Intelligent regulation and control method and system for active fault detection scheme of unmanned aerial vehicle formation

Abstract

The invention relates to the technical field of information detection, in particular to an intelligent regulation and control method and system for an active fault detection scheme of an unmanned aerial vehicle formation, and the method comprises the following steps: obtaining basic data information of a to-be-detected region, and dividing the to-be-detected region to obtain different region blocks and basic information of the different region blocks; according to the basic information, unmanned aerial vehicle formation forms and information detection devices of different area blocks are matched to obtain initial unmanned aerial vehicle detection schemes of different area blocks; evaluating the initial unmanned aerial vehicle detection scheme to obtain the formation tightness of the initial unmanned aerial vehicle detection scheme, the motion condition of the unmanned aerial vehicles and the pneumatic induction situation between the unmanned aerial vehicles; an evaluation analysis result of the detection scheme is obtained; and regulating and controlling the initial unmanned aerial vehicle detection scheme in combination with an evaluation analysis result to realize identification and detection of active fault information in different regions. The unmanned aerial vehicle detection scheme is regulated and controlled based on the condition of the to-be-detected area, and the pertinence and effectiveness of the active fault detection scheme are improved.

Description

Technical Field

[0001] This invention relates to the field of information detection technology, specifically to an intelligent control method and system for a UAV formation active fault detection scheme. Background Technology

[0002] As a key factor in earthquake disasters, the accurate detection of active faults, including their location and activity, is crucial for earthquake disaster prevention, rational urban planning, and ensuring the safety of engineering construction. However, the diverse geological conditions and complex geological environments of different regions pose significant challenges to active fault detection.

[0003] Existing methods for detecting active faults have significant limitations when dealing with complex geological environments. They struggle to adjust device parameters and detection methods based on the actual environmental conditions of the detection area. When faced with special geological structures, complex topography, or harsh weather conditions, existing detection schemes cannot fully utilize their capabilities, resulting in insufficient ability to capture the structural characteristics and activity changes of active faults. This makes it difficult to effectively detect the specific morphology and location of faults, and consequently, to comprehensively grasp the characteristic information of active faults.

[0004] There are also many problems in the process of UAVs performing reconnaissance missions. On the one hand, UAVs are susceptible to interference and restrictions from other equipment. Existing methods lack real-time monitoring and dynamic response to changes in UAV flight status and environmental dynamics, making it impossible to adjust the reconnaissance mission in a timely manner according to the actual situation. This not only increases the time and economic costs of the reconnaissance mission, but also negatively affects the accuracy and completeness of the reconnaissance data.

[0005] On the other hand, some methods, when simulating detection missions, only focus on a single influencing factor or a single predictive analysis result, lacking comprehensive analysis of multi-source data such as UAV flight data and environmental data. This results in an incomplete and inaccurate judgment of the detection mission, ultimately leading to deviations in the information detection results and increasing the purchase and use costs of UAV equipment.

[0006] Therefore, in response to the numerous problems encountered in the current active fault detection process, it is necessary to optimize the regional detection scheme and conduct effective analysis and simulation prediction of the UAV detection scheme to ensure that UAVs can still perform detection tasks efficiently and accurately in complex environments, avoid resource waste, reduce the economic cost of detection operations, and further provide technical support for earthquake disaster prevention, rational urban planning, and engineering construction. Summary of the Invention

[0007] During the acquisition of information on active faults, due to the complex and varied geological conditions in different regions, existing detection methods struggle to adjust the device parameters and detection methods according to the actual environmental conditions of the detection area. When faced with special geological structures or complex topography, existing detection schemes cannot fully utilize their performance, ultimately resulting in insufficient ability to capture the structural characteristics and activity changes of active faults, making it difficult to comprehensively grasp the characteristic information of active faults. This invention acquires basic data information of the area to be detected, defines and divides the area based on this basic data, and then divides the area into multiple different blocks, obtaining basic information for each block. Based on the basic information of different blocks, it designs UAV formations and configures information detection equipment to form initial UAV detection schemes for different blocks. This site-specific UAV detection scheme design fully considers the differences in geology and topography of different regions, helping to design UAV detection schemes according to actual environmental conditions. It can effectively cope with complex geological environments and help ensure the effective measurement and targeted detection of active fault characteristic information.

[0008] This invention evaluates and tests initial UAV detection schemes, obtaining multi-dimensional indicators such as formation tightness, UAV movement status, and aerodynamic induction behavior between UAVs. Based on these multi-dimensional indicators, an evaluation and analysis result of the initial scheme is obtained. This result is then used to intelligently adjust the initial scheme. The above analysis process enables real-time monitoring and dynamic response to changes in UAV flight status and environmental dynamics. This invention can adjust the UAV detection scheme in a timely manner according to actual conditions, improve the detection efficiency of active fault information, reduce the exploration cost of active faults, and ensure the accuracy and completeness of the detection data.

[0009] This invention includes a series of steps such as acquiring basic data information, defining and dividing regional blocks, matching UAV detection schemes, detection and evaluation, and intelligent control. It can realize targeted adjustment and optimization of UAV detection schemes, and can reasonably configure UAV formations and information detection equipment according to the actual conditions of different regional blocks, avoid unnecessary waste of resources, reduce the operating cost of active fault detection schemes, and further ensure the effective survey and acquisition of active fault information. Attached Figure Description

[0010] Figure 1 This is a flowchart of the intelligent control method for the UAV formation active fault detection scheme of the present invention; Figure 2 This is a schematic diagram showing the numbering and marking of different regions in the area to be detected in the intelligent control method of the UAV formation active fault detection scheme of the present invention. Figure 3 This is a schematic diagram of dual UAVs flying in the same plane, illustrating the intelligent control method for the UAV formation active fault detection scheme of the present invention. Figure 4 This is a structural diagram of the intelligent control system for the UAV formation active fault detection scheme of the present invention. Detailed Implementation

[0011] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0012] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0013] Please see Figure 1 To ensure the actual operational effectiveness of detection schemes in different regions, this invention predicts and optimizes the flight trajectory planning and detection methods in UAV detection schemes based on the actual environmental conditions of different regions. This ensures that UAV detection schemes can efficiently and accurately complete active fault detection tasks in complex and ever-changing detection environments, maximizing the accuracy and relevance of active fault detection results and providing data support for geological hazard assessment, urban planning, and engineering construction. This invention provides an intelligent control method for UAV formation active fault detection schemes, comprising the following steps: S1. Obtain basic data information of the area to be detected. Based on the above basic data information, define and divide the area to be detected to obtain different area blocks in the area to be detected. Based on the different area blocks and the basic data information, obtain the basic information of the different area blocks in the area to be detected. The specific steps and implementation content are as follows: First, geological, topographic, and meteorological data of the area to be explored are collected; then, the aforementioned geological, topographic, and meteorological data are integrated to obtain basic information about the area to be explored.

[0014] Before conducting active fault detection using UAV formations, it is necessary to comprehensively collect basic data on the area to be detected to ensure the smooth progress of subsequent active fault detection work. In this embodiment, to ensure the comprehensiveness of the basic data on the area to be detected, the relevant data covers various aspects such as geology, topography, and meteorology, which helps to provide information for subsequent UAV formation flight planning and simulation prediction.

[0015] I. Geological data collection for the area to be explored.

[0016] Geological maps, structural maps, and seismic activity maps of the area to be explored are crucial data for revealing the geological structural characteristics of the area. These maps can visually present the geological structural pattern, the distribution of major faults, and the lithological characteristics of the strata in the area to be explored. In this embodiment, detailed analysis of the geological maps can accurately determine the specific location and orientation of known faults within the exploration area, providing a scientific reference for the preliminary delineation of the exploration area. In an optional embodiment, the geological map shows a known fault in the area to be explored, trending northeast-southwest and 40 kilometers in length. When formulating the subsequent exploration plan, the scope can be appropriately expanded around this fault to ensure comprehensive coverage of any potentially active fault areas.

[0017] Relevant geological exploration reports, research papers, and other materials constitute the geological research findings for this region. These findings cover multiple aspects, including fault activity and geological evolution history, and are of great significance for assessing the potential hazard of active faults within the exploration area. Based on the above data, the detection accuracy and depth of the exploration area can be reasonably predicted. In an optional embodiment, if the geological report indicates that a fault within the exploration area has shown signs of activity within the past 50 years, and at a relatively rapid rate, then the detection accuracy and depth for this area need to be increased during the exploration process to more accurately obtain relevant information about the active fault.

[0018] Analyzing the mechanical properties and mineral composition of the rocks yields valuable insights for subsequent UAV reconnaissance planning and data analysis. In one optional embodiment, analyzing the rock's elastic modulus, Poisson's ratio, and other mechanical parameters provides a deeper understanding of the propagation characteristics of seismic waves within the rock, effectively ensuring the accuracy of the detection data and offering strong support for the accurate identification of active faults.

[0019] II. Collection of topographic data for the area to be explored.

[0020] The embodiment utilizes a Digital Elevation Model (DEM). The DEM data provides detailed information on the terrain undulations, slope, and aspect of the area to be surveyed, serving as a crucial basis for UAV flight altitude and route planning. Using DEM data, key parameters such as the maximum elevation difference and average slope within the survey area can be accurately calculated. In one optional embodiment, the maximum elevation difference within the survey area is 450 meters, and the average slope is 20 degrees. When planning the UAV flight altitude and route, the terrain factors of this area must be fully considered to ensure the safety of UAV flight.

[0021] Topographic maps at different scales can clearly mark important topographic features, such as mountains, rivers, and canyons. This information helps avoid collisions between drones and terrain obstacles during flight. By analyzing topographic maps, the complexity of the terrain in the area to be explored can be accurately determined, allowing for targeted adjustments to the drone formation's flight strategy and improving the safety and operational efficiency of the drone exploration program.

[0022] III. Collection of meteorological data for the area to be explored.

[0023] Historical meteorological data for the area to be explored mainly covers multiple aspects such as temperature, air pressure, wind speed, wind direction, and precipitation. Analyzing this meteorological data can provide a deeper understanding of the impact of meteorological conditions on UAV flight and the operation of exploration equipment. In one optional embodiment, the area to be explored experiences frequent thunderstorms and high wind speeds in summer. Therefore, when planning UAV flight times, it is necessary to fully consider the impact of relevant meteorological factors on UAV flight safety and the quality of exploration data, and to rationally select flight exploration periods to reduce the risks posed by meteorological factors.

[0024] Combining real-time weather forecast information can provide meteorological information for exploration missions, which helps to rationally arrange the flight time of the exploration plan before the mission is carried out, effectively avoid adverse weather conditions, closely monitor weather forecasts, and help to adjust the UAV exploration plan in a timely manner, ensuring that the exploration mission is carried out smoothly under safe and suitable conditions, and improving the success rate and data quality of the exploration plan.

[0025] In this embodiment, by collecting and analyzing geological maps, the location and orientation of known faults within the detection area can be accurately determined, providing a scientific reference for the preliminary delineation of the detection area; digital elevation model data can reflect in detail the topographic relief, slope, and aspect of the area to be detected, which is an important basis for the flight altitude and flight path planning of UAV formations; historical meteorological data can provide an in-depth understanding of the impact of meteorological conditions on UAV flight and the operation of detection equipment, thereby reducing the impact of meteorological factors on UAV flight safety and detection data quality, and further ensuring the reliability and accuracy of detection data.

[0026] Then, based on the above basic data, a preliminary analysis is conducted on the topographic features, geological structure, and seismic activity patterns of the area to be explored; and based on the topographic features, geological structure, and seismic activity patterns, the area to be explored is defined and divided, and different regional blocks within the area to be explored are obtained.

[0027] After collecting basic data on the area to be explored, this data needs to be analyzed to effectively understand its topographic features, geological structure, and seismic activity patterns. Based on the analysis results, the area to be explored should be scientifically and rationally defined and divided into different blocks, providing a basis for the division of operational units for subsequent UAV formation-based active fault exploration.

[0028] The steps for dividing the area to be probed into different blocks (working units) are as follows: Comprehensive geological data, including geological maps, structural maps, and seismic activity maps, were collected and systematically organized for the exploration area. Geological analysis methods were employed to deeply analyze the geological structural characteristics of the area, identifying key information such as the distribution, strike, dip, and lithology of major faults. In this embodiment, geological maps were used to determine the specific locations and extension directions of known faults, providing a geological basis for the division of regional blocks and ensuring that the results of the regional block (work unit) division conform to the actual geological conditions.

[0029] By combining digital elevation models (DEMs) and topographic maps, a comprehensive analysis of the topography of the exploration area is conducted. By combining the degree of topographic relief, slope, aspect changes, and typical topographic features such as mountains, rivers, and canyons, complex and relatively flat areas are further identified. This provides a reasonable topographic reference for the division of regional blocks (operational units), which is beneficial for subsequent analysis of the impact of topographic factors on UAV flight and exploration equipment performance.

[0030] Based on historical earthquake data of the area to be explored and its surrounding areas, statistical methods and seismological principles are used to analyze the spatial distribution patterns of seismic activity, identify earthquake-prone areas and potential earthquake-hazardous zones, and take the seismic activity patterns as an important factor in the division of regional blocks to further ensure the effectiveness and feasibility of the regional block (operational unit) division results.

[0031] In this embodiment, the topographic features, geological structure, and seismic activity patterns of the area to be explored are comprehensively analyzed. The area to be explored is then scientifically and rationally defined and divided, ultimately obtaining different regional blocks (operational units) within the area to be explored. This lays the foundation for the rational formulation and planning of subsequent exploration work.

[0032] Next, a region block coding and marking mechanism was set up. Through the above region block coding and marking mechanism, different region blocks were numbered and marked to obtain the corresponding number information of different region blocks.

[0033] After defining and dividing the different regions of the area to be explored, in order to facilitate the efficient implementation of subsequent flight mission planning, data collection and management, a region block coding and marking mechanism was established in the embodiment. This mechanism is used to number each divided region block, and each region block corresponds to a unique number.

[0034] In an optional embodiment, the numbering labels for different area blocks (work units) are as follows: Each region block (work unit) is assigned a unique number. In this embodiment, the number is determined based on various factors such as the geographical location, geological characteristics, or division order of the region block. A specific encoding format is used to represent the numbering information of different region blocks (work units). Here, m represents mountains, r represents rivers, c represents canyons, h represents hills, and p represents plains. The numbers 1, 2, and 3 represent different region blocks (work units) respectively. g represents earthquake-prone areas, z represents earthquake-prone areas, and d represents earthquake-prone areas.

[0035] In one optional embodiment, the numbering and marking information for different area blocks (work units) is as follows: 1.md represents the low-incidence earthquake zone of mountain range No. 1, where 1 represents the No. 1 zone (operation unit), m represents the landform type as mountain range, and d represents the No. 1 zone (operation unit) as a low-incidence earthquake zone; 3.rd represents the low-incidence earthquake zone of River No. 3, where 3 represents the No. 3 zone (operational unit), r represents the landform type as river, and d represents the No. 3 zone (operational unit) as a low-incidence earthquake zone; 5. c. z represents the earthquake-prone area of ​​Canyon No. 5, where 5 represents area No. 5 (operational unit), c represents the landform type as canyon, and z represents area No. 5 (operational unit) as the earthquake-prone area; 7. h. z represents the earthquake-prone area of ​​Hill 7, where 7 represents the 7th area (operation unit), h represents the terrain type as hilly, and z represents the 7th area (operation unit) as the earthquake-prone area. 9.pd represents the No. 9 plain low-incidence area block, where 9 represents the No. 9 area block (operation unit), p represents the landform type as plain, and d represents the No. 9 area block (operation unit) as a low-incidence area. The numbering of different regional blocks (operational units) provides a clear basis for the planning of UAV formation schemes. In an optional embodiment, information can be detected sequentially according to the numbering order of each regional block to avoid omissions or duplicate detections. Taking 1.md (low-incidence seismic area block of mountain range No. 1) as an example, it clearly indicates that this regional block (operational unit) is the first regional block to be detected. Combining its topographical features (mountain range) and seismic activity (low-incidence area), it helps to rationally plan the detection flight altitude, speed, and route, so as to ensure that the UAV detection scheme can complete the detection mission safely and efficiently.

[0036] To visually represent the division of different area blocks (work units) within the area to be detected, along with their corresponding numbering information, relevant schematic diagrams are provided in the embodiments. Please refer to the attached diagrams for details. Figure 2 .

[0037] based on Figure 2 It can be seen that by using the regional block coding and marking mechanism to scientifically and reasonably identify the area to be explored, key information such as the spatial distribution, landform type, and seismic activity of different regional blocks (operation units) can be displayed in an intuitive graphical way, providing a visual basis for subsequent exploration scheme planning, information exploration analysis, and results presentation.

[0038] Finally, based on the basic data information, different regional blocks, and numbering information, the basic information of different regional blocks in the area to be detected is matched.

[0039] After completing the division and numbering of different blocks in the area to be explored, in order to accurately grasp the relevant basic information and environmental conditions of different blocks (work units) and provide data support for the subsequent active fault detection work of UAV formation, this embodiment associates and matches the basic information of the area to be explored with the divided blocks (work units) and their corresponding numbering information.

[0040] In an optional embodiment, taking geological data from the basic information as an example, for the area block numbered 1.md (low-incidence earthquake zone block of Mountain Range No. 1), geological information associated with this area block is filtered from the geological data. The geological structural characteristics within this area block include the presence of concealed faults, the scale and nature of the faults, etc.; stratigraphic lithology information includes rock type, hardness, integrity, etc. Simultaneously, by combining the research results on fault activity in this area block from geological exploration reports and scientific papers, the historical activity of faults within this area block is further clarified, including activity frequency and intensity, which helps in designing UAV detection schemes based on the basic information of this area block.

[0041] Basic data was matched with different regional blocks and numbering information to establish basic information files for different regional blocks (work units). These files not only included geological, topographical, and meteorological information for different regional blocks (work units), but also enabled precise location and rapid retrieval of information through numbering information. During the exploration scheme planning phase, targeted exploration strategies were developed based on the basic information of different regional blocks (work units), effectively ensuring the smooth progress of UAV formation active fault exploration.

[0042] Furthermore, the method for obtaining basic information of different regional blocks in the area to be detected in the embodiments is only an optional condition of the present invention. In one or more other embodiments, the method for obtaining basic information of different regional blocks can be optimized according to the actual situation of the detection area. Optimizing the method for obtaining basic information according to the actual situation of the detection area can collect information closely related to the detection of active faults in a targeted manner, avoid unnecessary data collection work, and thus save time and resources.

[0043] S2. Match UAV formations for different regions based on basic information, configure information detection equipment for different regions based on their information detection requirements, and combine the UAV formations and information detection equipment to obtain initial UAV detection schemes for different regions. The specific steps and implementation details are as follows: Based on the needs of active fault detection and the basic information of different regional blocks, UAV formations for different regional blocks are designed. In the embodiments, the above-mentioned UAV formations mainly include the formation shape of the UAVs, their relative spatial positions, and their motion parameters.

[0044] In the process of designing a drone formation, drone selection needs to be combined with the detection requirements, taking into account key factors such as the drone's payload capacity, endurance, and flight stability to select a suitable drone model. Detection equipment needs to be installed on the drones, including ground-penetrating radar, high-precision cameras, and lidar. Ground-penetrating radar is used to detect underground geological structures, high-precision cameras are responsible for acquiring surface images, and lidar is used to acquire terrain elevation data. Formation formation needs to be based on the basic information of different area blocks (operation units), analyzing their area size and environmental complexity, and forming a drone formation of an appropriate number to ensure that the drone formation can fully cover the detection area.

[0045] Drone formation shapes can include linear formations, grid formations, and fan-shaped formations.

[0046] Linear formation is mainly suitable for probing along the strike of active faults. When the known active faults in a block are roughly distributed in a straight line, flying in a linear formation allows for rapid and efficient acquisition of probe data along the fault strike, enabling continuous monitoring and change detection of the fault.

[0047] Grid formation is used when comprehensive surveying of large areas is required. In plains or basins with complex geological structures, arranging drones in a grid pattern can achieve uniform coverage of the entire area, improving the comprehensiveness and accuracy of the survey.

[0048] Sector formations are often used to detect sector-shaped areas where active faults may exist. In areas with frequent seismic activity and unclear fault strikes, sector formations can detect potential faults from different angles, increasing the probability of fault detection.

[0049] In the parameter design process for spatial relative positions, in flat and open areas, such as plains, the horizontal spacing of UAVs is determined based on the coverage of the detection equipment. If the detection radius of the detection equipment is 400 meters, the horizontal spacing of the UAVs can be set to 600-900 meters to avoid detection blind spots. However, in canyon areas or other terrain areas, the horizontal spacing needs to be appropriately reduced due to the obstruction of detection signals caused by the undulating terrain. The horizontal spacing of the UAVs can be set to 400-800 meters to ensure the continuity and effectiveness of the detection signal.

[0050] In areas with significant topographical differences, such as mountains, hills, and canyons, drones should maintain a certain vertical height difference. In canyon areas with large elevation differences, the vertical height difference between adjacent drones can be set between 100 and 200 meters. This allows for the acquisition of more comprehensive geological information from different perspectives and reduces the interference of terrain on the detection.

[0051] The drone motion parameter settings are as follows: The flight speed is primarily determined by the sampling frequency and data transmission capability of the UAV detection equipment. A higher sampling frequency necessitates a slower flight speed to ensure sufficient data collection. In one optional embodiment, the detection equipment samples 15 times per second, and to guarantee data quality, the UAV's uniform flight speed can be set to 10-20 meters per second.

[0052] It is also necessary to develop a variable-speed flight strategy. When approaching areas suspected of having active faults or areas with complex geological structures, the flight speed should be appropriately reduced in order to detect geological information more meticulously. In relatively flat areas with simple geological conditions, the flight speed can be appropriately increased to improve detection efficiency.

[0053] The flight altitude needs to take into account the height of obstacles such as terrain, buildings, and trees, as well as the influence of meteorological conditions (such as wind speed and cloud height). In one optional embodiment, the flight altitude in hilly areas should be higher than the height of the hills, with a certain safety margin reserved.

[0054] The detection height is mainly determined based on the performance of the detection equipment and the requirements of the detection target. Different detection equipment has different requirements for detection height. For example, the detection effect of ground-penetrating radar will decrease as the height increases. Therefore, it is necessary to select an appropriate detection height based on the equipment parameters and detection needs.

[0055] A fixed heading angle refers to the UAV's ability to fly at a fixed heading angle when the orientation of an active fault is clear, in order to probe along the fault's orientation. In an optional embodiment, if the active fault in a block (work unit) is oriented east-west, the UAV's heading angle can be set to 90° or 270°. A variable heading angle refers to the need for the UAV to adjust its heading angle based on real-time detection data and geological models in areas with complex geological structures and unclear fault orientations, in order to better track the location and orientation of active faults.

[0056] Meanwhile, information detection devices for different regions are configured according to the information detection requirements of different regions. In this embodiment, the information detection devices mainly include the type of detection device, the number of devices, and the configuration of devices.

[0057] The selection of detection equipment type and configuration needs to be based on the requirements of active fault detection. Appropriate detection equipment should be selected, such as ground-penetrating radar, gravimeter, magnetometer, seismograph, etc. Different detection equipment have different detection principles and applicable ranges. Ground-penetrating radar is mainly used to detect shallow underground geological structures and faults, while gravimeter and magnetometer can be used to detect deep underground geological structures and changes in rock properties.

[0058] The number and layout of detection equipment are mainly determined based on the basic data and detection mission requirements of different area blocks (operation units), which determines the number and layout of detection equipment carried on each UAV. In an optional embodiment, for larger area blocks, large UAV formations can be used, and multiple detection devices can be carried on some UAVs to achieve multi-parameter, all-round information detection; for smaller area blocks, small UAV formations can be used, and key detection devices can be selected for deployment based on the detection focus.

[0059] The operating parameters of the detection equipment need to be adjusted according to the environmental conditions and detection requirements of different area blocks (work units), and the transmission power of the detection equipment needs to be adjusted accordingly. In an optional embodiment, when conducting deep geological exploration, it is necessary to increase the transmission power to enhance the strength of the detection signal; in shallow exploration or in areas sensitive to electromagnetic interference, the transmission power can be appropriately reduced; the information sampling frequency is mainly set according to the dynamic change characteristics of the detection target, and a suitable sampling frequency is set, where a higher sampling frequency can obtain more detailed geological information.

[0060] Furthermore, by combining the formation shape, spatial relative position, drone motion parameters, equipment type, number of devices, and equipment configuration, drone detection schemes for different area blocks can be obtained.

[0061] The UAV detection scheme for different area blocks (work units) also includes setting parameters related to collaborative operations.

[0062] Choosing an appropriate communication frequency ensures stable communication between drones and between drones and ground control stations. In an optional embodiment, a lower frequency (e.g., 433MHz) can be selected in remote mountainous areas to improve communication reliability and coverage.

[0063] The communication distance between UAVs is determined based on the formation size and the size of different area blocks (operation units). In this embodiment, the communication distance between UAVs should not be less than the maximum horizontal distance between adjacent UAVs in the formation to ensure that information can be transmitted in a timely and accurate manner.

[0064] The division of exploration tasks requires assigning different exploration tasks to each UAV based on its performance and the characteristics of the exploration equipment. In one optional embodiment, some UAVs are equipped with high-resolution ground-penetrating radar to conduct detailed exploration of active faults, while others are equipped with gravimeters and magnetometers to conduct geophysical field measurements on different blocks (work units).

[0065] Finally, the data processing tasks are assigned to different UAVs or ground control stations. In one optional embodiment, some UAVs have data processing capabilities, which can perform preliminary processing and analysis on the collected raw data, and then transmit the processing results to the ground control station; the ground control station is responsible for comprehensive processing and interpretation of all data.

[0066] The embodiment also equips the drone with various obstacle avoidance sensors, such as lidar, ultrasonic sensors, and visual sensors, to perceive obstacles in the surrounding environment in real time. In an optional embodiment, lidar can accurately measure the distance between the drone and obstacles, and visual sensors can identify the shape and position of obstacles.

[0067] By following the steps and implementation details above, and by combining the formation shape, spatial relative position, UAV motion parameters, equipment type, number of equipment, and equipment configuration, and taking into account parameters related to collaborative operations, UAV detection schemes for different area blocks can be obtained.

[0068] Furthermore, the design method of different regional block UAV detection schemes in this embodiment is only one optional condition of this embodiment. In other embodiments, the design method of regional block UAV detection schemes can be modified according to the information detection conditions and the actual situation of different regional blocks, so as to realize more intelligent detection decision-making, data processing and analysis, thereby promoting the continuous development of UAV detection technology.

[0069] S3. Based on the basic information, the initial UAV detection plan is tested and evaluated, and the formation tightness, UAV movement status, and aerodynamic induction status between UAVs are obtained. The specific steps and implementation content are as follows: First, based on the dynamic characteristics and collaborative effectiveness of UAVs, detection and evaluation indicators for UAV detection schemes are selected. In this embodiment, the detection and evaluation indicators mainly include the tightness of the formation, the movement status of UAVs, and the aerodynamic induction situation between UAVs.

[0070] Based on the dynamic characteristics and collaborative effectiveness of UAVs, suitable detection and evaluation indicators for UAV detection schemes are selected. In this embodiment, the selected detection and evaluation indicators mainly cover formation tightness, UAV motion status, and aerodynamic induced behavior between UAVs. Among them, formation tightness reflects the spatial density of the UAV formation, which has a significant impact on the coverage and efficiency of the detection mission; UAV motion status involves parameters such as flight speed, acceleration, and attitude, which are directly related to the execution quality and stability of the detection mission; the aerodynamic induced behavior between UAVs reflects the aerodynamic interaction between UAVs during flight, which changes the flight trajectory and performance of the UAVs, thus affecting the final actual detection effect.

[0071] To comprehensively characterize the coverage status information within the area to be detected, this embodiment sets the formation tightness, motion status, and aerodynamic induction status of the UAV formation. It predicts and analyzes the actual operation of the UAV formation. By analyzing the magnitude and trend of these influencing indicators, it is possible to predict in advance the stability problems that may occur during the formation's flight and take corresponding measures to adjust and optimize. This enables a comprehensive and accurate prediction and analysis of the actual operation of the UAV detection scheme from multiple dimensions, providing a basis for subsequent detection scheme evaluation and optimization.

[0072] In an optional embodiment, the implementation details of the formation tightness in the detection and evaluation indicators are as follows: First, a tightness reference value is obtained based on the drone's wingspan and the drone formation distance. In this embodiment, the tightness reference value is mainly calculated based on the drone's wingspan b and the drone formation distance R. Considering that the dynamic effects of each drone in the drone formation are coupled due to aerodynamic influences, the ratio of the drone formation distance R to the drone's wingspan b is introduced as the tightness reference value to quantify the degree of coupling. That is, the tightness reference value satisfies the following relationship: ; Then, based on the tightness reference value, the tightness of the formation of UAVs in different area blocks is analyzed.

[0073] Loose formation determination: When the ratio of the drone formation distance R to the drone wingspan b is greater than or equal to 3, that is... When the formation of drones in the area block (operation unit) is determined to be a loose formation, the coupling effect between drones is weak. Only when the deviation generated by each drone during flight is within the control error range of the detection equipment can the formation be considered to be in good condition.

[0074] Tight formation determination: when If the formation of drones in the area block (operation unit) is determined to be a tight formation, the coupling effect between drones is strong. In this tight formation state, it is also important to note that if any drone deviates, it will affect the control of other drones, thereby affecting the stability of the entire formation and the execution effect of the detection mission.

[0075] In an optional embodiment, the relevant implementation details for detecting and evaluating the drone's motion status as an indicator are as follows: The embodiment primarily constructs a drone motion model based on the drone detection scheme. Based on the drone formation information in the drone detection scheme, the movement of the drones is analyzed. In this embodiment, the drones participating in the information collection of the area to be detected are marked as D. It is assumed that a total of m drones participate in the detection task of this area block (operation unit). Based on the drone detection scheme, each drone is equipped with sensors for location positioning, target information perception, and obstacle identification. Furthermore, different drones can communicate with each other, and related drones within the communication range can maintain communication connections.

[0076] The above UAV motion model satisfies the following relationship: , in, This represents the location information of the drone at time t. This represents the flight speed of the drone at time t. This represents the direction angle of motion of the UAV at time t. This indicates the maximum flight speed of the drone. This indicates the maximum azimuth angle of the drone. , This indicates the position information of another drone communicating with a neighboring drone at time t. This indicates the safe distance between drones.

[0077] Furthermore, the above This indicates the horizontal coordinate position of the UAV at time t, which can be used to determine the specific horizontal position of the UAV on a two-dimensional plane (or a projection plane in three-dimensional space). This represents the ordinate position information of the drone at time t, and... By combining these methods, the exact location of the drone in space can be accurately described.

[0078] This parameter represents the horizontal coordinate position of another drone that is adjacent to and within the communication range of the drone at time t. This parameter can be used to determine the horizontal position of the adjacent drone and to calculate the distance between the two drones. This represents the ordinate position information of another drone adjacent to and within communication range of the drone at time t. Similarly, it represents the ordinate position information of another drone at time t. Together, they can determine the specific location of adjacent drones in space.

[0079] This represents the speed of the drone at time t. The speed mentioned above is a physical quantity that describes how fast the drone moves, and it determines the distance the drone travels per unit time.

[0080] This indicates the maximum speed limit for the drone. The drone's speed must not exceed this maximum speed value during flight. This limit is set to ensure the drone's flight safety and to prevent loss of control or excessive impact on the surrounding environment due to excessive speed.

[0081] The angle represents the direction of motion of the UAV at time t. It reflects the flight direction of the UAV at a certain moment. It is usually measured in a clockwise or counterclockwise direction, starting from a certain reference direction (such as due east). This parameter can be used to determine the flight orientation of the UAV in space.

[0082] This indicates the maximum directional angle of the drone, which limits the range of adjustment of the drone's flight direction. That is, the directional angle of the drone cannot exceed this maximum value during flight. Based on this, it helps to ensure the overall flight direction consistency of the drone formation and avoid problems such as formation chaos or collisions with other objects due to excessive directional adjustment.

[0083] This represents the safe distance between drones. To ensure the safe flight of a drone swarm, the distance between any two adjacent drones should not exceed this safe distance. In the model, this is calculated... To obtain the distance between the two drones and compare it with... Comparisons are made to ensure safe flight between any two adjacent drones in the detection scheme.

[0084] The drone motion model can quickly analyze the drone's position, speed, direction, and adjacent distances at different times, and then obtain the drone's motion status based on the above-mentioned position, speed, direction, and adjacent distances.

[0085] Calculating the motion state of drones can help improve the accuracy of prediction results from detection schemes.

[0086] pass Determining the horizontal and vertical coordinates of the UAV at time t allows for precise identification of its location on a two-dimensional plane (or a three-dimensional spatial projection plane). This enables accurate assessment of the UAV's distribution within the detection area when predicting and evaluating UAV detection schemes for different regions, providing a reliable basis for subsequent analysis of detection coverage and mission execution paths, thereby improving the accuracy of prediction and evaluation.

[0087] Accurately determining the speed and direction angle of a UAV at time t enables a dynamic description of its motion state. Understanding the changes in speed and direction of the UAV during the prediction and evaluation of UAV detection schemes in different area blocks helps analyze the dynamic behavior of the UAV during the detection process, thereby more accurately evaluating the effectiveness of the detection scheme.

[0088] The safety threshold of the drone motion model can ensure the flight safety of the drone and enhance the feasibility of the detection scheme.

[0089] The document specifies the maximum flight speed limit for drones to prevent them from going out of control or causing excessive impact on the surrounding environment due to excessive speed. When predicting and evaluating drone detection schemes for different areas, considering the speed limit can ensure that the scheme is executed within a safe range, reduce flight risks, and enhance the feasibility of the detection scheme.

[0090] Limiting the maximum azimuth angle of the drones helps ensure the overall flight direction consistency of the drone formation, avoiding formation chaos or collisions with other objects due to excessive directional adjustments. In predictive assessments, considering directional limitations allows for evaluation of the formation's flight stability in complex environments, which is beneficial for ensuring the successful implementation of detection strategies.

[0091] By calculating the distance between the two drones and comparing it with a safety threshold Comparisons ensure flight safety between any two adjacent UAVs in the detection scheme. Considering safe distances during the prediction and evaluation process can prevent collisions between UAVs, guarantee the safe flight of the formation, and improve the safety and reliability of the detection scheme.

[0092] The prediction and evaluation process involves a comprehensive analysis of the parameters in the UAV motion model. By analyzing the relationships between different parameters, it is beneficial to simulate and analyze the optimal flight path for different regional blocks (operational units). This helps UAV detection schemes for different regional blocks (operational units) complete the detection task in the shortest possible time, while ensuring detection quality and flight safety.

[0093] In an optional embodiment, the implementation and steps related to the detection and evaluation of the aerodynamic induced situation between UAVs are as follows: First, the amount of vortex rings generated by the UAV wing is analyzed based on fluid dynamics mechanisms.

[0094] In the field of fluid mechanics, vortex circulation is a key physical quantity that measures the degree of rotation of a fluid around a closed path. In the aerodynamic analysis system of UAV formations, it is used... The formula for calculating the vortex ring quantity generated by the drone's wing satisfies the following relationship: , in, This indicates the amount of vortex rings generated by the drone's wings. Indicates the lift of the drone. This indicates the air density of the area to be detected. This represents the flight speed of the drone at time t. This indicates that a characteristic length is related to the wing of the drone.

[0095] In the analysis of vortex circulation in UAV wings, a fluid dynamics mechanism must be used as the theoretical basis. In the aerodynamic analysis scenario of UAV formations, vortex circulation... Used to describe the vortex characteristics generated by the wings of unmanned aerial vehicles (UAVs).

[0096] The lift of a drone is a key parameter affecting the calculation of vortex rings. The magnitude of lift is constrained by multiple factors such as wing shape, angle of attack, flight speed, air density, and wing area. Analyzing and controlling the lift factors of drones helps to optimize their aerodynamic performance, thereby improving the stability and efficiency of formation flight.

[0097] Air density has a direct impact on the generation of aerodynamic forces. Air density is an indispensable parameter in the calculation of aerodynamic induced velocity. The greater the air density, the greater the aerodynamic induced velocity generated under the same conditions.

[0098] Characteristic lengths related to the drone wing, including wing span or mean chord length, can be incorporated into the vortex circulation calculation formula. This allows the vortex circulation to be normalized to dimensions related to the drone's size, thus providing a more accurate description of the drone's aerodynamic characteristics.

[0099] Then, based on the relative angle and frequency parameters of the UAVs, the sine and cosine transform coefficients are used to construct the angle correction coefficient analysis function between adjacent UAVs, and the angle correction coefficient is obtained through the angle correction coefficient analysis function.

[0100] The embodiment illustrates a schematic diagram of two unmanned aerial vehicles (UAVs) flying in the same plane; see details below. Figure 3 ,in This represents the relative angle between adjacent drones. To explore the characteristics of this relative angle in depth, it is necessary to first determine the sides of the half-wingspan of the two adjacent drones that are relatively close. Then, the midpoint of the half-wingspan of each drone (i.e., the point where the half-wingspan is closest) is used as a key reference point to analyze the relative angle between the two adjacent drones. , Indicates the vertical distance between adjacent drones. The wingspan (half the wingspan of the drone) is represented by D1, and D2 represents the other drone on the same plane adjacent to D1. The example shown illustrates two drones flying on the same plane. Figure 3 To lay the foundation for analysis, we further explored the angular influence relationship between adjacent UAVs.

[0101] Angle correction factor in drone formation flight It can be used to consider the relative angles between adjacent drones. Nonlinear factors affecting induced velocity. In actual flight scenarios, relative angles... The relationship between the relative angle and the induced velocity is not a simple linear one. When the angle approaches 0° or 180°, the induced velocity deviates from the result calculated by the original formula due to special aerodynamic effects. Therefore, introducing an angle correction coefficient allows for a more accurate analysis of the aforementioned nonlinear relationship.

[0102] Based on the relative angle and frequency parameters of the UAVs, sine and cosine transform coefficients were used to construct angle correction coefficients between adjacent UAVs, satisfying the following relationship: , in, Indicates the angle correction factor. Represents a constant term. Represents the sine term coefficient, Represents the sine term Angular frequency parameters in Indicates the relative angle between adjacent drones. Represents the cosine term coefficient, Represents the cosine term The angular frequency parameter in the text.

[0103] The above sine and cosine transform coefficients include and ,in Represents the sine term coefficient, Represents the cosine term The coefficient.

[0104] It is a constant term, representing the angle correction factor. The base value.

[0105] when hour, , ,at this time The above It can be viewed as an initial offset corresponding to the angle correction coefficient, without considering the influence of sine and cosine terms, and a constant term. It can be used to adjust the baseline level of the entire correction coefficient to accommodate the fundamental differences in the aerodynamic characteristics of different drone formations. Different drone models may have different relative angles due to factors such as wing shape and aerodynamic layout. The degree of influence of basic aerodynamics varies at different times. This can be used to illustrate this difference.

[0106] b is the sine term. The coefficient of θ determines the angle correction factor of the sine function. The degree of influence. The sine function has periodicity and symmetry. The size reflects the relative angle The intensity of the correction to the induced velocity in the form of a sine function when the change occurs. If The larger value indicates that the sine term has a significant impact on the correction coefficient, i.e., the relative angle. Changes in this will significantly alter the angle correction coefficient through the sine function, thus more strongly affecting the estimation of the induced velocity; conversely, if... The smaller the relative angle, the weaker the influence of the sinusoidal term. In UAV formations across different area blocks (operational units), the periodic variation of the relative angle may have a significant impact on the induced velocity. The value may be large.

[0107] It is a sine function The angular frequency parameter in the equation controls the period of the sine function. The period of the sine function. , The larger the period The smaller the value, the less the sine function changes with the relative angle. The faster the changes. In the aerodynamic analysis of UAV formations in different regional blocks (operational units), It can be used to describe relative angles The frequency characteristics of the effect on induced velocity. If the relative angle during actual flight... Even a tiny change can cause rapid fluctuations in the induction rate, so The value of is relatively large, which allows the sine function to more sensitively reflect this rapid change.

[0108] It is the cosine term The coefficient of the cosine function determines the angle correction factor. The degree of influence. The cosine function also has periodicity and symmetry. The size reflects the relative angle When the change occurs, the strength of the correction to the induced velocity is expressed in the form of a cosine function. The larger the value, the more significant the effect of the cosine term on the correction coefficient, i.e., the relative angle. Changes in the relative angle will significantly alter the angle correction factor through the cosine function, thus affecting the estimation of the induced velocity. Under the aerodynamic conditions of different blocks (working units), changes in the relative angle primarily affect the induced velocity through the form of a cosine function. The value may be large.

[0109] Cosine term The angular frequency parameter in the equation controls the period of the cosine function. The period of the cosine function is also... , The larger the period The smaller the value, the more the cosine function changes with the relative angle. The changes. Relative angles can be described Another frequency characteristic affecting the induced velocity. If the relative angle during the actual detection process of the area block (working unit)... Changes in frequency have a significant impact on the induction velocity within a specific frequency range, so adjustments can be made... The value of allows the cosine function to better match this frequency characteristic, thus more accurately describing the relationship between the relative angle and the induced velocity.

[0110] By rationally determining the relevant parameter values ​​based on the detection scheme for different regions (work units), the angle correction coefficient can be adjusted. To describe relative angles more accurately The nonlinear effects of the induction velocity can be further addressed by fitting and optimizing the parameters using experimental data or numerical simulations.

[0111] Next, a distance correction coefficient analysis function is constructed based on the vertical distance and linear term coefficient between adjacent UAVs, and the distance correction coefficient is obtained through the distance correction coefficient analysis function.

[0112] Based on the examples Figure 3 Information analysis reveals the altitude influence between adjacent drones, and a distance correction factor is set. With the vertical distance between adjacent drones The decay of induced velocity does not strictly follow the simple form of the original formula due to changes in velocity. At close range, stronger aerodynamic coupling effects may exist, resulting in slower induced velocity decay; while at long range, the induced velocity decay may be faster. Therefore, a distance correction factor is introduced. This reflects the nonlinear decay characteristic.

[0113] Based on the vertical distance and linear term coefficients between adjacent UAVs, a distance correction coefficient analysis function is constructed between adjacent UAVs. This distance correction coefficient analysis function satisfies the following relationship: , in, This represents the distance correction factor. Represents the coefficient of the linear term. Indicates the vertical distance between adjacent drones. This represents the coefficient of the quadratic term.

[0114] The coefficient of the linear term reflects the vertical distance between adjacent drones. Distance correction factor The degree of linear influence. When When it is positive, it increases with vertical distance. The increase of linear terms The value of will increase, causing the denominator to... This increases, thus leading to a distance correction factor. Decrease. This indicates that within the close-range range, The effect of this will cause the induced velocity to decrease at a certain linear rate as the vertical distance increases. If The relatively large value indicates that at close range, even a small increase in vertical distance can have a significant attenuation effect on the induced velocity, reflecting the influence of strong aerodynamic coupling effect on the attenuation of induced velocity at close range.

[0115] In the embodiment, It is considered one of the parameters describing the intensity of the close-range aerodynamic coupling effect. Because the aerodynamic characteristics of different detection schemes in different regions (operational units) are different, The value will also vary. For drone formations with complex wing shapes and strong aerodynamic interference, The value will be relatively large because the aerodynamic coupling effect is more significant at close range, and the induced velocity decays faster with distance.

[0116] The coefficients corresponding to the quadratic terms reflect the distance correction factor of the vertical distance h between adjacent UAVs. The degree of secondary impact. When When it is positive, it increases with vertical distance. The increase of the quadratic term The value of will increase rapidly, causing the denominator to... It increases more rapidly, thus affecting the distance correction factor. It decreases more rapidly. This indicates that over long distances, The effect of this will cause the induced velocity to decrease at a faster rate with increasing vertical distance. If The larger value indicates that at long distances, the increase in vertical distance has a more significant effect on the attenuation of induced velocity, reflecting the impact of the rapid weakening of aerodynamic coupling effect on the attenuation of induced velocity at long distances.

[0117] In the embodiment, This can be considered a parameter describing the attenuation rate of the aerodynamic coupling effect at long distances. Different detection schemes for different regional blocks (operational units) exhibit different aerodynamic characteristics at long distances. The values ​​will also vary. For some large drone formations, aerodynamic interference decreases rapidly at long distances. The value may be relatively large because the induced velocity decreases more rapidly with distance at this point.

[0118] By reasonably determining and The value of the distance correction factor can be adjusted. To more accurately describe the vertical distance between adjacent drones The nonlinear decay characteristics of the induced velocity are used to improve the accuracy of aerodynamic induced velocity estimation results in different regional block (work unit) detection schemes.

[0119] Due to the flight speed of drones This will affect the induced velocity. When the drone's speed is higher, the inertial effect of the airflow may be more significant, causing the change in induced velocity to differ from that at low speeds. Therefore, a velocity correction factor is introduced. Let's consider the above-mentioned relationships of influence.

[0120] Based on the airflow inertial effect and the speed of the UAV at different times, a speed correction coefficient analysis function was constructed between adjacent UAVs. The expression of the speed correction coefficient analysis function is as follows: , in, This represents the speed correction factor. Represents the speed of the drone in the linear term coefficient, This represents the speed of the drone at time t. Represents the speed of the drone in the quadratic term. The coefficient.

[0121] The speed of the drone in the linear term The coefficient reflects the vertical distance between adjacent drones. With drone speed Common speed correction coefficient The degree of linear influence.

[0122] From the above expression, It doesn't simply represent vertical distance; the velocity correction coefficient analysis function demonstrates a linear relationship between vertical distance and velocity in influencing induced velocity changes. When the UAV's velocity... When increasing, the linear term The value will increase, thus increasing the speed correction coefficient. The increase indicates that, within a certain range, as the speed of the UAV increases, the combined effect of vertical distance and speed leads to a greater degree of change in induced velocity.

[0123] In the embodiments This reflects a weighting of the effect of vertical distance on velocity-induced velocity changes. The effect of vertical distance on velocity-induced velocity varies under different detection schemes for different area blocks (work units). The value will also differ. When the formation flight space is relatively compact (small vertical distance), the impact of speed changes on induced velocity may be relatively small. The value may be relatively small; however, when the formation flight space is relatively open (large vertical distance), the impact of speed changes on induced velocity may be more significant. The value may be large.

[0124] The speed of the drone in the quadratic term The coefficient reflects the speed of the drone. Speed ​​correction factor The degree of secondary impact. When the drone speed When added, the quadratic term The value of increases rapidly at a rate equal to the square of the velocity, thus affecting the velocity correction coefficient. This has a significant impact, indicating that the effect of speed on induced velocity may exhibit stronger nonlinear characteristics during high-speed flight.

[0125] In the embodiments It is an important parameter describing the influence of airflow inertial effects and other factors on induced velocity during high-speed flight. Different models of UAVs, due to differences in wing shape, aerodynamic configuration, and other factors, will experience varying degrees of influence from airflow inertial effects on induced velocity during high-speed flight. The values ​​will also vary. For example, for some UAV detection schemes with larger wing areas and more complex aerodynamic characteristics, the interference from airflow is stronger during high-speed flight. The value may be large.

[0126] The h and [other values] are reasonably determined by different regional block (work unit) detection schemes. The value of the speed correction factor can be adjusted. To more accurately describe the speed of drones This study examines the impact on induced velocity, thereby improving the accuracy of aerodynamic induced velocity estimation results for UAV formations.

[0127] Finally, by combining the aforementioned vortex ring quantity, angle correction coefficient, distance correction coefficient, and velocity correction coefficient, an aerodynamic induced velocity estimation model is constructed between adjacent UAVs.

[0128] Based on the vortex ring quantity generated by the UAV wing, the distance correction coefficient, the angle correction coefficient, and the velocity correction coefficient, an aerodynamic induced velocity estimation model for adjacent UAVs is constructed. The above model satisfies the following relationship: , in, This represents the induced velocity between adjacent drones due to aerodynamic effects. This indicates the amount of vortex rings generated by the drone's wings. Represents pi (π). Indicates the vertical distance between adjacent drones. This represents the distance correction factor. Indicates the relative angle between adjacent drones. Indicates the angle correction factor. This represents the speed correction factor.

[0129] The induced velocity between adjacent UAVs due to aerodynamic effects is a key indicator for measuring the intensity of aerodynamic interaction between UAVs. This represents the vertical distance between adjacent drones. Based on the scenario of two drones flying in the same plane, if we consider aerodynamic effects within a two-dimensional plane, This represents the distance of a small deviation of the drone in the direction perpendicular to the flight plane; if considered from a general three-dimensional space perspective, It refers to the actual vertical distance between the two drones, and the aerodynamic induced velocity and Inversely proportional, The smaller the induced velocity, the greater the aerodynamic effect, indicating that the closer the drones are to each other, the more significant the aerodynamic impact.

[0130] The aerodynamic induced velocity estimation model described above is used to analyze the aerodynamic induced situation between UAVs.

[0131] The aerodynamic induced velocity between adjacent UAVs in a detection scheme within the same area block (operational unit) is quantified using an aerodynamic induced velocity estimation model. When multiple UAVs fly in formation, the aerodynamic induced velocities of each UAV are superimposed, leading to a significant change in the overall aerodynamic characteristics of the formation.

[0132] In an optional embodiment, regarding formation maintenance, excessive aerodynamic induced speeds between adjacent UAVs can cause them to deviate from their predetermined formation positions, affecting formation stability and flight performance. In terms of flight safety, excessive aerodynamic induced speeds can lead to collision risks between UAVs, especially in dense formations or complex maneuvering detection scenarios. Furthermore, aerodynamic induced situations also affect UAV energy consumption; due to aerodynamic induction, UAVs may require additional power to overcome aerodynamic interference, thereby increasing fuel consumption or battery power consumption and shortening flight time. Therefore, accurate analysis of aerodynamic induced situations is crucial for optimizing UAV formation flight strategies, ensuring flight safety, and improving flight efficiency in detection schemes for different area blocks (operation units).

[0133] S4. Based on the tightness of the formation, the movement of the UAVs, and the aerodynamic induction situation between the UAVs, the initial UAV detection schemes for different area blocks are evaluated, and the evaluation and analysis results of the initial UAV detection schemes are obtained.

[0134] In an optional embodiment, the formation tightness, UAV movement status, and aerodynamic induction situation between UAVs are simulated and analyzed by combining the basic information of different regional blocks to obtain the simulation analysis results of different detection and evaluation indicators; then, based on the simulation analysis results, the UAV detection schemes for different regional blocks are comprehensively analyzed to obtain the prediction and evaluation results of the UAV detection schemes for different regional blocks.

[0135] By comprehensively analyzing the basic information and detection scheme information of different regional blocks (operation units), simulation technology is used to conduct simulation analysis of the detection schemes for different regional blocks (operation units) in combination with formation tightness, UAV motion status, and aerodynamic induced behavior between UAVs. The formation tightness simulation covers aspects such as the spacing distribution between UAVs and changes in formation geometry; the UAV motion status simulation includes the prediction of dynamic parameters such as speed, acceleration, and flight trajectory; the aerodynamic induced behavior simulation between UAVs is based on an aerodynamic induced velocity estimation model to analyze the aerodynamic interactions between UAVs under different flight conditions. Through this series of simulation analyses, simulation analysis results of different detection and evaluation indicators in different detection schemes can be obtained. These indicators include formation stability indicators, UAV motion safety indicators, and aerodynamic interference impact indicators.

[0136] Subsequently, based on the simulation analysis results of detection schemes for different regional blocks (operational units), a comprehensive evaluation of UAV detection schemes for different regional blocks was conducted. During the evaluation process, multiple dimensions were considered, including detection range, detection accuracy, response time, and cost-effectiveness. Information such as formation, motion status, and aerodynamic induced behavior obtained from the simulation analysis was also incorporated to quantitatively analyze the applicability and effectiveness of different detection schemes in different regional blocks. Ultimately, predictive evaluation results of UAV detection schemes for different regional blocks were obtained, further providing a reference for the formulation and optimization of practical UAV detection schemes.

[0137] Furthermore, the evaluation and analysis method for different UAV detection schemes in this embodiment is only one optional condition of this embodiment. In other embodiments, the evaluation and analysis method for different UAV detection schemes can be optimized according to the actual conditions of different area blocks and the prediction and evaluation needs of UAV detection schemes. Other evaluation indicators can be incorporated based on the actual conditions of different area blocks, thereby more accurately reflecting the true performance of the detection schemes under different conditions, further ensuring that the evaluation results are closer to the actual situation, and improving the accuracy and pertinence of the scheme prediction and evaluation results.

[0138] S5. Based on the evaluation and analysis results, the initial UAV detection schemes for different regional blocks are intelligently adjusted, and the identification and detection of active fault information in different regions are achieved through the adjusted UAV detection schemes. The specific steps and implementation content are as follows: In this embodiment, the UAV detection scheme for different area blocks (operation units) is analyzed and judged by comprehensively considering the tightness of the formation, the movement of the UAVs, and the aerodynamic induction situation between the UAVs. The specific analysis content is as follows: 1. Analyze the formation tightness of UAV detection schemes in different area blocks (operation units).

[0139] Based on the UAV detection scheme and basic data for different regional blocks (operation units), the relative position deviation between UAVs is set as follows: After considering the influence of multiple drones overlapping, the ratio of the drone formation distance R to the drone wingspan b is used as the reference value for tightness, i.e. As a reference for determining the tightness or looseness of the formation.

[0140] Determining a good UAV formation in the detection scheme: At this point, it can be determined that the formation is well maintained. In this case, if the relative positional deviation between the drones is within the controller's control error λ range, the formation status is further confirmed to be acceptable. In the area block (operation unit) drone detection scheme, the drone wingspan... meters, distance of drone formation Rice, at this time Meanwhile, the control error of this area block (work unit) scheme... Set as Meters, the actual distance deviation between drones needs to not exceed Only when the distance is measured can it be determined that the formation is maintained in a qualified manner, and the UAV formation arrangement in the UAV detection scheme of this area block (operation unit) is feasible.

[0141] Judgment of loose UAV formations in the detection scheme: when At this time, the drone formation in the area block (operation unit) drone detection scheme is in a loose formation, and the coupling effect between drones is relatively weak. However, it is still necessary to ensure that the relative positional deviation between drones is within the control error range of the detection scheme in order to guarantee the formation status.

[0142] Judgment of tight UAV formation in detection scheme: when In this scenario, the drone formation in the area block (operation unit) drone detection scheme is a tight array, resulting in a strong coupling effect. Therefore, more precise monitoring of drone deviation is required, as drone deviation can significantly impact the control of other drones, especially when the drone's wingspan... meters, distance of drone formation Rice (at this time) If so, it is necessary to pay close attention to the relative position changes between drones in the drone detection scheme of the area block (work unit) to prevent the drones from deviating too much from the predetermined formation due to mutual influence.

[0143] 2. Analyze the movement of UAVs in different regional blocks (operation units) UAV detection schemes.

[0144] During the drone flight mission, the actual positions of different drones in the plan are continuously monitored using drone motion models. The deviation from the predetermined (target) position, if this deviation is within the allowable range of the scheme, indicates that the UAV in the UAV detection scheme can track the predetermined trajectory well. Furthermore, the UAV detection scheme sets the position tracking accuracy to... Meters, as long as the drone is in flight mission, its actual position The deviation from the predetermined position is always kept at If the drone position is within 1 meter, then the drone detection scheme for that area block (operation unit) can be considered qualified.

[0145] Real-time analysis of the actual speed of different drones in the scheme using drone motion models The deviation from the set (target) speed, if within the allowable range of the scheme, indicates that the UAV can control its speed well and maintain its motion state. Furthermore, this UAV detection scheme sets the speed control accuracy to... meters per second, if the actual speed of the drone during flight The deviation from the set speed is always in If the speed is within meters per second, the speed situation in the UAV detection scheme for that area block (operation unit) can be considered feasible.

[0146] Using the UAV motion model and basic data from different area blocks (operation units), the changes in the UAV's attitude angles (including pitch, roll, and yaw angles) are constantly monitored. If the changes in attitude angles are within the allowable range of the scheme, it indicates that the UAV can maintain good attitude stability. Furthermore, this UAV detection scheme sets the maximum range of attitude angle changes to be... If the drone's attitude angle changes constantly during flight, If the angle change is within a certain range, the UAV detection scheme for that area block (operation unit) can be deemed acceptable.

[0147] Based on the distance constraints in the UAV motion model, the distance between adjacent UAVs is monitored in real time during the flight mission, taking into account the basic data of different area blocks (operation units), to ensure that it does not exceed the safe distance. Furthermore, the safe distance in this drone detection scheme is set as follows: Meters, at a certain moment the distance between two adjacent drones is If the distance is meters, then the safety distance requirement is met; if the distance is... If the distance is less than 1 meter, it indicates a problem with the distance setting in the drone detection scheme for that area block (operation unit), which may pose a risk of drone collision.

[0148] By using an aerodynamic induced velocity estimation model for adjacent UAVs, the induced velocity between adjacent UAVs due to aerodynamic influences is calculated. The calculated induced velocity Compared with preset thresholds of different detection schemes, if the induced velocity If the threshold is exceeded, it indicates that the aerodynamic impact of this scheme is significant and may affect the formation of the drones, requiring appropriate adjustments. Furthermore, the induced velocity threshold in this drone detection scheme is set as follows: meters per second, if the calculated induced velocity If the value is meters per second, it indicates that the aerodynamic impact of this detection scheme is significant, and the UAV detection scheme for this area block (operation unit) needs to be adjusted.

[0149] Based on the above prediction and evaluation results, the formation shape, spatial relative position, drone motion parameters, equipment type, equipment quantity and equipment configuration in the drone detection schemes of different area blocks (operation units) are adjusted and optimized to obtain the adjusted and optimized drone detection schemes for different area blocks (operation units).

[0150] Based on the prediction and evaluation results of the UAV detection schemes for different regional blocks (operation units) in the embodiment, the detection schemes for each regional block (operation unit) are adjusted and optimized in a targeted manner. The adjustment and optimization include, but are not limited to, several key aspects such as formation shape, spatial relative position, UAV motion parameters, equipment type, number of equipment and equipment configuration.

[0151] Adjustments were made to the drone formation shape in the drone detection scheme: Based on the topography, weather conditions, and target characteristics of different areas (operation units), the UAV formation shape is adjusted accordingly. In complex canyon areas, to reduce the interference of terrain on the detection signal, the formation shape can be adjusted to a more compact triangle or rhombus shape, which can enhance the overall stability and anti-interference capability of the formation. In open plain areas, to expand the detection range, a loose linear or rectangular formation shape can be selected.

[0152] Adjustments to the spatial relative positions in the UAV detection scheme: Taking into account the aerodynamic induction effects, detection coverage, and safety distance requirements among the drones, the relative positions of the drones in space are adjusted. By optimizing the drone formation distance and wingspan ratio, it is ensured that the drones maintain a stable formation during flight while effectively avoiding flight instability caused by aerodynamic coupling effects. Simultaneously, based on the detection requirements and basic data of different area blocks (operation units), the horizontal and vertical spacing between drones can be reasonably adjusted to achieve the best detection results.

[0153] Adjustments were made to the drone motion parameters in the drone detection scheme: Based on adjustments to the formation shape and relative spatial position, the motion parameters of the UAVs are optimized accordingly. This includes, but is not limited to, setting parameters such as flight speed, flight altitude, flight heading angle, and flight position to ensure that the UAVs can fly stably along the predetermined trajectory and meet the detection requirements of different area blocks (operation units). In detection missions requiring rapid coverage of large areas, the flight speed can be appropriately increased; while in areas with high detection accuracy requirements, the flight speed needs to be reduced to ensure the accuracy and reliability of the detection data.

[0154] Adjustments were made to the type, quantity, and configuration of equipment in the drone detection scheme: Based on the basic data, detection targets, and geological conditions of different regional blocks (work units), appropriate detection equipment types should be selected for each block. For the detection of underground active faults, high-resolution ground-penetrating radar, electromagnetic detectors, and other equipment can be used; while for the monitoring of surface deformation, lidar, optical cameras, and other equipment can be employed. Simultaneously, the performance indicators, applicable range, and compatibility with other equipment of different devices need to be considered to ensure that the selected equipment can meet the requirements of the detection mission.

[0155] Simultaneously, based on the basic data of different regional blocks (operation units), the area, detection accuracy requirements, and detection capabilities of each regional block are obtained. The required number of devices for each regional block is then rationally determined. Through precise calculations and simulation analysis, it is ensured that the number of devices meets the requirements of the detection coverage while avoiding resource waste. In canyon areas with large detection areas and complex terrain, the number of devices can be appropriately increased to improve detection efficiency and data reliability.

[0156] Optimizing the equipment on the drones is crucial to ensuring maximum efficiency in their collaborative operation. This includes, but is not limited to, optimizing parameters such as the installation location, angle, and power of the detection equipment, as well as unifying the data transmission and communication protocols between the detection devices. By optimizing the equipment configuration of different detection schemes, the overall performance and stability of the detection schemes can be improved, further ensuring the accuracy and real-time nature of the detection data.

[0157] After the above series of adjustments and optimizations, the optimized UAV detection schemes for different regional blocks (operational units) can be obtained. To ensure the effective implementation of the optimized schemes, it is necessary to further analyze the impact of several key indicators, such as formation tightness, UAV movement, and aerodynamic induction between UAVs, on the adjusted UAV detection schemes. If the above-mentioned indicators are all within a reasonable range, it can be determined that the UAV formation is qualified or effective and can meet the detection needs of different regional blocks. If one or more indicators exceed the reasonable range, the reasons need to be analyzed in depth and corresponding measures need to be taken for adjustment. For example, if it is found that the UAV formation spacing is too small, resulting in enhanced aerodynamic coupling effect, the formation spacing can be adjusted appropriately; if the flight speed is too fast, resulting in excessive position tracking deviation, the flight speed can be reduced; if the attitude stability does not meet the requirements, the attitude control system of the UAV can be optimized and adjusted. Through continuous adjustments and optimizations, the stability and effectiveness of the optimized UAV detection schemes for different regional blocks (operational units) can be ensured, providing a guarantee and technical support for the subsequent intelligent detection of active faults.

[0158] Then, the drone detection scheme, which has been adjusted and optimized for different regional blocks, is used to intelligently detect different regional blocks in order to obtain detection information for different regional blocks. The optimized UAV detection scheme is used to conduct intelligent detection of different area blocks (work units). During the detection process, the UAV flies along a predetermined trajectory, collects various detection data in real time, and transmits the data back to the ground control center via wireless communication technology. The ground control center processes and analyzes the received data in real time to extract valuable information, such as geological structure information and surface deformation information.

[0159] Finally, by combining the detection information from different blocks in the area to be detected, a set of detection information for the area to be detected can be obtained. Based on the above set of detection information for the area to be detected, it is beneficial to effectively identify and accurately detect active faults in the area to be detected.

[0160] After the exploration mission is completed, the exploration information from different regional blocks (work units) is organized and aggregated. The exploration data from each regional block is classified, filtered, and integrated to remove noise and interference information, ensuring the accuracy and consistency of the data. Through organization and aggregation, a set of exploration information for the area to be explored is obtained. This set contains information on various aspects such as geology, topography, and surface deformation of different regional blocks within the area to be explored, providing comprehensive data support for subsequent active fault identification and exploration.

[0161] Based on the aforementioned set of detection information for the area to be explored, data analysis techniques and geological models can be used to effectively identify and accurately detect active faults within the area. The detected information can then be mined and analyzed, combining knowledge from geology, geophysics, and other disciplines to determine key parameters such as the location, strike, and activity of active faults in the area. Furthermore, 3D visualization technology can be used to visually display the distribution and activity characteristics of active faults in the area, providing scientific basis and decision support for geological disaster prevention and engineering planning in the region.

[0162] Through the above series of workflows, the UAV detection scheme for different blocks in the area to be detected was adjusted and optimized, intelligent detection was achieved, and active faults in the area to be detected were effectively identified and accurately detected, contributing to the development of geological exploration and disaster prevention and control and providing technical support.

[0163] Please see Figure 4 In an optional embodiment, to efficiently execute the intelligent control method for the UAV formation active fault detection scheme provided by the present invention, the present invention also provides an intelligent control system for the UAV formation active fault detection scheme. The intelligent control system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the specific steps of the relevant embodiments of the intelligent control method for the UAV formation active fault detection scheme provided by the present invention. The intelligent control system for the UAV formation active fault detection scheme of the present invention has a complete and stable structure, and can efficiently execute the intelligent control method for the UAV formation active fault detection scheme of the present invention, thereby improving the overall applicability and practical application capability of the present invention.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention.

Claims

1. An intelligent control method for unmanned aerial vehicle formation active fault detection scheme, characterized in that, The method comprises the following steps: obtaining basic information of a region to be detected, defining and dividing the region to be detected according to the basic information to obtain different region blocks in the region to be detected, and obtaining basic information of the different region blocks based on the different region blocks and the basic information; matching a UAV formation array of the different region blocks according to the basic information, configuring information detection equipment of the different region blocks based on information detection requirements of the different region blocks, and obtaining an initial UAV detection scheme of the different region blocks in combination with the UAV formation array and the information detection equipment; detecting and evaluating the initial UAV detection scheme according to the basic information, and obtaining tightness of the formation array, motion conditions of the UAVs, and aerodynamic induction situation among the UAVs of the initial UAV detection scheme; evaluating the initial UAV detection scheme of the different region blocks based on the tightness of the formation array, the motion conditions of the UAVs, and the aerodynamic induction situation among the UAVs, and obtaining evaluation analysis results of the initial UAV detection scheme; intelligently regulating and controlling the initial UAV detection scheme of the different region blocks in combination with the evaluation analysis results, and realizing identification and detection of information of different region active faults through an adjusted UAV detection scheme. 2.The intelligent regulation method for the unmanned aerial vehicle formation active fault detection scheme according to claim 1, characterized in that, The obtaining of the basic information of the region to be detected comprises: collecting geological data, terrain data, and meteorological data of the region to be detected; integrating the geological data, the terrain data, and the meteorological data to obtain the basic information of the region to be detected. 3.The method of claim 1, wherein, The defining and dividing of the region to be detected according to the basic information to obtain the different region blocks in the region to be detected, and the obtaining of the basic information of the different region blocks based on the different region blocks and the basic information comprise: preliminarily analyzing topographic and geomorphic features, geological structure patterns, and seismic activity regularities of the region to be detected according to the basic information; defining and dividing the region to be detected based on the topographic and geomorphic features, the geological structure patterns, and the seismic activity regularities, and obtaining the different region blocks in the region to be detected; setting a region block coding and marking mechanism, numbering the different region blocks by using the region block coding and marking mechanism, and obtaining corresponding number information of the different region blocks; matching the basic information of the different region blocks in the region to be detected according to the basic information, the different region blocks, and the number information. 4.The intelligent regulation method for UAV formation active fault detection scheme according to claim 1, wherein, The matching of the UAV formation array of the different region blocks according to the basic information, the configuring of the information detection equipment of the different region blocks based on the information detection requirements of the different region blocks, and the obtaining of the initial UAV detection scheme of the different region blocks in combination with the UAV formation array and the information detection equipment comprise: designing the UAV formation array of the different region blocks based on active fault detection requirements and the basic information, the UAV formation array comprising formation shape, spatial relative position, and UAV motion parameters; configuring the information detection equipment of the different region blocks according to information detection requirements of the different region blocks, the information detection equipment comprising detection equipment type, equipment quantity, and equipment configuration; The initial UAV detection scheme of different region blocks is designed in combination with the formation shape, the spatial relative position, the UAV motion parameter, the device type, the device quantity and the device configuration. 5.The intelligent regulation method for UAV formation active fault detection scheme according to claim 1, wherein, The initial UAV detection scheme is detected and evaluated according to the basic information, and the formation array tightness, the UAV motion condition and the aerodynamic induction situation between UAVs of the initial UAV detection scheme are obtained. The dynamic characteristics and the cooperative efficiency of the UAV detection scheme are analyzed. The detection evaluation indexes of the UAV detection scheme are screened in combination with the dynamic characteristics and the cooperative efficiency and the basic information, and the detection evaluation indexes include the formation array tightness, the UAV motion condition and the aerodynamic induction situation between UAVs. 6.The intelligent regulation method for UAV formation active fault detection scheme according to claim 5, wherein, The initial UAV detection scheme is detected and evaluated according to the basic information, and the formation array tightness, the UAV motion condition and the aerodynamic induction situation between UAVs of the initial UAV detection scheme are obtained. The UAV wingspan length and the array distance of the UAV formation are obtained according to the initial UAV detection scheme. The tightness reference value is set based on the UAV wingspan length and the array distance. The formation array tightness of different UAV formation arrays is evaluated according to the tightness reference value. The speed information, the position information and the motion direction information of the UAV are obtained according to the initial UAV detection scheme. The UAV motion model is established based on the speed information, the position information and the motion direction information. The motion position, the motion speed, the motion direction and the adjacent interval distance of the UAV at different time points are analyzed through the UAV motion model. The UAV motion condition is analyzed according to the motion position, the motion speed, the motion direction and the adjacent interval distance. 7.The method of claim 5, wherein, The initial UAV detection scheme is detected and evaluated according to the basic information, and the formation array tightness, the UAV motion condition and the aerodynamic induction situation between UAVs of the initial UAV detection scheme are obtained. The vortex ring quantity generated by the UAV wing is analyzed based on the fluid mechanics mechanism. The angle correction coefficient analysis function between adjacent UAVs is constructed according to the relative angle, the frequency parameter and the sine-cosine transformation coefficient of the UAV, and the angle correction coefficient is obtained through the angle correction coefficient analysis function. The distance correction coefficient analysis function between adjacent UAVs is constructed according to the vertical distance and the linear term coefficient between adjacent UAVs, and the distance correction coefficient is obtained through the distance correction coefficient analysis function. The speed correction coefficient analysis function between adjacent UAVs is constructed based on the airflow inertia effect and the speed of the UAV at different time points, and the speed correction coefficient is obtained through the speed correction coefficient analysis function. The aerodynamic induction velocity estimation model between adjacent UAVs is constructed in combination with the vortex ring quantity, the angle correction coefficient, the distance correction coefficient and the speed correction coefficient. The aerodynamic induction situation between UAVs is analyzed by using the aerodynamic induction velocity estimation model. 8.The method of claim 5, wherein, The initial UAV detection scheme of different regional blocks is evaluated based on the formation array tightness, the UAV movement condition and the aerodynamic induction situation among the UAVs, and evaluation analysis results of the initial UAV detection scheme are obtained, including: The formation array tightness, the UAV movement condition and the aerodynamic induction situation among the UAVs are simulated and analyzed in combination with the basic information of the different regional blocks, and formation array simulation results, movement condition simulation results and aerodynamic induction situation simulation results of the initial UAV detection scheme are obtained; The initial UAV detection scheme of different regional blocks is comprehensively analyzed based on the formation array simulation results, the movement condition simulation results and the aerodynamic induction situation simulation results, to obtain evaluation analysis results of the initial UAV detection scheme of different regional blocks. 9.The method of claim 8, wherein, The initial UAV detection scheme of different regional blocks is intelligently regulated in combination with the evaluation analysis results, and the identification and detection of active fault information of different regional blocks are realized through the adjusted UAV detection scheme, including: The formation shape, the spatial relative position, the UAV movement parameter, the equipment type, the equipment quantity and the equipment configuration in the initial UAV detection scheme are intelligently regulated based on the evaluation analysis results, to obtain the adjusted UAV detection scheme of different regional blocks; The active faults of different regional blocks are detected through the adjusted UAV detection scheme, to obtain active fault detection information of different regional blocks; The active fault detection information of different regional blocks is integrated to obtain an active fault detection information set of a to-be-detected region, to realize effective identification and accurate detection of active faults of the to-be-detected region.

10. An intelligent regulation system for unmanned aerial vehicle formation active fault detection scheme, characterized in that, The system comprises a processor, an input device, an output device and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the intelligent regulation method of the UAV formation active fault detection scheme according to any one of claims 1-9.

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