995results about "Fire alarms" patented technology

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 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 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 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 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.

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.

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 present invention relates to a wearable safety device 99 comprising: a device body 1 adapted to be secured to a user; a local alert system comprising at least one light source 65 and an audible alarm 35; an electronic control unit 51 comprising an alarm trigger and being configured to control the local alert system, wherein the electronic control unit is configured to continuously or repeatedly activate the light source to provide a visual alert, and wherein the electronic control unit is configured, upon actuation of the alarm trigger, to activate the audible alarm. The present invention also relates to a system comprising: a wearable safety device as aforementioned; and a software application for a user device, preferably a smartphone, wherein the software application is configured to broadcast an alert signal: based on a comparison between a planned route input by a user to the software application and data relating to the location of the user device; and / or upon actuation of the alarm trigger.

The invention discloses a mining industry multi-system joint alarm method, which comprises the following steps of: receiving alarm information from a plurality of alarm systems, performing protocol conversion according to a communication protocol format of the alarm information, and verifying in a unified standardized data format; based on a preset rule base, distributing a basic priority score for the alarm information verified to be valid; generating a comprehensive priority value of each alarm through weighted calculation in combination with the area weight and the time weight of the alarm, and forming a global priority queue; and according to the global priority queue, an alarm signal instruction is generated according to a priority sequence, and a multi-mode output device is driven to output an alarm signal. According to the invention, multi-system independent alarm is changed into multi-system intelligent joint alarm, and an automatic alarm center capable of getting through a data barrier, carrying out intelligent analysis and decision making and realizing ordered output is constructed, so that the efficiency and reliability of mining safety monitoring and emergency response are integrally improved.

Devices, systems, and methods for fire system floor plan layout generation are described herein. In some examples, one or more embodiments include a computing device comprising a memory and a processor to execute instructions stored in the memory to receive a floor plan layout of a space, define, via an object placement model utilizing the floor plan layout, placement locations of fire system objects for the floor plan layout, and generate, using the object placement model, a fire system floor plan for a fire system for the space including the fire system objects, wherein the fire system objects are located in the defined placement locations on the fire system floor plan from the floor plan layout.

The invention discloses a smart forest fire prevention risk monitoring system based on digital twinning, and the system comprises an environment collection module which is used for collecting multi-source environment state data in a forest region, and constructing an environment state data set; the risk modeling module is used for inputting the environment state data set into the improved T-GNN model to generate a risk evolution trajectory sequence; the abrupt change recognition module is used for executing fuzzy segmentation operation and constructing an initial abrupt change point set; the early warning generation module is used for calculating the trigger frequency and the weighted score value of the abrupt change point, and screening and generating an early warning trigger point set; constructing a delay response index table; and the track calibration module is used for performing error evaluation and completing calibration operation. According to the method, accurate identification and dynamic early warning of the forest fire danger evolution trend can be realized, and the method has the advantages of timely response, controllable error, high robustness and the like, and is suitable for intelligent fire prevention application scenes in a complex forest environment.

A fire early warning method based on multi-source information fusion, a system, and a medium. The method comprises single vehicle model training, model deployment, fire risk assessment, BIM display and anomaly information notification. In the fire early warning method based on multi-source information fusion, the system, and the medium, a vehicle temperature fault mode can be quickly and accurately identified by performing deep fusion and model training on image and location and temperature information of the vehicle, thereby improving the accuracy of fire early warning; and multi-source information is acquired in real time, such that when over temperature occurs in the vehicle, workers can quickly localize the vehicle and the vicinity thereof, so that measures can be taken promptly for the over-temperature vehicle, thereby preventing the occurrence of fires or reducing the severity of fire damage, and providing strong support for fire prevention and control work. By establishing a bridge BIM model, a bridge fire risk map is displayed on a display device or a user terminal, and a command department, workers and drivers can quickly ascertain information regarding the location, cause, severity, and vehicle density of a current bridge fire by means of the bridge fire risk map; the system has high environment adaptability, fire risk assessment can be carried out in different environments, the decision-making efficiency of the command department is improved, the workers can quickly arrive at a rescue site to carry out firefighting and rescue operations, and drivers in low-risk areas can quickly evacuate from the bridge or assist the workers in evacuating others, thereby reducing casualties and economic losses caused by fires as much as possible.

The embodiment of the invention provides a battery fire treatment method, device and equipment based on multi-modal data fusion, and relates to the technical field related to batteries. The method comprises the following steps: acquiring real-time multi-modal data acquired by a multi-modal sensor; the real-time multi-modal data at least comprises temperature, voltage, current, combustible gas concentration and sound wave data; according to the historical multi-modal database and the real-time multi-modal data, determining a comprehensive fault index through a dynamic weight model; the historical multi-modal database stores multi-modal data samples of each stage of thermal runaway development of the battery in the battery cell module in different fault modes, and the comprehensive fault index is used for indicating the fire risk degree; and outputting positioning information of the target area according to the comprehensive fault index and a preset spatial position mapping table, the preset spatial position mapping table is used for indicating a corresponding relation between the deployment position of the multi-modal sensor and the position of the target area. The method is used for achieving the effects of improving fire detection real-time performance and positioning precision and the like.

The invention relates to the technical field of power cable early warning, and discloses a power cable extremely-early fire early warning method and system based on multi-modal fusion, and the method comprises the steps: arranging a plurality of monitoring nodes in a monitoring region along the trend of a cable, and collecting multi-modal monitoring data through each monitoring node; preprocessing the multi-modal monitoring data to extract characteristic parameters; obtaining causal relationship characteristics based on the characteristic parameters, and calculating a causal chain strength index and a modal anomaly propagation consistency index; constructing a comprehensive feature vector of each monitoring node, and constructing a global feature vector based on the data of all the monitoring nodes; inputting the comprehensive feature vector and the global feature vector into a trained neural network model to obtain a comprehensive risk assessment value and abnormal probability distribution of each monitoring node; comparing the comprehensive risk assessment value with a multi-level early warning threshold value, and determining an early warning level in combination with an early warning continuity judgment mechanism; and outputting an early warning result. According to the invention, the operation safety and reliability of the power cable are improved.

The invention discloses a forest smoldering and reburning fire detection method and system based on distributed optical fiber temperature measurement, and the method comprises the steps: obtaining the temperature data, measured by an optical fiber, of a forest extinguished region, and carrying out the preprocessing of the temperature data through temperature calculation, dynamic noise reduction and drift compensation; constructing a BiLSTMAttenGAN fire detection model, wherein the BiLSTMAttenGAN fire detection model comprises a generator which takes real temperature data as input and reconstructed temperature data as output, and a discriminator which takes the real temperature data and the reconstructed temperature data as input and dichotomy judgment and abnormal score as output and comprises an animal body temperature interference elimination mechanism; and performing multi-level early warning on anomalies under different conditions based on the anomaly scores. According to the invention, the detection efficiency and accuracy of forest smoldering and reburning fires can be effectively improved, and powerful support is provided for prevention of forest smoldering and reburning fires.

The invention relates to the technical field of early warning management platforms, and particularly discloses a whole-plant comprehensive early warning management platform and method based on multi-source heterogeneous data fusion, and the platform comprises an image data collection layer, a data governance layer, an early warning analysis layer, an application interaction layer, and a safety protection layer. The image data acquisition layer covers data of the DCS system, the NCS system, the field inspection system, the fire alarm system, the video monitoring system and the intelligent monitoring system; the early warning analysis layer realizes alarm grading through a three-stage process of weight calculation, severity evaluation and score correction; and the application interaction layer is linked with the early warning analysis layer, a differential push strategy is triggered according to an alarm level, and the security protection layer meets the requirements of national energy group network security level protection of three levels or above. The core technical scheme (such as data atlas construction logic, an early warning algorithm model and a safety protection mechanism) of the method is not limited to specific unit types, and can be flexibly adapted to different types of thermal power projects such as gas turbines and coal-fired units with the capacity of 300MW-1000MW.

The invention discloses a forest fire intelligent early warning system, and the system comprises a fire image data collection module which collects fire image data and fire video data; the data preprocessing module is used for carrying out self-adaptive normalization processing on the collected fire picture data and fire video data; the YOLOv5 image recognition model is used for deploying a lightweight YOLOv5 model through edge calculation to judge whether the fire in the collected fire picture data and the fire video data is true or not; the AI large model performs real-time analysis and calculation according to a locally integrated large language model and a built-in knowledge base, and automatically outputs a forest fire emergency response scheme; the Rothermel model fire spreading prediction module is used for predicting a fire spreading path based on a Rothermel surface fire spreading model; the output display module is used for constructing a three-dimensional fire scene based on a visual data platform and displaying a prediction result; and the data storage module is used for storing fire picture data, fire video data and prediction related data in the process from occurrence to extinguishment of the fire.