Unmanned aerial vehicle task automatic planning and situation display system based on artificial intelligence

Abstract

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle task automatic planning and situation display system based on artificial intelligence. Comprising a task demand identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situation awareness and data fusion module, a three-dimensional situation display module, a task dynamic adjustment and feedback control module, a task data storage and historical analysis module and an emergency task response and environment interference adaptation module. By collecting task requirements and environment parameters in real time, the system can dynamically generate a task priority queue and optimize path planning based on priorities, it is ensured that the unmanned aerial vehicle has efficient response ability when executing tasks such as reconnaissance, monitoring, searching and rescue, the system combines a multi-source data fusion technology, the three-dimensional situation of the task environment is displayed in real time, and the system has good application prospects. Visual support is provided for decision making; the self-optimization capability and the adaptability to emergency situations lay a foundation for long-term intelligent development of unmanned aerial vehicle tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to an automatic unmanned aerial vehicle mission planning and situation display system based on artificial intelligence. Background Art

[0002] With the rapid development of UAV technology, UAVs have been widely used in military, civil, environmental monitoring and other fields. The efficiency and flexibility of UAV mission execution make it show great potential in scenarios such as reconnaissance, search and rescue, agricultural monitoring, etc. However, with the diversification and complexity of mission requirements, traditional mission planning and execution methods have gradually become insufficient, especially in adjusting mission priorities and real-time path planning in dynamic environments.

[0003] Existing technologies usually rely on static mission planning and simple path control, and lack the ability to dynamically respond to environmental changes and mission requirements. This approach not only leads to inefficient mission execution, but may also cause resource waste and mission failure. In addition, existing systems have limited capabilities in emergency mission response and are unable to identify and adjust mission priorities in a timely manner, thus failing to effectively ensure the successful execution of missions in emergencies. Therefore, there is an urgent need for a more intelligent and flexible UAV mission management system to cope with the needs of multi-task coordination and dynamic adjustment in complex environments. Summary of the invention

[0004] The present invention provides an automatic planning and situation display system for unmanned aerial vehicle tasks based on artificial intelligence.

[0005] The UAV mission automatic planning and situation display system based on artificial intelligence includes a mission requirement identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situation awareness and data fusion module, a three-dimensional situation display module, a mission dynamic adjustment and feedback control module, a mission data storage and history analysis module, and an emergency mission 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, the urgency of the task, and the importance of the task target; The multi-task path optimization module generates an optimized path that meets the multi-task priority based on the output of the task demand identification and priority dynamic adjustment module by using a reinforcement learning algorithm and an adaptive nonlinear path planning algorithm; The multi-dimensional situation awareness and data fusion module includes a multi-source sensor fusion unit, a data processing unit and a three-dimensional situation model generation unit, which collects real-time visual data, geographic location information and environmental parameters (such as temperature, humidity, wind speed, etc.) in the UAV mission area, generates a high-precision three-dimensional situation model through multi-source data fusion technology, and realizes multi-dimensional dynamic display of the mission environment; The three-dimensional situation display module displays the three-dimensional image of the UAV mission area in real time based on the three-dimensional situation model generated by the multi-dimensional situation perception and data fusion module, and combines the priority dynamic adjustment and path optimization information to show the real-time progress of the mission execution, and 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 in task execution (such as changes in task priority, blocked paths, or new task requirements) based on the real-time displayed task situation information, and adjusts multi-task path planning and priority queues in real time according to the feedback information; The task data storage and history analysis module is used to record the priority adjustment data, path planning adjustment records and three-dimensional data of multi-dimensional situation display during the task execution process, and perform data analysis after the task is completed, and optimize future task planning based on the analysis results; The emergency mission response and environmental interference adaptation module is used to prioritize the task priority queue and re-plan the path when emergency mission requirements or environmental interference (such as sudden obstacles, extreme weather, etc.) occur, to ensure that the UAV completes the mission efficiently and safely under complex conditions.

[0006] Optionally, the task requirement identification and priority dynamic adjustment module includes: Real-time mission requirement information collection: The preset mission data collection unit collects and identifies various mission requirement information in UAV mission scenarios, including reconnaissance, monitoring, and search and rescue missions, providing basic data for subsequent priority assessment; Environmental parameter acquisition and task adaptability analysis: Acquire environmental parameters related to the task area, including temperature, humidity, wind speed, and geographic location. Based on the 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. Task urgency and target importance assessment: Assign preliminary priorities to tasks based on task urgency and target importance; Task priority assessment model construction: Establish a task priority assessment model, using urgency, target importance and environmental adaptability as the main inputs to comprehensively calculate the priority score of the task; Dynamic generation and sorting of priority queues: Sort all tasks according to their priority scores, generate a task priority queue, and arrange tasks from high to low according to their priority scores; Real-time dynamic update of task priority queue: Dynamically adjust the priority queue based on real-time environment changes and task requirement updates.

[0007] Optionally, the multi-task path optimization module includes: Receiving task priority queues and confirming task target areas: Receive the task priority queues output by the task demand 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 areas of each task; Construction of 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 path length and resource consumption of the task; Initial path generation and reinforcement learning training: Under the guidance of the objective function of the path optimization model, the initial path of each task is generated according to the task priority queue, and the path generation process is trained through the reinforcement learning algorithm; Dynamic path adjustment of the path optimization model: Based on the 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.

[0008] Optionally, the multi-dimensional situation awareness and data fusion module includes: Multi-source sensor data acquisition: The multi-source data in the UAV mission area is collected in real time through the multi-source sensor fusion unit. The multi-source data includes visual data, geographic location information, and environmental parameters, such as temperature, humidity, and wind speed, to ensure that the multi-source sensor data covers the multi-dimensional information of the UAV mission environment; Data preprocessing and consistency calibration: Preprocess multi-source data through the data processing unit, including denoising, completion and format conversion to eliminate noise and data missing, and calibrate the consistency of multi-source data in space and time to ensure that each data is consistent in dimension; 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 input; Three-dimensional situation model generation: Through the three-dimensional situation model generation unit, a three-dimensional situation model is generated based on the output of the data fusion model. The three-dimensional situation model includes the geographical structure, environmental changes and obstacle distribution of the mission area.

[0009] Optionally, the multi-dimensional situation awareness and data fusion module further includes: Real-time processing and fusion preparation of multi-source data: The multi-source data in 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 situation information; Real-time multi-dimensional data fusion calculation: Based on the definition of data fusion model, real-time processed visual data, geographic location information and environmental parameters are fused to generate situation data with real-time dynamic characteristics; Dynamic update of 3D situation model: Utilize the 3D situation model generation unit, take the real-time updated situation data as input, and dynamically update the 3D situation model to ensure that changes in the mission environment can be reflected in the model immediately; Mission environment display of multi-dimensional situation information: The dynamically updated three-dimensional situation model is displayed in the control terminal, presenting the geographical structure, environmental characteristics and obstacle distribution information of the mission area in the form of three-dimensional images.

[0010] Optionally, the three-dimensional situation display module specifically includes: Input of 3D situation model: Receive the high-precision 3D situation model generated by the multi-dimensional situation awareness and data fusion module to ensure that the system can obtain the latest environmental status and mission information; Real-time status monitoring and updating: Real-time monitoring of the mission execution progress and environmental changes in 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; 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 mission area, obstacle distribution, and real-time progress of mission execution, so that operators can intuitively observe the current status; Real-time refresh and dynamic update: Dynamically update the 3D image in combination with real-time status monitoring information; Multi-level detail display function: Provides multi-level detail display function, allowing operators to select different levels of information to view as needed.

[0011] Optionally, the task dynamic adjustment and feedback control module specifically includes: Real-time task situation information reception: Receive 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; Abnormal situation monitoring: Real-time monitoring of abnormal situations during task execution, including changes in task priority, blocked paths, and new task requirements; Feedback information analysis: Based on the detected abnormal situations, the real-time feedback information is compared and analyzed with the historical task data. By calculating the impact of various abnormalities on the task execution efficiency, the difference between the current task status and the historical status is evaluated, and the direction that needs to be adjusted is determined; Dynamically adjust task path planning: According to feedback information and analysis results, multi-task path planning is adjusted in real time to optimize task execution efficiency. For example, if the path is blocked, the module will automatically calculate the new optimal path and incorporate it into the current task execution plan; Real-time adjustment of priority queue: Dynamically update the task priority queue based on the latest status of task execution.

[0012] Optionally, the task data storage and history analysis module specifically includes: Real-time recording of mission data: During the execution of the UAV mission, the mission 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 to form a complete mission execution file; Data storage management: The collected priority adjustment data, path planning adjustment records and 3D data are classified and stored according to the 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; Data analysis after task completion: After the task is completed, the stored data is analyzed; Optimize future mission planning: Generate optimization suggestions based on data analysis results to provide a reference for future drone mission planning.

[0013] Optionally, the emergency task response and environmental interference adaptation module includes: 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 quickly identifying new emergency tasks through preset thresholds; Priority queue adjustment: After identifying urgent tasks, adjust the task priority queue first, promote new urgent tasks to high priority, and re-evaluate the priority of existing tasks based on their urgency and importance; Path planning update: For new high-priority emergency tasks, the execution path is replanned, and a dynamic path optimization algorithm is used to ensure that the drone can reach the emergency task location in the shortest time, and the existing environmental conditions and obstacles are taken into account in the path planning.

[0014] Optionally, the emergency task response and environmental interference adaptation module further includes: Environmental monitoring and interference identification: Real-time monitoring of changes in the drone's operating environment and identification of potential environmental interference factors (such as sudden obstacles, extreme weather, etc.); Assessment of the impact of interference on tasks: Based on the identified environmental interference, assess the impact of environmental interference on the current task, including the potential impact on the 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.

[0015] Beneficial effects of the present invention: The present invention can quickly identify emergency tasks and dynamically adjust the priority queue by collecting and analyzing task requirements in real time, ensuring that the drone can respond and perform key tasks efficiently and accurately when faced with emergencies. This rapid response mechanism significantly improves the drone's ability to perform tasks in complex environments, especially in the fields of rescue and reconnaissance, and can effectively reduce the risk of mission failure.

[0016] The present invention uses a multi-task path optimization module to adjust the flight path of the drone in real time based on task priorities and environmental changes to ensure that each task is completed in the shortest time. Combined with the reinforcement learning algorithm and the adaptive nonlinear path planning algorithm, the system can continuously learn and optimize the path planning strategy, thereby significantly improving the efficiency and accuracy of task completion. This optimization mechanism not only improves the operating efficiency of the drone, but also reduces resource consumption.

[0017] The present invention enables the UAV to maintain an efficient operating state in a constantly changing environment 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. By real-time monitoring of environmental parameters and task status, the system can quickly identify and adapt to environmental interference to ensure the continuous execution of tasks. In addition, the self-optimization capability of the task data storage and historical analysis module gives the system the potential for long-term intelligent development, and can continuously improve decision-making levels and operational flexibility in future tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic diagram of a system flow of an embodiment of the present invention; Figure 2 The figure is a flowchart of a task requirement identification and priority dynamic adjustment module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0021] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0022] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0023] like Figure 1-Figure 2 As shown, the UAV task automatic planning and situation display system based on artificial intelligence includes a task requirement identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situation awareness and data fusion module, a three-dimensional situation display module, a task dynamic adjustment and feedback control module, a task data storage and history 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. It dynamically generates a task priority queue based on environmental parameters, the urgency of the task, and the importance of the task target. This module combines the priority evaluation model to adjust the weight of each task in real time to ensure the dynamic update of the priority queue. The multi-task path optimization module uses the reinforcement learning algorithm and adaptive nonlinear path planning algorithm based on the output of the task demand identification and priority dynamic adjustment module to generate an optimized path that meets the multi-task priority. 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 improve the efficiency of task completion; The multi-dimensional situation awareness and data fusion module includes a multi-source sensor fusion unit, a data processing unit, and a three-dimensional situation model generation unit. It collects real-time visual data, geographic location information, and environmental parameters (such as temperature, humidity, wind speed, etc.) in the UAV mission area, and generates a high-precision three-dimensional situation model through multi-source data fusion technology to achieve multi-dimensional dynamic display of the mission environment. The 3D situation display module is based on the 3D situation model generated by the multi-dimensional situation awareness and data fusion module. It displays the 3D image of the UAV mission area in real time, and combines the dynamic adjustment of priority 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, allowing the system to adapt to mission changes and environmental interference to ensure the accuracy of situation display. 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 in task execution (such as changes in task priority, blocked paths, or new task requirements), and adjusts multi-task path planning and priority queues in real time according to feedback information. This module ensures the flexibility and continuity of UAV task execution in complex environments by comparing and analyzing feedback data with historical task data. The mission data storage and history analysis module is used to record the priority adjustment data, path planning adjustment records and three-dimensional data of multi-dimensional situation display during the mission execution process, and to perform data analysis after the mission is completed. Based on the analysis results, the future mission planning is optimized, so that the system can achieve self-optimization and dynamic learning, providing a reference basis for the long-term intelligence of UAV missions; The emergency mission response and environmental interference adaptation module is used to adjust the task priority queue and re-plan the path when emergency mission requirements or environmental interference (such as sudden obstacles, extreme weather, etc.) occur, ensuring that the UAV can complete the mission efficiently and safely under complex conditions.

[0024] The task requirement identification and priority dynamic adjustment module includes: Real-time mission requirement information collection: The preset mission data collection unit collects and identifies various mission requirement information in UAV mission scenarios, including reconnaissance, monitoring, and search and rescue missions, providing basic data for subsequent priority assessment; Environmental parameter acquisition and task adaptability analysis: Acquire environmental parameters related to the task area, including temperature, humidity, wind speed, and geographic location. Based on the 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. Task urgency and target importance assessment: The tasks are initially prioritized based on their urgency and the importance of their targets. The urgency is assessed based on the time constraints of the task requirements and the urgency of task completion. The target importance is weighted based on the impact of the task on the overall task plan. Task priority evaluation model construction: A task priority evaluation model is established, with urgency, target importance and environmental adaptability as the main inputs, and the priority score of the task is comprehensively calculated. The calculation formula of the priority evaluation model is expressed as:

[0025] in, Score the priority of task i, represents the urgency score of task i, represents the importance score of the task objective, Scoring task suitability, parameters , , is the weighting coefficient to control the impact of each factor on the priority; Dynamic generation and sorting of priority queues: Sort all tasks according to their priority scores, generate a task priority queue, and arrange tasks from high to low according to their priority scores; Real-time dynamic update of task priority queue: Combined with real-time environmental changes and task requirement updates, the priority queue is dynamically adjusted. When new task requirements emerge or the urgency and importance of existing tasks change, the priority scores of related tasks are recalculated and the priority queue is updated to ensure that the drone can respond to key task requirements in real time in a dynamic task environment. Through real-time mission requirement information collection, environmental parameter analysis, mission urgency and target importance assessment, priority model construction and dynamic update, automatic priority allocation and real-time adjustment of multiple tasks are achieved, ensuring that UAV mission planning can flexibly respond to diverse mission requirements in complex scenarios and improve the efficiency and accuracy of mission execution.

[0026] The multi-task path optimization module includes: Receiving task priority queues and confirming task target areas: Receive the task priority queues output by the task demand 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 areas of each task, providing input data for path optimization; Construction of 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. The objective function is to minimize the total path length and resource consumption of the task, thereby providing optimization criteria for subsequent path planning. The objective function of the model is defined as follows:

[0027] in, is the number of tasks, is the priority weighting factor of task i, is the path length of task i, Estimate the resource consumption of task i; Initial path generation and reinforcement learning training: Under the guidance of the objective function of the path optimization model, the initial path of each task is generated according to the task priority queue. The path generation process is trained by the reinforcement learning algorithm. The objective function of the path optimization model is used as the reward mechanism of reinforcement learning. The reinforcement learning strategy is updated by the following formula:

[0028] in, Indicates in status Next select action The value of For immediate returns, is the learning rate, is the discount factor, is the new state after executing the action, For subsequent optional actions; Dynamic path adjustment of the path optimization model: Based on the 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 and node order of the task path in real time to ensure that the path of each task meets the priority requirements and optimal resource utilization; Path feasibility verification and adjustment: After the path optimization is completed, the feasibility of the path is verified according to the resource consumption and environmental parameters in the path optimization model. For the paths that do not meet the optimization conditions, the model makes further feedback adjustments according to the optimization objective function to ensure that the UAV path is executable under physical conditions and resource constraints, and finally form the optimal path set; Through multi-task priority queue reception, path optimization model construction, reinforcement learning training, real-time path adjustment and feasibility verification, multi-task adaptive path planning is achieved, ensuring the high efficiency of UAV mission execution in dynamic environments and the optimization of path selection, and improving the efficiency of task completion in complex multi-task scenarios.

[0029] The multi-dimensional situational awareness and data fusion module includes: Multi-source sensor data acquisition: The multi-source sensor fusion unit collects multi-source data in the UAV mission area in real time. The multi-source data includes visual data, geographic location information, and environmental parameters. The environmental parameters include temperature, humidity, and wind speed, ensuring that the multi-source sensor data covers multi-dimensional information of the UAV mission environment. Data preprocessing and consistency calibration: Preprocess multi-source data through the data processing unit, including denoising, completion and format conversion to eliminate noise and data missing, and calibrate the consistency of multi-source data in space and time to ensure that each data is consistent in dimension; 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 input. The core function of the data fusion model is defined as:

[0030] in, is a three-dimensional situation model. is a collection of visual data, is a collection of geographic location information. It is a set of environmental parameters. Through the fusion calculation of multi-source data, multi-dimensional situation information of the UAV mission area is generated. 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, environmental changes and obstacle distribution of the mission area, ensuring that the UAV operator can view the details of the mission environment in the model. The expression of the model is: ; in, is a three-dimensional situation model. A mapping function for converting situation data into a three-dimensional model; Through multi-source data collection, preprocessing, data consistency calibration, data fusion model construction and three-dimensional 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.

[0031] The multi-dimensional situational awareness and data fusion module also includes: Real-time processing and fusion preparation of multi-source data: The multi-source data in 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 input into the data processing unit for real-time processing and multi-source fusion to provide the latest situation information; Real-time multi-dimensional data fusion calculation: Based on the definition of the data fusion model, the real-time processed visual data, geographic location information and environmental parameters are multi-source fused. The fusion calculation process combines static information (such as geographic location) with dynamic information (such as wind speed and temperature) in the environment to generate situation data with real-time dynamic characteristics; Dynamic update of 3D situation model: Utilize the 3D situation model generation unit, take the real-time updated situation data as input, and dynamically update the 3D situation model to ensure that changes in the mission environment can be reflected in the model immediately. 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; Mission environment display of multi-dimensional situation information: The dynamically updated three-dimensional situation model is displayed in the control terminal, presenting the geographical structure, environmental characteristics and obstacle distribution information of the mission area in the form of a three-dimensional image. The display mode includes zooming, rotating and switching of information at different levels, so that operators can flexibly view different mission environment dimensions; Through real-time data processing, multi-dimensional data fusion calculation, dynamic update of three-dimensional situation model and mission environment display, 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 the path planning and execution of UAVs.

[0032] The three-dimensional situation display module specifically includes: Input of 3D situation model: Receive the high-precision 3D situation model generated by the multi-dimensional situation awareness and data fusion module to ensure that the system can obtain the latest environmental status and mission information; Real-time status monitoring and updating: Real-time monitoring of the mission execution progress and environmental changes in 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; 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 mission area, obstacle distribution, and real-time progress of mission execution, so that operators can intuitively observe the current status; Real-time refresh and dynamic update: Dynamically update the 3D image in combination with real-time status monitoring information. This step includes regularly refreshing the display content to ensure that the situation display accurately reflects the current mission environment. For example, if the mission environment changes (such as obstacles appearing or environmental parameters fluctuating), the system can immediately adjust the displayed 3D image; Multi-level detail display function: Provides multi-level detail display function, allowing operators to select different levels of information to view as needed. This function can realize focused display of specific tasks or areas, enhancing user experience and information acquisition efficiency; By receiving three-dimensional situation models, real-time status monitoring and updating, three-dimensional image generation and display, real-time refresh and dynamic update, and multi-level detail display functions, the situation display of the UAV mission area is ensured to be accurate and dynamic, thereby effectively supporting mission execution and environmental adaptability, and improving the overall intelligence level of the system.

[0033] The task dynamic adjustment and feedback control module specifically includes: Real-time task situation information reception: Receive 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; Abnormal situation monitoring: Real-time monitoring of abnormal situations during task execution, including changes in task priority, blocked paths, and new task requirements. By setting monitoring thresholds and conditions, abnormal events that may affect task execution can be quickly identified. Feedback information analysis: Based on the detected abnormal situations, the real-time feedback information is compared and analyzed with the historical task data. By calculating the impact of various abnormalities on the task execution efficiency, the difference between the current task status and the historical status is evaluated, and the direction that needs to be adjusted is determined; Dynamically adjust task path planning: According to feedback information and analysis results, multi-task path planning is adjusted in real time to optimize task execution efficiency. For example, if the path is blocked, the module will automatically calculate the new optimal path and incorporate it into the current task execution plan; Real-time adjustment of priority queues: Dynamically update the task priority queue based on the latest status of task execution, and adjust the weight of each task in combination with the priority evaluation model to ensure that high-priority tasks are given priority, thereby improving the flexibility and adaptability of task execution; By receiving real-time mission status 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 the success rate and execution efficiency of the mission.

[0034] The task data storage and history analysis module specifically includes: Real-time recording of mission data: During the execution of the UAV mission, the mission 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 to form a complete mission execution file; Data storage management: The collected priority adjustment data, path planning adjustment records and 3D data are classified and stored according to the 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; Data analysis after mission completion: After the mission is completed, the stored data is analyzed, including the impact of priority adjustment, the effectiveness of path planning, and the degree of support for mission execution by 3D situation display. Optimize future mission planning: Generate optimization suggestions based on data analysis results to provide reference for future UAV mission planning. Combined with machine learning algorithms, identify the most effective strategies and methods under different environments and mission conditions to improve mission success rate. Through real-time recording of mission data, data storage management, data analysis after mission completion, and optimization of future mission planning, we ensure that data from the drone mission execution process is effectively utilized, thus providing a solid foundation and support for long-term intelligence.

[0035] The emergency task response and environmental interference adaptation modules include: 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 quickly identifying new emergency tasks through preset thresholds; Setting a specific threshold, when the sensor data (such as the urgency of the received task request) exceeds the threshold, the system will trigger the urgent task identification; Priority queue adjustment: After identifying urgent tasks, the task priority queue is adjusted first, new urgent tasks are 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 weights of all tasks reflect their importance; Path planning update: For new high-priority emergency tasks, the execution path is replanned, and a dynamic path optimization algorithm is used to ensure that the drone can reach the emergency task location in the shortest time, and the existing environmental conditions and obstacles are taken into account in the path planning; By identifying urgent tasks, adjusting priority queues, and updating path planning, it ensures that drones can quickly respond to sudden mission demands, thereby improving the timeliness and effectiveness of mission execution.

[0036] The emergency mission response and environmental interference adaptation module also includes: Environmental monitoring and interference identification: Real-time monitoring of changes in the drone's operating environment, identification of potential environmental interference factors (such as sudden obstacles, extreme weather, etc.), and assessment of the impact on the drone's mission through sensor data and preset environmental models; Interference impact assessment on tasks: Based on the identified environmental interference, the impact of environmental interference on the current task is assessed, including the potential impact on the path, execution time and task priority. This assessment uses an environmental impact model to provide accurate interference assessment results; Dynamically adjust the mission execution strategy: Based on the evaluation results of environmental interference, dynamically adjust the mission execution strategy, including re-planning the mission path, adjusting the priority queue or modifying the mission execution plan to ensure that the UAV can complete the mission safely and efficiently under complex environmental conditions; Through environmental monitoring and interference identification, assessment of interference impact on missions and dynamic adjustment of mission execution strategies, we ensure that drones can effectively respond to environmental changes and improve the safety and stability of mission execution.

[0037] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0038] 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 principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. The UAV mission automatic planning and situation display system based on artificial intelligence is characterized by: It includes task requirement identification and priority dynamic adjustment module, multi-task path optimization module, multi-dimensional situation awareness and data fusion module, three-dimensional situation display module, task dynamic adjustment and feedback control module, task data storage and history analysis module and 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, the urgency of the task, and the importance of the task target; The multi-task path optimization module generates an optimized path that meets the multi-task priority based on the output of the task demand identification and priority dynamic adjustment module by using a reinforcement learning algorithm and an adaptive nonlinear path planning algorithm; The multi-dimensional situation awareness and data fusion module includes a multi-source sensor fusion unit, a data processing unit and a three-dimensional situation model generation unit, which collects real-time visual data, geographic location information and environmental parameters in the UAV mission area, generates a three-dimensional situation model through multi-source data fusion technology, and realizes multi-dimensional dynamic display of the mission environment; The three-dimensional situation display module displays the three-dimensional image of the UAV mission area in real time based on the three-dimensional situation model generated by the multi-dimensional situation perception and data fusion module, and combines the priority dynamic adjustment and path optimization information to show the real-time progress of the mission execution, and 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 in 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 history analysis module is used to record the priority adjustment data, path planning adjustment records and three-dimensional data of multi-dimensional situation display during the task execution process, and perform data analysis after the task is completed, and optimize future task planning based on the analysis results; The emergency task response and environmental interference adaptation module is used to preferentially adjust the task priority queue and re-plan the path when an emergency task demand or environmental interference occurs.

2. The artificial intelligence-based UAV mission automatic planning and situation display system according to claim 1 is 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, including reconnaissance, monitoring and search and rescue missions, through the preset mission data collection unit; Environmental parameter acquisition and task adaptability analysis: Obtain environmental parameters related to the task area, including temperature, humidity, wind speed, and geographic location. Based on the changes in environmental parameters, evaluate the adaptability of different tasks and filter out tasks that are not suitable for the current environment. Task urgency and target importance assessment: Assign preliminary priorities to tasks based on task urgency and target importance; Task priority assessment model construction: Establish a task priority assessment model, using urgency, target importance and environmental adaptability as the main inputs to comprehensively calculate the priority score of the task; Dynamic generation and sorting of priority queues: Sort all tasks according to their priority scores, generate a task priority queue, and arrange tasks from high to low according to their priority scores; Real-time dynamic update of task priority queue: Dynamically adjust the priority queue based on real-time environment changes and task requirement updates.

3. The artificial intelligence-based UAV mission automatic planning and situation display system according to claim 2 is characterized in that: The multi-task path optimization module includes: Receiving task priority queue and confirming task target area: Receive the task priority queue output by the task requirement identification and priority dynamic adjustment module, identify the high-priority tasks and confirm the target area of ​​each task; Construction of 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 path length and resource consumption of the task; Initial path generation and reinforcement learning training: Under the guidance of the objective function of the path optimization model, the initial path of each task is generated according to the task priority queue, and the path generation process is trained through the reinforcement learning algorithm; Dynamic path adjustment of the path optimization model: Based on the 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 artificial intelligence-based UAV mission automatic planning and situation display system according to claim 3 is characterized in that: The multi-dimensional situation awareness and data fusion module includes: Multi-source sensor data acquisition: Multi-source data in the UAV mission area is collected 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: Preprocess multi-source data through the data processing unit, including denoising, completion and format conversion, and calibrate the consistency of multi-source data in space and time; 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 input; Three-dimensional situation model generation: Through the three-dimensional situation model generation unit, a three-dimensional situation model is generated based on the output of the data fusion model. The three-dimensional situation model includes the geographical structure, environmental changes and obstacle distribution of the mission area.

5. The artificial intelligence-based UAV mission automatic planning and situation display system according to claim 4 is characterized in that: The multi-dimensional situation awareness and data fusion module also includes: Real-time processing and fusion preparation of multi-source data: The multi-source data in 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; Real-time multi-dimensional data fusion calculation: Based on the definition of data fusion model, real-time processed visual data, geographic location information and environmental parameters are fused from multiple sources to generate situation data with real-time dynamic characteristics; Dynamic update of 3D situation model: Utilize the 3D situation model generation unit, take the real-time updated situation data as input, and dynamically update the 3D situation model; Mission environment display of multi-dimensional situation information: The dynamically updated three-dimensional situation model is displayed in the control terminal, presenting the geographical structure, environmental characteristics and obstacle distribution information of the mission area in the form of three-dimensional images.

6. The artificial intelligence-based UAV mission automatic planning and situation display system according to claim 5 is characterized in that: The three-dimensional situation display module specifically includes: Input of the three-dimensional situation model: receiving the three-dimensional situation model generated by the multi-dimensional situation awareness and data fusion module; Real-time status monitoring and updating: 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 operation terminal; Real-time refresh and dynamic update: Dynamically update the 3D image in combination with real-time status monitoring information; Multi-level detail display function: Provides multi-level display function, allowing operators to select different levels of information to view as needed.

7. The artificial intelligence-based UAV mission automatic planning and situation display system according to claim 6 is characterized in that: The task dynamic adjustment and feedback control module specifically includes: Real-time task situation information reception: Receive real-time task situation information from the 3D situation display module, including current task priority, path status and new task requirements; Abnormal situation monitoring: Real-time monitoring of abnormal situations during task execution, including changes in task priority, blocked paths, and new task requirements; Feedback information analysis: Based on the detected abnormal situations, the real-time feedback information is compared and analyzed with the historical task data. By calculating the impact of various abnormalities on the task execution efficiency, the difference between the current task status and the historical status is evaluated, and the direction that needs to be adjusted is determined; Dynamically adjust task path planning: According to feedback information and analysis results, multi-task path planning is adjusted in real time to optimize task execution efficiency; Real-time adjustment of priority queue: Dynamically update the task priority queue based on the latest status of task execution.

8. The artificial intelligence-based UAV mission automatic planning and situation display system according to claim 7 is characterized in that: The task data storage and history analysis module specifically includes: Real-time recording of mission data: During the execution of the UAV mission, the mission priority adjustment data, path planning adjustment records and three-dimensional data generated by multi-dimensional situation display are recorded in real time; Data storage management: Classify and store the collected priority adjustment data, path planning adjustment records and three-dimensional data according to the predetermined format and structure; Data analysis after task completion: After the task is completed, the stored data is analyzed; Optimize future mission planning: Generate optimization suggestions based on data analysis results to provide a reference for future drone mission planning.

9. The artificial intelligence-based UAV mission automatic planning and situation display system according to claim 8, characterized in that: The emergency task response and environmental interference adaptation module includes: Emergency task identification: real-time monitoring of emergencies in the task environment, including the emergence of emergency task requirements; Priority queue adjustment: After identifying urgent tasks, adjust the task priority queue first, promote new urgent tasks to high priority, and re-evaluate the priority of existing tasks based on their urgency and importance; Path planning update: Replan the execution path for new high-priority urgent tasks.

10. The artificial intelligence-based UAV mission automatic planning and situation display system according to claim 9, characterized in that: The emergency task response and environmental interference adaptation module also includes: Environmental monitoring and interference identification: Real-time monitoring of changes in the drone's operating environment and identification of potential environmental interference factors; Assessment of the impact of interference on tasks: Based on the identified environmental interference, assess the impact of environmental interference on the current task, including the potential impact on the 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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