Auxiliary diagnosis method and system for intelligent first-aid kit

Abstract

The invention relates to the field of auxiliary diagnosis, and provides an auxiliary diagnosis method and system for an intelligent first-aid kit, and the method comprises the steps: monitoring all data of a patient in real time through a multi-sensor network; performing medical condition preliminary classification, personalized medical material suggestion generation and operation guide generation based on various data of the patient; an instruction corresponding to the operation guide is received, a guide system version is obtained, and functions and a service system of the intelligent first-aid kit are optimized; establishing a remote medical consultation channel, and transmitting data to a remote medical expert team; relevant data transmitted by a remote medical consultation channel are obtained, the condition of a patient is evaluated in real time in combination with an expert team, and medical guidance suggestions and key node records of the first-aid process are fed back. According to the technical scheme provided by the invention, the adaptability and the operation convenience of the intelligent first-aid kit in a complex emergency situation are remarkably improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of auxiliary diagnosis systems, and in particular, to an auxiliary diagnosis method and system for an intelligent first aid kit. Background Art

[0002] In emergency medical rescue scenarios, especially in remote areas or disaster sites, it is crucial to quickly and accurately identify the patient's condition and provide appropriate first aid measures. Smart first aid kits need to be able to monitor the patient's vital signs and environmental parameters in real time, automatically identify emergency medical conditions, and provide easy-to-understand and easy-to-implement first aid instructions for non-professionals. In addition, remote expert support functions are required to ensure that high-quality first aid services can be provided even in the absence of on-site professional medical resources.

[0003] Currently, smart first aid kits on the market are usually equipped with basic vital signs monitoring equipment and preset first aid supplies. Some more advanced systems have also introduced simple automatic diagnosis algorithms and remote communication modules to assist non-professionals in performing preliminary first aid treatment. However, most of these systems rely on static material configurations and limited remote support capabilities, and lack the ability to dynamically adjust according to specific circumstances.

[0004] The main problem with existing smart first aid kits is that they are not adaptable enough to complex emergencies. On the one hand, the recommended materials are often fixed and cannot be adjusted according to the actual situation, resulting in waste or shortage of materials; on the other hand, the existing operating instructions are mostly in text form, which is difficult for non-professionals to understand quickly and lack interactivity and situational awareness. In addition, telemedicine support is often limited to data transmission and lacks real-time professional guidance and feedback mechanisms, which affects the effectiveness of first aid. Summary of the invention

[0005] The embodiment of the present application provides an auxiliary diagnosis system for an intelligent first aid kit, which is used to solve the problems in the prior art that material recommendation plans are fixed, operation instructions are difficult to understand, and remote support is insufficient.

[0006] In a first aspect, an embodiment of the present application provides an auxiliary diagnosis method of a smart first aid kit, the method comprising:

[0007] Real-time monitoring of patients’ vital signs, environmental parameters, and emergency treatment progress data through a multi-sensor network;

[0008] Automatically identifying emergency medical conditions based on data acquired by the multi-sensor network and triggering an emergency response mechanism of the first aid kit, and preliminarily classifying the emergency medical conditions based on similar situations in a historical case database;

[0009] Based on the results of the preliminary classification, a genetic algorithm is used to dynamically adjust the emergency supplies recommendation plan. By combining the status of existing supplies in the first aid box with the preset disease model library, the most suitable combination of supplies is optimized by simulating the effects of different treatment paths. The failure mode and effects analysis technology is used to predict and analyze possible complications, and personalized medical supplies recommendations are obtained.

[0010] Generate an operation guide based on the personalized medical supplies recommendation using a mixed reality guidance system in conjunction with a semantic understanding algorithm, converting medical terminology into easy-to-understand instructions;

[0011] After receiving the instructions, interactive visual guidance is created through mixed reality, and situational awareness technology is introduced to obtain operational feedback and interactive information, and the guidance system version is obtained. In addition, the functions and service systems of the smart first aid kit are optimized in combination with the content of professional guidance exchanges;

[0012] Establish a telemedicine consultation channel to synchronously transmit vital sign data, emergency treatment progress data and interactive information to the telemedicine terminal;

[0013] Obtain relevant data transmitted by the telemedicine consultation channel, work with the expert team to conduct an immediate assessment of the patient's condition, and provide feedback on medical guidance and key node records of the emergency process.

[0014] Optionally, the automatic identification of emergency medical conditions based on the data acquired by the multi-sensor network and triggering of an emergency response mechanism of a first aid kit, and the preliminary classification of the emergency medical conditions according to similar situations in a historical case database, include:

[0015] Using the result of the preliminary classification, reconstruct the emergency medical situation and generate a scenario reconstruction model;

[0016] According to the scenario reconstruction model, a genetic algorithm is applied to encode the initial material allocation plan corresponding to the emergency medical situation, generate multiple candidate plans, and form a candidate plan set;

[0017] Based on the candidate solution set, combined with the status of existing materials in the first aid kit and the preset disease model library, the effects of different treatment paths are simulated, the fitness of each candidate solution is evaluated, and the fitness evaluation result is obtained;

[0018] Using the fitness evaluation results, the candidate solutions are selected, crossed and mutated, and the material allocation solution is iteratively updated until the material combination with the highest fitness is found, thereby obtaining the optimal material allocation solution;

[0019] According to the optimal material allocation plan, the failure mode and effect analysis technology is used to predict and analyze complications, identify potential risk points and their severity, and obtain complication prevention measures;

[0020] Based on the complication prevention measures, a personalized medical supplies recommendation is generated, wherein the personalized medical supplies recommendation includes basic first aid needs and additional supplies required for complication prevention.

[0021] Optionally, the scenario reconstruction model is based on which a genetic algorithm is applied to encode the initial material allocation plan corresponding to the emergency medical situation, to generate a plurality of candidate plans, and to form a candidate plan set, including:

[0022] Using the scenario reconstruction model, determine the types and quantity ranges of emergency supplies that may be needed under the emergency medical situation, and generate a supply demand framework;

[0023] Initializing the chromosome representation in the genetic algorithm, encoding the initial material allocation plan corresponding to the material demand framework into a chromosome to obtain an initial population; wherein the genes on each chromosome represent the quantity or type of a specific material;

[0024] According to the initial population and in combination with the status of existing materials in the first aid box, infeasible material allocation plans are eliminated to ensure that all material allocation plans are physically feasible, thereby generating an initial material allocation plan set;

[0025] By using the initial material allocation scheme set, applying the selection, crossover and mutation operations of the genetic algorithm, a new material allocation scheme is randomly generated to increase the diversity of the material allocation scheme, obtain multiple candidate schemes, and form a candidate scheme set.

[0026] Optionally, the optimal material allocation plan is used to predict and analyze complications using failure mode and effect analysis technology, identify potential risk points and their severity, and obtain complication prevention measures, including:

[0027] Using the optimal material allocation plan and combining it with the complication data in the preset disease model library, a comprehensive assessment of possible complications is conducted to generate a complication assessment list;

[0028] Based on the complication assessment list, the failure mode and effect analysis technology is applied to quantitatively analyze the occurrence probability, detection difficulty and severity of each complication to obtain a complication risk score;

[0029] Based on the complication risk score, high-risk complication types and their key triggering factors are identified to form a high-risk complication list;

[0030] Utilize the high-risk complication list to develop targeted prevention strategies, design specific prevention measures for each high-risk complication, and obtain complication prevention measures.

[0031] Optionally, after receiving the instruction, interactive visual guidance is created through mixed reality, and situational awareness technology is introduced to obtain operation feedback and interactive information, so as to obtain a guidance system version, and the functions and service systems of the smart first aid kit are optimized in combination with the content of professional guidance communication, including:

[0032] Using the personalized medical supplies recommendations, the specific procedures and supplies involved in the emergency treatment process are described in detail, and the medical terms are converted into daily language instructions in combination with the semantic understanding algorithm to obtain a preliminary operation guide;

[0033] Based on the preliminary operation guide, a mixed reality guidance system is applied to design corresponding three-dimensional visualization models and interactive elements for each operation, create interactive visual guidance, and generate enhanced operation guide;

[0034] According to the enhanced operation guide, a context-aware sensor is integrated into the first aid kit to monitor the user's operation behavior in real time, and collect the interaction data between the user and the system to obtain an operation feedback data set;

[0035] Utilize the operational feedback data set and combine it with machine learning algorithms to conduct in-depth analysis, identify common errors and difficulties, evaluate the effectiveness of the guidance system and user experience, and generate a user behavior analysis report;

[0036] Based on the user behavior analysis report, the content and form of the enhanced operation guide are adjusted and optimized according to the problems and improvement points found, so as to ensure the accuracy and friendliness of the guidance system and obtain the guidance system version.

[0037] Optionally, the operation feedback data set is used in combination with a machine learning algorithm to perform in-depth analysis, identify common errors and difficulties, evaluate the effectiveness of the guidance system and user experience, and generate a user behavior analysis report, including:

[0038] Using the operation feedback data set, preprocessing the user's operation behavior and interaction mode, removing outliers and noise, and obtaining clean operation behavior data;

[0039] Based on the cleaned operation behavior data, a classification algorithm is applied to classify different types of user behaviors, and the behavior patterns of successful operation completion and the behavior patterns of failure or difficulty are identified to obtain a behavior pattern classification result;

[0040] Based on the behavior pattern classification results, a clustering algorithm is used to analyze the common characteristics of similar behavior patterns, find out the key factors that lead to errors or difficulties, identify common errors and difficulties, and generate a behavior pattern analysis list;

[0041] Using the behavior pattern analysis list and combining it with the user satisfaction survey data, the effectiveness and user experience of the guidance system in actual use are evaluated to obtain a system effectiveness evaluation report;

[0042] Based on the system effectiveness evaluation report, combined with common problems and personalized needs in user behavior, a user behavior analysis report is generated.

[0043] Optionally, the remote medical consultation channel is a remote data transmission link constructed using Internet of Things technology and a secure encryption protocol.

[0044] Optionally, the remote data transmission link includes:

[0045] Collect and synchronously transmit the patient's vital signs data, emergency treatment progress and user interaction information with the smart first aid kit to the remote medical terminal in real time, and generate a real-time monitoring interface;

[0046] Based on the real-time monitoring interface, the expert team at the remote medical end conducts an immediate assessment of the patient's condition and provides professional medical guidance and suggestions through a two-way communication function.

[0047] Optionally, the real-time monitoring interface is also used to record important time nodes and events in the entire first aid process to obtain key node records of the first aid process.

[0048] In a second aspect, an embodiment of the present application provides an auxiliary diagnosis system of an intelligent first aid kit, which is used to perform the above method, and the system includes:

[0049] A multi-sensor network configured to monitor in real time vital sign data, environmental parameters, and emergency treatment progress data of a patient through the multi-sensor network;

[0050] A central processing unit, configured with a preliminary classification module, a prediction suggestion module and an instruction generation module;

[0051] The preliminary classification module automatically identifies emergency medical conditions based on data acquired by the multi-sensor network and triggers an emergency response mechanism of the first aid kit, and preliminarily classifies the emergency medical conditions based on similar conditions in a historical case database;

[0052] The preliminary recommendation module uses a genetic algorithm to dynamically adjust the first aid material recommendation plan based on the preliminary classification results, combines the status of existing materials in the first aid box with the preset disease model library, optimizes the most appropriate material combination by simulating the effects of different treatment paths, and uses the failure mode and effect analysis technology to predict and analyze possible complications to obtain personalized medical material recommendations;

[0053] The instruction generation module generates an operation guide based on the personalized medical supplies recommendation by using a mixed reality guidance system in conjunction with a semantic understanding algorithm, converting medical terms into easy-to-understand instructions;

[0054] The interactive visual guidance device is configured to create interactive visual guidance through mixed reality after receiving instructions, introduce situational awareness technology to obtain operation feedback and interactive information, obtain the guidance system version, and optimize the functions and service system of the smart first aid kit in combination with the content of professional guidance exchanges;

[0055] A remote communication link, configured to establish a remote medical consultation channel, and synchronously transmit vital sign data, emergency treatment progress data and interactive information to the remote medical terminal;

[0056] The telemedicine terminal is configured to obtain relevant data transmitted by the telemedicine consultation channel, conduct an immediate assessment of the patient's condition in conjunction with the expert team, and provide feedback on medical guidance and key node records of the emergency process.

[0057] In the embodiment of the present application, a multi-sensor network is used to monitor the patient's vital signs data and environmental parameters in real time, a central processing unit is used to automatically identify emergency medical conditions, and an emergency response mechanism of the first aid kit is triggered. The emergency medical conditions are preliminarily classified according to similar situations in the historical case database to obtain a preliminary classification result; based on the preliminary classification result, a genetic algorithm is used to dynamically adjust the first aid material recommendation plan, and the state of existing materials in the first aid kit and the preset disease model library are combined to optimize the most suitable material combination by simulating the effects of different treatment paths, and the failure mode and effect analysis technology is used to predict and analyze possible complications to obtain personalized medical material recommendations; the interactive visual guidance device uses a mixed reality guidance system The system cooperates with the semantic understanding algorithm to generate an operation guide according to the personalized medical supplies recommendation, converts medical terms into easy-to-understand instructions, creates interactive visual guidance through a mixed reality guidance system, introduces situational awareness technology to collect and analyze user operation feedback, and obtains a guidance system version. The improved guidance system version and the content of professional guidance exchanges are used to optimize the functions and service system of the smart first aid kit; a remote communication link is used to establish a remote medical consultation channel, and vital signs data, first aid treatment progress and user interaction information are synchronously transmitted to the remote medical terminal; relevant data transmitted by the remote medical consultation channel is obtained, and the patient's condition is immediately evaluated in conjunction with the expert team, and medical guidance suggestions and key node records of the first aid process are fed back.

[0058] The technical solution of this application has the following beneficial effects:

[0059] (1) Real-time monitoring of patients’ vital signs and environmental parameters through multi-sensor fusion technology, combined with preliminary classification based on historical case databases, can more accurately identify emergency medical conditions, reduce misjudgments and missed judgments, and provide a reliable basis for subsequent processing.

[0060] (2) Genetic algorithms are used to dynamically adjust the emergency material recommendation plan, combining the existing resource status with the preset disease model library to ensure that the material combination is both in line with the actual situation and can maximize the treatment effect. At the same time, the failure mode and effects analysis (FMEA) technology is used to predict complications and generate personalized medical material recommendations, making the preparation of emergency materials more accurate and comprehensive, and improving the efficiency and success rate of emergency treatment.

[0061] (3) Using a mixed reality guidance system with a semantic understanding algorithm, complex medical terms are converted into easy-to-understand operation guides, creating intuitive interactive visual guides to help non-professionals quickly master first aid skills. Introducing situational awareness technology to collect and analyze user feedback, continuously optimize the accuracy and friendliness of the guidance system, and improve the user experience and operational convenience.

[0062] (4) Establish a remote medical consultation channel to achieve the secure transmission of vital sign data, emergency treatment progress and user interaction information, so that remote experts can provide professional medical guidance in real time. Recording the key nodes of the emergency process not only helps to analyze and summarize experience and lessons after the event, but also supports the continuous improvement of the service system, ensuring that the functions and service system of the smart first aid kit are more complete, and enhancing the overall emergency capability and service quality.

[0063] Furthermore, the present invention generates operating guides based on personalized medical supplies recommendations by utilizing a mixed reality guidance system in conjunction with a semantic understanding algorithm, converting complex medical terms into easy-to-understand instructions, and creating interactive visual guidance, thereby significantly improving the accuracy and efficiency of operations performed by non-professionals in emergency medical situations. At the same time, contextual awareness technology is introduced to collect and analyze user operational feedback in real time, and in-depth analysis is performed in combination with machine learning algorithms to identify common errors and difficulties, evaluate the effectiveness of the guidance system and user experience, and continuously optimize the content and form of the operating guides to ensure the accuracy and friendliness of the guidance system. The final version of the guidance system not only improves the intuitiveness and ease of use of first aid operations, but also effectively solves the problems of difficult-to-understand operating guides, lack of interactivity, and contextual awareness in existing solutions through continuous improvement, greatly improving the reliability and user satisfaction of smart first aid kits in practical applications.

[0064] Furthermore, the present invention dynamically adjusts the first aid material recommendation plan by using a genetic algorithm based on the preliminary classification results, combines the existing material status in the first aid box with the preset disease model library, simulates the effects of different treatment paths, optimizes the most suitable material combination, and uses the failure mode and effect analysis technology to predict and analyze possible complications, and generates personalized medical material recommendations. This method not only significantly improves the accuracy and adaptability of material distribution, ensures the rational use and adequate preparation of first aid materials, but also greatly improves the comprehensiveness and safety of first aid treatment through effective prediction of complications and formulation of preventive measures. The personalized medical material recommendations finally generated not only cover basic first aid needs, but also take into account the additional materials required for complication prevention, effectively solving the problems of fixed material recommendations, inability to flexibly adjust according to actual conditions, and lack of complication prevention mechanisms in existing solutions, thereby greatly improving the rescue effect and success rate under emergency medical conditions.

[0065] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0067] Figure 1 Flow chart of an auxiliary diagnosis method of a smart first aid kit provided in an embodiment of the present application

[0068] Figure 2 A framework diagram of an auxiliary diagnosis system for an intelligent first aid kit provided in an embodiment of the present application;

[0069] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0071] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be performed in the order in which they appear in this article or may be performed in parallel. In addition, these processes may include more or fewer operations, and these operations may be performed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0072] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0073] Figure 1 A flowchart of an auxiliary diagnosis method of a smart first aid kit is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0074] The patient's vital signs, environmental parameters and emergency treatment progress data are monitored in real time through a multi-sensor network.

[0075] The multi-sensor network includes data from multiple sensors (such as vital signs sensors such as heart rate, blood pressure, and blood oxygen saturation, as well as environmental parameter sensors such as temperature and humidity) to monitor the patient's physical condition and surrounding environment in real time. These data are input into the processing system of the smart first aid kit to support subsequent emergency medical condition identification and classification. The historical case database contains a large amount of information on known medical cases, which is used to compare the current situation and make preliminary classifications.

[0076] Based on the data acquired by the multi-sensor network, emergency medical conditions are automatically identified and the emergency response mechanism of the first aid kit is triggered, and the emergency medical conditions are preliminarily classified according to similar situations in the historical case database.

[0077] In the embodiment of the present application, the patient's vital signs data and environmental parameters are collected in real time through a multi-sensor network, and similar situations in the historical case database are automatically identified and preliminarily classified, thereby triggering the emergency response mechanism of the first aid kit. This process not only improves the accuracy of emergency medical condition identification, but also provides a basis for subsequent material recommendations and operation guide generation.

[0078] Suppose a traffic accident occurs in a remote mountainous area and there is an injured person at the scene. After the smart first aid kit is activated, the built-in multi-sensor network immediately begins to monitor the injured person's heart rate, respiratory rate, body temperature and other vital signs, while recording environmental parameters such as temperature and humidity at the scene. The system compares these data with the historical case database and finds that they are similar to certain types of traumatic shock symptoms. It then triggers the emergency response mechanism and makes a preliminary classification of the emergency medical situation, laying the foundation for the next step of material distribution and guidance preparation.

[0079] Based on the results of preliminary classification, a genetic algorithm is used to dynamically adjust the recommended plan for first aid supplies. By combining the status of existing supplies in the first aid box with the preset disease model library, the most suitable combination of supplies is optimized by simulating the effects of different treatment paths. The failure mode and effects analysis technology is used to predict and analyze possible complications to obtain personalized medical supplies recommendations.

[0080] Genetic algorithm is a search algorithm that simulates natural selection and genetic mechanisms and is used to solve optimization problems. The preset disease model library contains a series of treatment paths and required material information for different diseases, which is used to guide the recommendation of emergency materials. Failure mode and effect analysis (FMEA) technology is used to assess the risk and severity of potential complications to ensure the safety and effectiveness of emergency measures.

[0081] In the embodiment of the present application, based on the preliminary classification results, the genetic algorithm is used to dynamically adjust the first aid material recommendation plan, combining the existing material status in the first aid box and the preset disease model library to simulate the effects of different treatment paths, and finally optimize the most suitable material combination. At the same time, FMEA technology is used to predict possible complications and generate personalized medical material recommendations to ensure that the configuration of first aid materials is both comprehensive and accurate.

[0082] Assuming that the injured person may be suffering from traumatic shock after preliminary classification, the system uses genetic algorithms to optimize the configuration of first aid supplies based on this classification result. Taking into account the existing resources in the first aid kit, the system simulates multiple treatment paths and selects the optimal combination of supplies, such as specific drugs and tourniquets. In addition, the system also uses FMEA technology to predict possible complications (such as infection or organ failure), and accordingly adds additional preventive supplies, generating a detailed personalized medical supplies recommendation.

[0083] A mixed reality guidance system is used in conjunction with a semantic understanding algorithm to generate operating instructions based on the personalized medical supplies recommendations, converting medical terms into easy-to-understand instructions.

[0084] After receiving the instructions, interactive visual guidance is created through mixed reality, and situational awareness technology is introduced to obtain operational feedback and interactive information to obtain the guidance system version. In addition, the functions and service system of the smart first aid kit are optimized based on the content of professional guidance and communication.

[0085] The mixed reality guidance system combines virtual reality and augmented reality technologies to provide intuitive visual guidance; the semantic understanding algorithm can convert complex medical terms into everyday language so that non-professionals can understand it; and the situational awareness technology monitors the user's operating behavior through integrated sensors and collects feedback data to improve the performance of the guidance system.

[0086] In the embodiment of this application, based on the personalized medical supplies recommendation, the mixed reality guidance system cooperates with the semantic understanding algorithm to generate easy-to-understand and easy-to-implement operation instructions, and creates interactive visual guidance through three-dimensional visualization models and interactive elements. The system also introduces contextual awareness technology to monitor user operations in real time and collect feedback data, continuously optimizing the accuracy and friendliness of the guidance system.

[0087] Assume that after the first responders receive personalized medical supplies recommendations, the mixed reality guidance system generates a detailed operation guide for them, in which complex medical terms have been converted into simple everyday language instructions. The system displays a 3D visualization model of each operation through AR glasses to help first responders quickly master the correct first aid methods. At the same time, the system's built-in situational awareness sensors monitor the first responders' operations in real time and collect feedback data to improve the performance of the guidance system in the future.

[0088] The improved guidance system version and the content of professional guidance exchanges are used to optimize the functions and service system of the smart first aid kit.

[0089] Establish a telemedicine consultation channel to synchronously transmit vital signs data, emergency treatment progress data and interactive information to the telemedicine terminal.

[0090] Obtain relevant data transmitted by the telemedicine consultation channel, work with the expert team to conduct an immediate assessment of the patient's condition, and provide feedback on medical guidance and key node records of the emergency process.

[0091] Telemedicine consultation channel refers to a stable communication link built through IoT technology and secure encryption protocol, which is used to transmit patients' vital signs data, emergency treatment progress and user interaction information. Key node records refer to the records of important time nodes and events in the entire emergency process, which are used for post-analysis and training.

[0092] In the embodiment of the present application, the functions and service system of the smart first aid kit are further optimized by using the improved guidance system version and professional guidance communication content. A remote medical consultation channel is established to achieve real-time transmission of information such as vital signs data, so that remote experts can provide professional medical guidance immediately. The system also records the key nodes in the first aid process, which is convenient for subsequent analysis and summary of lessons learned.

[0093] Assuming that the first aid personnel use the smart first aid kit on site, the system will transmit the injured person's vital signs data, first aid treatment progress and interactive information to the remote expert team through the remote medical consultation channel. After observing the specific situation of the injured person through the real-time monitoring interface, the remote expert provides professional medical guidance and suggestions. The system automatically generates key node records of the first aid process, which not only supports real-time professional guidance, but also provides valuable data for subsequent experience summary and system optimization.

[0094] In summary, this application covers a complete system from emergency medical condition identification, material recommendation optimization, operation guide generation to telemedicine support, aiming to provide an efficient, intelligent and user-friendly auxiliary diagnosis system to meet the diverse needs in emergency medical rescue scenarios. The smart first aid kit of this application not only improves the efficiency and success rate of first aid, but also significantly enhances the user experience and system reliability.

[0095] In order to solve the problems that the existing first aid material recommendation scheme is fixed, cannot be flexibly adjusted according to actual conditions, and lacks a complication prevention mechanism, in some embodiments, the emergency medical condition is automatically identified based on the data obtained by the multi-sensor network, and the emergency response mechanism of the first aid kit is triggered, and the emergency medical condition is preliminarily classified according to similar situations in the historical case database, including:

[0096] Using the result of the preliminary classification, the emergency medical situation is reconstructed to generate a scenario reconstruction model; based on the scenario reconstruction model, a genetic algorithm is used to encode the initial material allocation plan corresponding to the emergency medical situation, generate multiple candidate plans, and form a candidate plan set; based on the candidate plan set, combined with the status of existing materials in the first aid kit and the preset disease model library, the effects of different treatment paths are simulated, the fitness of each candidate plan is evaluated, and a fitness evaluation result is obtained; using the fitness evaluation result, the candidate plans are selected, crossed and mutated, and the material allocation plan is iteratively updated until the material combination with the highest fitness is found to obtain an optimal material allocation plan; based on the optimal material allocation plan, the failure mode and effect analysis technology is used to predict and analyze complications, identify potential risk points and their severity, and obtain complication prevention measures; based on the complication prevention measures, personalized medical material recommendations are generated, and the personalized medical material recommendations include basic first aid needs and additional materials required for complication prevention.

[0097] In this embodiment, the scenario reconstruction model includes integrating multiple vital sign data and environmental parameters to generate specific scenarios that reflect emergency medical conditions; the candidate solution set is a series of possible material combinations generated by encoding the initial material allocation plan; the fitness evaluation result is the data obtained after quantitatively evaluating the effectiveness and feasibility of each candidate solution in a specific situation; and the complication prevention measures are strategies formulated after identifying potential risk points and their severity based on FMEA technology.

[0098] In the embodiment of the present application, the emergency medical situation is firstly reconstructed using the preliminary classification results to generate a detailed scenario reconstruction model; secondly, the initial material allocation plan is encoded using a genetic algorithm according to the scenario reconstruction model to form multiple candidate plans and constitute a candidate plan set; thirdly, based on the candidate plan set combined with the existing material status in the first aid kit and the preset disease model library, the effects of different treatment paths are simulated, the fitness of each candidate plan is evaluated, and the fitness evaluation result is obtained; finally, the candidate plans are selected, crossed and mutated using the fitness evaluation result, and the material allocation plan is iteratively updated until the material combination with the highest fitness is found, and the FMEA technology is used to predict and analyze complications according to the optimal material allocation plan, identify potential risk points and their severity, and finally generate personalized medical material recommendations, covering basic first aid needs and additional materials required for complication prevention.

[0099] Here is a specific example:

[0100] Suppose a traffic accident occurred in a remote mountainous area, and there was an injured person at the scene. After the smart first aid kit was started, the system first reconstructed the emergency medical situation based on the preliminary classification results and generated a detailed scenario reconstruction model; then, the system applied genetic algorithms to encode the initial material allocation plan based on this model, formed multiple candidate plans and constituted a candidate plan set; next, based on these candidate plan sets, the system combined the existing material status in the first aid kit and the preset disease model library, simulated the effects of different treatment paths, evaluated the fitness of each candidate plan, and obtained the fitness evaluation results; finally, the system used these evaluation results to select, cross and mutate the candidate plans, iteratively updated the material allocation plan, found the material combination with the highest fitness, and used FMEA technology based on this optimal plan to predict and analyze possible complications, identify potential risk points and their severity, and finally generate a detailed personalized medical material recommendation, which not only covers basic first aid needs, but also considers the additional materials required for complication prevention. Through the above process, the smart first aid kit ensures the accuracy and comprehensiveness of material configuration, greatly improving the safety and effectiveness of first aid treatment.

[0101] In order to solve the problem that the existing emergency material recommendation plan is fixed and cannot be flexibly adjusted according to the actual situation, in some embodiments, according to the scenario reconstruction model, a genetic algorithm is applied to encode the initial material allocation plan corresponding to the emergency medical situation, generate multiple candidate plans, and form a candidate plan set, including:

[0102] The scenario reconstruction model is used to determine the types and quantity ranges of first aid supplies that may be required under the emergency medical situation, and a material demand framework is generated; the chromosome representation method in the genetic algorithm is initialized, and the initial material allocation plan corresponding to the material demand framework is encoded into a chromosome to obtain an initial population; wherein the genes on each chromosome represent the quantity or type of specific supplies; based on the initial population and in combination with the status of existing supplies in the first aid kit, infeasible material allocation plans are eliminated to ensure that all material allocation plans are physically feasible, and an initial material allocation plan set is generated; using the initial material allocation plan set, the selection, crossover and mutation operations of the genetic algorithm are applied to randomly generate new material allocation plans to increase the diversity of material allocation plans, obtain multiple candidate plans, and form a candidate plan set.

[0103] In this embodiment, the material demand framework includes determining the types and quantity ranges of first aid materials that may be needed in emergency medical situations, which are used to guide subsequent material allocation; the chromosome representation is the basic unit in the genetic algorithm, and the genes on each chromosome represent the quantity or type of specific materials, which are used to encode the initial material allocation plan; the initial population refers to a collection of multiple initial material allocation plans formed after encoding; infeasible material allocation plans refer to those plans that cannot be implemented due to physical limitations (such as the capacity of the first aid kit).

[0104] In an embodiment of the present application, a scenario reconstruction model is first used to determine the types and quantity ranges of first aid supplies that may be needed in an emergency medical situation, and a material demand framework is generated; secondly, the chromosome representation method in the genetic algorithm is initialized, and the initial material allocation plan corresponding to the material demand framework is encoded as a chromosome to obtain an initial population; again, based on the initial population and the status of existing materials in the first aid kit, infeasible material allocation plans are eliminated to ensure that all material allocation plans are physically feasible, and an initial material allocation plan set is generated; finally, the initial material allocation plan set is used to apply the selection, crossover and mutation operations of the genetic algorithm to randomly generate new material allocation plans, increase the diversity of the material allocation plans, obtain multiple candidate plans, and constitute a candidate plan set.

[0105] Here is a specific example:

[0106] Suppose a serious natural disaster occurred in a remote mountainous area, with multiple injured people on the scene. After the smart first aid kit is activated, the system first uses the scenario reconstruction model to determine the types and quantity ranges of first aid supplies that may be needed in emergency medical situations, and generates a detailed material demand framework; then initializes the chromosome representation in the genetic algorithm, encodes the initial material allocation plan corresponding to the material demand framework into multiple chromosomes, and forms an initial population; then, based on these initial populations and the existing material status in the first aid kit, it eliminates infeasible material allocation plans, ensures that all material allocation plans are physically feasible, and generates an initial material allocation plan set; finally, using this plan set, through selection, crossover and mutation operations, randomly generates new material allocation plans, increases the diversity of the plans, and finally obtains multiple candidate plans and forms a candidate plan set. Through the above process, the smart first aid kit ensures that the material allocation plan not only meets actual needs, but also has a high degree of flexibility and feasibility, thereby significantly improving the effect and efficiency of first aid treatment.

[0107] In order to solve the problem that there is a lack of complication prevention mechanism in the existing emergency material recommendation plan, in some embodiments, according to the optimal material allocation plan, the failure mode and effect analysis technology is used to predict and analyze complications, identify potential risk points and their severity, and obtain complication prevention measures, including:

[0108] Utilizing the optimal material allocation plan and combining it with the complication data in the preset disease model library, a comprehensive assessment of possible complications is conducted to generate a complication assessment list; based on the complication assessment list, the failure mode and effects analysis technology is applied to quantitatively analyze the probability of occurrence, detection difficulty and severity of each complication to obtain a complication risk score; based on the complication risk score, high-risk complication types and their key triggering factors are identified to form a high-risk complication list; utilizing the high-risk complication list, targeted prevention strategies are formulated, specific prevention measures are designed for each high-risk complication, and complication prevention measures are obtained.

[0109] In this embodiment, the complication assessment list includes a detailed list generated based on the optimal material allocation plan and complication data in the preset disease model library, which is used to comprehensively evaluate possible complications; the complication risk score is the data obtained after quantitative analysis of the probability of occurrence, detection difficulty and severity of each complication, which is used to identify high-risk complications; the high-risk complication list is formed based on the complication risk score, which includes possible and serious complication types and their key triggering factors; complication prevention measures are specific strategies designed for high-risk complications, aiming to deal with potential problems in advance.

[0110] In the embodiment of the present application, firstly, the optimal material allocation plan is used in combination with the complication data in the preset disease model library to conduct a comprehensive assessment of possible complications and generate a complication assessment list; secondly, based on the complication assessment list, the FMEA technology is used to quantitatively analyze the probability of occurrence, detection difficulty and severity of each complication to obtain a complication risk score; thirdly, based on the complication risk score, high-risk complication types and their key triggering factors are identified to form a high-risk complication list; finally, the high-risk complication list is used to formulate targeted prevention strategies, design specific prevention measures for each high-risk complication, and finally obtain complication prevention measures.

[0111] Here is a specific example:

[0112] Suppose a traffic accident occurred in a remote mountainous area, with multiple injured people at the scene. After the smart first aid kit is activated, the system first uses the optimal material allocation plan combined with the complication data in the preset disease model library to conduct a comprehensive assessment of possible complications and generate a detailed complication assessment list; then based on this assessment list, the FMEA technology is used to quantitatively analyze the probability of occurrence, detection difficulty and severity of each complication, and obtain the complication risk score; next, based on these risk scores, high-risk complication types and their key triggering factors are identified to form a high-risk complication list; finally, using this list, the system formulates targeted prevention strategies and designs specific preventive measures for each high-risk complication, such as adding specific drugs or equipment to ensure that potential complications can be dealt with in advance during the first aid process. Through the above process, the smart first aid kit not only provides accurate material allocation, but also effectively prevents the occurrence of complications, greatly improving the safety and success rate of first aid treatment.

[0113] In order to solve the problem that the existing first aid operation guide is difficult to understand, lacks interactivity and situational awareness, in some embodiments, after receiving the instruction, interactive visual guidance is created through mixed reality, and situational awareness technology is introduced to obtain operation feedback and interactive information, and the guidance system version is obtained. In combination with the content of professional guidance communication, the functions and service systems of the smart first aid box are optimized, including:

[0114] Utilizing the personalized medical supplies recommendation, the specific processes and required supplies involved in the first aid process are described in detail, and the medical terms are converted into daily language instructions in combination with the semantic understanding algorithm to obtain a preliminary operation guide; based on the preliminary operation guide, the mixed reality guidance system is applied to design corresponding three-dimensional visualization models and interactive elements for each operation, create interactive visual guidance, and generate an enhanced operation guide; according to the enhanced operation guide, contextual awareness sensors are integrated into the first aid kit to monitor the user's operating behavior in real time, and collect interaction data between the user and the system to obtain an operation feedback data set; utilizing the operation feedback data set, combined with a machine learning algorithm, an in-depth analysis is performed to identify common errors and difficulties, evaluate the effectiveness of the guidance system and user experience, and generate a user behavior analysis report; based on the user behavior analysis report, the content and form of the enhanced operation guide are adjusted and optimized for the problems and improvement points found to ensure the accuracy and friendliness of the guidance system, and obtain a guidance system version.

[0115] In this embodiment, the preliminary operation guide describes in detail the specific process and required materials of the first aid process based on personalized medical supplies recommendations, and converts medical terms into daily language instructions in combination with semantic understanding algorithms; the enhanced operation guide refers to the application of a mixed reality guidance system to design a three-dimensional visualization model and interactive elements for each operation, providing intuitive interactive visual guidance; the operation feedback data set is composed of context-aware sensors integrated in the first aid kit that monitor the user's operation behavior in real time and collect interaction data between the user and the system; the user behavior analysis report is a document generated after an in-depth analysis of the operation feedback data set is performed using a machine learning algorithm to identify common errors and difficulties, and evaluate the effectiveness of the guidance system and user experience.

[0116] In the embodiment of the present application, personalized medical supplies recommendations are first used to describe in detail the specific processes and required supplies involved in the first aid process, and the semantic understanding algorithm is used to convert complex medical terms into easy-to-understand daily language instructions to obtain a preliminary operation guide; secondly, based on the preliminary operation guide, a mixed reality guidance system is applied to design corresponding three-dimensional visualization models and interactive elements for each operation, create interactive visual guidance, and generate an enhanced operation guide; again, according to the enhanced operation guide, a context-aware sensor is integrated into the first aid kit to monitor the user's operating behavior in real time, and collect interaction data between the user and the system to obtain an operation feedback data set; finally, the operation feedback data set is used in combination with a machine learning algorithm for in-depth analysis to identify common errors and difficulties, evaluate the effectiveness of the guidance system and user experience, generate a user behavior analysis report, and based on this, adjust and optimize the content and form of the enhanced operation guide to ensure the accuracy and friendliness of the guidance system, and finally obtain a guidance system version.

[0117] Here is a specific example:

[0118] Suppose a natural disaster occurs in a remote mountainous area, and there are multiple injured people at the scene. After the smart first aid kit is activated, the system first uses personalized medical supplies recommendations to describe in detail the specific processes and materials involved in the first aid process, and combines semantic understanding algorithms to convert complex medical terms into easy-to-understand daily language instructions to obtain a preliminary operation guide; then based on this preliminary operation guide, the system uses a mixed reality guidance system to design corresponding three-dimensional visualization models and interactive elements for each operation, creates interactive visual guidance, and generates an enhanced operation guide; next, according to the enhanced operation guide, the system integrates context-aware sensors in the first aid kit to monitor the user's operation behavior in real time, and collects user-system interaction data to obtain an operation feedback data set; finally, the system uses these operation feedback data sets, combined with machine learning algorithms for in-depth analysis, identifies common errors and difficulties, evaluates the effectiveness of the guidance system and user experience, generates a user behavior analysis report, and adjusts and optimizes the content and form of the enhanced operation guide based on this report, ensuring the accuracy and friendliness of the guidance system, and finally obtains the guidance system version. Through the above process, the smart first aid kit not only provides intuitive and easy-to-understand operation guides, but also improves the user experience and first aid efficiency through continuous improvement.

[0119] In order to solve the problem that the existing guidance system is difficult to identify common errors and difficulties in user operations, and to evaluate its effectiveness and user experience, in some embodiments, the operation feedback data set is used in combination with a machine learning algorithm for in-depth analysis to identify common errors and difficulties, evaluate the effectiveness and user experience of the guidance system, and generate a user behavior analysis report, including:

[0120] The operation feedback data set is used to pre-process the user's operation behavior and interaction pattern, remove outliers and noise, and obtain clean operation behavior data; based on the clean operation behavior data, a classification algorithm is applied to classify different types of user behaviors, identify the behavior patterns of successfully completing operations and the behavior patterns of failures or encountering difficulties, and obtain behavior pattern classification results; based on the behavior pattern classification results, a clustering algorithm is used to analyze the common characteristics of similar behavior patterns, find out the key factors that lead to errors or difficulties, identify common errors and difficulties, and generate a behavior pattern analysis list; using the behavior pattern analysis list, combined with user satisfaction survey data, evaluate the effectiveness of the guidance system in actual use and the user experience, and obtain a system effectiveness evaluation report; based on the system effectiveness evaluation report, combined with common problems and personalized needs in user behavior, a user behavior analysis report is generated.

[0121] In this embodiment, the operation feedback data set includes data on the user's operation behavior and interaction pattern, which is used to remove outliers and noise to obtain clean operation behavior data; the behavior pattern classification result is obtained by classifying different types of user behaviors to identify the behavior patterns of successfully completing operations and the behavior patterns of failures or encountering difficulties; the behavior pattern analysis list analyzes the common characteristics of similar behavior patterns through clustering algorithms, finds out the key factors that lead to errors or difficulties, and identifies common errors and difficulties; the system effectiveness evaluation report is a document generated after combining user satisfaction survey data to evaluate the effectiveness of the guidance system in actual use and user experience; the user behavior analysis report is a comprehensive report generated based on the system effectiveness evaluation report, combined with common problems and personalized needs in user behavior.

[0122] In an embodiment of the present application, firstly, an operation feedback data set is used to pre-process the user's operation behavior and interaction pattern to remove outliers and noise, and obtain clean operation behavior data; secondly, based on the clean operation behavior data, a classification algorithm is applied to classify different types of user behaviors, and the behavior patterns of successful operation completion and the behavior patterns of failure or difficulty are identified to obtain a behavior pattern classification result; again, based on the behavior pattern classification result, a clustering algorithm is used to analyze the common characteristics of similar behavior patterns, find out the key factors leading to errors or difficulties, identify common errors and difficulties, and generate a behavior pattern analysis list; finally, the behavior pattern analysis list is used, combined with the user satisfaction survey data, to evaluate the effectiveness of the guidance system in actual use and the user experience, to obtain a system effectiveness evaluation report, and based on this, a user behavior analysis report is generated.

[0123] Here is a specific example:

[0124] Suppose a large-scale public incident occurs in a city, and there are many injured people who need first aid at the scene. After the smart first aid kit is started, the system first uses the operation feedback data set to pre-process the user's operation behavior and interaction mode, removes outliers and noise, and obtains clean operation behavior data; then, based on these clean data, the classification algorithm is applied to classify different types of user behaviors, and the behavior patterns of successful operation and failure or difficulty are identified, and the behavior pattern classification results are obtained; next, based on this classification result, the clustering algorithm is used to analyze the common characteristics of similar behavior patterns, find out the key factors leading to errors or difficulties, identify common errors and difficulties, and generate a behavior pattern analysis list; finally, using this list, combined with user satisfaction survey data, the effectiveness and user experience of the guidance system in actual use are evaluated, and a system effectiveness evaluation report is obtained, and a user behavior analysis report is generated based on this. Through the above process, the smart first aid kit can not only identify and solve common problems in user operations, but also continuously optimize the performance of the guidance system, significantly improving the user experience and first aid efficiency.

[0125] Based on the functional evaluation report and combined with the content of professional guidance and communication, determine the functional points and service measures that need to be enhanced or added, and form a functional optimization list; based on the functional optimization list, develop and integrate new functional modules and service measures into the smart first aid kit to generate an enhanced version of the smart first aid kit.

[0126] In order to solve the problem that the existing smart first aid kit functions and service systems lack flexibility and remote support capabilities, in some embodiments, the remote medical consultation channel and the remote medical terminal include:

[0127] Utilize Internet of Things technology and secure encryption protocols to build a stable and reliable remote medical consultation channel and obtain a remote data transmission link.

[0128] The remote data transmission link collects and synchronously transmits the patient's vital signs data, emergency treatment progress and user interaction information with the smart first aid kit to the remote medical terminal in real time, generating a real-time monitoring interface; based on the real-time monitoring interface, the expert team at the remote medical terminal conducts an immediate assessment of the patient's condition, and provides professional medical guidance and suggestions through a two-way communication function, obtaining professional guidance and communication content; using the real-time monitoring interface, important time nodes and events in the entire first aid process are recorded, obtaining key node records of the first aid process.

[0129] In this embodiment, the function evaluation report is a document generated based on problems encountered in actual use and improvement suggestions, and is used to evaluate the existing functions and service processes of the smart first aid box; the function optimization list is a list of function points and service measures that need to be enhanced or added based on the function evaluation report and professional guidance communication content; the enhanced smart first aid box refers to an upgraded version formed after the development and integration of new function modules and service measures; the remote data transmission link is a stable and reliable communication link built through Internet of Things technology and secure encryption protocol to ensure the safe transmission of data; the real-time monitoring interface is a visualization interface generated based on the remote data transmission link, which collects and synchronously transmits the patient's vital signs data, first aid treatment progress and user interaction information with the smart first aid box to the remote expert team in real time; the professional guidance communication content is the remote expert team's instant assessment of the patient's condition and the professional medical guidance suggestions provided through the two-way communication function; the key node record of the first aid process refers to the record of important time nodes and events in the entire first aid process.

[0130] In the embodiment of the present application, firstly, based on the function evaluation report and in combination with the content of professional guidance and communication, the function points and service measures that need to be enhanced or added are determined to form a function optimization list; secondly, according to the function optimization list, new function modules and service measures are developed and integrated into the smart first aid kit to generate an enhanced version of the smart first aid kit; again, the Internet of Things technology and security encryption protocol are used to build a stable and reliable remote medical consultation channel to obtain a remote data transmission link; finally, based on the remote data transmission link, the patient's vital signs data, first aid treatment progress and user interaction information with the smart first aid kit are collected and synchronously transmitted to the remote expert team in real time to generate a real-time monitoring interface; and based on the real-time monitoring interface, the remote expert team conducts an immediate assessment of the patient's condition, provides professional medical guidance and suggestions, and records important time nodes and events in the entire first aid process to obtain key node records of the first aid process.

[0131] Here is a specific example:

[0132] Suppose a serious traffic accident occurred in a remote area, with multiple injured people at the scene. After the smart first aid kit is started, the system first determines the functional points and service measures that need to be enhanced or added based on the functional evaluation report and the content of professional guidance and communication, and forms a functional optimization list; then, based on this list, new functional modules and service measures are developed and integrated, such as auxiliary diagnostic tools for specific diseases or optimized material recommendation logic, to generate an enhanced smart first aid kit; next, a stable and reliable telemedicine consultation channel is built using the Internet of Things technology and secure encryption protocol, and a remote data transmission link is obtained; finally, based on this link, the system collects and synchronously transmits the patient's vital signs data, the progress of first aid treatment, and the user's interaction information with the smart first aid kit to the remote expert team in real time, generating a real-time monitoring interface. Based on this interface, the remote expert team conducts an immediate assessment of the patient's condition and provides professional medical guidance and suggestions through the two-way communication function. At the same time, the system records the important time nodes and events in the entire first aid process, and obtains the key node records of the first aid process. Through the above process, the smart first aid kit not only optimizes its functions and service system, but also establishes an efficient telemedicine support mechanism, which significantly improves the rescue effect and success rate in emergency medical conditions.

[0133] Figure 2 The framework diagram of the auxiliary diagnosis system of a smart first aid kit is provided for executing the above method, such as Figure 2 As shown, the system comprises:

[0134] A multi-sensor network configured to monitor in real time vital sign data, environmental parameters, and emergency treatment progress data of a patient through the multi-sensor network;

[0135] A central processing unit, configured with a preliminary classification module, a prediction suggestion module and an instruction generation module;

[0136] The preliminary classification module automatically identifies emergency medical conditions based on data acquired by the multi-sensor network and triggers an emergency response mechanism of the first aid kit, and preliminarily classifies the emergency medical conditions based on similar conditions in a historical case database;

[0137] The preliminary recommendation module uses a genetic algorithm to dynamically adjust the first aid material recommendation plan based on the preliminary classification results, combines the status of existing materials in the first aid box with the preset disease model library, optimizes the most appropriate material combination by simulating the effects of different treatment paths, and uses the failure mode and effect analysis technology to predict and analyze possible complications to obtain personalized medical material recommendations;

[0138] The instruction generation module generates an operation guide based on the personalized medical supplies recommendation by using a mixed reality guidance system in conjunction with a semantic understanding algorithm, converting medical terms into easy-to-understand instructions;

[0139] The interactive visual guidance device is configured to create interactive visual guidance through mixed reality after receiving instructions, introduce situational awareness technology to obtain operation feedback and interactive information, obtain the guidance system version, and optimize the functions and service system of the smart first aid kit in combination with the content of professional guidance exchanges;

[0140] A remote communication link, configured to establish a remote medical consultation channel, and synchronously transmit vital sign data, emergency treatment progress data and interactive information to the remote medical terminal;

[0141] The telemedicine terminal is configured to obtain relevant data transmitted by the telemedicine consultation channel, conduct an immediate assessment of the patient's condition in conjunction with the expert team, and provide feedback on medical guidance and key node records of the emergency process.

[0142] In one possible design, Figure 2 The auxiliary diagnosis system of the smart first aid kit of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0143] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0144] The processing component 32 is used for: using multi-sensor fusion technology to monitor the patient's vital sign data and environmental parameters in real time, automatically identifying emergency medical conditions, triggering the emergency response mechanism of the first aid kit, preliminarily classifying the emergency medical conditions according to similar situations in the historical case database to obtain a preliminary classification result; based on the preliminary classification result, dynamically adjusting the first aid supplies recommendation plan by using a genetic algorithm, combining the status of existing supplies in the first aid kit with a preset disease model library, optimizing the most suitable supply combination by simulating the effects of different treatment paths, predicting and analyzing possible complications by using failure mode and effect analysis technology to obtain personalized medical supply suggestions; using a mixed reality guidance system in cooperation with a semantic understanding algorithm to generate an operation guide according to the personalized medical supply suggestions, converting medical terms into easy-to-understand instructions, creating an interactive visual guidance through the mixed reality guidance system, introducing context awareness technology to collect and analyze the operation feedback of the user to obtain a guidance system version; using the improved guidance system version and the content of professional guidance communication to optimize the functions and service system of the intelligent first aid kit, establishing a remote medical consultation channel, and synchronously transmitting the vital sign data, first aid treatment progress and user interaction information to a remote expert team to obtain a remote medical consultation channel and key node records of the first aid process.

[0145] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the processes in the method implemented by the above system. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0146] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0147] Of course, the computing device may necessarily further include other components, such as an input / output interface, a display component, a communication component, etc.

[0148] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0149] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0150] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0151] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 An auxiliary diagnosis method for an intelligent first aid kit according to the embodiment shown.

[0152] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0153] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0154] 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 methods described in each embodiment or some parts of the embodiments.

[0155] 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. An auxiliary diagnosis method for an intelligent first aid kit, characterized in that: The method comprises: Real-time monitoring of patients’ vital signs, environmental parameters, and emergency treatment progress data through a multi-sensor network; Automatically identifying emergency medical conditions based on data acquired by the multi-sensor network and triggering an emergency response mechanism of the first aid kit, and preliminarily classifying the emergency medical conditions based on similar situations in a historical case database; Based on the results of the preliminary classification, a genetic algorithm is used to dynamically adjust the emergency supplies recommendation plan. By combining the status of existing supplies in the first aid box with the preset disease model library, the most suitable combination of supplies is optimized by simulating the effects of different treatment paths. The failure mode and effects analysis technology is used to predict and analyze possible complications, and personalized medical supplies recommendations are obtained. Using a mixed reality guidance system in conjunction with a semantic understanding algorithm to generate an operating guide based on the personalized medical supplies recommendations, translating medical terminology into easy-to-understand instructions; After receiving the instructions, interactive visual guidance is created through mixed reality, and situational awareness technology is introduced to obtain operational feedback and interactive information, and the guidance system version is obtained. In addition, the functions and service systems of the smart first aid kit are optimized in combination with the content of professional guidance exchanges; Establish a telemedicine consultation channel to synchronously transmit vital sign data, emergency treatment progress data and interactive information to the telemedicine terminal; Obtain relevant data transmitted by the telemedicine consultation channel, work with the expert team to conduct an immediate assessment of the patient's condition, and provide feedback on medical guidance and key node records of the emergency process.

2. The method according to claim 1, characterized in that The method of automatically identifying emergency medical conditions based on the data acquired by the multi-sensor network and triggering the emergency response mechanism of the first aid kit, and preliminarily classifying the emergency medical conditions according to similar situations in the historical case database, includes: Using the result of the preliminary classification, reconstruct the emergency medical situation and generate a scenario reconstruction model; According to the scenario reconstruction model, a genetic algorithm is applied to encode the initial material allocation plan corresponding to the emergency medical situation, generate multiple candidate plans, and form a candidate plan set; Based on the candidate solution set, combined with the status of existing materials in the first aid kit and the preset disease model library, the effects of different treatment paths are simulated, the fitness of each candidate solution is evaluated, and the fitness evaluation result is obtained; Using the fitness evaluation results, the candidate solutions are selected, crossed and mutated, and the material allocation solution is iteratively updated until the material combination with the highest fitness is found, thereby obtaining the optimal material allocation solution; According to the optimal material allocation plan, the failure mode and effect analysis technology is used to predict and analyze complications, identify potential risk points and their severity, and obtain complication prevention measures; Based on the complication prevention measures, a personalized medical supplies recommendation is generated, wherein the personalized medical supplies recommendation includes basic first aid needs and additional supplies required for complication prevention.

3. The method according to claim 2, characterized in that The initial material allocation plan corresponding to the emergency medical situation is encoded by applying a genetic algorithm according to the scenario reconstruction model to generate multiple candidate plans and form a candidate plan set, including: Using the scenario reconstruction model, determine the types and quantity ranges of emergency supplies that may be needed under the emergency medical situation, and generate a supply demand framework; Initializing the chromosome representation in the genetic algorithm, encoding the initial material allocation plan corresponding to the material demand framework into a chromosome to obtain an initial population; wherein the genes on each chromosome represent the quantity or type of a specific material; According to the initial population and in combination with the status of existing materials in the first aid box, infeasible material allocation plans are eliminated to ensure that all material allocation plans are physically feasible, thereby generating an initial material allocation plan set; By using the initial material allocation scheme set, applying the selection, crossover and mutation operations of the genetic algorithm, a new material allocation scheme is randomly generated to increase the diversity of the material allocation scheme, obtain multiple candidate schemes, and form a candidate scheme set.

4. The method according to claim 2, characterized in that: According to the optimal material allocation plan, the failure mode and effect analysis technology is used to predict and analyze complications, identify potential risk points and their severity, and obtain complication prevention measures, including: Using the optimal material allocation plan and combining it with the complication data in the preset disease model library, a comprehensive assessment of possible complications is conducted to generate a complication assessment list; Based on the complication assessment list, the failure mode and effect analysis technology is applied to quantitatively analyze the occurrence probability, detection difficulty and severity of each complication to obtain a complication risk score; Based on the complication risk score, high-risk complication types and their key triggering factors are identified to form a high-risk complication list; Utilize the high-risk complication list to develop targeted prevention strategies, design specific prevention measures for each high-risk complication, and obtain complication prevention measures.

5. The method according to claim 1, characterized in that After receiving the instruction, the interactive visual guidance is created through mixed reality, and the situational awareness technology is introduced to obtain operation feedback and interactive information, and the guidance system version is obtained. In addition, the functions and service systems of the smart first aid kit are optimized in combination with the content of professional guidance exchanges, including: Using the personalized medical supplies recommendations, the specific procedures and supplies involved in the emergency treatment process are described in detail, and the medical terms are converted into daily language instructions in combination with the semantic understanding algorithm to obtain a preliminary operation guide; Based on the preliminary operation guide, a mixed reality guidance system is applied to design corresponding three-dimensional visualization models and interactive elements for each operation, create interactive visual guidance, and generate enhanced operation guide; According to the enhanced operation guide, a context-aware sensor is integrated into the first aid kit to monitor the user's operation behavior in real time, and collect the interaction data between the user and the system to obtain an operation feedback data set; Utilize the operational feedback data set and combine it with machine learning algorithms to conduct in-depth analysis, identify common errors and difficulties, evaluate the effectiveness of the guidance system and user experience, and generate a user behavior analysis report; Based on the user behavior analysis report, the content and form of the enhanced operation guide are adjusted and optimized according to the problems and improvement points found, so as to ensure the accuracy and friendliness of the guidance system and obtain the guidance system version.

6. The method according to claim 5, characterized in that The operation feedback data set is used in combination with a machine learning algorithm to perform in-depth analysis, identify common errors and difficulties, evaluate the effectiveness of the guidance system and user experience, and generate a user behavior analysis report, including: Using the operation feedback data set, preprocessing the user's operation behavior and interaction mode, removing outliers and noise, and obtaining clean operation behavior data; Based on the cleaned operation behavior data, a classification algorithm is applied to classify different types of user behaviors, and the behavior patterns of successful operation completion and the behavior patterns of failure or difficulty are identified to obtain a behavior pattern classification result; Based on the behavior pattern classification results, a clustering algorithm is used to analyze the common characteristics of similar behavior patterns, find out the key factors that lead to errors or difficulties, identify common errors and difficulties, and generate a behavior pattern analysis list; Using the behavior pattern analysis list and combining it with the user satisfaction survey data, the effectiveness and user experience of the guidance system in actual use are evaluated to obtain a system effectiveness evaluation report; Based on the system effectiveness evaluation report, combined with common problems and personalized needs in user behavior, a user behavior analysis report is generated.

7. The method according to claim 1, characterized in that The remote medical consultation channel is a remote data transmission link constructed using Internet of Things technology and secure encryption protocols.

8. The method according to claim 7, characterized in that The remote data transmission link comprises: Collect and synchronously transmit the patient's vital signs data, emergency treatment progress and user interaction information with the smart first aid kit to the remote medical terminal in real time, and generate a real-time monitoring interface; Based on the real-time monitoring interface, the expert team at the remote medical end conducts an immediate assessment of the patient's condition and provides professional medical guidance and suggestions through a two-way communication function.

9. The method according to claim 8, characterized in that The real-time monitoring interface is also used to record important time nodes and events in the entire first aid process, and obtain key node records of the first aid process.

10. An auxiliary diagnosis system for an intelligent first aid box, used to execute the method according to any one of claims 1 to 9, characterized in that: The system comprises: A multi-sensor network configured to monitor in real time vital sign data, environmental parameters, and emergency treatment progress data of a patient through the multi-sensor network; A central processing unit, configured with a preliminary classification module, a prediction suggestion module and an instruction generation module; The preliminary classification module automatically identifies emergency medical conditions based on data acquired by the multi-sensor network and triggers an emergency response mechanism of the first aid kit, and preliminarily classifies the emergency medical conditions based on similar conditions in a historical case database; The preliminary recommendation module uses a genetic algorithm to dynamically adjust the first aid material recommendation plan based on the preliminary classification results, combines the status of existing materials in the first aid box with the preset disease model library, optimizes the most appropriate material combination by simulating the effects of different treatment paths, and uses the failure mode and effect analysis technology to predict and analyze possible complications to obtain personalized medical material recommendations; The instruction generation module generates an operation guide based on the personalized medical supplies recommendation by using a mixed reality guidance system in conjunction with a semantic understanding algorithm, converting medical terms into easy-to-understand instructions; The interactive visual guidance device is configured to create interactive visual guidance through mixed reality after receiving instructions, introduce situational awareness technology to obtain operation feedback and interactive information, obtain the guidance system version, and optimize the functions and service system of the smart first aid kit in combination with the content of professional guidance exchanges; A remote communication link, configured to establish a remote medical consultation channel, and synchronously transmit vital sign data, emergency treatment progress data and interactive information to the remote medical terminal; The telemedicine terminal is configured to obtain relevant data transmitted by the telemedicine consultation channel, conduct an immediate assessment of the patient's condition in conjunction with the expert team, and provide feedback on medical guidance and key node records of the emergency process.

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