Intelligent pre-hospital emergency medical response system and method based on emergency unmanned aerial vehicle

Abstract

The invention relates to the technical field of emergency medical rescue, and discloses an intelligent pre-hospital emergency medical response system and method based on a first-aid unmanned aerial vehicle, and the system comprises an emergency receiving module, an intelligent analysis and scheduling module, the first-aid unmanned aerial vehicle, an unmanned aerial vehicle control module, and a remote medical guidance module. After the system receives emergency information, an intelligent analysis and scheduling module analyzes the information to determine medical requirements and emergency levels and generates a scheduling instruction; the unmanned aerial vehicle control module controls the first-aid unmanned aerial vehicle to fly to the scene, and collects and returns scene information; and the remote medical guidance module enables remote experts to perform real-time guidance on the site and control the release of first-aid materials. According to the invention, the problems of traditional pre-hospital emergency response delay, insufficient field information acquisition and lack of early professional guidance are solved, the emergency response and professional treatment capability are preposed through the unmanned aerial vehicle, and the overall efficiency and quality of pre-hospital emergency are improved through air-ground cooperation and data closed-loop optimization.

Description

Technical Field

[0001] This invention relates to the field of emergency medical rescue technology, specifically to an intelligent pre-hospital emergency medical response system and method based on emergency medical drones. Background Technology

[0002] Pre-hospital emergency medical services are a crucial part of the modern healthcare system, and their response efficiency and treatment quality directly affect the survival rate and prognosis of critically ill patients. In the routine pre-hospital emergency care process, after receiving a request for assistance, the emergency command center primarily dispatches ground ambulances carrying professional medical personnel to the scene to provide assistance.

[0003] However, this response model, which relies entirely on ground transportation, faces inherent technical limitations in practical applications. The speed of ground ambulances is severely constrained by objective factors such as urban traffic congestion and poor road conditions in remote areas or complex terrain, making it difficult to guarantee emergency response time and causing patients to miss the optimal window for treatment.

[0004] Furthermore, before medical personnel arrive at the scene, the command center and dispatched personnel's understanding of the specific situation is usually limited to the one-sided verbal description from the person seeking help. Because those seeking help are often emotionally distressed or lack professional knowledge in emergency situations, the information they provide is often incomplete and inaccurate, creating an information barrier between the rescue team and the scene. This lag and uncertainty in information acquisition prevents dispatched medical personnel from making targeted rescue preparations en route, and upon arrival at the scene, they need to spend additional time assessing the environment and injuries, further consuming valuable treatment time.

[0005] Meanwhile, the time between the call for help and the arrival of the ambulance often leaves a gap in professional medical intervention at the scene. Although dispatchers can provide remote verbal guidance over the phone, the accuracy and effectiveness of this guidance are significantly reduced due to the lack of visual information at the scene. More importantly, core emergency equipment such as automated external defibrillators (AEDs) and specialized hemostatic kits must also arrive with the ambulance, making it impossible to implement early and effective interventions for emergencies such as cardiac arrest and severe trauma in a timely manner. Therefore, shortening response time, eliminating information barriers, and bringing effective medical intervention capabilities forward are urgent technical problems that need to be solved in the field of pre-hospital emergency care. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent pre-hospital emergency medical response system and method based on emergency medical drones, which solves the problems of discontinuous control trajectory, static and unadjustable emotion mapping, lack of user feedback closed loop, weak adaptive capability, and insufficient fusion of multi-dimensional control information in existing technologies.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent pre-hospital emergency medical response system and method based on emergency medical drones. The first aspect of the present invention provides an intelligent pre-hospital emergency medical response system based on emergency medical drones, the system comprising: The emergency event receiving module is used to receive emergency event information, including the location of the event. The intelligent analysis and dispatch module is connected to the emergency event receiving module. It is used to perform intelligent analysis on the emergency event information to determine the analysis results of medical needs and emergency levels, and generate dispatch instructions based on the analysis results, real-time environmental data and preset emergency resources status. Emergency medical drones are aircraft equipped with image acquisition equipment, two-way communication equipment, and emergency supplies delivery mechanisms. The drone control module, which is connected to the intelligent analysis and scheduling module, is used to control the emergency drone to fly autonomously to the event location according to the scheduling instructions, and to collect on-site information through the image acquisition device. The remote medical guidance module is used to receive on-site information transmitted back by the emergency drone and establish a two-way communication connection between the remote medical expert and the on-site personnel.

[0008] In one specific embodiment, the intelligent analysis and scheduling module is specifically used to perform the following operations: First, it integrates voice text, sensor data, and event location from the emergency event information to form standardized input data; second, based on a preset intelligent scenario assessment model, it calculates the integrated data to assess the emergency level and predict medical needs; finally, based on real-time environmental data of the emergency drone and ground rescue resources, as well as a preset emergency resource status (including location, availability, and payload), it calculates using a preset optimization algorithm to generate a scheduling instruction containing the optimal drone route and material delivery strategy. The core function of the intelligent analysis and scheduling module can be described by the following function model: ; in, For emergency information, For real-time environmental data, This is the preset emergency medical resource status. This is the generated scheduling instruction. (This function...) It includes an intelligent context assessment process. : ; in, This indicates the fusion processing of input information. For predicted medical needs, The level of emergency as assessed.

[0009] Preferably, the remote medical guidance module is specifically used to: receive and display in real time the on-site video and audio information transmitted back by the emergency drone through the image acquisition device to remote medical experts; at the same time, enable remote medical experts to provide real-time, step-by-step emergency operation voice guidance to on-site personnel through the two-way communication device of the emergency drone, and the guidance content is matched with the emergency supplies deployed by the emergency drone.

[0010] In one specific embodiment, the system further includes a mobile terminal for ground-based medical resources. The intelligent analysis and dispatch module is also used to simultaneously send the on-site information collected by the emergency drone and the preliminary assessment information generated by the remote medical guidance module to the mobile terminal for ground-based medical resources before the ground-based medical resources arrive at the event location.

[0011] Preferably, the system further includes a drone station. The drone control module is also used to control the emergency drone to autonomously return to the drone station after the on-site rescue mission is completed, and to trigger automatic charging, emergency supplies replenishment, and system self-check processes.

[0012] In one specific embodiment, the intelligent analysis and scheduling module is further configured to: match the determined medical needs to a preset type of emergency medical supply package, and include the identification information of the emergency medical supply package type in the scheduling instruction. Accordingly, before dispatching the emergency medical drone, the drone control module verifies the emergency medical supplies carried by the drone based on the identification information.

[0013] In one specific embodiment, the drone control module includes: An environmental data receiving unit is used to acquire information on wind speed, obstacles, and no-fly zones around the flight path of the emergency medical drone in real time. The route evaluation unit is used to compare the information obtained by the environmental data receiving unit with the preset route to determine whether there is a safety or efficiency risk to the preset route. The dynamic path planning unit is used to recalculate and generate a dynamically adjusted flight path that avoids obstacles and no-fly zones and compensates for wind speed effects, based on the real-time acquired information, when the flight path evaluation unit determines that a risk exists. The function of the dynamic path planning unit can be described by the following functional model: ; in, For the preset route, To obtain real-time information about the surrounding environment of the flight path, To dynamically adjust the generated flight path.

[0014] Preferably, the emergency medical supplies deployed by the emergency drone include a vital sign monitoring device that can connect to the patient. Accordingly, the remote medical guidance module is also used to receive and display in real time the vital sign data collected and transmitted back by the vital sign monitoring device, thereby assisting remote medical experts in assessment and guidance.

[0015] In one specific embodiment, the system further includes a data management and optimization module, which is used to archive all data in the entire emergency response process, including the dispatch instructions, UAV flight data, on-site information and remote guidance records, and to iteratively optimize the dispatch strategy of the intelligent analysis and dispatch module based on the analysis of the archived data.

[0016] A second aspect of the present invention provides an intelligent pre-hospital emergency medical response method based on an emergency medical drone, the method comprising the following steps: S1. Receiving steps: Receive emergency event information containing the location of the event; S2. Analysis and Dispatch Steps: The emergency event information is intelligently analyzed to determine medical needs and emergency levels. Based on the analysis results, real-time environmental data, and preset emergency resource status, dispatch instructions are generated. S3. Drone dispatch steps: According to the dispatch instructions, control the emergency medical drone to fly autonomously to the event location and collect on-site information through the image acquisition equipment of the emergency medical drone; S4. Remote support steps: Establish a communication connection between the emergency medical drone and the remote medical guidance center, transmit the on-site information, and conduct remote medical guidance and emergency supplies delivery through the emergency medical drone.

[0017] Preferably, in step S2, the analysis and scheduling step, the specific operations include: The emergency event information is integrated with voice text, sensor data, and event location. Based on a pre-set intelligent situation assessment model, the emergency level is assessed and medical needs are predicted. Finally, based on real-time environmental data of emergency medical drones and ground medical resources, as well as the preset status of emergency medical resources, a dispatch instruction containing the optimal drone route and material delivery strategy is generated.

[0018] In one specific embodiment, the remote medical guidance operation in step S4, the remote support step, specifically includes: The emergency medical drone transmits the on-site video and audio information collected by the drone back to the remote medical guidance center in real time. Through the two-way communication equipment of the emergency medical drone, telemedicine experts can provide on-site personnel with step-by-step voice guidance for emergency medical operations that matches the emergency medical supplies being deployed.

[0019] Preferably, the method further includes an air-ground coordination step: Before ground-based medical resources arrive at the incident location, the on-site information collected by the emergency drone, as well as the preliminary assessment information generated during the remote medical guidance process, are simultaneously sent to the mobile terminal of the ground-based medical resources.

[0020] In one specific embodiment, the method further includes a data archiving and optimization step after step S4 and the remote support step: Record and store the entire process data from S1 to S4, including scheduling instructions, UAV flight data, on-site information and remote guidance records, and use the entire process data to conduct retrospective analysis and optimization of the intelligent analysis model and scheduling strategy in S2.

[0021] This invention provides an intelligent pre-hospital emergency medical response system and method based on emergency medical drones. It has the following beneficial effects: 1. This invention generates dispatch instructions through an intelligent analysis and dispatch module, and the drone control module controls the emergency drone to autonomously fly to the incident location. Because the emergency drone uses an aerial flight path, it is not limited by ground traffic congestion, road conditions, or complex terrain, thus enabling emergency resources to be delivered to the scene as quickly as possible, shortening the response time from the occurrence of an incident to the intervention of professional forces, and solving the response delay problem caused by reliance on ground transportation in existing technologies.

[0022] 2. This invention utilizes image acquisition equipment mounted on an emergency medical drone to immediately capture video and audio information from the scene upon arrival, and transmits this information in real time via data link to a remote medical guidance module and a mobile terminal for ground-based medical resources. This technical solution enables remote medical experts and dispatched ground medical personnel to obtain objective, visual, first-hand information about the scene before arrival, overcoming the information barrier caused by existing technologies that rely solely on verbal descriptions from those seeking help, and providing a basis for remote decision-making and on-site preparation.

[0023] 3. This invention utilizes the emergency medical supply delivery mechanism of an emergency drone to rapidly deliver emergency medical supplies matching the on-site medical needs to the patient. Furthermore, through a remote medical guidance module and the drone's two-way communication equipment, remote medical experts can provide on-site personnel with step-by-step professional emergency medical operation guidance based on real-time transmitted images. This technical solution brings key emergency medical equipment and professional medical guidance capabilities forward, solving the problem of the inability to implement effective early treatment measures in existing technologies. Attached Figure Description

[0024] Figure 1 This is a perspective view of the present invention; Figure 2 This is a bottom view of the present invention; Figure 3 This is a diagram of the internal structure of the present invention; Figure 4 This is a block diagram of the system functional modules of the present invention; Figure 5 This is a flowchart of the method of the present invention; Figure 6 This is a functional block diagram of the intelligent analysis and scheduling module of the present invention; Figure 7 This is a functional block diagram of the unmanned aerial vehicle (UAV) control module of the present invention. Figure 8 This is a schematic diagram of the system application scenario of the present invention. Detailed Implementation

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] See attached document Figure 1 - Appendix Figure 4 The present invention provides an intelligent pre-hospital emergency medical response system based on an emergency medical drone, comprising: an emergency event receiving module, an intelligent analysis and dispatching module, an emergency medical drone, a drone control module, and a remote medical guidance module.

[0027] In a preferred embodiment, the system further includes a ground-based emergency medical resource mobile terminal, a drone station, and a data management and optimization module.

[0028] The emergency event receiving module, intelligent analysis and dispatch module, drone control module, remote medical guidance module, and data management and optimization module can be software program modules, hardware circuits, or combinations thereof deployed on cloud servers or local server clusters. They are connected and exchange information through a preset data communication protocol.

[0029] An emergency medical drone is an aircraft equipped with hardware including image acquisition equipment for collecting on-site information, two-way communication equipment for remote communication, and an emergency medical supplies delivery mechanism. The drone control module is connected to the emergency medical drone via a wireless communication link to send control commands and receive data.

[0030] The overall workflow of the system in this embodiment of the invention is as follows: First, the emergency event receiving module receives emergency event information and transmits it to the intelligent analysis and dispatch module. The intelligent analysis and dispatch module processes the information and, in conjunction with external real-time environmental data and preset emergency resource status, generates dispatch instructions.

[0031] The dispatch command was sent to the drone control module. Based on this command, the drone control module directed the emergency medical drone to autonomously fly to the incident location. Upon arrival, the drone used its image acquisition equipment to collect on-site information and transmitted this information back to the remote medical guidance module in real time.

[0032] The telemedicine guidance module presents on-site information to telemedicine experts and establishes two-way communication between experts and on-site personnel for remote guidance and control of emergency supplies deployment. Throughout the response process, the intelligent analysis and dispatch module can also synchronize relevant information to mobile terminals for ground-based medical resources. After the mission is completed, the emergency drone returns to its designated drone station. All process data is archived by the data management and optimization module.

[0033] One of the core functions of the intelligent analysis and scheduling module is to execute an analysis and scheduling function. It can be expressed by the following formula: ; In the formula, For the generated scheduling instructions; This refers to emergency event information received by the emergency event receiving module; This is for real-time acquisition of environmental data, including weather information, traffic conditions, etc. The system presets and updates the status of emergency medical resources in real time, including the current location, availability, and payload information of emergency medical drones and ground medical resources.

[0034] This function Internally, it includes an intelligent situational assessment process for determining medical needs and urgency levels. It can be expressed by the following formula: ; In the formula, This indicates the input of emergency event information. The standardized fusion process extracts and integrates voice text, sensor data, and event locations from the information. The medical needs are the output after the function calculation; This is the urgency level output after function calculation. These two outputs will be used to generate the final dispatch instruction. The key input parameters.

[0035] In one specific embodiment, this intelligent context evaluation model A neural network model employing a multi-task learning architecture from deep learning can be used. For example, it could be a Transformer encoder-based model that effectively processes and understands the features of the text sequence after speech conversion, while simultaneously fusing other numerical sensor data and location data from the input. The output layer of this model is designed with two parallel branches, one of which outputs the emergency level through a Softmax activation function. One branch calculates the classification probability, while the other branch outputs a multi-label vector representing the specific medical need through a sigmoid activation function. .

[0036] See attached document Figure 4 and attached Figure 5 This section details the specific implementation method of the system from receiving emergency event information to generating dispatch instructions.

[0037] The emergency event receiving module is used to acquire emergency event information from one or more sources. In one specific embodiment, the module has multiple communication interfaces, such as a voice interface for receiving calls from the public telephone network and a network data interface for receiving data from automated alarm devices.

[0038] When a call is received via the voice interface, the module records the call audio stream and obtains the initial geographic coordinates of the call source through the location service. When information is received via the network data interface, this information is a structured data packet containing precise geographic coordinates and sensor readings. All received raw information, including timestamps, geographic coordinates, and data content, is formatted and transmitted to the intelligent analysis and scheduling module.

[0039] The intelligent analysis and scheduling module receives emergency event information transmitted by the emergency event receiving module. Then, a series of processing operations are performed.

[0040] See attached document Figure 6 This module includes an information fusion unit, an intelligent evaluation unit, and a scheduling strategy generation unit.

[0041] The information fusion unit is used for preprocessing and feature extraction of raw emergency event information. For audio streams, the unit's built-in speech-to-text engine converts them into text sequences. Subsequently, the natural language processing component analyzes the text sequence, extracting keywords and entities related to injury descriptions, event types, and the number of people involved. For structured sensor data, the unit directly extracts key indicators. Finally, the information fusion unit integrates the processed text features, sensor indicators, event location coordinates, and timestamps into a standardized event feature vector, which serves as input for subsequent processing.

[0042] The intelligent assessment unit receives the event feature vector output by the information fusion unit and performs calculations to determine medical needs and urgency levels. This unit is equipped with a pre-trained intelligent situational assessment model. This model can be a multi-task classification and regression model, taking the event feature vector as input and outputting a value including the urgency level. and medical needs Analysis results. Emergency level. It is a discrete value, for example, from level 1 to level 4, used to characterize the severity of an event. Medical needs. It is a vector used to describe the specific medical procedures or supplies that are predicted to be needed, such as external defibrillation, wound hemostasis, or airway management.

[0043] In addition, the intelligent assessment unit also performs the matching of medical needs with emergency supplies. The system has a pre-installed supplies database that stores different types of emergency supply kits, their corresponding identification information, and applicable scenarios. The intelligent assessment unit then calculates the medical needs... The system queries and matches one or more of the most suitable emergency medical supply package types in the database and outputs the corresponding identification information to the dispatch strategy generation unit.

[0044] The scheduling strategy generation unit is the core of the decision-making process. It receives the analysis results from the intelligent evaluation unit. and ), and acquire real-time external environmental data. and the system's preset emergency medical resource status . The data structure dynamically records the location, battery level, and payload status (whether it carries a supply package and the type of supply package) of each emergency medical drone, as well as the location, personnel configuration, and mission status of each ground medical resource.

[0045] Based on all the input information, the scheduling strategy generation unit executes an optimization algorithm to calculate the optimal scheduling scheme. The objective function of this algorithm is to minimize the overall response time while satisfying resource matching and flight safety constraints. The calculation result ultimately generates a structured scheduling instruction. The "comprehensive response time" can be defined as a weighted sum function, which includes the weighted sum of the estimated flight time of the drone, the preparation and verification time of supplies, and the estimated arrival time of ground rescue resources. The "constraints" include: the drone's battery power must meet the energy requirements for the mission's round trip and hovering; the selected drone site must have the required first-aid supply kit; and the planned initial flight path must not cross known permanent or temporary no-fly zones.

[0046] The optimization algorithm, which could be an improved genetic algorithm or ant colony algorithm, searches for the optimal solution that minimizes the objective function value from all available combinations of drones and ground resources, while satisfying all constraints. The instruction explicitly specifies the dispatched emergency medical drone's number, the type of emergency medical supplies package it needs to carry, and a pre-planned optimal drone flight path.

[0047] In scenarios involving coordination with ground-based medical resources, the dispatch strategy generation unit, while generating dispatch instructions, also sends the event location, preliminary analysis results, and information on dispatched emergency drones to the mobile terminals of the dispatched ground-based medical resources via a data interface. This operation allows ground personnel to understand the situation on-site in advance while en route, preparing for response upon arrival.

[0048] See attached document Figure 8 This section details how the system controls emergency drones to safely and accurately execute flight missions after receiving dispatch instructions.

[0049] The drone control module is the central execution control unit connecting the intelligent analysis and scheduling module and the emergency medical drone. This module receives scheduling commands from the intelligent analysis and scheduling module. It then parses the data into a specific sequence of flight control commands.

[0050] Before executing a dispatch mission, the UAV control module first performs a material verification process. Dispatch instructions. This includes identification information for the type of emergency medical supply package determined by the intelligent assessment unit. The drone control module sends a query command to the drone station to which the designated emergency medical drone belongs, or directly queries the status of the drone's onboard sensors, to confirm that the emergency medical supply package currently carried by the drone matches the identification information in the command. Only after successful verification will the subsequent takeoff and dispatch procedures be executed, ensuring that the drone carries the correct relief supplies.

[0051] In a preferred embodiment, each first-aid supply kit is affixed with a unique radio frequency identification (RFID) tag or QR code, which stores information such as the type and expiration date of the supply kit. The drone station's helipad or the mounting mechanism of the first-aid drone is equipped with a corresponding RFID reader or image scanning device. The supply verification process is as follows: the drone control module sends the supply identification information from the dispatch command to the drone station; the station control system drives the reader to scan the supply kit tag mounted on the drone and compares the read information with the command information; if the comparison matches, a verification pass signal is returned.

[0052] After successful verification, the drone control module sends takeoff commands and initial flight path data to the emergency medical drone. Upon receiving the commands, the emergency medical drone autonomously takes off from its parking drone station and flies to the incident location along the initial optimal flight path planned by the scheduling strategy generation unit.

[0053] Throughout the autonomous flight, the UAV control module continuously monitors and dynamically adjusts the flight status.

[0054] See attached document Figure 7 and attached Figure 8 The UAV control module includes an environmental data receiving unit, a flight path evaluation unit, and a dynamic path planning unit.

[0055] The environmental data receiving unit continuously acquires real-time environmental information about the area surrounding and ahead of the emergency medical drone's flight path through airborne sensors (such as barometers, anemometers, and obstacle avoidance radar) and external data links (such as real-time weather services and air traffic control information services). This information includes, but is not limited to, wind speed, wind direction, air pressure, temperature, whether there are obstacles ahead (such as flocks of birds or buildings), and whether there are any temporarily established no-fly zones.

[0056] The flight path assessment unit receives information from the environmental data receiving unit in real time and compares and evaluates it with the flight path currently being executed by the emergency medical drone. For example, the unit calculates whether the impact of current wind speed and direction on the drone's flight speed and energy consumption exceeds preset thresholds, or determines whether the preset flight path will cross newly appearing obstacles or no-fly zones. The assessment result is a risk status indicator used to indicate whether the current flight path needs to be adjusted.

[0057] When the route assessment unit determines that a risk exists, i.e., the risk status flag is activated, the dynamic route planning unit is triggered. The function of this unit can be represented by the following functional model: ; In the formula, For the pre-set route segments where risks currently exist, The environmental data receiving unit receives real-time environmental information that may lead to risks. The dynamic path planning unit executes a path search algorithm to... As a constraint, a dynamically adjusted flight path is recalculated and generated to avoid the aforementioned obstacles and no-fly zones, and to compensate for the impact of wind speed. The new route It is then sent to the flight control system of the emergency medical drone for execution, thereby replacing the original risky flight path.

[0058] Upon completion of the mission, or upon receiving a return-to-home command or autonomously determining that the mission has ended, the drone control module controls the emergency medical drone to return to the designated drone station. After landing and positioning, automated equipment within the drone station is triggered, performing a series of maintenance procedures on the emergency medical drone. These procedures include automatically charging the drone, replacing consumed emergency medical supply kits and replenishing them manually via a robotic arm, and initiating self-checks of the drone's hardware and software systems. The status of all maintenance procedures is recorded and transmitted back to the system to update the emergency medical drone's resource status.

[0059] Specifically, the supply storage area within the drone site can be an automated medicine cabinet-style shelf. Upon receiving a supply replenishment instruction, the robotic arm retrieves the corresponding type and latest batch of first-aid supply kits and installs them onto the drone's mounting mechanism. Simultaneously, the charging port automatically aligns and connects until the battery management system reports charging completion. The self-test program automatically checks the drone's power system, flight control software, communication links, and onboard sensors, generating a self-test report which is then uploaded to the data management and optimization module.

[0060] Once the emergency drone arrives over the location of the incident, its onboard image acquisition equipment (such as a visible light high-definition zoom camera and an infrared thermal imager) begins to work, collecting video and audio information from the scene, i.e., on-site information. The drone control module establishes a high-bandwidth, low-latency encrypted data transmission link between the emergency drone and the remote medical guidance module, transmitting the collected on-site information back in real time.

[0061] The telemedicine guidance module is a human-computer interaction platform integrating communication and data display functions for use by telemedicine experts. This module receives and decodes real-time audio and video streams transmitted from emergency drones and displays them on the expert's monitoring screen. Through this platform, experts can clearly observe the on-site environment, the patient's specific location, and their general condition.

[0062] Based on real-time assessment of on-site information, telemedicine experts initiate two-way voice communication with the on-site personnel via a telemedicine guidance module. The expert's voice commands are transmitted via a data link to the emergency drone, which then broadcasts them to the on-site personnel using its onboard two-way communication equipment (including a speaker and microphone). Simultaneously, the on-site personnel's voices are also captured by the microphone and transmitted back to the expert. In this way, the expert can provide precise, step-by-step voice guidance for emergency procedures to the on-site personnel.

[0063] Under expert guidance or according to a pre-set deployment logic, the drone control module controls the emergency medical drone's emergency supplies delivery mechanism to accurately deliver emergency supplies packages matching the guidance to a safe area near the patient. For example, if the expert determines it is cardiac arrest, a package containing an automated external defibrillator (AED) is delivered, and then on-site personnel are given voice instructions on how to open the package, attach the electrodes, and administer the defibrillation.

[0064] In a preferred embodiment, the deployed emergency medical supplies include a portable vital sign monitoring device that can connect to the patient, such as a multi-parameter monitoring patch integrating ECG, blood oxygen saturation, and blood pressure monitoring functions. Once on-site personnel connect the device to the patient, the vital sign data collected by the device is transmitted wirelessly (e.g., via Bluetooth or Wi-Fi) to an emergency drone, which then transmits the data back to the remote medical guidance module via the main data link. This module displays the received vital sign data as real-time curves and numerical values, synchronized with the on-site video feed, providing objective data support for the assessment and guidance of remote medical experts.

[0065] After the entire emergency response process concludes, the data management and optimization module is responsible for archiving and analyzing the data generated throughout the process. This module collects and stores all relevant data from various components of the system, forming a complete event database. Archived data includes, but is not limited to, scheduling instructions generated by the intelligent analysis and scheduling module. Complete flight data of the emergency medical drone recorded by the drone control module. (Including flight path, speed, altitude, and power consumption); on-site information saved by the remote medical guidance module. (Audio and video recordings) and remote guidance records (Recordings of conversations between experts and the scene, and records of vital signs data).

[0066] The data management and optimization module performs offline iterative optimization of the system's core algorithm model based on statistical analysis and machine learning modeling of a large amount of archived event data. For example, by analyzing the response time and rescue results of different types of events, this module can adjust the optimization algorithm parameters of the scheduling strategy generation unit in the intelligent analysis and scheduling module. By analyzing the correlation between remote guidance records and patient prognosis, the intelligent situation assessment model can be optimized. Medical needs The accuracy of predictions will be improved. The optimized new model and strategy parameters will be redeployed into the system, enabling it to make more accurate and effective decisions in subsequent emergency responses.

[0067] For example, the data management and optimization module can establish a correlation model between different event characteristics, different types of material delivery, different remote guidance strategies, and final patient clinical prognostic indicators (if available) through retrospective analysis. By using reinforcement learning, the model can be evaluated by using an increased probability of positive prognosis as a reward signal. The decision-making logic is trained. At the same time, by modeling the actual flight energy consumption data of all UAVs, the energy consumption assessment model in the UAV control module can be continuously optimized, so that the dynamic path planning unit can more accurately balance flight time and energy consumption when planning routes.

Claims

1. An intelligent pre-hospital emergency medical response system based on first-aid drone, characterized in that, The application relates to an emergency response system, comprising: an emergency event receiving module for receiving emergency event information containing an event location; an intelligent analysis and dispatching module for intelligently analyzing the emergency event information to determine an analysis result of medical needs and an emergency level, and generating a dispatching instruction according to the analysis result, real-time environmental data and a preset first-aid resource state; a first-aid unmanned aerial vehicle (UAV) equipped with an image acquisition device, a two-way communication device and a first-aid material delivery mechanism; a UAV control module for controlling the first-aid UAV to autonomously fly to the event location according to the dispatching instruction, and collecting on-site information through the image acquisition device; a remote medical guidance module for receiving the on-site information returned by the first-aid UAV and establishing a two-way communication connection between a remote medical expert and the on-site location.

2. The drone-based smart pre-hospital emergency medical response system according to claim 1, wherein, The intelligent analysis and dispatching module is specifically used for: fusing voice text, sensor data and an event location in the emergency event information; evaluating the emergency level and predicting medical needs based on a preset intelligent context evaluation model; generating a dispatching instruction containing an optimal UAV route and a material delivery strategy based on real-time environmental data of the first-aid UAV and ground rescue resources and a preset first-aid resource state, including location, availability and load.

3. The drone-based smart pre-hospital emergency medical response system of claim 1, wherein, The remote medical guidance module is specifically used for: real-time receiving and displaying on-site video and audio information returned by the first-aid UAV through the image acquisition device; enabling a remote medical expert to provide real-time and step-by-step first-aid operation voice guidance to on-site personnel through the two-way communication device of the first-aid UAV, wherein the guidance content matches the first-aid materials delivered by the first-aid UAV.

4. The drone-based smart pre-hospital emergency medical response system of claim 1, wherein, The application further comprises a ground rescue resource mobile terminal, and the intelligent analysis and dispatching module is further used for: synchronously sending on-site information collected by the first-aid UAV and preliminary evaluation information generated by the remote medical guidance module to the ground rescue resource mobile terminal before the ground rescue resource arrives at the event location.

5. The drone-based smart pre-hospital emergency medical response system according to claim 1, wherein, The application further comprises a UAV station, and the UAV control module is further used for: controlling the first-aid UAV to autonomously return to the UAV station after completing an on-site rescue task, and triggering an automatic charging, first-aid material replenishment and system self-checking process.

6. The drone-based smart pre-hospital emergency medical response system according to claim 1, wherein, The intelligent analysis and dispatching module is further used for: matching a determined medical need to a preset first-aid material package type, and including identification information of the first-aid material package type in the dispatching instruction; the UAV control module verifies the carried first-aid materials according to the identification information before dispatching the first-aid UAV.

7. The drone-based smart pre-hospital emergency medical response system according to claim 1, wherein, The UAV control module comprises: an environmental data receiving unit for real-time obtaining wind speed, obstacles and no-fly zone information around a flight path of the first-aid UAV; a route evaluation unit for comparing information obtained by the environmental data receiving unit with a preset route, so as to determine whether the preset route has safety or efficiency risks; a dynamic path planning unit for, when the route evaluation unit determines that there is a risk, re-computing and generating a dynamic adjustment route that avoids the obstacles and no-fly zones and compensates for the wind speed effect based on the real-time obtained information.

8. The drone-based smart pre-hospital emergency medical response system according to claim 1, wherein, The emergency supplies dropped by the emergency unmanned aerial vehicle include a vital sign monitoring device connectable with the patient; The remote medical guidance module is further configured to receive and display in real time vital sign data collected and transmitted by the vital sign monitoring device, thereby assisting the remote medical expert in evaluation and guidance.

9. The drone-based smart pre-hospital emergency medical response system according to claim 1, wherein, The data management and optimization module is further configured to archive all data in the entire emergency response process, including the dispatching instruction, unmanned aerial vehicle flight data, field information and remote guidance record, and iteratively optimize the dispatching strategy of the intelligent analysis and dispatching module based on analysis of the archived data.

10. A method for intelligent pre-hospital emergency medical response based on emergency drone, characterized in that, The method comprises the following steps: S1, receiving emergency event information containing an event location; S2, intelligently analyzing the emergency event information and determining medical needs and emergency levels, and generating a dispatching instruction based on the analysis result, real-time environmental data and preset emergency resource status; S3, controlling the emergency unmanned aerial vehicle to autonomously fly to the event location according to the dispatching instruction, and collecting field information through the image collection device of the emergency unmanned aerial vehicle; S4, establishing a communication connection between the emergency unmanned aerial vehicle and the remote medical guidance center, transmitting the field information, and conducting remote medical guidance and emergency supply dropping through the emergency unmanned aerial vehicle.

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