A forest fire fighting remote command system based on artificial intelligence meteorological large model and digital twin technology

Abstract

The present application belongs to the technical field of safety warning, and discloses a forest fire fighting remote command system based on artificial intelligence meteorological large model and digital twin technology. A laser radar monitoring module collects fire field basic information and transmits it to an artificial intelligence meteorological large model prediction module for trend prediction. The prediction result and the monitoring data are fused by a digital twin situation display module and then the fire field situation information is displayed. A commander makes decisions based on a three-dimensional visual fire field environment, and a command decision and emergency response module generates a hierarchical response and a combat scheme. A personnel, material and air-ground collaborative scheduling module executes specific tasks accordingly. The present application has the following advantages: fire field information acquisition is more real-time and complete; prediction capability is significantly improved and dynamically updated; multi-source data deep fusion and digital twin dynamic display are realized; a complete hierarchical response and command closed loop is formed; and cross-regional and cross-department resource scheduling is more efficient.

Description

Technical Field

[0001] This invention belongs to the field of safety early warning technology and relates to a remote command system for forest fire fighting based on artificial intelligence meteorological big data model and digital twin technology. Background Technology

[0002] Forest fires are among the most significant natural disasters threatening ecological, climate, and social security. They are characterized by their sudden onset, rapid spread, significant influence from weather conditions and terrain, and high difficulty in firefighting. Mountainous forest areas, due to their complex terrain, poor transportation, and weak communication, face immense challenges in obtaining fire information, forecasting, and responding to emergencies. Once a fire breaks out, it can easily escalate into a large-scale disaster in a short period, causing damage to forest resources, deterioration of 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 development trends, and formulate scientific emergency response decision-making mechanisms and firefighting measures accordingly. However, existing methods for obtaining fire information mainly rely on satellite remote sensing, fixed video surveillance, and manual patrols, which have significant shortcomings: 1) Satellite remote sensing has a wide coverage area, but due to imaging cycles and cloud cover, data transmission is delayed, making it difficult to achieve real-time updates at the minute or hour level; 2) While fixed video surveillance can provide intuitive images, it is limited by terrain obstructions and equipment layout, and cannot fully cover large areas of mountainous forest areas. Furthermore, due to current technological limitations, the misjudgment rate of forest fire points is high; 3) Manual patrols suffer from slow response, numerous blind spots, inaccessibility to uninhabited areas, and delayed information feedback. Due to these limitations, key fire elements such as fire location, smoke particulate concentration, and near-surface wind speed and direction are often not accurately obtained in the first instance, leading to delayed situation assessment and delayed emergency response.

[0004] On the other hand, predicting the spread of wildfires is also a weak link in current forest fire prevention and control. Existing technologies mainly rely on traditional numerical weather models or empirical wildfire spread models. Numerical weather models are computationally complex and have long update cycles, and are usually used to analyze historical wildfire events. They are insufficient in characterizing small-scale topographic effects in mountainous areas and thermal disturbances in fire fields, making it difficult to meet the needs of short-term forecasts at the minute to hour level. While empirical wildfire spread models are computationally efficient, they mainly rely on empirical formulas to extrapolate fire line expansion and have limited ability to simulate complex meteorological and wildfire-wind field coupling processes (even ignoring real-time forecasts and using manual input modes), resulting in insufficient prediction accuracy. More importantly, these models generally lack rapid assimilation and fusion with real-time observation data, and cannot quickly incorporate observation results such as fire location, local wind speed and direction into the calculation, leading to large deviations between the predicted results and the actual fire situation, making it difficult to provide a reliable basis for emergency dispatch.

[0005] Furthermore, forest fire emergency response involves multiple data sources, including lidar, satellite remote sensing, ground weather stations, drone patrols, and video surveillance. These data sources are complex in format, and their temporal resolution and coordinate systems are inconsistent, making integration within a unified framework difficult. Existing command platforms can only display a single type of data, lacking the dynamic and comprehensive visualization capabilities to track fire points, smoke transport, wind field evolution, and fire spread trends within the same spatial-temporal coordinate system. Commanders struggle to form 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 level. Fire prediction results cannot be directly translated into tiered response strategies and resource allocation plans. The deployment of firefighting forces mainly relies on the experience of commanders, lacking intelligent support tools and resulting in low execution efficiency. Insufficient verification methods such as drone inspections and high-point monitoring lead to the inability to quickly correct deviations between assessment, prediction, and actual fire conditions, making it difficult to form a closed-loop chain of "prediction-verification-re-prediction." Cross-regional and multi-departmental resource allocation also suffers from information silos. Personnel, equipment, and aerial firefighting resources are scattered across different management units, lacking a unified online catalog and dynamic availability tracking. Dispatch mainly relies on manual communication, making it difficult to achieve global optimization of resource allocation. This is especially pronounced in mountainous fire areas where communication conditions are poor, exacerbating the risks of information delays and conflicts in air-to-ground coordinated operations.

[0007] In summary, the existing forest fire prevention and control system has significant shortcomings in real-time fire information perception, spread trend prediction, data integration and display, intelligent command and decision-making, and cross-regional collaborative dispatch. 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 dispatch to solve these problems.

[0008] In terms of observation technology, lidar, cloud measuring instruments, and multi-source sensors have been gradually applied to acquire key quantities such as boundary layer wind fields, smoke echoes, and aerosol concentrations, providing information with high spatiotemporal resolution and offering new means for fire environment perception. However, most existing applications remain at the level of monitoring and independent display, and the observation data and spread trend prediction models have not been deeply integrated, making it impossible to directly form forward-looking trend information.

[0009] In 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 abrupt changes in mountain wind fields or fire-wind field coupling. Coupled atmosphere-fire behavior models are more physically accurate and can consider the feedback of fire source heat flux on local wind fields, but they are computationally expensive, difficult to implement in operational settings, and cannot meet the real-time requirements of emergency scenarios. Furthermore, these models have limited ability to rapidly assimilate real-time observation data, making it difficult to quickly update prediction results and resulting in model results that fail to reflect the latest fire situation in a timely manner.

[0010] In recent years, some emergency management platforms have begun to introduce digital twin technology, unifying the display of satellite remote sensing, ground observation, video surveillance, and model prediction results in a three-dimensional scene, thus improving the intuitiveness of situational awareness. However, existing digital twin platforms are mainly used for information visualization and lack deep integration with emergency graded response, cross-regional intelligent resource scheduling, and air-ground collaborative operations. They cannot directly convert model simulation results into executable tasks and resource allocation instructions, thus limiting their overall application value.

[0011] To address the aforementioned issues, existing technical solutions improve individual aspects, but fail to form a complete system. Some solutions focus on observation, utilizing equipment such as lidar and cloud measuring instruments to acquire real-time information on fire location, smoke transport, and near-surface wind fields, thus improving the spatiotemporal resolution of fire information. However, these solutions are primarily geared towards monitoring departments, with data processing and result presentation remaining relatively independent. Observational results cannot be quickly integrated into predictive models, nor do they directly serve emergency command.

[0012] Another approach relies on empirical fire spread models to predict fire development trends, generating fire line propagation paths in a short time, 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 observation data. If fire conditions change abruptly, the model results can easily deviate from the actual fire situation.

[0013] Some solutions attempt to introduce coupled atmosphere-wildfire behavior models to enhance the physical realism of predictions, providing a more comprehensive description of wildfire-wind field coupling and topographic impacts. However, these models are computationally intensive and have long update cycles, making them difficult to apply in real-time under emergency command scenarios. Furthermore, the model results are mostly output in the form of meteorological field data or fireline maps, lacking linkage mechanisms with emergency tiered response, resource allocation, and air-ground collaborative operations, thus failing to directly support tactical-level decision-making.

[0014] Some emergency management platforms primarily emphasize data visualization, using digital twin technology to present multi-source observations and model results in a 3D scene, improving the situational awareness experience. However, these platforms lack the ability to integrate the prediction, verification, and scheduling processes, failing to achieve rapid verification of prediction results, automated tiered response triggering, and intelligent scheduling of cross-departmental resources, resulting in significant breaks in the command chain.

[0015] In summary, while existing technological solutions possess certain advantages in individual aspects such as observation, prediction, or visualization, they suffer from common problems including a disconnect between monitoring and prediction, a break in the link between prediction and command, insufficient data fusion, and inefficient resource allocation. A complete closed-loop system encompassing "real-time monitoring—intelligent prediction—situation visualization—remote command—cross-regional resource optimization and allocation" has not yet been established. These issues have become major bottlenecks restricting the improvement of forest fire emergency response capabilities.

[0016] The analysis of the aforementioned background technology and existing technical solutions reveals that current forest fire prevention and control technologies still have significant shortcomings in fire monitoring, trend prediction, data fusion, remote command, and cross-regional resource allocation, making it difficult to meet the real-time, accuracy, and intelligence requirements of modern emergency response systems. The main drawbacks are as follows:

[0017] (1) Insufficient timeliness and completeness in obtaining fire scene information;

[0018] Current observation methods mainly rely on satellite remote sensing, fixed video surveillance, or manual patrols. Due to limitations in imaging cycles, terrain obstruction, and manpower, it is difficult to obtain core information such as fire location, smoke particulate concentration, and near-surface wind speed and direction in a timely manner, and the coverage and resolution are limited. Although advanced observation equipment such as lidar has high spatiotemporal resolution, it is mostly used alone, and the observation results are isolated, failing to achieve efficient collaboration with other information sources.

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

[0020] Existing fire spread prediction models are mainly divided into two categories: empirical models, which are fast to calculate but lack prediction accuracy, and coupled atmospheric-wildfire behavior models, which have high calculation accuracy but long operating cycles and are not suitable for real-time emergency applications. Both types of models generally lack the ability to quickly assimilate real-time observation data from lidar, UAV inspections, etc., and the prediction results deviate from the actual situation on the ground, making it difficult to meet the needs of short-term forecasts at the minute to hour level.

[0021] (3) Data from multiple sources is difficult to integrate, resulting in a limited and unbalanced situational awareness display;

[0022] Forest fire emergency response involves multiple data sources, including satellites, radar, video, and ground weather stations. However, existing systems lack unified standards in data format, coordinate reference, and time synchronization, resulting in fragmented information from multiple sources. This makes it impossible to comprehensively present fire points, smoke transport, wind field evolution, and spread trends within a unified spatial-temporal framework, limiting situational awareness. Commanders struggle to fully grasp the fire situation using existing platforms, leading to insufficient decision-making basis.

[0023] (4) The decision-making chain is incomplete, and there is a lack of closed loop between command and execution;

[0024] Existing platforms mostly remain at the stage of information display and manual analysis, unable to directly translate forecast results into tiered response measures and resource allocation plans. The deployment of firefighting forces relies on commanders' experience, lacks standardized decision support, and is inefficient and prone to errors. Furthermore, there is insufficient verification and dynamic correction between forecasts and actual fire conditions, lacking a closed-loop chain of "prediction-verification-re-prediction," resulting in low accuracy in command and dispatch.

[0025] (5) Cross-regional resource scheduling is inefficient and lacks coordination;

[0026] Firefighting personnel, vehicles, equipment, and aerial firefighting resources are scattered across different management units, lacking a unified online resource catalog and dynamic availability monitoring. Cross-departmental information communication and dispatching rely heavily on manual methods, making it difficult to achieve optimal resource allocation globally. Especially when communication conditions are poor and the fire scene is complex, air-ground coordinated operations suffer from information lag and operational conflicts, resulting in low resource utilization and constraining overall firefighting effectiveness.

[0027] To address the aforementioned shortcomings, this invention constructs a novel remote command system for forest fire fighting, using a digital twin as the remote command center and integrating real-time lidar monitoring with AI-powered meteorological model prediction. Structurally and functionally, it connects the monitoring, prediction, decision-making, execution, and dispatching stages, achieving closed-loop linkage across the entire chain. Specific objectives are as follows:

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

[0029] (2) Achieve deep coupling between observation data and prediction models. Quickly inject real-time observation results into the big data model of artificial intelligence meteorology to generate fire spread trend predictions at the minute to hour level, and maintain consistency between predictions and actual fire conditions through multiple rounds of updates.

[0030] (3) Construct a unified data fusion and digital twin potential display platform. Integrate observation and prediction information under a unified coordinate and time benchmark, and dynamically display fire points, smoke, wind fields and spread trends in three dimensions, so that commanders can intuitively grasp the overall picture of the fire.

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

[0032] (5) Achieve intelligent scheduling of resources across regions and departments. Construct a unified resource catalog and scheduling algorithm to realize online management and global optimal allocation of emergency forces from multiple departments, and ensure efficient collaboration between ground firefighting, UAV inspection and aerial firefighting. Summary of the Invention

[0033] The purpose of this invention is to solve the core problems in existing technologies, such as delayed fire information, disconnect between prediction and command, insufficient data fusion, and inefficient scheduling and coordination, by deeply integrating digital twin technology, lidar monitoring, and artificial intelligence meteorological models. This will build a remote and precise command system for forest fire fighting that can be implemented across regions and levels, and comprehensively improve the real-time performance, scientific nature, and operational safety of forest fire emergency response.

[0034] The technical solution of this invention:

[0035] A remote command system for forest fire fighting based on artificial intelligence meteorological big data models and digital twin technology includes a lidar monitoring module, an artificial intelligence meteorological big data 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 fire scene information and transmits it to the artificial intelligence meteorological big data model prediction module for trend prediction. The prediction results and monitoring data are then fused with the digital twin potential display module to display the fire scene situation information. Commanders make decisions based on the three-dimensional visualized fire scene environment. The command decision-making and emergency response module generates tiered responses and operational plans, which are then executed by the personnel, material, and air-ground coordinated dispatch module. Unmanned aerial vehicles (UAVs) inspect and monitor to verify the actual fire scene situation, and the feedback data is then entered into the remote command system for forest fire fighting to form a closed loop, ensuring dynamic consistency between prediction, command, and execution.

[0036] The aforementioned lidar monitoring module is deployed in key locations within important forest areas and mountainous forest regions, responsible for the real-time collection of basic fire information. The lidar monitoring module acquires monitoring data through observation units, inverts the echo characteristics of the monitoring data, and obtains the coordinates of the fire location, smoke aerosol concentration, aerosol profile, wind speed, wind direction, and boundary layer structure near the ground and in the boundary layer. This lidar monitoring module enables high-frequency dynamic sensing of key fire elements, with a data refresh cycle down to the minute level, ensuring the availability of first-hand information for prediction and command.

[0037] The AI-powered meteorological big data prediction module is responsible for making short-term predictions of fire spread trends and meteorological elements based on lidar monitoring results, ground meteorological station data, and regional meteorological background fields. The regional meteorological background fields are historical data of the station over the past few days based on reanalysis data. This AI-powered meteorological big data prediction module enables forward-looking predictions of fire situations from minutes to hours, providing a scientific basis for subsequent command and resource deployment.

[0038] The digital twin situation 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. This digital twin situation display module displays the fire situation in a dynamic and intuitive way, enabling commanders to clearly grasp the fire development process and future trends.

[0039] The command and 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, tiered response triggering, operational plan generation, and dynamic correction. Through the command and decision-making and emergency response module, real-time risk assessment, automatic tiered response triggering, intelligent operational plan generation, and closed-loop dynamic correction are achieved, avoiding complete reliance on commanders' experience and judgment, effectively improving response speed and decision-making scientificity, and providing reliable technical support for cross-regional and multi-departmental joint firefighting.

[0040] The personnel, material, and air-ground coordinated dispatch module dynamically dispatches various firefighting resources based on the task instructions generated by the command decision and emergency response module, enabling multi-departmental and multi-level collaborative operations. This module also enables efficient cross-departmental and cross-regional coordination, significantly improving resource utilization and operational safety.

[0041] The beneficial effects of this invention are:

[0042] (1) Fire scene information is acquired more in real time and is more complete. Existing technologies mostly rely on single observation methods. This invention achieves high-frequency data acquisition at the minute level through a lidar monitoring module, and standardizes and processes multiple elements such as fire location, smoke particulate matter concentration, wind speed and direction, which significantly improves the comprehensiveness and timeliness of fire scene information.

[0043] (2) Significantly improved predictive capability and dynamic updates. This invention employs an artificial intelligence meteorological large-scale model prediction module to rapidly assimilate lidar observation results into the model, achieving fire spread trend prediction at the minute to hour level, and possessing multi-round update capability, ensuring that the prediction results are synchronized with the actual fire situation. Compared with the shortcomings of existing semi-empirical or coupled models with prediction lag, 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 situation display module of this invention can integrate observation and prediction information in three dimensions under a unified coordinate and time reference, intuitively displaying the fire point, smoke diffusion, wind field and fire spread trend, providing commanders with a clear overall situation.

[0045] (4) Forming a complete hierarchical response and command closed loop. Unlike the shortcomings of existing technologies such as fragmented information and incomplete command chains, the command decision-making and emergency response module of this invention can determine the risk level based on multiple indicators such as the fire spread speed, the exposure of threat targets, and meteorological risks, and automatically trigger hierarchical emergency response to generate combat plans. Combined with UAV inspection and high-point monitoring verification, a dynamic closed loop of "prediction-verification-re-prediction" is formed, improving the accuracy of decision-making and the speed of response.

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

[0047] In summary, this invention organically integrates real-time lidar monitoring, AI-based meteorological model prediction, and digital twin technology to form a complete chain of monitoring, prediction, display, command, and dispatch, significantly improving the real-time performance, scientific rigor, and operational safety of forest fire fighting—something that is difficult to achieve with existing technologies. Detailed Implementation

[0048] The specific embodiments of the present invention will be further described below in conjunction with the technical solution.

[0049] A remote command system for forest fire fighting based on artificial intelligence meteorological big data models and digital twin technology includes a lidar monitoring module, an artificial intelligence meteorological big data 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 fire scene information and transmits it to the artificial intelligence meteorological big data model prediction module for trend prediction. The prediction results and monitoring data are then fused with the digital twin potential display module to display the fire scene situation information. Commanders make decisions based on the three-dimensional visualized fire scene environment. The command decision-making and emergency response module generates tiered responses and operational plans, which are then executed by the personnel, material, and air-ground coordinated dispatch module. Unmanned aerial vehicles (UAVs) inspect and monitor to verify the actual fire scene situation, and the feedback data is then entered into the remote command system for forest fire fighting to form a closed loop, ensuring dynamic consistency between prediction, command, and execution.

[0050] The lidar monitoring module is deployed in key locations in important forest areas and mountainous forest areas to collect basic fire information in real time. The lidar monitoring module acquires monitoring data through observation units, inverts the echo characteristics of the monitoring data, and obtains the coordinates of the fire point, smoke aerosol concentration, aerosol profile, wind speed, wind direction and boundary layer structure near the ground and boundary layer.

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

[0052] (1.2) Data processing: The location coordinates of the fire point and the smoke aerosol concentration are inverted by using the echo signal intensity, pulse time delay and Doppler frequency shift, and the wind profile algorithm is combined to calculate the wind speed, wind direction, boundary layer structure and aerosol profile of the near ground and boundary layer in real time;

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

[0054] This lidar monitoring module enables high-frequency dynamic sensing of key fire elements, with a data refresh cycle of up to minutes, ensuring the availability of first-hand information for prediction and command.

[0055] The AI-powered meteorological big data prediction module is responsible for making short-term predictions of fire spread trends and meteorological elements based on lidar monitoring results, ground meteorological station data, and regional meteorological background fields. The regional meteorological background fields are historical data from the past few days for the station, based on reanalysis data.

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

[0057] (2.2) Model framework: an artificial intelligence meteorological model trained on deep neural networks and large-scale meteorological data, which has a fire spread prediction module embedded in it to simulate the fire-wind interaction and the spread of fire with changes in terrain and meteorology; among them, the large-scale meteorological data includes 500hPa geopotential height, 200hPa wind speed and sea level pressure.

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

[0059] (2.4) Real-time update mechanism: Each time new lidar and monitoring data are received, the artificial intelligence meteorological big data model can be triggered to quickly recalculate, so as to realize the dynamic update of the prediction results and keep the prediction structure consistent with the actual fire site.

[0060] This AI-powered meteorological big data model prediction module enables forward-looking fire predictions at the minute to hour level, providing a scientific basis for subsequent command and resource deployment.

[0061] The aforementioned digital twin situation 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;

[0062] (3.1) Data fusion: The real-time monitoring data of lidar is fused with the prediction results of the artificial intelligence meteorological big data model according to a unified coordinate reference and time scale;

[0063] (3.2) 3D modeling: Based on high-precision terrain grid data, a 3D visualized fire scene environment is established, and the location of fire points, smoke diffusion range, high-resolution wind field, temperature and humidity field, and fire spread range are dynamically rendered;

[0064] (3.3) Interactive function: It supports commanders to view relevant parameters, historical retrospectives and trend projections in the three-dimensional visualized fire scene environment, and can directly deploy control commands based on the three-dimensional visualized fire scene environment, including evacuation routes, deployment areas and fire extinguishing points;

[0065] (3.4) Risk warning: Real-time prompts will be displayed in the form of layers to indicate fire threat areas, obstructed evacuation routes and helicopter operation windows.

[0066] This digital twin situation display module presents the fire situation in a dynamic and intuitive way, enabling commanders to clearly grasp the development process and future trends of the fire.

[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, graded response triggering, operation plan generation and dynamic correction;

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

[0069] First, based on the fire situation information from the digital twin situation 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 advance rate of the fire line and the expansion rate of the fire area;

[0072] (4.1.2) Exposure of threatened targets: A comprehensive analysis of the area and exposure degree of residential areas, infrastructure, and important forest land within the fire coverage area;

[0073] (4.1.3) Meteorological hazard index, combined with predicted wind speed, probability of sudden wind direction change, temperature and humidity to calculate fire risk index;

[0074] (4.1.4) Accessibility of firefighting forces: Assess the average time for firefighting teams to reach the fire scene, road accessibility, and the operational window for drone and helicopter firefighting.

[0075] (4.1.5) Historical fire data comparison indicators, referencing the difficulty of handling and the level of loss of similar fires in the past to classify and match them;

[0076] Risk levels are determined using a multi-indicator weighted comprehensive scoring method, and are divided into Level 1 fire (general fire), Level 2 fire (relatively large fire), Level 3 fire (major fire), and Level 4 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 forces can control the fire within 1 hour;

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

[0079] Level 3 fire: Spreading speed 30-60m / min, threat range 5-10km², the fire is developing rapidly and requires the deployment of more than 100 professional teams, more than two helicopters, and a large number of vehicles and materials to form a cross-regional, multi-force joint firefighting effort;

[0080] Special level fire: The spread rate is greater than 60m / min, the threat range exceeds 10km², the fire may spread in leaps or secondary fires, and cross-regional command and the activation of the highest level of response are required.

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

[0082] Based on the determined risk level, the command decision-making and emergency response module automatically calls upon a pre-set response plan library to generate corresponding emergency response instructions at different risk levels:

[0083] A Level 1 fire will automatically notify the local emergency response team to be dispatched and to strengthen fire monitoring.

[0084] A level-two fire alert triggered regional joint defense, deploying reinforcements and drones for monitoring.

[0085] Level III and Special Level fires will automatically request support from higher-level emergency departments and aerial firefighting, and will enter a regional or cross-provincial joint response mechanism.

[0086] The forest fire fighting remote command system combines the predicted fire development direction and speed to generate operational plan suggestions, including priority protection targets, evacuation routes, fire line blocking positions, helicopter operation windows, and drone patrol routes.

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

[0088] The command and decision-making and emergency response module works in real time with the drone inspection and high-point monitoring system to verify the fire situation and the execution of operations;

[0089] If the verification results show that the actual development of the fire deviates from the prediction results, the forest fire fighting remote command system will immediately trigger a risk reassessment and update the prediction results, correct the response measures, and achieve 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 collaborative scheduling module through a unified task management platform, forming a detailed task list that specifies the task objective, the executing unit, the task start time and the end time.

[0092] The command, decision-making and emergency response module monitors the mission execution status in real time, issues alerts for delays, resource shortages or security risks, and can automatically escalate the response level and request reinforcements when necessary.

[0093] Through the command and decision-making and emergency response module, the system enables real-time risk assessment, automatic triggering of tiered responses, intelligent generation of operational plans, and closed-loop dynamic correction. This avoids relying entirely on the commander's experience and judgment, effectively improving response speed and the scientific nature of decision-making, and providing reliable technical support for cross-regional and multi-departmental joint firefighting efforts.

[0094] The personnel, material, and air-ground coordinated dispatch module dynamically dispatches various firefighting resources based on the task instructions generated by the command decision and emergency response module, enabling multi-departmental and multi-level collaborative operations.

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

[0096] Scheduling algorithm: Taking into account road conditions, fire situation, operational safety and arrival timeliness, automatically generates the optimal task-resource matching scheme and combat route;

[0097] Air-ground coordination: The forest fire fighting remote command system marks no-fly zones, workable airspace and fire danger zones through three-dimensional visualization of the fire scene environment, avoiding air-ground conflicts and ensuring coordinated operations between helicopter water drops, drone inspections and ground fire fighting;

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

[0099] This personnel and material coordination module enables efficient cross-departmental and cross-regional coordination, significantly improving resource utilization and operational safety.

Claims

1. A remote command system for forest fire fighting based on an artificial intelligence meteorological big data model and digital twin technology, characterized in that, The remote command system for forest fire fighting includes a lidar monitoring module, an artificial intelligence meteorological big data 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 fire information and transmits it to the artificial intelligence meteorological big data model prediction module for trend prediction. The prediction results and monitoring data are then integrated with the digital twin potential display module to display the fire situation information. Commanders make decisions based on the 3D visualized fire environment. The command decision-making and emergency response module generates tiered responses and operational plans, which are then executed by the personnel, material, and air-ground coordinated dispatch module. Unmanned aerial vehicles (UAVs) inspect and monitor to verify the actual fire situation, and the feedback data is then entered into the remote command system for forest fire fighting to form a closed loop, ensuring dynamic consistency between prediction, command, and execution. The lidar monitoring module is deployed in key locations in important forest areas and mountainous forest areas to collect basic fire information in real time. The lidar monitoring module acquires monitoring data through observation units, inverts the echo characteristics of the monitoring data, and obtains the coordinates of the fire point, smoke aerosol concentration, aerosol profile, wind speed, wind direction and boundary layer structure near the ground and boundary layer. (1.1) Equipment composition: The observation unit consists of lidar, cloud measuring instrument and auxiliary meteorological sensors, which has all-weather and all-round automatic monitoring capabilities; (1.2) Data processing: The location coordinates of the fire point and the smoke aerosol concentration are inverted by using the echo signal intensity, pulse time delay and Doppler frequency shift, and the wind profile algorithm is combined to calculate the wind speed, wind direction, boundary layer structure and aerosol profile of the near ground and boundary layer in real time; (1.3) Data output: Output the fire point location coordinates, wind speed, wind direction, aerosol profile and smoke aerosol concentration profile data after quality control, and upload them to the artificial intelligence meteorological big model prediction module and digital twin potential display module according to the unified data interface protocol. The AI-powered meteorological big data prediction module is responsible for making short-term predictions of fire spread trends and meteorological elements based on lidar monitoring results, ground meteorological station data, and regional meteorological background fields. The regional meteorological background fields are historical data from the past few days of the stations based on reanalysis data. (2.1) Input data: fire location, wind speed, wind direction, aerosol profile, smoke aerosol concentration, and temperature and humidity obtained from the ground weather station from the lidar monitoring module; (2.2) Model framework: an artificial intelligence meteorological model trained on deep neural networks and large-scale meteorological data, which has a fire spread prediction module embedded in it to simulate the fire-wind interaction and the spread of fire with changes in terrain and meteorology; among them, the large-scale meteorological data includes 500hPa geopotential height, 200hPa wind speed and sea level pressure. (2.3) Prediction results: Output the location of the fire point, high-resolution wind field, temperature and humidity field, smoke diffusion range and fire spread range for the next few hours; among which, the fire spread range includes the burning area and the speed of fire line advance; (2.4) Real-time update mechanism: Each time new lidar and monitoring data are received, the artificial intelligence meteorological big data model can be triggered to quickly recalculate, so as to realize the dynamic update of the prediction results and keep the prediction structure consistent with the actual fire scene. The aforementioned digital twin situation 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; (3.1) Data fusion: The real-time monitoring data of lidar is fused with the prediction results of the artificial intelligence meteorological big data model according to a unified coordinate reference and time scale; (3.2) 3D modeling: Based on high-precision terrain grid data, a 3D visualized fire scene environment is established, and the location of fire points, smoke diffusion range, high-resolution wind field, temperature and humidity field, and fire spread range are dynamically rendered; (3.3) Interactive function: It supports commanders to view relevant parameters, historical retrospectives and trend projections in the three-dimensional visualized fire scene environment, and can directly deploy control commands based on the three-dimensional visualized fire scene environment, including evacuation routes, deployment areas and fire extinguishing points; (3.4) Risk warning: Real-time display of fire threat areas, obstructed evacuation routes, and helicopter operation windows in the form of layers; 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, graded response triggering, operation plan generation and dynamic correction; (4.1) Risk assessment and judgment criteria; First, based on the fire situation information from the digital twin situation 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 advance rate of the fire line and the expansion rate of the fire area; (4.1.2) Exposure of threatened targets: A comprehensive analysis of the area and exposure degree of residential areas, infrastructure, and important forest land within the fire coverage area; (4.1.3) Meteorological hazard index, combined with predicted wind speed, probability of sudden wind direction change, temperature and humidity to calculate fire risk index; (4.1.4) Accessibility of firefighting forces: Assess the average time for firefighting teams to reach the fire scene, road accessibility, and the operational window for drone and helicopter firefighting. (4.1.5) Historical fire data comparison indicators, with reference to the difficulty of handling and the level of loss of previous fires for classification and matching; Risk levels are determined using a multi-indicator weighted comprehensive scoring method, and are divided into Level 1 fire, Level 2 fire, Level 3 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 forces can control the fire within 1 hour; Level II fire: Spreading speed 10-30m / min, threat range 1-5km², unstable weather conditions, firefighting forces need to coordinate with multiple units; Level 3 fire: Spreading speed 30-60m / min, threat range 5-10km², the fire is developing rapidly and requires the deployment of more than 100 professional teams, more than two helicopters, and a large number of vehicles and materials to form a cross-regional, multi-force joint firefighting effort; Special level fire: The spread rate is greater than 60m / min, the threat range exceeds 10km², the fire shows jumping spread or secondary fire, and cross-regional command and the highest level of response are required. (4.2) Hierarchical response triggering and decision generation; Based on the determined risk level, the command decision-making and emergency response module automatically calls upon a pre-set response plan library to generate corresponding emergency response instructions at different risk levels: A Level 1 fire will automatically notify the local emergency response team to be dispatched and to strengthen fire monitoring. A level-two fire alert triggered regional joint defense, deploying reinforcements and drones for monitoring. Level III and Special Level fires will automatically request support from higher-level emergency departments and aerial firefighting, and will enter a regional or cross-provincial joint response mechanism. The forest fire fighting remote command system combines the predicted fire development direction and speed to generate operational plan suggestions, including priority protection targets, evacuation routes, fire line blocking positions, helicopter operation windows, and drone patrol routes. (4.3) Real-time verification and closed-loop correction; The command and decision-making and emergency response module works in real time with the drone inspection and high-point monitoring system to verify the fire situation and the execution of operations; If the verification results show that the actual development of the fire deviates from the prediction results, the forest fire fighting remote command system will immediately trigger a risk reassessment and update the prediction results, correct the response measures, and realize 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 collaborative scheduling module through a unified task management platform, forming a detailed task list that specifies the task objective, the executing unit, the task start time and the end time. The command, decision-making and emergency response module monitors the mission execution status in real time, alerts for delays, insufficient resources or security risks, and can automatically escalate the response level and request reinforcements. The personnel, material, and air-ground coordinated dispatch module dynamically dispatches various firefighting resources based on the task instructions generated by the command decision and emergency response module, enabling multi-departmental and multi-level collaborative operations. Resource Catalog: The forest fire fighting remote command system has a built-in unified emergency resource database, covering personnel, vehicles, fire fighting equipment, fire fighting helicopters and drones, water sources, and updates resource location and availability in real time; Scheduling algorithm: Taking into account road conditions, fire situation, operational safety and arrival timeliness, automatically generates the optimal task-resource matching scheme and combat route; Air-ground coordination: The forest fire fighting remote command system marks no-fly zones, workable airspace and fire danger zones through three-dimensional visualization of the fire scene environment, avoiding air-ground conflicts and ensuring coordinated operations between helicopter water drops, drone inspections and ground fire fighting; Execution tracking: The command center tracks the progress and effectiveness of mission execution in real time and updates the dispatch plan as the fire situation changes.