dynamically adjust the order of taking out according to the state of the first aid items in the first aid plan to obtain an optimized order of taking out;

[0048] Based on the optimized removal order, the interactive interface of the portable multifunctional first aid kit is used, combined with SLAM technology and object tracking algorithm, and through an augmented reality guidance module, when a user opens the portable multifunctional first aid kit, the removal path and order of the first aid items are displayed to help the user quickly locate and remove the required first aid items, and generate intuitive removal instructions;

[0049] Based on the facial expression recognition algorithm and the sound emotion recognition algorithm, the emotional state of the user is evaluated, and according to the emotional state, the speech speed, volume and tone of the voice prompt on the portable multifunctional first aid kit are adjusted in combination with the personalized speech synthesis technology and the emotional feedback loop mechanism to generate a personalized voice prompt;

[0050] Based on the order of taking out the first aid items in the taking out guide and the usage method in the personalized voice prompt, an operation guide is generated using natural language processing technology and image enhancement technology.

[0051] Optionally, the receiving module is further used to:

[0052] Receiving an emergency medical situation, the user inputs symptom information related to the emergency medical situation through an interactive interface on the portable multifunctional first aid kit to obtain an initial symptom description of the emergency medical situation;

[0053] Using natural language processing technology to perform preliminary analysis on the initial symptom description, and applying a text analysis algorithm to extract keywords and phrases in the initial symptom description to obtain preliminary analysis results;

[0054] According to the preliminary parsing results, the semantic features in the symptom description are quantified and encoded through a semantic analysis network, and medical entity features are extracted from entity relationships in a medical knowledge base, and the semantic features and the medical entity features are quantified and encoded in a high-dimensional manner to obtain a structured data form;

[0055] Based on the structured data form, an evaluation index system is constructed, and a multi-dimensional evaluation algorithm is used to evaluate the relevance and importance of each symptom in the initial symptom description, so as to calculate the matching score of each symptom in the initial symptom description and screen out key symptom information.

[0056] In an embodiment of the present application, a receiving module is used to receive an emergency medical situation, and a user inputs symptom information related to the emergency medical situation through an interactive interface on the portable multifunctional first aid kit, outputs a symptom description of the emergency medical situation, and identifies key symptom information from the symptom description; a conversion module is used to use a preset symptom classification model in the portable multifunctional first aid kit to parse and convert the key symptom information to obtain a structured data form, and apply a multi-dimensional evaluation and weight distribution algorithm in combination with the data form to select a first aid plan from multiple preset first aid plans included in the symptom classification model; a guidance module is used to optimize the order of taking out first aid items in the first aid plan based on the first aid plan combined with the intelligent distribution system in the portable multifunctional first aid kit, using a path planning algorithm, to ensure that the first aid items required by the user are provided as quickly as possible, and adjust the order according to the augmented reality guidance module and the emotional computing. A voice prompt with a tone of voice is used to display and guide the correct use of the first aid items in the first aid plan, and generate an operation guide; a communication module is used to trigger the establishment of a remote communication connection based on the information of the first aid scene and the operation guide when the first aid scene exceeds the scope of the first aid plan, and use image and video analysis algorithms to evaluate the key areas from the situation at the first aid scene, and transmit and analyze the information of the key areas to the expert platform established by the remote communication connection through an encrypted communication protocol to obtain treatment advice; a statistical module is used to count the number and usage of first aid supplies in the first aid plan, record the first aid supplies used in each first aid process, and update the number of first aid supplies in the portable multifunctional first aid box to generate a usage log; a generation module is used to collect and process key information of the first aid scene based on the treatment advice and the usage log to generate a first aid report. The technical solution of this application has the following beneficial effects:

[0057] This application reduces human errors through automated and intelligent process design to ensure efficient and accurate first aid process; provides customized first aid plans and operation guides based on user symptom input and real-time feedback to enhance the effectiveness of first aid; uses augmented reality technology and emotional computing to adjust voice prompts so that users can respond calmly even in tense situations and increase their confidence in first aid operations; in complex situations or situations beyond local processing capabilities, instantly connects to expert platforms to obtain professional treatment advice to ensure that patients receive the best treatment; uses encrypted communication protocols to ensure secure transmission of information and protect patient privacy and data security; automatically generates first aid reports to provide detailed information for subsequent treatment and case analysis, which helps to improve first aid strategies and training.

[0058] Furthermore, the embodiment of the present application also utilizes the conversion module to include an extraction unit, a construction unit, an update unit and an optimization unit. The extraction unit is based on the symptom classification model preset in the portable multifunctional first aid box, and uses a deep learning semantic analysis network to quantify and encode semantic features from key symptom information, and extracts medical entity features in combination with entity relationships in the medical knowledge base, performs high-dimensional quantification and encoding on these features, and generates a high-dimensional structured data form. The construction unit constructs an evaluation index system based on this data form, and evaluates the relevance and applicability of the preset first aid plan in combination with a multi-dimensional evaluation algorithm of integrated learning, and calculates the matching score. The update unit sets the initial weight value based on the matching score, applies the Bayesian optimization algorithm, and searches for the optimal weight combination through iterative updates, and performs a comprehensive scoring process on the preset first aid plan to obtain the optimal comprehensive scoring result. The optimization unit adopts a reinforcement learning strategy based on the comprehensive scoring result, and uses a Q learning algorithm to optimize the selection strategy of the preset first aid plan within the framework of the Markov decision process, thereby improving the accuracy and efficiency of the selection, and finally selecting the most suitable first aid plan from multiple preset first aid plans.

[0059] Through the above method, deep learning semantic analysis network and knowledge graph technology are introduced, and the conversion module of this application can efficiently convert the key symptom information input by the user into a high-dimensional structured data form, ensuring the accuracy and comprehensiveness of the symptom description. The construction unit accurately evaluates the correlation and applicability between each preset first aid plan and the key symptom information through the evaluation index system and multi-dimensional evaluation algorithm, calculates the matching score, and improves the scientificity and rationality of the first aid plan selection. The update unit uses the Bayesian optimization algorithm and the genetic algorithm to dynamically adjust the weights and search for the optimal combination, further improving the accuracy of the comprehensive score. The optimization unit continuously optimizes the selection strategy of the first aid plan within the framework of the Markov decision process through reinforcement learning strategies and Q learning algorithms, significantly improving the accuracy and efficiency of the selection, ensuring that the most suitable first aid plan can be quickly provided in emergency medical situations, greatly improving the success rate of first aid and the patient's chance of survival.

[0060] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following briefly introduces the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0062] Figure 1 FIG. is a schematic structural diagram of a first aid system based on a portable multifunctional first aid kit provided for an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the following clearly and completely describes the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0064] In some processes described in the specification, claims and the above drawings of the present application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The operation numbers such as 101 and 102 are only used to distinguish different operations, and the numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0065] The following clearly and completely describes the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0066] Figure 1 A flowchart of a first aid system based on a portable multifunctional first aid kit is provided for an embodiment of the present application. As Figure 1 shown, the method includes:

[0067] Step 101: a receiving module is used to receive an emergency medical situation, a user inputs symptom information related to the emergency medical situation through an interactive interface on a portable multifunctional first aid kit, outputs a symptom description of the emergency medical situation, and identifies key symptom information from the symptom description;

[0068] In this step, the receiving module is the starting point of the entire first aid process, and is responsible for receiving the emergency medical condition-related symptom information input by the user through the interactive interface on the portable multi-functional first aid kit. This module not only includes interactive methods such as touch screen, voice input and buttons, but also integrates natural language processing technology to parse the symptom description entered by the user and identify key symptom information. Specifically, the receiving module can understand the user's spoken description, extract key words and phrases, and form structured symptom data, providing a basis for subsequent processing.

[0069] In actual operation, when a user faces an emergency medical situation, he or she first inputs symptom information related to the emergency medical situation through the interactive interface of the portable multifunctional first aid kit. After receiving this information, the system uses natural language processing technology to perform a preliminary analysis of the symptom description, extracting keywords and phrases to ensure that subsequent processing can accurately understand the user's input content. Then, the system further analyzes these keywords based on the medical knowledge base, identifies the key symptom information that best reflects the condition, and outputs a detailed symptom description report.

[0070] For example, in a home environment, a user discovers that a family member suddenly has chest pain and difficulty breathing. The user immediately opens the portable multi-functional first aid kit and enters keywords such as "chest pain" and "difficulty breathing" through the touch screen or voice command. The system responds quickly, using the built-in natural language processing algorithm to parse these symptom descriptions, identify the possibility of "acute myocardial infarction", and generate a detailed symptom description report, providing a solid foundation for subsequent steps.

[0071] Step 102: A conversion module is used to parse and convert the key symptom information using the symptom classification model preset in the portable multifunctional first aid kit to obtain a structured data form, and apply a multi-dimensional evaluation and weight distribution algorithm to the data form to select a first aid plan from multiple preset first aid plans included in the symptom classification model;

[0072] In this step, the core task of the conversion module is to deeply analyze and convert the key symptom information to generate a high-dimensional structured data form. This module uses a deep learning semantic analysis network to quantify and encode semantic features from key symptom information, and combines medical knowledge graph technology to extract entity relationships from the medical knowledge base to generate medical entity features. By performing high-dimensional quantification and encoding of these features, the conversion module provides accurate data support for subsequent evaluation. In addition, the conversion module also includes a construction unit for constructing an evaluation index system based on a high-dimensional structured data form, and applying a multi-dimensional evaluation and weight distribution algorithm to select the most appropriate preset first aid plan.

[0073] In actual operation, after the conversion module receives the key symptom information from the receiving module, it first uses the deep learning semantic analysis network to quantify and encode this information and extract semantic features. At the same time, combined with the medical knowledge graph technology, entity relationships are extracted from the medical knowledge base to generate medical entity features. Next, these features are quantified and encoded in high dimensions to form a structured data form. Based on this data form, the construction unit constructs an evaluation index system, applies multi-dimensional evaluation and weight distribution algorithms, evaluates the relevance and applicability of the preset first aid plan, calculates the matching score, and finally selects the most suitable first aid plan.

[0074] For example, continuing with the above example of a home environment, once the system identifies the possibility of "acute myocardial infarction", the conversion module starts immediately. It uses a deep learning semantic analysis network to quantify and encode keywords such as "chest pain" and "dyspnea", and combines medical knowledge graph technology to extract relevant entity relationships from the medical knowledge base, such as heart function and blood pressure levels, to generate detailed medical entity features. Subsequently, the conversion module quantifies and encodes these features in a high-dimensional manner, constructs an evaluation index system, evaluates multiple preset first aid plans, calculates the matching score, and finally selects the first aid plan that best suits the current symptoms, namely cardiopulmonary resuscitation and immediate administration of nitroglycerin.

[0075] Step 103: A guidance module is used to optimize the order of taking out the first aid items in the first aid plan based on the first aid plan and the intelligent distribution system in the portable multifunctional first aid box, using a path planning algorithm to ensure that the first aid items required by the user are provided as quickly as possible, and to display and guide the correct use of the first aid items in the first aid plan according to the voice prompts adjusted by the augmented reality guidance module and the emotional calculation, and generate an operation guide;

[0076] In this step, the guidance module is tasked with optimizing the order in which first aid items are taken out and providing intuitive operation guides to guide users to use first aid items correctly. This module combines the intelligent distribution system and path planning algorithm in the portable multifunctional first aid kit to ensure that users can obtain the required first aid items as quickly as possible. In addition, the guidance module also integrates the augmented reality guidance module and emotional computing technology to adjust the tone and speed of voice prompts, helping users stay calm in tense situations and improving operational efficiency and accuracy.

[0077] In actual operation, the guidance module optimizes the order of taking out first aid items based on the selected first aid plan, combined with the intelligent distribution system and path planning algorithm. The system will display a clear path and order for taking out items to help users quickly locate and take out the required items. At the same time, the guidance module uses augmented reality technology to display the location and usage of items in real time when the user opens the first aid kit. In addition, the system will adjust the tone and speed of the voice prompts according to the user's facial expressions and voice emotional state, and generate personalized voice guidance to ensure that users can correctly perform first aid measures in the shortest time.

[0078] For example, after confirming cardiopulmonary resuscitation and taking nitroglycerin immediately as the first aid plan, the guidance module is immediately started. It combines the intelligent distribution system and the path planning algorithm to optimize the order of taking out first aid items, ensuring that users can get the automated external defibrillator (AED) and nitroglycerin tablets as quickly as possible. Through augmented reality technology, the system displays the location of the AED and drugs in real time when the user opens the first aid kit, and provides detailed instructions for use. At the same time, the system monitors the user's facial expressions and voices, and after detecting the user's nervousness, adjusts the tone and speed of the voice prompts, and guides the user on how to correctly operate the AED and take drugs in a gentle and firm voice, helping the user stay calm in tense situations.

[0079] Step 104: A communication module is used to trigger the establishment of a remote communication connection based on the information of the first aid scene and the operation guide when the first aid scene exceeds the scope of the first aid plan, and use image and video analysis algorithms to evaluate key areas from the situation at the first aid scene, and transmit and analyze the information of the key areas to the expert platform established by the remote communication connection through an encrypted communication protocol to obtain treatment suggestions;

[0080] In this step, the communication module is responsible for establishing a remote communication connection and transmitting information about key areas to the expert platform when the emergency scene is beyond the local processing range. The module uses image and video analysis algorithms to evaluate the situation at the emergency scene, identify key areas, and ensure the security of information transmission through encrypted communication protocols. The communication module also has a real-time monitoring and feedback mechanism, enabling the expert platform to obtain the latest emergency scene information in a timely manner and provide professional treatment advice.

[0081] In actual operation, when a situation occurs at the emergency scene that is beyond the scope of local processing, the communication module triggers the establishment of a remote communication connection. The system uses cameras and sensors to capture images and video data of the emergency scene, evaluates the situation on the scene through image and video analysis algorithms, and identifies key areas. Subsequently, the communication module uses encrypted communication protocols to ensure the security of information transmission and transmits information about key areas to the remote expert platform. Professionals on the expert platform use artificial intelligence-assisted diagnostic tools to evaluate the specific situation at the emergency scene, provide professional treatment advice, and provide feedback to on-site users through secure channels.

[0082] For example, after the user completes CPR and takes medication, the patient's condition has not improved, and the system detects a situation that is beyond the local processing range. The communication module immediately triggers the establishment of a remote communication connection, uses the camera to capture the image and video data of the emergency scene, evaluates the scene through image and video analysis algorithms, and identifies the patient's electrocardiogram and respiratory status as key areas. The system transmits this information to the remote expert platform through an encrypted communication protocol. Professionals on the expert platform use artificial intelligence-assisted diagnostic tools to evaluate the specific situation at the emergency scene, confirm that they need to be immediately sent to the hospital for further treatment, and provide feedback to the user through a secure channel, instructing the user to call an ambulance and prepare for transfer.

[0083] Step 105: A statistical module is used to count the number and usage of first aid supplies in the first aid plan, record the first aid supplies used in each first aid process, update the number of first aid supplies in the portable multifunctional first aid box, and generate a usage log; in this step, the statistical module includes the first aid supply quantity statistics, usage frequency tracking, and real-time update of the inventory status of first aid supplies in the portable multifunctional first aid box. This module not only records the first aid supplies and their types actually used in each first aid process, but also generates a detailed usage log for subsequent analysis and management. These data not only help to understand the consumption pattern of first aid supplies, but also provide a reference for future first aid preparations, ensuring that the first aid box is always in the best condition and ready to respond to emergency medical situations. Through accurate statistics and real-time updates, the statistical module enhances the reliability of the system and the efficiency of resource management.

[0084] In actual operation, the statistics module first counts the number and usage of first aid supplies according to the list of first aid supplies specified in the first aid plan, combined with the first aid items actually taken out and used by the user. Whenever the user takes out or uses first aid supplies from the portable multi-functional first aid kit, the statistics module automatically records this information and updates the number of remaining first aid supplies in the first aid kit. In addition, the statistics module also generates a detailed usage log, which contains information such as the timestamp of each first aid event, the type and quantity of first aid supplies used, etc. Based on this data, the statistics module can predict future first aid supply needs, remind users or managers to replenish first aid supplies in a timely manner, and ensure that the first aid kit is always in the best condition and ready to respond to emergency medical situations at any time. The entire process ensures the effective use and timely replenishment of first aid resources, and improves the reliability and practicality of the system.

[0085] For example, after the user completes CPR and takes medication, the patient's condition has not improved, and the system detects a situation that is beyond the local processing range. The communication module immediately triggers the establishment of a remote communication connection, uses the camera to capture the image and video data of the emergency scene, evaluates the scene through image and video analysis algorithms, and identifies the patient's electrocardiogram and respiratory status as key areas. The system transmits this information to the remote expert platform through an encrypted communication protocol. Professionals on the expert platform use artificial intelligence-assisted diagnostic tools to evaluate the specific situation at the emergency scene, confirm that they need to be immediately sent to the hospital for further treatment, and provide feedback to the user through a secure channel, instructing the user to call an ambulance and prepare for transfer.

[0086] Step 106: A generation module is used to collect and process key information of the first aid scene based on the treatment suggestion to generate a first aid report.

[0087] In this step, the task of the generation module is to collect and process key information from the first aid scene and generate a detailed first aid report after obtaining treatment recommendations from the expert platform. This module not only records all important information during the first aid process, such as the symptom description entered by the user, the first aid plan selected, the first aid items used, and the treatment recommendations of the experts, but also generates a detailed report containing metadata such as timestamps and geographic locations. The first aid report can not only serve as a reference for subsequent treatment, but can also be used for case analysis and improvement of first aid strategies.

[0088] In actual operation, after receiving the treatment recommendations from the expert platform, the generation module begins to collect and process key information from the first aid scene. The system records all important information during the first aid process, including the symptom description entered by the user, the first aid plan selected, the first aid items used, the treatment recommendations from the experts, etc. At the same time, the generation module also generates a detailed report containing metadata such as timestamps and geographic locations. Finally, the system integrates this information into a complete first aid report for subsequent treatment and case analysis.

[0089] For example, after it is confirmed on the remote expert platform that the patient needs to be immediately sent to the hospital for further treatment, the generation module is immediately activated. It records all the important information during the first aid process, such as the symptom descriptions entered by the user, like "chest pain", "difficulty breathing", etc., the selected first aid plans for cardiopulmonary resuscitation and taking nitroglycerin, the AED and medications used, the treatment suggestions of the experts, etc. At the same time, the generation module generates a detailed first aid report containing metadata such as timestamps and geographical locations, ensuring that the subsequent treatment team can comprehensively understand the first aid process and providing valuable reference for the further treatment of the patient.

[0090] Through the collaborative work of the receiving module, conversion module, guidance module, communication module, statistical module, and generation module, the present invention realizes a complete closed-loop process from the user inputting symptom information to generating a first aid report. The receiving module ensures the accuracy and comprehensiveness of the symptom description, the conversion module improves the scientificity and rationality of the first aid plan selection, the guidance module enhances the efficiency of retrieving first aid items and the accuracy of user operations, the communication module guarantees the immediacy and security of remote professional support. Through the accurate recording and real-time update of the statistical module, the first aid system can efficiently manage first aid resources, ensure that the first aid supplies in the first aid kit are sufficient, greatly improving the first aid efficiency and the reliability of the system. The generation module provides detailed information for subsequent treatment and case analysis. This series of steps significantly improves the success rate of first aid and the survival probability of the patient, ensuring that personalized first aid guidance and support can be provided quickly and accurately in emergency medical situations.

[0091] To solve the problem that symptom classification and first aid plan selection in the prior art are not intelligent and accurate enough, in some embodiments, the conversion module described in step 102 includes:

[0092] The extracting unit is used to quantify and encode semantic features from the key symptom information based on the symptom classification model preset in the portable multifunctional first aid kit by using a deep learning semantic analysis network, and to extract medical entity features from the key symptom information in combination with entity relationships in a medical knowledge base based on knowledge graph technology, and to perform high-dimensional quantification and encoding on the semantic features and the medical entity features to obtain a high-dimensional structured data form; the constructing unit is used to construct an evaluation index system based on the data form, and to evaluate the relevance and applicability of the preset first aid plan in the symptom classification model based on the evaluation index system combined with a multi-dimensional evaluation algorithm of integrated learning, so as to calculate a matching score between the preset first aid plan and the key symptom information; the updating unit is used to update the first aid plan according to the data form. Matching score, applying Bayesian optimization algorithm to set initial weight value, and updating the initial weight value through iteration, and using genetic algorithm to search for optimal weight combination in the updated weight value, based on the optimal weight combination, comprehensively scoring the preset first aid plan to obtain the optimal comprehensive scoring result; optimization unit, used to adopt reinforcement learning strategy based on the comprehensive scoring result, establish a reinforcement learning framework based on Markov decision process from the selection process of the preset first aid plan, and use Q learning algorithm to optimize the selection strategy of the preset first aid plan within the reinforcement learning framework, so as to improve the accuracy and efficiency of the selection of the preset first aid plan, and select the first aid plan with the highest comprehensive score from the multiple preset first aid plans as the most suitable first aid plan.

[0093] In this embodiment, the extraction unit uses a deep learning semantic analysis network to quantify and encode semantic features from key symptom information, and extracts entity relationships from the medical knowledge base in combination with knowledge graph technology to generate medical entity features. These features together constitute a high-dimensional structured data form, providing accurate data support for subsequent evaluation. The construction unit constructs an evaluation index system based on this high-dimensional structured data form, and applies a multi-dimensional evaluation algorithm to evaluate the relevance and applicability of the preset first aid plan, and calculates the matching score between each plan and the key symptom information. The updating unit sets the initial weight value based on the matching score, applies the Bayesian optimization algorithm, and updates these weight values ​​through iteration, uses the genetic algorithm to search for the optimal weight combination, and performs a comprehensive scoring process on the preset first aid plan to obtain the optimal comprehensive scoring result. Finally, based on the comprehensive scoring result, the optimization unit adopts a reinforcement learning strategy, and uses the Q learning algorithm to optimize the selection strategy of the preset first aid plan within the framework of the Markov decision process, ensuring that the selection process is both efficient and accurate, and finally selects the plan with the highest comprehensive score from multiple preset first aid plans as the most suitable first aid plan.

[0094] In the embodiment of the present application, the conversion module first receives the key symptom information from the receiving module, and then quantifies and encodes it using the deep learning semantic analysis network to extract semantic features. Then, combined with the knowledge graph technology, relevant entity relationships are extracted from the medical knowledge base to generate medical entity features. These features are further quantified and encoded in high dimensions to form a structured data form. Based on this data form, the construction unit constructs an evaluation index system, defines multiple evaluation dimensions, and evaluates the relevance and applicability of the preset first aid plan in combination with the multi-dimensional evaluation algorithm of integrated learning, and calculates the matching score between each plan and the key symptom information. The updating unit sets the initial weight value based on these matching scores, applies the Bayesian optimization algorithm, and updates these weight values ​​through iteration, uses the genetic algorithm to search for the optimal weight combination in the updated weight values, and performs a comprehensive scoring process on the preset first aid plan to obtain the optimal comprehensive scoring result. Finally, the optimization unit establishes a reinforcement learning framework based on the Markov decision process based on the comprehensive scoring result, and uses the Q learning algorithm to continuously optimize the selection strategy of the preset first aid plan to ensure that each selection is optimal, and finally selects the plan with the highest comprehensive score from multiple preset first aid plans as the most suitable first aid plan.

[0095] The following is a specific embodiment: Assume that the user enters symptom information such as "chest pain" and "dyspnea" on the portable multifunctional first aid kit. The extraction unit first uses a deep learning semantic analysis network to quantify and encode these symptoms to generate a semantic feature vector. Then, combined with the knowledge graph technology, the entity relationships related to "chest pain" and "dyspnea", such as heart function, blood pressure level, etc., are extracted from the medical knowledge base to generate medical entity features. These features are further quantified and encoded in high dimensions to form a structured data form. Next, the construction unit constructs an evaluation index system based on this data form and defines multiple evaluation dimensions, such as symptom severity, effectiveness of first aid measures, and patient history. Combined with the multi-dimensional evaluation algorithm of integrated learning, multiple preset first aid plans are evaluated, and the matching score of each plan with "chest pain" and "dyspnea" is calculated. For example, the cardiopulmonary resuscitation plan may get a higher matching score because it can quickly relieve the patient's dyspnea symptoms. Subsequently, the update unit sets the initial weight values ​​based on these matching scores using the Bayesian optimization algorithm, and updates these weight values ​​through iterations. It uses the genetic algorithm to search for the optimal weight combination in the updated weight values, and performs a comprehensive scoring process on the preset first aid plan to obtain the optimal comprehensive scoring result. Finally, based on the comprehensive scoring results, the optimization unit establishes a reinforcement learning framework based on the Markov decision process, and uses the Q learning algorithm to continuously optimize the selection strategy of the preset first aid plan, confirming that the cardiopulmonary resuscitation plan is the most suitable first aid plan, and guiding the user to immediately perform cardiopulmonary resuscitation operations to ensure that the patient receives timely and effective treatment.

[0096] In order to solve the problem that the evaluation index system in the prior art is not comprehensive and accurate enough, in some embodiments, the construction unit in step 102 includes:

[0097] A definition subunit is used to define and construct a multi-dimensional evaluation index system based on the high-dimensional structured data form, and set quantitative standards for each dimension in the evaluation index system according to medical standards and emergency needs to obtain a comprehensive evaluation index system, wherein the multiple dimensions include symptom type, severity, urgency and medical history; an evaluation subunit is used to use the dimensional standards in the comprehensive evaluation index system, combined with the multi-dimensional evaluation algorithm of integrated learning, to evaluate the relevance and applicability of the preset emergency plan in the symptom classification model, so as to calculate the similarity or correlation score of the preset emergency plan in each dimension in the comprehensive evaluation index system, and obtain a preliminary dimensional score; an analysis subunit is used to perform a comprehensive comparison and analysis of the preset emergency plan and the key symptom information through an integrated learning algorithm based on the preliminary dimensional score, so as to summarize the similarity or correlation score of each dimension in the comprehensive evaluation index system, and obtain a comprehensive matching score between the preset emergency plan and the key symptom information. Optionally, the evaluation subunit is also used to: based on each dimensional standard in the comprehensive evaluation index system, using the set quantitative standards, compare the key symptom information and each preset first aid plan in the symptom classification model to obtain a preliminary score of each preset first aid plan in the symptom classification model in each dimension; apply an integrated learning multi-dimensional evaluation algorithm to evaluate the preliminary score of each preset first aid plan in each dimension, and obtain a preliminary dimension score after comprehensive evaluation through multiple machine learning models; based on the preliminary dimensional score after comprehensive evaluation, standardize the performance of the preset first aid plan in each dimension in the comprehensive evaluation index system to generate a standardized dimensional score; summarize the standardized dimensional scores to calculate the similarity or correlation score of the preset first aid plan in each dimension in the comprehensive evaluation index system to obtain a preliminary dimensional score between the preset first aid plan and the key symptom information.

[0098] In this embodiment, the construction unit includes a definition subunit, an evaluation subunit, and an analysis subunit, which aims to build a comprehensive evaluation index system and select the most suitable first aid plan based on a high-dimensional structured data form. The definition subunit defines multiple dimensions, such as symptom type, severity, urgency, medical history, and their quantitative standards based on medical standards and first aid needs. The evaluation subunit uses these quantitative standards, combined with an integrated learning algorithm, to evaluate the relevance and applicability of the preset first aid plan and calculate the preliminary dimension score. The analysis subunit summarizes these scores and generates the final comprehensive matching score through comprehensive comparison and analysis to ensure that the selected first aid plan is both scientific and accurate.

[0099] In an embodiment of the present application, the construction unit first receives the high-dimensional structured data generated by the extraction unit, and then constructs a multi-dimensional evaluation index system by defining sub-units. The evaluation sub-unit uses this system to compare key symptom information with preset first aid plans, calculate preliminary dimension scores, and apply an integrated learning algorithm for standardization. Finally, the analysis sub-unit performs a comprehensive comparison and analysis based on the standardized dimension scores to obtain the final comprehensive matching score, thereby selecting the first aid plan that best suits the current situation. The entire process ensures that the evaluation results are comprehensive and accurate, significantly improving the efficiency and success rate of first aid.

[0100] The following is a specific embodiment:

[0101] Suppose the user enters symptom information of "chest pain" and "dyspnea". After the extraction unit generates high-dimensional structured data, it defines subunits to construct an evaluation index system that includes dimensions such as symptom type and severity. The evaluation subunit evaluates multiple preset first aid plans, such as cardiopulmonary resuscitation and drug treatment, and calculates preliminary dimension scores. For example, cardiopulmonary resuscitation scores higher in the "urgency" dimension. The analysis subunit summarizes these scores, confirms that cardiopulmonary resuscitation is the most suitable plan, and guides the user to perform the operation immediately to ensure that the patient receives timely and effective treatment. Through this process, the construction unit improves the intelligence and accuracy of first aid plan selection.

[0102] In order to solve the problem that the selection of emergency plan in the prior art is not dynamically adjusted and optimized, in some embodiments, the updating unit in step 102 is further used to:

[0103] Obtain historical first aid cases, apply the Bayesian optimization algorithm, set a preliminary weight configuration according to the information provided by the matching score, and dynamically adjust the initial weight value by combining the data of the historical first aid cases and the user input feedback obtained from the interactive interface in the portable multifunctional first aid box using the Bayesian optimization algorithm; update the initial weight value using the iterative mechanism of the Bayesian optimization algorithm, and collect and monitor the user's selection and use feedback of the preset first aid plan through the interactive interface on the portable multifunctional first aid box, evaluate the effectiveness of the selection and use feedback of the preset first aid plan according to the actual effect after the first aid is completed, and obtain the weight value after multiple iterations of optimization; use the genetic algorithm to search for a preliminary weight combination in the weight value after multiple iterations of optimization, based on the preliminary weight combination, select the best performing weight combination through the selection, crossover and mutation operations of the genetic algorithm, and evaluate the fitness of the weight combination according to the comprehensive scoring function to obtain the optimal weight combination; comprehensively score the preset first aid plan for the dimensions, apply the optimal weight combination to the matching score of each dimension in the evaluation index system, calculate the comprehensive score of each preset first aid plan, and obtain the optimal comprehensive scoring result. Optionally, the method of searching for a preliminary weight combination in the weight values ​​using a genetic algorithm, and based on the preliminary weight combination, screening out the best performing weight combination through selection, crossover and mutation operations of the genetic algorithm, and evaluating the fitness of the weight combination according to a comprehensive scoring function to obtain an optimal weight combination includes: applying a selection operation of the genetic algorithm, and evaluating the fitness of each of the preliminary weight combinations using a comprehensive scoring function to select a weight combination with a higher fitness as a candidate for the next generation population, and obtaining a high-quality candidate weight combination; selecting two weight combinations from the candidate weight combinations as parents, exchanging part of the weight values ​​of the two weights, generating a child weight combination, and generating a diversified new generation population based on the child weight combination by using crossover and mutation operations; performing a mutation operation of the genetic algorithm on the child weight combination according to the new generation population to generate a mutated child weight combination; applying a comprehensive scoring function to evaluate the fitness of the mutated child weight combination, and screening out the best performing weight combination through multiple rounds of evolutionary iterations, and obtaining the optimal weight combination based on the best weight combination showing the highest fitness in the evaluation index system.

[0104] In this embodiment, the task of the update unit is to dynamically adjust the initial weight value by obtaining historical first aid cases and user feedback, and use the Bayesian optimization algorithm and genetic algorithm for multiple iterative optimization, and finally obtain the optimal weight combination. Specifically, the update unit first applies the Bayesian optimization algorithm to set the initial weight configuration, and dynamically adjusts the initial weight value in combination with the data of historical first aid cases and user input feedback. Then, these weight values ​​are continuously updated through an iterative mechanism, and user selection and use feedback on the preset first aid plan are collected to evaluate its effectiveness. Next, the genetic algorithm is used to search for the initial weight combination in the weight values ​​after multiple iterative optimization, and the best performing weight combination is screened out through selection, crossover and mutation operations, and its fitness is evaluated according to the comprehensive scoring function, and finally the optimal weight combination is obtained. These optimized weight values ​​are applied to each dimension in the evaluation index system, and the comprehensive score of each preset first aid plan is calculated to ensure the selection of the most suitable first aid plan.

[0105] In the embodiment of the present application, the update unit first obtains the historical first aid case data, and applies the Bayesian optimization algorithm to set the preliminary weight configuration. These weight values ​​are based on the information provided by the matching score, and are dynamically adjusted in combination with the historical first aid cases and user input feedback. Through the iterative mechanism of the Bayesian optimization algorithm, the update unit continuously adjusts the initial weight value, and collects and monitors the user's selection and use feedback of the preset first aid plan through the interactive interface on the portable multifunctional first aid box. The effectiveness of these feedbacks is evaluated according to the actual effect after the first aid is completed, and after multiple iterations of optimization, a better weight value is obtained. Subsequently, the update unit uses a genetic algorithm to search for a preliminary weight combination in these optimized weight values, and selects the best performing weight combination through selection, crossover and mutation operations. Finally, the fitness of these weight combinations is evaluated according to the comprehensive scoring function, and the optimal weight combination is obtained, and it is applied to each dimension in the evaluation index system to calculate the comprehensive score of each preset first aid plan, and finally the optimal comprehensive score result is obtained. The whole process ensures the accuracy and efficiency of the first aid plan selection and significantly improves the success rate of first aid.

[0106] The following is a specific embodiment:

[0107] Suppose the user enters symptom information such as "chest pain" and "dyspnea". After the extraction unit generates high-dimensional structured data, the construction unit evaluates multiple preset first aid plans to obtain a preliminary matching score. The update unit obtains historical first aid case data, applies the Bayesian optimization algorithm to set the preliminary weight configuration, and dynamically adjusts the weight value based on user feedback. For example, historical data shows that cardiopulmonary resuscitation has a higher success rate under similar symptoms, so it is given a higher weight. Through multiple iterative optimizations, the update unit collects user feedback on the first aid plan, evaluates the effectiveness of the feedback based on the actual results, and gradually optimizes the weight value. The weight combination of the cardiopulmonary resuscitation plan that was finally confirmed showed the highest fitness and became the optimal weight combination. These optimized weight values ​​are applied to the evaluation index system to calculate the comprehensive score of each preset first aid plan to ensure that the most suitable first aid plan is selected.

[0108] In order to solve the problem of lack of professional support when the emergency scene is beyond the local processing range in the prior art, in some embodiments, the communication module in step 104 further includes:

[0109] A monitoring subunit is used to monitor the situation at the first aid scene based on the intelligent monitoring system in the portable multifunctional first aid kit, and to establish contact with the expert platform in a timely manner to establish a remote communication connection when a situation beyond the scope of the first aid plan is detected at the first aid scene and professional help is needed; an analysis subunit is used to obtain the first aid scene information through the remote communication connection, in combination with the operation guide, using image and video analysis algorithms, and applying computer vision technology to identify and mark key areas of the first aid scene to obtain key area information; an encryption subunit is used to securely transmit and optimize the key area information based on the encryption communication protocol combined with the compression algorithm to ensure that the expert platform quickly receives high-quality image and video data and generates securely transmitted data packets; an evaluation subunit is used to evaluate the situation at the first aid scene based on the securely transmitted data packets, with the help of artificial intelligence-assisted diagnosis tools by professionals on the expert platform to determine the best treatment plan. Based on the treatment plan, the expert platform introduces a historical case library and a medical knowledge map to generate detailed treatment recommendations. Optionally, the analysis subunit includes: based on the intelligent monitoring system in the portable multifunctional first aid kit, through a remote communication connection, when the first aid scene appears a situation beyond the scope of the first aid plan, the portable multifunctional first aid kit captures the picture of the first aid scene through a camera and a sensor to obtain image and video data of the first aid scene; compares the image and video data of the first aid scene with the operation guide to determine the first aid links and item locations that need to be focused on, generates preliminary analysis results, based on the preliminary analysis results, uses image and video analysis algorithms, applies computer vision technology, identifies and marks key areas of the first aid scene; based on the key areas, through multi-angle view analysis and time series data processing, refines and evaluates the specific situation of the first aid scene, generates a detailed dynamic evaluation report, converts the dynamic evaluation report into a structured data form, records the coordinates, type and attributes of each of the key areas, and obtains accurate key area information.

[0110] In this embodiment, the task of the communication module is to ensure that when the emergency scene is beyond the scope of local processing, a remote communication connection is established with the expert platform in a timely manner, and high-quality images and video data are provided to obtain professional treatment advice. The monitoring subunit monitors the situation at the emergency scene in real time based on the intelligent monitoring system in the portable multi-functional first aid box. When it is detected that professional help is needed, the monitoring subunit will immediately establish contact with the expert platform and start the remote communication connection. The analysis subunit uses image and video analysis algorithms, combined with computer vision technology, to identify and mark the key areas of the emergency scene and generate key area information. The encryption subunit securely transmits and optimizes the information of these key areas according to the encryption communication protocol and compression algorithm, ensuring that the expert platform quickly receives high-quality images and video data. The evaluation subunit evaluates the situation at the emergency scene based on the received data packets, with the help of artificial intelligence-assisted diagnostic tools, determines the best treatment plan, and generates detailed treatment recommendations.

[0111] In the embodiment of the present application, the communication module first monitors the situation of the emergency scene in real time through the monitoring subunit. When it is detected that the emergency scene exceeds the local processing range and requires professional help, the monitoring subunit immediately establishes a remote communication connection with the expert platform. Next, the analysis subunit obtains the emergency scene information through the remote communication connection, combines the operation guide, uses image and video analysis algorithms, and applies computer vision technology to identify and mark the key areas of the emergency scene, and generates key area information. The encryption subunit encrypts and compresses these key area information to ensure the secure transmission and optimized processing of the data, and generates a securely transmitted data packet. Finally, based on these data packets, the evaluation subunit uses artificial intelligence-assisted diagnostic tools to evaluate the specific situation of the emergency scene, determine the best treatment plan, and introduce historical case libraries and medical knowledge maps to generate detailed treatment recommendations. The whole process ensures the efficient, secure transmission and professional evaluation of emergency scene information, significantly improving the efficiency and success rate of emergency treatment.

[0112] The following is a specific embodiment:

[0113] Suppose the user enters the symptom information of "chest pain" and "dyspnea" on the portable multifunctional first aid kit and selects cardiopulmonary resuscitation as the first aid plan. However, during the execution, the patient's condition did not improve. The monitoring subunit detected that the first aid scene was beyond the local processing range and immediately established a remote communication connection with the expert platform. At this time, the analysis subunit captures the screen of the first aid scene through cameras and sensors to obtain image and video data. It compares these data with the operation guide, determines the first aid links and item locations that need to be focused on, and generates preliminary analysis results. Then, using image and video analysis algorithms, computer vision technology is applied to identify and mark the key areas of the first aid scene, such as the patient's electrocardiogram, respiratory status, etc. Through multi-angle view analysis and time series data processing, the specific situation of the first aid scene is refined and evaluated, and a detailed dynamic evaluation report is generated to record the coordinates, type and attributes of each key area to obtain accurate key area information. The encryption subunit encrypts and compresses these key area information to generate data packets for secure transmission. After receiving these data, professionals on the expert platform use artificial intelligence-assisted diagnosis tools to evaluate the first aid scene, and combine historical case libraries and medical knowledge graphs to generate detailed treatment recommendations, such as immediate hospitalization for further treatment. Through this process, the communication module not only improves the efficiency and security of information transmission at the emergency scene, but also ensures the timeliness and accuracy of professional treatment recommendations, greatly improving the success rate of first aid and the patient's chance of survival.

[0114] In order to solve the problems in the prior art that the order of taking out first aid items is not optimized enough and the emotional state of the user is not fully considered, in some embodiments, the guidance module in step 103 is also used to:

[0115] Based on the first aid plan, combined with the intelligent distribution system in the portable multifunctional first aid box, a three-dimensional spatial positioning algorithm is used, and through visual sensors and lidar technology, the first aid items in the portable multifunctional first aid box are located, and the coordinates of each first aid item in the three-dimensional space are recorded, and the deep learning model is applied to identify and classify the first aid items to obtain accurate location information of the first aid items; according to the location information of the first aid items, a variety of path planning algorithms and machine learning prediction models are applied to calculate the optimal path from opening the portable multifunctional first aid box to taking out all the required first aid items, and the order of taking out is dynamically adjusted according to the status of the first aid items in the first aid plan to obtain the optimized order of taking out; based on the optimized order of taking out, the portable multifunctional first aid box is used to calculate the optimal path from opening the portable multifunctional first aid box to taking out all the required first aid items, and the order of taking out is dynamically adjusted according to the status of the first aid items in the first aid plan to obtain the optimized order of taking out; based on the optimized order of taking out, the portable multifunctional first aid box is used to calculate the optimal path from opening the portable multifunctional first aid box to taking out all the required first aid items, and the first aid plan ... The interactive interface of the portable multifunctional first aid kit combines SLAM technology and object tracking algorithm, and through an augmented reality guidance module, displays the path and order of taking out the first aid items when the user opens the portable multifunctional first aid kit, so as to help the user quickly locate and take out the required first aid items, and generate intuitive taking out instructions; based on the facial expression recognition algorithm and the sound emotion recognition algorithm, the emotional state of the user is evaluated, and according to the emotional state, combined with personalized speech synthesis technology and emotional feedback loop mechanism, the speed, volume and tone of the voice prompt on the portable multifunctional first aid kit are adjusted to generate personalized voice prompts; based on the order of taking out the first aid items in the taking out instructions and the method of use in the personalized voice prompts, natural language processing technology and image enhancement technology are used to generate an operation guide.

[0116] In this embodiment, the task of the guidance module is to accurately locate the first aid items based on the first aid plan, combined with the intelligent distribution system in the portable multifunctional first aid box, and use the three-dimensional space positioning algorithm, visual sensor and laser radar technology to record its coordinates in the three-dimensional space. The first aid items are identified and classified by applying the deep learning model to obtain accurate location information. Then, based on this location information, a variety of path planning algorithms and machine learning prediction models are used to calculate the optimal path from opening the first aid box to taking out all the required first aid items, and dynamically adjust the order of taking out to ensure that the required first aid items are provided as quickly as possible. Next, the guidance module combines SLAM technology and object tracking algorithm to display the path and order of taking out the first aid items under the guidance of augmented reality, helping users to quickly locate and take out the required items and generate intuitive removal instructions. In addition, the guidance module evaluates the user's emotional state through facial expression recognition algorithms and sound emotion recognition algorithms, combines personalized speech synthesis technology and emotional feedback loop mechanism, adjusts the speed, volume and tone of the voice prompts, and generates personalized voice prompts. Finally, natural language processing technology and image enhancement technology are used to generate detailed operation guides to ensure that users can use first aid items correctly.

[0117] In the embodiments of the present application, the guidance module first, based on the first aid plan, in combination with the intelligent allocation system, uses the three-dimensional space positioning algorithm and visual sensors, lidar technology to accurately locate the first aid items in the first aid kit and record the coordinates of each item in the three-dimensional space. The first aid items are identified and classified through a deep learning model to ensure the accuracy of the location information. Next, according to these location information, the guidance module applies a variety of path planning algorithms and machine learning prediction models to calculate the optimal path from opening the first aid kit to taking out all the required first aid items, and dynamically adjusts the taking-out order according to the status of the first aid items in the first aid plan to obtain the optimized taking-out order. Then, the guidance module combines SLAM technology and object tracking algorithms. When the user opens the first aid kit, the taking-out path and order of the first aid items are displayed through the augmented reality guidance module to generate an intuitive taking-out guide to help the user quickly locate and take out the required items. In addition, the guidance module evaluates the user's emotional state through facial expression recognition algorithms and voice emotion recognition algorithms, and adjusts the speed, volume, and intonation of the voice prompt according to the emotional state to generate personalized voice prompts to help the user stay calm and operate correctly. Finally, the guidance module uses natural language processing technology and image enhancement technology to generate detailed operation guides to ensure that users can correctly use the first aid items and improve the first aid efficiency and success rate.

[0118] The following is a specific embodiment:

[0119] Suppose the user inputs symptom information of "chest pain" and "difficulty breathing" and selects cardiopulmonary resuscitation as the first aid plan. The guidance module first uses the three-dimensional space positioning algorithm and visual sensors, lidar technology to accurately locate the first aid items such as AED and nitroglycerin tablets in the first aid kit and record their coordinates in the three-dimensional space. These items are identified and classified through a deep learning model to ensure the accuracy of the location information. Next, according to these location information, the guidance module applies the A* path planning algorithm and machine learning prediction models to calculate the optimal path from opening the first aid kit to taking out the AED and nitroglycerin tablets, and dynamically adjusts the taking-out order according to the first aid plan to ensure that the user can obtain the required items most quickly. When the user opens the first aid kit, the guidance module combines SLAM technology and object tracking algorithms to display the taking-out path and order of the AED and drugs under augmented reality guidance to generate an intuitive taking-out guide to help the user quickly locate and take out the required items. At the same time, the guidance module evaluates the user's emotional state through facial expression recognition algorithms and voice emotion recognition algorithms and finds that the user is very nervous, so it adjusts the speed of the voice prompt to be slower, the volume to be moderate, and the intonation to be gentle, generating a personalized voice prompt: "Please don't panic and follow the instructions step by step." Finally, the guidance module uses natural language processing technology and image enhancement technology to generate detailed operation guides to ensure that users can correctly use the AED and drugs, improving the first aid efficiency and success rate.

[0120] In order to solve the problem that the initial symptom description of emergency medical situations in the prior art is not accurate and comprehensive enough, in some embodiments, the receiving module in step 101 is further used to:

[0121] An emergency medical situation is received, and the user inputs symptom information related to the emergency medical situation through an interactive interface on a portable multifunctional first aid kit to obtain an initial symptom description of the emergency medical situation; the initial symptom description is preliminarily parsed using natural language processing technology, and keywords and phrases in the initial symptom description are extracted using a text analysis algorithm to obtain a preliminary parsing result; based on the preliminary parsing result, the semantic features in the symptom description are quantified and encoded using a semantic analysis network, and medical entity features are extracted from entity relationships in a medical knowledge base, and the semantic features and the medical entity features are high-dimensionally quantified and encoded to obtain a structured data form; based on the structured data form, an evaluation index system is constructed, and a multi-dimensional evaluation algorithm is used to evaluate the relevance and importance of each symptom in the initial symptom description, so as to calculate a matching score for each symptom in the initial symptom description, and screen out key symptom information.

[0122] In this embodiment, the task of the receiving module is to receive the symptom information related to the emergency medical situation input by the user through the interactive interface on the portable multifunctional first aid kit, generate an initial symptom description, and perform a preliminary analysis on it using natural language processing technology and text analysis algorithms. Specifically, the receiving module first obtains the symptom information input by the user to obtain an initial symptom description. Then, the initial symptom description is preliminarily analyzed by applying natural language processing technology, and the keywords and phrases therein are extracted using a text analysis algorithm to form a preliminary analysis result. Next, based on these preliminary analysis results, the receiving module quantifies and encodes the semantic features in the symptom description through a semantic analysis network, and extracts entity relationships from a medical knowledge base to generate medical entity features. Finally, the received semantic features and medical entity features are quantified and encoded in high dimensions and converted into a structured data form. In addition, the receiving module constructs an evaluation index system based on this structured data, uses a multi-dimensional evaluation algorithm to evaluate the relevance and importance of each symptom, calculates the matching score of each symptom, and screens out key symptom information.

[0123] In an embodiment of the present application, the receiving module first receives the emergency medical condition-related symptom information input by the user through the interactive interface on the portable multifunctional first aid kit, and generates an initial symptom description. Then, the initial symptom description is preliminarily parsed and processed using natural language processing technology to extract key words and phrases therein to form a preliminary parsing result. Next, the receiving module quantifies and encodes the semantic features in the symptom description through a semantic analysis network, and extracts medical entity features in combination with the entity relationships in the medical knowledge base. These features are quantified and encoded in a high-dimensional manner to form a structured data form. Based on this structured data, the receiving module constructs an evaluation index system, uses a multi-dimensional evaluation algorithm to evaluate the relevance and importance of each symptom, calculates the matching score of each symptom, and screens out key symptom information. The entire process ensures the accurate parsing of the initial symptom description and the effective extraction of key symptom information, significantly improving the scientificity and accuracy of the subsequent first aid plan selection.

[0124] The following is a specific embodiment:

[0125] Suppose the user enters symptom information such as "chest pain", "dyspnea", and "sweating" on the portable multifunctional first aid kit. The receiving module first generates an initial symptom description: "The patient feels chest pain, accompanied by dyspnea and heavy sweating." Next, the receiving module uses natural language processing technology to perform preliminary parsing on this description, extracting keywords and phrases such as "chest pain", "dyspnea", and "sweating" to form a preliminary parsing result. Then, the semantic features in these symptom descriptions are quantified and encoded through the semantic analysis network, and medical entity features such as "heart problems" and "respiratory system problems" are extracted in combination with the entity relationships in the medical knowledge base. After high-dimensional quantification and encoding, these features form a structured data form. Based on this structured data, the receiving module constructs an evaluation index system, uses a multi-dimensional evaluation algorithm to evaluate the relevance and importance of each symptom, and calculates the matching score of each symptom. For example, the matching score of "chest pain" is 90, "dyspnea" is 85, and "sweating" is 70. Finally, the receiving module screens out key symptom information and confirms that "chest pain" and "dyspnea" are the main symptoms, providing a basis for further selection of emergency plans. Through this process, the receiving module not only improves the parsing accuracy of the initial symptom description, but also ensures the effective extraction of key symptom information, greatly improving the scientificity and accuracy of subsequent first aid plan selection.

[0126] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the system described in each embodiment or some parts of the embodiment.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A first aid system based on a portable multifunctional first aid kit, characterized in that: include: A receiving module, for receiving an emergency medical condition, wherein a user inputs symptom information related to the emergency medical condition through an interactive interface on the portable multifunctional first aid kit, outputs a symptom description of the emergency medical condition, and identifies key symptom information from the symptom description; a conversion module, for parsing and converting the key symptom information using the symptom classification model preset in the portable multifunctional first aid kit to obtain a structured data form, applying a multi-dimensional evaluation and weight distribution algorithm in combination with the data form to select a first aid plan from a plurality of preset first aid plans included in the symptom classification model; A guidance module, which is used to optimize the order of taking out the first aid items in the first aid plan based on the first aid plan and the intelligent distribution system in the portable multifunctional first aid box, using a path planning algorithm to ensure that the first aid items required by the user are provided as quickly as possible, and to display and guide the correct use of the first aid items in the first aid plan according to the voice prompts adjusted by the augmented reality guidance module and the emotional calculation, and generate an operation guide; A communication module, for triggering the establishment of a remote communication connection based on information of the first aid scene in combination with the operation guide when a situation beyond the scope of the first aid plan occurs at the first aid scene, and using image and video analysis algorithms to evaluate key areas from the situation at the first aid scene, and transmitting and analyzing the information of the key areas to an expert platform established by the remote communication connection through an encrypted communication protocol to obtain treatment suggestions; A statistics module, used to count the number and usage of first aid supplies in the first aid plan, record the first aid supplies used in each first aid process, update the number of first aid supplies in the portable multifunctional first aid box, and generate a usage log; A generating module is used to collect and process key information of the first aid scene based on the treatment suggestion and the usage log to generate a first aid report.

2. The system according to claim 1, characterized in that The conversion module comprises: An extraction unit is used to quantify and encode semantic features from the key symptom information based on a symptom classification model preset in the portable multifunctional first aid kit by using a deep learning semantic analysis network, and to extract medical entity features from the key symptom information in combination with entity relationships in a medical knowledge base based on a knowledge graph technology, and to perform high-dimensional quantification and encoding on the semantic features and the medical entity features to obtain a high-dimensional structured data form; A construction unit is used to construct an evaluation index system based on the data form, and evaluate the relevance and applicability of the preset first aid plan in the symptom classification model based on the evaluation index system combined with a multi-dimensional evaluation algorithm of integrated learning, so as to calculate a matching score between the preset first aid plan and the key symptom information; An updating unit, configured to set an initial weight value by applying a Bayesian optimization algorithm according to the matching score, and update the initial weight value by iteration, and search for an optimal weight combination in the updated weight values ​​by using a genetic algorithm, and perform a comprehensive scoring process on the preset first aid plan based on the optimal weight combination to obtain an optimal comprehensive scoring result; The optimization unit is used to adopt a reinforcement learning strategy based on the comprehensive scoring result to establish a reinforcement learning framework based on the Markov decision process from the selection process of the preset first aid plan, and use a Q learning algorithm to optimize the selection strategy of the preset first aid plan within the reinforcement learning framework to improve the accuracy and efficiency of the selection of the preset first aid plan, and select the first aid plan with the highest comprehensive score from the multiple preset first aid plans as the most suitable first aid plan.

3. The system according to claim 2, characterized in that The construction unit comprises: A definition subunit is used to define and construct a multi-dimensional evaluation index system based on the high-dimensional structured data form, and set quantitative standards for each dimension in the evaluation index system according to medical standards and emergency needs to obtain a comprehensive evaluation index system, wherein the multiple dimensions include symptom type, severity, urgency, and medical history; An evaluation subunit, used to evaluate the relevance and applicability of the preset first aid plan in the symptom classification model by using the dimensional standards of each dimension in the comprehensive evaluation index system in combination with the multi-dimensional evaluation algorithm of ensemble learning, so as to calculate the similarity or relevance score of the preset first aid plan in each dimension in the comprehensive evaluation index system, and obtain a preliminary dimension score; The analysis subunit is used to perform a comprehensive comparison and analysis of the preset first aid plan and the key symptom information based on the preliminary dimension scores through an integrated learning algorithm, so as to summarize the similarity or correlation scores of each dimension in the comprehensive evaluation index system, and obtain a comprehensive matching score between the preset first aid plan and the key symptom information.

4. The system according to claim 3, characterized in that The evaluation subunit is further used for: Based on each dimension standard in the comprehensive evaluation index system, using the set quantitative standards, comparing the key symptom information with each preset first aid plan in the symptom classification model, and obtaining a preliminary score of each preset first aid plan in the symptom classification model in each dimension; Applying a multi-dimensional evaluation algorithm of integrated learning to evaluate the preliminary score of each preset first aid plan in each dimension, and obtaining the preliminary dimension score after comprehensive evaluation through multiple machine learning models; Based on the preliminary dimension scores after the comprehensive evaluation, the performance of the preset first aid plan in each dimension in the comprehensive evaluation index system is standardized to generate standardized dimension scores; The standardized dimension scores are summarized to calculate the similarity or correlation scores of the preset first aid plan in each dimension in the comprehensive evaluation index system, and obtain the preliminary dimension scores between the preset first aid plan and the key symptom information.

5. The system according to claim 2, characterized in that The updating unit is further used for: Obtaining historical first aid cases, applying a Bayesian optimization algorithm, setting a preliminary weight configuration based on information provided by the matching score, and dynamically adjusting the initial weight value by combining the data of the historical first aid cases and user input feedback obtained from an interactive interface in the portable multifunctional first aid box using the Bayesian optimization algorithm; The initial weight value is updated by using the iterative mechanism of the Bayesian optimization algorithm, and the selection and use feedback of the user on the preset first aid plan are collected and monitored through the interactive interface on the portable multifunctional first aid box, and the effectiveness of the selection and use feedback of the preset first aid plan is evaluated according to the actual effect after the first aid is completed, so as to obtain the weight value after multiple iterations of optimization; Using a genetic algorithm to search for a preliminary weight combination in the weight values ​​after multiple iterations of optimization, based on the preliminary weight combination, through the selection, crossover and mutation operations of the genetic algorithm, the best performing weight combination is screened out, and the fitness of the weight combination is evaluated according to a comprehensive scoring function to obtain the optimal weight combination; The preset first aid plan is scored comprehensively on the dimensions, the optimal weight combination is applied to the matching scores of each dimension in the evaluation index system, the comprehensive score of each preset first aid plan is calculated, and the optimal comprehensive score result is obtained.

6. The system according to claim 5, characterized in that The method of searching for a preliminary weight combination in the weight values ​​by using a genetic algorithm, screening out the best performing weight combination based on the preliminary weight combination through selection, crossover and mutation operations of the genetic algorithm, and evaluating the fitness of the weight combination according to a comprehensive scoring function to obtain the optimal weight combination includes: Applying a selection operation of a genetic algorithm, using a comprehensive scoring function to evaluate the fitness of each of the preliminary weight combinations, so as to select weight combinations with higher fitness as candidates for the next generation population, and obtain high-quality candidate weight combinations; Select two weight combinations from the candidate weight combinations as parents, exchange some weight values ​​of the two weights to generate offspring weight combinations, and generate a diversified new generation population based on the offspring weight combinations by using crossover operations and mutation operations; According to the new generation population, performing a mutation operation of a genetic algorithm on the offspring weight combination to generate a mutated offspring weight combination; A comprehensive scoring function is applied to evaluate the fitness of the mutated offspring weight combination, and the best performing weight combination is screened out through multiple rounds of evolutionary iterations. The optimal weight combination is obtained based on the fact that the best weight combination exhibits the highest fitness in the evaluation index system.

7. The system according to claim 1, characterized in that The communication module further includes: A monitoring subunit is used to monitor the situation at the emergency scene based on the intelligent monitoring system in the portable multifunctional first aid kit, and to establish contact with the expert platform in a timely manner to establish a remote communication connection when a situation beyond the scope of the first aid plan is detected at the emergency scene and professional help is needed; An analysis subunit, configured to identify and mark key areas of the first aid scene based on the first aid scene information obtained through the remote communication connection, in combination with the operation guide, using image and video analysis algorithms, and applying computer vision technology to obtain key area information; The encryption subunit is used to securely transmit and optimize the key area information according to the encryption communication protocol combined with the compression algorithm to ensure that the expert platform quickly receives high-quality image and video data and generates a data packet for secure transmission; The evaluation subunit is used for evaluating the situation at the emergency scene based on the securely transmitted data packet, and for professionals on the expert platform to use artificial intelligence-assisted diagnosis tools to determine the best treatment plan. Based on the treatment plan, the expert platform introduces a historical case library and a medical knowledge graph to generate detailed treatment recommendations.

8. The system according to claim 7, characterized in that The analysis subunit is further used for: Based on the intelligent monitoring system in the portable multifunctional first aid kit, through remote communication connection, when the first aid scene appears a situation beyond the scope of the first aid plan, the portable multifunctional first aid kit captures the picture of the first aid scene through the camera and sensor to obtain the image and video data of the first aid scene; Compare the image and video data of the first aid scene with the operation guide to determine the first aid links and item locations that need to be focused on, generate preliminary analysis results, and based on the preliminary analysis results, use image and video analysis algorithms and apply computer vision technology to identify and mark key areas of the first aid scene; Based on the key areas, through multi-angle view analysis and time series data processing, the specific situation of the emergency scene is evaluated in detail, and a detailed dynamic evaluation report is generated. The dynamic evaluation report is converted into a structured data form, and the coordinates, type and attributes of each key area are recorded to obtain accurate key area information.

9. The system according to claim 1, characterized in that The guidance module is also used to: Based on the first aid plan, combined with the intelligent distribution system in the portable multifunctional first aid box, the first aid items in the portable multifunctional first aid box are located by using a three-dimensional spatial positioning algorithm, through visual sensors and laser radar technology, and the coordinates of each first aid item in the three-dimensional space are recorded. The first aid items are identified and classified by applying a deep learning model to obtain accurate location information of the first aid items; According to the location information of the first aid items, a variety of path planning algorithms and machine learning prediction models are applied to calculate the optimal path from opening the portable multifunctional first aid box to taking out all the required first aid items, and dynamically adjust the order of taking out according to the state of the first aid items in the first aid plan to obtain an optimized order of taking out; Based on the optimized removal order, the interactive interface of the portable multifunctional first aid kit is used, combined with SLAM technology and object tracking algorithm, and through an augmented reality guidance module, when a user opens the portable multifunctional first aid kit, the removal path and order of the first aid items are displayed to help the user quickly locate and remove the required first aid items, and generate intuitive removal instructions; Based on the facial expression recognition algorithm and the sound emotion recognition algorithm, the emotional state of the user is evaluated, and according to the emotional state, the speech speed, volume and tone of the voice prompt on the portable multifunctional first aid kit are adjusted in combination with the personalized speech synthesis technology and the emotional feedback loop mechanism to generate a personalized voice prompt; Based on the order of taking out the first aid items in the taking out guide and the usage method in the personalized voice prompt, an operation guide is generated using natural language processing technology and image enhancement technology.

10. The system according to claim 1, characterized in that The receiving module is further used for: Receiving an emergency medical situation, the user inputs symptom information related to the emergency medical situation through an interactive interface on the portable multifunctional first aid kit to obtain an initial symptom description of the emergency medical situation; Using natural language processing technology to perform preliminary analysis on the initial symptom description, and applying a text analysis algorithm to extract keywords and phrases in the initial symptom description to obtain preliminary analysis results; According to the preliminary parsing results, the semantic features in the symptom description are quantified and encoded through a semantic analysis network, and medical entity features are extracted from entity relationships in a medical knowledge base, and the semantic features and the medical entity features are quantified and encoded in a high-dimensional manner to obtain a structured data form; Based on the structured data form, an evaluation index system is constructed, and a multi-dimensional evaluation algorithm is used to evaluate the relevance and importance of each symptom in the initial symptom description, so as to calculate the matching score of each symptom in the initial symptom description and screen out key symptom information.

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