223 results about "Emergency response systems" patented technology

The invention relates to the technical field of dispatching and emergency response, in particular to a supply chain multi-node real-time collaborative dispatching and emergency response system and method, and the system comprises a distributed data collection module which is used for obtaining the inventory data, logistics state and equipment operation parameters of each node in real time; the digital twin modeling engine is used for constructing a dynamic virtual mapping model of the supply chain network; the collaborative decision center generates a multi-objective optimization scheduling scheme based on a reinforcement learning algorithm; the emergency response trigger is used for automatically starting a graded emergency plan through abnormal mode recognition; according to the method, second-level response is realized through millisecond-level data synchronization and edge calculation, so that decision timeliness is improved, cross-node cooperation efficiency is improved by adopting multi-agent game and federated learning, and the punctuality rate of orders and the toughness index of the network are improved through multi-target Pareto optimization on the premise of controllable cost.

The invention relates to the technical field of low-altitude safety emergency, and discloses a low-altitude safety emergency system based on large model application. The system comprises a low-altitude situation awareness module which constructs a dynamic situation prediction model based on historical flight data, collects a low-altitude target track coordinate sequence, radar echoes and communication signal characteristics in real time, and outputs a theoretical security situation value. The multi-source data fusion analysis module performs three-dimensional difference analysis on theoretical and actually measured situation values, which relates to time domain trend deviation, space domain coverage offset and event sequence matching degree, and generates a target-level difference feature matrix; and the risk propagation path deduction module inputs the matrix into a geographic topology analysis network, generates a probability thermal distribution diagram of an abnormal risk propagation path in combination with airspace node communication parameters and a target position, and locates a risk influence area. And the intelligent disposal strategy generation module configures parameters according to the graph, starts high-frequency monitoring for a high-probability area, and performs communication disturbance test on an adjacent target.

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.

The invention provides a disaster early warning information issuing method and system based on cooperation of a multi-modal product and an intelligent agent. The method and system are used for improving comprehensiveness and accuracy of disaster early warning and timeliness and effectiveness of emergency response. The method comprises the following steps: acquiring a multi-source monitoring data set comprising a satellite cloud picture data unit and a ground observation data unit, wherein the multi-source monitoring data set is acquired and generated by monitoring nodes distributed at different spatial positions in real time; joint feature analysis processing is performed on the multi-source monitoring data set through a multi-agent cooperative processing network, disaster risk trend features are generated, and the disaster risk trend features reflect a time evolution rule and a spatial diffusion mode of a disaster occurrence probability; calling a reinforcement learning algorithm to carry out early warning strategy optimization processing on the disaster risk trend characteristics, and generating an optimized early warning strategy containing early warning content types and delivery opportunities; and executing early warning content delivery operation according to the optimized early warning strategy, and linking an emergency response system to complete emergency response cooperative operation.

The invention belongs to the technical field of extreme weather emergency management, and particularly relates to an extreme weather event prediction and emergency response system. The problems of multi-source data splitting, disaster chain prediction missing, static plan stiffness and lack of closed-loop evolution in the prior art are solved. A global disaster map is constructed through real-time fusion of multi-source data, a physical mechanism and machine learning are coupled to realize refined deduction of extreme weather and secondary disasters, a self-adaptive emergency scheme is generated based on a dynamic resource library and an optimization algorithm, and disaster feedback data is utilized to drive model and strategy iteration, so that the disaster situation feedback data can be used for driving the model and strategy iteration. And a'prediction-decision-feedback-evolution 'intelligent closed loop is formed. The method has the advantages that information islands are eliminated, the disaster chain modeling bottleneck is broken through, resource scheduling is dynamically optimized, a self-evolution mechanism is established, and the extreme weather response efficiency is improved.

The invention relates to the technical field of chemical safety emergency, in particular to a quick plugging emergency system for leakage of a chemical storage tank. The system comprises a leakage sensing data acquisition module, a leakage feature extraction module, a plugging decision generation module, a leakage trend prediction module, a plugging decision optimization module and a plugging execution control module. The leakage sensing data acquisition module acquires multi-sensor leakage data flow in real time; the leakage feature extraction module combines preset leakage physical constraint information and analyzes data to determine a leakage source and diffusion feature information; the plugging decision generation module generates an initial plugging scheme according to a built-in decision space and a fitness constraint condition; the leakage trend prediction module predicts leakage development to obtain prediction feature information; a plugging decision optimization module optimizes the initial scheme according to the initial scheme to form an optimization scheme; and the plugging execution control module drives the execution device to complete plugging. The system can improve the leakage detection accuracy, decision scientificity and plugging effectiveness, and guarantees the operation safety of the chemical storage tank.

The invention relates to the technical field of smart park integrated management, and discloses a smart park comprehensive management and control system and method based on multi-modal data analysis, and the method comprises the steps: obtaining a park multi-dimensional real-time data flow, and extracting a current situation feature vector and a personnel distribution thermodynamic diagram; analyzing the historical emergency event data sequence by using a space-time convolutional neural network, and outputting an event type identification vector; and generating a dynamic evacuation path planning and resource scheduling scheme based on a graph optimization algorithm, constructing a minimum cost flow optimization problem taking the weighted sum of time cost and waiting time as a target function, and solving to obtain an optimal people flow allocation scheme. The problem that emergency response cannot adapt to dynamic environment changes is solved. Experiential knowledge cannot be effectively inherited and utilized. And the emergency response system is slow in response and improper in resource allocation.

The invention discloses an AI multi-agent layered hierarchical emergency response system supporting an elastic power grid, and relates to the technical field of substation power response, the AI multi-agent layered hierarchical emergency response system comprises a global situation awareness module, a regional autonomous control module and an equipment toughness execution module, the global situation awareness module integrates multi-source disaster monitoring information, establishes a disaster chain deduction model, and sends the disaster chain deduction model to the regional autonomous control module; quantifying regional disaster intensity and generating a risk map; the regional autonomous control module extracts a high-risk region to divide a balanced island, realizes second-level splitting and black start through an intelligent agent, and constructs an autonomous stability coefficient to evaluate the stability of the island; and the equipment toughness execution module identifies a potential fault area, drives an intelligent agent to dynamically reconstruct a power grid topology, links distributed energy to perform differential energy supplementation according to a load gap, and calculates a recovery speed coefficient to judge the core load recovery quality. The system improves disaster pre-judgment precision and emergency response efficiency, ensures reliable power supply of medical and industrial core loads, and enhances disaster resistance elasticity of a power grid.

The invention relates to the technical field of path planning, in particular to a multi-agent dynamic nuclear emergency evacuation path planning method and system based on deep reinforcement learning. In particular to a multi-agent optimization path generation and dynamic adjustment method based on a deep reinforcement learning technology, and aims to improve the real-time performance, the safety and the execution efficiency of a public evacuation path in an emergency nuclear event. According to the scheme, the optimal evacuation path is automatically explored through deep reinforcement learning, a path model does not need to be manually preset, complex factors such as pollution dynamic diffusion and traffic jam under the nuclear accident scene can be dealt with, and the efficiency and safety of emergency evacuation path planning are greatly improved. The system is suitable for public emergency response systems in high-risk environments such as nuclear accidents, earthquakes, fire disasters and urban explosions.

The invention discloses a help-calling emergency response system based on an intelligent communication network, particularly relates to the field of intelligent communication, and comprises a multi-source data acquisition layer, a preprocessing and feature extraction layer, a progressive analysis engine, a decision fusion center and an execution control layer. According to the method, the data integrity and the anti-interference capability are remarkably improved, the time-space reference unification module adopts a PTP protocol and a coordinate system conversion technology, accurate alignment of multi-source data is achieved, a multi-dimensional feature matrix is generated through time-frequency conjoint analysis, and the problem that the feature extraction dimension of a traditional system is single is solved; the progressive analysis engine triggers a hierarchical response strategy based on dynamic fusion of a transmission robustness index, a space confidence coefficient and an environmental risk value, combines terrain refraction compensation and a timeliness attenuation mechanism to optimize decision logic, breaks through the limitation of a fixed threshold response mode, adapts to communication quality fluctuation and environmental sudden change, reduces rescue delay risk, and improves rescue efficiency. And the resource scheduling accuracy and the system intelligence level are improved.

The invention provides an emergency situation understanding system based on a multi-modal large model. The emergency situation understanding system comprises a multi-modal data acquisition module, a data preprocessing module, a multi-modal large model deployment module, a situation understanding and question and answer generation module and a tool registration and management module. Through four core innovations of multi-modal fusion, edge intelligence, dynamic optimization and cross-domain collaboration, the bottleneck problems of a traditional emergency system in the aspects of data utilization, real-time performance, safety, intelligence and the like are solved, and the response speed, decision-making precision and collaboration efficiency of emergency management are remarkably improved; and efficient and reliable technical support is provided for the fields of public safety, disaster rescue, military emergency and the like.

The invention provides a traffic hub large passenger flow emergency decision-making method and system based on swarm intelligent emergence and a large model, and a readable storage medium, and the system comprises a distributed sensing module which is responsible for carrying out edge intelligent processing at a data source; the group intelligent emerging module is responsible for forming a global control strategy in a self-organizing manner through local interaction among intelligent agents; the large model element agent evaluation module is responsible for carrying out macroscopic monitoring and evolution guidance on an agent group; and an emergency decision linkage execution module. The method has the beneficial effects that the mode conversion from the traditional single center decision to the distributed collaborative autonomous decision is realized, the robustness, the self-adaptability and the response real-time performance of the system in a dynamic uncertain environment are remarkably enhanced, and finally a closed-loop autonomous emergency response system integrating sensing, decision making, execution and optimization is formed; and the intelligent level and the operation safety guarantee capability of high-speed rail station large passenger flow management are comprehensively improved.

The invention discloses an old people fall monitoring and multi-terminal linkage emergency response system, and relates to the technical field of medical treatment, and the system comprises the steps: carrying out the non-inductive collection of human body dynamic information in a weak light visual blind area environment; the voice interaction verification judgment module comprises a far-field voice excitation unit; and setting a priority queue management mechanism, and confirming a behavior perception output tumble instruction. According to the invention, closed-loop management of fall monitoring and emergency treatment of the old people is realized, and the system has remarkable beneficial effects; the privacy of the user is effectively protected, and the false alarm rate and the invalid scheduling of nursing resources are obviously reduced; the event discrimination accuracy and the individual suitability are improved, and it is ensured that high-risk events are not missed; the timeliness and accuracy of emergency response are improved, and safe, intelligent and humanized technical support is provided for intelligent old-age care and home health monitoring.

The invention discloses a disaster emergency system based on big data, which comprises a data collection module, a fire-fighting early warning module and a fire-fighting emergency module, and is characterized in that the data collection module is used for collecting visual data and environmental comprehensive data in a monitored target area in real time, and inputting comprehensive information of all people entering a building into the system; the fire-fighting early warning module is used for detecting whether a fire occurs in a target area or not, further analyzing the spreading trend and the danger degree of the fire and adjusting the operation mode of the spraying system according to an analysis result, and the fire-fighting emergency module is used for obtaining the positions of trapped persons in a building, shunting the trapped persons and sending the trapped persons to the fire-fighting early warning module. The system comprises a data collection module and a distribution module, an evacuation route is planned for each distribution group, the data collection module comprises a data input module, a visual module and a sensor module, the data input module is used for inputting comprehensive information of personnel entering the building into the system, and the system has the advantage of improving evacuation efficiency and accuracy.

The invention relates to a multi-source alarm fusion emergency system linkage disposal method. The method comprises the following steps: acquiring a multi-dimensional heterogeneous sensing data stream in a monitoring area and generating a basic evidence set object; calculating a global conflict quantitative index and performing adaptive confidence correction on the high-conflict evidence; performing weighted fusion on the corrected evidence by using the dynamically updated sensor reliability weight vector to generate a target safety state probability; determining an alarm response level according to the probability and driving equipment to respond cooperatively; and finally, iteratively updating the sensor weight based on the execution feedback data. The method has the advantages that accurate fusion judgment of multi-source heterogeneous evidences in a complex interference environment is achieved, and it is ensured that the system has the self-adaptive adjustment capacity for sensor performance changes.

The invention relates to the technical field of intelligent old-age care and health monitoring. The invention provides a tumble detection and emergency response system for the elderly at home based on multi-modal sensing fusion, and the system comprises a sensing module which is used for collecting the multi-modal data of the elderly and at least comprises a depth camera array, an inertial measurement unit, a millimeter wave radar, an audio sensor array, and a pressure sensing pad; the decision-making module is used for receiving, synchronizing and processing the multi-modal data, comprises a dynamic confidence fusion engine and is used for calculating a dynamic confidence score for the data streams acquired by the sensing module in real time and carrying out information fusion through a collaborative game algorithm based on the dynamic confidence score of each data stream to obtain a multi-modal data fusion result; generating a fall detection decision with a situation tag; and the response module is used for executing grading emergency response operation and comprises a local alarm unit, a remote communication unit and an interface unit linked with a community service and emergency center.

The invention belongs to the technical field of chemical delivery monitoring, and particularly relates to a chemical leakage early warning and emergency response system. According to the method, various factors such as gas concentration, storage pressure and transmission temperature can be comprehensively considered, the risk level of current chemical transmission is judged, and the feedback frequency of the sensor is dynamically adjusted according to the risk level, so that the monitoring efficiency and accuracy are improved; multi-modal data and a preset safety threshold can be compared in real time, once abnormal data is found, an early warning signal is triggered immediately, related data is recorded as risk data, then a corresponding emergency regulation and control instruction is generated according to the risk deviation value of the risk data, the risk data is regulated and controlled to be below the safety threshold, the safety of chemical delivery is ensured, and the safety of chemical delivery is improved. In the risk regulation and control process, the regulation and control amount of the risk data under the regulation and control instruction is monitored in real time through a sliding window mechanism, so that secondary accidents are avoided, and the safety in the chemical conveying process is effectively improved.

The invention relates to the technical field of laboratory intelligent management, and discloses a hazardous chemical substance laboratory supervision emergency system, which takes a central processing unit as a core, and integrates seven modules of multi-dimensional perception, ledger registration, laboratory digital twinborn model library, access cross validation, behavior identification intervention, emergency response strategy and mobile terminal interaction assistant. A complete closed-loop management system from hazardous chemical substance taking compliance verification, personnel operation safety monitoring to intelligent emergency response is constructed, and the hazardous chemical substance laboratory safety management level is comprehensively improved. The system can realize real-time linkage of data of each module, break information islands, avoid access violation from the source, block operation risks in real time and efficiently deal with emergencies, meanwhile, improves emergency efficiency by means of mobile terminal interaction assistant navigation, comprehensively strengthens the full life cycle supervision ability of hazardous chemicals, remarkably reduces the occurrence rate of laboratory safety accidents, and has a good application prospect. And the event emergency disposal efficiency is improved, and the intelligence and refinement level of overall safety management is improved.

The invention relates to the technical field of seismology and artificial intelligence crossing, and discloses a non-natural seismic event identification and judgment method based on deep learning, and the method comprises the steps: building a seismic data collection module, a seismic data analysis module, an early warning module, a judgment module, a comprehensive optimization module and a verification module, the seismic data analysis module is based on four internal units, and the problems of misjudgment, poor adaptability and low efficiency of a traditional method are solved in a targeted mode. The early warning module performs graded response according to an analysis result, realizes second-level early warning and is linked with an emergency system; the judgment module is used for realizing accurate classification of events by utilizing ensemble learning; the comprehensive optimization module dynamically updates a model and a data strategy based on an early warning and judgment result; the verification module guarantees the reliability of the system through cross verification and blind measurement, and finally achieves the effects of improving the recognition accuracy of the non-natural seismic event, shortening the cross-regional deployment period, reducing the false alarm rate and improving the data utilization rate.

A system for real-time prediction of heart disease and to support clinical decisions; the system includes: a patient data acquisition module configured to receive multimodal inputs, including physiological signals selected from electrocardiographic waveforms, blood pressure readings, heart rate readings, and oxygen saturation readings, as well as demographic and lifestyle information, including age, gender, cholesterol levels, smoking habits, and family medical history; a preprocessing engine configured to perform data cleansing, imputation of missing values, categorical coding, and feature scaling, so that the input data is normalized and converted into a format suitable for machine learning; a processing core comprising a multi-core CPU / GPU system-on-chip operationally coupled with a secure storage unit, wherein the processing core is configured to execute a variety of pre-trained machine learning models, including Naive Bayes, Random Forest, Logistic Regression and Decision Tree classifiers; a model evaluation unit configured to calculate validation metrics such as precision, recall, F1 score and area under the curve for each of the models and dynamically select the model with optimal performance to generate real-time predictions for the risk of heart disease; a display interface configured to present predictive results in the form of risk probability values, confidence indices, and actionable recommendations that are mapped to clinical treatment guidelines; and a communication interface configured to transmit emergency alerts based on high-risk predictions to remote caregivers, hospitals, and emergency response systems via wireless communication protocols such as WLAN, Bluetooth, and 4G / 5G cellular connections.

The invention relates to an emergency processing system and method for atmospheric pollution source influence based on DeepSeek, and belongs to the technical field of environmental monitoring, and the method comprises the steps: collecting atmospheric pollutant concentration data, meteorological data and basic information data of surrounding buildings; preprocessing the data; carrying out deep analysis on the preprocessed data based on a DeepSeek model, and judging whether to trigger early warning or not and the early warning degree in combination with a pollutant exceeding standard knowledge base; when abnormal data is found, the constructed knowledge base is retrieved; combining the data retrieved by the knowledge base; the current atmospheric pollution and building influence conditions are retrieved and compared with existing plan cases; and a detailed alarm condition is output, and a targeted emergency plan is generated at the same time. According to the method, based on the DeepSeek data analysis and learning ability, the influence of multiple factors is fully considered, the position of the influenced building is accurately identified, the influence degree is evaluated, and a more reliable basis is provided for environment decision making.

A cloud-based intelligence to emergency evacuation systems using Wi-Fi access points over data communication systems. A layout of emergency exits for a building being monitored and AP locations within the building layout is configured. A density map is generated from a number of stations within radio range of each AP including stations that are connected and stations that are not connected. An emergency that requires evacuation of people from the building from an emergency system associated with the building is detected. In response, an optimal evacuation route from each AP based on the density map and the building layout is generated. Evacuation instructions for each AP can be displayed on the stations within range of each AP.

The invention discloses an intelligent microphone starting method based on voice recognition, and relates to the technical field of voice recognition. The method comprises the following steps: acquiring a user voice signal and surrounding environment audio data to obtain a mixed audio signal stream; identifying an abnormal waveform and determining a frequency feature vector; determining a waveform fluctuation amplitude and obtaining an enhanced waveform stable representation; recognizing syllable interval duration and analyzing interval shortening degree; analyzing the urgent speech speed characteristics to determine a time domain stretching compensation value, and adjusting the syllable interval duration to obtain a syllable sequence; identifying spectrum features of the environment background audio and determining candidate wake-up words; performing similarity matching with an emergency wake-up word template to determine an intention recognition result; and analyzing the confidence score to obtain an emergency response instruction execution signal. Through multi-level signal processing and intelligent feature extraction, high-precision voice intention recognition in a complex environment is realized, the robustness and reliability of an emergency response system are remarkably improved, and rapid help seeking of a user in a crisis scene is guaranteed.

The invention relates to the technical field of engineering safety monitoring, in particular to an engineering safety monitoring system and method.The method comprises the steps that an intelligent monitoring terminal collects original data in a graded mode through a multi-mode sensing array, and core parameters are reserved preferentially after the original data are preprocessed through an edge computing unit; the dynamic data transmission module implements differential transmission according to data importance, high-priority parameters are transmitted through an encryption channel, and conventional parameters are transmitted through a universal Internet of Things channel; the self-adaptive data processing unit is internally provided with a dynamic threshold model fusing engineering mechanics characteristics and an anomaly recognition algorithm, and is used for processing transmission data to recognize anomaly; the grading early warning and linkage disposal module receives the abnormal result through a data interaction protocol, generates a corresponding disposal instruction and is in butt joint with an existing emergency system to achieve reuse. Therefore, the problems of low data processing efficiency, insufficient anomaly recognition accuracy, poor system suitability and the like in existing engineering monitoring are solved, and the reliability and response speed of engineering safety monitoring are improved.

The invention belongs to the technical field of emergency management, and discloses a three-proofing emergency command method and system based on meteorological big data. Comprising the following steps: collecting disaster meteorological associated data, and identifying meteorological evolution modes corresponding to various disasters; collecting current meteorological data and environment state data in real time, and generating a three-proofing risk progressive curve; acquiring weather forecast data, calculating weather similarities between the weather forecast data and the weather evolution modes, predicting disaster risk factors of various disasters in combination with a three-proofing risk progressive curve, and determining intervention disasters; according to disaster risk factors of the intervention disaster, feasible time periods and expected effects of different artificial influence weather operations are analyzed; the optimal operation type and the optimal time window are intelligently identified, three-proofing emergency resources are intelligently scheduled, and a preventive resource deployment scheme is generated; according to the invention, a full-chain intelligent three-proofing emergency command system is constructed, and the technical bottlenecks of a traditional emergency system in the aspects of early warning timeliness, intervention capability, resource allocation and the like are broken through.

An emergency response data system (ERDS) provides AI-based real-time intelligence during a response to an incident. The system includes a monitoring module configured to receive and process incident metadata from an emergency system, such as a Computer-Aided Dispatch (CAD) system, wherein the incident metadata includes location, type of emergency, units dispatched, and comments. An analysis module analyzes the incident metadata and identifies changes in incident circumstances, including comparing current comments with historical comments corresponding to the location. A data retrieval module accesses and retrieves public record information corresponding to the location, including property data, tax records, real estate records, historical records, and permits. An insight generation module generates real-time insights and updates regarding the incident, including reclassifying the type of emergency based on the analyzed metadata and retrieved public records. An alert module transmits the real-time insights to emergency personnel, including field responders and Emergency Communication Centers (ECC).

The invention relates to a mine area collaborative early warning and emergency response system based on'scene-quick response chain-disposal ', and belongs to the technical field of mine safety intellectualization. The system adopts a'distributed deployment and centralized management and control 'mode, and covers five-layer architecture of perception, transmission, data, decision and application. Through the technologies of multi-dimensional perception, high-reliability communication, intelligent decision making and the like, the problems of response lag, unsmooth information, low decision making efficiency and the like in traditional emergency response are solved. Around the targets of cross-subject information intercommunication and the like, multi-subject efficient linkage is realized through full-process logic. The system also has the functions of redisk optimization and rule iteration, can continuously improve the emergency response capability, and provides powerful technical support for the emergency response of a mine area.

The invention relates to the technical field of heat dissipation of electronic equipment, in particular to a chip heat dissipation optimization system based on a multi-layer stacking structure. A heat conduction module; a heat dissipation module; a heat recovery module; the temperature monitoring and control module monitors the temperature of each layer of chip and the temperature difference of an inlet and an outlet of cooling liquid in real time, judges the working state of the whole system and controls the whole system; by arranging the heat conduction and heat dissipation modules, adopting heat conduction pores in gradient distribution and a mixed phase change material, and combining a nano-porous graphene substrate, cooperation of hot spot rapid heat conduction and non-hot spot buffer energy storage is achieved, the defect that a traditional phase change material is single is overcome, the dual-mode cooling pore channel design is achieved, seamless switching between a conventional system and an emergency system is achieved, and the energy storage efficiency is improved. The heat dissipation capacity is greatly improved, 0.1 mm resolution ratio temperature field reconstruction is achieved through the temperature monitoring and control module, hot spots are predicted 300 ms ahead of time, and temperature control is more accurate.

There is disclosed a method of controlling a helicopter (1) comprising: at least one thermal engine (11); at least one rotor (3), which is operatively connectable to thermal engine (11) to be drive, in use, at a set rotational speed (ωSET); an emergency system (40) comprising: at least one electric motor (41), which is operatively connectable to rotor (3) and an electrical power source (42), which is electrically connected to electric motor (41); the method comprises the steps of i) operatively connecting thermal engine (11) to rotor (3) and keeping electric motor (41) disconnected by rotor (3) or idle, in a normal operating condition of thermal engine (11); ii) detecting if thermal engine (11) is in at least partial failure condition; iii) accelerating electric motor (41) up to when said current rotational speed (ω) equals set value (ωSET), in case of at least partial failure of said thermal engine (11); step iii) comprises the steps of iv) sensing a plurality of parameters (ω, ωERR, ϕ, ϕERR, IIT, ττ / S, τMGB, Powerτ / S, PowerMGB) of thermal engine (11) and rotor (3); and v) identifying an at least partial failure of thermal engine (11), on the basis of parameters (ω, ωERR, ϕ, ϕERR, IIT, τ, P.

A system and method for initiating a discreet emergency response for a user of a mobile communication device having front and rear facing cameras, a microphone and a GPS receiver, the method including providing an application on the device capable of receiving a session initiation input and, responsive to the session initiation input, activating a potential emergency session with a remote dispatcher or monitoring center. This includes transmitting an emergency data stream to be accessible to the remote dispatcher or monitoring center. The emergency data stream including a first video stream for video images from a front-facing camera, a second video stream from a rear-facing cameras an audio stream from a microphone, and a stream of geolocation coordinates from the GPS receiver.