609 results about "Fire hazard" patented technology

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 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 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 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, 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 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 discloses a fire early warning method and device based on sensing parameter fusion and a medium, belongs to the technical field of fire prediction, and is used for solving the technical problems that existing fire early prediction is relatively difficult, relevance among sensing data is easily ignored, and fire early warning and recognition of fire early features are not facilitated. The method comprises the following steps: monitoring an environment state in an indoor building in real time to obtain initial environment parameter data; performing environmental parameter numerical value change calculation on the initial environmental parameter data in the previous time node and the current environmental parameter data in the current time node; performing dynamic analysis on a plurality of environmental parameters in the environmental parameter change rate under related time sequence relevance to determine abnormal environmental parameter information; performing positioning processing on the abnormal environment parameter information in a fire hazard local area to obtain an abnormal environment subarea; and performing real-time smoke detection processing related to smoke sensing characteristics on the abnormal environment partition to obtain a smoke detection result.

The invention discloses a scene preprocessing method for fire simulation, and relates to the technical field of building fire simulation, and the method comprises the steps: calculating a three-dimensional thermal weight through employing a fire propagation dynamics model, and generating a fire dangerous region thermal diagram; through a tensor field generation algorithm, fire behavior main diffusion direction and each axial anisotropic parameter are extracted, and fire behavior grid parameters are generated; through a hybrid grid generator, performing hexahedron dominant grid construction and increasing the grid density of a fire behavior dangerous area to obtain a fire behavior optimization hybrid grid; performing Reynolds number verification and boundary layer matching on the fire behavior optimization hybrid grid through a fire behavior feature adaptive algorithm, and performing iterative correction to generate a fire simulation correction grid; according to the invention, through the hybrid grid generator, the self-adaptive distribution of the grid density of the fire hazard area and the smooth connection of the transition area are realized, so that the grid distribution of the fire hazard area is accurately matched with the fire spreading characteristics.

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

The invention provides a chamber fire flashover prediction method, device and system and a training method of a chamber fire flashover prediction model. The chamber fire flashover prediction method comprises the following steps: acquiring a temperature data sequence acquired by using a wearable temperature sensor and a radiation heat flow data sequence acquired by using a wearable radiation heat flow sensor; respectively inputting the temperature data sequence and the radiation heat flow data sequence into a chamber fire hazard flashover prediction model, and outputting predicted temperature data and predicted radiation heat flow data at a future moment; and when the predicted temperature data is greater than or equal to the flashover temperature critical value or the predicted radiation heat flow data is greater than or equal to the flashover radiation heat flow critical value, sending an alarm signal.

The invention relates to the technical field of fire fighting, and discloses an intelligent control system for a fireproof electronic display screen, and the system comprises a temperature monitoring module which collects the temperature data of each peripheral region in real time through a sensor array which is uniformly distributed in a region where the number of through holes at the back of a screen body exceeds a preset number; the smoke monitoring module collects the environment smoke concentration in real time through sensors installed at the positions of a plurality of through holes in the back of the screen body. The probe monitoring module acquires monitoring videos outside the screen body and at the perforation by means of a high-definition probe at the edge of the screen body or at the perforation position; the multi-source data analysis module establishes a three-dimensional temperature field model, a smoke diffusion track and fire source characteristic data based on temperature, smoke concentration and internal and external monitoring videos; and the intelligent control module triggers the fire-fighting linkage system according to the data and the multi-stage early warning rule, and intelligently controls the display screen at the same time. The intelligent level of fire prevention of the electronic display screen is improved, fire hazards can be automatically and efficiently dealt with without close attention of workers, and the management efficiency is improved.

The invention discloses a community fire risk dynamic assessment method and system based on AI prediction, and belongs to the technical field of fire prevention and control, and the method comprises the steps: S1, collecting and preprocessing multi-source data of a community, and constructing a comprehensive risk factor database; s2, constructing a risk prediction model, taking the preprocessed multi-source data as input, identifying a key risk factor, and outputting a fire risk probability; s3, calculating the dynamic weight of each index by fusing the static expert experience and the real-time risk contribution degree; s4, based on the dynamic weight and the risk probability, calculating a comprehensive risk value and dividing risk levels; s5, constructing a prevention and control strategy library based on the risk level and the key risk factor; according to the community fire risk dynamic assessment method and system based on AI prediction, the change rule of the fire risk factors in the community can be captured in real time, potential fire hazards can be predicted in advance, and a targeted prevention and control strategy can be automatically generated.

The invention provides a smoke analysis AI system based on a quantum field theory tensor network, the algorithm is provided with a multi-camera fusion module, a quantum state coding module, a dynamic Hamiltonian construction module, a tensor network evolution module and a chaos detection module, and by monitoring a smoke diffusion Lyapunov index in real time, the sudden change from smoke to fire behavior is warned in advance; according to the algorithm, a quantum multi-body system theory and computer vision are combined, smoke diffusion is modeled into a phase change process of a quantum spin chain, high-dimensional quantum state information is compressed by using a tensor network, and a mutation behavior of a chaotic edge is detected in combination with a Lyapunov exponent. The method solves the problem of early fire hazard false alarm under the complex operation condition of the transformer substation in a traditional method, improves the initiative and accuracy of monitoring and prevention of the extremely early smoke fire hazard of the transformer substation, provides more reliable guarantee for the safe operation of the transformer substation, and has wide application prospects and important practical significance.

The invention discloses a fire early warning cooperative system composed of a thermal imaging double-spectrum intelligent ball, an intelligent analysis platform and a fire extinguishing unmanned aerial vehicle, which is applied to the technical field of fire early warning, and comprises the thermal imaging double-spectrum intelligent ball, an integrated visible light sensor and a thermal imaging sensor, and a double-spectrum fusion monitoring technology is adopted to carry out omnibearing and all-weather monitoring; the intelligent analysis platform simulates a fire spreading path based on a fire spreading prediction model of a fluid dynamics and machine learning algorithm, generates a three-dimensional fire danger thermodynamic diagram, and completes a whole-process response from regional early warning to command center decision making to unmanned aerial vehicle cluster task distribution through a multi-level early warning mechanism; the fire extinguishing unmanned aerial vehicle adopts a multi-model collaborative operation system to meet the fire extinguishing requirements of different fire scenes. According to the invention, in a complex and changeable fire scene, the fire hazard can be quickly and accurately identified, and the efficient disposal of fire extinguishing operation is realized.

The invention relates to the technical field of intelligent safety monitoring, and discloses a fire hazard identification method and system based on a multi-modal large model. According to the method, standardized preprocessing is carried out on field multi-modal data through a unified timestamp system and a quality evaluation mechanism; vision, text and sensor features are extracted and aligned by using a pre-trained deep learning model, adaptive feature fusion is realized by using an attention mechanism of scene perception, hidden danger recognition and confidence evaluation are completed through a hierarchical classification network and an uncertainty quantification method, and structured rectification suggestions are generated based on template matching and multi-target optimization. And progressive model updating of experience playback and collaborative management of RESTful API (Application Program Interface) are supported. According to the invention, the accuracy and efficiency of fire hazard identification can be effectively improved, and intelligent decision support is provided for safety management.

The invention discloses an intelligent fire-fighting hidden danger identification method based on multi-modal fusion, and the method comprises the steps: collecting multi-source heterogeneous data in a building environment, and carrying out the unified processing of the data, and obtaining standardized multi-modal original input data; event anchor point detection is carried out on the multi-modal input, key events are extracted, and a corresponding multi-modal event sequence is generated; constructing an event graph containing time and causal edges, and outputting an alignment event stream by using a space-time coupling causal alignment module; importing BIM and air duct structure information to establish a spatial topological graph, and fusing event streams to generate spatial constraint features; constructing a self-evolution semantic decision map based on the fusion features, and dynamically adjusting node weights and edge connection relationships; and carrying out hidden danger identification and grade division on the real-time data by using the decision map, and outputting an early warning signal and positioning information. According to the method, high-precision identification and intelligent early warning of fire-fighting hidden dangers are realized through space-time causal alignment, spatial topology fusion and self-evolution semantic decision of multi-modal data.

The invention discloses a method and a system for realizing intelligent prevention and control of an electrical fire in an enclosed space, relates to the technical field of intelligent monitoring and prevention and control of fire, and aims to solve the problems of insufficient environmental protection property, hysteresis quality and intelligence in the prevention and control of the electrical fire in the enclosed space. The method comprises the following steps: acquiring multi-modal sensing data and preprocessing the multi-modal sensing data to generate a feature vector; based on the vector, early warning of the fire hazard is realized through a warning model; when early warning is triggered, the positioning model is used for accurately positioning a fire source and evaluating the risk; according to an evaluation result, determining an optimal fire extinguishing strategy through a decision model; and the fire-extinguishing execution module is driven to release fire-extinguishing agents. The system comprises a multi-mode sensing module, a data processing and early warning module, a positioning and evaluating module, a fire extinguishing decision module and a fire extinguishing execution module. According to the technical scheme, full-chain intelligent and automatic prevention and control can be achieved, the intelligent, precise and environment-friendly efficiency of fire prevention and control is improved, and the fire risk and loss are reduced.

Multispectral imaging and related techniques are provided to detect fire hazards and alerts that are false positive indicators of the fire hazards. The embodiments are directed to systems for identifying pre-fire hazards using thermal images, visual images, and different rules corresponding to temperature configuration levels. The embodiment are also directed to identifying false positive indicators of fire hazards using regions of interest in the thermal and visual images, identifying objects that have hot components which may generate a false positive indicator for a fire hazard, and identifying sun reflections which may generate a false positive indicator for the fire hazard.

The present disclosure relates to a method of fire hazard plant control of a processing plant (100), wherein the processing plant (100) is provided with a transporting arrangement (101) for transporting items to a detection zone (IZ) and a classification arrangement (102) configured to classify items present in said detection zone based on at least one feature of the item and optionally to sort items based on item classification. The method comprises a step of detecting at least one item as a fire hazard (FH). The method comprises a step of initiating, in response to said fire hazard detection, a fire hazard response action. A control unit (103) and a processing plant (100) is also provided.

The invention provides a fire safety risk assessment and intelligent decision-making method based on big data analysis, and the method comprises the steps: carrying out the analysis of a risk monitoring request when a target platform receives the risk monitoring request, obtaining the information of monitoring equipment, obtaining the multi-source fire monitoring data of the equipment, generating a fire risk assessment result, and carrying out the analysis of the fire risk assessment result. And displaying an evaluation report interface containing the result and the risk threshold configuration control. And storing the evaluation result according to the matched risk threshold information. If the intelligent fire-fighting system device obtains new fire-fighting monitoring data, matching processing is carried out according to the stored evaluation result, and if the matching result shows that the risk level is higher than a preset threshold value, a protection assembly of the control device executes corresponding decision-making operation. The system can timely find fire hazards and take effective measures, improves the fire safety management level, reduces the fire risk, and guarantees the personnel and property safety.

The invention relates to the technical field of unmanned aerial vehicles, and particularly discloses an unmanned aerial vehicle take-off and landing cabin which is characterized in that cabin doors are symmetrically arranged above a cabin body and connected with the two sides of the cabin body, and the two cabin doors are closed at the top of the cabin body to cover the cabin body; a stop two-dimensional code is arranged on the top and / or the side surface of the cabin door The parking platform is arranged in the cabin body, and a lifting mechanism is arranged at the bottom of the cabin body; a guiding two-dimensional code is arranged in the cabin body, and the express cabinet and the storage room are connected to the lower portion of the cabin body. The management system carries out real-time monitoring and data statistics on the use state, energy supply and unmanned aerial vehicle access records of the take-off and landing cabin. The cabin body is made of a high-strength waterproof material and a sealing structure and is matched with a cabin door closing design, so that severe environments such as wind, rain and dust can be effectively resisted, and the unmanned aerial vehicle is prevented from being interfered by the environment in the taking-off, landing and parking processes; smoke, temperature and vibration sensors are integrated, fire hazards, temperature abnormity and illegal invasion are detected in real time, alarm and emergency measures are automatically triggered when abnormity occurs, and the safety of equipment and the unmanned aerial vehicle is guaranteed.

The invention relates to a cable tunnel bridge fire prediction method and device, electronic equipment and a storage medium. The cable tunnel bridge fire hazard prediction method comprises the following steps: establishing a cable tunnel bridge fire hazard numerical simulation model, establishing a cable tunnel bridge fire hazard simulation model based on FDS software, obtaining key parameters such as heat release rate, temperature distribution and flame spread boundary under different working conditions, and constructing a multi-time sequence sample data set; performing state discretization on continuous variables such as temperature and a spreading range, and estimating a node prior probability in combination with a statistical frequency; constructing a dynamic Bayesian network topological structure according to a causal relationship among a heat source, temperature and spread, and setting a cross-time slice node dependence path to realize time sequence modeling; inputting a training sample and performing parameter learning to generate a conditional probability table; in the prediction stage, multi-step reasoning is achieved through forward propagation, flame spreading range probability distribution and interval estimation of multiple time steps in the future are obtained, and the specific position where the flame arrives is predicted.

The invention relates to the technical field of electrical fire intelligent early warning, in particular to an electrical fire intelligent early warning and handling method based on multi-source data fusion, which comprises a data acquisition system, and the output end of the data acquisition system is connected with a multi-source data fusion platform. The output end of the multi-source data fusion platform is provided with an intelligent analysis and early warning system, the output end of the intelligent analysis and early warning system is provided with a disposal decision system, the output end of the disposal decision system is provided with a feedback optimization system, and the multi-source data fusion platform, the intelligent analysis and early warning system, the disposal decision system and the feedback optimization system are integrated. The processing decision-making system and the feedback optimization system are both in communication connection with the data acquisition system. According to the invention, through the data acquisition-fusion analysis-intelligent early warning-decision processing-feedback optimization closed-loop system, the whole-process intelligent management of electrical fire prevention and control is realized, the cooperation work of each component breaks through the limitation of single parameter monitoring, and the three-dimensional acquisition of video data provides multi-dimensional data support for fire hazard identification.

The invention discloses a fire safety early warning method and system based on wireless networking, and relates to the technical field of fire safety early warning. According to the fire safety early warning method based on the wireless networking, electric power and environment monitoring data of a plurality of areas in the wireless networking of a shopping mall are acquired in real time, and an electric fire danger induction index and a fire danger environment disturbance index are analyzed; meanwhile, shopping mall monitoring image angle sequence data of each area is obtained, a visual potential safety hazard index is analyzed based on a pre-trained fire safety identification model, the safety condition of each area is comprehensively evaluated according to an electrical fire danger induction index and a fire danger environment disturbance index, and finally a comprehensive safety perception index of each area is generated. According to the invention, fire safety early warning is carried out on the corresponding area through the comprehensive safety perception index of each area, so that fire hazards in multi-area and complex layout environments such as shopping malls and the like are identified and positioned in real time, the accuracy of fire safety early warning is effectively improved, and the overall fire safety level is improved.

The invention discloses a phosphorus-nitrogen synergistic flame-retardant benzoxazine / epoxy resin composite material and a preparation method thereof. Firstly, a phenolic compound and DOPO react to generate an intermediate product, and then the intermediate product, an amine compound and paraformaldehyde are subjected to a dehydration condensation reaction to generate the phosphorus-nitrogen synergistic flame retardant benzoxazine. The phosphorus-nitrogen synergistic flame-retardant benzoxazine is added into epoxy resin for thermocuring to obtain the composite material. The prepared benzoxazine flame retardant contains phosphorus and nitrogen elements, the nitrogen-based flame retardant has the advantages of low pollution, low smoke, good compatibility with EP and the like, and a quenching effect, a dilution effect and a carbon layer barrier effect can be generated in the combustion process to prevent further combustion of flames. The composite material has better flame retardant property, can effectively reduce the risk of fire hazard caused by overheating or short circuit of equipment, and ensures the safe and stable operation of energy equipment.

The invention discloses a fire alarm inspection system and method based on big data, and relates to the technical field of intelligent fire protection and public safety. The problems that a traditional fire fighting system is serious in data island phenomenon, single in risk perception dimension, static and rigid in early warning model, high in false alarm rate and low in inspection efficiency are solved. According to the method, a fire-fighting theme database is constructed, and based on a multi-dimensional dynamic analysis model, comprehensive analysis of space aggregation risks, time sequence evolution trends and equipment linkage logic is realized, and dynamic fire danger hidden danger indexes are calculated; a key monitoring object is positioned through a visual thermodynamic diagram, grading alarm is triggered, differentiated inspection tasks and an optimal route are generated based on the intelligence, and emergency disposal linkage is supported; closed-loop management from global risk perception, intelligent analysis and early warning to precise inspection treatment is realized, and early recognition and early warning accuracy and resource scheduling efficiency of fire hazards are remarkably improved.

The invention discloses an intelligent control system and method for an active fire extinguishing device of a high-voltage switch cabinet, and the system comprises the steps: collecting multi-source heterogeneous environmental parameters in the cabinet, carrying out the LoRa wireless transmission, introducing a dynamic thermal drift compensation mechanism based on an Arrhenius model, and carrying out the zero drift correction and sensitivity dual compensation of the original data of a sensor. A high-fidelity physical monitoring value is reconstructed through high-order polynomial nonlinear mapping, and the risk of false alarm and missing alarm caused by thermal-sensitive coupling is eliminated. After a fire hazard is recognized, the block type fire extinguishing device is excited in a linkage mode, inert gas and free radical groups are instantaneously released through an oxidation-reduction reaction to conduct total flooding type suppression, negative feedback adjustment and automatic termination of the reaction rate are achieved according to the environment temperature change, and therefore accurate early warning is ensured, and meanwhile the safety of the fire hazard is improved. Efficient blocking and reset safety protection of live-line fire disasters are realized.

The invention provides a multi-mode linkage control and remote state monitoring system for fire-fighting equipment. The multi-mode linkage control and remote state monitoring system is characterized in that multi-mode data of the fire-fighting equipment is acquired in real time through a sensor array; building a building digital twin model based on the three-dimensional point cloud data and a BIM model, training in combination with a generative adversarial network GANs, performing analog simulation on the fire development trend, and performing grade division and risk assessment on the fire; formulating a dynamic linkage rule of the fire-fighting equipment, and respectively controlling linkage of different fire-fighting equipment according to different risk degrees of the fire and states of the fire-fighting equipment; optimizing the linkage rule of the fire fighting equipment based on a reinforcement learning algorithm; the state of the fire-fighting equipment is remotely monitored in real time, and timely processing is performed if the state is judged to be abnormal. The fire hazard identification accuracy is improved through multi-modal data linkage, the processing efficiency is improved based on digital twin dynamic mapping, the linkage rule is intelligently adjusted according to the fire hazard and the equipment state, and multi-modal linkage control and remote state monitoring of the fire-fighting equipment are realized.