Human-computer interaction method and system in emergency scene

Abstract

The invention provides a man-machine interaction method and system in an emergency scene. The method comprises the following steps: analyzing received multimedia information of a first-aid requester to obtain a first-aid situation perception model; according to the first-aid situation perception model, performing selection processing on a possible first-aid response strategy by using a deep reinforcement learning algorithm, and analyzing a relationship between a symptom and a possible cause of disease of the requester in combination with a medical knowledge map technology to generate a first-aid guidance scheme; based on the first-aid guidance scheme, performing real-time optimization processing on a rescue path through a traffic flow prediction algorithm, dynamically adjusting a driving route of a rescue vehicle in combination with road condition information collected by the Internet of Things technology, and generating an optimal rescue path and a latest rescue progress notification; and by using the optimal rescue path and the latest rescue progress notice, performing visualization processing on the first-aid operation process, and generating intuitive visual guidance. According to the technical scheme provided by the invention, the first-aid response speed, accuracy and effectiveness are remarkably improved.

Claims

1. A human-computer interaction method in an emergency scenario, characterized in that: include: Using multimodal sentiment analysis technology and geographic information system, the multimedia information received from the first aid requester is analyzed and processed to obtain the first aid situation awareness model; Based on the emergency situation awareness model, a deep reinforcement learning algorithm is used to select possible emergency response strategies, and the relationship between the requester's symptoms and possible causes of the disease is analyzed in combination with medical knowledge graph technology to generate an emergency guidance plan; Based on the emergency guidance plan, the rescue route is optimized in real time through the traffic flow prediction algorithm, and the driving route of the rescue vehicle is dynamically adjusted in combination with the road condition information collected by the Internet of Things technology to generate the optimal rescue route and the latest rescue progress notification; The optimal rescue path and the latest rescue progress notification are used to visualize the emergency operation process and generate intuitive visual guidance.

2. The method according to claim 1, characterized in that: According to the first aid situation awareness model, a deep reinforcement learning algorithm is used to select possible first aid response strategies, and the relationship between the requester's symptoms and possible causes is analyzed in combination with medical knowledge graph technology to generate a first aid guidance plan, including: Utilizing the emergency situation awareness model, the emotional state and location information of the requester are extracted and processed to obtain emotional location features; Based on the emotional position features, a deep reinforcement learning algorithm is used to evaluate and process multiple emergency response strategies to obtain the optimal response strategy; According to the optimal response strategy, combined with medical knowledge graph technology, the requester's symptoms and possible causes are analyzed for correlation, and the most relevant disease type and its urgency are obtained; Using the most relevant disease types and their urgency, a set of first aid guidance plans are generated.

3. The method according to claim 2, characterized in that The first aid situation awareness model is used to extract and process the emotional state and location information of the requester to obtain emotional location features, including: Use natural language processing technology to parse and process the voice or text information of the first aid requester, identify key words and expressions related to emotions, and obtain emotional state data; Based on the emotional state data and in combination with environmental factors, the location of the requester is accurately located to generate location information; Performing comprehensive analysis and processing based on the emotional state data and location information to evaluate the correlation between emotions and geographic locations and obtain emotion-location correlation analysis results; The emotion position association analysis result is used to perform fusion processing to obtain emotion position features.

4. The method according to claim 2, characterized in that: According to the optimal response strategy, combined with the medical knowledge graph technology, the requester's symptoms and possible causes are analyzed for correlation, and the most relevant disease type and its urgency are obtained, including: Using the symptom identification information included in the optimal response strategy, a preliminary classification process is performed on the symptom manifestations of the requester to obtain a symptom classification result; Based on the symptom classification results, combined with the disease and symptom relationship database in the medical knowledge graph, the correlation between the requester's symptoms and known diseases is matched to generate a potential disease list; Based on the potential disease list, the medical knowledge graph technology is used to further analyze the typical symptoms, common complications and pathogenesis of each disease, evaluate the possibility and urgency of each disease, and obtain a disease possibility and urgency assessment report; The disease possibility and urgency assessment report is used to obtain the most relevant disease types and their urgency.

5. The method according to claim 1, characterized in that Based on the emergency guidance plan, the rescue path is optimized in real time through the traffic flow prediction algorithm, and the driving route of the rescue vehicle is dynamically adjusted in combination with the road condition information collected by the Internet of Things technology to generate the optimal rescue path and the latest rescue progress notification, including: Initializing the initially planned rescue path using the emergency degree and location information provided in the first aid guidance plan to obtain an initial rescue path; Based on the initial rescue path, the current and expected traffic conditions are analyzed and processed by a traffic flow prediction algorithm, factors that may affect the rescue time are predicted, and traffic condition prediction results are generated; According to the traffic condition prediction results, combined with the road condition information collected in real time by the Internet of Things technology, the driving route of the rescue vehicle is dynamically adjusted to obtain the optimal rescue route; Utilize the optimal rescue path, continuously monitor the actual driving situation, maintain communication with the requester, and generate the latest rescue progress notification.

6. The method according to claim 5, characterized in that The method of dynamically adjusting the route of the rescue vehicle based on the traffic condition prediction result and combining the road condition information collected in real time by the Internet of Things technology to obtain the optimal rescue route includes: Using the traffic condition prediction results, factors that may currently affect the rescue time are identified and processed to obtain potential traffic influencing factors; Based on the potential traffic influencing factors, combined with the specific road condition information collected in real time by the Internet of Things technology, comprehensive analysis and processing are performed to generate real-time road condition updates; According to the real-time road condition update, the driving route of the rescue vehicle is replanned to avoid adverse road conditions and obtain an adjusted driving route; The adjusted driving route is continuously optimized until the shortest time and the best safety conditions are achieved, thereby generating an optimal rescue path.

7. The method according to claim 1, characterized in that The optimal rescue path and the latest rescue progress notification are used to visualize the emergency operation process and generate intuitive visual guidance, including: Using the optimal rescue path and combining it with map data, the rescue vehicle's route is visualized to obtain a dynamic route map; Based on the dynamic route map, the current location and estimated arrival time of the rescue vehicle are updated and displayed in real time according to the latest rescue progress notification, generating a real-time progress display; According to the real-time progress display, combined with the specific symptoms of the requester and the generated first aid guidance plan, the first aid operation steps are demonstrated through augmented reality technology to obtain a first aid operation demonstration sequence; The first aid operation demonstration sequence is utilized to generate intuitive visual guidance.

8. A human-computer interaction system in an emergency scenario, characterized in that: include: The parsing module is used to parse and process the multimedia information received from the first aid requester by using multimodal sentiment analysis technology and geographic information system to obtain a first aid situation awareness model; A selection module is used to select possible emergency response strategies based on the emergency situation perception model using a deep reinforcement learning algorithm, analyze the relationship between the requester's symptoms and possible causes of disease in combination with medical knowledge graph technology, and generate an emergency guidance plan; An adjustment module is used to optimize the rescue path in real time based on the first aid guidance plan through a traffic flow prediction algorithm, dynamically adjust the driving route of the rescue vehicle in combination with the road condition information collected by the Internet of Things technology, and generate an optimal rescue path and the latest rescue progress notification; The processing module is used to visualize the emergency operation process using the optimal rescue path and the latest rescue progress notification to generate intuitive visual guidance.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a human-computer interaction method in a first aid scenario as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a human-computer interaction method in a first aid scenario as described in any one of claims 1 to 7 is implemented.

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