Fire extinguishing task planning and efficiency evaluation system for fixed-wing aircraft

Abstract

The invention relates to the technical field of fixed-wing aircraft fire extinguishing, in particular to a fixed-wing aircraft fire extinguishing task planning and efficiency evaluation system. Comprising a dynamic environment modeling module, an intelligent task planning module, a fire extinguishing agent distribution prediction module, a high-precision bomb dropping control module, an efficiency real-time evaluation module, a heterogeneous data collaboration module, a self-adaptive model iteration module and a man-machine collaborative decision-making module. The dynamic environment modeling module is used for real-time meteorological data fusion and three-dimensional terrain reconstruction. Based on a quantum annealing multi-objective optimization and multiphase flow coupling correction method, a flight path considering efficiency and precision is generated through environmental data fusion and path optimization, then fire extinguishing agent distribution is corrected and predicted based on physical simulation and data driving, and finally accurate execution of a bomb dropping trajectory is ensured through anti-interference guidance. A'planning-prediction-execution 'closed loop is formed, and the problems that a traditional scheme is low in efficiency and the fire extinguishing task of the fixed-wing aircraft cannot be planned and evaluated are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of fixed-wing aircraft fire extinguishing, and particularly to a fixed-wing aircraft fire extinguishing mission planning and effectiveness evaluation system. Background Art

[0002] Traditional forest fire extinguishing mainly relies on helicopter bucket operations. The application of fixed-wing aircraft is limited due to the lack of accurate bomb dropping verification methods. The existing technology cannot effectively calculate the distribution of fire extinguishing agents and verify the accuracy of mathematical models, resulting in low fire extinguishing efficiency and serious waste of resources. At the same time, the fire scene is affected by multiple factors such as flight state, natural environment, and fire extinguishing agent characteristics, and there is an urgent need for dynamic planning and real-time correction capabilities. Precise bomb dropping needs to solve problems such as airflow disturbance and navigation error, and at the same time, it is necessary to verify the coverage effect of fire extinguishing agents to reduce personnel and property losses.

[0003] Therefore, the general bomb dropping path depends on the pilot's visual judgment, which is easily interfered by the fire scene smoke, with low accuracy. The distribution of fire extinguishing agents lacks quantitative evaluation, making it difficult to optimize the dropping strategy. The traditional mathematical model does not consider real-time environmental changes, resulting in prediction deviations, lacking a dynamic correction mechanism, and the simulation results being disconnected from the actual scenario. The single-machine data is not shared, and the model optimization is limited to local experience. The test data is easily tampered with or lost, and the verification process cannot be traced. At the same time, the fire spreads quickly, but the traditional method takes a long time to adjust the path and cannot cope with sudden fires.

[0004] Based on this, the present invention provides a fixed-wing aircraft fire extinguishing mission planning and effectiveness evaluation system to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a fixed-wing aircraft fire extinguishing mission planning and effectiveness evaluation system to solve the problems mentioned in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A fixed-wing aircraft fire extinguishing mission planning and effectiveness evaluation system, including a dynamic environment modeling module, an intelligent mission planning module, a fire extinguishing agent distribution prediction module, a high-precision bomb dropping control module, an effectiveness real-time evaluation module, a heterogeneous data collaboration module, an adaptive model iteration module, and a human-machine collaborative decision-making module;

[0008] The dynamic environment modeling module is used for real-time meteorological data fusion and three-dimensional terrain reconstruction;

[0009] The intelligent mission planning module is used for multi-objective path optimization and dynamic priority adjustment;

[0010] The fire extinguishing agent distribution prediction module is used for fluid dynamic simulation and historical data correction;

[0011] The high-precision bomb dropping control module is used for timing synchronization and navigation fusion;

[0012] The real-time effectiveness evaluation module is used for thermal field monitoring and local effectiveness feedback;

[0013] The heterogeneous data collaboration module is used for data archiving and cross-aircraft parameter sharing;

[0014] The adaptive model iteration module is used for hyperparameter self-optimization and structural dynamic adaptation;

[0015] The human-machine collaborative decision-making module is used for three-dimensional situation projection and instruction semantic parsing.

[0016] Preferably, the dynamic environment modeling module further includes a real-time meteorological data fusion unit and a three-dimensional terrain reconstruction unit;

[0017] The real-time meteorological data fusion unit is used for integrating multi-source meteorological data and airborne sensor information, dynamically updating flight environment parameters, and providing real-time input for path planning;

[0018] The three-dimensional terrain reconstruction unit is used for constructing a high-precision terrain model, predicting the fire extinguishing agent diffusion path and obstacle distribution, and supporting the safety assessment of the bomb dropping point.

[0019] Preferably, the intelligent task planning module further includes a multi-objective path optimization unit and a dynamic priority adjustment unit;

[0020] The multi-objective path optimization unit is used for generating a global flight trajectory that meets the requirements of bomb dropping accuracy, fuel efficiency, and fire suppression;

[0021] The dynamic priority adjustment unit is used for dynamically adjusting the bomb dropping sequence and path according to the real-time thermal radiation data of the fire scene to cope with sudden changes in the fire situation.

[0022] Preferably, the fire extinguishing agent distribution prediction module further includes a fluid dynamics simulation unit and a historical data correction unit;

[0023] The fluid dynamics simulation unit is used for simulating the aerial diffusion process of the fire extinguishing agent and outputting a three-dimensional model of the coverage range and concentration distribution;

[0024] The historical data correction unit is used for correcting the simulation parameters based on historical bomb dropping results to improve the matching degree between the prediction model and the actual scenario.

[0025] Preferably, the high-precision bomb dropping control module further includes a timing synchronization unit and a navigation fusion unit;

[0026] The timing synchronization unit is used for coordinating the aircraft attitude, speed, and bomb release action to reduce the influence of airflow disturbance on the dropping accuracy;

[0027] The navigation fusion unit is used to integrate multi-source navigation data, correct the missile guidance trajectory in real time, and ensure that the landing point error is controllable.

[0028] Preferably, the real-time effectiveness evaluation module further includes a thermal field monitoring unit and a local effectiveness feedback unit;

[0029] The thermal field monitoring unit is used to collect real-time data on the change of fire field temperature and quantify the cooling effect of the area covered by the fire extinguishing agent;

[0030] The local effectiveness feedback unit is used to generate fire extinguishing effectiveness indicators and drive the dynamic adjustment of the mission planning module.

[0031] Preferably, the heterogeneous data collaboration module further includes a data certification unit and a cross-aircraft parameter sharing unit;

[0032] The data certification unit is used to perform distributed storage and verification on flight parameters and bomb dropping records to ensure that the test data cannot be tampered with;

[0033] The cross-aircraft parameter sharing unit is used to realize collaborative optimization of multi-aircraft model parameters and avoid the limitations of single-aircraft data.

[0034] Preferably, the adaptive model iteration module further includes a hyperparameter self-optimization unit and a structure dynamic adaptation unit;

[0035] The hyperparameter self-optimization unit is used to automatically adjust the parameter configuration of the fire extinguishing agent diffusion model and shorten the model training period;

[0036] The structure dynamic adaptation unit is used to adjust the logical architecture of the mission planning model according to the scale of the fire field and the environmental complexity.

[0037] Preferably, the human-machine collaborative decision-making module further includes a three-dimensional situation projection unit and an instruction semantic analysis unit;

[0038] The three-dimensional situation projection unit is used to map the real-time situation of the fire field and the bomb dropping path to an interactive three-dimensional sand table to assist in human decision-making;

[0039] The instruction semantic analysis unit is used to convert the pilot's voice instructions into mission planning parameters to achieve natural language interaction control.

[0040] Based on the above system, the present invention also proposes a method for mission planning and effectiveness evaluation of a fixed-wing aircraft for fire extinguishing, including the following steps:

[0041] S1. Integrate real-time satellite meteorological data of wind speed, wind direction, temperature and humidity and airborne sensor data, dynamically correct flight environment parameters, construct a high-precision three-dimensional terrain model through LiDAR point cloud and GIS matching, and predict the fire extinguishing agent diffusion path and obstacle distribution;

[0042] S2. Based on multi-objective constraints such as fuel efficiency, fire suppression requirements, and bombing accuracy, use an optimization algorithm to generate the optimal flight path. According to the real-time thermal imaging data of the fire scene, dynamically adjust the bombing priority and path to cope with sudden changes in the fire situation;

[0043] S3. Simulate the diffusion, atomization, and surface coverage processes of the fire extinguishing agent affected by airflows in the air to generate a three-dimensional concentration distribution model. Combine historical bombing measurement data to dynamically correct the simulation parameters of viscosity and diffusion coefficient to improve the prediction accuracy;

[0044] S4. Integrate GNSS and inertial navigation data to real-time correct the missile guidance trajectory, suppress the deviation caused by airflow disturbance. By synchronizing the aircraft attitude and the missile release timing sequence, ensure that the landing point error ≤ 5 meters. Real-time collect the fire scene temperature change data, evaluate the cooling effect of the area covered by the fire extinguishing agent, and feedback the results to the path planning module to drive the next round of task optimization.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] The present invention improves the credibility of environmental parameters through the real-time integration of satellite meteorology, LiDAR terrain, and airborne sensors. Predicts the diffusion path through three-dimensional terrain reconstruction, avoids dangerous areas, generates the global optimal path by comprehensively considering fuel efficiency, fire suppression, and bombing accuracy, reorders the bombing sequence in real-time based on thermal imaging data, quickly responds to sudden changes in the fire situation, simulates the diffusion process through CFD coupled with the discrete element method, dynamically corrects the viscosity and diffusion coefficient by combining historical data, through the fusion of Beidou / GNSS and inertial navigation, the landing point error ≤ 5 meters, far exceeding the traditional manual bombing accuracy, the flight parameters and bombing records are stored with anti-tampering to ensure the reliability of test data, breaks through the limitations of single-machine data through multi-aircraft model parameter sharing, improves the global accuracy, quantifies the cooling effect of the fire extinguishing agent through infrared thermal radiation, feedback drives path optimization, automatically adjusts the model parameters and logical architecture to adapt to complex fire scenes. At the same time, the real-time fire scene situation is visualized to assist the pilot in making quick decisions, and the voice commands are directly mapped to task parameters to reduce the operation complexity;

[0047] In summary, based on the quantum annealing multi-objective optimization and multiphase flow coupling correction method, the present invention first generates a flight path that takes into account both efficiency and accuracy through environmental data fusion and path optimization, then predicts the distribution of the fire extinguishing agent based on physical simulation and data-driven correction, and finally ensures the accurate execution of the bombing trajectory through anti-interference guidance, forming a "planning - prediction - execution" closed loop, solving the problems of low efficiency of traditional solutions and the inability to plan and evaluate the fixed-wing aircraft fire extinguishing task. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Shows the flow chart of the fixed-wing aircraft fire extinguishing task planning and effectiveness evaluation method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0049] Next, in combination with the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] Embodiment 1

[0051] Please refer to Figure 1 , the present invention proposes a fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation system, including a dynamic environment modeling module, an intelligent mission planning module, a fire extinguishing agent distribution prediction module, a high-precision bomb dropping control module, an effectiveness real-time evaluation module, a heterogeneous data collaboration module, an adaptive model iteration module, and a human-machine collaborative decision-making module;

[0052] Among them, it should be noted that the dynamic environment modeling module is used for real-time meteorological data fusion and three-dimensional terrain reconstruction, the intelligent mission planning module is used for multi-objective path optimization and dynamic priority adjustment, the fire extinguishing agent distribution prediction module is used for fluid dynamic simulation and historical data correction, the high-precision bomb dropping control module is used for time sequence synchronization and navigation fusion, the effectiveness real-time evaluation module is used for thermal field monitoring and local effectiveness feedback, the heterogeneous data collaboration module is used for data archiving and cross-aircraft parameter sharing, the adaptive model iteration module is used for hyperparameter self-optimization and structural dynamic adaptation, and the human-machine collaborative decision-making module is used for three-dimensional situation projection and instruction semantic analysis;

[0053] In this embodiment, it should also be noted that the dynamic environment modeling module further includes a real-time meteorological data fusion unit and a three-dimensional terrain reconstruction unit;

[0054] Furthermore, the real-time meteorological data fusion unit is used to integrate multi-source meteorological data and airborne sensor information, dynamically update flight environment parameters, and provide real-time input for path planning. The three-dimensional terrain reconstruction unit is used to construct a high-precision terrain model, predict the fire extinguishing agent diffusion path and obstacle distribution, and support the safety evaluation of the bomb dropping point;

[0055] Through the data interface of the satellite meteorological data receiver and the airborne sensor, the wind speed, wind direction, temperature, and humidity parameters are collected in real time, and the multi-source data is calibrated and integrated using the weighted fusion technology to dynamically update the flight environment database and provide high-confidence input for the path planning module;

[0056] Through airborne LiDAR scanning to obtain ground point cloud data, combined with the GIS geographic information layer, a high-precision three-dimensional terrain model is generated using the spatial interpolation algorithm, and the dangerous area is marked through the obstacle recognition algorithm to support the prediction of the fire extinguishing agent diffusion path;

[0057] In this embodiment, it should also be noted that the intelligent task planning module further includes a multi-objective path optimization unit and a dynamic priority adjustment unit;

[0058] Furthermore, the multi-objective path optimization unit is used to generate a global flight trajectory that meets the requirements of bombing accuracy, fuel efficiency, and fire suppression. The dynamic priority adjustment unit is used to dynamically adjust the bombing order and path according to the real-time thermal radiation data of the fire scene to cope with sudden changes in the fire situation;

[0059] Based on the fuel consumption model of the aircraft, the fire spread prediction model, and the bombing accuracy constraint, a multi-objective optimization problem is constructed. The heuristic search algorithm is used to generate the globally optimal flight path, and the waypoint sequence is output through the visualization interface;

[0060] The thermal radiation distribution data of the fire scene is collected in real time through an infrared thermal imaging camera. Combining with the fire spread speed prediction model, the urgency of each fire area is dynamically calculated, the bombing priority is reallocated, and the flight path is corrected through the heading angle adjustment instruction;

[0061] In this embodiment, it should also be noted that the fire extinguishing agent distribution prediction module further includes a fluid dynamics simulation unit and a historical data correction unit;

[0062] Furthermore, the fluid dynamics simulation unit is used to simulate the aerial diffusion process of the fire extinguishing agent and output a three-dimensional model of the coverage range and concentration distribution. The historical data correction unit is used to correct the simulation parameters based on the historical bombing results to improve the matching degree between the prediction model and the actual scenario;

[0063] Based on the computational fluid dynamics framework, the physical property parameters of flight altitude, wind speed, fire extinguishing agent density, and viscosity are input to simulate the aerial diffusion and surface coverage process after the release of the fire extinguishing agent, and a three-dimensional concentration distribution heat map is generated;

[0064] By comparing the actual coverage range in the historical bombing records retrieved from the database with the simulation results, the viscosity and diffusion coefficient parameters of the fluid model are adjusted using the error backpropagation mechanism to improve the subsequent prediction accuracy;

[0065] In this embodiment, it should also be noted that the high-precision bombing control module further includes a timing synchronization unit and a navigation fusion unit;

[0066] Furthermore, the timing synchronization unit is used to coordinate the aircraft attitude, speed, and bomb release action to reduce the impact of airflow disturbance on the delivery accuracy. The navigation fusion unit is used to integrate multi-source navigation data and real-time correct the bomb guidance trajectory to ensure that the landing point error is controllable;

[0067] The flight control computer collects the aircraft attitude pitch angle, roll angle, airspeed, and bomb bay status in real time, designs a timing trigger logic to ensure that the bomb release is strictly matched with the flight state, and offsets the influence of airflow disturbance;

[0068] Integrate the Beidou / GNSS positioning signal and the data of the inertial navigation system (INS), eliminate the positioning noise through a filtering algorithm, and output the guidance trajectory correction instruction of the bomb body in real time to ensure that the landing point error ≤ 5 meters;

[0069] In this embodiment, it should also be noted that the effectiveness real-time evaluation module further includes a thermal field monitoring unit and a local effectiveness feedback unit;

[0070] Furthermore, the thermal field monitoring unit is used to collect the real-time data of the fire field temperature change and quantify the cooling effect of the fire extinguishing agent coverage area, and the local effectiveness feedback unit is used to generate the fire extinguishing effectiveness index and drive the dynamic adjustment of the mission planning module;

[0071] Deploy a multi-spectral infrared sensor array to collect the real-time data of the fire field temperature distribution, identify the fire extinguishing agent coverage area through the temperature gradient analysis algorithm, and calculate the regional average cooling rate;

[0072] Quantify the cooling rate and coverage area index into the fire extinguishing effectiveness index (MEI), compare the data differences before and after bomb dropping in real time through the edge computing node, generate an effectiveness evaluation report and feedback it to the mission planning module;

[0073] In this embodiment, it should also be noted that the heterogeneous data collaboration module further includes a data storage and verification unit and a cross-aircraft parameter sharing unit;

[0074] Furthermore, the data storage and verification unit is used for distributed storage and verification of flight parameters and bomb dropping records to ensure that the test data cannot be tampered with, and the cross-aircraft parameter sharing unit is used to realize the collaborative optimization of multi-aircraft model parameters and avoid the limitations of single-aircraft data;

[0075] Adopt a lightweight blockchain node network to perform hash encryption and distributed storage on the key data of flight parameters and bomb dropping records, and ensure data integrity and anti-tampering through the consensus mechanism;

[0076] Upload the fluid correction coefficient parameters optimized by the single-aircraft model to the cloud federated learning platform through an encrypted communication channel, aggregate multi-aircraft data to generate global optimization parameters and distribute them to each terminal;

[0077] In this embodiment, it should also be noted that the adaptive model iteration module further includes a hyperparameter self-optimization unit and a structure dynamic adaptation unit;

[0078] Further, the hyperparameter self-optimization unit is used to automatically adjust the parameter configuration of the fire extinguishing agent diffusion model, shortening the model training cycle, and the structure dynamic adaptation unit is used to adjust the logical architecture of the mission planning model according to the fire scale and environmental complexity;

[0079] Based on historical training data and real-time bombing results, automatically adjust the learning rate and iteration times hyperparameters of the fire extinguishing agent diffusion model, and accelerate model convergence through parallel computing;

[0080] According to the fire area and environmental terrain undulation of the fire scene, dynamically adjust the logical branches and decision weights of the mission planning model to adapt to different fire extinguishing scenarios;

[0081] In this embodiment, it should also be noted that the human-machine collaborative decision-making module further includes a three-dimensional situation projection unit and an instruction semantic parsing unit;

[0082] Further, the three-dimensional situation projection unit is used to map the real-time situation of the fire scene and the bombing path to an interactive three-dimensional sand table to assist manual decision-making, and the instruction semantic parsing unit is used to convert the pilot's voice instructions into mission planning parameters to achieve natural language interaction control;

[0083] Call the digital twin engine through the AR head-mounted device, and superimpose the real-time fire scene heat map, aircraft track and bombing path on the three-dimensional geographic sand table to support the pilot's touch interaction and perspective switching;

[0084] Deploy a natural language processing (NLP) engine to parse the pilot's voice instructions. In practical applications, for example:

[0085] "Prioritize covering the northeast area";

[0086] Extract keywords and map them to mission planning parameters, and calculate and feedback the bombing coordinate offset in practical applications;

[0087] In summary, in this embodiment, the dynamic environment modeling module of the present invention provides real-time environment input for mission planning and fire extinguishing agent prediction. The output of the intelligent mission planning module directly controls the bombing action and effectiveness evaluation process. The heterogeneous data collaboration module ensures the data credibility of model iteration and multi-aircraft collaboration. The human-machine collaborative decision-making module feeds back the manual intervention signal to the mission planning to form a closed-loop optimization link.

[0088] Embodiment 2

[0089] Please refer to Figure 1 , in practical applications, based on the above-mentioned fixed-wing aircraft fire extinguishing mission planning and effectiveness evaluation method of the system, specifically, it includes the following steps:

[0090] (1) Integrate satellite meteorological data of wind speed, wind direction, temperature and humidity and airborne sensor data in real time, and dynamically correct flight environment parameters;

[0091] Meteorological data fusion:

[0092] Input satellite weather data: wind speed v w 、wind directionθ w Or temperature and humidity H;

[0093] Airborne sensor data: air pressure P or temperature T;

[0094] In this step, the wind speed v is w , see formula (1):

[0095]

[0096] By weighted fusion of satellite and sensor wind speed data, the weight α is calculated by the variance of the two measurements. Dynamic calculation to improve the reliability of environmental parameters;

[0097] By matching LiDAR point cloud with GIS, a high-precision three-dimensional terrain model is constructed to predict the diffusion path of fire extinguishing agents and the distribution of obstacles;

[0098] Terrain Elevation Modeling:

[0099] Input LiDAR point cloud data {(x i ,y i ,z i )}, GIS terrain layer, see formula (2):

[0100]

[0101] Interpolate the discrete LiDAR point cloud based on the kernel function K, weight w i The point cloud density is adaptively adjusted to generate a continuous terrain elevation model for predicting the diffusion path of the fire extinguishing agent;

[0102] (2) Based on the multi-objective constraints of fuel efficiency, fire suppression requirements and bombing accuracy, the optimization algorithm is used to generate the optimal flight path and optimize the target definition, as shown in formula (3):

[0103]

[0104] The constraints are:

[0105]

[0106] Comprehensively consider fuel consumption, fire spread suppression error and bombing accuracy, and balance multi-objective optimization through weights λ1 / λ2 / λ3 to generate the global optimal path P;

[0107] Dynamically adjust the bomb - dropping priority and path according to the real - time thermal imaging data of the fire scene to cope with sudden changes in the fire situation;

[0108] Adopt the quantum annealing strategy, and the Hamiltonian is shown in Equation (4):

[0109]

[0110] Map the path optimization to the problem of minimizing the energy of the spin system. J ij represents the correlation strength of the path segment, γ is the penalty coefficient for constraint violation, and jump out of the local optimal solution through the quantum tunneling effect;

[0111] (3) Simulate the diffusion, atomization and surface coverage process of the fire - extinguishing agent in the air affected by the air flow, generate a three - dimensional concentration distribution model, and the control equations for multiphase flow simulation modeling are shown in Equation (5):

[0112]

[0113] Simulate the diffusion process of the water / chemical agent of the fire - extinguishing agent in the air affected by the air flow. F drag is the discrete particle drag force, which is used to calculate the atomization degree and coverage range;

[0114] Combine the measured data of historical bomb - dropping, dynamically correct the simulation parameters of viscosity and diffusion coefficient, improve the prediction accuracy, and the data - driven error correction is shown in Equation (6):

[0115]

[0116] Based on the measured data of historical bomb - dropping and the simulation results Through the coefficient β k Dynamically correct the fluid viscosity μ to improve the prediction accuracy;

[0117] (4) Integrate GNSS and inertial navigation data, real - time correct the missile guidance trajectory, suppress the deviation caused by air - flow disturbance, and ensure that the landing point error ≤ 5 meters by synchronizing the aircraft attitude and the missile body release timing sequence. The anti - interference guidance of the bomb - dropping trajectory, where the state - estimation equation for navigation data fusion is shown in Equation (7):

[0118]

[0119] Integrate Beidou / GNSS position data z k and inertial navigation data, and through the Kalman gain K k Real - time estimate the missile body state (position, velocity), and suppress the trajectory deviation caused by air - flow disturbance;

[0120] Collect real-time data on the temperature changes at the fire site, evaluate the cooling effect of the fire extinguishing agent coverage area, and feedback the results to the path planning module to drive the optimization of the next round of tasks. The trajectory correction strategy and adaptive weight adjustment are shown in Equation (8):

[0121]

[0122] According to the measurement noise variances σ of GNSS and inertial navigation 2 , dynamically allocate weights, give priority to trusting high-precision data sources, and ensure that the circular error probability of the landing point is ≤ 5 meters;

[0123] In summary, according to the steps in this embodiment, the present invention is based on the quantum annealing multi-objective optimization and multiphase flow coupling correction method. First, through environmental data fusion and path optimization, a flight path that takes into account both efficiency and accuracy is generated. Then, based on physical simulation and data-driven correction, the distribution of the fire extinguishing agent is predicted. Finally, through anti-interference guidance, the bomb dropping trajectory is accurately executed, forming a "planning - prediction - execution" closed loop.

[0124] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0125] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation system, characterized in that, It includes a dynamic environment modeling module, an intelligent task planning module, a fire extinguishing agent distribution prediction module, a high-precision bomb dropping control module, an effectiveness real-time evaluation module, a heterogeneous data collaboration module, an adaptive model iteration module, and a human-machine collaborative decision-making module; The dynamic environment modeling module is used for real-time meteorological data fusion and three-dimensional terrain reconstruction; The intelligent task planning module is used for multi-objective path optimization and dynamic priority adjustment; The fire extinguishing agent distribution prediction module is used for fluid dynamic simulation and historical data correction; The high-precision bomb dropping control module is used for time sequence synchronization and navigation fusion; The effectiveness real-time evaluation module is used for thermal field monitoring and local effectiveness feedback; The heterogeneous data collaboration module is used for data storage and cross-machine parameter sharing; The adaptive model iteration module is used for hyperparameter self-optimization and structural dynamic adaptation; The human-machine collaborative decision-making module is used for three-dimensional situation projection and instruction semantic analysis.

2. The fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation system according to claim 1, wherein The dynamic environment modeling module further includes a real-time meteorological data fusion unit and a three-dimensional terrain reconstruction unit; The real-time meteorological data fusion unit is used to integrate multi-source meteorological data and airborne sensor information, dynamically update flight environment parameters, and provide real-time input for path planning; The three-dimensional terrain reconstruction unit is used to construct a high-precision terrain model, predict the diffusion path of the fire extinguishing agent and the distribution of obstacles, and support the safety evaluation of the bomb dropping point.

3. The fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation system according to claim 2, wherein, The intelligent task planning module further includes a multi-objective path optimization unit and a dynamic priority adjustment unit; The multi-objective path optimization unit is used to generate a global flight trajectory that meets the requirements of bomb dropping accuracy, fuel efficiency, and fire suppression; The dynamic priority adjustment unit is used to dynamically adjust the bomb dropping order and path according to the real-time thermal radiation data of the fire field to cope with sudden changes in the fire situation.

4. The fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation system according to claim 3, characterized in that, The fire extinguishing agent distribution prediction module further includes a fluid dynamic simulation unit and a historical data correction unit; The fluid dynamic simulation unit is used to simulate the aerial diffusion process of the fire extinguishing agent and output a three-dimensional model of the coverage range and concentration distribution; The historical data correction unit is used to correct the simulation parameters based on historical bomb dropping results and improve the matching degree between the prediction model and the actual scenario.

5. The fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation system according to claim 4, characterized in that, The high-precision bomb dropping control module further includes a time sequence synchronization unit and a navigation fusion unit; The time sequence synchronization unit is used to coordinate the aircraft attitude, speed, and bomb body release action to reduce the impact of airflow disturbance on the dropping accuracy; The navigation fusion unit is used to integrate multi-source navigation data and real-time correct the guidance trajectory of the bomb body to ensure that the landing point error is controllable.

6. The fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation system according to claim 5, characterized in that, The effectiveness real-time evaluation module further includes a thermal field monitoring unit and a local effectiveness feedback unit; The thermal field monitoring unit is used to collect real-time data on the temperature change of the fire field and quantify the cooling effect of the area covered by the fire extinguishing agent; The local effectiveness feedback unit is used to generate fire extinguishing effectiveness indicators and drive the dynamic adjustment of the task planning module.

7. The fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation system according to claim 6, wherein, The heterogeneous data collaboration module further includes a data storage unit and a cross-machine parameter sharing unit; The data storage unit is used for distributed storage and verification of flight parameters and bomb dropping records to ensure that the test data cannot be tampered with; The cross-machine parameter sharing unit is used to achieve collaborative optimization of multi-machine model parameters and avoid the limitations of single-machine data.

8. The fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation system according to claim 7, characterized in that, The adaptive model iteration module further includes a hyperparameter self-optimization unit and a structure dynamic adaptation unit; The hyperparameter self-optimization unit is used to automatically adjust the parameter configuration of the fire extinguishing agent diffusion model and shorten the model training cycle; The structure dynamic adaptation unit is used to adjust the logical architecture of the task planning model according to the scale of the fire scene and the environmental complexity.

9. The fixed-wing aircraft fire extinguishing mission planning and effectiveness evaluation system according to claim 8, characterized in that, The human-machine collaborative decision-making module further includes a three-dimensional situation projection unit and an instruction semantic analysis unit; The three-dimensional situation projection unit is used to map the real-time situation of the fire scene and the bomb dropping path to an interactive three-dimensional sand table to assist manual decision-making; The instruction semantic analysis unit is used to convert the pilot's voice instructions into task planning parameters to achieve natural language interactive control.

10. A method for fixed-wing aircraft fire-fighting mission planning and effectiveness evaluation according to claims 1-9, characterized in that It includes the following steps: S1. Integrate real-time satellite meteorological data of wind speed, wind direction, temperature, and humidity and airborne sensor data, dynamically correct flight environment parameters, construct a high-precision three-dimensional terrain model through the matching of LiDAR point cloud and GIS, and predict the fire extinguishing agent diffusion path and obstacle distribution; S2. Based on multi-objective constraints such as fuel efficiency, fire suppression requirements, and bomb dropping accuracy, use an optimization algorithm to generate the optimal flight path, and dynamically adjust the bomb dropping priority and path according to the real-time thermal imaging data of the fire scene to cope with sudden changes in the fire situation; S3. Simulate the diffusion, atomization, and surface coverage process of the fire extinguishing agent affected by air flow in the air, generate a three-dimensional concentration distribution model, and dynamically correct the simulation parameters of viscosity and diffusion coefficient in combination with historical bomb dropping measurement data to improve prediction accuracy; S4. Integrate GNSS and inertial navigation data, correct the missile guidance trajectory in real time, suppress the deviation caused by air flow disturbance, ensure that the landing point error ≤ 5 m by synchronizing the aircraft attitude and the missile body release timing, collect the fire scene temperature change data in real time, evaluate the cooling effect of the fire extinguishing agent coverage area, and feedback the results to the path planning module to drive the next round of task optimization.

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