76 results about "Wild Fires" patented technology

The invention discloses a forest carbon flux statistical method and system under the influence of a wildfire, and the method comprises the steps: obtaining forest multi-dimensional data including fire point data, and constructing a multi-scale collaborative database; constructing a carbon source-carbon sink dynamic coupling model according to the multi-scale collaborative database; obtaining a forest multi-dimensional remote sensing index, and constructing a correlation matrix; correcting the carbon source-carbon sink dynamic coupling model according to the correlation matrix; and generating a forest spatialization decision map based on the multi-scale collaborative database and the carbon source-carbon sink dynamic coupling model. According to the technical scheme provided by the invention, by fusing forest multi-scale data, a nationwide unified grid system is constructed, and large-scale forest fire carbon flux statistics and accurate quantification are realized; a carbon source-carbon sink dynamic coupling model is constructed, a forest multi-dimensional remote sensing index is used for correction, and the monitoring precision of the fire influence is remarkably improved; and the emission factors are dynamically calibrated, so that the inter-region difference is reduced, and the universality of the method is enhanced.

A service inputs building characteristics for a building into a machine learning model configured to output a probability that a given building will be lost should a fire reach the building, and receives as output from the model a building loss factor for the building. The service determines determining an exposure measurement for the building by performing simulations, over a plurality of candidate environmental parameters, of whether a simulated fire would encroach on the building. The service determines a building damage potential measurement based on the building loss factor, the exposure measurement, and an intensity measurement, and generates for display a graphical user interface showing fire risk for the building based on the building damage potential measurement.

The invention discloses a forest fire rapid identification and accurate fire point positioning method based on air-ground cooperation. Comprising the following steps: a forest fire panoramic monitoring and positioning method based on a double-axis holder, a wildfire target intelligent identification algorithm based on multi-modal image fusion analysis, and a low-power-consumption air-ground cooperative communication network oriented to forest fire monitoring. The invention relates to an unmanned aerial vehicle-based forest wildfire target maneuvering inspection and positioning method and an air-ground fusion wildfire target accurate positioning method. According to the invention, 360-degree panoramic image acquisition is realized by using the double-shaft holder, and meanwhile, a fire source position self-adaptive adjustment mechanism is provided; multi-modal image fusion analysis is adopted in the image processing layer to resist interference, blind areas are compensated, and the fire source recognition accuracy is improved; a low-power-consumption air-ground cooperative communication network is constructed, so that the cost is reduced, and flight is continued; and the monitoring limitation is broken through by means of flexible inspection of the unmanned aerial vehicle, a closed-loop system of air-ground cooperation-information complementation-intelligent verification is formed, and the monitoring stability in a complex scene is remarkably enhanced.

A system, method, and device for predicting a risk of wildfire are provided. The system includes a satellite imaging device for collecting data covering a target area and a processing server including an indices module for processing the data to generate key vegetation indices, a map generation module configured to generate weather and static maps, an analysis module configured to analyze historical wildfire data to identify past fire locations in the target area, an integration module configured to integrate the received, generated, and analyzed data to obtain a comprehensive dataset for the target area, and a risk prediction module configured to analyze the comprehensive dataset using a risk prediction model trained to predict the risk of wildfire for the targeted area, the risk prediction model including a machine-learning-based pattern detection model for receiving the comprehensive dataset as an input and generate prediction data describing a predicted risk as an output.

The invention discloses a wildfire monitoring method based on a multi-source time sequence remote sensing image, and relates to the technical field of remote sensing geoscience application, and the method comprises the steps: obtaining multi-source remote sensing data, constructing a time sequence waveband data set, and constructing a high-temperature thermal anomaly index through the atmospheric top reflectivity of a three-waveband spectrum; based on the high-temperature thermal anomaly index, extracting a first-level fire point pixel, a saturated pixel and a high-radiance pixel from the multi-source remote sensing data, and converting the saturated pixel and the high-radiance pixel into the first-level fire point pixel; extracting saturated pixels and high-radiance pixels from missed fire cluster cores in the multi-source remote sensing data; based on limiting conditions, building coverage area data and a high-frequency ground object mask, noise pixels in the first-level fire point pixels are removed, and second-level fire point pixels are obtained; transmitting to a local client and a cloud geographic space analysis platform, and creating a fire point list. Multi-source long-time sequence optical remote sensing data are combined, and the spatial and temporal distribution of the large-area wildfire is quickly and automatically identified.

The application provides a WUI division method and device based on house wild fire risk, equipment and storage medium, determines the weighted fuel grade of each fuel grid in the region to be divided; for each house in the region to be divided, the building wild fire risk index of the house is calculated and obtained; for each fuel grid, the vegetation danger index of the fuel grid is calculated and obtained; from all the houses in the region to be divided, the houses with building wild fire risk index greater than a preset first threshold value are screened out as risk houses; for each risk house, from all the fuel grids falling within the buffer area corresponding to the risk house, the fuel grids with vegetation danger index greater than a preset second threshold value are screened out as risk fuel grids corresponding to the risk house; for each risk house, the risk house and the risk fuel grid corresponding to the risk house are regionally merged to obtain a plurality of forest town junction domains in the region to be divided. The scheme of the application improves the accuracy of forest town junction domain division.

The application discloses a kind of wild fire smoke cluster and its boundary vector automatic identification method, system, storage medium and electronic equipment, belong to satellite remote sensing and disaster monitoring technical field.Method includes: obtaining satellite active fire point, fire event and UVAI data;Fire source area is positioned based on fire event geographic location and initial plume pixel is marked;The spatially continuous smoke cluster connected domain is identified by iterative region growing algorithm;According to the specific distribution form of connected domain pixel in satellite pixel matrix, the accurate closed boundary vector is automatically generated using matching geometric algorithm;Finally, using multiple criteria, the spatial isolated independent smoke cluster is screened out.The application avoids the defects that traditional image method is easily disturbed by environment from physical principle, realizes fully automated processing without training data, and can output vector boundary that can be directly used for diffusion and emission evaluation, significantly improves the accuracy, automation degree and application value of wild fire smoke cluster monitoring.

This invention discloses a method for predicting unsteady runoff response considering forest disturbance. The method includes data decomposition of the unsteady runoff sequence to obtain a basic runoff sequence and a disturbed runoff sequence; time-varying coefficient regression to obtain a basic runoff mapping function; correlation analysis and sensitivity analysis; using the correlation analysis results to correct the sensitivity analysis results to obtain optimized disturbance sensitivity; constructing a disturbed runoff prediction model; inputting the watershed characteristics and disturbance data of the watershed to be predicted into the basic runoff mapping function and the disturbed runoff prediction model respectively to obtain predicted basic runoff and predicted disturbed runoff; and superimposing these to obtain the predicted unsteady runoff. This method can quantify the disturbed runoff resulting from the interaction of wildfire disturbance, logging disturbance, vegetation adjustment disturbance, and conventional climate disturbance, and incorporates a competition-cooperative improved double cumulative curve (C-MDMC) to quantify the interaction effects between disturbances, outputting disturbed runoff prediction results. This provides a refined decision-making basis for watershed multi-scenario adaptive water resource management.

An apparatus and method are provided for reducing methane emissions from ruminant animals by combusting methane gas extracted from the rumen. The system comprises a conduit configured to transport methane from the animal's rumen to a combustion module mounted externally or implanted partially or fully in a subdorsal position. The combustion module includes a pressure-activated valve, air intake, ignition system, and combustion chamber enclosed by a heat-absorbing roof structure. A control unit monitors internal gas pressure and triggers a spark ignition circuit when combustion conditions are satisfied. An upward-facing camera inhibits ignition if flammable obstructions are detected above the module. A water-filled thermal buffer integrated into the chamber roof moderates exhaust temperature, reducing wildfire risk. Power is supplied by a solar panel and rechargeable battery. The system intermittently converts methane into carbon dioxide and water vapor, significantly mitigating the greenhouse gas impact of enteric fermentation in ruminant livestock.

A multi-modal combustion interruption system comprises a detection subsystem configured to identify combustion events, an electromagnetic emission subsystem having pulsed energy source and directional emission structure, a control processor implementing adaptive modulation based on measured combustion response, and a safety enforcement subsystem configured to control emission activation based on presence detection. The system may be configured in various modes from baseline electromagnetic emission through enhanced configurations incorporating optional ionization enhancement, particle generation, and acoustic energy subsystems. Adaptive modulation enables parameter selection based on measured combustion response rather than predetermined values. The modular architecture enables configuration selection based on deployment type and application requirements. Fixed installations may utilize grid power for industrial facility protection. Mobile platforms may employ onboard power for emergency response or wildfire intervention. Configuration selection may consider environmental compatibility including omission of particle generation for water-sensitive equipment environments or incorporation of multiple enhancement subsystems for demanding applications.

Machine-learning-based methods and systems for vegetation treatment project design. The approach autonomously creates, evaluates, and optimizes wildfire risk mitigation activities to intelligently reduce wildfire risk to communities in a resource-constrained environment. The methods and / or systems are accompanied by a cloud-enabled user interface that provides decision support to vegetation management specialists charged with implementing wildfire risk reduction measures.

An intelligent sewage system designed for use in municipalities around the Wildland Urban Interface incorporates a decentralized network of wastewater treatment units to process wastewater from a plurality of parcels. The decentralized wastewater treatment units spread a Biological Oxygen Demand (BOD) reduction of the wastewater throughout the system and effluent is delivered efficiently through a pressure sewage system. Non-potable and potable water supplies are generated and delivered to individually separable water distribution zones throughout the municipality. The system also provides an auxiliary high-pressure, high-flow non-potable water supply to compensate during depressurization events and bolster the water distribution zones in the event of a wildfire emergency event. The intelligent sewage system also incorporates a comprehensive wildfire defense network and a supervisory control and data acquisition system which work in concert to harden the municipality against wildfire risks and combat active wildfires.

A system, method, and device for predicting a risk of wildfire are provided. The system includes a satellite imaging device for collecting data covering a target area and a processing server including an indices module for processing the data to generate key vegetation indices, a map generation module configured to generate weather and static maps, an analysis module configured to analyze historical wildfire data to identify past fire locations in the target area, an integration module configured to integrate the received, generated, and analyzed data to obtain a comprehensive dataset for the target area, and a risk prediction module configured to analyze the comprehensive dataset using a risk prediction model trained to predict the risk of wildfire for the targeted area, the risk prediction model including a machine-learning-based pattern detection model for receiving the comprehensive dataset as an input and generate prediction data describing a predicted risk as an output.

Environmentally-clean wildfire inhibitor liquid biochemical solutions produced from an aqueous mixture of alkali metal salt derived from a non-polymerized saturated carboxylic acid such as oxalic acid, and dissolved in water along with a dispersing and coalescing agent, realized as an ester of a non-polymerized saturated carboxylic acid, and dissolved in the water to provide a liquid fire inhibitor solution that can be sprayed on combustible surfaces to form thin alkali metal salt crystalline coatings on the combustible surfaces when and as water molecules in the liquid fire inhibitor evaporate to the environment during drying operations, to inhibit fire ignition, flame spread and smoke development.

This invention discloses an integrated prediction method and system for fire spread coupled with smoke and meteorological feedback. The method first estimates forest fire emission parameters in real time based on satellite remote sensing data and converts them into spatiotemporal dynamic emission fluxes identifiable by the numerical weather chemistry (WRF-Chem) model. Grid interpolation is then used to match the WRF-Chem meteorological and chemical grid. Secondly, based on WRF-Chem simulations, the bidirectional coupling process between smoke aerosols and the meteorological field is simulated to generate a dynamic meteorological field incorporating smoke radiation feedback. Finally, the dynamic meteorological field is input into the weather-wildfire coupled simulation and the WRF-Fire weather research and forecasting model to simulate the feedback effect of smoke on fire spread. This invention reveals for the first time an aerosol-dominated positive feedback chain: fire emissions, through the synergistic effect of thermodynamic heating and radiative forcing, provide scientific support for disaster prevention decision-making and improve emergency response efficiency.

A service inputs wind data and utility data corresponding to a utility component into a first machine learning model to determine an outage risk prediction representing a probability that the utility component will have an outage. The service determines a probability of ignition at a vicinity of the utility component, and determines a set of wildfire impact measurements by simulating a wildfire in the vicinity of the utility component. The service inputs the outage risk prediction, the probability of ignition, and the set of wildfire impact measurements into a second machine learning model, and receives as output from the second machine learning model, a catastrophic wildfire risk score corresponding to the utility component. The service outputs a graphical representation on a dashboard representing the catastrophic wildfire risk score.

Provided are a device and a method for detecting a wildfire. The wildfire detecting device may include: an image data acquisition module acquiring image data captured by a shooting device; an image data storage module storing the image data acquired through the image data acquisition module in a storage space; a wildfire detection module performing wildfire detection by using a deformable convolution based deep learning model to which smoke characteristics widely diffused are reflected by dynamically adjusting a filter position by using a learnable offset from the image data; and a wildfire direction and position estimation module estimating a direction and a position of the wildfire with respect to a case where it is determined that the wildfire occurs by the wildfire detection module.

The invention relates to the technical field of deep learning and computer vision, and discloses a YOLO and DeepLabv3 + fused forest burned area intelligent identification method, which comprises the following steps: collecting remote sensing image data of various temporal and spatial resolutions covering a research area, preprocessing the collected data, unifying all the data to the same spatial reference system and pixel size, and identifying the area of the research area. Forming a time-space consistent multi-modal remote sensing data set; on the basis of existing global overfire area products and historical wildfire records, sample labels of overfire areas and non-overfire areas are sketched and generated on the multi-source remote sensing images; a two-stage model cascade architecture is adopted, and an optimal parameter set is searched to minimize an overall loss function; and the trained integrated deep learning model and the multi-source remote sensing image are utilized to realize rapid identification of the forest fire land. According to the method, the accuracy, the robustness and the cross-regional adaptability of forest fire land identification are improved, and the problems of misjudgment and missed judgment caused by data isomerism and environment complexity in an existing method are solved.

A system, method, and device for predicting a risk of wildfire are provided. The system includes a satellite imaging device for collecting data covering a target area and a processing server including an indices module for processing the data to generate key vegetation indices, a map generation module configured to generate weather and static maps, an analysis module configured to analyze historical wildfire data to identify past fire locations in the target area, an integration module configured to integrate the received, generated, and analyzed data to obtain a comprehensive dataset for the target area, and a risk prediction module configured to analyze the comprehensive dataset using a risk prediction model trained to predict the risk of wildfire for the targeted area, the risk prediction model including a machine-learning-based pattern detection model for receiving the comprehensive dataset as an input and generate prediction data describing a predicted risk as an output.

A system may be configured for continuous wildfire risk assessment and notification in remote terrain environments. The system may obtain environmental sensor readings from a first sensor suite, where the readings include at least temperature, humidity, and windspeed data local to the first sensor suite. The system may calculate a Hot, Dry, and Windy (HDW) index value using the environmental data and determine whether the HDW index value satisfies a threshold value. Responsive to the HDW index value satisfying the threshold value, the system may activate a second sensor suite characterized by a higher energy consumption than the first sensor suite. The system may then obtain smoke and infrared readings from the second sensor suite, detect wildfire conditions based on the smoke and infrared readings, and transmit a wildfire alert to a remote system responsive to detecting the wildfire conditions.

The invention discloses a sky-ground integrated wildfire disaster defense method and defense system, and the method specifically comprises the following steps: S1, carrying out the change detection of remote sensing data through a central processing platform, constructing a multi-factor dynamic risk fusion model, and generating an initial target region list; s2, constructing a fire scene state vector and a meteorological threat vector of the initial target area, calculating a spreading potential index, and obtaining a dynamic task priority score; s3, the fire extinguishing unmanned aerial vehicle conducts fire extinguishing treatment; and S4, constructing a time sequence aligned truth value sample library, and optimizing the multi-factor dynamic risk fusion model to complete a data closed loop. According to the invention, multi-source dynamic information can be fused to carry out fire point identification, deep cooperation of sky, ground, air and a central processing platform is realized, full-process automation from monitoring and early warning to precise disposal is completed, the system is suitable for complex scenes through task priorities, the monitoring precision of fire defense is improved, and the fire defense precision is continuously improved.

A method of parameter training of a fire spread model predicting fire spread for a specific region is provided, the method comprising: assigning default combustion parameters for the fire spread model, and fine-tuning the combustion parameters for the specific region through an iterative process. In this way, solutions may be developed to efficiently obtain region-specific models predicting the evolution and spread of wildfires without the need for expert knowledge or a curated dataset, thus providing reliable fire spread models for any location on Earth within a short response time in the event of wildfire occurrences.

The invention relates to a system (1) for establishing a fire barrier in a terrain with the aim of stopping the spreading of a wildfire. The system comprises a series of rods (2) which each comprises a plurality of nozzles (3) arranged at predetermined locations along a longitudinal axis of the rod at least when it is in an operation configuration. Each of the nozzles is in fluid connection with an inlet (4) via a first tube (5) for allowing pressurized water from an associated water supply to flow from the inlet to the nozzle so that the nozzle can atomize the water and provide a mist of water to the surroundings during use. The system may further comprise a movable carriage (11) for storing the series of rods before they are deployed and for transporting the series of rods along a line of deployment in the terrain during deployment of the rods into the operation configuration.

A detection and prediction system, method, and server are provided. The system includes data collecting devices configured to connect to a network and a server for receiving environmental data from the devices, the data including location data, historical environmental data, and real-time environmental data, populating a map of a geographic area where the devices are disposed, receiving weather data through the wildfire detection network, determining a probability of a wildfire at a plurality of locations within the geographic area from the environmental data and the weather data, and determining whether an anomaly has occurred within the geographic area based on the real-time environmental data.

A service inputs wind data and utility data corresponding to a utility component into a first machine learning model to determine an outage risk prediction representing a probability that the utility component will have an outage. The service determines a probability of ignition at a vicinity of the utility component, and determines a set of wildfire impact measurements by simulating a wildfire in the vicinity of the utility component. The service inputs the outage risk prediction, the probability of ignition, and the set of wildfire impact measurements into a second machine learning model, and receives as output from the second machine learning model, a catastrophic wildfire risk score corresponding to the utility component. The service outputs a graphical representation on a dashboard representing the catastrophic wildfire risk score.

There is provided networks, systems and displays for providing derived data and predictive information for use in emergencies; and in particular for use in wildfire emergencies. More particularly, there is provided systems, equipment and networks for the monitoring and collecting of raw data regarding fire emergencies, both real time and historic. In embodiments, this raw data is then analyzed to provide derived data, predictive data, virtual data, and combinations and variations of this data, which depending upon the nature of this data may be packaged, distributed, displayed and used in various setting and applications to mitigate, avoid and manage the emergency, including a wildfire emergency.

The invention discloses a wildfire puff and boundary vector automatic identification method and system thereof, a storage medium and electronic equipment, and belongs to the technical field of satellite remote sensing and disaster monitoring. The method comprises the following steps: acquiring satellite active fire points, fire events and UVAI data; positioning a fire source area based on the geographic position of the fire event and marking an initial smoke plume pixel; identifying a smoke puff connected domain with continuous space through an iterative region growing algorithm; automatically generating an accurate closed boundary vector by adopting a matched geometric algorithm according to the specific distribution pattern of the connected domain pixels in the satellite pixel matrix; and finally, screening out the independent puff with isolated space by utilizing multiple criteria. According to the method, the defect that a traditional image method is easily interfered by the environment is avoided from the physical principle, full-automatic processing without training data is achieved, the vector boundary which can be directly used for diffusion and emission evaluation can be output, and the accuracy, the automation degree and the application value of wildfire puff monitoring are remarkably improved.

Prediction and identification of the impact of wild fires on watershed health through monitoring, reporting, and validation of sensor-geospatial fusion networks can be provided by a system comprising: a first sensor in a watershed for measuring an in-stream water quality indicator; a remote sensing data source; a water treatment plant that extracts water from the watershed; and a machine learning model in communication with the first sensor, the remote sensing data source, and the water treatment plant, the machine learning model configured to generate a time series estimate of water quality in the watershed damaged by a wild fire based on a source data sequence from the first sensor and a remote data sequence from the remote sensing data source, and further configured to modify a configuration of the water treatment plant based on the time series estimate of water quality.

Systems and methods for facilitating a fire inspection of a building structure. Multiple digital images (e.g., a video) of the outside walls of the building structure are processed to generate a three-dimensional (3D) sparse point cloud. An estimated perimeter of the building structure is determined based on the sparse point cloud and aligned with a geospatial perimeter to generate a geospatially-aligned 3D sparse point cloud. The geospatially-aligned cloud is then volume-rendered to generate a 3D reconstruction of the outside walls. One or more portions of this 3D reconstruction are displayed on a display screen, based on a position, orientation, and / or field of view (FOV) of an observation / inspection point relative to the building structure. A fire inspection may be performed based on respective displayed portions of the 3D reconstruction, in which one or more displayed objects that pose an ignition risk may be tagged and annotated for wildfire mitigation actions.