1135 results about "Fire hazard" patented technology

The invention discloses a forest fire detection and disappearance method and system, and relates to the technical field of forest fire prevention and data processing, and the method comprises the steps: carrying out the fire inspection of a planned forest region through a plurality of inspection unmanned planes, and transmitting an image and airborne sensor data back to a command center in real time; identifying a fire behavior by using an artificial intelligence method, and performing fire behavior judgment and fire hazard grade division; the command center screens fire points according to the thermal images and updates the positions of the fire points in real time; the fire scene spreading trend is judged according to the returned data, and a basis is provided for the dispatching task of throwing the fire extinguishing bombs by the fire extinguishing unmanned aerial vehicle; using an MAPPO multi-agent reinforcement learning algorithm to train an optimal dynamic fire point collaborative disappearance regulation strategy; and after the open fire is extinguished, the inspection unmanned aerial vehicle continues to inspect to identify a re-ignition point or a smoke point, and secondary large-area fire is prevented. According to the method, efficient collaborative fire extinguishing, dynamic environment adaptation and resource optimization configuration can be achieved, and robustness is kept when part of intelligent agents fail.

The invention provides a forest fire prevention unmanned aerial vehicle patrol early warning system and method based on edge calculation, and relates to the technical field of unmanned aerial vehicle patrol early warning. The patrol early warning system comprises an unmanned aerial vehicle platform, a data acquisition module, a data processing and analysis module, a communication and early warning module and a ground control and scheduling platform. According to the invention, calculation and data storage are close to a data source through edge calculation, the data collected by the unmanned aerial vehicle is processed in real time at an edge calculation node without being transmitted to a remote cloud and returning a result, the data processing and response time is greatly shortened, fire hazards can be found in seconds or even shorter time, and early warning can be given out. And the system can analyze data in real time in the flight process of the unmanned aerial vehicle, provides real-time decision support for a ground control center, and can quickly formulate a fire-fighting scheme according to information such as fire behavior and wind direction when a fire occurs, so that the fire-fighting efficiency is improved.

The invention relates to the technical field of fire hazard monitoring, and discloses a fire hazard remote monitoring and multistage early warning mechanism integration system which integrates a data acquisition module, a heat gathering map generation module, an image fusion module, a data calculation module, a risk prediction module, an early warning notification module and the like. Through an infrared temperature measurement unit, a smoke concentration acquisition unit and an image acquisition unit, the system can acquire temperature, smoke and image information of a key area in real time. A heat gathering map generation and image fusion technology is utilized, a fusion image is systematically generated, a fire scene and temperature distribution thereof are visually displayed, and the fire source positioning efficiency is improved. Meanwhile, the system analyzes the gradient change of the temperature, the heat accumulation area and the smoke concentration through a data calculation module, constructs a risk prediction model in combination with a machine learning algorithm, and achieves the refined prediction of the fire risk. And according to a prediction result, the early warning notification module automatically sends a corresponding early warning message to ensure rapid transmission and timely response of the early warning message. According to the invention, the intelligent, precise and efficient level of fire monitoring is improved.

The invention discloses a community fire early warning method combined with a knowledge graph, and relates to the technical field of fire early warning, and the method comprises the steps: carrying out the traversing of community region information, carrying out the data collection, and constructing a community fire knowledge graph according to a data collection result; performing feature extraction based on the community fire knowledge graph to obtain a plurality of fire features; constructing a fire risk assessment model to perform fire risk assessment on the community, and generating a plurality of risk assessment results; monitoring the community in real time, and generating a trigger instruction; and starting a fire early warning mechanism according to the trigger instruction, and generating a fire response decision. The technical problems that in the prior art, a traditional fire early warning method is inaccurate in evaluation and cannot effectively prevent fire in advance are solved, and the technical effects of improving the accuracy of fire risk evaluation, improving the accuracy of fire prevention and effectively reducing fire hazards are achieved by combining the knowledge graph.

The invention provides a forest fire prevention early warning method and system based on a multi-source sensor, and the method comprises the steps: firstly obtaining a multi-source sensing monitoring data set of a target forest region, including data subsets of temperature, humidity, smoke concentration, visible light images and the like, and collecting each subset by at least two heterogeneous sensor nodes; performing space-time alignment processing on the multi-source sensing monitoring data set to generate a space-time distribution characteristic set containing various characteristics such as temperature gradient, calling a dynamic anomaly detection model to compare fire danger indexes, generating a fire danger grade quantitative score and an anomaly fluctuation index set, and performing data processing on the fire danger grade quantitative score and the anomaly fluctuation index set; the method comprises the steps of obtaining a fire hazard grade quantitative score, matching a multi-level early warning strategy based on the fire hazard grade quantitative score and a preset early warning threshold interval, generating a dynamic fire hazard early warning instruction, triggering fire extinguishing resource scheduling according to the dynamic fire hazard early warning instruction, generating a forest region escape path optimization strategy in combination with an abnormal fluctuation index, and achieving efficient and accurate forest fire prevention early warning and coping.

The invention discloses a forest fire hidden danger identification method and device for a power transmission line based on a multi-modal large model, and the method comprises the steps: constructing a forest fire hidden danger identification model for a smog or flame-containing image outputted by a conventional target detection model, and enabling the model to guide a dialogue set through the combination with forest fire hidden danger identification, thereby achieving the recognition of the forest fire hidden danger. The dialogue context and the image visual features are fused, so that the deep fusion of the text semantics and the firework image features is realized; and finally, inputting the deeply fused features into a large language model, so that a forest fire hidden danger output dialogue of the power transmission line can be obtained by utilizing the deep semantic understanding capability of the large language model, and then a forest fire hidden danger recognition result of the power transmission line is obtained. Therefore, according to the invention, the judgment of whether the smoke and fire can damage the power transmission line is realized, the technology upgrade from smoke and fire existence detection to equipment threat research and judgment is completed, and on the basis, the problems of resource waste and untimely crisis response caused by reporting all smoke and fire information in the traditional technology can be avoided.

The invention relates to the technical field of fire safety, in particular to an intelligent fire-fighting electrical fire monitoring method, device and equipment and a medium, and the method comprises the steps: obtaining electrical parameters; according to the electrical parameters, a fire hazard identification model is established by using an LSTM algorithm and a CNN algorithm, the operation state of the electrical equipment is predicted according to the fire hazard identification model, an operation state trend prediction result is obtained, and a potential fire hazard type is identified; according to a fire hazard type identification result, in combination with a use environment of electrical equipment and a threshold determination algorithm, performing quantitative evaluation on a fire hazard risk to obtain a corresponding risk level, and when the risk level exceeds a set multi-layer threshold, automatically starting a linkage control mechanism; therefore, the problems of response lagging, high labor cost, high intermittent fault omission ratio and the like in the prior art are solved.

The invention belongs to the technical field of fire detection, and discloses a method and a system for artificial intelligence identification, research and judgment of fire alarm, and the method comprises the following steps: carrying out the framing of input video data, converting a video stream into continuous image frames frame by frame, carrying out the timestamp marking of each frame of image, and obtaining a framed image; according to the method, flames are positioned by analyzing red and orange tones of the images, early signs of a fire can be more accurately captured by analyzing flame shapes and diffusion behaviors, the area change rate of flames and smoke in continuous frames can be analyzed, the dynamic change of flame diffusion speed and smoke coverage can be beneficially understood, and the method is suitable for popularization and application. The fire hazard early warning is more timely and accurate, the fire hazard risk level is evaluated according to the flame dynamic model, and the system is allowed to adjust the sensitivity and threshold of the early warning signal according to the real-time data, so that the fire hazard early warning scheme is more targeted and effective, faster response can be provided at the initial stage of the fire hazard, and casualties and property loss are greatly reduced.

The invention provides a power transmission line fire reason identification system and method based on electrical characteristic parameters, and relates to the technical field of safety monitoring and fault diagnosis of a power system. Comprising a data acquisition module, a data processing module, a fire risk assessment module, a multi-dimensional analysis module, an early warning module, a multi-source data fusion module, an after-event analysis module and a self-learning mechanism module. Abnormal conditions in line operation can be found in time, fire hazards caused by delayed detection are effectively avoided, the efficiency and timeliness of line monitoring are improved, monitored electrical data are analyzed and processed by adopting a machine learning algorithm, abnormal modes can be automatically recognized, potential causes of fire occurrence can be judged, and the method is suitable for popularization and application. The system not only depends on single threshold alarm, but also provides more accurate fire early warning through deep analysis of historical data and operation trend.

The invention relates to the technical field of electric bicycles, and discloses a data-driven electric bicycle battery fire hazard detection and protection method, which comprises the following steps: S1, multi-modal data acquisition and fusion, S2, data preprocessing and feature extraction, S3, data-driven intelligent detection: classifying normal and abnormal states through a supervised learning model, S4, hierarchical protection strategy, and S5, data-driven intelligent detection. S5, cloud data optimization and model continuous learning; S6, model data updating; S7, intelligent linkage and long-term optimization; S8, cloud risk data acquisition; and low, middle and high three-level protection mechanisms are adopted. Through dynamic risk assessment, charging and discharging power is limited in a medium risk, a charger is automatically disconnected, a user is prompted to stop operation, active protection is triggered in a high risk, and sound-light alarm and remote notification are triggered at the same time, so that accidents are prevented from further spreading.

The invention relates to the technical field of fire protection and security, in particular to a security and disinfection integrated multi-modal fusion embedded edge calculation method and system, and provides a security and disinfection integrated multi-modal fusion embedded edge calculation method which comprises the following steps: constructing a data set; the AI visual recognition model uses an improved YOLOv8 model to train a data set and execute target detection, meanwhile, an optical flow method is adopted to extract motion features of flames or smog in continuous key frame images in a monitoring video, and a spreading prediction model is established to send out an early warning signal in advance; and the fusion processing module establishes a dynamic threshold adjustment model based on the environmental parameters to optimize an early warning trigger threshold, and transmits the early warning trigger threshold to the fire-fighting linkage control module to verify and trigger fire-fighting linkage operation. AI intelligent analysis and fire-fighting linkage disposal of fire hazards are realized, false alarm of early warning information is effectively prevented, the discovery and disposal efficiency of the fire hazards is improved, and the fire hazards are eliminated in the bud.

The invention relates to the technical field of fire alarm, in particular to a fire ground-air integrated monitoring and early warning system and method, and the system comprises a flame and smoke recognition module which collects image data from a ground monitoring camera and an air unmanned aerial vehicle, carries out the feature extraction of the image data, generates a feature data set, and carries out the early warning based on the feature data set. And judging existence of flames and smog by using image analysis, and generating a fire identification result. According to the invention, multi-source heterogeneous data obtained by the ground monitoring camera, the aerial unmanned aerial vehicle and the infrared thermal imaging equipment are integrated for collaborative analysis, so that high-precision feature extraction of fire hazards and judgment of fire behaviors are realized, and the accuracy and response efficiency of fire recognition are improved; the unmanned aerial vehicle is used for dynamically collecting air thermal imaging data in real time, and an alarm is quickly formed based on temperature abnormal points, so that an abnormal temperature area can be timely positioned and pre-warned in the early stage of a disaster.

The invention discloses a cable tunnel fire risk feature identification system and identification method, and belongs to the technical field of cable tunnel safety monitoring. The whole stage of a fire is covered through multi-class cooperative detection, the target identification precision and scene adaptability are greatly improved, and the safety of the cable tunnel is improved. According to the whole system, a flame and smoke detection unit and a flame / smoke special detection unit are innovatively added to a detection engine module, an original heat source detection unit and an original human body detection unit are combined, a heat source-flame-smoke-human body four-category collaborative detection framework is formed, high-precision recognition is achieved on the basis of a YOLO model framework, and the detection efficiency is improved. Compared with the problems that a traditional system is high in single target detection omission ratio and cannot cover the whole stage of smoldering-initial open fire-violent combustion of a fire, the system has the advantages that the recognition rate of early flame and weak smoke is increased, the false alarm rate is greatly reduced, meanwhile, a heat source of operation and maintenance personnel is prevented from being misjudged as a fire hazard through human body detection, and the safety of the fire hazard is improved. And cable monitoring and personnel safety protection are both considered.

The embodiment of the invention relates to the technical field of fire spreading prediction, and particularly discloses a fire spreading trend AI prediction method and system based on a high-rise building. According to the embodiment of the invention, fire alarm information is received; acquiring smoke monitoring data of the target fire building, and performing smoke spreading prediction on the smoke monitoring data; selecting an auxiliary monitoring building; flame monitoring data transmitted by the auxiliary monitoring building are acquired, and flame spreading prediction is carried out; aI prediction of fire hazards and visual display of the fire spreading trend are carried out. Smoke monitoring data of a target fire building can be acquired, auxiliary monitoring analysis is performed, an auxiliary monitoring building is selected, flame monitoring data transmitted by the auxiliary monitoring building is acquired, and AI prediction of fire hazards and visual display of a fire spreading trend are performed by integrating a smoke prediction result and a flame prediction result. The defect of single data acquisition is overcome, and the accuracy of fire spreading trend prediction is effectively ensured in combination with flame monitoring of an auxiliary monitoring building.

The invention belongs to the technical field of fire-fighting supervision, and particularly relates to an intelligent fire-fighting alarm system and method based on regionalized monitoring, and the system comprises a regionalized monitoring network module, an intelligent data analysis module, an early warning information issuing module, an emergency evacuation plan module, a fire-fighting facility linkage module, and a regional fire-fighting supervision terminal. Environmental parameters in a target area are comprehensively collected in real time through the regionalized monitoring network module, the intelligent data analysis module identifies a fire disaster based on the collected data, quick response and emergency evacuation are performed after the fire disaster is identified, loss caused by the fire disaster is effectively reduced, and the safety of the fire disaster is improved. After fire early warning is removed, fire hazards are accurately analyzed and fed back in time, regional fire control management personnel can master the severity degree of each fire in detail, and regional fire control safety supervision is enhanced in time by comprehensively evaluating and analyzing the regional fire control potential safety hazard degree; the regional fire-fighting hidden danger is obviously reduced; and the regional fire-fighting management difficulty is reduced.

The invention discloses a building outer wall fire fighting system based on an unmanned aerial vehicle and a fire extinguishing method thereof, and belongs to the technical field of fire fighting, the building outer wall fire fighting system comprises fixed fire fighting equipment and mobile fire fighting equipment, the fixed fire fighting equipment is arranged on a building body, and the mobile fire fighting equipment is in butt joint with the fixed fire fighting equipment according to fire conditions; and the mobile fire-fighting equipment and the fixed fire-fighting equipment are electrically connected with the fire-fighting control center. The fire early warning cameras and the smoke alarm are arranged on the periphery of the roof parapet wall and used for discovering a fire in the early stage; the outer wall fire-fighting vertical pipe and the fire-fighting power supply are connected with a fire-fighting system of the whole building, and the unmanned aerial vehicle obtains a water source and the power supply through an adapter on the outer wall fire-fighting vertical pipe, so that the unmanned aerial vehicle can continuously work until a building fire is successfully extinguished; the building builds a three-dimensional fire-fighting network by means of indoor and outer wall fire-fighting systems, and the internal and external fire-fighting systems cooperate with each other to accelerate fire extinguishing of the building, reduce fire hazards and guarantee personal and property safety.

The invention relates to the technical field of data processing, and discloses a fire scene situation awareness and early warning method and system. The method comprises the following steps: performing gridding segmentation through infrared thermal imaging and visible light fusion, identifying similar electrical equipment and calculating a temperature baseline deviation value and an abnormal propagation coefficient, constructing a heat conduction coupling matrix to predict an abnormal diffusion probability, determining a fire development stage and generating a graded coloring situation visualization graph. According to the invention, the problem that a traditional infrared thermal imaging technology cannot realize intelligent identification of similar electrical equipment and prediction of a temperature anomaly propagation path is solved, and the accuracy of electrical fire hazard monitoring is improved.

The invention relates to the technical field of unmanned aerial vehicle inspection, and provides an unmanned aerial vehicle-based forest fire prevention inspection and disposal method, device and system. When an inspection unmanned aerial vehicle equipped with a multi-modal sensor is deployed to cruise according to a preset route, a visible light camera of the inspection unmanned aerial vehicle continuously collects forest region videos and transmits the videos to edge computing equipment of an inspection airport; fire hazards are analyzed in real time through a target recognition algorithm, and when a fire source is detected, position information is reported to a background terminal immediately; the system automatically dispatches the nearest fire extinguishing airport to dispatch the fire extinguishing unmanned aerial vehicle, and secondary accurate positioning is performed on a fire source by using the airborne infrared vision module, so that accurate throwing of the fire extinguishing bomb is ensured. The whole system enhances the real-time analysis capability through the edge computing device, breaks through the single-machine computing power limitation, also realizes the full-process automation from fire behavior recognition to fire extinguishing treatment, and significantly improves the response efficiency and treatment safety of forest fire prevention.

The invention discloses a high-rise fire intelligent positioning voice alarm method and system, and particularly relates to the technical field of voice alarm, and the method comprises the steps: taking the concentration of different types of smog generated by combustion of different materials, the brightness value of flame, and the spreading speed information of flame as the input of a fuzzy Bayesian neural network model; taking the fire hazard grade probability distribution as the output of a fuzzy Bayesian neural network model, determining the fire hazard grade of the fire hazard area according to the fire hazard grade probability distribution, collecting the crowd flow information and the area geographical location information in the monitoring area, and carrying out the comprehensive analysis of the crowd flow information and the geographical location information in the monitoring area. According to the invention, the fire hazard monitoring efficiency and accuracy are improved, the specific condition of the fire hazard is combined with the alarm level, and the flexibility and effectiveness of the fire hazard emergency response are realized.

The invention provides a control method of a forest fire prevention early warning system, which comprises the following steps: aiming at different types of litters, carrying out a combustion characteristic test, and measuring ignition points, heat values and combustion rate parameters of the litters to obtain combustion characteristic data of the different types of litters; on the basis of grading of the litter layer fire danger, analyzing spatial distribution characteristics of the litter layer fire danger, identifying a fire danger high-incidence area, and representing spatial aggregation and relevance of the litter layer fire danger by adopting a spatial autocorrelation analysis method; analyzing dynamic change characteristics of the litter layer fire danger through a litter layer fire danger assessment result, summarizing a seasonal change rule and an annual change trend of the litter layer fire danger by adopting a time sequence analysis method, and identifying a fire danger high-incidence time period; a geographical detector method is adopted to quantitatively analyze the influence degree of terrain, weather and vegetation on the litter layer fire danger, the dominant key factors are identified, and the key factors influencing the litter layer fire danger are explored.

An indoor fire hazard flashover risk prediction method based on a flame form and remote sensing data comprises the following steps: using fire hazard simulation software to simulate fire hazard data, and constructing a fire hazard data set; ignition is conducted, an indoor fire flame image is obtained through a high-definition camera, and the obtained image is processed; outputting the real-time heat release rate HRR of a fire point from the processed image through a residual network Resnet model; a methane laser remote sensing detector is used for monitoring and acquiring methane gas concentration data of an indoor flue gas layer at a specific height in real time; and building a fire hazard flashover risk prediction model based on the fire source heat release rate and the methane gas concentration, and predicting the flashover risk in advance. According to the invention, accurate identification and prediction of the flashover risk of the fire in the early stage of fire development are realized, and an early warning signal is sent when the fire is about to enter the flashover stage, so that casualties and property loss caused by the fire are reduced.

The invention provides a monitoring method and a control device for fire hazards of a lithium battery energy storage system, and belongs to the technical field of thermal runaway early warning, the method comprises the steps that the thermal state parameters of a lithium battery are confirmed, and the thermal state parameters are used for reflecting the heat state of the lithium battery in the using process; according to the thermal state parameters, a thermal control risk characteristic value of the lithium battery is determined, and the thermal control risk characteristic value is used for reflecting the thermal runaway risk of the lithium battery; according to the thermal control risk characteristic value and a multi-parameter fusion technology, determining a comprehensive index reflecting the thermal control risk of the lithium battery; selecting a machine learning algorithm according to the thermal control risk characteristic value, and determining a thermal runaway risk prediction model according to the machine learning algorithm; and collecting real-time thermal state parameters of the lithium battery, and determining the current thermal runaway state of the lithium battery according to the real-time thermal state parameters of the lithium battery and the thermal runaway risk prediction model. Whether the lithium battery is about to be subjected to thermal runaway can be predicted, the monitoring accuracy is improved, an early warning signal is sent out in time, and safe operation of the system is ensured.

The invention discloses a device and a method for three-dimensional intelligent monitoring and prevention of spontaneous ignition in a composite goaf during short-distance coal seam group mining, and the method comprises the following steps: firstly, determining the overall layout and construction of a directional drilling hole in a high-position fissure zone, and then sequentially installing a three-loop switching device, an expansion bag plug and an intelligent analysis control box; during normal gas extraction, different drilling gas is extracted to detect and analyze the concentration change of the index gas, the position and depth of the position are determined when a natural ignition hidden danger occurs, a closed nitrogen injection space is constructed, nitrogen is accurately injected, after coal in the area is fully inerted and cooled, gas extraction is recovered, and the index gas detection is enhanced; the upper natural fire risk of the composite goaf is eliminated. According to the method, through the mode that one hole is used for multiple purposes by replacing a roadway with a hole, the natural ignition hidden danger of the upper portion of the composite goaf can be intelligently monitored while gas of an adjacent layer and the goaf is treated, the specific position of a hidden danger area is determined in a short time, nitrogen is accurately injected, and natural ignition three-dimensional intelligent monitoring and prevention are truly achieved; a powerful support is provided for solving the problem that the upper natural ignition hidden danger cannot be monitored and prevented due to the complex internal environment of the composite goaf at present, and safe production of the mining working face of the close-range coal seam group is guaranteed.

The invention relates to the field of hidden danger identification, in particular to a hidden danger identification method in fire inspection based on a multi-modal large model, which comprises the following steps of: 1, establishing the multi-modal large model; 2, model training; 3, fine adjustment of the model; 4, deploying and using; 5, displaying a result; according to the method, the multi-modal large model is constructed, and the image data and the corresponding text description collected in the inspection process are effectively utilized, so that the fire hazard safety problem is accurately identified, the hidden danger identification capability is enhanced, and the fire hazard identification accuracy is remarkably improved. According to the fusion analysis mode, the limitation of single-mode data is made up, and misjudgment and missed judgment caused by insufficient information are reduced.

The invention, which relates to the technical field of fire safety, discloses a multi-modal intelligent identification method for fire-fighting hidden troubles based on a large model, comprising the following steps: S1, acquiring multi-source heterogeneous feature data through a sensor network deployed in a fire-fighting water pump room to obtain an initial feature set; s2, performing unified scale conversion on the initial feature set, calculating heterogeneous bridging parameters according to the environmental parameters and the equipment operation data, constructing a fusion feature vector, inputting the fusion feature vector into a preset neural network for feature weight adaptive adjustment, and outputting an adjusted weight vector; according to the fire-fighting hidden danger intelligent identification method based on the large-model multi-mode, intelligent closed loop from hidden danger identification to prevention and control decision making is realized, and the intelligent, dynamic and precise management level of a fire-fighting safety system is comprehensively improved.

The invention discloses an environment monitoring method based on visual identification, and the method comprises the steps: S1, shooting an image of a monitoring region in real time through an image collection module, transmitting the image to a controller after the image is processed, and transmitting the image to a terminal platform through a data transmission module electrically connected with the controller; and S2, when the terminal platform detects that the flame and smoke information is marked in the received picture, alarm information is sent out. According to the invention, the image acquisition module is adopted to shoot the condition of the monitoring area in real time, the flame and smoke information of the monitoring area is accurately identified through the improved YOLOv8 algorithm, and the terminal platform sends out an alarm signal for the abnormal condition and takes corresponding measures in time, so that the harm caused by information hysteresis can be avoided, and the fire hazard can be reduced.

The invention discloses a fire positioning method based on heat source gradient identification, and belongs to the technical field of fire positioning, and the method specifically comprises the steps: obtaining a two-dimensional temperature distribution image of a fire scene; drawing an isotherm, and marking a potential fire source area based on a preset temperature threshold value; calculating the area of each potential fire source region, screening out an undetermined region from the potential fire source regions, calculating the temperature change rate of a central isotherm of the undetermined region in unit time, and screening out a candidate region from the undetermined region; marking a plurality of points on the peripheral isotherm of the candidate area at equal intervals, generating a temperature gradient path in the reverse direction of the temperature gradient based on each point, calculating the temperature gradient of each path, obtaining the candidate area with the temperature gradient mean value larger than a threshold value, and marking the candidate area as an initial fire source area; continuously drawing vertical lines into the central isotherm from a tangent line of the intersection point on the central isotherm, and marking the intersection point of the vertical lines as an initial fire source center point; according to the invention, rapid positioning of the initial fire source center in the fire scene is realized.

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.

The invention relates to the technical field of fire early warning, and particularly discloses a fire-fighting electrical fire early warning method and system based on the Internet of Things, and the method comprises the steps: configuring an edge computing gateway, and collecting a current waveform signal and a high-frequency electromagnetic radiation signal of an electrical loop based on the edge computing gateway; extracting harmonic characteristics and electromagnetic radiation characteristics; constructing a risk value prediction and propagation model based on the graph neural network model according to the power grid topological structure, generating a data packet when the coupling relationship between the harmonic characteristics and the electromagnetic radiation characteristics meets a joint triggering condition, inputting the data packet into the risk value prediction and propagation model, and transmitting the data packet to the power grid. A dynamic risk value and a fault source position are output based on a risk value prediction and propagation model, through bimodal acquisition, electrical fire hazards can be found in an early stage, real-time feature extraction is performed by using an edge computing gateway, the data transmission amount is reduced, modeling is performed on power grid topology through a graph neural network model, a fault propagation path can be accurately predicted, and the fault propagation efficiency is improved. And the early warning precision is improved.

The invention belongs to the technical field of fire safety, and particularly relates to a control method of a fire detection alarm. Firstly, a distributed sensor network is deployed to collect temperature and smoke data, self-calibration is carried out, and abnormal data is eliminated. Then, through three-level progressive anomaly screening, namely, first-level preliminary screening monitoring mutation features and second-level feature verification, a space-time matrix is constructed, CNN analysis is used for the space-time matrix, third-level scene adaptation is combined with historical data to calculate the probability, and an anomaly screening signal is output. A visual camera is used for collecting images to generate a visual evidence matrix, a sensor data vector and an abnormal screening signal are combined to calculate a comprehensive confidence coefficient, and an alarm signal is triggered if the comprehensive confidence coefficient is larger than a threshold value; and finally, through time window sliding verification taking one minute as a period, determining an alarm when an alarm signal is greater than or equal to three periods. According to the method, the data accuracy is improved, fire hazards are accurately identified, the false alarm rate is reduced, and the alarm timeliness and accuracy are guaranteed.