2848results about "Fire alarms" patented technology

The invention provides a charging pile line fire-fighting early warning method based on big data and a storage medium, and the method comprises the steps: collecting a line operation data set of a target charging pile cluster, the line operation data set comprises multi-source time sequence monitoring data, carrying out the cross-modal feature alignment of the multi-source time sequence monitoring data, and generating a time-space correlation feature matrix; inputting the space-time correlation feature matrix into a pre-trained fire risk prediction model, generating a line abnormal risk probability distribution diagram, generating a layered early warning signal set according to risk levels corresponding to space nodes in the line abnormal risk probability distribution diagram, and triggering a dynamic protection mechanism based on the layered early warning signal set. The dynamic protection mechanism includes performing a current cut-off operation on a high-risk line segment, and performing a power attenuation operation on an adjacent line segment. According to the invention, the reliability and the intelligent level of the charging pile line fire-fighting early warning system can be comprehensively improved.

The invention relates to the technical field of environment monitoring, in particular to a forest fire monitoring method based on edge intelligence, which comprises the following steps: S1, deploying edge nodes and sensing equipment; s2, collecting data acquired by the sensing equipment in real time, and performing primary processing on the data; s3, fire risk intelligent identification and decision making are carried out through the edge nodes; s4, emergency response and event management; and S5, user interaction and feedback. The edge intelligence technology is introduced, the architecture of the forest fire monitoring system is optimized, and local processing and real-time analysis of data are realized, so that the response speed and the detection precision of fire monitoring are improved, and the method is suitable for further popularization and application.

The invention provides a new energy automobile fire dynamic risk assessment method and system based on multi-modal spatial-temporal feature fusion. The new energy automobile safety monitoring and fault early warning method comprises the steps that a multi-source data acquisition layer obtains multi-source heterogeneous data, and space-time marking is carried out; the feature fusion preprocessing layer generates a space-time consistency data stream through a space-time alignment module and missing value filling; the deep learning evaluation layer extracts multi-modal features by using a dedicated network, and outputs feature scores through time-space dependence modeling fusion; and the dynamic early warning decision-making layer generates a dynamic risk score in combination with the thermal runaway probability, the risk gradient and the aging factor, constructs a five-level risk map and executes vehicle-cloud collaborative graded early warning. According to the new energy automobile fire dynamic risk assessment method and system based on multi-modal spatial-temporal feature fusion, the limitation of traditional single data static assessment is broken through, accurate dynamic early warning is achieved, the early warning time is longer than or equal to 15 min, the false alarm rate is reduced to 4.7%, and the thermal runaway prediction accuracy rate reaches 98.3%.

The invention provides a thermal power plant environment monitoring and early warning method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: forming a preprocessing data stream for collected environment monitoring data through an edge calculation node; and carrying out real-time data stream processing by utilizing a distributed message queue, constructing a hierarchical distributed data storage structure, and carrying out homomorphic encryption and desensitization processing on data to form an encrypted data set. Based on an encrypted data set, a long-short term memory network, a random forest algorithm and a limit gradient boosting tree are adopted to construct a layered anomaly detection engine, environmental parameter spatial-temporal characteristics are extracted through a multi-head attention mechanism and residual connection, an early warning threshold value is iteratively calculated based on a Bayesian optimization algorithm, and an early warning level judgment standard is dynamically adjusted. And generating early warning information. And finally, through a three-dimensional visualization module and a knowledge inference engine based on a graph neural network and a deep reinforcement learning algorithm, visualization of early warning information and generation of a self-adaptive emergency processing strategy are realized respectively.

An intelligent safety supervision system applied to a ship is provided. An image acquisition module is configured to acquires high-definition images in real time. An automatic recognition module is configured to obtains ship dynamic and static data. A ship server to-performs feature recognition on the ship dynamic and static data to obtain a data processing result, to-transmits the ship dynamic and static data and the data processing result, and receives alarm indication information. An alarm module outputs an alarm. A ship client displays the data processing result, and determines whether to transmit the alarm indication information according to the data processing result. A communication module receives and transmits the ship dynamic and static data and the data processing result. A shore-side supervision system includes a ship safety supervision big data analysis platform for performing secondary feature recognition on the ship dynamic and static data, so as to obtain a secondary data processing result.

The invention discloses a temperature collecting, monitoring and alarming method for palm fire fighting, relates to the technical field of temperature monitoring for fire fighting, and is used for solving the problem of inaccurate temperature monitoring and prediction. According to the method, environmental factor data are synchronously collected through temperature monitoring points, the correlation between each factor and the temperature is calculated after denoising, multi-factor characteristics are extracted to generate an output characteristic vector of temperature prediction, the output characteristic vector is constructed into a time sequence matrix to be input into a long-short-term memory network, and temperature trend characteristics are extracted; the method comprises the following steps: predicting a future trend and comparing the future trend with a safety threshold to judge whether early warning is triggered or not, meanwhile, identifying temperature changes of different frequencies by adopting fast Fourier transform, a weighted moving average method and an exponential smoothing method through short-term, medium-term and long-term scale temperature trend analysis, setting a three-level early warning mechanism and dynamically adjusting the threshold, and finally, carrying out early warning. Based on early warning feedback data, a Q-learning algorithm is adopted to optimize a threshold value and an alarm strategy, a self-adaptive learning closed loop is formed, and the early fire early warning capability is improved.

The invention relates to the technical field of public safety and intelligent fire protection, in particular to a real-time processing method and system based on fire alarm data, and the method comprises the steps: environment steady state reconstruction: accessing non-fire environment dynamic parameters; presetting thermotechnical static parameters of the building space; constructing a space thermal inertia differential equation; generating an ideal reference baseline; knowledge-driven simulation: receiving the ideal reference baseline; calling a synthesis operator in a preset disaster interference knowledge base; executing dynamic superposition injection; generating a virtual sensor state flow which has physical characteristics and contains environmental background characteristics; homomorphic judgment: executing double difference calculation; obtaining a real residual feature vector; geometric homomorphism verification is executed; outputting fire alarm triggering, interference filtering or fault prompting instructions; according to the method, the problem of false alarm caused by non-stable fluctuation of the environment background in the background technology is solved, and dynamic fusion and scene adaptation of the standard signal model and the real environment background are realized.

The invention discloses a building intelligent electrical fire monitoring method and system, and relates to the field of fire monitoring, and the method comprises the steps: obtaining distribution box parameters of different floors in a target monitoring area in a preset time period, and the distribution box parameters comprise a leakage current value and a temperature value; performing multi-scale decomposition on the parameters of the distribution box to obtain a plurality of intrinsic mode functions and residual trends; performing feature extraction on each intrinsic mode function and the residual trend to obtain an electrical fire feature vector; constructing a fire risk assessment model according to the electrical fire feature vector; and predicting a fire risk level according to the fire risk assessment model. Aiming at neglecting a nonlinear relation between leakage current and temperature of a distribution box in the prior art, the building electrical fire monitoring precision is improved based on multivariable time-delay analysis, ensemble empirical mode decomposition and the like.

The invention discloses a metro fire detection method based on YOLOv8. The metro fire detection method is used for improving the accuracy and response speed of an alarm system. According to the invention, a YOLOv8 target detection algorithm is adopted, a network structure is optimized in combination with deep learning, and a weighted focusing loss function and multi-modal data fusion method is designed, so that the detection precision is improved. A data set covers images and sensor data of different subway environments, an attention mechanism and multi-scale detection are introduced, and the flame and smoke recognition capability is improved. And by combining temperature sensor data, false alarms are reduced. The optimization strategy aims at the carriage and platform environment, the robustness of the model under the conditions of low illumination, reflection and shielding is enhanced, and real-time detection is realized by means of TensorRT accelerated reasoning. The system can operate independently, does not need a network, can be linked with a subway FAS system, and improves the early warning efficiency. Experiments show that the detection performance of the method is better than that of a traditional method, the reasoning speed exceeds 50 frames per second, and the method has wide application prospects.

The invention discloses a hazardous chemical substance storage risk artificial intelligence identification method and device and a medium, and relates to the technical field of hazardous chemical substance management. The method comprises the following steps: acquiring product information, operation time and access frequency of hazardous chemicals, and respectively acquiring three-dimensional images of a warehouse and a preset distance outside the warehouse; stacking the hazardous chemical substances according to the product information, the operation time and the access frequency to obtain a stack of the hazardous chemical substances, and obtaining a three-dimensional image of the stack; environment data of a warehouse and a preset distance outside the warehouse are collected, three-dimensional modeling is carried out according to the three-dimensional image, product information is integrated, and a digital twinborn model of hazardous chemical substance storage is obtained to reflect dynamic changes of the warehouse environment; and according to the dynamic change, identifying whether the stacking height of the hazardous chemical substances, the adjacent stacking distance and the environmental data exceed preset thresholds or not. According to the method, artificial intelligence, three-dimensional modeling and digital twinning are comprehensively applied, and intelligent identification and management of hazardous chemical substance storage risks are realized.

The invention relates to the technical field of power monitoring, in particular to a power transmission line forest fire monitoring and early warning method based on meteorology and three-dimensional visual support, which comprises the following steps: S1, acquiring multi-source data of a target area and preprocessing the multi-source data; s2, predicting the fire risk of the target area by adopting a TFT model in combination with multi-source data; s3, constructing a visual three-dimensional geographic model; when the predicted fire risk level exceeds a preset safety threshold value, early warning is triggered, fire early warning information is pushed and displayed in the three-dimensional geographic model, and the fire early warning information comprises geographic boundary coordinates, the fire risk level and electric power facility information. The fire risk can be monitored in real time, the fire occurrence probability is analyzed in combination with meteorological data, and the fire development trend and the risk area are displayed through a three-dimensional visual interface.

The invention discloses an unattended fire alarm system based on real-time comprehensive analysis of environmental characteristics, and relates to the field of unattended fire alarm, and the system comprises a dust distribution simulation module which builds a dynamic dust concentration distribution model in an industrial production area, and analyzes the distribution condition of dust in the space in real time; the secondary risk area marking module is used for performing three-dimensional coverage on the electrostatic field distribution of the monitoring area and the distribution of the dust in the space, and marking an overlapped area as a secondary risk area; the first-level risk area labeling module is used for judging the dust-gas collaborative explosion risk existing in the current risk area based on the electrostatic discharge energy and carrying out first-level risk labeling; the explosion propagation path simulation module is used for establishing an explosion propagation path diagram based on the propagation attenuation condition of explosion energy estimation and thermal radiation; and the risk alarm module is used for coloring and marking the graded risk area and the explosion propagation path diagram and sending out an alarm signal.

The invention discloses a power transmission channel forest fire risk early warning method and system based on spatial-temporal feature fusion, and relates to the technical field of risk early warning. The method comprises the following steps: constructing a power transmission channel risk grid and fusing historical multi-source data to generate a static flammability background map; the method comprises the following steps: collecting real-time visual and micrometeorological data, and after space-time alignment processing, respectively extracting visual risk features and environmental flammability trend features by using a double-branch space-time feature extraction network; inputting the static background and the dynamic characteristics into a space-time fusion module to obtain a comprehensive forest fire risk index; when the index exceeds a threshold, triggering a forest fire spreading deduction model corrected by combining a power transmission corridor effect, predicting fire spreading, evaluating a line tripping probability, and issuing a graded early warning and emergency strategy; according to the method, the defects of serious data islands and inaccurate early warning are overcome, the crossing from pure fire point monitoring to risk situation dynamic prediction is realized, and the accuracy of forest fire early warning and the intelligent level of power grid prevention and control are remarkably improved.

The invention discloses a forest fire risk prediction method and system based on machine learning, and the method comprises the steps: taking data of a monitoring data source of a preset forest in a specified time period as to-be-analyzed data; extracting space-time distribution characteristics of the to-be-analyzed data and the reference data source, performing area division on the space-time distribution characteristics according to similarity, taking an area where the to-be-analyzed data with the similarity higher than a similarity threshold value is located as a candidate area, and otherwise, taking the area as a fuzzy area; performing autocorrelation analysis on the fuzzy region based on the undetermined data source, adding the fuzzy region of which the autocorrelation is higher than an autocorrelation threshold to the candidate region, and performing time-varying influence analysis on the environmental data and the human activity data to obtain a fire driving coefficient; and constructing a forest fire risk prediction model according to the fire driving coefficient and the candidate region, optimizing the forest fire risk prediction model according to a fire spread rule, and outputting a prediction result.

The invention relates to the technical field of intelligent fire early warning, in particular to an urban fire intelligent early warning system which comprises an urban structure intelligent analysis module, a fire dynamic early warning simulation module, a fire prevention strategy optimization module and an environment influence intelligent analysis module. According to the method, the urban graph model is generated by using the geographic positions, materials, fire data and building age data of the urban buildings, the relevance and characteristics between the buildings are deeply analyzed, and the fire diffusion influence is simulated through the graph convolutional network, so that the prediction precision of the fire propagation path and speed is remarkably improved; the evacuation difficulty and risk level of a building under multiple fire source points are calculated in an auxiliary manner, a basis is provided for scientific planning of evacuation routes and fireproof isolation areas, emergency response and resource allocation are optimized, meteorological, population dynamics and holiday activity data analysis are integrated, the effect of environmental factors in fire early warning is enhanced, the accuracy and timeliness of early warning are ensured, and the early warning efficiency is improved. And the overall efficiency of an urban fire prevention system is improved.

The invention relates to the technical field of pipe gallery fire analysis, in particular to an underground pipe gallery fire feature correlation analysis system based on an improved Apriori algorithm, and aims to comprehensively collect underground pipe gallery data through multi-modal sensing equipment and pre-process the underground pipe gallery data into a standardized three-dimensional time sequence data set to provide a high-quality data basis for subsequent analysis; a multi-dimensional feature association matrix is constructed based on the three-dimensional time sequence data set, feature association strength is quantified, and a multi-dimensional feature association basis is provided for fire risk mining; a fire risk rule knowledge base is constructed through a space-time weighted support degree optimization Apriori algorithm, effective association rules are accurately extracted, and the accuracy and reliability of fire risk early warning are improved; based on the fire risk rule knowledge base, a decision tree model is established, association rule weights of branches of the decision tree are analyzed, fire risk grades are divided, a composite early warning instruction set is generated, different coping strategies can be adopted for risks of different grades, and the pertinence and effectiveness of emergency response are improved.

The invention discloses a fire fighting system intelligent management method and device based on intelligent property management, and relates to the technical field of data management. The intelligent management method and device for the fire fighting system based on the intelligent property management comprises the following steps: S1, dynamically triggering and collecting a multi-source fire fighting data set through multiple protocols, and temporarily storing the multi-source fire fighting data set at the edge in a normalized manner; s2, constructing an event full-period window and a ternary association index of the event, the time sequence data and the original message; s3, performing double-track storage of the semantic analysis data and the original message, and constructing multi-dimensional index association; s4, reconstructing a visual playback alarm process through edge cloud increment synchronization and a multi-dimensional time sequence chain; and S5, performing transient analysis, protocol upgrading and responsibility evidence obtaining based on the cloud data collaborative link and the original message data set. According to the method, the complete acquisition of the fire-fighting data and the reproducibility of the event process are effectively improved, and the problem that in the prior art, only polling recording of single-point alarm is carried out, and an original message is discarded, so that the transient process and message-level evidence is lost is solved.

The invention discloses a fire-fighting fireproof door alarm information transmission device and method based on the Internet of Things, and relates to the technical field of fire-fighting alarm, and the method comprises the following steps: carrying out the multi-band continuous scanning of a wireless communication environment of a fireproof door alarm device deployment region after a fire emergency mode is started, identifying a target communication protocol frequency band used by the fireproof door alarm device, and obtaining spectrum parameter information of each frequency band to form a to-be-analyzed communication frequency band set; and extracting a working frequency band used by the peripheral wireless equipment based on a pre-established environment wireless equipment frequency band database. Frequency spectrum mapping is constructed through frequency spectrum scanning and an environment database, intelligent interference prediction is realized in combination with a support vector machine, coding parameters are dynamically adjusted based on interference intensity, the transmission reliability of key alarm information in a high-interference environment is improved, feedforward recognition and closed-loop regulation and control capabilities are achieved, and the method is suitable for popularization and application. And the problems of slow response and unstable transmission of a traditional system are effectively solved.

The invention relates to the technical field of data analysis, in particular to a fire monitoring and recognition system based on multi-sensor fusion. Comprising a multi-modal sensor data fusion processing module, a fire occurrence probability intelligent evaluation module, a fire occurrence probability real-time prediction module, a multi-level detection and feedback module and an automatic control and emergency response module. Extracting multi-modal fire data fusion features through a data fusion algorithm to capture fire signs; a three-dimensional fire monitoring model is created, and model parameters and performance evaluation are adjusted, so that the fire risk is quickly judged; the probability of fire occurrence in the current monitoring area state is predicted, so that emergency measures can be planned in advance subsequently; fire identification is carried out through a multi-level verification mechanism, the reliability of the system is improved, and the response speed is improved by dividing fire grades and constructing an emergency response strategy library. The invention is used for solving the technical problems of incomplete monitoring and insufficient management and control in current fire prevention and management.

The invention relates to the technical field of fire early warning, in particular to an electrical fire monitoring and early warning method and system, and the method comprises the steps: collecting the current electrical parameter data of a monitored object, and obtaining the electrical parameter data before multiple historical fire early warning and in multiple normal states; analyzing the first necessity of each historical fire early warning according to the characteristics of abnormal data in the data collected before each historical fire early warning and the correlation between the abnormal data and the fire early warning; analyzing the second necessity of each historical fire early warning according to the difference between any two kinds of data in each historical normal state and the correlation between the abnormal condition and the fire early warning; and combining the first necessity and the second necessity to calculate the disaster early warning necessity of each moment in the current and previous preset time periods so as to judge the current disaster early warning necessity. According to the invention, the historical correlation change among various electrical parameters is analyzed, the fire risk is evaluated more comprehensively, and the accuracy of early warning is improved.

The invention discloses a multi-modal data fusion-based household aging transformation demand evaluation and scheme generation system. According to the system, technologies such as attitude sensing and image acquisition are comprehensively applied, acceleration and angular velocity data of a user are acquired by means of an attitude sensing module, and behavior image data are acquired by an image acquisition module. Fusion analysis is carried out on the multi-mode signals through edge AI processing equipment, accurate recognition of the behavior state of the old people is achieved, and alarm is triggered when an abnormal posture is detected. Meanwhile, the system utilizes the environment sensing module to assist in behavior judgment and path planning, has the advantages of non-intrusive type, high real-time performance, high accuracy and the like, can effectively improve the life safety and behavior monitoring intelligence level of the old people under an unattended condition, is suitable for a safety monitoring scene in a family old people nursing environment, and has a wide application prospect. Good popularization and application values are realized.

To provide a smoke tester that can easily perform an operation test of a fire sensor.SOLUTION: A smoke tester 10 is a smoke tester that performs an operation test of a fire sensor using a testing smoke, which comprises: a cup 20 that is placed over a fire sensor; a smoking unit 30 that generates the testing smoke in the cup 20; and an air pump 50 that generates air flow in the cup 20. The smoke tester is mounted on a drone.SELECTED DRAWING: Figure 4

The invention discloses a fire detection and early warning method and system based on improved YOLOv8, and belongs to the technical field of image recognition, and the method comprises the steps: building a fire data set, marking the fire data set, and dividing the fire data set into a training set, a verification set and a test set; the YOLOv8 network is improved; the original YOLOv8 network and the improved YOLOv8 network are trained, evaluated, adjusted and tested, and an optimal model is determined; deploying the optimal model to an edge AI development board of a ground station; a real-time video stream of the unmanned aerial vehicle is obtained through the unmanned aerial vehicle camera and is transmitted to the edge AI development board for fire detection; a fire signal is detected by using the edge AI development board, and early warning is carried out through the fire alarm module; the system comprises a fire data set establishment and processing module, a YOLOv8 network improvement module, a YOLOv8 network training module, an optimal model deployment module, a fire detection module and a fire early warning module. The method has the technical advantages of high precision and light weight.

In one embodiment, a wearable electronic apparatus for a user can include at least a first wireless communication component, an output device configured to present a message, a microphone configured to receive a voice input from the user, the voice input being at least in view of the message being presented, and a controller configured to execute computer program code, including at least code for (i) analyzing the voice input received from the user, (ii) determining an apparatus-control output based on the analyzed voice input, (iii) capturing, via the microphone, audio data pertaining to spoken words from the user, (iv) evaluating a physical condition of the user from the audio data, (v) determining whether another person should be alerted of the physical condition of the user, and (vi) alerting the another person based on the determining.

The invention discloses a concrete filled steel tube structure fire early warning method and device based on multi-agent cooperative detection, and belongs to the technical field of concrete filled steel tube structure fire early warning. The invention discloses a concrete filled steel tube structure fire early warning method and device based on multi-agent cooperative detection. The method comprises the following steps that S1, a fire early warning threshold value is set; s2, collecting real-time physical state data of the concrete-filled steel tube structure; s3, performing cross validation on the fire signal; s4, comparing the composite fire behavior index with a set fire early warning threshold value; and S5, sending out corresponding fire early warning information. According to the invention, the problem that a fire detection system for detecting a single parameter in the prior art cannot accurately distinguish a real fire source from environmental interference so as to cause false alarm is solved, compared with single-point monitoring, early fire characteristics such as local temperature abnormity and a smoke diffusion path can be accurately captured, the false alarm rate is obviously reduced, and the system is suitable for popularization and application. And the accuracy of fire early warning is ensured.

The invention discloses a fire early warning method and system based on multi-source information fusion and a medium. The method comprises single vehicle model training, model deployment, fire risk assessment, BIM modeling display and abnormal information notification. According to the fire early warning method and system based on multi-source information fusion and the medium, deep fusion and model training are carried out on the image, positioning and temperature information of the vehicle, the vehicle temperature fault mode can be rapidly and accurately recognized, the accuracy of fire early warning is improved, multi-source information is obtained in real time, and when the vehicle has an overtemperature phenomenon, the fire early warning efficiency is improved. A worker can quickly locate the vehicle and nearby the vehicle, measures can be taken for the overtemperature vehicle in time, fire disasters are prevented or the fire disaster damage degree is reduced, and powerful support is provided for fire prevention and control work. A bridge fire risk map is displayed on a display device or a user terminal by establishing a bridge BIM model, a command department, workers and drivers can quickly grasp information such as a disaster site, a fire reason, a disaster situation degree and a vehicle density degree of a current bridge fire through the bridge fire risk map, and the environment adaptability of the system is high; according to the invention, the fire risk can be evaluated in different environments, the decision-making efficiency of a command department is improved, a worker can quickly arrive at a rescue site to carry out fire extinguishing and rescue work, and a driver in a low-risk area can quickly evacuate from a bridge or assist the worker in personnel evacuation. And casualties and economic losses caused by fire disasters can be reduced as much as possible.

The invention discloses a coal mine goaf fire monitoring method and system based on multi-source data fusion, and relates to the technical field of coal mine goaf fire monitoring, and the method comprises the steps: collecting the concentration, concentration and air pressure of each wireless sensing node in a coal mine goaf in real time; according to the concentration of each node, a dual-parameter dynamic coupling gradient field traceability algorithm of fusion / gas expansion and chemical reaction mechanism is adopted to calculate a fire source coordinate; performing anomaly detection according to the concentration of each node, and positioning an air leakage point by adopting a three-dimensional oxygen concentration gradient field positioning algorithm and combining air pressure compensation for an oxygen concentration anomaly point; based on goaf point cloud data, a goaf three-dimensional grid model is constructed, and a fire source spreading path, an air leakage channel and gas concentration distribution are dynamically and visually displayed in combination with multi-source monitoring data and fire source and air leakage point positioning results. The goaf fire prevention and control system can achieve all-around prevention and control of goaf fire.

The invention, which relates to the technical field of the energy storage equipment, discloses a fire multi-stage early warning system for energy storage equipment, and the system comprises the steps: collecting the operation temperature data of a lithium battery pack, generating an operation temperature value, and starting a fire early warning mechanism if the operation temperature value exceeds an operation temperature threshold value; if the abnormal degree of the fire monitoring data of the potential risk area exceeds the expectation, a first-level alarm instruction is sent out and corresponding feedback data is received, if the feedback data is not consistent with the expectation, a second-level alarm instruction is sent out, the existing fire risk is processed, and after a traversal area is identified according to the fire state data, the traversal area is identified. An emergency escape path is planned in the early warning area, and if the emergency escape path does not exist, a third-level alarm instruction is sent out; and if the secondary alarm instruction is not received, the battery pack temperature adjustment knowledge graph is matched with a corresponding processing strategy. The lithium battery pack is maintained in a targeted manner, the running state of the lithium battery pack is improved, and subsequent potential safety hazards and fire risks are reduced.

An evacuation guidance system includes: an evacuation guidance management system that acquires information of a disaster which has occurred in a marine vessel, acquires passenger position information in the marine vessel, formulates an evacuation route, and specifies a passenger to be subjected to evacuation guidance and a position of the passenger; and a mobile body that performs control to move an own device to the position of the specified passenger on the basis of the specified passenger and the position of the passenger and to move the own device along the evacuation route while outputting guidance information to the passenger.

The invention discloses a fire-fighting early warning management system and method, and the system comprises a monitoring module which is used for obtaining and analyzing real-time feature monitoring data of preset monitoring equipment, determining abnormal equipment, abnormal feature monitoring data and corresponding abnormal change features, and generating an early warning work order; the determination module is used for determining historical abnormal feature monitoring data, fire behavior change features, historical false abnormal feature monitoring data and hidden danger change features based on the historical fire behavior logs and the historical hidden danger logs; the analysis module is used for performing similarity analysis to obtain a first similarity and a second similarity; and the notification module is used for setting the credibility of the early warning work order according to the first similarity and the second similarity and generating a corresponding notification instruction, and by improving the judgment accuracy of the early warning work order, whether the early warning work order is a false alarm or a real fire behavior is accurately judged, and the accuracy and the early warning efficiency of fire fighting early warning are further improved.