1821 results about "Emergency encounter" patented technology

A method to optimize human resource management within emergency medical services (EMS) according to one approach may have the steps of: receiving inputs from at least one or more the data sources such as traffic conditions, weather, incident location of emergency or non emergency call, call type, dispatch type, latitude and longitude of incident location, age, sex, chief complaint, incident date and time, holiday, day of the week, call classification, emergency department population status, incoming EMS service requests, and the like and combinations thereof; providing a scheduling module for building one or more predictive assessment values; the scheduling module outputting automatically a suggested scheduling template; employing one or more machine learning models to generate the one or more predictive assessment values that relate to the comparison of the inputs of the evolution of data over a time interval using machine learning; employing a global positioning systems (GPS) device to provide geo-location information of EMS equipment.

A disclosed method includes monitoring a plurality of emergency event queues at an emergency network entity; determining that an emergency event in one of the emergency event queues corresponds to an emergency type that can be responded to using an unmanned aerial vehicle; determining that an unmanned aerial vehicle is available that has capabilities corresponding to the emergency type; establishing an unmanned aerial vehicle control link between the unmanned aerial vehicle and the emergency network entity; and deploying the unmanned aerial vehicle to the emergency event location and providing data from the unmanned aerial vehicle on a display of the emergency network entity.

The invention relates to the technical field of emergency response, in particular to an emergency resource scheduling and path planning method, which comprises the following steps that a dynamic weight evaluation system is constructed based on emergency resource scheduling factors, and the weight evaluation system can dynamically calculate and adjust factor weights so as to determine that the most critical resource is scheduled to the most required place under the emergency condition; and constructing a deep reinforcement learning model, and using historical emergency response data as a training set, so that the intelligent agent learns and optimizes a strategy of selecting an optimal path under different conditions in a simulated or historical emergency scene. Efficient emergency response: through comprehensive application of a dynamic weight evaluation system and a deep reinforcement learning model, the system can quickly identify and preferentially schedule the most critical resources to the most needed place, and meanwhile, the optimal path is planned, so that the emergency response speed and efficiency are remarkably improved.

Described herein are systems, devices, methods, and media for an emergency response data exchange system. The system provides an operations center emergency management application with a textual and / or graphical notification of emergency communications initiated on a premises managed by the operations center. The system exchanges operations center data (e.g., floorplans, video feeds, point of contact information, building access controls) with an emergency communications center (ECC) and / or with a first responder device to facilitate reducing emergency response times. The system selectively establishes communications (e.g., messaging windows) between the operations center, ECC, and / or first responder device to facilitate real-time information sharing between relevant parties of an emergency.

The invention provides a compressor air system management and control system based on an Internet of Things smart cloud platform. The compressor air system management and control system comprises an edge computing unit, the smart cloud platform and an intelligent terminal. And the edge calculation unit collects the operation parameters of the compressor in real time through the Internet of Things sensor, processes the collected data, generates preprocessed data, and executes a high-priority local quick response control task. And the smart cloud platform receives the data uploaded by the edge computing unit, performs fault prediction on the running state of the compressor by using a neural network model, dynamically calculates and optimizes running parameters, and sends an optimization result to the edge computing unit to execute adjustment. The intelligent terminal interacts with the intelligent cloud platform and the edge computing unit to display the real-time operation state and the fault prediction result of the compressor; in an emergency, the intelligent terminal cooperates with the edge computing unit through direct communication to execute localized emergency management. The operation efficiency, the fault response capability and the energy consumption optimization level of the compressor system are improved.

The invention discloses an AI nursing whiteboard system and method based on a large model agent, the system comprises a nursing whiteboard terminal, an AI intelligent chain platform and a database, the nursing whiteboard terminal comprises an AI nursing assistant, and a whiteboard control system is arranged in the nursing whiteboard terminal. Through the functions of integration of multi-source data, intelligent early warning, accurate question and answer and the like, various diagnosis and treatment data, such as vital signs, medical advices, nursing records and the like, of a patient can be rapidly integrated and presented in a clear and visual mode, a large amount of time and energy are saved, the patient can rapidly know the condition of the patient, and the diagnosis and treatment efficiency is improved. Vital signs and illness state changes of a patient can be monitored in real time, early warning can be immediately given out once abnormal conditions such as heart rate and blood pressure exceeding normal ranges occur, a nurse can know the abnormal conditions in the first time and take corresponding measures, the response speed to emergency situations of the patient is greatly increased, and when the nurse encounters a question in the aspect of medical knowledge, the nurse can timely take corresponding measures. Inquiry may be made by an AI care assistant.

The invention relates to the technical field of human resources, in particular to a human resource intelligent allocation optimization system, which comprises an occupational analysis and prediction module, an occupational path planning module, an emergency allocation management module and a human resource optimization module. According to the invention, by integrating the occupational history and skill data of the medical personnel, accurate prediction and dynamic deployment of occupational paths are realized, talent matching is more accurate through accurate occupational conversion point analysis, resource waste is reduced, and occupational training recommendation is customized for hospital business targets, so that the pertinence and effectiveness of training are improved; the talent training process is optimized, the training effect of professional talents is improved, under the emergency condition, the system can respond in time through real-time data monitoring, rapid and effective manpower allocation is carried out, it is ensured that a hospital can keep efficient operation before emergency medical requirements, through continuous data updating and feedback, a manpower resource allocation strategy is continuously optimized, and the training effect of the professional talents is improved. And the adaptive capacity and the management efficiency of the hospital in a changeable environment are improved.

The invention relates to the technical field of data communication, and discloses a rail transit station-level agent implementation method and system. The method comprises the following steps: constructing a professional body base through an ARM processor, designing a LiteIP security communication bus based on an FPGA, constructing a multi-core CPU cluster to construct an intelligent body center, establishing a real-time and entity database to form a data basis, fusing federated learning and a neural network to generate a decision, and executing edge calculation to realize station-level autonomous control. According to the invention, millisecond-level data exchange and intelligent cooperative control in the station-level system are realized, and the response speed and the processing efficiency of the system in an emergency are remarkably improved.

The invention provides an unmanned aerial vehicle enhanced positioning method and system fusing GNSS and UWB, and the method comprises the steps: receiving a flight task instruction from a ground station, starting a GNSS module through the flight task instruction, obtaining an initial position, and building a communication connection with a plurality of fixed beacon nodes through a UWB module; adjusting the position estimation of the unmanned aerial vehicle according to the initial position and the real-time distance measurement information in combination with wide-area positioning data to obtain accurate position estimation, and when the unmanned aerial vehicle enters a signal blocking area, switching to UWB to carry out accurate positioning processing on the unmanned aerial vehicle and generating a fusion positioning result; updating the positioning strategy in case of emergency in the process of executing the specific task to obtain a positioning scheme; all GNSS and UWB positioning data acquired during the flight period are integrated, analyzed and processed to generate a flight path report, and the flight path report is uploaded to a cloud server; according to the technical scheme provided by the invention, the positioning precision and service reliability of the unmanned aerial vehicle in different environments are improved, and smooth completion of tasks is ensured.

The invention relates to the technical field of medical emergency monitoring, in particular to an ICU emergency response system which comprises a preliminary risk assessment module, an emergency recognition module, a treatment scheme optimization module, a vital sign monitoring module, an emergency response coordination module, a disease evolution prediction module, a resource allocation management module and a medical decision support module. According to the method, feature engineering and logistic regression analysis are combined with medical history and real-time vital signs of a patient, emergency medical events are recognized by applying pattern recognition and natural language processing, a treatment scheme is optimized by using a support vector machine and a decision tree, personalized treatment suggestions are provided, and the time sequence and a biometric method are used for finely monitoring the vital signs; health risks are early warned, resources are reasonably allocated in emergency response through a resource scheduling algorithm and a priority queue, illness state prediction is assisted through machine learning and probability statistics, resource utilization is improved through linear programming and a resource optimization strategy, and waste is reduced.

An embodiment related to a system, comprising: a processor; a sensor; an image sensor; a display; and a communication module; wherein the processor is configured to measure a physiological characteristic of a passenger of a vehicle; capture an image of the passenger of a vehicle; process the physiological characteristic of the passenger and determine whether the physiological characteristic is outside a predefined threshold; process the image, via the processor comprising an image processing algorithm and artificial intelligence, for extracting a feature; detect the health emergency and a severity of the health emergency based on the feature extracted; generate an alert signal; display the alert signal and at least one of a detail of the health emergency of the passenger; and an action based on the health emergency and a severity of the health emergency. In an embodiment, the system is configured to be a component of the vehicle.

The invention discloses a fusion reward-based reinforcement learning sparse graph reasoning method and system, which ensure accurate and timely response in emergency response decision by introducing a rule set and a fusion reward mechanism. Meanwhile, a fusion embedding module is constructed, semantic association information and graph structure information in the dam emergency response knowledge graph are expressed in a combined mode, more complete and accurate knowledge expression is provided for reasoning of an intelligent agent, and a complex emergency response scene can be dealt with. Besides, through a rule-based reward shaping mechanism, a terminal reward is combined to realize a fused reward function, the reinforcement learning model is further guided to generate a high-quality explainable reasoning path, and it is ensured that the generated decision path can be rapidly understood and executed in an emergency. According to the method, the accuracy and practicability of the sparse knowledge graph reasoning task in the dam emergency response system are remarkably improved, and application in the fields of knowledge graph completion, disaster emergency decision making, intelligent early warning and the like can be effectively supported.

The invention provides a tracheal intubation automatic navigation method based on an artificial intelligence analysis anatomical structure, and relates to the technical field of tracheal navigation, and the tracheal intubation automatic navigation method comprises the following steps: carrying out image scanning on a user to obtain real-time airway image information; constructing a human airway image anatomical model, and performing anatomical structure analysis on the real-time airway image information according to the human airway image anatomical model to obtain anatomical structure data; connecting an electromagnetic navigation mechanical control system; the path planning subsystem analyzes the anatomical structure data to obtain a first intubation navigation path; and the path control subsystem synchronously generates intubation control parameters according to the first intubation navigation path, and controls a mechanical arm of intubation equipment to perform tracheal intubation on the user according to the intubation control parameters. The technical problem that in the prior art, due to the limitation that tracheal intubation operation depends on experience and skills of doctors, the intubation success rate is not high under the emergency condition can be solved, and the technical effect of improving the intubation success rate is achieved.

The invention relates to the technical field of intelligent control, in particular to an intelligent linkage control system for tunnel electromechanical equipment, which comprises an environment sensing module, a data analysis module, an intelligent decision module, a linkage control module, a user interaction module and an emergency response module, wherein the environment sensing module is used for acquiring environment parameters in a tunnel; the data analysis module is used for generating an environment trend prediction and anomaly detection result; the intelligent decision module is used for generating a linkage control strategy in combination with a preset fuzzy logic algorithm; the linkage control module is used for adjusting electromechanical equipment in the tunnel to realize intelligent linkage control of the equipment; the user interaction module is used for providing multiple interaction modes; and the emergency response module is used for immediately starting emergency measures when an emergency occurs. According to the invention, through real-time monitoring, intelligent adjustment and effective emergency response, the safety, reliability and emergency processing capability of the tunnel are significantly improved.

The invention discloses an emergency response decision optimization system and method for an intelligent refrigerant system, and the method comprises the steps: carrying out the automatic data collection, crawling and collecting the text data of an emergency plan, a historical emergency event and a risk factor related to the refrigerant system, carrying out the intelligent text preprocessing, and carrying out the decision optimization. Potential association relationships in the data are mined through an association rule mining algorithm, and a multi-layer complex network model is constructed. The multi-layer complex network model is input to a convolutional neural network through an embedded layer, key features are automatically extracted, and the model is trained, so that optimal emergency response strategies in different risk scenes are learned. The system receives risk input data in real time through an application program interface, and rapidly outputs optimized emergency resource configuration and coping measure combination to assist a decision maker to make an optimal decision in an emergency.

The invention relates to a subway station pedestrian emergency evacuation system and method based on identification guidance, in particular to a dynamic identification guidance system optimized in combination with Legon simulation software. According to the system, a dynamic identification device is deployed in a subway station, Legon simulation software is combined to simulate and analyze people flow in the station in real time, and an optimal evacuation path is provided. In an emergency, the system dynamically adjusts the guiding direction of the evacuation identification by using simulation data and environment monitoring information. Legion simulation software can accurately simulate driving behaviors of passengers in different environments and under different congestion conditions, so that the accuracy of an evacuation scheme is improved. Through a simulation test, the system can significantly reduce the evacuation time, improve the evacuation speed, optimize the distribution of personnel in the station, reduce the residence time in a high-density area, and ensure the safe evacuation of passengers in an emergency.

The utility model relates to the technical field of mobile charging piles, and discloses an environment-friendly cabinet type mobile charging pile which comprises a main body mechanism, a track mechanism and a driving mechanism, racks are arranged on the two sides of a track main body, an emergency stop button is located at the front end of a box body, and the driving mechanism is located at the rear end of the box body. The charging pile is used for rapidly cutting off a power supply in an emergency and ensuring use safety, the interactive terminal provides an operation interface for a user and has the functions of charging starting, charging state monitoring, setting adjustment and the like, and the shock pad absorbs vibration and impact and protects the structure of the charging pile from being damaged. According to the environment-friendly cabinet type movable charging pile, the charging cabinet body is designed to be movable, single charging equipment is allowed to serve a plurality of parking spaces, the utilization rate of a charging station is remarkably improved, and the situation that the parking spaces are occupied by non-electric vehicles or the electric vehicles are vacant due to full charging is reduced through the design; therefore, the same charging resource can serve more users, especially in urban areas with insufficient parking spaces.

The invention belongs to the technical field of air traffic management, and discloses a control tower and flight guarantee command room decision instruction transmission method based on edge nodes, priorities are distributed for instructions through an intelligent grading model, the high-priority instructions can reserve resources in advance and are transmitted preferentially, the resources can be preempted in emergency, and the control tower and flight guarantee command room decision instruction transmission efficiency is improved. A multi-path transmission strategy is automatically switched when a certain path fails, data integrity is ensured in combination with fragment verification and block chain recording, instruction loss caused by a single point of failure is avoided through edge-cloud redundant backup, and the problems that instruction transmission is prone to interference and insufficient in safety in a traditional method are solved; through intelligent priority grading, resources are inclined to high-priority instructions, resource waste is avoided, a collaborative scheduling mechanism of an edge node cluster can dynamically adjust resource allocation according to a real-time load, and a transmission quality feedback and adaptive optimization strategy is continuously optimized, so that the system can still efficiently process a large number of instructions in a flight take-off and landing intensive period.

Disclosed are system and techniques for classifying events such as emergencies. A system can include a computer system to perform operations including: receiving sensor signals from a group of devices at a location, determining whether one or more of the sensor signals exceed expected threshold levels, in response to determining that the one or more of the sensor signals exceed the expected threshold levels, correlating the sensor signals, classifying the correlated sensor signals into an emergency event based on applying an artificial intelligence (AI) model to the correlated sensor signals, the AI model having been trained to classify the correlated sensor signals into a type of emergency, determine a spread of the emergency event, and determine a severity level of the emergency event, generating, based on information associated with the classified emergency event as output from the AI model, emergency response information, and returning the emergency response information.

The invention discloses a remote monitoring robot for health monitoring of old people, and relates to the technical field of monitoring robots, the remote monitoring robot comprises a mobile robot module, and the mobile robot module is in communication connection with a deep learning algorithm module, a voice module, an anomaly detection module, a positioning module and an emergency safety guarantee module. According to the remote monitoring robot for health monitoring of the old people, by integrating a deep learning algorithm and a mobile robot technology, a safe living space is provided for the old people, activities of the old people are monitored in real time, and it is ensured that the old people can be timely helped when encountering health crisis through a fall-down detection and emergency situation feedback mechanism; when it is detected that the old man falls down through a YOLOv5 algorithm, help seeking information is immediately sent to a preset guardian, in addition, the mobile following function of the robot provides continuous monitoring when the old man goes out, and the robot can closely follow the old man at home or outdoors to ensure that no dead angle exists in monitoring.

The invention discloses a coal mine fireproof simulation training method based on a virtual reality technology, relates to the technical field of coal mine fireproof simulation training, is used for solving the problem that a virtual scene and student operation are asynchronous due to sensor data delay or misalignment in the training process, and comprises the following steps: collecting action data of a student in real time; comprise posture change, hand movement and position coordinate information. Seamless synchronization of the sensor and the virtual scene is ensured through the time synchronization algorithm and the dynamic compensation model, and the safety and effectiveness of training are improved. In combination with a sliding window, a prediction algorithm and a redundancy detection mechanism, the system has high fault-tolerant capability, and training continuity and stability are guaranteed. The dynamic path planning under the fire spreading trend is realized by adopting the A * algorithm, and the guidance is quickly adjusted and corrected when the trainee deviates from the path, so that the real-time response capability and the immersion of training are improved, and the trainee is helped to quickly make a decision and act in an emergency.

The invention relates to the technical field of data transmission, in particular to an intelligent emergency calling method and system. The method comprises the following steps: when an emergency event occurs, detecting potential bottleneck and congestion in a network, analyzing a current available transmission path based on real-time network data, evaluating the quality and priority of the path, and collecting real-time emergency data at the same time; distributing the emergency data to corresponding network slices through a predefined slicing strategy; and after the network slices are distributed, detecting the demand of emergency data traffic, evaluating the real-time slice capacity, judging whether the resources in the slices meet the demand of the emergency data traffic, and if the resources in the slices do not meet the demand of the data traffic, performing adjustment according to the real-time condition of the slices. According to the intelligent emergency response method and system, it can be ensured that the key task data can preferentially obtain necessary resources under the emergency condition, interference of low-priority data is avoided, and it is ensured that the key task data can be reliably transmitted.

The invention relates to a subway emergency evacuation identification system utility test and evaluation method, in particular to a simulation experiment evaluation method based on BIM (Building Information Model), Unity (game engine) and VR (Virtual Reality) technologies. According to the method, firstly, a subway emergency evacuation identification system experiment test platform integrating BIM, Unity and VR technologies is constructed; the platform can simulate the performance of the subway emergency evacuation identification system in a fire emergency event, and assesses different emergency evacuation identification systems. Further, according to the method, volunteers are recruited for experimental testing, and evacuation behavior parameters and visual gazing parameters of passengers are collected and analyzed. Finally, an all-dimensional and multi-dimensional index system is constructed, and the effectiveness of the emergency evacuation identification system is comprehensively evaluated by adopting a coupling coordination degree model, so that the safety, reliability and high efficiency of the emergency evacuation identification system in an emergency are ensured.

The invention provides a safe and reliable data center machine room cooling channel, and belongs to the technical field of data center heat dissipation and cooling devices. Comprising a bottom plate component, a vertical frame component, data center cabinets, a fire-fighting linkage skylight system, an access door system, an access control system and a monitoring sensing system, the data center cabinets are arranged between the bottom plate component and the vertical frame component in pairs, and the fire-fighting linkage skylight system is located at the top ends of the data center cabinets and the vertical frame component and can be in linkage with the monitoring sensing system. The channel door systems are located on the two sides of the cold channel respectively and can be in linkage with the access control system and the monitoring sensing system, and the purposes of safe opening and closing and emergency opening and closing are achieved. Meanwhile, an intelligent lighting system is further installed on the outer bottom face of the bottom short frame in the fire-fighting linkage skylight system, conventional lighting management can be achieved, highlight continuous lighting can be conducted in emergency, and reliable guarantee is provided for efficient operation, safety management and energy-saving control of a data center.

The present invention discloses a home-based elderly care comprehensive service management system based on the Internet of Things, and relates to the technical field of the Internet of Things. In order to solve the problems in the prior art that the detection and response speed for emergencies is poor, especially the lack of more intelligent means for emergency handling of elderly people without anyone to accompany them, and the service content fails to fully combine the specific needs of users for intelligent push, and the service model is relatively single; the present invention collects the health status, activity trajectory and environmental data of users in real time through Internet of Things devices, builds a comprehensive perception system, dynamically adjusts and pushes personalized service content according to the user's health records, behavioral habits and actual needs, compares user behavior feature data with historical habits, effectively determines the degree of behavioral deviation, and generates early warning reports in time, which not only improves the real-time and accuracy of behavior monitoring, but also provides customized services according to the personalized needs of users, discovers abnormal behaviors in time, and ensures user safety.

The invention relates to the technical field of intelligent assistance, and discloses an auxiliary system based on an intelligent wearable device used in daily life, and the system comprises a health signal monitoring and analysis module which is used for monitoring the heart rate, blood oxygen, sleep and body temperature of a user, generating a health report, and according to the health data of the user; the safety and emergency calling module is used for calling for help, positioning and safety monitoring in emergency; the smart home control module is used for controlling smart home appliances through Bluetooth and Wi-Fi, adjusting indoor temperature and humidity and being linked with a safety system; and the education and learning module is used for providing online learning and knowledge acquisition functions. Real-time monitoring of key health indexes such as heart rate, blood oxygen, body temperature and sleep of a user is achieved, and early warning is given out in time when abnormal conditions are detected. Besides, the system provides multi-level safety guarantee for the user through the functions of emergency call, position sharing, home monitoring and the like, and is suitable for daily health management of the old and chronic disease patients.

The invention relates to the technical field of medical intubation, in particular to patient-assisted breathing intubation equipment for nursing, comprising an intubation bed divided into an upper plank bed and a lower plank bed; the head adjusting mechanism comprises a pillow groove formed in the top end of the upper plate bed, an adjusting groove is formed in the inner wall of the pillow groove, and a first supporting plate is fixedly connected to an opening of the adjusting groove. 3, sensing the change of the exhaled air temperature of the patient through the thermistor plate, namely the human body exhaled air temperature (6-37 DEG C vs and the room temperature of 24 DEG C), and when exhaled air flow is in contact with the thermistor plate, triggering the change of a current signal by the temperature change. After the PLC detects sudden change of current, the single-shaft motor is automatically controlled to drive the headrest to rotate, the elevation angle of the head is accurately adjusted to the optimal intubation position through magnetic attraction transmission of the electromagnetic block and the permanent magnet block, a nurse does not need to manually and repeatedly adjust the elevation angle, and especially time is saved in an emergency. The preset position of the thermistor plate ensures the angle accuracy.

The invention relates to the technical field of detection towed body release, in particular to an emergency release device of an underwater towing system. The underwater towing system comprises a depth-keeping towed body and a detection towed body, the detection towed body and the depth-keeping towed body are connected through a photoelectric composite cable, the photoelectric composite cable is composed of a towing cable and a communication cable, and the communication cable is connected with a detection instrument in the detection towed body through a plug connector male head and a plug connector female head which are mutually plugged; the emergency release device is arranged at one end in the detection towed body and comprises a towing cable release mechanism and a communication cable release mechanism; the driving piece drives the trailing cable releasing mechanism to be separated from the trailing cable and drives the communication cable releasing mechanism to be separated from the communication cable; the emergency release device is suitable for releasing the detection towed body in an underwater towing system, depends on a mechanical transmission structure, is stable and reliable, and can realize autonomous separation of the detection towed body when an emergency situation or communication loss occurs, so that the safety and the recovery rate of the detection towed body are improved, and the potential heavy loss is reduced.

The invention discloses a medical care data intelligent evaluation system based on multi-modal data fusion, and belongs to the technical field of medical care. The problems that subjective deviation exists in the prior art, evaluation results of different medical workers on the same patient may be different, the medical data source is single, and patient information cannot be comprehensively reflected are solved. Through real-time feedback of nursing personnel, the frequency of abnormal data in a feedback result within a period of time is analyzed, so that the updating frequency of the model is dynamically adjusted, and misjudgment caused by model deviation is reduced; according to the method, the abnormal data in the multi-modal data are identified by calculating Z-score, the time sequence data are set to be high priority, and the abnormal data at the high priority are preferentially fed back to the nursing personnel, so that the response speed of the nursing personnel to abnormal conditions is increased, the distribution of nursing resources is optimized, and the nursing efficiency is improved. Therefore, nursing personnel can deal with emergencies in time, and nursing quality and patient safety are improved.