Unmanned aerial vehicle cluster intelligent control system

Abstract

The invention relates to the technical field of unmanned aerial vehicle intelligent control, in particular to an unmanned aerial vehicle cluster intelligent control system, which comprises a data acquisition module, a first determination module, a second determination module, a judgment module, a control module and an adjustment module. According to the invention, through the highly integrated sensing and decision-making module, complex climate changes can be quickly responded in a polar region, the unmanned aerial vehicle task region can be accurately positioned and adjusted, and the system can efficiently identify ponding grids, temporary grids and observation grids by collecting spectral reflectivity, temperature and crack width data in real time and combining intelligent analysis, so that the accuracy of the system is improved. In practical application, the system not only can flexibly adjust the reflectivity threshold value according to the environment change of the grids, but also can optimize the distribution of the unmanned aerial vehicle among the plurality of grids through comparison and fluctuation calculation, so that the accuracy of the distribution of the unmanned aerial vehicle among the plurality of grids is improved. The problem that the response speed of the system is low when the system faces emergencies due to the fact that the system depends on a single control strategy is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) intelligent control, and in particular to an unmanned aerial vehicle (UAV) cluster intelligent control system. Background Art

[0002] With the rapid development of drone technology and the increasing demand for its application, drone swarm systems are widely used to perform monitoring, rescue, exploration and other tasks, especially in complex environments such as polar regions and disaster areas. These tasks usually require the system to efficiently and accurately complete large-scale real-time data collection and task planning. However, when faced with large-scale cluster scheduling, existing systems often have problems such as untimely response and uneven resource utilization, and are difficult to cope with dynamic changes in complex environments. Therefore, improving the intelligence level and autonomous decision-making ability of drone swarms has become the key to solving the above problems.

[0003] The patent document with the publication number CN119002518A discloses a control method for a swarm UAV system based on DPPO deep reinforcement learning, which includes: 1) Construction of a three-level structure of a swarm UAV system: constructing a three-level structure of cluster-formation-single machine, analyzing the collaborative relationship and topological configuration characteristics between formations within the cluster and between UAVs within the formation, and forming a three-level dynamic control system; 2) Multi-dimensional situation perception and feature extraction: designing a deep neural network model, processing the mission area features, formation coverage features, UAV location features and damage area features through the ResNet module, using the LSTM module to extract the temporal features of high-dimensional information, and using the Attention mechanism to focus on the interaction within the formation and across levels to obtain multi-dimensional situation information; 3) Control strategy generation based on distributed proximal strategy optimization DPPO: applying the advantage Actor-Critic policy gradient algorithm, combined with the experience replay technology for asynchronous update, by maximizing the policy reward expectation, using TD (λ), V-trace and UPGO algorithms to train and update the neural network, and generate a dynamic control strategy for the swarm UAV system; 4) Design and training of alliance learning mechanism: By designing alliance games and virtual self-learning mechanisms, including three types of agents: main agent, main explorer and alliance explorer, distributed learning training is carried out to optimize the control strategy of the cluster drone system; 5) Dynamic control strategy execution: During the execution of the drone cluster mission, the formation topology structure and drone path planning are adjusted in real time based on the generated control strategy to ensure the continuity of mission execution and the optimization of system performance.

[0004] It can be seen that the control method of the swarm UAV system based on DPPO deep reinforcement learning has the following problems: the method relies on a large amount of high-dimensional data and historical data for training in multi-dimensional situational awareness and feature extraction, and updates the control strategy through reinforcement learning. This requires a large amount of training data, and is affected by factors such as data quality, sampling frequency and scene complexity, resulting in differences in the stability of model performance in specific environments; reinforcement learning algorithms such as DPPO often encounter problems such as slow convergence and local optimal solutions during training. Although a variety of algorithms are used for optimization (such as TD (λ), V-trace, UPGO), the training effect of reinforcement learning is still uncertain in complex dynamic environments, resulting in long training time or unstable strategy performance; this method generates dynamic control strategies through deep learning models, and the response speed of the system is slow in emergency situations, especially when the UAV cluster faces emergencies or environmental changes, which will delay adjustments and affect the execution efficiency and continuity of the task. Summary of the invention

[0005] To this end, the present invention provides a drone swarm intelligent control system, which is used to overcome the problem of low response speed of the system in the face of emergencies due to reliance on a single control strategy in the prior art through a dynamic monitoring and adjustment mechanism based on multiple real-time data.

[0006] The polar regions are ice areas with many cracks. Due to long-term climate change, ice sliding and other natural factors, cracks of different sizes and widths have formed on the surface of the ice sheet in this area. The changes in these cracks not only directly affect the stability of the ice sheet, but also have an impact on the local ecological environment and global sea level changes. In this environment, the use of drone clusters for efficient and accurate real-time monitoring is particularly critical.

[0007] To achieve the above object, the present invention provides a drone swarm intelligent control system, comprising: A data acquisition module is used to obtain real-time spectral reflectance, real-time temperature and real-time ice sheet crack width collected by a preset number of drones in each grid to be determined in the polar region divided by grids; A first determination module, connected to the data acquisition module, for determining a number of waterlogging grids and non-waterlogging grids according to the real-time spectral reflectance and a preset reflectance threshold; A second determination module, which is connected to the first determination module and the data acquisition module respectively, and is used to determine a number of temporary grids according to the real-time temperature of each of the water accumulation grids and the real-time ice sheet crack width; a determination module, which is connected to the second determination module and the data acquisition module respectively, and is used to determine a number of observation grids according to the real-time spectral reflectance and the real-time ice sheet crack width of any two adjacent temporary grids; A control module connected to the determination module, for controlling a plurality of the drones to move from the non-waterlogging grid to the observation grid according to the number of grids, grid positions and the preset number of the observation grid; An adjustment module is connected to the data acquisition module, the control module and the first determination module respectively, and is used to adjust the preset reflectivity threshold based on the temporary grid determined after scheduling a number of the drones.

[0008] Furthermore, the first determining module includes: A reflectivity comparison unit, used to compare the real-time spectral reflectivity with the preset reflectivity threshold to form a reflectivity comparison result; A first determination unit is connected to the reflectivity comparison unit, and is used to determine that the grid to be determined is the waterlogged grid when the reflectivity comparison result is that the real-time spectral reflectivity is greater than the preset reflectivity threshold, and to determine that the grid to be determined is the non-waterlogged grid when the reflectivity comparison result is that the real-time spectral reflectivity is less than or equal to the preset reflectivity threshold.

[0009] Furthermore, the second determining module includes: A temperature fluctuation calculation unit, used to calculate the standard deviation of the real-time temperature within a preset temporary determined time period to form a temperature fluctuation value; A first width fluctuation calculation unit, used for calculating the standard deviation of the real-time ice sheet crack width within the preset temporary determination time length to form a first width fluctuation value; A second determining unit is connected to the temperature fluctuation calculating unit and the first width fluctuation calculating unit respectively, and is used to determine a plurality of temporary grids according to the temperature fluctuation value and the width fluctuation value.

[0010] Further, the second determining unit includes: A temperature fluctuation curve drawing subunit is used to draw a variation curve of the temperature fluctuation value to form a temperature fluctuation curve; A first width fluctuation curve drawing subunit, used for drawing a variation curve of the first width fluctuation value to form a first width fluctuation curve; a consistency calculation subunit, which is connected to the temperature fluctuation curve drawing subunit and the width fluctuation curve drawing subunit respectively, and is used to calculate the cosine similarity of the temperature fluctuation curve and the width fluctuation curve to form a change consistency; A temporary determination subunit is connected to the consistency calculation subunit, and is used to determine that the waterlogging grid is a temporary grid when the change consistency is less than a preset consistency threshold.

[0011] Furthermore, the determination module includes: A reflectivity fluctuation calculation unit, used to calculate the standard deviation of the real-time spectral reflectivity of a single temporary grid within a preset determination time period to form a reflectivity fluctuation value; A second width fluctuation calculation unit, used for calculating the standard deviation of the real-time ice sheet crack width of a single temporary grid within the preset determination time period to form a second width fluctuation value; a synchronization degree calculation unit, connected to the reflectivity fluctuation calculation unit and the second width fluctuation calculation unit respectively, for calculating the synchronization degree according to the reflectivity fluctuation value and the second width fluctuation value; A determination unit is connected to the synchronization calculation unit and is used to determine a number of observation grids according to the synchronization between any two adjacent temporary grids.

[0012] Furthermore, the synchronization degree calculation unit includes: A reflectivity fluctuation curve drawing subunit is used to draw a change curve of the reflectivity fluctuation value to form a reflectivity fluctuation curve; A second width fluctuation curve drawing subunit, used for drawing a variation curve of the second width fluctuation value to form a second width fluctuation curve; The synchronization degree calculation subunit is connected to the reflectivity fluctuation curve drawing subunit and the second width fluctuation curve drawing subunit respectively, and is used to calculate the cosine similarity of the reflectivity fluctuation curve and the second width fluctuation curve to form the synchronization degree.

[0013] Furthermore, the determination unit includes: A synchronization deviation calculation subunit, used for calculating the relative deviation of the synchronization degree of any two adjacent temporary grids to form a synchronization deviation; A determination subunit is connected to the synchronization deviation calculation subunit, and is used to determine that the temporary grid is the observation grid when the synchronization deviation is greater than a preset synchronization deviation threshold.

[0014] Furthermore, the control module comprises: An observation ratio calculation unit is used to calculate the ratio of the number of grids of the observation grid to the number of grids of all grids to be determined, so as to form an observation ratio; A control unit is connected to the observation ratio calculation unit, and is used to control a plurality of the drones to move from the non-waterlogging grid to the observation grid according to the observation ratio and the preset number.

[0015] Furthermore, the control unit comprises: A distribution degree calculation subunit, used for calculating the standard deviation of the distances from all the grid positions to the preset center point to form a distribution degree when the observation proportion is greater than a preset proportion threshold; a dispatch quantity calculation subunit, connected to the distribution degree calculation subunit, for calculating the total dispatch quantity according to the relative deviation between the distribution degree and the preset distribution degree threshold, the preset dispatch coefficient, the number of grids of the observation grid and the preset number when the distribution degree is greater than the preset distribution degree threshold; A control subunit is connected to the dispatch quantity calculation subunit, and is used to control the total dispatch quantity of the drones to be moved evenly from the non-waterlogged grids to each of the observation grids.

[0016] Furthermore, the adjustment module includes: A grid fluctuation calculation unit, used to calculate the standard deviation of the number of grids of the temporary grid within a preset adjustment time to form a grid number fluctuation value; An adjustment unit is connected to the grid fluctuation calculation unit and is used to increase the preset reflectivity threshold according to a relative deviation between the grid quantity fluctuation value and the preset quantity fluctuation threshold and a preset adjustment coefficient when the grid quantity fluctuation value is greater than the preset quantity fluctuation threshold.

[0017] Compared with the prior art, the beneficial effect of the present invention is that, through a highly integrated perception and decision-making module, it can quickly respond to complex climate changes in polar regions, accurately locate and adjust the UAV mission area. The system collects spectral reflectance, temperature and crack width data in real time, combined with intelligent analysis, and can efficiently identify waterlogging grids, temporary grids and observation grids to achieve accurate task allocation and scheduling. In practical applications, the system can not only flexibly adjust the reflectance threshold according to the environmental changes of the grid, but also optimize the allocation of UAVs among multiple grids through comparison and fluctuation calculation, avoid resource waste and improve collection efficiency. In addition, the adaptive adjustment mechanism of the system can quickly adjust the number and distribution of UAVs according to real-time data fluctuations to ensure that an efficient working state can be maintained in various extreme environments. Overall, the system significantly improves the task execution capability, response speed and data acquisition accuracy of UAV clusters in complex polar environments, effectively reduces operating costs and improves the comprehensiveness and accuracy of polar environmental monitoring, and effectively solves the problem of low response speed of the system in the face of emergencies due to reliance on a single control strategy.

[0018] Furthermore, by comparing the real-time spectral reflectance with the preset reflectance threshold, it is possible to accurately and quickly determine whether there is water accumulation in each grid, reducing the complexity of manual judgment and improving the efficiency and accuracy of data processing.

[0019] Furthermore, by calculating the fluctuations of temperature and crack width, areas with unstable characteristics can be identified, providing a scientific basis for the determination of temporary grids, thereby enhancing the ability to accurately identify waterlogged areas. By monitoring the fluctuations of temperature and crack width in real time, the accuracy of drone dispatch can be ensured, and subsequent environmental monitoring and data collection can be further optimized, the response speed and flexibility of the system can be improved, and the reliability and accuracy of waterlogging identification in polar regions can be improved.

[0020] Furthermore, by analyzing the consistency of temperature and crack width fluctuations, temporary grids can be effectively identified, which helps to further optimize the scheduling and observation strategies of drones. By analyzing the consistency of temperature and crack width changes, areas with unstable or abnormal changes can be accurately determined to avoid misjudgment and improve the adaptability and processing efficiency of the system in complex environments.

[0021] Furthermore, by calculating the synchronization of the spectral reflectance and crack width fluctuation values ​​of the temporary grids, grids showing similar change trends can be effectively identified, ensuring a more accurate selection of the observation grid, reducing the interference of errors, and improving the accuracy and reliability of the overall grid division and monitoring.

[0022] Furthermore, by calculating the synchronization degree, it is possible to effectively identify whether the spectral reflectance and crack width change trends of adjacent grids in the same time period are consistent, thereby providing a basis for further screening of temporary grids and improving the accuracy of grid determination. Using cosine similarity for synchronization degree calculation can eliminate misjudgments caused by instantaneous fluctuations and ensure the stability and reliability of the determination.

[0023] Furthermore, by comparing the synchronization deviation with the preset synchronization deviation threshold, the synchronization difference between temporary grids can be accurately judged, ensuring that the temporary grid will only be judged as an observation grid when the changes are obviously inconsistent. This can effectively filter out grids that do not meet the observation conditions, improve the accuracy of data analysis, and optimize the subsequent UAV scheduling and regional monitoring efficiency.

[0024] Furthermore, by calculating the observation ratio and rationally dispatching drones, it is possible to ensure that the observation grid is adequately covered by drones, while avoiding waste of resources, optimizing the allocation of drones, and improving the efficiency and accuracy of polar region monitoring.

[0025] Furthermore, by dynamically adjusting the number of dispatches and allocation of drones, the target area can be covered efficiently, avoiding over-concentration or sparse distribution, thereby improving the work efficiency of drones in polar region monitoring and the comprehensiveness of data collection.

[0026] Furthermore, by dynamically adjusting the reflectivity threshold, we can avoid over-reliance on unstable grid data, improve the robustness and adaptability of the system, and ensure that the system can respond flexibly and optimize the task allocation and resource scheduling of drones when the environment changes significantly. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the UAV swarm intelligent control system of this embodiment; Figure 2 This is a determination logic diagram of the first determination unit of this embodiment for determining a waterlogging grid and a non-waterlogging grid; Figure 3 A decision logic diagram for temporarily determining a subunit decision temporary grid for this embodiment; Figure 4 This is a decision logic diagram for the decision subunit in this embodiment to decide the observation grid. DETAILED DESCRIPTION

[0028] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0029] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0030] See also Figure 1 As shown, it is a schematic diagram of the drone cluster intelligent control system of this embodiment; This embodiment provides a drone swarm intelligent control system, including: A data acquisition module is used to obtain real-time spectral reflectance, real-time temperature and real-time ice sheet crack width collected by a preset number of drones in each grid to be determined in the polar region divided by grids; A first determination module, connected to the data acquisition module, for determining a number of waterlogging grids and non-waterlogging grids according to the real-time spectral reflectance and a preset reflectance threshold; A second determination module, which is connected to the first determination module and the data acquisition module respectively, and is used to determine a number of temporary grids according to the real-time temperature of each of the water accumulation grids and the real-time ice sheet crack width; A determination module, which is connected to the second determination module and the data acquisition module respectively, and is used to determine a number of observation grids according to the real-time spectral reflectance and the real-time ice sheet crack width of any two adjacent temporary grids; A control module connected to the determination module, for controlling a plurality of the drones to move from the non-waterlogging grid to the observation grid according to the number of grids, grid positions and the preset number of the observation grid; An adjustment module is connected to the data acquisition module, the control module and the first determination module respectively, and is used to adjust the preset reflectivity threshold based on the temporary grid determined after scheduling a number of the drones.

[0031] The data acquisition module collects data simultaneously through multiple drones in each grid area, and ensures the representativeness and accuracy of the acquired data through averaging. Specifically, each drone is equipped with a spectral reflectance sensor, temperature sensor and crack width measurement equipment, which continuously collect data at different locations in the same area. Through real-time data transmission, the module summarizes the collection results of multiple drones in each grid and calculates the average values ​​of spectral reflectance, temperature and ice sheet crack width in each grid area. These average values ​​reflect the overall status of the environmental characteristics within the grid and serve as the basis for subsequent analysis and decision-making, thus ensuring the comprehensiveness and high accuracy of the data.

[0032] The preset number refers to the number of drones participating in data collection in each grid area, which depends on the size of the area, the complexity of the environment, and the accuracy requirements of data collection. According to actual conditions, the preset number is usually between 5 and 20. In this embodiment, it is set to 10 to ensure a high data collection density and representativeness, and a reasonable number of drones to avoid waste of resources.

[0033] The preset reflectivity threshold refers to the spectral reflectivity value used to distinguish between waterlogged and non-waterlogged grids. It depends on the reflectivity characteristics of different types of ground or surfaces in a specific spectral band. It is usually set between 0.3 and 0.5. In this embodiment, it is set to 0.4, which can better identify waterlogged areas and non-waterlogged areas, avoid misjudgment, and improve the accuracy of the system.

[0034] Through a multi-level data collection and analysis process, grids are intelligently divided and dynamically scheduled in the polar region. First, the data acquisition module collects spectral reflectance, temperature and crack width data in real time for subsequent analysis. The first determination module determines the waterlogged and non-waterlogged grids based on the spectral reflectance and the preset threshold. The second determination module further analyzes the temperature and crack width of the waterlogged grid to determine the temporary grid. Then, the judgment module determines the final observation grid based on the synchronization of adjacent grids. The control module dispatches the drone from the non-waterlogged grid to the observation grid based on the number, location and preset number of observation grids. Finally, the adjustment module dynamically adjusts the preset reflectance threshold according to the drone scheduling situation, thereby improving the adaptability and accuracy of the system.

[0035] Through the highly integrated perception and decision-making modules, it can quickly respond to complex climate changes in polar regions, accurately locate and adjust the UAV mission area. The system collects spectral reflectance, temperature and crack width data in real time, and combined with intelligent analysis, it can efficiently identify waterlogging grids, temporary grids and observation grids, and realize accurate task allocation and scheduling. In practical applications, the system can not only flexibly adjust the reflectance threshold according to the environmental changes of the grid, but also optimize the allocation of UAVs among multiple grids through comparison and fluctuation calculation, avoid resource waste and improve collection efficiency. In addition, the system's adaptive adjustment mechanism can quickly adjust the number and distribution of UAVs according to real-time data fluctuations to ensure that it can maintain an efficient working state in various extreme environments. Overall, the system significantly improves the task execution capability, response speed and data collection accuracy of UAV clusters in complex polar environments, effectively reduces operating costs and improves the comprehensiveness and accuracy of polar environmental monitoring, and effectively solves the problem of low response speed of the system in the face of emergencies due to reliance on a single control strategy.

[0036] Please continue reading Figure 2 As shown, it is a determination logic diagram of the first determination unit of this embodiment for determining a waterlogging grid and a non-waterlogging grid; The first determining module comprises: A reflectivity comparison unit, used to compare the real-time spectral reflectivity with the preset reflectivity threshold to form a reflectivity comparison result; A first determination unit is connected to the reflectivity comparison unit, and is used to determine that the grid to be determined is the waterlogged grid when the reflectivity comparison result is that the real-time spectral reflectivity is greater than the preset reflectivity threshold, and to determine that the grid to be determined is the non-waterlogged grid when the reflectivity comparison result is that the real-time spectral reflectivity is less than or equal to the preset reflectivity threshold.

[0037] The reflectivity comparison unit compares the real-time spectral reflectivity with the preset reflectivity threshold to form a reflectivity comparison result. If the real-time spectral reflectivity is greater than the preset reflectivity threshold, the first determination unit determines that the grid is a waterlogged grid; conversely, if the real-time spectral reflectivity is less than or equal to the preset reflectivity threshold, it is determined to be a non-waterlogged grid. This process realizes the automatic identification of waterlogging in the polar region.

[0038] By comparing the real-time spectral reflectance with the preset reflectance threshold, it is possible to accurately and quickly determine whether there is water accumulation in each grid, reducing the complexity of manual judgment and improving the efficiency and accuracy of data processing.

[0039] Specifically, the second determination module includes: The temperature fluctuation calculation unit is used to calculate the standard deviation of the real-time temperature within a preset temporary determination time period to form a temperature fluctuation value, wherein: , R is the temperature fluctuation value, Ti is the real-time temperature at each moment within the preset temporary determination time, and T0 is the average value of all real-time temperatures; The first width fluctuation calculation unit is used to calculate the standard deviation of the real-time ice sheet crack width within the preset temporary determination time length to form a first width fluctuation value, wherein: Y is the first width fluctuation value, Wi is the real-time ice sheet crack width at each moment within the preset temporary determination time, W0 is the average of all real-time ice sheet crack widths, and N is the real-time temperature or the number of real-time ice sheet crack widths; A second determining unit is connected to the temperature fluctuation calculating unit and the first width fluctuation calculating unit respectively, and is used to determine a plurality of temporary grids according to the temperature fluctuation value and the width fluctuation value.

[0040] The preset temporary determination time length is the time period used to calculate the real-time temperature and crack width fluctuations, which depends on the speed of environmental changes and the requirements for the system's response to changes. It usually takes into account factors such as weather fluctuations and ice sheet crack change cycles. It is usually set between a few hours and a day. In this embodiment, it is set to 12 hours, which can balance real-time performance and the stability of data fluctuations, avoid misjudgment of fluctuations in too short a time, and at the same time ensure that the change trend can be captured over a sufficiently long period of time, which helps to more accurately judge the temporary state of the area and improve the response accuracy and stability of the system.

[0041] The temperature fluctuation calculation unit calculates the standard deviation of the real-time temperature within the preset temporary determination time to obtain the temperature fluctuation value; at the same time, the first width fluctuation calculation unit calculates the standard deviation of the real-time ice sheet crack width to obtain the first width fluctuation value. Then, the second determination unit determines a number of temporary grids based on the changes in the temperature fluctuation value and the crack width fluctuation value. This process can effectively identify areas with fluctuations within a specific time period, providing a basis for further observation and analysis.

[0042] By calculating the fluctuations of temperature and crack width, areas with unstable characteristics can be identified, providing a scientific basis for the determination of temporary grids, thereby enhancing the ability to accurately identify waterlogged areas. By monitoring the fluctuations of temperature and crack width in real time, the accuracy of drone dispatch can be ensured, and subsequent environmental monitoring and data collection can be further optimized, the response speed and flexibility of the system can be improved, and the reliability and accuracy of waterlogging identification in polar regions can be improved.

[0043] Please continue reading Figure 3 As shown, it is a decision logic diagram of temporarily determining a subunit to determine a temporary grid in this embodiment; The second determining unit includes: A temperature fluctuation curve drawing subunit is used to draw a variation curve of the temperature fluctuation value to form a temperature fluctuation curve; A first width fluctuation curve drawing subunit, used for drawing a variation curve of the first width fluctuation value to form a first width fluctuation curve; a consistency calculation subunit, which is connected to the temperature fluctuation curve drawing subunit and the width fluctuation curve drawing subunit respectively, and is used to calculate the cosine similarity of the temperature fluctuation curve and the width fluctuation curve to form a change consistency; The formula for calculating the change consistency is: , Q is the consistency of change.

[0044] A temporary determination subunit is connected to the consistency calculation subunit, and is used to determine that the waterlogging grid is a temporary grid when the change consistency is less than a preset consistency threshold.

[0045] The preset consistency threshold is used to determine whether the similarity between the temperature fluctuation curve and the crack width fluctuation curve is high enough, so as to determine whether it is the standard value of the temporary grid. It depends on the required sensitivity and accuracy. It is usually set between 0.7 and 0.9 according to the actual application scenario, the range of changes in environmental factors and the system's tolerance for abnormal changes. In this embodiment, it is set to 0.8, which helps to balance the risks of misjudgment and missed judgment, and ensures that only when the changes in temperature and crack width fluctuations are highly consistent, it is judged as a temporary grid, thereby improving the accuracy and stability of the system.

[0046] The temperature fluctuation value and the crack width fluctuation value are used to draw the change curve, and the temperature fluctuation curve and the width fluctuation curve are generated respectively. Then, the cosine similarity of the two fluctuation curves is calculated, that is, the change consistency. If the change consistency is less than the preset consistency threshold, the waterlogging grid is judged to be a temporary grid, indicating that the state of the area has changed greatly and has temporary instability, which requires further observation and processing.

[0047] By analyzing the consistency of temperature and crack width fluctuations, temporary grids can be effectively identified, which helps to further optimize the scheduling and observation strategies of drones. By analyzing the consistency of temperature and crack width changes, areas with unstable or abnormal changes can be accurately determined to avoid misjudgment and improve the adaptability and processing efficiency of the system in complex environments.

[0048] Specifically, the determination module includes: The reflectivity fluctuation calculation unit is used to calculate the standard deviation of the real-time spectral reflectivity of a single temporary grid within a preset determination time to form a reflectivity fluctuation value, wherein: , U is the reflectivity fluctuation value, Pi is the real-time spectral reflectivity at each moment within the preset determination time, and P0 is the average value of all real-time spectral reflectivity; The second width fluctuation calculation unit is used to calculate the standard deviation of the real-time ice sheet crack width of a single temporary grid within the preset determination time to form a second width fluctuation value, wherein: F is the second width fluctuation value, Wi' is the real-time ice cover crack width at each moment within the preset determination time, W0' is the average value of all real-time ice cover crack widths, and n is the real-time spectral reflectance or the number of real-time ice cover crack widths; a synchronization degree calculation unit, connected to the reflectivity fluctuation calculation unit and the second width fluctuation calculation unit respectively, for calculating the synchronization degree according to the reflectivity fluctuation value and the second width fluctuation value; A determination unit is connected to the synchronization calculation unit and is used to determine a number of observation grids according to the synchronization between any two adjacent temporary grids.

[0049] The preset judgment duration refers to the time period used when judging the temporary grid synchronization, which depends on the geographical environment, data collection frequency and actual monitoring needs. It is usually set between a few minutes and a few hours. In this embodiment, it is set to 30 minutes, which can ensure that the collected data has a sufficient range of variation to avoid interference from instantaneous fluctuations. At the same time, it will not cause excessive environmental changes due to excessive duration, thereby ensuring the stability and accuracy of the judgment.

[0050] The reflectivity fluctuation calculation unit and the second width fluctuation calculation unit respectively calculate the standard deviation of the spectral reflectivity and the crack width in the temporary grid within the preset judgment time length to obtain the reflectivity fluctuation value and the second width fluctuation value. The synchronization calculation unit calculates the synchronization degree according to the two fluctuation values. Finally, the judgment unit determines whether any two adjacent temporary grids meet the conditions according to their synchronization degrees, thereby determining a number of observation grids.

[0051] By calculating the synchronization of the spectral reflectance and crack width fluctuation values ​​of the temporary grid, the grids showing similar change trends can be effectively identified, ensuring a more accurate selection of the observation grid, reducing the interference of errors, and improving the accuracy and reliability of the overall grid division and monitoring.

[0052] Specifically, the synchronization calculation unit includes: A reflectivity fluctuation curve drawing subunit is used to draw a change curve of the reflectivity fluctuation value to form a reflectivity fluctuation curve; A second width fluctuation curve drawing subunit, used for drawing a variation curve of the second width fluctuation value to form a second width fluctuation curve; a synchronization degree calculation subunit, which is connected to the reflectivity fluctuation curve drawing subunit and the second width fluctuation curve drawing subunit respectively, and is used to calculate the cosine similarity of the reflectivity fluctuation curve and the second width fluctuation curve to form a synchronization degree; The formula for calculating the synchronization degree is: , Q is the consistency of change, and G is the synchronization.

[0053] By plotting the change curves of reflectivity fluctuation value and crack width fluctuation value, the reflectivity fluctuation curve and the second width fluctuation curve are formed respectively. Then, the cosine similarity between the two fluctuation curves is calculated to obtain the synchronization value. The higher the synchronization, the more consistent the fluctuation trend between the two, reflecting the synchronous change of the water accumulation grid and the crack width.

[0054] By calculating the synchronization degree, it is possible to effectively identify whether the spectral reflectance and crack width change trends of adjacent grids in the same time period are consistent, thereby providing a basis for further screening of temporary grids and improving the accuracy of grid determination. Using cosine similarity to calculate the synchronization degree can eliminate misjudgments caused by instantaneous fluctuations and ensure the stability and reliability of the determination.

[0055] Please continue reading Figure 4 As shown, it is a decision logic diagram of the decision subunit in this embodiment for deciding the observation grid; The determination unit comprises: A synchronization deviation calculation subunit, used for calculating the relative deviation of the synchronization degree of any two adjacent temporary grids to form a synchronization deviation; A determination subunit is connected to the synchronization deviation calculation subunit, and is used to determine that the temporary grid is the observation grid when the synchronization deviation is greater than a preset synchronization deviation threshold.

[0056] The preset synchronization deviation threshold is a standard value used to determine whether a temporary grid is an observation grid. It depends on the expected range of changes in factors such as temperature fluctuations and crack width changes within the grid, and how to distinguish normal fluctuations from abnormal fluctuations. It is usually set between 0.1 and 1.0. In this embodiment, it is set to 0.5 to ensure that abnormal grids that require attention can be effectively identified and to avoid over-response to normal slight changes to a large extent, thereby improving the stability and practicality of the system.

[0057] First, the relative deviation between the synchronization degrees of two adjacent temporary grids is calculated by the synchronization deviation calculation subunit to obtain the synchronization deviation value. Then, the determination subunit determines whether the temporary grid belongs to the observation grid according to whether the synchronization deviation is greater than the preset synchronization deviation threshold. If the synchronization deviation is greater than the threshold, it is considered that the changes of the adjacent grids are inconsistent, and thus it is determined to be an observation grid.

[0058] By comparing the synchronization deviation with the preset synchronization deviation threshold, the synchronization difference between temporary grids can be accurately judged, ensuring that the temporary grid will only be judged as an observation grid when the changes are obviously inconsistent. This can effectively filter out grids that do not meet the observation conditions, improve the accuracy of data analysis, and optimize the subsequent drone scheduling and regional monitoring efficiency.

[0059] Specifically, the control module includes: An observation ratio calculation unit is used to calculate the ratio of the number of grids of the observation grid to the number of grids of all grids to be determined, so as to form an observation ratio; A control unit is connected to the observation ratio calculation unit, and is used to control a plurality of the drones to move from the non-waterlogging grid to the observation grid according to the observation ratio and the preset number.

[0060] The observation ratio calculation unit calculates the ratio of the number of observation grids to the total number of grids to be determined, i.e., the observation ratio. Then, the control unit selects and controls several drones from the non-waterlogged grids according to the relationship between the ratio and the preset number, and moves them to the observation grids for further observation or data collection.

[0061] By calculating the observation ratio and rationally dispatching drones, we can ensure that the observation grid is adequately covered by drones, avoid wasting resources, optimize the allocation of drones, and improve the efficiency and accuracy of polar region monitoring.

[0062] Specifically, the control unit includes: A distribution degree calculation subunit, used for calculating the standard deviation of the distances from all the grid positions to the preset center point to form a distribution degree when the observation proportion is greater than a preset proportion threshold; a dispatch quantity calculation subunit, connected to the distribution degree calculation subunit, for calculating the total dispatch quantity according to the relative deviation between the distribution degree and the preset distribution degree threshold, the preset dispatch coefficient, the number of grids of the observation grid and the preset number when the distribution degree is greater than the preset distribution degree threshold; A control subunit is connected to the dispatch quantity calculation subunit, and is used to control the total dispatch quantity of the drones to be moved evenly from the non-waterlogged grids to each of the observation grids.

[0063] Among them, the calculation of the total dispatch quantity first calculates an adjustment factor, which is obtained by multiplying the preset quantity and a multiplier determined by the adjustment coefficient and the relative deviation. Then, the calculated adjustment factor is multiplied by the number of observation grids to finally obtain the total number of drones that need to be dispatched.

[0064] The preset percentage threshold is an important parameter used by the control unit to determine whether the drone scheduling needs to be adjusted. It depends on the coverage requirements and area size of the actual task, and determines when more drones need to be dispatched to the observation grid. It is usually set between 10% and 30%. In this embodiment, it is set to 20%, which can balance the needs of regional coverage and resource scheduling, and help ensure that the observation grid has enough drones for effective monitoring.

[0065] The preset distribution threshold is a standard value used to evaluate the distribution of the observation grid, which is set depending on the size of the grid, the complexity of the task and the characteristics of the area, and is usually set between 10% and 40%. In this embodiment, it is set to 25%, which helps to adjust the distribution of drones in time to achieve uniform area coverage.

[0066] The preset scheduling coefficient is an adjustment parameter used to calculate the number of drones to be scheduled. It depends on the actual mission requirements, the capabilities of the drones, and the regional conditions. It is usually set between 1.0 and 2.0. In this embodiment, it is set to 1.5, which can moderately increase the number of scheduled drones when the distribution degree is high to ensure the efficiency of mission completion.

[0067] When the observation ratio is greater than the preset ratio threshold, the distribution degree calculation subunit calculates the standard deviation of the distance from the grid position to the preset center point to form the distribution degree. If the distribution degree is greater than the preset threshold, the dispatch quantity calculation subunit calculates the total number of dispatches required based on the distribution degree, deviation, dispatch coefficient and preset number. Finally, the control subunit reasonably dispatches drones from non-waterlogged grids based on the calculation results and evenly distributes them to each observation grid for task execution.

[0068] By dynamically adjusting the number of dispatches and drone allocation, the target area can be covered efficiently, avoiding excessive concentration or sparse distribution, thereby improving the work efficiency and comprehensiveness of data collection of drones in polar region monitoring.

[0069] Specifically, the adjustment module includes: A grid fluctuation calculation unit, used to calculate the standard deviation of the number of grids of the temporary grid within a preset adjustment time to form a grid number fluctuation value; An adjustment unit is connected to the grid fluctuation calculation unit and is used to increase the preset reflectivity threshold according to a relative deviation between the grid quantity fluctuation value and the preset quantity fluctuation threshold and a preset adjustment coefficient when the grid quantity fluctuation value is greater than the preset quantity fluctuation threshold.

[0070] First, the standard deviation of the temporary number of grids within the preset adjustment time is calculated by the grid fluctuation calculation unit to obtain the grid number fluctuation value. Then, the adjustment unit makes a judgment based on the relative deviation between the grid number fluctuation value and the preset number fluctuation threshold and the preset adjustment coefficient. If the grid number fluctuation value is greater than the preset threshold, the adjustment unit will increase the preset reflectivity threshold to adapt to the new environmental changes.

[0071] By dynamically adjusting the reflectivity threshold, we can avoid over-reliance on unstable grid data, improve the robustness and adaptability of the system, ensure that the system can respond flexibly in the case of large environmental changes, and optimize the task allocation and resource scheduling of drones.

[0072] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An intelligent control system for drone swarms, characterized in that: include: A data acquisition module is used to obtain real-time spectral reflectance, real-time temperature and real-time ice sheet crack width collected by a preset number of drones in each grid to be determined in the polar region divided by grids; A first determination module, connected to the data acquisition module, for determining a number of waterlogging grids and non-waterlogging grids according to the real-time spectral reflectance and a preset reflectance threshold; A second determination module, which is connected to the first determination module and the data acquisition module respectively, and is used to determine a number of temporary grids according to the real-time temperature of each of the water accumulation grids and the real-time ice sheet crack width; A determination module, which is connected to the second determination module and the data acquisition module respectively, and is used to determine a number of observation grids according to the real-time spectral reflectance and the real-time ice sheet crack width of any two adjacent temporary grids; A control module connected to the determination module, for controlling a plurality of the drones to move from the non-waterlogging grid to the observation grid according to the number of grids, grid positions and the preset number of the observation grid; An adjustment module is connected to the data acquisition module, the control module and the first determination module respectively, and is used to adjust the preset reflectivity threshold based on the temporary grid determined after scheduling a number of the drones.

2. The UAV swarm intelligent control system according to claim 1 is characterized in that: The first determining module comprises: A reflectivity comparison unit, used to compare the real-time spectral reflectivity with the preset reflectivity threshold to form a reflectivity comparison result; A first determination unit is connected to the reflectivity comparison unit, and is used to determine that the grid to be determined is the waterlogged grid when the reflectivity comparison result is that the real-time spectral reflectivity is greater than the preset reflectivity threshold, and to determine that the grid to be determined is the non-waterlogged grid when the reflectivity comparison result is that the real-time spectral reflectivity is less than or equal to the preset reflectivity threshold.

3. The UAV swarm intelligent control system according to claim 2 is characterized in that: The second determining module comprises: A temperature fluctuation calculation unit, used to calculate the standard deviation of the real-time temperature within a preset temporary determined time period to form a temperature fluctuation value; A first width fluctuation calculation unit, used for calculating the standard deviation of the real-time ice sheet crack width within the preset temporary determination time length to form a first width fluctuation value; A second determining unit is connected to the temperature fluctuation calculating unit and the first width fluctuation calculating unit respectively, and is used to determine a plurality of temporary grids according to the temperature fluctuation value and the width fluctuation value.

4. The UAV swarm intelligent control system according to claim 3 is characterized in that: The second determining unit includes: A temperature fluctuation curve drawing subunit is used to draw a variation curve of the temperature fluctuation value to form a temperature fluctuation curve; A first width fluctuation curve drawing subunit, used for drawing a variation curve of the first width fluctuation value to form a first width fluctuation curve; a consistency calculation subunit, which is connected to the temperature fluctuation curve drawing subunit and the width fluctuation curve drawing subunit respectively, and is used to calculate the cosine similarity of the temperature fluctuation curve and the width fluctuation curve to form a change consistency; A temporary determination subunit is connected to the consistency calculation subunit, and is used to determine that the waterlogging grid is a temporary grid when the change consistency is less than a preset consistency threshold.

5. The UAV swarm intelligent control system according to claim 4 is characterized in that: The determination module comprises: A reflectivity fluctuation calculation unit, used to calculate the standard deviation of the real-time spectral reflectivity of a single temporary grid within a preset determination time period to form a reflectivity fluctuation value; A second width fluctuation calculation unit, used for calculating the standard deviation of the real-time ice sheet crack width of a single temporary grid within the preset determination time period to form a second width fluctuation value; a synchronization degree calculation unit, connected to the reflectivity fluctuation calculation unit and the second width fluctuation calculation unit respectively, for calculating the synchronization degree according to the reflectivity fluctuation value and the second width fluctuation value; A determination unit is connected to the synchronization calculation unit and is used to determine a number of observation grids according to the synchronization between any two adjacent temporary grids.

6. The UAV swarm intelligent control system according to claim 5 is characterized in that: The synchronization degree calculation unit comprises: A reflectivity fluctuation curve drawing subunit is used to draw a change curve of the reflectivity fluctuation value to form a reflectivity fluctuation curve; A second width fluctuation curve drawing subunit, used for drawing a variation curve of the second width fluctuation value to form a second width fluctuation curve; The synchronization degree calculation subunit is connected to the reflectivity fluctuation curve drawing subunit and the second width fluctuation curve drawing subunit respectively, and is used to calculate the cosine similarity of the reflectivity fluctuation curve and the second width fluctuation curve to form the synchronization degree.

7. The UAV swarm intelligent control system according to claim 6, characterized in that: The determination unit comprises: A synchronization deviation calculation subunit, used for calculating the relative deviation of the synchronization degree of any two adjacent temporary grids to form a synchronization deviation; A determination subunit is connected to the synchronization deviation calculation subunit, and is used to determine that the temporary grid is the observation grid when the synchronization deviation is greater than a preset synchronization deviation threshold.

8. The UAV swarm intelligent control system according to claim 7, characterized in that: The control module comprises: An observation ratio calculation unit is used to calculate the ratio of the number of grids of the observation grid to the number of grids of all grids to be determined, so as to form an observation ratio; A control unit is connected to the observation ratio calculation unit, and is used to control a plurality of the drones to move from the non-waterlogging grid to the observation grid according to the observation ratio and the preset number.

9. The UAV swarm intelligent control system according to claim 8, characterized in that: The control unit comprises: A distribution degree calculation subunit, used for calculating the standard deviation of the distances from all the grid positions to the preset center point to form a distribution degree when the observation proportion is greater than a preset proportion threshold; a dispatch quantity calculation subunit, connected to the distribution degree calculation subunit, for calculating the total dispatch quantity according to the relative deviation between the distribution degree and the preset distribution degree threshold, the preset dispatch coefficient, the number of grids of the observation grid and the preset number when the distribution degree is greater than the preset distribution degree threshold; A control subunit is connected to the dispatch quantity calculation subunit, and is used to control the total dispatch quantity of the drones to be moved evenly from the non-waterlogged grids to each of the observation grids.

10. The UAV swarm intelligent control system according to claim 9, characterized in that: The adjustment module comprises: A grid fluctuation calculation unit, used to calculate the standard deviation of the number of grids of the temporary grid within a preset adjustment time to form a grid number fluctuation value; An adjustment unit is connected to the grid fluctuation calculation unit and is used to increase the preset reflectivity threshold according to a relative deviation between the grid quantity fluctuation value and the preset quantity fluctuation threshold and a preset adjustment coefficient when the grid quantity fluctuation value is greater than the preset quantity fluctuation threshold.

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