1967results about "Fire alarm electric actuation" patented technology

The invention relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing 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 relates to the technical field of fire control management, in particular to a fire rescue intelligent management method and system based on a multi-mode AI large model. The method comprises the following steps: acquiring real-time environment monitoring data of a fire scene; carrying out environmental parameter analysis on the fire scene real-time environmental monitoring data, and carrying out gas change trend prediction to generate a scene gas parameter change trend; acquiring a fire scene thermal imaging image; performing temperature distribution visual identification and continuous frame temperature change rate calculation according to the fire scene thermal imaging image so as to generate a fire source area temperature change rate; performing multi-time ignition trend prediction according to the temperature change rate of the fire source area and the on-site gas parameter change trend so as to generate a multi-time ignition trend prediction map; and identifying a to-be-rescued area point based on the fire scene thermal imaging image. According to the invention, real-time and accurate distribution of on-site fire rescue resources is realized, and the rescue management efficiency and safety are improved.

The invention discloses an electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

The invention relates to the technical field of communication machine room safety protection, and discloses a communication machine room intelligent fire monitoring and fire extinguishing system based on edge computing, which comprises a fire parameter acquisition module, a temperature characteristic analysis module, a smoke dynamic detection module, a multi-source fusion judgment module, a regulation and control parameter generation module and the like. The fire parameter acquisition module acquires parameters such as temperature gradient, smoke concentration and equipment current, judges fire risk and triggers cooperative monitoring; the temperature characteristic analysis module extracts temperature distribution characteristics and quantitatively evaluates hot spot abnormity; the smoke dynamic detection module analyzes the smoke diffusion trend; the multi-source fusion judgment module is combined with multiple parameters to generate a fire extinguishing or optimizing signal; and the regulation and control parameter generation module is matched with the strategy to generate injection and ventilation parameters. The system also corrects a risk index through an equipment coupling analysis module, and forms closed-loop control by using a dynamic feedback execution module. According to the system, multi-source data fusion monitoring and intelligent fire extinguishing are realized, and the fire prevention and control accuracy of the communication machine room is improved.

The invention relates to the technical field of fire early-stage recognition, in particular to a multi-source information-based conveying belt fire early-stage recognition and early-warning system, which comprises a sudden change detection module, a synchronous analysis module, a spatial trend recognition module, a coupling fluctuation screening module and a probability evaluation module. According to the invention, through multi-source information linkage acquisition, collaborative monitoring of parameters such as along-line temperature, smoke, gas, images, load and heat source temperature difference, combined characteristic analysis among parameters and synchronous response trend determination are driven, and active revelation of early risk hidden dangers and dynamic discrimination of spatial distribution consistency and fluctuation continuity are realized. Potential abnormal focusing locking under a high-interference complex working condition is promoted, collaborative fluctuation between a load and a heat source temperature difference further eliminates environmental noise influence, risk weight dynamic adjustment strengthens classification sensitivity of probability identification, fire risk clustering division promotes accurate mastering of distribution of tiny initial hidden dangers, and the probability identification accuracy is improved. And the reliability of fire early warning in a coal mine area conveying belt scene is obviously improved.

The invention discloses an electrical fire monitoring method based on inherent residual current automatic compensation, and relates to the technical field of electrical safety monitoring, and the method comprises the following steps: S100, collecting a residual current signal, executing multi-component time-frequency deconstruction processing, extracting an energy distribution characteristic through wavelet packet transformation, and extracting a mutability index in combination with short-time spectrum entropy analysis, and constructing a preliminary distribution map of higher harmonic interference, and determining boundary features of interference signals in a time domain and a frequency domain. According to the method, high-frequency interference signals are accurately positioned through time-frequency deconstruction, wavelet packet analysis and spectral entropy indexes, non-fault harmonic components are effectively eliminated in combination with amplitude-frequency coupling recognition and a recursive rejection strategy, closed-loop control is constructed by introducing an adaptive compensation and stability backtracking mechanism, accurate recognition and dynamic correction of real electric leakage risks are achieved, and the method is suitable for large-scale popularization and application. The identification precision and the safety reliability of the monitoring system in a complex industrial environment are obviously improved, and the method has good engineering adaptability.

The invention provides a fire early warning method and system based on three-dimensional modeling, and the method comprises the steps: determining the transaction indexes of fire danger at different space units through the spatial structure characteristics of a three-dimensional digital twin model in combination with the state evolution mode of a physical field, and determining a risk cloud picture through all transaction indexes; extracting a risk sub-domain of the fire according to the risk cloud picture, and further generating a heat flux density field matrix among different space units; determining risk fitting degrees of different space units by combining the heat flow diffusion information with the monitoring information of the fire behavior in the target building, and performing time sequence aggregation on the fire risk in the target building according to all the risk fitting degrees and the local temperature field of each space unit to obtain fire danger propagation potential energy; and performing fire diffusion situation monitoring on the target building according to the fire danger propagation potential energy, and outputting a fire diffusion early warning signal. By adopting the scheme of the invention, the diffusion track of the fire heat flow can be accurately identified in the three-dimensional model.

The invention discloses an intelligent forest fire monitoring method and device, electronic equipment and a medium, and the monitoring method employs a multi-mode deep learning model based on a double-attention mechanism to enhance the capturing capability of early flame thermal radiation characteristics and smoke form characteristics, and greatly improves the recognition sensitivity. In combination with triple verification and a confidence coefficient decision-making mechanism, errors caused by environmental interference are effectively avoided, and the recognition reliability is comprehensively improved; meanwhile, based on intelligent gridding three-dimensional monitoring of terrain complexity and vegetation types, the blind area coverage rate is greatly reduced, normalized inspection and post-disaster quick response are performed through the unmanned aerial vehicle platform, the monitoring coverage range is enlarged, and the response efficiency is improved.

The invention discloses a distribution box fire early warning method and system based on multi-source information fusion, and particularly relates to the technical field of distribution box fire early warning. Temperature, smoke, acoustic vibration and current harmonic signals are synchronously acquired, time sequence alignment and amplitude normalization are performed, and a dynamic baseline is generated in real time; extracting multi-scale statistics and morphological characteristics according to an adaptive window, and calibrating an operation mode through unsupervised clustering; then retrieving a matched baseline signature in a historical feature library, and calculating a multi-modal standardization deviation of a current window; mapping the deviation index into a graph node, combining a covariance edge weight, a phase synchronization index and a smoke discrete index, obtaining a coupling risk coefficient through a fusion model, and outputting a comprehensive risk index; and finally, carrying out multi-scale rate and acceleration analysis on the comprehensive risk index, triggering three-level early warning of attention, warning and danger by adopting a dynamic threshold, and correcting the threshold by utilizing operation and maintenance feedback self-learning, thereby effectively solving the problems of early warning response lag and unknown early warning level.

The invention relates to the technical field of fire safety monitoring, in particular to a firework identification system and method based on multi-modal fusion perception, and the core of the scheme is a visible light, multispectral and temperature three-modal framework: feature extraction optimization of each modal, improved YOLOv8s for visible light branches, dynamic background modeling and flame color screening, and multi-modal fusion perception. False positive is rejected by a multispectral branch depending on a waveband ratio and an index, an error compensation algorithm is introduced into a thermopile branch, and finally, a final smoke and fire area and the confidence coefficient thereof are determined by associating a three-mode area through collaborative decision. According to the scheme, the complex environment adaptability and the recognition reliability can be improved, the false alarm risk is reduced, the extremely-early smoke and fire detection capability is enhanced, good real-time performance and deployment flexibility are achieved, and the method is suitable for various types of fire safety monitoring scenes.

The invention discloses an electrical fire real-time monitoring system based on a wireless sensor network, and relates to the technical field of electrical fire monitoring. The cloud monitoring platform is in communication connection with an electrical data acquisition module, an electrical fire abnormity analysis module, an abnormity early warning module, a node management configuration module and a visual monitoring module, and all the modules are in electric signal connection; the electrical data acquisition module is used for acquiring monitoring data of electrical nodes by using a deployed wireless sensor and transmitting the monitoring data to the cloud monitoring platform through a wireless communication technology, and the monitoring data comprises current, voltage, temperature, humidity and leakage current. Multi-parameter fusion monitoring is carried out through the wireless sensors deployed at the key nodes of the electrical equipment, compared with a traditional wired sensor, the wireless sensors do not need wiring, installation is flexible, early signals of an electrical fire can be captured more comprehensively, and the accuracy of fire early warning is remarkably improved.

The invention provides a distributed fire situation awareness and cooperative early warning method for a building group, and relates to the technical field of building groups, and the method comprises the steps: collecting fire data through smoke sensing, temperature sensing and infrared imaging nodes, generating a fire feature vector through a distributed preprocessing algorithm, and constructing a multi-dimensional fire incidence matrix; a building group fire dynamic evolution model is generated based on graph structure neural network training, a fire spreading prediction result is calculated in combination with a building group digital twinborn platform, and differential early warning instructions are generated. According to the invention, intelligent sensing, dynamic prediction and cooperative early warning of the fire behavior of the building group are realized, and the accuracy and timeliness of fire early warning are improved.

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 discloses a tunnel fire position positioning and fire source risk analysis method and system, and relates to the technical field of tunnel fire detection, and the method comprises the steps: periodically collecting image, temperature, smoke, harmful gas concentration, infrared radiation and humidity data at each monitoring point in a tunnel, and dynamically adjusting the monitoring frequency according to the humidity; feature extraction is performed on the collected data, a factor set and a fuzzy evaluation set for fire judgment are constructed, and a fuzzy algorithm is utilized to calculate the membership degree of each feature to fire evaluation; a comprehensive membership value vector is generated in combination with the feature weight and is used for judging whether a fire occurs or not and performing preliminary positioning; initial monitoring equipment and front and back adjacent equipment are set, and the fire position is further accurate through fuzzy analysis of data of the front and back adjacent equipment; performing risk assessment on the fire according to the fire position and the abnormal pixel point proportion; intelligent detection, precise positioning and risk analysis of the tunnel fire are realized, and the tunnel safety management level is effectively improved.

The invention relates to the technical field of data processing, in particular to a data acquisition method and system based on internet-of-things equipment, and the method comprises the steps: installing a smoke concentration sensor and a temperature sensor in a cargo area of a warehouse, and calculating the smoke concentration and temperature of the cargo area according to the deviation degrees and change rates of the smoke concentration and temperature at the current moment; determining a smoke concentration risk index and a temperature risk index, and determining a comprehensive risk index by combining the mutual information of the smoke concentration and the temperature; and determining a comprehensive value index according to the respective uncertainty of the historical smoke concentration sequence and the historical temperature sequence and the similarity between the historical smoke concentration sequence and the historical temperature sequence and the corresponding standard sequence, and adjusting the acquisition frequency of the sensor by using the comprehensive risk index and the comprehensive value index to realize a self-adaptive data acquisition strategy. According to the method, environmental changes can be responded in time, potential risks can be recognized in time, and generation of excessive redundant data can be avoided.

The invention provides an industrial fire-fighting emergency method based on visual edge calculation. The method comprises the following steps: synchronously acquiring visual data, thermal data and gas data at the edge end of a factory; processing and feature extraction are respectively carried out on the collected data to obtain visual features, thermal features and gas features, after the visual features, the thermal features and the gas features are fused to obtain multi-modal fusion features, whether a fire happens or not is predicted, and if yes, a fire prediction picture is generated; presetting a time sequence analysis model, generating a fire emergency task flow according to a result output by the time sequence analysis model, dividing the priority of the fire emergency task flow, and submitting the fire emergency task flow to a workflow engine to distribute the fire emergency task flow to a corresponding execution unit; and on the basis of the steps S11 to S14, collecting multi-modal data of different factories, and pre-training corresponding fire emergency models. According to the invention, by synchronously collecting and fusing the multi-modal data of the treatment plant area, the corresponding industrial fire-fighting emergency scheme of the plant area is generated, and the fire-fighting emergency response time is remarkably shortened.

The invention discloses a fire-fighting early warning system and method based on artificial intelligence, and relates to the technical field of intelligent fire-fighting early warning, and the system comprises an environment sensing module which comprises a multispectral sensor, a temperature sensor, a smoke sensor and a high-definition camera, and is used for collecting fire characteristic spectral data and transmitting the data to a data analysis module; the data analysis module comprises an edge calculation unit and a data fusion unit, and is used for performing feature extraction and anomaly detection on the fire feature spectrum data through a deep learning model to generate a fire risk assessment result; the early warning decision module comprises a risk assessment unit, an early warning grading unit and a communication unit, and is used for determining an early warning grade according to a fire risk assessment result and triggering equipment of the execution feedback module; and the execution feedback module comprises a spraying system, an emergency lighting system and an alarm and is used for executing fire emergency measures according to the instruction of the early warning decision module. According to the invention, comprehensive improvement of fire prevention and control efficiency is realized through cooperative work of multiple modules.

The invention discloses a multistage fire-fighting and intelligent heat management combined system and method for an energy storage system, a battery cluster is provided with a single fire extinguishing nozzle, a composite detector monitors the state of the battery cluster in real time, and when any battery cluster meets a spraying condition, the fire extinguishing nozzle is triggered to spray a fire extinguishing material; the internal state of the energy storage cabin is monitored in real time through a composite detector, and when the interior of the energy storage cabin meets the spraying condition, a spraying nozzle is automatically started to continuously spray for preset spraying time; according to the centralized liquid cooling unit, a dual-redundancy compressor is combined with an ethylene glycol aqueous solution circulating system to conduct dynamic flow regulation and control, the opening degree of an electromagnetic valve of the dual-redundancy compressor is dynamically adjusted through a composite detector, and the internal temperature of an energy storage cabin is controlled; dynamically distributing flow according to the energy storage time period by adopting an intelligent temperature control algorithm; after the multi-stage fire-fighting module acts, the centralized liquid cooling unit is started for cooling; when the temperature detected by the composite detector exceeds a high-temperature threshold value, the fan is started for air replacement, and the multi-stage fire-fighting module is triggered to act.

The invention discloses a thermal disaster alarm system and electronic equipment for a lithium ion battery energy storage power station, and relates to the technical field of thermal disaster monitoring, and the system comprises a power station dividing module which is used for dividing the lithium ion battery energy storage power station into M thermal disaster risk control monitoring areas; the node configuration module is used for configuring M edge thermal runaway prediction nodes; the thermal runaway suppression decision module is used for outputting a real-time thermal runaway prevention and control strategy; the decision generation module is used for generating a real-time fire extinguishing risk control decision according to the evolution result; the risk level mapping module is used for obtaining a real-time risk level; and the alarm signal sending module is used for sending the thermal disaster alarm signal to a power station operation and maintenance center. The technical problem that the lithium ion battery energy storage power station fire hazard risk early warning and prevention and control response speed is low is solved, and the technical effects of accurate thermal runaway risk prediction and timely prevention and control based on edge node real-time prediction and cloud decision optimization and improvement of power station fire hazard early warning and emergency response efficiency are achieved.

The invention provides a power transmission line forest fire risk assessment method and system driven by a flame combustion model, relates to the technical field of electrical engineering, establishes a multi-source data fusion fire point monitoring system, and comprehensively utilizes three monitoring modes of satellite remote sensing, a ground monitoring station and unmanned aerial vehicle inspection to collect fire point data information. According to the method, multi-source cooperative complementary monitoring is realized, the satellite remote sensing, the ground monitoring station and the unmanned aerial vehicle are comprehensively used for routing inspection, the limitation of a traditional monitoring means is overcome, the satellite remote sensing coverage is wide, and large-area fire points can be found in time; a ground monitoring station carries out precise monitoring in a close-range and complex terrain area; the unmanned aerial vehicle is flexible in routing inspection and high in resolution, low-altitude detailed reconnaissance can be realized, and the three parts cooperate to comprehensively improve the monitoring accuracy and reduce missing report and false report.

The invention relates to the technical field of fire alarm, and particularly discloses a distributed power distribution cabinet fire alarm system based on multi-source data fusion, which adopts a time sequence analysis technology based on deep learning to carry out time sequence modeling on the temperature in a power distribution cabinet, the environment temperature and load current data so as to capture the time sequence change trend of the temperature, the environment temperature and the load current data; based on the power distribution cabinet environment temperature time sequence characteristics, environment interference filtering is carried out on the cabinet internal temperature time sequence characteristics to obtain pure characterization of the cabinet internal temperature time sequence change mode, and then time sequence interaction response excitation analysis is carried out on the power distribution cabinet internal temperature time sequence change characteristics after load current and environment interference filtering. Therefore, the intelligent early warning prompt of the fire risk is realized based on the time sequence correlation response mode between the load and the temperature of the power distribution cabinet. Therefore, the accuracy of fire alarm can be effectively improved, the situations of false alarm and missing alarm are reduced, and the safe operation of the power distribution cabinet is ensured.

The invention discloses a forest fire dynamic monitoring method and system based on multi-source data fusion, and relates to the technical field of fire dynamic monitoring, and the method comprises the steps: carrying out the calculation of the fire occurrence probability of a forest region, and outputting a dynamic risk thermodynamic diagram; non-uniform deployment of integrated sensing units is carried out to form a ground fire sensing network; large-range abnormal hot spot distribution is obtained; performing abnormal hot spot cooperative verification, and outputting a real-time fire risk alarm; matching a high-credibility unmanned aerial vehicle according to the fire risk alarm to execute real-time fire image acquisition; fire spreading modeling is carried out in combination with real-time meteorological data, and a fire spreading trend prediction result is obtained; and executing high-response fire extinguishing management and control of the forest area. According to the invention, the technical problem of low fire extinguishing management and control efficiency caused by inaccurate forest fire monitoring risk in the prior art is solved, and the technical effects of realizing dynamic monitoring and efficient management and control of forest fire and improving the efficiency and timeliness of fire extinguishing management and control are achieved.

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 discloses a digital twinning-based police cloud Internet of Things equipment three-dimensional scene building method and system, and belongs to the technical field of digital twinning, and the method comprises the steps: laying sensing equipment in a target region; establishing a three-dimensional entity model of the target area in the cloud server, laying a corresponding virtual model in the entity model based on the installation information of the sensing equipment, and accessing data from the sensing equipment into the virtual model to generate a digital twin model; the first troubleshooting unit analyzes the scene video and the environment data collected by the sensing equipment in real time, and if an environment risk is detected, a disposal scheme is generated; the second troubleshooting unit processes the scene video into a multi-section video, analyzes the multi-section video in combination with environmental data, and determines suspicious personnel and risk degree of the target area; and screening suspicious persons whose risk degrees are greater than a first threshold value as high-risk risks. According to the invention, safety management personnel can pay attention to and deal with high risks in time, and the emergency response speed and accuracy are effectively improved.

The invention discloses an intelligent fire evacuation system and method based on dynamic environment perception, and relates to the technical field of fire fighting and safety protection, and the method comprises the steps: collecting real-time data in a fire scene; dividing the evacuation area into a plurality of grid units by using a two-dimensional rasterization technology to form a real-time updated environment map; in combination with dynamic modeling data, an optimal evacuation path is generated by using an improved path planning algorithm; an evacuation path is transmitted to trapped people in real time in a visual and auditory mode, and the visual and auditory mode comprises the use of a dynamic indicator lamp, a marker lamp, an electronic screen and a voice broadcast system; the guidance content is adjusted in combination with real-time data, and the evacuation efficiency is optimized; fire fighting equipment is linked, and the evacuation path environment is optimized; and the evacuation state is monitored through a dynamic data feedback mechanism. According to the invention, an evacuation path is calculated and updated in real time by integrating a sensor network, fire scene dynamic modeling and an intelligent path planning algorithm, and a safe and efficient evacuation scheme is provided for trapped people in a fire.

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 invention discloses a multi-stage power transmission line forest fire identification early warning analysis method and system, and relates to the field of power transmission line forest fire early warning. Power transmission line region division information is acquired, a target key patrol region is extracted, and a forest fire risk level of the target key patrol region is calculated; acquiring a forest fire alarm preset threshold value of the target key patrol area; according to the forest fire risk level, determining an adjustment amplitude of a forest fire alarm preset threshold, and obtaining a parameter interval of the forest fire alarm preset threshold; environment information of the target key patrol area is identified, a specific value in a parameter interval is determined according to the environment information, and a preliminary threshold value is obtained; and identifying the forest fire development situation of the target key patrol area, determining an adjustment coefficient according to the forest fire development situation, and endowing the adjustment coefficient with a preliminary threshold to obtain an alarm threshold. Multi-stage identification early warning is realized by adopting a dynamic threshold value mode, real-time adjustment is performed according to environment change, false alarm and missing alarm are greatly reduced, and early warning accuracy is improved.

The invention relates to the technical field of energy storage power station fire monitoring, and discloses a fire detection and early warning method and system. The method comprises the steps that real-time monitoring signals are acquired and preprocessed, and after a preliminary abnormal index is determined, changes of temperature and gas concentration are analyzed and evaluated to obtain a fire risk pre-judgment score; if the preset risk threshold value is exceeded, potential fire source area distribution is judged according to a pre-established dynamic fire feature database, fire source positioning coordinates are obtained, and consistency is verified; abnormal points of the temperature and the gas concentration are classified, a fire initial symptom set is determined, the dynamic fire feature database is updated, judgment is conducted again, and the accuracy of early warning signals is determined; and if the accuracy is higher than the preset accuracy threshold, generating a comprehensive risk assessment report, obtaining a fire identification result, extracting an optimized feature vector from the fire identification result, and generating a fire source region distribution map and an abnormal early warning signal. According to the invention, fire source positioning can be realized, the false alarm and missing alarm rate is obviously reduced, and the emergency response speed is improved.

The invention belongs to the technical field of safety protection, and particularly relates to an energy storage equipment safety protection early warning system applied to an industrial park, which adopts a module-cluster-cabin three-level architecture to realize real-time monitoring and dynamic evaluation of thermal runaway risk, the architecture integrates multi-source data, and when the risk exceeds a preset threshold value, the energy storage equipment safety protection early warning system is used for early warning the thermal runaway risk. The system automatically starts graded fire extinguishing measures and a fire-fighting linkage mechanism, and meanwhile, secondary analysis is carried out on an early warning result through a cloud platform to optimize model parameters, so that the accuracy and the efficiency of the system are ensured; the system constructs an integrated monitoring, response and self-correction prevention and control process, and effectively overcomes the problems of early warning delay, high false alarm rate, single protection means, data islanding and the like in the prior art through a multi-parameter fusion early warning, accurate recognition, graded fire extinguishing and intelligent linkage mechanism. And a solid guarantee is provided for fire safety in a high-density energy storage environment.