1965 results about "Fire safety" patented technology

The invention discloses an energy storage power station fire safety monitoring system based on a big data Internet of Things, and relates to the technical field of fire safety monitoring. A multi-dimensional sensor network is adopted to monitor parameters in real time, a multi-dimensional parameter model under normal working conditions is established based on a multivariable Gaussian distribution model, and the real-time monitoring of the parameters is realized. In the early stage of thermal runaway, abnormity is recognized through the Mahalanobis distance, an early warning signal can be triggered immediately when the abnormity occurs, the defects that in a traditional method, the monitoring means is single, and response lags behind are overcome, fire early warning is more accurate and timely, and the concentration of harmful gas is monitored in real time in combination with a gas detection module; sensor data and gas concentration data are fused through Bayesian inference, the fire risk is evaluated more comprehensively, the comprehensiveness of fire risk evaluation is remarkably improved, a fire area is positioned through a fire source positioning algorithm, and the fire extinguishing range is adjusted in real time according to the fire risk level and the dynamic change of fire source diffusion.

The invention discloses an AFIS intelligent fire-fighting monitoring system based on the Internet of Things technology, and relates to the technical field of fire-fighting safety, and the AFIS intelligent fire-fighting monitoring system comprises a front-end module, a central monitoring module, a data analysis module, an early warning module and a fault diagnosis module. The front-end module is used for monitoring fire-related environment data and fire-fighting equipment states through a plurality of sensor nodes; installing and deploying various types of sensor nodes in the key area; the collected original data is subjected to primary processing, including data cleaning, denoising, standardization and the like, so that the accuracy and reliability of the data are ensured; according to the invention, through integration of various sensor nodes, real-time monitoring of environment parameters of a key area and the state of fire-fighting equipment is realized, so that the fire prevention and response efficiency is obviously improved, the system can wirelessly transmit data by using the Internet of Things technology, the complexity of installation and maintenance is reduced, and the monitoring range is expanded.

The invention belongs to the technical field of fire safety, and discloses a fire-fighting equipment detection early warning method and system based on a visual camera, and the method comprises the following steps: obtaining multi-focal-length image frames of a fixed spray head and a fire extinguisher edge region, judging the continuous change of gray scale in the continuous image frames, and carrying out the detection early warning of the fire-fighting equipment based on the visual camera. Comparing the gray level distribution difference under the long focus and the short focus, identifying the dynamic trajectory of the contour line angular point of the fire-fighting equipment association structure, analyzing the delay of the function action response, marking as an abnormal region, and generating a fire-fighting equipment collaborative early warning information set; according to the invention, by extracting and analyzing the multi-focal-length image frames of the edge areas of important facilities such as a fixed spray head and a fire extinguisher, fine changes of the fire-fighting facilities under differentiated focal lengths can be captured, working states and potential faults of the fire-fighting facilities can be judged, functional abnormalities caused by equipment aging or damage can be recognized in advance, early warning can be carried out in real time, and the safety of the fire-fighting facilities can be improved. And a higher guarantee level is brought to fire safety.

The present invention discloses an intelligent control system for fire safety management, which relates to the field of safety management technology, including a multimodal sensor module and a digital twin modeling module, wherein the multimodal sensor module and the digital twin modeling module are connected and communicated through a data transmission network, and the digital twin modeling module includes an edge computing node in the region and a central data processing server, and constructs a three-dimensional digital twin model of a building and its environment by receiving and fusing data from the multimodal sensor module and utilizing real-time data processing and model updating algorithms. The present invention realizes the function of constructing and continuously updating a digital twin model of a building and its environment by designing a multimodal sensor module and a digital twin modeling module, and reflects the internal structure of the building, environmental parameters and personnel flow status in real time, solves the problems of data dispersion, lack of real-time performance and static model in traditional systems, and ensures the accuracy and timeliness of fire risk assessment.

The invention belongs to the technical field of fire sensing and detection, and provides a fire sensing method and related device for a double-layer cable tunnel structure, and the method comprises the steps: respectively collecting the temperature, smoke and image data of a double-layer cable tunnel, and carrying out the double-layer data linkage; respectively constructing a temperature distribution model, a smoke diffusion model and an image target detection model according to the collected temperature, smoke and image data; and performing temperature, smoke and image multi-modal data fusion based on the temperature distribution model, the smoke diffusion model and the image target detection model, and when the fused data meets a set fire threshold, determining that a fire occurs. Through the fire sensing and verification method aiming at the characteristics of the double-layer cable tunnel, scientific and reliable decision support is provided for improving tunnel fire safety management, the safety of personnel and equipment in the tunnel is ensured, and meanwhile, the standardization process of the fire sensing technology is promoted.

The invention relates to the technical field of fire safety, and provides a smart campus multi-dimensional fire safety monitoring method and system. The method comprises the following steps: collecting multi-source security data of a campus building; extracting features based on a preset risk dimension, and generating a fire risk feature vector; constructing a risk model according to historical fire cases, and scoring the feature vectors to obtain a real-time risk score; when the score exceeds a dynamic threshold value, triggering a grading early warning instruction; an emergency scheme is generated according to the early warning, and fire fighting equipment and a personnel evacuation system are linked to execute operation. The technical problem that an existing campus fire safety monitoring system lacks a multi-dimensional, real-time and comprehensive risk assessment means and is difficult to accurately judge different fire risks and deal with the fire risks in real time is solved, and accurate fire risk early warning and efficient emergency response are achieved through multi-source data fusion and intelligent analysis. And the campus fire safety management level is improved.

The invention provides a method for constructing a differential diffusion small flame model for buoyancy-driven plume, and the method is applied and expanded to a pool fire combustion process in the field of fire safety. According to the method, the buoyancy driven plume is solved by using the small flame model, the solving number of component equations of the buoyancy plume is greatly reduced through coordinate conversion dimensionality reduction processing, and the main characteristics of the buoyancy plume can be reproduced with relatively high precision and a faster calculation rate. According to the method, high-precision numerical solution of buoyancy plume is realized, and pool fire combustion of gas fuel and liquid fuel and numerical solution of all plume driven by buoyancy can be processed.

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 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 an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

The invention discloses an intelligent AI detection method based on an infrared thermogram and automobile diagnosis, according to the technical scheme, the fireproof safety detection efficiency of an electric automobile is remarkably improved through multi-modal data fusion and intelligent analysis, and a multi-dimensional data basis is constructed through synchronous acquisition of dual-band infrared and vehicle operation parameters; secondly, a dynamic correlation model realizes accurate triggering of abnormal current on heat map enhancement, partition gradient processing is combined with dynamic threshold adjustment to optimize detection sensitivity for thermal characteristics of different parts, three-dimensional thermal field reconstruction breaks through surface detection limitation, internal potential overheat points can be identified, and the detection sensitivity is improved; and finally, closed-loop diagnosis is formed through case library intelligent matching, so that the fault positioning precision reaches a component level, and the whole scheme is coordinated through a multi-level technology, so that the problems of low infrared thermal image contrast and relatively poor detail distinguishing capability due to the fact that electric vehicle fireproof safety detection is carried out only by depending on thermal infrared images in the prior art are solved.

The invention relates to the technical field of highway tunnel safety monitoring, and discloses a highway tunnel intelligent fire safety early warning system, which comprises a data acquisition and preprocessing module used for acquiring video monitoring data, environment sensor data and vehicle positioning data in a tunnel in real time; the dynamic risk assessment module is used for constructing a risk field model based on vehicle trajectory and smoke diffusion space-time coupling; the emergency resource elastic adaptation module is used for automatically adjusting the induction screen and the fire fighting equipment according to the risk intensity; according to the method, the limitation of traditional threshold alarm is avoided, fire early warning nodes are greatly advanced through multi-source data dynamic fusion, long-term stability of a monitoring benchmark is guaranteed by utilizing an optical flow compensation and pollution self-repairing mechanism, and the self-adaptive migration of a calculation task is realized by utilizing the optical flow compensation and pollution self-repairing mechanism. And the initiative and reliability of tunnel safety prevention and control are obviously improved.

The invention discloses a fire safety detection system based on edge-cloud collaboration, and belongs to the technical field of fire safety, the fire safety detection system comprises an edge end and a cloud end, the edge end comprises a sensor module and a fire response module, the cloud end comprises a fire analysis module and an auxiliary traceability module, and the sensor module is connected with the auxiliary traceability module. Comprising a smoke sensor, a light scattering sensor and an infrared sensor and is used for receiving field data in real time, transmitting the field data to the edge end for judgment and transmitting a control signal into the fire-fighting response module based on a judgment result of the edge end. According to the fire safety detection system based on side cloud cooperation, on the basis of the side cloud cooperation technology, the edge end is responsible for field monitoring and emergency alarm, the cloud end is responsible for data integration, analysis, information transmission and auxiliary alarm, data support is provided for subsequent fire decision making, meanwhile, the fire spreading trend and the fire grade are analyzed and predicted in real time, and the fire safety detection efficiency is improved. And the fire treatment efficiency can be improved.

The invention belongs to the technical field of fire monitoring, and particularly relates to an early warning system and method based on fire safety. According to the method, the multi-modal environment data of the monitoring area is comprehensively analyzed, a confidence evaluation function and a dynamic weight distribution mechanism are introduced, the accuracy and reliability of early warning are improved, and the early warning accuracy and reliability are improved by calculating and analyzing the characteristic parameters such as the forward change rate and the historical fire occurrence probability of each sub-environment data. The confidence of the sub-environment data is obtained by combining a confidence evaluation function, an important basis is provided for subsequent data processing, fine processing of the sub-environment data is realized through independent analysis of the sub-environment data, the sensitivity and accuracy of early warning are further improved, and the accuracy of early warning is improved through weighted fusion of the sub-environment data. According to the method, the fire safety early warning index is obtained, the risk grade of the monitoring area is evaluated according to the index, the corresponding early warning signal is output, and the real-time early warning of the fire safety state of the monitoring area is realized.

The invention discloses a multi-mode sensing fire detection method based on a Transform time sequence feature fusion model, and relates to the technical field of fire safety monitoring, the method comprises a flame detector combining video, multi-band infrared and ultraviolet sensing technologies, the detector is mainly composed of an image sensor, four infrared sensors and one ultraviolet sensor, the image sensor is mainly used for sensing wavelength information within the range of 400 nm to 800 nm, the infrared sensor is particularly sensitive to detection of heat radiation, and the ultraviolet sensor can detect ultraviolet light generated by flames. According to the invention, the method can adapt to the condition of missing of actual data and partial sensor data, and the designed multi-modal data fusion module carries out effective feature extraction on the sensor data and image data, so that the generalization ability of the model is improved; according to the method, the correlation between different sensor data and the depth feature information of the specific time sequence mode of the fire can be captured from long-distance context information, and the historical data and the current data are adaptively fused.

The invention relates to the technical field of fire safety, and discloses a partition controller linkage method, device and equipment and a storage medium. The method comprises the following steps: acquiring original fire sensing data acquired by a plurality of fire-fighting sensors in each independent partition, and performing preprocessing of a plurality of sensing types; extracting space-time dimension features from the preprocessed data, generating fire behavior space-time feature vectors, and performing multi-source evidence weight fusion; performing fire abnormal mode identification and evaluation on the fused fire spatio-temporal feature vector to obtain a partition preliminary fire evaluation result, transmitting the partition preliminary fire evaluation result to a main controller, and performing global fire situation fusion based on a building physical structure and a partition connection relationship to obtain an overall fire situation evaluation result; and generating a multi-partition cooperative fire extinguishing execution scheme based on the overall fire situation assessment result, and controlling fire fighting equipment in each partition to perform linkage fire fighting. According to the invention, multi-source data fusion and cross-partition collaborative linkage of building fire are realized, and the fire detection accuracy and the fire-fighting response efficiency are 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.

The invention relates to the technical field of intelligent fire safety linkage control, in particular to an intelligent locking linkage control system and method based on fire monitoring, and the system comprises an acquisition module, a modeling module, a simulation engine module, a decision center module and an execution module. The acquisition module acquires physical quantity data of a key area of a building, wherein the physical quantity data comprises high-temperature radiation spectrum offset, aerosol particle swarm distribution characteristics and local heat convection intensity. The modeling module generates an environmental interference confidence index through convolutional neural network fusion features, and starts an incremental clustering algorithm to update an interference knowledge base when the confidence is insufficient. And the execution module dynamically compresses the delay window according to the risk map, and triggers a cross-level linkage mechanism through a people flow density threshold. And the environment sampling frequency adjustment coefficient is fed back to the acquisition module, the execution state data reverse drive modeling module iteratively updates the knowledge base, a Bayesian optimizer is combined to screen high-value characteristics to reconstruct simulation parameters, and full-link closed-loop learning and continuous evolution of the false alarm suppression capability are realized.

The invention discloses an intelligent fire-fighting risk prediction and dynamic early warning method and system based on multi-source data fusion, and relates to the technical field of intelligent fire-fighting risk prediction, and the method comprises the steps: collecting multi-source fire-fighting data, and constructing a fire-fighting safety state multi-dimensional data matrix; constructing a risk prediction model based on the fire safety state multi-dimensional data matrix to carry out real-time risk scoring; and executing dynamic early warning through a risk prediction result. According to the method, through the multi-source data acquisition and fusion module, multi-type fire-fighting data are comprehensively acquired and standardized, a structured and multi-dimensional fire-fighting safety state matrix is constructed, and the problems of data dispersion and information splitting are solved. In combination with a risk collaborative prediction and scoring module, the risk level of each region is dynamically output based on multi-factor analysis, and the accuracy and foresight of risk prediction are improved. Through a dynamic early warning generation and linkage module, real-time grading early warning and linkage of a fire fighting system are realized, and rapid response and intelligent prevention and control of a high-risk area are realized.

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 relates to the technical field of intelligent fire monitoring, in particular to an intelligent monitoring method and system for construction environment safety. The method comprises the following steps: acquiring a first fire possibility of a to-be-monitored area according to texture features of smoke areas in all continuous target video frame grey-scale maps; acquiring a second fire possibility of the to-be-monitored area according to the movement condition of the smoke area between the adjacent target video frame grey-scale maps; acquiring a third fire possibility of the to-be-monitored region according to the area change and texture change of the smoke region between adjacent target video frame grey-scale maps in combination with the smoke duration; and the fire possibility of the to-be-monitored area is evaluated so as to carry out safety monitoring. According to the method, the possibility that the smoke area in the continuous target video frame grey-scale map is the fire smoke is analyzed based on the distinguishing texture features and the distinguishing motion features between the fire smoke and the non-fire smoke, the interference of other non-fire smoke in the construction site is reduced, and the fire safety intelligent monitoring accuracy of the construction site is improved.

The invention discloses an intelligent fire-fighting AI collaborative early warning system and method fusing GIS and multi-source data, and relates to the technical field of fire-fighting safety, and the system comprises a GIS space modeling module which is used for constructing a digital twinborn model through integrating a building BIM model, fire-fighting facility distribution, a population density thermodynamic diagram, sensing equipment layout and real-time environment data; the multi-source data fusion module is used for integrating sensing equipment data, real-time environment data, meteorological information, manually reported hidden dangers and a historical fire case library, and outputting the data after standardization processing; the AI collaborative early warning engine module is used for accessing standardized data, realizing building fire dynamic risk assessment and outputting graded warning information; the 3D simulation drilling platform is used for calling the digital twinborn model, receiving graded alarm information and supporting fire spreading simulation, emergency drilling and plan optimization; and the block chain evidence storage module is used for storing alarm information and drill records. According to the invention, risk assessment and accurate prediction of the fire hazard can be realized.

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 a deep learning method for fire risk monitoring and early warning, and belongs to the technical field of fire safety. Through integration of various sensors (such as an infrared imager, a smoke detector and a temperature sensor), the method realizes omnibearing perception of a monitoring area, and significantly improves the accuracy and reliability of fire monitoring. A convolutional neural network (CNN) and a long and short term memory network (LSTM) are adopted, so that the system can effectively reduce false alarm and missing alarm, monitors key parts in real time, collects multiple parameters such as temperature and voltage, and dynamically evaluates the fire risk through intelligent analysis. In addition, an efficient communication link between the early warning system and other safety systems is established, and it is ensured that emergency measures can be taken quickly when the fire risk is detected. The system also has the functions of target tracking and behavior recognition so as to judge the authenticity and severity of the fire. Through modular design and environmental adaptability optimization, the system still operates stably in various complex environments.

The invention relates to the field of urban building fire-fighting management, and discloses an urban building fire-fighting safety intelligent management platform based on BIM and GIS multi-source fusion, and the platform comprises a multi-dimensional sensing module which is used for accessing BIM building detail data and GIS geographic space data, and collecting the multi-dimensional data of a building in real time; the data fusion module is used for performing space-time alignment and feature fusion on the BIM building detail data, the GIS geographic space data and the multi-dimensional data by adopting a space-time attention mechanism, and performing standardization processing to obtain fused data; and the coupling calculation module is used for utilizing a multi-physics field coupling algorithm. Through comprehensive acquisition and access of BIM building detail data, GIS geographic space data and real-time multi-dimensional data, space-time alignment and feature fusion of multi-source data are completed by means of a space-time attention mechanism, and a fire dynamic model including interaction of a temperature field, a structural mechanical field and a smoke diffusion field is constructed by using a multi-physics field coupling algorithm.

The invention discloses a power distribution room remote monitoring and fire-fighting inspection system based on a cloud platform, and relates to the technical field of fire-fighting safety monitoring. The system comprises a remote monitoring module, a fire safety monitoring module and an inspection task scheduling module. And the remote monitoring module acquires and stores the running state data of the power distribution equipment in real time to realize continuous monitoring. The fire safety monitoring module obtains environment data of a power distribution room through an environment sensing unit, establishes a fire prediction model through a random forest algorithm in combination with an equipment operation state, and generates a fire prediction index. And the early warning unit simulates a fire spreading process based on the fire prediction index and predicts a fire spreading direction and an influence area. And the inspection task scheduling module intelligently distributes inspection tasks and adjusts task execution strategies in real time according to the fire risk assessment result in combination with the geographic position and the working load of the inspection personnel. The fire risk is accurately predicted and the inspection task is dynamically scheduled, so that the fire-fighting potential safety hazard of the power distribution room can be timely found and eliminated.

The invention belongs to the technical field of fire safety early warning, and discloses a fire detection alarm method and system. The method comprises the following steps: monitoring the environmental smoke concentration in real time through a smoke detector arranged in a battery compartment to obtain smoke parameters; synchronously monitoring the environment temperature change through a temperature sensing detector to obtain a temperature parameter; comparing the smoke parameter with a preset smoke threshold value, and comparing the temperature parameter with a preset temperature threshold value; determining an alarm signal grade according to a comparison result, and triggering a corresponding alarm signal based on the alarm signal grade; and after the alarm signal is triggered, corresponding alarm measures and corresponding fire extinguishing measures are executed according to the grade of the triggered alarm signal. Through the above mode, composite grading alarm can be carried out, the accuracy of fire alarm is improved, targeted fire extinguishing measures are taken for different alarms, and the fire extinguishing efficiency is also improved.

The invention relates to the technical field of fire safety management, and discloses a fire safety management supervision platform system. The system comprises a building information acquisition module for acquiring building structure data and environment monitoring data of a target building in real time; the risk area division module is used for dividing a plurality of independent fire risk areas according to the building structure data and distributing area identifiers; the historical data indexing module is used for indexing a historical fire accident data set and a fire-fighting facility maintenance record set of each region according to the region identifier; the dynamic risk analysis module fuses the environment monitoring data and the historical fire accident data set to generate a real-time fire risk coefficient sequence; the fire-fighting resource scheduling module is used for generating a fire-fighting resource allocation scheme based on the real-time fire risk coefficient sequence and the facility maintenance record set; and the emergency response decision module generates a multi-stage fire emergency response strategy according to the resource allocation scheme. The system improves the timeliness and pertinence of fire safety management.

The invention discloses a real-time judgment method for roof collapse caused by a fire between nuclear power station oil storage tanks, and relates to the technical field of fire safety design of roof structures between nuclear power station oil storage tanks. Acquiring time sequence temperature data of the grid nodes; establishing a three-dimensional heat conduction model of the roof structure; top plate structure heating surface temperature field mapping is realized; carrying out three-dimensional heat transfer simulation on the top plate structure; analyzing the instability critical condition of the roof structure. According to the real-time judgment method for the roof collapse caused by the fire between the oil storage tanks of the nuclear power station, time sequence temperature field mapping of the heated surface of a complex roof structure between CFD numerical simulation software and FEM software in different fire scenes between the oil storage tanks of the nuclear power station can be realized; real-time structure response behaviors of the nuclear power station oil storage tank roof structure in different fire scenes are simulated, and the method can be used for fire safety design and post-disaster damage evaluation of the nuclear power station oil storage tank roof structure.

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.