1231 results about "Fire safety" patented technology

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 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 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 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 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 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 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.

The invention discloses a fire safety intelligent evaluation system and a closed-loop management and control method, and the method comprises the steps: analyzing national standard and local standard texts through a natural language processing technology, disassembling terms into a three-stage quantifiable index system, and constructing a dynamic index library comprising at least 200 subdivisions; a dynamic rule base is generated based on a BERT-NER model, a standard parameter threshold value is automatically extracted, and version difference comparison and real-time updating are supported; deploying a fire-fighting special BERT model to analyze an unstructured document, extracting violation events and converting the violation events into structured evaluation factors, aggregating multi-source sensor data and video streams through an edge computing gateway, and adding space-time tags to realize millisecond-level real-time monitoring; generating a comprehensive score in combination with the static weight and the dynamic weight, constructing a risk prediction model based on an Attention-LSTM network, inputting historical hidden danger data, environment variables and enterprise operation data, and outputting future risk probability distribution; according to the invention, compliance management is efficient, assessment accuracy is improved, risk response is real-time, and a management and control process is digital.

The invention relates to the technical field of urban fire safety rescue, in particular to a low-altitude fire fighting system and method based on urban CIM and Beidou positioning. According to the system and the method, full space-time accurate positioning is realized through the Beidou differential base station and the UWB indoor positioning equipment; integrating the urban information model system and unmanned aerial vehicle cluster data, and constructing a multi-source heterogeneous data set; a coordinate system is unified based on a space-time reference calibration module, and fire behavior prediction, path planning and resource scheduling are realized through AI computing nodes; the unmanned aerial vehicle task distribution module is used for realizing multi-vehicle cooperative rescue; and iteratively optimizing system parameters through a closed-loop adaptive optimization module. The method is suitable for fire emergency rescue in an urban complex building environment, and the rescue response speed, the decision-making precision and the cooperative combat capability are remarkably improved.

The invention discloses a building fire-fighting automatic verification and evacuation path verification system based on a rule engine, and relates to the technical field of building fire-fighting intellectualization. Comprising the steps that a rule generation module analyzes a fire-fighting standard text and generates an executable rule set; the model analysis module is used for extracting component attributes and a spatial topological relation from the building information model; the real-time sensing module collects environment state data. The rule engine module matches the component attributes with the executable rule set to generate a first verification result; spatial topology and real-time sensing data are fused, the dynamic passing weight of each path segment is calculated, and an evacuation path network model is constructed. The path verification module verifies the safety of a path from a preset point to an exit based on the model, and generates a path verification result. And the result generation module outputs fire-fighting compliance data. According to the invention, through the dynamic optimal evacuation path planning from the static fire-fighting standard examination of the building design to the occurrence of the disaster, the guarantee capability and the emergency response efficiency of the building fire-fighting safety are improved.

The invention provides an AI-based security risk analysis method, and relates to the technical field of data processing, and the method comprises the steps: dynamically determining three spatial reference positions through a spatial topological relation analysis algorithm based on spatial coordinates in a business data result; generating a dynamic risk partition based on the spatial reference position; executing a space grid division operation on the dynamic risk partition to form a discretized space analysis unit; calculating a grid deformation gradient tensor as a risk distribution adjustment value based on the geometric deformation parameters of the spatial analysis unit; and generating spatial risk thermodynamic data points describing spatial risk distribution according to the structured semantic information, the knowledge enhancement context, the business data result and the risk distribution adjustment value. According to the method, the risk analysis target, the key entity and the spatial constraint condition can be accurately extracted, and the convenience of man-machine interaction and the accuracy of semantic understanding are improved.

The invention relates to an intelligent fire-fighting potential safety hazard evaluation system and evaluation method, and relates to the technical field of fire-fighting safety management, and the system comprises a multi-dimensional sensing module, a state vectorization module, a regional risk quantification module, a global risk evaluation module and a model optimization module. Multi-dimensional data is converted into a unified state vector through the multi-dimensional sensing module and the state vectorization module, and the unified state vector is compared with a dynamic normal state, so that the accuracy of hidden danger recognition is improved. The regional risk quantification module calculates the internal risk index of each region, and the global risk assessment module integrates the indexes to form a comprehensive risk assessment result. The model optimization module dynamically adjusts model parameters based on historical data and real-time feedback, and the prediction accuracy is improved. According to the invention, comprehensive monitoring and intelligent assessment of fire risks are realized, the efficiency and practicability of fire safety work are significantly improved, and it is ensured that potential fire risks are effectively coped with in a complex and changeable environment.

The invention discloses an impedance remodeling method for a grid-connected converter, and relates to the field of fire safety, and the method comprises the steps: respectively recording a virtual impedance transfer function and a virtual admittance transfer function into a current inner loop universal controller structure under a d / q coordinate system, and obtaining a series virtual impedance control structure and a parallel virtual admittance control structure; the series virtual impedance control structure and the parallel virtual admittance control structure are subjected to time domain and frequency domain conversion to obtain a series virtual impedance Norton equivalent circuit and a parallel virtual admittance Norton equivalent circuit respectively; based on the influence of virtual impedance and virtual admittance on the converter, multiple compensation links are added in a control framework of the grid-connected converter, so that the harmonic voltage and harmonic current of a grid-connected point are reduced, and the converter operates more stably.

The invention relates to the technical field of fire safety, in particular to a fire simulation early warning system based on intelligent fire protection multi-source data fusion, which comprises a sensor health degree dynamic evaluation module, an intelligent sensor health degree dynamic evaluation module and a fire simulation early warning module, establishing a two-dimensional feature library of the state of the sensor and environmental interference; and based on the feature library data, a fuzzy comprehensive evaluation method is adopted. By filtering unreliable data of the sensor, distinguishing multi-source contradictory data, dynamically adjusting weights according to fire behavior stages, iteratively updating data credibility and optimizing parameters in a closed-loop mode, the problems of false alarm and missing alarm in the prior art are effectively solved, and the accuracy and practicability of the intelligent fire-fighting early warning system are improved.

The invention discloses an intelligent layering and guiding device for highway tunnel fire smoke and a control method, and relates to the technical field of tunnel fire safety, a smoke flow Froude number is introduced as a direct control target, the parameter represents the ratio of smoke flow inertia force to buoyancy force, the numerical value directly determines the stability of a layering interface, and the layering interface can be directly controlled. The Froude number of each subarea of the tunnel is calculated in real time through a sensing network and is compared with a critical value calibrated through an experiment, when a real-time value is lower than the critical value, it is indicated that inertia force is relatively insufficient, the buoyancy effect is dominant, and instability and entrainment are likely to happen to an interface between a smoke layer and lower cold air, and then according to a control algorithm, the smoke layer and the lower cold air are controlled. The micro jet fan applies directional inertia force to a smoke layer by adjusting the angle and the speed, the intelligent induction smoke outlet optimizes local pressure difference and flow by adjusting the opening degree and assists in maintaining flowing, the smoke flowing state is actively locked in a stable layered flow interval, and mixing and sedimentation caused by flow state instability are prevented.

The embodiment of the invention provides a converter station fire-fighting digital management and control system, and the system comprises a converter station data processing module, a fire-fighting daily management module, and a fire-fighting emergency processing module, and the converter station data processing module is used for obtaining the fire-fighting internet-of-things data of a converter station, and transmitting the fire-fighting internet-of-things data to the fire-fighting daily management module. Performing abnormal data detection on the acquired fire-fighting internet-of-things data by using an abnormal data detection model; the fire-fighting daily management module is used for performing fire-fighting daily management on the converter station according to the fire-fighting internet-of-things data of the converter station; and the fire-fighting emergency processing module is used for determining the converter station with a dangerous case or a dangerous case trend according to at least one of the fire-fighting internet-of-things data of the converter station and the management result of the fire-fighting daily management module, and performing fire-fighting emergency processing on the converter station with the dangerous case or the dangerous case trend. Therefore, the digital fire-fighting management and control capability of the converter station is enhanced, and the accuracy and effectiveness of fire-fighting safety management and control of the converter station are improved.

The invention relates to the technical field of security control systems, in particular to an escape door linkage method and system based on fire signals, and the method comprises the following steps: S1, obtaining local control suggestions corresponding to all microcontrollers; s2, based on all the obtained local control suggestions, generating a final global control instruction through linkage analysis; s3, the final global control instruction is sent to a door body driving motor so as to control a door body to execute corresponding opening and closing actions; wherein the local control suggestion comprises the target opening angle, the movement speed and the driving torque. According to the invention, the reliability of fire security and protection can be improved.

The invention discloses a multi-terminal linkage control method and system for a fire alarm scene, and relates to the technical field of energy storage system fire safety, and the method comprises the steps: configuring a composite cluster-level detector for each cluster of batteries of a target energy storage system, and collecting cluster-level external environment data; the method comprises the following steps: acquiring internal state data of a battery pack through a BMS, and uploading the two types of data to an edge calculation unit; the edge calculation unit performs fire risk assessment on the data and determines fire risk assessment parameters of the battery cluster; and determining a battery cluster fire extinguishing decision result, and finally performing multi-end linkage fire extinguishing control on the fire extinguishing device through the result. The technical problems that a traditional fire detection and fire extinguishing control scheme is slow in response, high in false alarm and missing alarm rate and incapable of giving consideration to internal and external states when dealing with thermal runaway of the energy storage system are solved, and the technical effects of early-stage accurate early warning and rapid linkage fire extinguishing and effective improvement of the fire safety level of the energy storage system are achieved.

The invention relates to the technical field of fire safety, and discloses an AI-based intelligent fire scene risk assessment system, which comprises a data acquisition module used for acquiring multi-source heterogeneous data in a fire scene; the knowledge graph construction module is used for constructing and updating a dynamic heterogeneous knowledge graph in real time based on the multi-source heterogeneous data; the cross-modal fusion processing module is used for carrying out feature extraction and fusion on the multi-source heterogeneous data by adopting a BioNet algorithm, distributing dynamic weights to different modal data by adopting a multi-head attention mechanism, and generating a fusion feature vector; the space-time risk prediction module is used for predicting the risk evolution trend of the fire-fighting scene in a future preset time period by adopting a space-time Transform model; the dynamic allocation module is used for scheduling fire-fighting resources by adopting a DBO algorithm based on a result output by the space-time Transform model, and dynamically allocating fire extinguishing equipment, emergency channels and rescue strategies; according to the invention, the accuracy and timeliness of risk assessment are improved.

The invention relates to the field of fire safety engineering, and discloses a water pressure transient detection method, system and device for multi-physical quantity fusion and data processing, and the method comprises the following steps: arranging pressure, flow and temperature sensors at key positions of a pipe network, and collecting real-time data; denoising processing is carried out on the collected data, and interference signals are filtered out; fusing the multi-source data by using a Kalman filtering algorithm to obtain an accurate estimated value of each physical quantity; carrying out water pressure transient modeling and state prediction based on the hydrodynamic model; combining a prediction result and sensor data, and adopting a machine learning algorithm to identify an abnormal mode; once an abnormality is detected, local alarm and emergency control response are triggered immediately. Water pressure, flow and temperature data are fused, modeling prediction and intelligent identification are combined, and efficient early warning and response of abnormal water pressure are achieved; the system is high in integration level and has local calculation and control capability, and the accuracy, the real-time performance and the intelligent level of pipe network monitoring are remarkably improved.

The invention provides an intelligent building fire safety early warning system based on intelligent fire protection, and relates to the field of fire safety, and the system comprises a distributed data collection module which is used for achieving data real-time collection and network disconnection local processing through heterogeneous sensor networking and dual-mode communication; the edge processing module is used for carrying out edge processing on the output standardized environment data flow by adopting a fire behavior triple verification model and a dynamic weight algorithm, and carrying out cross-node model sharing in combination with a federated learning framework so as to obtain a fire risk score and a fire source positioning coordinate; the decision execution module is used for dynamically planning an evacuation path; the feedback module is used for performing real-time rendering fluid dynamics fire spreading simulation on the evacuation path and generating multi-terminal early warning; and the dynamic evacuation optimization module simulates and optimizes the exit position and the path planning through fluid dynamics to obtain the optimized path planning. The method and the device are used for overcoming the defects of cloud processing delay and failure during network disconnection in the prior art.