Forest fire suppression remote command system based on artificial intelligence meteorological big model and digital twinborn technology

Abstract

The invention belongs to the technical field of safety early warning, and discloses a forest fire suppression remote command system based on an artificial intelligence meteorological big model and a digital twinborn technology. The laser radar monitoring module collects fire scene basic information and transmits the fire scene basic information to the artificial intelligence meteorological large model prediction module for trend prediction. A prediction result and monitoring data are fused through the digital twinborn situation display module and then fire scene situation information is displayed. A commander makes a decision based on the three-dimensional visual fire scene environment, the command decision and emergency response module generates a hierarchical response and combat scheme, and the personnel, material and air-ground collaborative scheduling module executes specific tasks according to the hierarchical response and combat scheme. The method has the advantages that the fire scene information can be acquired in real time and completely; the prediction capability is obviously improved and dynamically updated; multi-source data deep fusion and digital twinning dynamic display are realized; a complete hierarchical response and command closed loop is formed; and cross-region and cross-department resource scheduling is more efficient.

Description

Technical Field

[0001] The present invention belongs to the field of safety warning technology and relates to a remote command system for forest fire fighting based on an artificial intelligence meteorological large model and digital twin technology. Background Art

[0002] Forest fires are one of the most significant natural disasters currently threatening ecological, climate, and social security. They are characterized by sudden onset, rapid spread, significant influence from meteorological conditions and topography, and significant difficulty in extinguishing. In mountainous forested areas, complex terrain, inaccessible transportation, and poor communications present significant challenges in obtaining fire information, forecasting, and responding to emergencies. Once a fire breaks out, it can easily cause widespread damage in a short period of time, causing damage to forest resources, deteriorating the ecological environment, and posing a serious threat to the lives and property of residents.

[0003] The core requirement of the current forest fire prevention and control system is the ability to obtain timely and accurate fire information, predict fire trends, and formulate scientific, graded emergency response decision-making mechanisms and firefighting measures accordingly. However, existing fire information acquisition relies primarily on satellite remote sensing, fixed video surveillance, and manual patrols, which have significant shortcomings: 1) Satellite remote sensing has wide coverage, but data transmission lags due to imaging cycles and cloud obstruction, making it difficult to achieve real-time updates on the order of minutes or hours. 2) While fixed video surveillance provides intuitive images, it is limited by terrain and equipment layout, preventing comprehensive coverage of large mountainous forest areas. Furthermore, due to existing technical limitations, the misidentification rate of forest fires is high. 3) Manual patrols suffer from slow response times, numerous blind spots, inaccessible areas, and delayed information feedback. Due to these limitations, key fire information, such as fire location, smoke particle concentration, and near-surface wind speed and direction, is often not accurately captured immediately, resulting in delayed situation assessment and emergency response.

[0004] On the other hand, predicting wildfire spread trends is also a weak link in current forest fire prevention and control. Existing technologies primarily rely on traditional numerical weather models or empirical wildfire spread models. Numerical weather models are computationally complex and have long update cycles. They are typically used to analyze historical wildfire events, but they inadequately capture small-scale terrain effects and thermal disturbances in mountainous areas, making them inadequate for short-term forecasts on the minute to hour scale. While empirical wildfire spread models are computationally efficient, they rely primarily on empirical formulas to infer fire front expansion, have limited ability to simulate complex meteorological and wildfire-wind field coupling (sometimes ignoring real-time forecasts and relying on manual input), and thus lack predictive accuracy. More critically, these models generally lack rapid assimilation and integration with real-time observational data, failing to rapidly incorporate observations such as fire location and local wind speed and direction. This results in significant deviations between predictions and actual fire conditions, making it difficult to provide a reliable basis for emergency response.

[0005] Furthermore, forest fire emergency response involves multiple data sources, including lidar, satellite remote sensing, ground-based meteorological stations, drone inspections, and video surveillance. These data come in complex formats, inconsistent temporal resolutions, and inconsistent coordinate bases, making them difficult to integrate within a unified framework. Existing command platforms often display only a single type of data and lack the ability to dynamically and comprehensively visualize fire points, smoke transport, wind field evolution, and fire spread trends within a unified spatial-temporal coordinate system. This makes it difficult for commanders to develop a comprehensive and intuitive understanding of the overall situation, limiting their decision-making basis.

[0006] More significantly, the existing emergency response system lacks a closed-loop linkage mechanism at the decision-making and execution levels. Fire predictions cannot be directly translated into tiered response strategies and resource scheduling plans. Firefighting deployment relies primarily on commanders' experience, lacks intelligent support tools, and results in low execution efficiency. Inadequate verification methods, such as drone inspections and high-point monitoring, prevent rapid correction of discrepancies between assessments, predictions, and actual fire conditions, making it difficult to establish a closed-loop "prediction-verification-re-prediction" system. Cross-regional and multi-departmental resource scheduling also suffers from information isolation. Personnel, equipment, and aerial firefighting resources are dispersed across different management units, lacking a unified online directory and dynamic availability tracking. Scheduling relies primarily on manual communication, making it difficult to achieve global optimization in resource allocation. This is especially true in mountainous fires, where communication conditions are poor, leading to more pronounced information delays and conflict risks in air-ground coordinated operations.

[0007] In summary, the existing forest fire prevention and control system has obvious shortcomings in real-time perception of fire information, spread trend prediction, data integration and display, intelligent command and decision-making, and cross-regional coordinated scheduling. There is an urgent need for a new system that can integrate multi-source data in real time, realize prediction and command linkage, and support cross-regional scheduling to solve these problems.

[0008] In terms of observation technology, lidar, cloud meters, and multi-source sensors have been gradually applied to obtain key quantities such as boundary layer wind fields, smoke echoes, and aerosol concentrations. These sensors can provide information with high temporal and spatial resolution, offering new means for sensing fire environments. However, existing applications mostly remain at the monitoring and independent display level, lacking a deep integration of observation data with spread trend prediction models, and thus unable to directly generate forward-looking trend information.

[0009] In terms of predictive modeling, empirical fire spread models can quickly simulate scenarios, but they lack the ability to depict complex dynamic processes and struggle to reflect important mechanisms such as sudden changes in mountain wind fields or wildfire-wind field coupling. Coupled atmosphere-fire behavior models offer greater physical realism and can account for the feedback of fire source heat flux on local wind fields. However, these models are computationally expensive and difficult to operationalize, making them difficult to meet the real-time requirements of emergency scenarios. Furthermore, these models have limited ability to rapidly assimilate real-time observational data, making it impossible to quickly update predictions. This makes it difficult for model results to promptly reflect the latest fire situation.

[0010] In recent years, some emergency management platforms have begun to incorporate digital twin technology, combining satellite remote sensing, ground observations, video surveillance, and model predictions in a unified three-dimensional display, enhancing the intuitiveness of situational awareness. However, existing digital twin platforms primarily focus on information visualization and lack deep integration with tiered emergency response, cross-regional intelligent resource scheduling, and air-ground collaborative operations. Furthermore, they are unable to directly translate model deduction results into executable tasks and resource allocation instructions, limiting their overall application value.

[0011] To address these issues, existing technical solutions each address a single aspect, but overall they fall short of forming a complete system. Some solutions focus on observation, utilizing equipment like lidar and cloud meters to obtain real-time information on fire location, smoke transport, and near-surface wind patterns, improving the spatiotemporal resolution of fire information. However, these solutions primarily target monitoring departments, with relatively independent data processing and result presentation. Observational results cannot be quickly integrated into prediction models, nor do they directly contribute to emergency response operations.

[0012] Another approach relies on empirical fire spread models to predict fire trends. These models can quickly generate fire extension paths, making them suitable for scenario analysis and risk assessment. However, these models have limited predictive accuracy and lack the ability to quickly integrate with real-time observational data. If fire conditions suddenly change, the model results can easily deviate from the actual fire situation.

[0013] Some proposals have also attempted to incorporate coupled atmosphere-wildfire behavior models to enhance the physical realism of predictions, enabling a more comprehensive description of wildfire-wind field coupling and terrain influences. However, these models are computationally intensive and require long update cycles, making them difficult to implement in real-time emergency command scenarios. Furthermore, model results are mostly output in the form of meteorological element fields or fire line diagrams, lacking mechanisms for linking with tiered emergency response, resource scheduling, and air-ground collaborative operations, making them difficult to directly support tactical-level decision-making.

[0014] Some emergency management platforms primarily emphasize data visualization, leveraging digital twin technology to present multi-source observations and model results in three-dimensional scenarios, improving situational awareness. However, these platforms lack the ability to integrate prediction, verification, and dispatch processes. They are unable to rapidly verify predictions, trigger automated hierarchical responses, or intelligently dispatch cross-departmental resources, leaving significant gaps in the chain of command.

[0015] Overall, while existing technical solutions offer certain advantages in individual aspects such as observation, prediction, and visualization, they suffer from common issues such as disconnection between monitoring and prediction, a gap between prediction and command, insufficient data integration, and inefficient resource scheduling. A complete closed-loop system encompassing real-time monitoring, intelligent prediction, situation visualization, remote command, and optimized cross-regional resource scheduling has yet to be established. These issues have become a major bottleneck hindering the improvement of forest fire emergency response capabilities.

[0016] Through the analysis of the above background technology and existing technical solutions, it can be seen that the existing forest fire prevention and control technology still has obvious shortcomings in fire scene monitoring, trend prediction, data fusion, remote command and cross-regional resource scheduling, and it is difficult to meet the real-time, accurate and intelligent requirements of the modern emergency response system. The main shortcomings are as follows:

[0017] (1) The timeliness and completeness of fire scene information acquisition are insufficient;

[0018] Existing observation methods primarily rely on satellite remote sensing, fixed video surveillance, or manual inspections. These methods are limited by imaging cycles, terrain obstructions, and manpower constraints. Key information such as fire location, smoke particle concentration, and near-surface wind speed and direction is difficult to obtain immediately, and their coverage and resolution are limited. Advanced observation equipment such as lidar (LiDAR) offers high spatial and temporal resolution, but these are often used in isolation, resulting in isolated observations and a failure to effectively collaborate with other information sources.

[0019] (2) The monitoring and forecasting links are disconnected, and trend forecasts are lagging behind;

[0020] Existing fire spread prediction models fall into two main categories: empirical models offer fast computational speed but lack accuracy, while coupled atmosphere-fire behavior models offer high accuracy but long runtimes, making them unsuitable for real-time emergency response. Both models generally lack the ability to rapidly assimilate real-time observational data from sources like lidar and drone inspections. Their predictions deviate from actual on-site conditions, making them inadequate for short-term forecasts ranging from minute to hourly.

[0021] (3) It is difficult to integrate multi-source data, and the situation display is single;

[0022] Forest fire emergency response involves multiple data sources, including satellites, radar, video, and ground-based weather stations. However, existing systems lack unified standards for data formats, coordinate references, and time synchronization. This fragments multi-source information and makes it impossible to comprehensively present fire locations, smoke transport, wind field evolution, and spread trends within a unified spatial and temporal framework. This limits situational awareness. Commanders struggle to fully grasp the fire situation through existing platforms, leaving them with insufficient basis for decision-making.

[0023] (4) The decision-making chain is incomplete, and there is no closed loop between command and execution;

[0024] Existing platforms are mostly limited to information display and manual analysis, unable to directly translate prediction results into tiered response measures and resource scheduling plans. Firefighting deployment relies on commanders' experience, lacks standardized decision-making support, and is inefficient and prone to errors. Furthermore, there is insufficient verification and dynamic correction between predictions and actual fire conditions, lacking a closed-loop "prediction-verification-re-prediction" system, resulting in low command and dispatch accuracy.

[0025] (5) Low efficiency in cross-regional resource scheduling and insufficient coordination;

[0026] Firefighting personnel, vehicles, equipment, and aerial firefighting resources are dispersed across different management units. There's a lack of a unified online resource directory and dynamic availability monitoring. Cross-departmental information communication and scheduling often rely on manual processes, making it difficult to achieve optimal resource allocation. Especially in the face of poor communication conditions and complex firefighting environments, air-ground collaborative operations are subject to information lags and operational conflicts, resulting in low resource utilization and hindering overall firefighting effectiveness.

[0027] To address these shortcomings, the present invention constructs a new remote command system for forest fire fighting that uses a digital twin as the remote command center, integrating real-time lidar monitoring with large-scale artificial intelligence meteorological model predictions. This system structurally and functionally connects monitoring, prediction, decision-making, execution, and scheduling, achieving a closed-loop linkage across the entire chain. The specific objectives are as follows:

[0028] (1) Improve the real-time and completeness of fire scene information. This invention uses a laser radar to collect multiple information elements such as fire location, smoke particle concentration, wind speed and direction at high frequency, and integrates it with multi-source observation methods such as ground meteorological stations and drone inspections to form a global dynamic perception capability.

[0029] (2) Deeply couple observation data with prediction models. Rapidly inject real-time observation results into the AI ​​meteorological model to generate minute-to-hour fire spread trend forecasts, and maintain consistency between the forecasts and actual fire conditions through multiple rounds of updates.

[0030] (3) Build a unified data fusion and digital twin potential display platform. Integrate observation and prediction information under a unified coordinate and time base, and dynamically display the fire point, smoke, wind field and spread trend in three dimensions, so that commanders can intuitively grasp the overall picture of the fire scene.

[0031] (4) Form a complete closed loop of command and decision-making. The system can automatically generate emergency response recommendations based on graded levels, verify prediction results through drone inspections and high-point monitoring, continuously revise the fire situation, and promote the full-link cycle of "prediction-verification-re-prediction" to ensure accurate and efficient decision-making.

[0032] (5) Realize cross-regional and cross-departmental intelligent resource scheduling. Build a unified resource directory and scheduling algorithm to achieve online management and global optimal allocation of emergency forces from multiple departments, and ensure efficient coordination between ground firefighting, drone inspections, and aerial firefighting. Summary of the Invention

[0033] The purpose of this invention is to solve the core problems of existing technologies such as fire scene information lag, prediction and command disconnection, insufficient data fusion, and inefficient scheduling and coordination through the deep integration of digital twin technology, lidar monitoring and artificial intelligence meteorological models, and to build a remote and precise command system for forest fire fighting that can cross regions and levels, so as to comprehensively improve the real-time, scientific nature and operational safety of forest fire emergency response.

[0034] The technical solution of the present invention:

[0035] A remote command system for forest fire fighting based on artificial intelligence meteorological big model and digital twin technology, including a lidar monitoring module, an artificial intelligence meteorological big model prediction module, a digital twin ecological potential display module, a command decision and emergency response module and a personnel, material and air-ground coordinated dispatching module; the lidar monitoring module collects basic information of the fire scene and transmits it to the artificial intelligence meteorological big model prediction module for trend prediction, and the prediction results and monitoring data are integrated with the digital twin potential display module to display the fire situation information; the commander makes decisions based on the three-dimensional visualized fire scene environment, the command decision and emergency response module generates a hierarchical response and combat plan, and the personnel, material and air-ground coordinated dispatching module performs specific tasks accordingly; drone inspections and monitoring verify the actual situation of the fire scene, and the feedback data enters the remote command system for forest fire fighting to form a closed loop, ensuring dynamic consistency of prediction, command and execution.

[0036] The LiDAR monitoring module, deployed at key locations in key forest areas and mountainous forest zones, is responsible for collecting real-time basic fire information. The module acquires monitoring data through observation units and inverts the echo characteristics of this data to obtain the coordinates of the fire point, smoke aerosol concentration, aerosol profile, and wind speed, direction, and boundary layer structure near the ground and in the boundary layer. This LiDAR monitoring module enables high-frequency dynamic perception of key fire elements, with data refresh cycles as low as minutes, ensuring first-hand information required for prediction and command.

[0037] The artificial intelligence meteorological large model prediction module is responsible for making short-term predictions of fire spread trends and meteorological elements based on the monitoring results of the lidar, ground meteorological station data and the regional meteorological background field; among them, the regional meteorological background field is the historical data of the station over the past few days based on reanalysis data; through this artificial intelligence meteorological large model prediction module, forward-looking predictions of fire conditions at the minute to hour level are achieved, providing a scientific basis for subsequent command and resource deployment.

[0038] The digital twin potential display module is responsible for integrating monitoring data and prediction results to construct a digital twin of the real fire scene and obtain fire situation information; the digital twin potential display module displays the fire situation in a dynamic and intuitive manner, enabling commanders to clearly grasp the fire development process and future trends.

[0039] The command decision-making and emergency response module relies on the fire situation information provided by the digital twin potential display module to complete fire risk assessment, hierarchical response triggering, combat plan generation and dynamic correction; through the command decision-making and emergency response module, the command decision-making and emergency response module realizes real-time risk judgment, automatic triggering of hierarchical response, intelligent generation of combat plans and closed-loop dynamic correction, avoiding complete reliance on the commander's experience and judgment, effectively improving response speed and scientific decision-making, and providing reliable technical support for cross-regional and multi-department joint firefighting.

[0040] The personnel, materials, and air-ground collaborative scheduling module dynamically dispatches various firefighting resources according to the task instructions generated by the command decision-making and emergency response modules, realizing multi-department and multi-level collaborative operations; through this personnel, materials, and air-ground collaborative scheduling module, efficient cross-departmental and cross-regional coordination is achieved, significantly improving resource utilization and combat safety.

[0041] Beneficial effects of the present invention:

[0042] (1) Fire scene information is acquired in a more real-time and complete manner. Existing technologies mostly rely on a single observation method. The present invention uses a lidar monitoring module to achieve high-frequency data collection at the minute level, and standardizes multiple factors such as fire location, smoke particle concentration, wind speed and direction, significantly improving the comprehensiveness and timeliness of fire scene information.

[0043] (2) The prediction capability is significantly improved and dynamically updated. This invention uses an artificial intelligence meteorological large-scale model prediction module to quickly assimilate lidar observation results into the model, achieving minute-to-hour fire spread trend predictions. It also has the ability to update multiple rounds, keeping the prediction results synchronized with the actual fire situation. Compared with the shortcomings of existing semi-empirical or coupled models that have a lag in prediction, this invention can more accurately reflect the dynamic changes of the fire scene.

[0044] (3) Achieve deep fusion of multi-source data and dynamic display of digital twins. Existing technologies mostly display data independently. The digital twin potential display module of the present invention can present the three-dimensional fusion of observation and prediction information under a unified coordinate and time base, intuitively displaying the fire point, smoke diffusion, wind field and fire spread trend, and provide commanders with a clear overall situation.

[0045] (4) Forming a complete hierarchical response and command closed loop. Unlike the existing technologies that have information fragmentation and incomplete command chains, the command decision-making and emergency response module of the present invention can determine the risk level based on multiple indicators such as fire spread speed, threat target exposure, and meteorological risk, and automatically trigger emergency hierarchical response and generate combat plans. In conjunction with drone inspections and high-point monitoring verification, a dynamic closed loop of "prediction-verification-re-prediction" is formed, improving decision-making accuracy and response speed.

[0046] (5) More efficient cross-regional and cross-departmental resource scheduling. This invention establishes a unified resource directory and scheduling algorithm through the personnel, material, and air-ground collaborative scheduling modules, achieving dynamic tracking of resource availability and global optimal allocation. Air-ground operation conflicts can be early-warned and optimized in the digital twin scenario, solving the problem of inefficient cross-regional resource coordination in existing technologies.

[0047] Overall, the present invention organically integrates real-time monitoring by lidar, prediction by artificial intelligence meteorological large-scale models, and digital twin technology to form a complete link of monitoring-prediction-display-command-dispatch, significantly improving the real-time, scientific nature, and operational safety of forest fire fighting, which is difficult to achieve with existing technologies. DETAILED DESCRIPTION

[0048] The specific implementation of the present invention is further described below in conjunction with the technical solution.

[0049] A remote command system for forest fire fighting based on artificial intelligence meteorological big model and digital twin technology, including a lidar monitoring module, an artificial intelligence meteorological big model prediction module, a digital twin ecological potential display module, a command decision and emergency response module and a personnel, material and air-ground coordinated dispatching module; the lidar monitoring module collects basic information of the fire scene and transmits it to the artificial intelligence meteorological big model prediction module for trend prediction, and the prediction results and monitoring data are integrated with the digital twin potential display module to display the fire situation information; the commander makes decisions based on the three-dimensional visualized fire scene environment, the command decision and emergency response module generates a hierarchical response and combat plan, and the personnel, material and air-ground coordinated dispatching module performs specific tasks accordingly; drone inspections and monitoring verify the actual situation of the fire scene, and the feedback data enters the remote command system for forest fire fighting to form a closed loop, ensuring dynamic consistency of prediction, command and execution.

[0050] The LiDAR monitoring module is deployed in key locations in key forest areas and mountainous forest regions, responsible for real-time collection of basic fire scene information. The LiDAR monitoring module obtains monitoring data through an observation unit, inverts the echo characteristics of the monitoring data, and obtains the coordinates of the fire point, smoke aerosol concentration, aerosol profile, wind speed and direction near the ground and boundary layer, and boundary layer structure.

[0051] (1.1) Equipment composition: The observation unit consists of a lidar, a cloud meter, and auxiliary meteorological sensors, with all-weather, all-round automatic monitoring capabilities;

[0052] (1.2) Data processing: Use echo signal strength, pulse time delay, and Doppler frequency shift to invert the fire point coordinates and smoke aerosol concentration. Combined with the wind profiling algorithm, calculate the wind speed, wind direction, boundary layer structure, and aerosol profile near the ground and in the boundary layer in real time.

[0053] (1.3) Data output: Output the quality-controlled fire point location coordinates, wind speed, wind direction, aerosol profile, and smoke aerosol concentration profile data, and upload them to the artificial intelligence meteorological model prediction module and digital twin potential display module according to the unified data interface protocol.

[0054] This lidar monitoring module can achieve high-frequency dynamic perception of key fire elements, and the data refresh cycle can reach minutes, ensuring first-hand information required for prediction and command.

[0055] The AI ​​meteorological model prediction module is responsible for making short-term predictions of fire spread trends and meteorological elements based on the monitoring results of the lidar, ground weather station data, and the regional meteorological background field; the regional meteorological background field is the historical data of the station over the past few days based on reanalysis data;

[0056] (2.1) Input data: fire location, wind speed, wind direction, aerosol profile, smoke aerosol concentration from the lidar monitoring module, and temperature and humidity obtained from the ground meteorological station;

[0057] (2.2) Model Framework: An AI-powered meteorological model trained on deep neural networks and large-scale meteorological data, with a built-in fire spread prediction module, is used to simulate fire-wind interactions and the spread of fire as it changes with topography and meteorology. The large-scale meteorological data includes 500hPa geopotential height, 200hPa wind speed, and sea level pressure.

[0058] (2.3) Prediction results: Output the fire location, high-resolution wind field, temperature and humidity field, smoke spread range, and fire spread range for the next few hours; the fire spread range includes the burning area and the speed of the fire line;

[0059] (2.4) Real-time update mechanism: Every time new lidar and monitoring data is received, the AI ​​meteorological model can be triggered to quickly recalculate, dynamically updating the prediction results and keeping the prediction structure consistent with the actual fire scene.

[0060] This artificial intelligence meteorological large model prediction module can achieve forward-looking predictions of fire conditions at the minute to hour level, providing a scientific basis for subsequent command and resource deployment.

[0061] The digital twin ecological situation display module is responsible for integrating monitoring data and prediction results, constructing a digital twin of the real fire scene, and obtaining fire situation information;

[0062] (3.1) Data fusion: The real-time monitoring data from LiDAR is integrated with the prediction results of the AI ​​meteorological model according to a unified coordinate reference and time scale;

[0063] (3.2) 3D Modeling: Build a 3D fire scene visualization based on high-precision terrain grid data, dynamically rendering the fire location, smoke spread, high-resolution wind field, temperature and humidity field, and fire spread range;

[0064] (3.3) Interactive function: Commanders can view relevant parameters, historical backtracking, and trend deduction in a 3D fire scene visualization environment. They can also directly deploy control instructions based on the 3D fire scene visualization environment, including evacuation routes, deployment areas, and fire extinguishing points;

[0065] (3.4) Risk warning: Fire threat areas, blocked evacuation routes and helicopter operation windows are displayed in real time in the form of layers.

[0066] The digital twin potential display module displays the fire situation in a dynamic and intuitive way, allowing commanders to clearly grasp the fire development process and future trends.

[0067] The command decision-making and emergency response module relies on the fire situation information provided by the digital twin situation display module to complete fire risk assessment, hierarchical response triggering, combat plan generation and dynamic correction;

[0068] (4.1) Risk assessment and determination criteria;

[0069] First, based on the fire situation information from the digital twin potential display module, a comprehensive risk assessment of the fire development trend is conducted;

[0070] Risk indicators include:

[0071] (4.1.1) Fire spread rate (m / min or km / h), calculated by predicting the fire front advance speed and the fire area expansion rate;

[0072] (4.1.2) Exposure of threatened targets: a comprehensive analysis of the areas of settlements, infrastructure, and important forest land within the fire zone and their exposure levels;

[0073] (4.1.3) Meteorological danger index, which is calculated by combining the predicted wind speed, probability of sudden change in wind direction, temperature, and humidity;

[0074] (4.1.4) Accessibility of firefighting forces, assessing the average time it takes for firefighting teams to reach the fire scene, road accessibility, and the operational window for drones and helicopters to extinguish fires;

[0075] (4.1.5) Comparison indicators based on historical fire data, with reference to the difficulty of handling and loss levels of similar fires in the past, and matching them by grade;

[0076] The risk level is determined based on a multi-index weighted comprehensive scoring method and is divided into Level 1 fire (general fire), Level 2 fire (large fire), Level 3 fire (major fire) and Level 3 fire (extremely high risk fire):

[0077] Level 1 fire: The spread rate is less than 10m / min, the threat area is less than 1km², the weather conditions are relatively stable, and the firefighting force can control the fire within 1 hour;

[0078] Level 2 fire: Spread speed 10-30m / min, threat range 1-5km², unstable weather conditions, firefighting forces need to coordinate with multiple units;

[0079] Level 3 fire: The fire spreads at a speed of 30-60m / min, threatens a range of 5-10km², and is rapidly developing, requiring a professional team of more than 100 people, two or more helicopters, and a large number of vehicles and supplies to form a cross-regional, multi-force joint firefighting operation;

[0080] Special fire: The spread speed is greater than 60m / min, the threat range exceeds 10km², and the fire may jump or spread to a secondary fire scene, requiring cross-regional command and the activation of the highest level of response;

[0081] (4.2) Hierarchical response triggering and decision making;

[0082] Based on the determined risk level, the command decision and emergency response module automatically calls the preset response plan library to generate the corresponding emergency graded response instructions:

[0083] A level one fire will automatically notify the local emergency team to dispatch and strengthen fire scene monitoring;

[0084] Level 2 fires trigger regional joint defense, calling in reinforcements and drone monitoring;

[0085] Level 3 and special fires will automatically request support from higher-level emergency departments and aviation firefighting, and enter into regional or cross-provincial linkage mechanisms;

[0086] The remote command system for forest fire fighting combines the predicted direction and speed of fire development to generate combat plan recommendations, including priority protection targets, evacuation routes, fire line blocking locations, helicopter operation windows, and drone patrol routes;

[0087] (4.3) Real-time verification and closed-loop correction;

[0088] The command decision-making and emergency response modules are linked in real time with drone inspections and high-point monitoring systems to verify the fire situation and operational execution;

[0089] If the verification results show that the actual development of the fire deviates from the prediction results, the remote command system for forest fire fighting will immediately trigger a risk reassessment and update the prediction results, and revise the response measures, thus realizing a dynamic closed loop of "prediction-verification-re-prediction-re-response";

[0090] (4.4) Task decomposition and instruction management;

[0091] All response instructions are issued to the personnel, materials and air-ground coordinated dispatch module through a unified task management platform, forming a detailed task list that specifies the task objectives, execution units, task start time and deadline;

[0092] The command decision-making and emergency response module monitors the task execution status in real time, issues warnings for delays, insufficient resources, or security risks, and automatically upgrades the response level and requests reinforcement resources when necessary.

[0093] Through the command decision-making and emergency response module, the command decision-making and emergency response module realizes real-time risk assessment, automatic triggering of graded responses, intelligent generation of combat plans and closed-loop dynamic correction, avoiding complete reliance on commanders' experience and judgment, effectively improving response speed and scientific decision-making, and providing reliable technical support for cross-regional and multi-department joint firefighting.

[0094] The personnel, materials and air-ground coordinated dispatch module dynamically dispatches various firefighting resources according to the task instructions generated by the command decision and emergency response module, realizing multi-department and multi-level coordinated operations;

[0095] Resource Catalog: The remote command system for forest fire fighting has a built-in unified emergency resource database covering personnel, vehicles, firefighting equipment, firefighting helicopters and drones, and water sources, and updates resource locations and availability in real time;

[0096] Dispatch algorithm: Automatically generates the optimal task-resource matching plan and combat path by comprehensively considering road conditions, fire scene situation, operational safety, and arrival timeliness;

[0097] Air-ground coordination: The remote command system for forest fire fighting uses 3D visualization of the fire scene to mark no-fly zones, operational airspace, and fire danger zones, avoiding air-ground conflicts and ensuring coordinated operations between helicopter water drops, drone inspections, and ground-based firefighting.

[0098] Execution tracking: The command center tracks the progress of mission execution and operational effectiveness in real time, and updates the dispatch plan on a rolling basis as the fire situation changes.

[0099] Through this personnel, materials and air-ground collaborative scheduling module, efficient cross-departmental and cross-regional coordination can be achieved, significantly improving resource utilization and combat safety.

Claims

1. A remote command system for forest fire fighting based on artificial intelligence meteorological large model and digital twin technology, characterized by: The remote command system for forest fire fighting includes a lidar monitoring module, an artificial intelligence meteorological model prediction module, a digital twin potential display module, a command decision-making and emergency response module, and a personnel, material, and air-ground coordinated dispatch module; the lidar monitoring module collects basic information about the fire scene and transmits it to the artificial intelligence meteorological model prediction module for trend prediction. The prediction results and monitoring data are integrated with the digital twin potential display module to display the fire situation information; commanders make decisions based on the three-dimensional visualized fire environment, the command decision-making and emergency response module generates graded responses and combat plans, and the personnel, material, and air-ground coordinated dispatch module performs specific tasks accordingly; drone inspections and monitoring verify the actual situation of the fire scene, and the feedback data enters the remote command system for forest fire fighting to form a closed loop, ensuring dynamic consistency of prediction, command, and execution.

2. The forest fire fighting remote command system based on artificial intelligence meteorological large model and digital twin technology according to claim 1 is characterized in that: The LiDAR monitoring module is deployed in key locations in key forest areas and mountainous forest regions, responsible for real-time collection of basic fire scene information. The LiDAR monitoring module obtains monitoring data through an observation unit, inverts the echo characteristics of the monitoring data, and obtains the coordinates of the fire point, smoke aerosol concentration, aerosol profile, wind speed and direction near the ground and boundary layer, and boundary layer structure. (1.1) Equipment composition: The observation unit consists of a lidar, a cloud meter, and auxiliary meteorological sensors, with all-weather, all-round automatic monitoring capabilities; (1.2) Data processing: Use echo signal strength, pulse time delay, and Doppler frequency shift to invert the fire point coordinates and smoke aerosol concentration. Combined with the wind profiling algorithm, calculate the wind speed, wind direction, boundary layer structure, and aerosol profile near the ground and in the boundary layer in real time. (1.3) Data output: Output the quality-controlled fire point location coordinates, wind speed, wind direction, aerosol profile, and smoke aerosol concentration profile data, and upload them to the artificial intelligence meteorological model prediction module and digital twin potential display module according to the unified data interface protocol.

3. The forest fire fighting remote command system based on artificial intelligence meteorological large model and digital twin technology according to claim 1 is characterized in that: The AI ​​meteorological model prediction module is responsible for making short-term predictions of fire spread trends and meteorological elements based on the monitoring results of the lidar, ground weather station data, and the regional meteorological background field; the regional meteorological background field is the historical data of the station over the past few days based on reanalysis data; (2.1) Input data: fire location, wind speed, wind direction, aerosol profile, smoke aerosol concentration from the lidar monitoring module, and temperature and humidity obtained from the ground meteorological station; (2.2) Model Framework: An AI-powered meteorological model trained on deep neural networks and large-scale meteorological data, with a built-in fire spread prediction module, is used to simulate fire-wind interactions and the spread of fire as it changes with topography and meteorology. The large-scale meteorological data includes 500hPa geopotential height, 200hPa wind speed, and sea level pressure. (2.3) Prediction results: Output the fire location, high-resolution wind field, temperature and humidity field, smoke spread range, and fire spread range for the next few hours; the fire spread range includes the burning area and the speed of the fire line; (2.4) Real-time update mechanism: Every time new lidar and monitoring data is received, the AI ​​meteorological model can be triggered to quickly recalculate, dynamically updating the prediction results and keeping the prediction structure consistent with the actual fire scene.

4. The forest fire fighting remote command system based on artificial intelligence meteorological large model and digital twin technology according to claim 1 is characterized in that: The digital twin ecological situation display module is responsible for integrating monitoring data and prediction results, constructing a digital twin of the real fire scene, and obtaining fire situation information; (3.1) Data fusion: The real-time monitoring data from LiDAR is integrated with the prediction results of the AI ​​meteorological model according to a unified coordinate reference and time scale; (3.2) 3D Modeling: Build a 3D fire scene visualization based on high-precision terrain grid data, dynamically rendering the fire location, smoke spread, high-resolution wind field, temperature and humidity field, and fire spread range; (3.3) Interactive function: Commanders can view relevant parameters, historical backtracking, and trend deduction in a 3D fire scene visualization environment. They can also directly deploy control instructions based on the 3D fire scene visualization environment, including evacuation routes, deployment areas, and fire extinguishing points; (3.4) Risk warning: Fire threat areas, blocked evacuation routes and helicopter operation windows are displayed in real time in the form of layers.

5. The forest fire fighting remote command system based on artificial intelligence meteorological large model and digital twin technology according to claim 1 is characterized in that: The command decision-making and emergency response module relies on the fire situation information provided by the digital twin situation display module to complete fire risk assessment, hierarchical response triggering, combat plan generation and dynamic correction; (4.1) Risk assessment and determination criteria; First, based on the fire situation information from the digital twin potential display module, a comprehensive risk assessment of the fire development trend is conducted; Risk indicators include: (4.1.1) Fire spread rate, calculated by predicting the fire front advance speed and the fire area expansion rate; (4.1.2) Exposure of threatened targets: a comprehensive analysis of the areas of settlements, infrastructure, and important forest land within the fire zone and their exposure levels; (4.1.3) Meteorological danger index, which is calculated by combining the predicted wind speed, probability of sudden change in wind direction, temperature, and humidity; (4.1.4) Accessibility of firefighting forces, assessing the average time it takes for firefighting teams to reach the fire scene, road accessibility, and the operational window for drones and helicopters to extinguish fires; (4.1.5) Comparison indicators based on historical fire data, with reference to the difficulty of handling and loss levels of previous fires for classification and matching; The risk level is determined according to a multi-index weighted comprehensive scoring method and is divided into level one fire, level two fire, level three fire and special level fire: Level 1 fire: The spread rate is less than 10m / min, the threat area is less than 1km², the weather conditions are stable, and the firefighting force can control the fire within 1 hour; Level 2 fire: Spread speed 10-30m / min, threat range 1-5km², unstable weather conditions, firefighting forces need to coordinate with multiple units; Level 3 fire: The fire spreads at a speed of 30-60m / min, threatens a range of 5-10km², and is rapidly developing, requiring a professional team of more than 100 people, two or more helicopters, and a large number of vehicles and supplies to form a cross-regional, multi-force joint firefighting operation; Special fire: The fire spreads faster than 60m / min, threatens a range exceeding 10km², and has jump spread or secondary fires, requiring cross-regional command and the activation of the highest level of response; (4.2) Hierarchical response triggering and decision making; Based on the determined risk level, the command decision and emergency response module automatically calls the preset response plan library to generate the corresponding emergency graded response instructions: A level one fire will automatically notify the local emergency team to dispatch and strengthen fire scene monitoring; Level 2 fires trigger regional joint defense, calling in reinforcements and drone monitoring; Level 3 and special fires will automatically request support from higher-level emergency departments and aviation firefighting, and enter into regional or cross-provincial linkage mechanisms; The remote command system for forest fire fighting combines the predicted direction and speed of fire development to generate combat plan recommendations, including priority protection targets, evacuation routes, fire line blocking locations, helicopter operation windows, and drone patrol routes; (4.3) Real-time verification and closed-loop correction; The command decision-making and emergency response modules are linked in real time with drone inspections and high-point monitoring systems to verify the fire situation and operational execution; If the verification results show a deviation between the actual fire development and the predicted results, the remote command system for forest fire fighting will immediately trigger a risk reassessment and update the prediction results, revising the response measures, thus achieving a dynamic closed loop of "prediction-verification-re-prediction-re-response"; (4.4) Task decomposition and instruction management; All response instructions are issued to the personnel, materials and air-ground coordinated dispatch module through a unified task management platform, forming a detailed task list that specifies the task objectives, execution units, task start time and deadline; The command decision-making and emergency response module monitors the task execution status in real time, issues alarms for delays, insufficient resources or security risks, and can automatically upgrade the response level and request reinforcement resources.

6. The forest fire fighting remote command system based on artificial intelligence meteorological large model and digital twin technology according to claim 1 is characterized in that: The personnel, materials and air-ground coordinated dispatch module dynamically dispatches various firefighting resources according to the task instructions generated by the command decision-making and emergency response modules, thus realizing multi-department and multi-level coordinated operations. Resource Catalog: The remote command system for forest fire fighting has a built-in unified emergency resource database covering personnel, vehicles, firefighting equipment, firefighting helicopters and drones, and water sources, and updates resource locations and availability in real time; Dispatch algorithm: Automatically generates the optimal task-resource matching plan and combat path by comprehensively considering road conditions, fire scene situation, operational safety, and arrival timeliness; Air-ground coordination: The remote command system for forest fire fighting uses 3D visualization of the fire scene to mark no-fly zones, operational airspace, and fire danger zones, avoiding air-ground conflicts and ensuring coordinated operations between helicopter water drops, drone inspections, and ground-based firefighting. Execution tracking: The command center tracks the progress of mission execution and operational effectiveness in real time, and updates the dispatch plan on a rolling basis as the fire situation changes.