1283results about "Fire alarms" patented technology

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 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 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 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 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.

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.

A method and system of providing guidance and information to an end-user during an emergency situation. A network of beacon devices is arranged throughout different rooms of a physical structure, such as a house. When an emergency situation such as a fire is detected, for example via data collected from one or more smart sensors installed in the house, a navigation server can activate the beacon device network. Each beacon device will begin to provide navigation cues such as audio content and / or light display that guide the end-user to the nearest designated exit.

The invention relates to the technical field of radar echo denoising, in particular to a radar echo signal filtering noise self-adaptive suppression method and a radar echo signal filtering noise self-adaptive suppression system. Acquiring radar station position and beam pointing data, and synchronously acquiring forest vegetation data and environment interference data of a beam pointing target area to form radar environment data; the radar environment data are identified through the multi-band decision model, and multi-modal waveform parameters are obtained; based on the multi-modal waveform parameters, radar equipment is controlled to perform periodic detection on the target area, and radar echo data are obtained; the method comprises the steps of identifying radar echo data and radar environment data, extracting dynamic spectrum characteristics in the radar echo data, performing clutter suppression processing on the dynamic spectrum characteristics by using the radar environment data, calculating the confidence probability that an early fire exists in a target area, and executing space-time consistency joint verification; according to the invention, fire early warning is carried out through the confidence probability of the fire.

The invention relates to a fire hazard degree comprehensive assessment method integrating forest lightning stroke parameters, and belongs to the technical field of forest fire early warning. The method comprises the following steps: acquiring a meteorological factor, a vegetation factor, a terrain factor, a lightning stroke factor, an exposure factor and a resistance factor in a determined spatial scale resolution; processing the meteorological factors through a meteorological index model to obtain a meteorological fire danger index; inputting the vegetation factor, the terrain factor and the lightning stroke factor into an ignition probability index model to obtain a lightning stroke ignition probability; based on the vegetation factor, the terrain factor and the meteorological fire danger index, obtaining a combustible flammability index through a fire danger index model; performing dimensionless processing on the exposure degree factor and the resistance degree factor, and calculating the exposure degree and the resistance degree; inputting the exposure degree E and the resistance degree R into a fire hazard degree comprehensive model to obtain a comprehensive fire hazard index; and performing automatic grading according to the threshold value and issuing the lightning fire early warning information in real time. According to the invention, the accuracy of comprehensive assessment of the fire hazard degree can be improved.

The invention discloses an emergency linkage control system for oil depot fire hazard real-time monitoring, and relates to the technical field of safety monitoring, the emergency linkage control system comprises an emergency linkage control center, the emergency linkage control center is in communication connection with the following modules: a multi-source data fusion sensing module used for constructing a multi-mode sensor network, and collecting multi-source heterogeneous data in real time, carrying out fusion analysis, and generating a hidden danger dynamic distribution diagram. Real-time high-frequency acquisition of oil depot environment, equipment and disaster evolution data is realized by constructing a multi-mode sensor network and integrating various sensors, data noise is eliminated and a hidden danger dynamic distribution diagram is generated in combination with a spatio-temporal data fusion engine, so that the monitoring data covers the whole area of the oil depot and the time resolution reaches a second level; compared with a traditional threshold triggering mode, the system can dynamically capture multi-source heterogeneous data of the key area of the oil depot, discover the hidden danger evolution trend in advance, shorten the disaster early warning time and remarkably improve the active suppression capability.

The invention provides an emergency broadcast playing method and system for disasters in an outdoor scene, and relates to the technical field of emergency broadcasts, cameras in a region are adopted to monitor the evacuation state of personnel in the outdoor scene in real time, and an emergency broadcast system and a rescue system are triggered through the evacuation state to perform linkage, so that bidirectional information transmission is realized; and the priority of the broadcast content played by the emergency broadcast is adjusted according to different instruction information in bidirectional information transmission, so that the broadcast content has pertinence, and rescue workers can receive key instructions.

The invention discloses an HRR-based acoustic fire hazard type identification method, device and equipment and a medium, and relates to the technical field of fire hazard detection, and the method comprises the steps: obtaining an original acoustic signal through transmitting a high-frequency modulation ultrasonic wave and receiving a flame region reflection echo, obtaining channel impulse response data through channel estimation, and carrying out the channel impulse response data; flame features are extracted, a heat release rate curve is calculated and generated, finally fire type identification and fire behavior stage determination are completed based on the curve, dense smoke, shielding and illumination interference can be resisted, accurate quantification of the heat release rate is realized, fire type identification and fire behavior development stage determination can be finely carried out, and the accuracy of fire behavior determination is improved. And a stable and reliable technical support is provided for fire monitoring and emergency decision making.

The invention discloses a fire-fighting monitoring method and system based on multi-sensor fusion, a medium and equipment. The method comprises the following steps: acquiring equipment fire-fighting data acquired by multiple sensors in real time; performing preprocessing and abnormal data processing on the equipment fire-fighting data to obtain target fire-fighting data; performing fire prediction on the target fire-fighting data based on a space-time fusion model to obtain a fire probability; performing fire-fighting early warning judgment on the target fire-fighting data based on a risk level triggering condition; and generating a fire-fighting control instruction according to the fire probability and the fire-fighting early warning judgment result, and feeding back the fire-fighting control instruction to the equipment end. Therefore, the problem of hidden fire source recognition delay of an existing system is solved, and millisecond-level fire early warning and graded response closed-loop treatment are achieved.

The invention discloses a lightning fire occurrence risk grade forecasting method and device and a storage medium. Belongs to the technical field of forest fire prediction. The method comprises the following steps: acquiring hour-scale real-time data of a target area; extracting a real-time numerical value corresponding to each key factor in a predefined key factor set from the real-time data; inputting the real-time numerical values corresponding to all the key factors into a pre-trained risk level prediction model to obtain a lightning fire occurrence probability corresponding to each lightning in the target area; and according to the occurrence probability of the lightning fire, determining the risk level of the lightning fire caused by each lightning in the target area. Therefore, scientific screening of key factors of lightning fire forecasting is realized, hour-scale real-time data acquisition is adopted, and data processing and model forecasting logic adapted to hour-scale dynamic change are adopted, so that forecasting accuracy and real-time performance are effectively improved.

The invention discloses a multi-source data fusion energy storage system fire risk real-time early warning method and system, and the method comprises the steps: collecting energy storage system multi-source fire risk correlation parameters, and screening a key parameter set through a data correlation mapping mechanism; calculating a preliminary thermal runaway probability value by adopting a random forest integration model for thermal runaway probability prediction; outputting a time sequence parameter prediction sequence by using the variational auto-encoder-gating cycle unit time sequence prediction model; inputting the two into a self-adaptive gradient lifting tree algorithm model to obtain a comprehensive fire risk value; and comparing a preset threshold interval to generate a corresponding grade early warning signal. The system comprises six units which are sequentially associated and cooperatively work. According to the method and the system, the problems of redundant parameter processing and single algorithm in the prior art are solved, the early warning accuracy and real-time performance are improved, the operation safety of the energy storage system is ensured, the complex association of multi-source parameters is fully processed through multi-algorithm cooperation, and the prediction accuracy and the evaluation comprehensiveness are greatly improved.

The invention provides a cable-type temperature-sensing fire detection method and system with an intelligent positioning function, and relates to the technical field of artificial intelligence, and the method comprises the steps: laying an optical fiber temperature-sensing cable along a preset path, obtaining the real-time three-dimensional coordinates of a temperature measurement point through employing a reflective mark point and a multi-view three-dimensional reconstruction technology, enabling the temperature data to be correlated with the coordinates to form a temperature field data set, and obtaining a temperature field data set; and generating three-dimensional temperature field grid data by adopting a weight optimization method based on distance attenuation, and giving temperature early warning and positioning information according to the three-dimensional temperature field grid data. According to the invention, accurate positioning and real-time early warning of temperature abnormity at the early stage of the fire disaster are realized, and the accuracy and response speed of fire disaster monitoring are improved.

Example implementations include a method, apparatus and computer-readable medium of object / action recognition using a text-based classification model, comprising generating an image vector configured to represent one or more features of the first image. Additionally, the implementations further include computing a vector distance between the image vector and each of a first text vector and a second text vector, wherein the first text vector is configured to represent a first text, and wherein the second text vector is configured to represent a second text. Additionally, the implementations further include classifying the first image according to the first text or the second text based on which computed vector distance indicates a highest similarity between the image vector and either the first text vector or the second text vector relative to the other of the first text vector or the second text vector.

A method and apparatus relating to providing a remote device for a fire alarm control system are, in turn, provided. The aspects include configuring each at least two replaceable sub-devices to provide at least one of a notification and a detection of a fire event. The aspects include configuring a housing to receive and connect to the at least two replaceable sub-devices.

The invention discloses a fire risk assessment early warning system, and relates to the technical field of fire early warning, and the system comprises a data collection module which collects the real-time data of related fire risk factors in a fire risk assessment target environment; the feature extraction module is used for extracting feature parameters of the fire risk factors and setting a dynamic risk threshold value; the risk judgment module is used for judging whether the characteristic parameters exceed a threshold value or not according to a set dynamic risk threshold value; the risk probability calculation module is used for analyzing the abnormal development trend of the fire risk based on the abnormal data, establishing a fire risk probability evaluation model and predicting the risk probability of fire occurrence; and the early warning module is used for evaluating the fire state of the fire risk evaluation target environment according to a risk probability evaluation result. According to the invention, fire risk factors are monitored in real time, analysis is carried out by using a time sequence and a sliding window, an early warning mechanism is dynamically adjusted, the fire risk is evaluated by using machine learning, the fire probability is accurately predicted, and the fire risk is reduced.

A smoke alarm includes a housing having a generally hollow interior, a printed circuit board arranged within the interior of the housing, and an optical chamber assembly connected to the printed circuit board. The optical chamber assembly has an interior chamber and at least one smoke entry location. A substantial entirety of the interior chamber is external to the housing. At least one light device for evaluating particles is arranged within an interior chamber of the optical chamber assembly.

To enable obtaining a smoke sensor that can prevent erroneous sensing of smoke.SOLUTION: A smoke sensor (100) comprises: a smoke sensing part (50) for sensing smoke generated in a monitoring area to output a smoke sensing level indicating concentration of the sensed smoke; and a state monitoring part (20) for monitoring a state of an inner face of a flow channel (41) for allowing air in the monitoring area to pass through the smoke sensing part (50), or an object face (31) as a face of an interior of the smoke sensing part (50). The state monitoring part (20) has: a monitoring light-emitting part (21) for emitting light toward the object face (31); and a monitoring light-receiving part (22) for receiving reflected light of light emitted from the monitoring light-emitting part (21), from the object face (31) to output a light-receiving level indicating intensity of the received light as a monitoring light level.SELECTED DRAWING: Figure 2

An apparatus, including a personal monitoring device and a drone. The personal monitoring device monitors a movement of the individual and detects a deviation from an expected position or location, or detects a deviation from an expected travel route, by the individual, generates a message containing information regarding when the individual deviated from the expected position or location or deviated from the expected travel route, and transmits the message or information contained in the message to the drone. An operation of the drone is activated, the drone travels to the position or location of the personal monitoring device, records video information or video and audio information, and transmits the video information or the video and audio information to a user communication device or to a central processing computer.

An apparatus may include a one or more sensor devices configured to capture data of an environment, such as infrared data and images, and a computing device in communication with the one or more sensor devices. The computing device may be configured to receive the data, determine one or more hazardous events based on analyzing the data, and cause one or more indications associated with the one or more hazardous events to be output.

The invention discloses a wind power plant fire intelligent sensing and early warning method based on multi-sensor fusion, and belongs to the technical field of fire sensing and early warning, and the method comprises the steps: obtaining data information collected by a plurality of sensors in real time through the plurality of sensors disposed in a wind power plant, and carrying out the data preprocessing, according to the method, the importance weights of the environmental parameters are dynamically adjusted, the importance degrees of the parameters are reflected in real time according to environmental changes, the accuracy of environmental condition monitoring and modeling is improved, the space-time correlation model is formed in combination with the multi-dimensional time sequence matrix and the space correlation matrix, the uncertainty of the environmental state is comprehensively evaluated, and the accuracy of environmental condition monitoring and modeling is improved. The dynamic environment modeling and prediction are carried out, the time and space factors are fully considered, the fire key features are extracted by adaptively adjusting the weight according to the change of the dynamic environment model and the environment parameters to the fire sensitivity, the information related to the fire can be captured in a more targeted manner, and the accuracy and effectiveness of fire feature extraction are improved.

To make it easier for a person in charge of calling with a master device of a disaster prevention monitoring system to grasp a situation of a site where a fire or the like has occurred.SOLUTION: A disaster prevention monitoring system according to the present invention includes a master device, a slave device, a fire receiver, and a call wiring connecting the master device and the slave device, in which the master device includes a master device handset and a display device, the slave device includes a slave device main body, a handset, and a camera device, the handset is capable of making a call with the master device handset by an audio signal via the call wiring, the camera device is movable, and a video signal obtained by photographing a fire can be sent to the master device via the call wiring or the video wiring and displayed on the display device.SELECTED DRAWING: Figure 2

The invention relates to an accident and fire precursor detection system, in particular to a system for automatically detecting overheating of an electrical equipment component, a method of using the same and a testing method. An electrical equipment element overheating automatic detection system comprises a gas sensor used for detecting gas released when a controlled element or a thermal activation gas emission material installed on the controlled element is heated; a controller associated with or combined with the gas sensor has a function of generating an overheat alarm and / or transmitting overheat information to other devices when the controlled element is heated, which function can be achieved when the sensor is affected by gas released when the controlled element is heated or a thermally activated gas release material mounted thereon, and meanwhile, the existence of background gas in the controlled area is considered. Meanwhile, the invention discloses a testing method of the system and a method for automatically detecting overheating of an electrical equipment element. According to the invention, by timely detecting the defect that the electrical equipment is overheated, the operation reliability and safety of the electrical equipment are improved.

The invention discloses an intelligent gas alarm method and system, and belongs to the technical field of gas safety monitoring. According to the method, multi-source sensing data of gas, flame, human body and valve states are integrated, and after missing value filling, abnormal value detection and correction, preprocessing of moving average filtering and feature extraction based on a fixed time window, the data are input into a support vector machine classification model for risk level identification and judgment; executing a multi-mode linkage control strategy according to the risk level, the valve state signal and the human body existence state signal; and anonymized feature data generated by the model and corresponding event tags are uploaded to a cloud end for incremental learning and model iterative updating based on weighted security performance indexes. According to the invention, multifunctional collaborative linkage and intelligent decision making are realized, the alarm threshold can be dynamically optimized, and the accuracy, environmental adaptability and long-term self-evolution capability of the system are remarkably improved.

The invention relates to the technical field of fire safety, in particular to a fire operation whole-course acousto-optic identification monitoring and early warning system, which comprises front-end data acquisition equipment and rear-end data processing equipment in two-way communication with the front-end data acquisition equipment, the front-end data acquisition equipment comprises a visual information acquisition device and a sound information acquisition device and is used for acquiring a visual light source signal and a voiceprint signal of a fire operation site, encrypting the visual light source signal and the voiceprint signal and transmitting the signals to the rear-end data processing equipment; the rear-end data processing equipment comprises a feature extraction and analysis module and a platform early warning module; the feature extraction and analysis module is used for carrying out feature extraction on the received visual light source signal and voiceprint signal and carrying out fire operation danger signal identification based on extracted feature parameters; and the platform early warning module is used for determining the current risk level and carrying out risk early warning based on the fire operation danger signal identification result, so that pre-identification and timely alarm of the danger signal in the fire operation process are realized.

The invention discloses an intelligent identification method and system for an initial fire type of a kitchen. The method comprises the following steps: acquiring multi-modal environment data in the kitchen; the multi-modal environment data comprises visible light image data, infrared thermal imaging data and gas concentration data; the multi-mode environment data are preprocessed; performing fire feature extraction on the preprocessed multi-modal environment data to obtain each multi-modal initial fire feature; the multi-modal initial fire features comprise visible light image features, infrared thermal imaging features and gas concentration features; and inputting the multi-modal initial fire features into a deep learning model of multi-modal feature fusion, and outputting a judgment result of the kitchen initial fire type. According to the intelligent identification method and system for the initial fire type of the kitchen, the type of the initial fire of the kitchen can be rapidly and accurately identified in a complex environment of the kitchen, and a basis is provided for subsequently taking targeted fire extinguishing measures.