Intelligent remote emergency rescue management system capable of rapidly matching resources

Abstract

This invention proposes an intelligent remote emergency rescue management system capable of rapidly matching resources, relating to the field of remote emergency medical technology. The alarm receiving module triggers the case analysis module to generate priorities, the resource scheduling module allocates instructions to the drone control module, and after the drone completes deployment, the information matching module generates navigation information. Data from the person scanning the code is fed back to the system via the data association unit, forming a complete closed loop of resource scheduling-deployment-feedback. Through real-time data interaction between modules, the traditional emergency resource matching time is shortened from 10-20 minutes to within 2 minutes, while simultaneously improving the accuracy and utilization rate of drone AED deployment, achieving a qualitative leap in emergency response efficiency.

Description

Technical Field

[0001] This invention relates to the field of remote emergency rescue technology, and in particular to an intelligent remote emergency rescue management system that can quickly match resources. Background Technology

[0002] Current traditional emergency medical services systems suffer from significant drawbacks: resource allocation relies on human experience, resulting in noticeable response delays and difficulty in quickly matching optimal resources in emergencies; new equipment such as drones lacks intelligent linkage with case requirements, leading to low accuracy in emergency equipment deployment; and information silos are severe, with the caller's location, case type, and resource status unable to be correlated in real time, delaying crucial rescue time. For example, existing systems typically retrieve fixed plans after a call is received manually, but priorities cannot be adjusted promptly when cases change dynamically, and drone deployment relies on fixed coordinates, failing to dynamically adjust navigation paths based on the location of the person scanning the code at the scene.

[0003] Furthermore, the identity of the person scanning the code was not automatically matched with the case data, requiring secondary manual verification, which further slowed down the response time. These problems resulted in inefficient allocation of medical resources, making it difficult to meet the stringent time requirements for "golden 4 minutes" rescue in emergencies such as cardiac arrest. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, this invention proposes an intelligent remote emergency rescue management system capable of rapidly matching resources, comprising: an alarm receiving module, used to receive alarm information and generate case data including case type and incident location; The case analysis module, connected to the alarm receiving module, is used to classify cases based on case data and generate resource scheduling priorities. The resource scheduling module, connected to the case analysis module, is used to generate dispatch instructions based on resource scheduling priorities. The drone control module, connected to the resource scheduling module, is used to respond to dispatch commands and control the drone to carry emergency medical equipment to the designated drop point. The information matching module, connected to the drone control module, is used to generate a QR code containing navigation information of the delivery point and send it to the caller's terminal. The data association unit, connected to the information matching module, is used to match the data of the person scanning the code with the case data; Among them, the matching coefficient of the UAV control module, information matching module, and data association unit is K. In the formula, ε d v represents the absolute error of the drone deployment. h t represents the movement speed of the person scanning the code. d The time interval from the generation of the QR code to its reception by the caller's terminal is α, where α is the spatial sensitivity coefficient and Δs is the real-time distance between the location of the person scanning the code and the designated delivery point.

[0005] Some implementations also include: The QR code feedback unit, connected to the data association unit, is used to receive QR code information and extract the location data and identity information of the QR code scanner. The expert guidance module, connected to the data association unit, is used to initiate online expert guidance based on the matching results. The case tracking module is used to collect real-time information on the status of emergency medical resources and on-site feedback. The archiving module is used to store case processing records.

[0006] In some implementations, the alarm receiving module performs the following steps: Receive alarm information sent by the caller's terminal. The alarm information includes at least one data type, such as voice, text, or geolocation. By using speech recognition technology to extract keywords from speech content, or by using text parsing technology to perform semantic analysis on text content, a set of keywords for case types can be generated. The case type keywords are matched according to the preset classification rules to generate case type tags; Associate case type tags with geographical locations in alarm information to generate case data containing unique case numbers; The case data is transmitted to the case analysis module.

[0007] In some implementations, the case analysis module performs the following steps: Receive the case number from the case data and retrieve similar case records that match the case type tag from the historical case database; Extract the processing priority, resource consumption data and historical response time of similar case records, and calculate the average resource demand weight; Dynamic resource scheduling priorities are generated based on disposal priorities and average resource demand weights, where the priority calculation formula is a weighted comprehensive score. The dynamic resource scheduling priority is bound to the location coordinates of the incident in the case data to form a resource scheduling instruction and output it to the resource scheduling module.

[0008] In some implementations, the resource scheduling module performs the following steps: Based on resource scheduling priority, available vehicles are selected from the emergency vehicle database and dispatch instructions are generated. The selection criteria include the vehicle's current location, vehicle type, and equipment configuration. Synchronously query the drone's availability status in the drone control module. If the drone is in standby mode and its battery level is higher than a preset threshold, generate a drone start command containing the coordinates of the deployment point. The dispatch command is sent to the corresponding emergency vehicle terminal, and the drone start command is sent to the drone control module; If there are insufficient emergency vehicles or drones in the current area, send a dispatch request containing the case number and resource requirements to the cross-regional resource dispatch center.

[0009] In some implementations, the drone control module performs the following steps: Receive the drone start command and control the drone to carry the AED device to fly along the planned route to the delivery point coordinates. The planned route is generated based on real-time traffic data and airspace control information. The flight path is captured in real time by an airborne camera, and obstacles are detected using image recognition technology. If an obstacle is detected, an obstacle avoidance algorithm is triggered to replan the flight path. Upon arrival at the delivery point, the AED device is released via the onboard robotic arm, and a completion signal containing the delivery point coordinates and release time is sent to the information matching module via the wireless communication module.

[0010] In some implementations, the information matching module performs the following steps: Upon receiving the completion signal, the coordinates of the delivery point are parsed and a QR code containing the shortest path navigation link is generated. The navigation link is dynamically generated through the map service interface. Send the QR code to the caller's terminal via SMS or instant messaging tools, and record the sending time and the caller's terminal reception status. The unique identifier of the QR code is associated with the case number in the case data, and the association is stored in the matching relationship table of the data association unit; If the person calling for help does not scan the QR code within the preset time, a second push notification will be triggered and the case will be marked as a high-priority case.

[0011] In some implementations, the QR code feedback unit performs the following steps: Receive the scanning request uploaded after the user's terminal scans the QR code, and parse the unique identifier in the scanning request; Extract the real-time location data of the user's terminal; the location data is obtained through GPS or base station positioning technology. Retrieve the mobile phone number and identity verification information of the user's terminal from the user registration database; The unique identifier, real-time location data, and mobile phone number are encapsulated into a structured QR code scanning feedback data packet and sent to the data association unit.

[0012] In some implementations, the data association unit performs the following steps: Retrieve the associated case number and location coordinates from the matching table based on the unique identifier; The difference in straight-line distance between the real-time location data of the scanner's terminal and the coordinates of the incident location is calculated. The difference in straight-line distance is generated by a latitude and longitude coordinate transformation algorithm. If the difference in straight-line distance is less than the preset threshold, it is determined to be a successful match and a matching result containing the case number and the mobile phone number of the person who scanned the code is generated; If the difference in straight-line distance is greater than or equal to a preset threshold, a manual review process is triggered and the case is marked as pending verification. At the same time, an exception notification is sent to the case tracking module.

[0013] In some implementations, the expert guidance module performs the following steps: Receive the case number from the matching results, and retrieve the case type label and patient vital signs information from the case data; Based on the case type tag, the corresponding emergency medical experts are matched from the expert database. The matching criteria include the expert's qualifications, online status, and historical response speed. Establish an audio and video communication link between the expert terminal and the scanning terminal, and transmit patient vital signs data and on-site environmental video in real time through the link; First aid guidance and AED usage instructions are simultaneously pushed to the scanner's terminal and the emergency vehicle's terminal. The guidance is generated based on standardized procedures in the case database.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: Through multi-module collaborative design, the above-mentioned problems are effectively solved: the alarm receiving module receives alarm information and generates case data containing case type and incident location, providing structured input for subsequent analysis; the case analysis module classifies cases based on case data and generates resource scheduling priorities, dynamically adapting to the needs of different urgency levels; the resource scheduling module generates dispatch instructions according to priorities, ensuring that emergency vehicles and drones are allocated as needed, reducing resource idleness; the drone control module responds to dispatch instructions and controls drones to carry emergency equipment to designated drop points, avoiding obstacles by combining real-time environmental data, improving the drop success rate; the information matching module generates a QR code containing drop point navigation information and sends it to the caller's terminal, shortening the equipment acquisition time through intuitive navigation; the data association unit matches the scanner's data with the case data, eliminating information silos and ensuring accurate association between on-site personnel and cases. Each module forms a closed-loop link: the alarm receiving module triggers the case analysis module to generate priorities, the resource scheduling module allocates instructions to the drone control module, the information matching module generates navigation information after the drone completes drop, and the scanner's data is fed back to the system through the data association unit, forming a complete closed loop of resource scheduling-drop-feedback. Through real-time data interaction between modules, the traditional emergency resource matching time is shortened from 10-20 minutes to within 2 minutes, while improving the accuracy and utilization rate of drone AED deployment, achieving a qualitative leap in emergency response efficiency. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 The diagram shown is a structural schematic of an intelligent remote emergency rescue management system that can quickly match resources, according to an embodiment of the present invention.

[0017] Figure 2 The diagram shown is a structural schematic of an intelligent remote emergency rescue management system that can quickly match resources, according to another embodiment of the present invention.

[0018] Figure 3 The diagram shown is a schematic representation of the processing flow of an intelligent remote emergency rescue management system that can quickly match resources, according to another embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] The specific embodiments of the present invention will be described below.

[0021] like Figure 1 As shown, this invention proposes an intelligent remote emergency rescue management system that can quickly match resources, including: an alarm receiving module, used to receive alarm information and generate case data containing case type and incident location; The case analysis module, connected to the alarm receiving module, is used to classify cases based on case data and generate resource scheduling priorities. The resource scheduling module, connected to the case analysis module, is used to generate dispatch instructions based on resource scheduling priorities. The drone control module, connected to the resource scheduling module, is used to respond to dispatch commands and control the drone to carry emergency medical equipment to the designated drop point. The information matching module, connected to the drone control module, is used to generate a QR code containing navigation information of the delivery point and send it to the caller's terminal. The data association unit, connected to the information matching module, is used to match the data of the person scanning the code with the case data; Among them, the matching coefficient of the UAV control module, information matching module, and data association unit is K. In the formula, ε d v represents the absolute error of the drone deployment. h t represents the movement speed of the person scanning the code. d The time interval from the generation of the QR code to its reception by the caller's terminal is α, where α is the spatial sensitivity coefficient and Δs is the real-time distance between the location of the person scanning the code and the designated delivery point.

[0022] Specifically, ε d The deviation of the actual drone drop point from the designated drop point, expressed in meters; v h The average speed of the person scanning the code from the time they scan it to the time they arrive at the designated delivery point, expressed in meters per second; t d The unit is seconds; α controls the attenuation of the matching effect by the distance deviation, and is derived from historical data training, with the unit being meters. -1 Δs is measured in meters.

[0023] K represents the dynamic coupling result of the drone emergency equipment deployment efficiency and the respondent's behavior when scanning the code, directly reflecting the probability that "the emergency equipment can be quickly obtained by the person calling for help." For example, its numerical criterion is: K≥1: High matching quality (emergency equipment can be obtained by the person scanning the code within the golden rescue time); 0.5≤K<1: Medium matching quality (requires manual guidance); K<0.5: Matching failed (backup rescue plan needs to be activated).

[0024] The K-value acts like a dynamic matching quality table, integrating several previously independent factors—the accuracy of drone-deployed emergency equipment, the speed of the caller's journey to the deployment point, the system's internal processing delays, and the actual spatial deviation between the caller's scan and the emergency equipment—into a comprehensive matching coefficient. The core effect of this coefficient is to reveal in real-time the potential probability of the emergency equipment being quickly and reliably obtained by the caller. A high K-value indicates a smooth and efficient deployment-response chain, allowing the system to maintain its established strategy. Conversely, a decrease in the K-value reveals bottlenecks or risks in the matching chain (such as excessive deployment deviation, obstructed personnel movement, or slow system response). This allows the system to anticipate potential matching failures and dynamically trigger corresponding remedial measures, such as automatically enhancing navigation guidance, prioritizing the allocation of nearby personnel, or requesting immediate expert intervention. Ultimately, by continuously monitoring and responding to changes in the K-value, the entire system significantly improves the overall efficiency and reliability of emergency resource coordination from deployment to user, gaining valuable time windows in time-sensitive emergency rescue scenarios.

[0025] The alarm receiving module receives alarm information sent from the caller's terminal. This alarm information can be of at least one data type, including voice, text, or geolocation. For example, if the caller sends a voice alarm via a mobile application, the module uses voice recognition technology to extract keywords (such as "cardiac arrest" or "traffic accident"), generates a case type tag, and associates it with GPS location information to form case data containing a case number. After receiving the case data, the case analysis module retrieves handling records of similar cases from the historical database and generates a dynamic resource scheduling priority based on resource consumption and response time. For example, if the case type is cardiac arrest, the priority is automatically raised to the highest level. The resource scheduling module filters available emergency vehicles and drones based on priority, generating dispatch instructions and drone launch instructions. After responding to the instructions, the drone control module controls the drone carrying the AED device to fly to the delivery point, avoiding obstacles in real time. The information matching module generates a QR code containing a navigation link and pushes it to the caller's terminal. The data association unit matches the location of the person scanning the code with the case data to ensure accurate resource delivery.

[0026] Each module is connected through a data stream. The alarm receiving module triggers the analysis module to generate priorities, the resource scheduling module allocates instructions, and after the drone is deployed, the information matching module generates navigation information. The scanned data is fed back to the system to form a closed loop, reducing the traditional resource matching time from 10-20 minutes to within 2 minutes.

[0027] The alarm receiving module quickly generates structured case data, the case analysis module dynamically generates resource scheduling priorities, the resource scheduling module accurately allocates emergency resources, the drone control module enables efficient deployment of emergency equipment, the information matching module provides navigation guidance, and the data association unit eliminates information silos. These modules are interconnected to form a closed loop, reducing the traditional resource matching time from 10-20 minutes to within 2 minutes, thus improving emergency response speed and resource utilization.

[0028] like Figure 2 As shown, some implementations also include: The QR code feedback unit, connected to the data association unit, is used to receive QR code information and extract the location data and identity information of the QR code scanner. The expert guidance module, connected to the data association unit, is used to initiate online expert guidance based on the matching results. The case tracking module is used to collect real-time information on the status of emergency medical resources and on-site feedback. The archiving module is used to store case processing records.

[0029] The QR code scanning feedback unit receives the request uploaded after the user's terminal scans the QR code, parses the unique identifier, and extracts the user's location data and mobile phone number. For example, after the user scans the QR code using WeChat, the unit obtains the real-time location through base station positioning and binds it to the user's registration information. The data association unit calculates the difference in straight-line distance between the user's location and the incident location. If the difference is less than a preset threshold (e.g., 200 meters), a successful match is determined, and a matching result is generated. The expert guidance module initiates online guidance based on the matching result, such as assigning a cardiology expert to guide on-site personnel in using an AED via audio and video. The case tracking module collects real-time data on the location of emergency vehicles, the status of drones, and the patient's vital signs. If there is a vehicle delay or the patient's condition deteriorates, a secondary dispatch is triggered. The archiving module stores case processing records, including resource usage data and guidance records, for subsequent analysis and optimization.

[0030] The QR code feedback unit, data association unit, expert guidance module, case tracking module, and archiving module form a closed-loop feedback link, which interacts with the main link through the data association unit to achieve full-process management of resource scheduling, deployment, guidance, and tracking.

[0031] The QR code scanning feedback unit extracts the location and identity information of the person scanning the code, the data association unit verifies the location matching, the expert guidance module provides real-time remote guidance, the case tracking module monitors resource status, and the archiving module stores complete case records. This closed-loop feedback chain collaborates with the main system to ensure the traceability of the emergency response process and the optimization of resource scheduling, while also improving the success rate of on-site treatment through expert intervention.

[0032] In some implementations, the alarm receiving module performs the following steps: Receive alarm information sent by the caller's terminal. The alarm information includes at least one data type, such as voice, text, or geolocation. By using speech recognition technology to extract keywords from speech content, or by using text parsing technology to perform semantic analysis on text content, a set of keywords for case types can be generated. The case type keywords are matched according to the preset classification rules to generate case type tags; Associate case type tags with geographical locations in alarm information to generate case data containing unique case numbers; The case data is transmitted to the case analysis module.

[0033] The alarm receiving module uses speech recognition technology to extract keywords from the caller's voice, such as identifying keywords like "patient unconscious" and "difficulty breathing," or it uses text parsing technology to perform semantic analysis on SMS alarms, generating a set of case type keywords. Pre-defined classification rules map these keywords to standardized case type tags (such as "cardiac arrest" and "traumatic bleeding"). For example, the keyword "cardiac arrest" triggers a cardiogenic case tag. The case type tag is associated with the geographical location (such as latitude and longitude coordinates) in the alarm information, generating case data with a unique case number. After the case data is transmitted to the case analysis module, a resource scheduling process is triggered.

[0034] In alternative solutions, the alarm receiving module can be integrated with a natural language processing model to improve classification accuracy in complex contexts.

[0035] The alarm receiving module automatically classifies cases using voice recognition and text parsing technologies, generates standardized case type tags, and associates these tags with geographical locations to form unique case data. This reduces human intervention errors, improves alarm receiving efficiency, and provides accurate input for subsequent resource allocation.

[0036] In some implementations, the case analysis module performs the following steps: Receive the case number from the case data and retrieve similar case records that match the case type tag from the historical case database; Extract the processing priority, resource consumption data and historical response time of similar case records, and calculate the average resource demand weight; Dynamic resource scheduling priorities are generated based on disposal priorities and average resource demand weights, where the priority calculation formula is a weighted comprehensive score. The dynamic resource scheduling priority is bound to the location coordinates of the incident in the case data to form a resource scheduling instruction and output it to the resource scheduling module.

[0037] The case analysis module retrieves similar case records from the historical case database that match the case type tag, such as retrieving handling data for all "cardiac arrest" cases within the past 30 days. It extracts handling priority, resource consumption (e.g., AED usage count, number of medical personnel), and average response time, calculating resource demand weights. The priority calculation formula is a weighted comprehensive score, for example, response time accounts for 60%, and resource consumption accounts for 40%. Dynamic resource scheduling priorities are bound to the coordinates of the incident location to generate resource scheduling instructions. For example, high-priority cases are automatically assigned to the nearest ambulance and standby drones.

[0038] In an alternative approach, the case analysis module can employ a machine learning model to dynamically adjust weight parameters based on real-time data.

[0039] The case analysis module dynamically generates resource scheduling priorities based on historical data, and optimizes decisions by combining handling records and resource consumption weights. This avoids the rigidity of fixed plans, adapts to the needs of different emergency scenarios, and improves the rationality of resource allocation.

[0040] In some implementations, the resource scheduling module performs the following steps: Based on resource scheduling priority, available vehicles are selected from the emergency vehicle database and dispatch instructions are generated. The selection criteria include the vehicle's current location, vehicle type, and equipment configuration. Synchronously query the drone's availability status in the drone control module. If the drone is in standby mode and its battery level is higher than a preset threshold, generate a drone start command containing the coordinates of the deployment point. The dispatch command is sent to the corresponding emergency vehicle terminal, and the drone start command is sent to the drone control module; If there are insufficient emergency vehicles or drones in the current area, send a dispatch request containing the case number and resource requirements to the cross-regional resource dispatch center.

[0041] The resource dispatch module filters available vehicles from the emergency vehicle database, using criteria such as the distance between the vehicle's current location and the incident site, vehicle type (e.g., whether it is equipped with a defibrillator), and equipment configuration status. For example, it filters ambulances within 5 kilometers that are equipped with an AED. Simultaneously, it queries the drone control module for the drone's battery level and mission status. If the battery is sufficient and the drone is not currently on a mission, it generates a drone start command containing the deployment point coordinates. The dispatch command is sent to the vehicle terminal, and the drone command is sent to the control module. If resources are insufficient within the area, a dispatch request is sent to the cross-regional dispatch center, such as requesting drone support from adjacent areas.

[0042] In alternative solutions, the resource scheduling module can integrate traffic big data to predict vehicle arrival times and optimize routes.

[0043] The resource scheduling module generates dispatch instructions by comprehensively considering vehicle status, drone availability, and regional resource conditions, supporting cross-regional resource allocation. This addresses the issue of insufficient local resources, ensures coordinated operation of emergency vehicles and drones, and minimizes response time.

[0044] In some implementations, the drone control module performs the following steps: Receive the drone start command and control the drone to carry the AED device to fly along the planned route to the delivery point coordinates. The planned route is generated based on real-time traffic data and airspace control information. The flight path is captured in real time by an airborne camera, and obstacles are detected using image recognition technology. If an obstacle is detected, an obstacle avoidance algorithm is triggered to replan the flight path. Upon arrival at the delivery point, the AED device is released via the onboard robotic arm, and a completion signal containing the delivery point coordinates and release time is sent to the information matching module via the wireless communication module.

[0045] After receiving the start command, the drone control module generates a planned flight path based on real-time traffic data and airspace control information. For example, it avoids peak-hour traffic and no-fly zones. The onboard camera captures environmental images of the flight path and uses image recognition technology to detect obstacles (such as high-voltage lines and trees), triggering an obstacle avoidance algorithm to replan the flight path. Upon arrival at the delivery point, the onboard robotic arm releases the AED device and sends a completion signal containing coordinates and time to the information matching module via the wireless communication module.

[0046] In alternative solutions, drones can use lidar instead of cameras for obstacle detection, improving obstacle avoidance accuracy in complex environments.

[0047] The drone control module plans flight paths and avoids obstacles based on real-time environmental data, accurately deploying AED devices. This improves the safety and success rate of drone flights in complex environments, preventing device delays or damage.

[0048] In some implementations, the information matching module performs the following steps: Upon receiving the completion signal, the coordinates of the delivery point are parsed and a QR code containing the shortest path navigation link is generated. The navigation link is dynamically generated through the map service interface. Send the QR code to the caller's terminal via SMS or instant messaging tools, and record the sending time and the caller's terminal reception status. The unique identifier of the QR code is associated with the case number in the case data, and the association is stored in the matching relationship table of the data association unit; If the person calling for help does not scan the QR code within the preset time, a second push notification will be triggered and the case will be marked as a high-priority case.

[0049] After receiving the completion signal, the information matching module parses the coordinates of the delivery point and generates the shortest path navigation link through the map service interface. For example, it calls the Gaode Map API to generate a walking navigation route. The QR code contains the navigation link and a unique identifier, which is pushed to the caller's terminal via SMS or WeChat. The unique identifier is associated with the case number and stored in the matching table of the data association unit. If the caller does not scan the code within a preset time (e.g., 3 minutes), the system triggers a second push and marks it as a high-priority case, giving it priority in resource allocation.

[0050] In alternative solutions, the navigation link can be embedded with AR navigation functionality to guide the caller to quickly locate the AED device.

[0051] The information matching module dynamically generates navigation links and pushes QR codes to guide callers to quickly obtain AED devices. By linking case data with unique identifiers, it ensures accurate matching between the person scanning the code and the case, reducing the time required for secondary confirmation.

[0052] In some implementations, the QR code feedback unit performs the following steps: Receive the scanning request uploaded after the user's terminal scans the QR code, and parse the unique identifier in the scanning request; Extract the real-time location data of the user's terminal; the location data is obtained through GPS or base station positioning technology. Retrieve the mobile phone number and identity verification information of the user's terminal from the user registration database; The unique identifier, real-time location data, and mobile phone number are encapsulated into a structured QR code scanning feedback data packet and sent to the data association unit.

[0053] The QR code scanning feedback unit parses the unique identifier in the scanning request, such as extracting a 32-bit hash value from the QR code. It obtains the real-time location data of the scanner's terminal via GPS or cell tower triangulation and retrieves the phone number and identity information (such as a first aid certificate number) from the user registration database. The unique identifier, location data, and phone number are encapsulated into a structured data packet and sent to the data association unit. For example, the data packet format is JSON, containing the fields "case_id", "location", and "phone".

[0054] In alternative solutions, the barcode feedback unit can support NFC near-field communication and is compatible with barcode scanning on non-smart terminals.

[0055] The QR code scanning feedback unit parses the scanning request and encapsulates structured data, obtaining the scanner's location and identity information in real time. This provides a verification basis for the data association unit, preventing fraudulent or erroneous scanning from interfering with case processing.

[0056] In some implementations, the data association unit performs the following steps: Retrieve the associated case number and location coordinates from the matching table based on the unique identifier; The difference in straight-line distance between the real-time location data of the scanner's terminal and the coordinates of the incident location is calculated. The difference in straight-line distance is generated by a latitude and longitude coordinate transformation algorithm. If the difference in straight-line distance is less than the preset threshold, it is determined to be a successful match and a matching result containing the case number and the mobile phone number of the person who scanned the code is generated; If the difference in straight-line distance is greater than or equal to a preset threshold, a manual review process is triggered and the case is marked as pending verification. At the same time, an exception notification is sent to the case tracking module.

[0057] The data association unit retrieves the case number and incident location coordinates from the matching table based on the unique identifier. It calculates the difference in straight-line distance between the scanner's location and the incident location, for example, using the Haversine formula to calculate latitude and longitude distance. If the difference is less than a preset threshold (e.g., 200 meters), a successful match is confirmed, a matching result is generated, and pushed to the expert guidance module. If the difference exceeds the threshold, manual review is triggered, and the case is marked as pending verification. Simultaneously, the case tracking module is notified to intervene and investigate. An alternative solution could use a planar coordinate system to simplify distance calculations, sacrificing some accuracy to improve response speed.

[0058] The data association unit verifies the match between the person scanning the code and the incident location through distance calculation, automatically triggering manual review of abnormal cases. This prevents misallocation of resources, ensures that AED devices are obtained by those who actually need them, and improves the accuracy of resource utilization.

[0059] In some implementations, the expert guidance module performs the following steps: Receive the case number from the matching results, and retrieve the case type label and patient vital signs information from the case data; Based on the case type tag, the corresponding emergency medical experts are matched from the expert database. The matching criteria include the expert's qualifications, online status, and historical response speed. Establish an audio and video communication link between the expert terminal and the scanning terminal, and transmit patient vital signs data and on-site environmental video in real time through the link; First aid guidance and AED usage instructions are simultaneously pushed to the scanner's terminal and the emergency vehicle's terminal. The guidance is generated based on standardized procedures in the case database.

[0060] The expert guidance module retrieves case type tags and patient vital signs data (such as heart rate and blood oxygen) based on the case number in the matching results. It matches qualified, online, and fast-responding emergency medical experts from the expert database, prioritizing online doctors with experience in handling cardiac arrest. An audio-visual communication link is established between the expert terminal and the user's terminal to transmit patient data and on-site video in real time. Emergency medical guidance operation guidelines are generated based on standardized procedures in the case database, such as "CPR compression rate 100-120 times / minute." The guidelines are simultaneously pushed to the user's terminal and the emergency vehicle terminal to ensure on-site and rear-area collaboration. Alternatively, the expert guidance module can integrate an AI-assisted system to automatically generate emergency medical advice when the expert is offline.

[0061] The expert guidance module matches experts based on case type and establishes an audio-visual guidance link, simultaneously pushing standardized first aid guidelines. This enables real-time collaboration between on-site and remote medical resources, improving the effectiveness of rescue efforts by non-professionals and reducing the risk of operational errors.

[0062] like Figure 3As shown, in other embodiments, the processing of each step improves emergency response speed and resource utilization.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent remote emergency rescue management system capable of rapidly matching resources, characterized in that, include: The alarm receiving module is used to receive alarm information and generate case data including the case type and the location of the incident; The case analysis module, connected to the alarm receiving module, is used to classify cases based on the case data and generate resource scheduling priorities. A resource scheduling module, connected to the case analysis module, is used to generate dispatch instructions based on the resource scheduling priority. The drone control module, connected to the resource scheduling module, is used to control the drone to carry emergency medical equipment to a designated delivery point in response to the dispatch command. An information matching module, connected to the drone control module, is used to generate a QR code containing navigation information of the delivery point and send it to the caller's terminal. A data association unit, connected to the information matching module, is used to match the data of the person scanning the code with the case data; Among them, the matching coefficient of the UAV control module, information matching module, and data association unit is K. In the formula, ε d v represents the absolute error of the drone deployment. h t represents the movement speed of the person scanning the code. d The time interval from the generation of the QR code to its reception by the caller's terminal is α, where α is the spatial sensitivity coefficient and Δs is the real-time distance between the location of the person scanning the code and the designated delivery point.

2. The intelligent remote emergency rescue management system for rapid resource matching according to claim 1, characterized in that, Also includes: The QR code feedback unit, connected to the data association unit, is used to receive QR code information and extract the location data and identity information of the QR code scanner. The expert guidance module, connected to the data association unit, is used to initiate online expert guidance based on the matching results; The case tracking module is used to collect real-time information on the status of emergency medical resources and on-site feedback. The archiving module is used to store case processing records.

3. The intelligent remote emergency rescue management system for rapid resource matching according to claim 2, characterized in that, The alarm receiving module performs the following steps: Receive alarm information sent by the caller's terminal, wherein the alarm information includes at least one data type among voice, text, or geographic location; The speech content is extracted using speech recognition technology, or the text content is semantically analyzed using text parsing technology to generate a set of keywords for case types. The case type keyword set is matched according to the preset classification rules to generate case type tags; The case type label is associated with the geographical location in the alarm information to generate case data containing a unique case number; The case data is transmitted to the case analysis module.

4. The intelligent remote emergency rescue management system for rapid resource matching according to claim 3, characterized in that, The case analysis module performs the following steps: Receive the case number from the case data and retrieve similar case records that match the case type tag from the historical case database; Extract the processing priority, resource consumption data, and historical response time of the similar case records, and calculate the average resource demand weight; Dynamic resource scheduling priorities are generated based on the aforementioned disposal priorities and average resource demand weights, wherein the priority calculation formula is a weighted comprehensive score. The dynamic resource scheduling priority is bound to the location coordinates of the incident in the case data to form a resource scheduling instruction, which is then output to the resource scheduling module.

5. The intelligent remote emergency rescue management system for rapid resource matching according to claim 4, characterized in that, The resource scheduling module performs the following steps: Based on the resource scheduling priority, available vehicles are selected from the emergency vehicle database and a dispatch instruction is generated. The selection criteria include the vehicle's current location, vehicle type, and equipment configuration. The availability status of the drone in the drone control module is queried synchronously. If the drone is in standby mode and the battery level is higher than a preset threshold, a drone start command containing the coordinates of the deployment point is generated. The dispatch command is sent to the corresponding emergency vehicle terminal, and the drone start command is sent to the drone control module; If there are insufficient emergency vehicles or drones in the current area, send a dispatch request containing the case number and resource requirements to the cross-regional resource dispatch center.

6. The intelligent remote emergency rescue management system for rapid resource matching according to claim 5, characterized in that, The UAV control module performs the following steps: Upon receiving the drone start command, control the drone to carry the AED device and fly along a planned route to the coordinates of the delivery point. The planned route is generated based on real-time traffic data and airspace control information. The flight path is captured in real time by an airborne camera, and obstacles are detected using image recognition technology. If an obstacle is detected, an obstacle avoidance algorithm is triggered to replan the flight path. Upon arrival at the delivery point, the AED device is released via an onboard robotic arm, and a completion signal containing the delivery point coordinates and release time is sent to the information matching module via a wireless communication module.

7. The intelligent remote emergency rescue management system for rapid resource matching according to claim 6, characterized in that, The information matching module performs the following steps: Upon receiving the completion signal, the coordinates of the delivery point are parsed and a QR code containing the shortest path navigation link is generated, wherein the navigation link is dynamically generated through a map service interface; The QR code is pushed to the caller's terminal via SMS or instant messaging tools, and the push time and the caller's terminal reception status are recorded. The unique identifier of the QR code is associated with the case number in the case data, and the association is stored in the matching relationship table of the data association unit; If the person calling for help does not scan the QR code within a preset time, a second push notification will be triggered and the case will be marked as a high-priority case.

8. The intelligent remote emergency rescue management system for rapid resource matching according to claim 7, characterized in that, The QR code feedback unit performs the following steps: Receive the scanning request uploaded after the user's terminal scans the QR code, and parse the unique identifier in the scanning request; Extract the real-time location data of the scanning terminal, which is obtained through GPS or base station positioning technology; Retrieve the mobile phone number and identity verification information of the user's terminal from the user registration database; The unique identifier, real-time location data, and mobile phone number are encapsulated into a structured QR code scanning feedback data packet and sent to the data association unit.

9. The intelligent remote emergency rescue management system for rapid resource matching according to claim 8, characterized in that, The data association unit performs the following steps: The case number and location coordinates of the incident are retrieved from the matching table based on the unique identifier. Calculate the straight-line distance difference between the real-time location data of the scanner's terminal and the coordinates of the incident location. The straight-line distance difference is generated by a latitude and longitude coordinate conversion algorithm. If the difference in the straight-line distance is less than a preset threshold, it is determined to be a successful match and a matching result containing the case number and the mobile phone number of the person who scanned the code is generated; If the difference in straight-line distance is greater than or equal to the preset threshold, a manual review process is triggered and the case is marked as pending verification. At the same time, an abnormal notification is sent to the case tracking module.

10. The intelligent remote emergency rescue management system for rapid resource matching according to claim 9, characterized in that, The expert guidance module performs the following steps: Receive the case number from the matching result, and retrieve the case type label and patient vital sign information from the case data; Based on the case type label, a corresponding emergency medical expert is matched from the expert database. The matching criteria include the expert's qualifications, online status, and historical response speed. Establish an audio and video communication link between the expert terminal and the scanning terminal, and transmit the patient's vital signs data and on-site environment video in real time through the link; First aid guidance and AED usage instructions are simultaneously pushed to the scanner's terminal and the emergency vehicle's terminal. The guidance is generated based on standardized procedures in the case database.

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