Artificial Intelligence-Based Unmanned Aerial Vehicle (UAV) Mission Automated Planning and Situation Display System

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) technology, specifically to an AI-based UAV mission automatic planning and situation display system. The system includes a mission requirement identification and dynamic priority adjustment module, a multi-mission path optimization module, a multi-dimensional situational awareness and data fusion module, a three-dimensional situation display module, a mission dynamic adjustment and feedback control module, a mission data storage and historical analysis module, and an emergency mission response and environmental interference adaptation module. By collecting mission requirements and environmental parameters in real time, the system can dynamically generate a mission priority queue and optimize path planning based on priorities, ensuring that UAVs have efficient response capabilities when performing reconnaissance, monitoring, search, and rescue missions. The system combines multi-source data fusion technology to display the three-dimensional situation of the mission environment in real time, providing intuitive support for decision-making. The self-optimization capability and adaptability to emergency situations of this invention lay the foundation for the long-term intelligent development of UAV missions.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to an artificial intelligence-based UAV mission automatic planning and situation display system. Background Technology

[0002] With the rapid development of drone technology, drones have been widely used in various fields such as military, civilian, and environmental monitoring. The mission execution efficiency and flexibility of drones have demonstrated enormous potential in scenarios such as reconnaissance, search and rescue, and agricultural monitoring. However, as mission requirements become more diverse and complex, traditional mission planning and execution methods are gradually proving insufficient, especially in adjusting mission priorities and real-time path planning in dynamic environments.

[0003] Existing technologies typically rely on static task planning and simple path control, lacking the ability to dynamically respond to environmental changes and task requirements. This approach not only leads to low task execution efficiency but can also result in resource waste and task failure. Furthermore, existing systems have limited capabilities in emergency task response, failing to promptly identify and adjust task priorities, thus hindering successful task execution in unforeseen circumstances. Therefore, a more intelligent and flexible UAV mission management system is urgently needed to address the demands for multi-task coordination and dynamic adjustment in complex environments. Summary of the Invention

[0004] This invention provides an artificial intelligence-based automatic mission planning and situation display system for unmanned aerial vehicles (UAVs).

[0005] The AI-based unmanned aerial vehicle (UAV) mission automatic planning and situation display system includes a mission requirement identification and priority dynamic adjustment module, a multi-mission path optimization module, a multi-dimensional situation perception and data fusion module, a three-dimensional situation display module, a mission dynamic adjustment and feedback control module, a mission data storage and historical analysis module, and an emergency mission response and environmental interference adaptation module.

[0006] The task requirement identification and priority dynamic adjustment module is used to collect and identify various task requirement information in the task scenario in real time, including reconnaissance, monitoring and search and rescue, and dynamically generate a task priority queue based on environmental parameters and the urgency of the task and the importance of the task objectives.

[0007] The multi-task path optimization module generates optimized paths that conform to the priorities of multiple tasks based on the output of the task requirement identification and priority dynamic adjustment module, using reinforcement learning algorithm and adaptive nonlinear path planning algorithm.

[0008] The multi-dimensional situational awareness and data fusion module includes a multi-source sensor fusion unit, a data processing unit, and a three-dimensional situational model generation unit. It collects real-time visual data, geographical location information, and environmental parameters (such as temperature, humidity, wind speed, etc.) within the UAV mission area, and generates a high-precision three-dimensional situational model through multi-source data fusion technology to realize a multi-dimensional dynamic display of the mission environment.

[0009] The three-dimensional situation display module is based on the three-dimensional situation model generated by the multi-dimensional situation perception and data fusion module. It displays the three-dimensional image of the UAV mission area in real time, and combines priority dynamic adjustment and path optimization information to show the real-time progress of mission execution. It also has the functions of real-time refresh and multi-level detail display.

[0010] The task dynamic adjustment and feedback control module works in conjunction with the three-dimensional situation display module. Based on the real-time displayed task situation information, it monitors abnormal situations during task execution (such as changes in task priority, path obstruction, or the emergence of new task requirements), and adjusts the multi-task path planning and priority queue in real time according to the feedback information.

[0011] The task data storage and historical analysis module is used to record priority adjustment data, path planning adjustment records, and three-dimensional data of multi-dimensional situation display during task execution. After the task is completed, data analysis is performed, and future task planning is optimized based on the analysis results.

[0012] The emergency mission response and environmental interference adaptation module is used to prioritize adjusting the mission priority queue and replan the path when there are emergency mission requirements or environmental interference (such as sudden obstacles, extreme weather, etc.), so as to ensure that the UAV can complete the mission efficiently and safely under complex conditions.

[0013] Optionally, the task requirement identification and priority dynamic adjustment module includes:

[0014] Real-time mission requirement information collection: Collect and identify various mission requirement information in UAV mission scenarios through a preset mission data collection unit, including reconnaissance, monitoring and search and rescue missions, to provide basic data for subsequent priority assessment;

[0015] Environmental parameter acquisition and task adaptability analysis: Acquire environmental parameters related to the task area, including temperature, humidity, wind speed and geographical location. Based on changes in environmental parameters, evaluate the adaptability of different tasks and filter out tasks that are not suitable for the current environment to ensure that the priority queue generated subsequently can effectively respond to the current environment.

[0016] Task urgency and objective importance assessment: Initial priority allocation of tasks based on task urgency and objective importance;

[0017] Task Priority Assessment Model Construction: Establish a task priority assessment model, using urgency, target importance, and environmental adaptability as the main inputs, and comprehensively calculate the task priority score;

[0018] Dynamic generation and sorting of priority queue: Sort all tasks according to their priority scores, generate a task priority queue, and arrange tasks from highest to lowest priority score;

[0019] Real-time dynamic updates to the task priority queue: The priority queue is dynamically adjusted based on real-time environmental changes and updated task requirements.

[0020] Optionally, the multi-task path optimization module includes:

[0021] Task priority queue reception and task target area confirmation: Receive the task priority queue output by the task requirement identification and priority dynamic adjustment module, identify high-priority tasks (the top 15% of the task priority queue are defined as high-priority tasks) and confirm the target area of ​​each task.

[0022] Construction of a multi-task path optimization model: Based on reinforcement learning algorithm and adaptive nonlinear path planning algorithm, a multi-task path optimization model is constructed. The path optimization model takes task priority queue, path distance, task resource consumption and environmental parameters as input variables, and the objective function is to minimize the total task path length and resource consumption.

[0023] Initial path generation and reinforcement learning training: Guided by the objective function of the path optimization model, initial paths for each task are generated according to the task priority queue, and the path generation process is trained through reinforcement learning algorithm;

[0024] Dynamic path adjustment in the path optimization model: Based on a real-time updated priority queue, the path is dynamically adjusted using the objective function of the path optimization model;

[0025] Path feasibility verification and adjustment: After path optimization is completed, the feasibility of the path is verified based on the resource consumption and environmental parameters in the path optimization model.

[0026] Optionally, the multi-dimensional situational awareness and data fusion module includes:

[0027] Multi-source sensor data acquisition: Multi-source data within the UAV mission area is acquired in real time through a multi-source sensor fusion unit. The multi-source data includes visual data, geographic location information, and environmental parameters, including temperature, humidity, and wind speed, ensuring that the multi-source sensor data covers multi-dimensional information of the UAV mission environment.

[0028] Data preprocessing and consistency calibration: The data processing unit preprocesses multi-source data, including denoising, completion and format conversion, to eliminate noise and missing data, and performs spatial and temporal consistency calibration on the multi-source data to ensure that the data are consistent in dimensions.

[0029] Data fusion model construction: Construct a data fusion model based on multi-source data fusion technology, taking visual data, geographic location information and environmental parameters as inputs;

[0030] 3D Situation Model Generation: The 3D situation model generation unit generates a 3D situation model based on the output of the data fusion model. The 3D situation model includes the geographical structure of the mission area, environmental changes, and obstacle distribution.

[0031] Optionally, the multi-dimensional situational awareness and data fusion module further includes:

[0032] Multi-source data real-time processing and fusion preparation: Multi-source data within the UAV mission area is collected and integrated in real time through the multi-source sensor fusion unit, and the collected multi-source data is input into the data processing unit for real-time processing and multi-source fusion to provide the latest situational information;

[0033] Real-time multidimensional data fusion computing: Based on the definition of the data fusion model, real-time processed visual data, geographic location information and environmental parameters are fused from multiple sources to generate situational data with real-time dynamic characteristics;

[0034] Dynamic updating of the 3D situation model: Using the 3D situation model generation unit, the real-time updated situation data is used as input to dynamically update the 3D situation model, so as to ensure that changes in the mission environment can be reflected in the model in real time.

[0035] Multi-dimensional situational information display of the mission environment: The dynamically updated three-dimensional situational model is displayed on the control terminal, presenting the geographical structure, environmental features and obstacle distribution information of the mission area in the form of three-dimensional images.

[0036] Optionally, the three-dimensional situation display module specifically includes:

[0037] Input to the 3D situation model: Receives a high-precision 3D situation model generated by the multi-dimensional situation awareness and data fusion module to ensure that the system can acquire the latest environmental status and mission information;

[0038] Real-time status monitoring and updates: Real-time monitoring of mission execution progress and environmental changes within the UAV mission area, including dynamic adjustment of mission priorities and path optimization information, to ensure that the system can obtain all dynamic information related to the mission in a timely manner;

[0039] 3D Image Generation and Display: Based on the input 3D situation model, a 3D image is generated through a preset graphics processing unit and displayed in real time on the operation terminal. The image display includes the geographical structure of the task area, the distribution of obstacles, and the real-time progress of task execution, enabling operators to intuitively observe the current status.

[0040] Real-time refresh and dynamic update: Combine real-time status monitoring information to dynamically update the 3D image;

[0041] Multi-level detail display function: Provides a multi-level detail display function, allowing operators to select different levels of information to view as needed.

[0042] Optionally, the task dynamic adjustment and feedback control module specifically includes:

[0043] Real-time task situation information reception: Receives real-time task situation information from the 3D situation display module, including current task priority, path status and new task requirements, to ensure that the system obtains the latest task execution environment;

[0044] Anomaly monitoring: Real-time monitoring of anomalies during task execution, including changes in task priority, path obstruction, and new task requirements;

[0045] Feedback information analysis: Based on the detected anomalies, the real-time feedback information is compared and analyzed with historical task data. By calculating the impact of various anomalies on task execution efficiency, the difference between the current task status and the historical status is assessed, and the direction of adjustment that needs to be determined is identified.

[0046] Dynamically adjust task path planning: Based on feedback information and analysis results, adjust multi-task path planning in real time to optimize task execution efficiency. For example, if the path is blocked, the module will automatically calculate a new optimal path and incorporate it into the current task execution plan.

[0047] Real-time adjustment of priority queue: The task priority queue is dynamically updated based on the latest task execution status.

[0048] Optionally, the task data storage and historical analysis module specifically includes:

[0049] Real-time recording of mission data: During the execution of UAV missions, priority adjustment data, path planning adjustment records, and three-dimensional data generated by multi-dimensional situation display are recorded in real time to ensure that all important information related to the mission is fully recorded and a complete mission execution archive is formed.

[0050] Data storage management: Collected priority adjustment data, path planning adjustment records, and 3D data are classified and stored according to a predetermined format and structure. Data storage management includes data compression and encryption to improve storage efficiency and data security, ensuring that future data analysis is not affected.

[0051] Data analysis after task completion: After the task is completed, the stored data is analyzed;

[0052] Optimize future mission planning: Based on data analysis results, generate optimization suggestions to provide a reference for future drone mission planning.

[0053] Optionally, the emergency task response and environmental disturbance adaptation module includes:

[0054] Emergency Task Identification: Real-time monitoring of emergencies in the task environment, including the emergence of emergency task requirements (such as emergency rescue requests, sudden reconnaissance requirements, etc.), and rapid identification of new emergency tasks through preset thresholds;

[0055] Priority queue adjustment: After an urgent task is identified, the task priority queue is adjusted first, the new urgent task is promoted to high priority, and the priority of existing tasks is re-evaluated according to their urgency and importance;

[0056] Path planning update: For new high-priority emergency tasks, the execution path is replanned using a dynamic path optimization algorithm to ensure that the UAV can reach the emergency task location in the shortest possible time, and the existing environmental conditions and obstacles are taken into account in the path planning.

[0057] Optionally, the emergency task response and environmental interference adaptation module further includes:

[0058] Environmental monitoring and interference identification: Real-time monitoring of changes in the UAV's operating environment and identification of potential environmental interference factors (such as sudden obstacles, extreme weather, etc.);

[0059] Interference impact assessment: Based on the identified environmental interference, assess the extent of its impact on the current task, including its potential impact on path, execution time, and task priority;

[0060] Dynamically adjust task execution strategies: Based on the evaluation results of environmental interference, dynamically adjust task execution strategies, including replanning task paths, adjusting priority queues, or modifying task execution plans.

[0061] The beneficial effects of this invention are:

[0062] This invention, through real-time data collection and analysis of mission requirements, can quickly identify urgent tasks and dynamically adjust priority queues, ensuring that UAVs can respond efficiently and accurately to and execute critical tasks in the face of emergencies. This rapid response mechanism significantly improves the mission execution capabilities of UAVs in complex environments, especially in fields such as rescue and reconnaissance, effectively reducing the risk of mission failure.

[0063] This invention, through a multi-task path optimization module, allows the system to adjust the UAV's flight path in real time based on task priorities and environmental changes, ensuring the completion of various tasks in the shortest possible time. Combining reinforcement learning algorithms and adaptive nonlinear path planning algorithms, the system can continuously learn and optimize path planning strategies, thereby significantly improving the efficiency and accuracy of task completion. This optimization mechanism not only improves the UAV's operational efficiency but also reduces resource consumption.

[0064] This invention, through the collaborative work of a multi-dimensional situational awareness and data fusion module, a three-dimensional situational display module, and a task dynamic adjustment and feedback control module, enables unmanned aerial vehicles (UAVs) to maintain efficient operation in constantly changing environments. By monitoring environmental parameters and task status in real time, the system can quickly identify and adapt to environmental interference, ensuring continuous task execution. Furthermore, the self-optimization capability of the task data storage and historical analysis module gives the system long-term intelligent development potential, enabling it to continuously improve decision-making and operational flexibility in future missions. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a schematic diagram of the system flow according to an embodiment of the present invention;

[0067] Figure 2 This is a flowchart illustrating the task requirement identification and priority dynamic adjustment module in an embodiment of the present invention. Detailed Implementation

[0068] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0069] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0070] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0071] like Figure 1-Figure 2 As shown, the AI-based UAV mission automatic planning and situation display system includes a mission requirement identification and priority dynamic adjustment module, a multi-mission path optimization module, a multi-dimensional situation perception and data fusion module, a three-dimensional situation display module, a mission dynamic adjustment and feedback control module, a mission data storage and historical analysis module, and an emergency mission response and environmental interference adaptation module.

[0072] The task requirement identification and priority dynamic adjustment module is used to collect and identify various task requirement information in the task scenario in real time, including reconnaissance, monitoring and search and rescue. It dynamically generates a task priority queue based on environmental parameters, the urgency of the task and the importance of the task objectives. This module combines a priority evaluation model to adjust the weight of each task in real time to ensure the dynamic updating of the priority queue.

[0073] The multi-task path optimization module, based on the output of the task requirement identification and priority dynamic adjustment module, uses reinforcement learning algorithm and adaptive nonlinear path planning algorithm to generate optimized paths that meet the priorities of multiple tasks. This module dynamically adjusts the path planning according to the real-time priority queue to ensure that each task is executed on the optimal path and improves the task completion efficiency.

[0074] The multi-dimensional situational awareness and data fusion module includes a multi-source sensor fusion unit, a data processing unit, and a three-dimensional situational model generation unit. It collects real-time visual data, geographic location information, and environmental parameters (such as temperature, humidity, and wind speed) within the UAV mission area. Through multi-source data fusion technology, it generates a high-precision three-dimensional situational model to achieve a multi-dimensional dynamic display of the mission environment.

[0075] The 3D situation display module is based on a 3D situation model generated by the multi-dimensional situation perception and data fusion module. It displays a 3D image of the UAV mission area in real time, and combines priority dynamic adjustment and path optimization information to show the real-time progress of mission execution. It also has real-time refresh and multi-level detail display functions, enabling the system to adapt to changes in mission and environmental interference, and ensuring the accuracy of situation display.

[0076] The task dynamic adjustment and feedback control module works in collaboration with the three-dimensional situation display module. Based on the real-time displayed task situation information, it monitors abnormal situations during task execution (such as changes in task priority, path obstruction, or the emergence of new task requirements), and adjusts the multi-task path planning and priority queue in real time according to the feedback information. This module ensures the flexibility and continuity of UAV task execution in complex environments through comparative analysis of feedback data and historical task data.

[0077] The task data storage and historical analysis module is used to record priority adjustment data, path planning adjustment records, and three-dimensional data of multi-dimensional situation display during the task execution process. After the task is completed, data analysis is performed, and future task planning is optimized based on the analysis results. This enables the system to achieve self-optimization and dynamic learning, providing a reference for the long-term intelligentization of UAV missions.

[0078] The Emergency Mission Response and Environmental Interference Adaptation module is used to prioritize and replan the mission priority queue and route when emergency mission requirements or environmental interference (such as sudden obstacles or extreme weather) occur, ensuring that the UAV can complete the mission efficiently and safely under complex conditions.

[0079] The task requirement identification and dynamic priority adjustment module includes:

[0080] Real-time mission requirement information collection: Collect and identify various mission requirement information in UAV mission scenarios through a preset mission data collection unit, including reconnaissance, monitoring and search and rescue missions, to provide basic data for subsequent priority assessment;

[0081] Environmental parameter acquisition and task adaptability analysis: Acquire environmental parameters related to the task area, including temperature, humidity, wind speed and geographical location. Based on changes in environmental parameters, evaluate the adaptability of different tasks and filter out tasks that are not suitable for the current environment to ensure that the priority queue generated subsequently can effectively respond to the current environment.

[0082] Task urgency and objective importance assessment: Tasks are initially prioritized based on their urgency and objective importance. Urgency is assessed based on the time constraints of the task requirements and the urgency of task completion, while objective importance is weighted according to the impact of the task on the overall task plan.

[0083] Task Priority Assessment Model Construction: A task priority assessment model is established, using urgency, goal importance, and environmental adaptability as the main inputs. The priority score of the task is calculated comprehensively. The calculation formula for the priority assessment model is expressed as follows:

[0084]

[0085] in, Rate the priority of task i. This indicates the urgency score of task i. The score indicates the importance of the task objective. For task adaptability scoring, parameters , , These are weighting coefficients to control the impact of each factor on the priority.

[0086] Dynamic generation and sorting of priority queue: Sort all tasks according to their priority scores, generate a task priority queue, and arrange tasks from highest to lowest priority score;

[0087] Real-time dynamic update of task priority queue: Combine real-time environmental changes and task requirement updates to dynamically adjust the priority queue. When new task requirements appear or the urgency and importance of existing tasks change, the priority score of relevant tasks is recalculated and the priority queue is updated to ensure that the UAV can respond to critical task requirements in real time in a dynamic task environment.

[0088] By collecting real-time task requirements information, analyzing environmental parameters, assessing task urgency and target importance, constructing priority models, and dynamically updating them, the system achieves automatic priority allocation and real-time adjustment of multiple tasks. This ensures that UAV mission planning can flexibly respond to diverse task requirements in complex scenarios, thereby improving the efficiency and accuracy of task execution.

[0089] The multi-task path optimization module includes:

[0090] Task priority queue reception and task target area confirmation: Receives the task priority queue output by the task requirement identification and priority dynamic adjustment module, identifies high-priority tasks (the top 15% of the task priority queue are defined as high-priority tasks) and confirms the target area of ​​each task, providing input data for path optimization;

[0091] Construction of a Multi-Task Path Optimization Model: Based on reinforcement learning and adaptive nonlinear path planning algorithms, a multi-task path optimization model is constructed. The model takes task priority queues, path distances, task resource consumption, and environmental parameters as input variables. The objective function is to minimize the total task path length and resource consumption, thus providing optimization criteria for subsequent path planning. The objective function of the model is defined as follows:

[0092]

[0093] in, For the number of tasks, The priority weighting factor for task i. Let be the path length of task i. Estimate the resource consumption for task i;

[0094] Path initialization and reinforcement learning training: Guided by the objective function of the path optimization model, initial paths are generated for each task according to the task priority queue. The path generation process is trained using a reinforcement learning algorithm. The objective function of the path optimization model serves as the reward mechanism for reinforcement learning, and the reinforcement learning policy is updated using the following formula:

[0095]

[0096] in, Indicates the state Select action value, For immediate returns, For learning rate, As a discount factor, The new state after the action is performed. This is an optional action for later use;

[0097] Dynamic path adjustment of the path optimization model: Based on a real-time updated priority queue, the path is dynamically adjusted using the objective function of the path optimization model. During the adjustment process, the path optimization model calculates the weight of the task path and the node order in real time to ensure that the path of each task meets the priority requirements and the optimal resource utilization.

[0098] Path feasibility verification and adjustment: After the path optimization is completed, the feasibility of the path is verified based on the resource consumption and environmental parameters in the path optimization model. For paths that do not meet the optimization conditions, the model is further adjusted according to the optimization objective function to ensure that the UAV path is feasible under physical conditions and resource constraints, and finally form the optimal path set.

[0099] By implementing multi-task priority queue reception, path optimization model construction, reinforcement learning training, real-time path adjustment, and feasibility verification, adaptive path planning for multiple tasks is achieved, ensuring the high efficiency of UAV missions in dynamic environments and the optimization of path selection, thereby improving the mission completion efficiency in complex multi-task scenarios.

[0100] The multi-dimensional situational awareness and data fusion module includes:

[0101] Multi-source sensor data acquisition: Multi-source data within the UAV mission area is acquired in real time through a multi-source sensor fusion unit. The multi-source data includes visual data, geographic location information, and environmental parameters, including temperature, humidity, and wind speed, ensuring that the multi-source sensor data covers multi-dimensional information of the UAV mission environment.

[0102] Data preprocessing and consistency calibration: The data processing unit preprocesses multi-source data, including denoising, completion and format conversion, to eliminate noise and missing data, and performs spatial and temporal consistency calibration on the multi-source data to ensure that the data are consistent in dimensions.

[0103] Data fusion model construction: Construct a data fusion model based on multi-source data fusion technology, taking visual data, geographic location information, and environmental parameters as inputs. The core function of the data fusion model is defined as follows:

[0104]

[0105] in, It is a three-dimensional situation model. For visual data sets, A collection of geographic location information, As a set of environmental parameters, multi-dimensional situational information of the UAV mission area is generated through the fusion calculation of multi-source data;

[0106] 3D Situation Model Generation: The 3D situation model generation unit generates a 3D situation model based on the output of the data fusion model. The 3D situation model includes the geographical structure of the mission area, environmental changes, and obstacle distribution, ensuring that the UAV operator can view the details of the mission environment in the model. The model expression is:

[0107] ;

[0108] in, It is a three-dimensional situation model. A mapping function to convert situational data into a three-dimensional model;

[0109] Through multi-source data acquisition, preprocessing, data consistency calibration, data fusion model construction, and 3D situation model generation, a multi-dimensional dynamic display of the UAV mission environment is achieved, providing high-precision environmental perception support for UAV mission planning and execution.

[0110] The multi-dimensional situational awareness and data fusion module also includes:

[0111] Multi-source data real-time processing and fusion preparation: Multi-source data within the UAV mission area is collected and integrated in real time through the multi-source sensor fusion unit. The multi-source data includes visual data, geographic location information, and environmental parameters to ensure the timeliness and comprehensiveness of the data. The collected multi-source data is then input into the data processing unit for real-time processing and multi-source fusion to provide the latest situational information.

[0112] Real-time multidimensional data fusion computing: Based on the definition of the data fusion model, real-time processed visual data, geographic location information and environmental parameters are fused from multiple sources. This fusion computing process combines static information (such as geographic location) and dynamic information (such as wind speed and temperature) in the environment to generate situational data with real-time dynamic characteristics.

[0113] Dynamic updating of the 3D situation model: Using the 3D situation model generation unit, the real-time updated situation data is used as input to dynamically update the 3D situation model, so as to ensure that changes in the mission environment can be reflected in the model in real time. For example, as environmental parameters change (such as temperature or wind speed fluctuations), the system can generate corresponding dynamic effects in the model to help operators quickly grasp environmental changes.

[0114] Multi-dimensional situational information display of the mission environment: The dynamically updated three-dimensional situational model is displayed on the control terminal, presenting the geographical structure, environmental features and obstacle distribution information of the mission area in the form of three-dimensional images. The display modes include zooming, rotation and switching of information at different levels, so that operators can flexibly view different dimensions of the mission environment.

[0115] Through real-time data processing, multi-dimensional data fusion calculation, dynamic updating of the three-dimensional situation model, and display of the mission environment, dynamic visualization of the mission environment is achieved, ensuring that operators can grasp the environmental dynamics of the mission area in real time, and providing comprehensive environmental support for UAV path planning and execution.

[0116] The 3D situation display module specifically includes:

[0117] Input to the 3D situation model: Receives a high-precision 3D situation model generated by the multi-dimensional situation awareness and data fusion module to ensure that the system can acquire the latest environmental status and mission information;

[0118] Real-time status monitoring and updates: Real-time monitoring of mission execution progress and environmental changes within the UAV mission area, including dynamic adjustment of mission priorities and path optimization information, to ensure that the system can obtain all dynamic information related to the mission in a timely manner;

[0119] 3D Image Generation and Display: Based on the input 3D situation model, a 3D image is generated through a preset graphics processing unit and displayed in real time on the operation terminal. The image display includes the geographical structure of the task area, the distribution of obstacles, and the real-time progress of task execution, enabling operators to intuitively observe the current status.

[0120] Real-time refresh and dynamic update: Combine real-time status monitoring information to dynamically update the 3D image. This step includes periodically refreshing the displayed content to ensure that the situation display accurately reflects the current task environment. For example, if the task environment changes (such as the appearance of obstacles or fluctuations in environmental parameters), the system can immediately adjust the displayed 3D image.

[0121] Multi-level detail display function: Provides a multi-level detail display function, allowing operators to select different levels of information to view as needed. This function can achieve focused display of specific tasks or areas, enhancing user experience and information acquisition efficiency;

[0122] By receiving 3D situation models, real-time status monitoring and updates, 3D image generation and display, real-time refresh and dynamic updates, and multi-level detail display functions, the system ensures accurate and dynamic situation display of the UAV mission area, thereby effectively supporting mission execution and environmental adaptability, and improving the overall intelligence level of the system.

[0123] The task dynamic adjustment and feedback control module specifically includes:

[0124] Real-time task situation information reception: Receives real-time task situation information from the 3D situation display module, including current task priority, path status and new task requirements, to ensure that the system obtains the latest task execution environment;

[0125] Anomaly monitoring: Real-time monitoring of anomalies during task execution, including changes in task priority, path obstruction, and new task requirements. By setting monitoring thresholds and conditions, it can quickly identify abnormal events that may affect task execution.

[0126] Feedback information analysis: Based on the detected anomalies, the real-time feedback information is compared and analyzed with historical task data. By calculating the impact of various anomalies on task execution efficiency, the difference between the current task status and the historical status is assessed, and the direction of adjustment that needs to be determined is identified.

[0127] Dynamically adjust task path planning: Based on feedback information and analysis results, adjust multi-task path planning in real time to optimize task execution efficiency. For example, if the path is blocked, the module will automatically calculate a new optimal path and incorporate it into the current task execution plan.

[0128] Real-time adjustment of priority queue: The task priority queue is dynamically updated based on the latest task execution status. Combined with the priority evaluation model, the weight of each task is adjusted to ensure that high-priority tasks are processed first, thereby improving the flexibility and adaptability of task execution.

[0129] By receiving real-time mission situation information, monitoring abnormal situations, analyzing feedback information, dynamically adjusting mission path planning, and adjusting priority queues in real time, the flexibility and continuity of UAV mission execution in complex environments are ensured, thereby improving mission success rate and execution efficiency.

[0130] The task data storage and historical analysis module specifically includes:

[0131] Real-time recording of mission data: During the execution of UAV missions, priority adjustment data, path planning adjustment records, and three-dimensional data generated by multi-dimensional situation display are recorded in real time to ensure that all important information related to the mission is fully recorded and a complete mission execution archive is formed.

[0132] Data storage management: Collected priority adjustment data, path planning adjustment records, and 3D data are classified and stored according to a predetermined format and structure. Data storage management includes data compression and encryption to improve storage efficiency and data security, ensuring that future data analysis is not affected.

[0133] Data analysis after task completion: After the task is completed, the stored data is analyzed, including in-depth discussions on the impact of priority adjustments, the effectiveness of path planning, and the degree of support of the 3D situation display for task execution.

[0134] Optimize future mission planning: Based on data analysis results, generate optimization suggestions to provide a reference for future drone mission planning. Combine machine learning algorithms to identify the most effective strategies and methods under different environmental and mission conditions to improve mission success rate.

[0135] By recording mission data in real time, managing data storage, analyzing data after mission completion, and optimizing future mission planning, we ensure that data from the execution of UAV missions is effectively utilized, thereby providing a solid foundation and support for long-term intelligentization.

[0136] The emergency mission response and environmental disturbance adaptation module includes:

[0137] Emergency Task Identification: Real-time monitoring of emergencies in the task environment, including the emergence of emergency task requirements (such as emergency rescue requests, sudden reconnaissance requirements, etc.), and rapid identification of new emergency tasks through preset thresholds;

[0138] Set a specific threshold, and when sensor data (such as the urgency of received task requests) exceeds the threshold, the system will trigger emergency task identification;

[0139] Priority queue adjustment: After an urgent task is identified, the task priority queue is adjusted first, the new urgent task is promoted to high priority, and the priority of existing tasks is re-evaluated according to their urgency and importance. This adjustment uses a priority evaluation model to ensure that the real-time weight of all tasks reflects their importance.

[0140] Path planning update: For new high-priority emergency tasks, the execution path is replanned using a dynamic path optimization algorithm to ensure that the UAV can reach the emergency task location in the shortest possible time, and the existing environmental conditions and obstacles are taken into account in the path planning.

[0141] By identifying urgent tasks, adjusting priority queues, and updating path planning, drones can quickly respond to sudden mission demands, thereby improving the timeliness and effectiveness of mission execution.

[0142] The emergency mission response and environmental disturbance adaptation module also includes:

[0143] Environmental monitoring and interference identification: Real-time monitoring of changes in the UAV operating environment, identification of potential environmental interference factors (such as sudden obstacles, extreme weather, etc.), and assessment of the impact on UAV missions through sensor data and preset environmental models;

[0144] Interference impact assessment: Based on the identified environmental interference, assess the extent of its impact on the current task, including its potential impact on path, execution time, and task priority. This assessment uses an environmental impact model to provide accurate interference assessment results.

[0145] Dynamically adjust mission execution strategies: Based on the assessment results of environmental interference, dynamically adjust mission execution strategies, including replanning mission paths, adjusting priority queues, or modifying mission execution plans, to ensure that UAVs can complete missions safely and efficiently under complex environmental conditions;

[0146] By monitoring the environment and identifying interference, assessing the impact of interference on the mission, and dynamically adjusting mission execution strategies, we can ensure that UAVs can effectively cope with environmental changes and improve the safety and stability of mission execution.

[0147] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0148] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. An AI-based automatic mission planning and situation display system for unmanned aerial vehicles (UAVs), characterized in that: It includes a task requirement identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situational awareness and data fusion module, a three-dimensional situational display module, a task dynamic adjustment and feedback control module, a task data storage and historical analysis module, and an emergency task response and environmental interference adaptation module, among which; The task requirement identification and priority dynamic adjustment module is used to collect and identify various task requirement information in the task scenario in real time, including reconnaissance, monitoring and search and rescue, and dynamically generate a task priority queue based on environmental parameters and the urgency of the task and the importance of the task objectives. The multi-task path optimization module generates optimized paths that conform to the priorities of multiple tasks based on the output of the task requirement identification and priority dynamic adjustment module, using reinforcement learning algorithm and adaptive nonlinear path planning algorithm. The multi-dimensional situational awareness and data fusion module includes a multi-source sensor fusion unit, a data processing unit, and a three-dimensional situational model generation unit. It collects real-time visual data, geographic location information, and environmental parameters within the UAV mission area, and generates a three-dimensional situational model through multi-source data fusion technology to achieve multi-dimensional dynamic display of the mission environment. The three-dimensional situation display module is based on the three-dimensional situation model generated by the multi-dimensional situation perception and data fusion module. It displays the three-dimensional image of the UAV mission area in real time, and combines priority dynamic adjustment and path optimization information to show the real-time progress of mission execution. It also has the functions of real-time refresh and multi-level detail display. The task dynamic adjustment and feedback control module works in conjunction with the three-dimensional situation display module to monitor abnormal situations during task execution based on the real-time displayed task situation information, and adjust the multi-task path planning and priority queue in real time according to the feedback information. The task data storage and historical analysis module is used to record priority adjustment data, path planning adjustment records, and three-dimensional data of multi-dimensional situation display during task execution. After task completion, it performs data analysis and optimizes future task planning based on the analysis results, including: Real-time recording of mission data: During the execution of UAV missions, priority adjustment data, path planning adjustment records, and 3D data generated by multi-dimensional situation display are recorded in real time; Data storage management: Collected priority adjustment data, path planning adjustment records, and 3D data are classified and stored according to predetermined formats and structures; Data analysis after task completion: After the task is completed, the stored data is analyzed; Optimize future mission planning: Based on data analysis results, generate optimization suggestions to provide a reference for future drone mission planning; The emergency task response and environmental interference adaptation module is used to prioritize adjusting the task priority queue and replan the path when an emergency task requirement or environmental interference occurs.

2. The AI-based automatic UAV mission planning and situation display system according to claim 1, characterized in that, The task requirement identification and priority dynamic adjustment module includes: Real-time mission requirement information collection: Collect and identify various mission requirement information in UAV mission scenarios through a preset mission data collection unit, including reconnaissance, monitoring and search and rescue missions; Environmental parameter acquisition and task adaptability analysis: Acquire environmental parameters related to the task area, including temperature, humidity, wind speed and geographical location. Based on changes in environmental parameters, assess the adaptability of different tasks and filter out tasks that are not suitable for the current environment. Task urgency and objective importance assessment: Initial priority allocation of tasks based on task urgency and objective importance; Task Priority Assessment Model Construction: Establish a task priority assessment model, using urgency, target importance, and environmental adaptability as the main inputs, and comprehensively calculate the task priority score; Dynamic generation and sorting of priority queue: Sort all tasks according to their priority scores, generate a task priority queue, and arrange tasks from highest to lowest priority score; Real-time dynamic updates to the task priority queue: The priority queue is dynamically adjusted based on real-time environmental changes and updated task requirements.

3. The AI-based automatic UAV mission planning and situation display system according to claim 2, characterized in that, The multi-task path optimization module includes: Task priority queue reception and task target area confirmation: Receive the task priority queue output by the task requirement identification and dynamic priority adjustment module, identify high-priority tasks and confirm the target area of ​​each task; Construction of a multi-task path optimization model: Based on reinforcement learning algorithm and adaptive nonlinear path planning algorithm, a multi-task path optimization model is constructed. The path optimization model takes task priority queue, path distance, task resource consumption and environmental parameters as input variables, and the objective function is to minimize the total task path length and resource consumption. Initial path generation and reinforcement learning training: Guided by the objective function of the path optimization model, initial paths for each task are generated according to the task priority queue, and the path generation process is trained through reinforcement learning algorithm; Dynamic path adjustment in the path optimization model: Based on a real-time updated priority queue, the path is dynamically adjusted using the objective function of the path optimization model; Path feasibility verification and adjustment: After path optimization is completed, the feasibility of the path is verified based on the resource consumption and environmental parameters in the path optimization model.

4. The AI-based automatic UAV mission planning and situation display system according to claim 3, characterized in that, The multi-dimensional situational awareness and data fusion module includes: Multi-source sensor data acquisition: Multi-source data within the UAV mission area is acquired in real time through a multi-source sensor fusion unit. The multi-source data includes visual data, geographic location information, and environmental parameters, including temperature, humidity, and wind speed. Data preprocessing and consistency calibration: The data processing unit preprocesses multi-source data, including denoising, completion and format conversion, and performs spatial and temporal consistency calibration on the multi-source data; Data fusion model construction: Construct a data fusion model based on multi-source data fusion technology, taking visual data, geographic location information and environmental parameters as inputs; 3D Situation Model Generation: The 3D situation model generation unit generates a 3D situation model based on the output of the data fusion model. The 3D situation model includes the geographical structure of the mission area, environmental changes, and obstacle distribution.

5. The AI-based automatic UAV mission planning and situation display system according to claim 4, characterized in that, The multi-dimensional situational awareness and data fusion module also includes: Preparation for real-time processing and fusion of multi-source data: The multi-source sensor fusion unit collects and integrates multi-source data within the UAV mission area in real time, and inputs the collected multi-source data into the data processing unit for real-time processing and multi-source fusion. Real-time multidimensional data fusion computing: Based on the definition of the data fusion model, real-time processed visual data, geographic location information and environmental parameters are fused from multiple sources to generate situational data with real-time dynamic characteristics; Dynamic updating of the 3D situation model: Using the 3D situation model generation unit, the real-time updated situation data is used as input to dynamically update the 3D situation model. Multi-dimensional situational information display of the mission environment: The dynamically updated three-dimensional situational model is displayed on the control terminal, presenting the geographical structure, environmental features and obstacle distribution information of the mission area in the form of three-dimensional images.

6. The AI-based automatic UAV mission planning and situation display system according to claim 5, characterized in that, The three-dimensional situation display module specifically includes: Input to the 3D situation model: Receives a 3D situation model generated by the multi-dimensional situation awareness and data fusion module; Real-time status monitoring and updates: Real-time monitoring of mission execution progress and environmental changes within the UAV mission area, including dynamic adjustment of mission priorities and path optimization information; 3D image generation and display: Based on the input 3D situation model, a 3D image is generated through a preset graphics processing unit and displayed in real time on the operating terminal; Real-time refresh and dynamic update: Combine real-time status monitoring information to dynamically update the 3D image; Multi-level detail display function: Provides a multi-level display function, allowing operators to select different levels of information to view as needed.

7. The AI-based automatic UAV mission planning and situation display system according to claim 6, characterized in that, The task dynamic adjustment and feedback control module specifically includes: Real-time task situation information reception: Receives real-time task situation information from the 3D situation display module, including current task priority, path status and new task requirements; Anomaly monitoring: Real-time monitoring of anomalies during task execution, including changes in task priority, path obstruction, and new task requirements; Feedback information analysis: Based on the detected anomalies, the real-time feedback information is compared and analyzed with historical task data. By calculating the impact of various anomalies on task execution efficiency, the difference between the current task status and the historical status is assessed, and the direction of adjustment that needs to be determined is identified. Dynamically adjust task path planning: Adjust multi-task path planning in real time based on feedback information and analysis results to optimize task execution efficiency; Real-time adjustment of priority queue: The task priority queue is dynamically updated based on the latest task execution status.

8. The AI-based automatic UAV mission planning and situation display system according to claim 7, characterized in that, The emergency task response and environmental disturbance adaptation module includes: Emergency Task Identification: Real-time monitoring of unexpected situations in the task environment, including the emergence of emergency task requirements; Priority queue adjustment: After an urgent task is identified, the task priority queue is adjusted first, the new urgent task is promoted to high priority, and the priority of existing tasks is re-evaluated according to their urgency and importance; Path planning update: For new high-priority urgent tasks, the execution path is replanned.

9. The AI-based automatic mission planning and situation display system for unmanned aerial vehicles according to claim 8, characterized in that, The emergency task response and environmental disturbance adaptation module also includes: Environmental monitoring and interference identification: Real-time monitoring of changes in the UAV's operating environment and identification of potential environmental interference factors; Interference impact assessment: Based on the identified environmental interference, assess the extent of its impact on the current task, including its potential impact on path, execution time, and task priority; Dynamically adjust task execution strategies: Based on the evaluation results of environmental interference, dynamically adjust task execution strategies, including replanning task paths, adjusting priority queues, or modifying task execution plans.

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