Geological collaborative surveying and mapping operation control method and system based on unmanned aerial vehicle

Abstract

The invention relates to the field of geological surveying and mapping, and discloses a geological collaborative surveying and mapping operation control method and system based on an unmanned aerial vehicle, and the method comprises the following steps: S1, collecting environment dynamic data of a geological surveying and mapping region in real time through a sensor group carried by a plurality of unmanned aerial vehicles, and constructing a dynamically updated three-dimensional environment model; wherein the sensor group comprises a camera, a laser radar and an inertial measurement unit; s2, based on a preset surveying and mapping task target, initial surveying and mapping sub-regions are allocated to the unmanned aerial vehicles, and conflict-free flight paths are planned; s3, monitoring the flight state of the unmanned aerial vehicle and environment dynamic data in real time, and analyzing and processing to obtain a terrain sudden change index, a meteorological disturbance index and an unmanned aerial vehicle health index; and if any one of the following trigger conditions is detected: at least one of the geomorphic mutation index, the meteorological disturbance index and the unmanned aerial vehicle health index exceeds the respective corresponding preset threshold interval, dynamic task redistribution and path optimization are triggered.

Description

Technical Field

[0001] The present invention relates to the field of geological surveying and mapping, and in particular to a method and system for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles. Background Art

[0002] Geological surveying and mapping is an important basic work in the fields of resource exploration, environmental monitoring and disaster warning. Traditional geological surveying and mapping methods mainly rely on manual field surveys and satellite remote sensing technology, but these methods have problems such as low efficiency, high cost and insufficient accuracy, especially in complex terrain and dangerous areas, and are difficult to meet the needs of modern geological surveys.

[0003] In recent years, the rapid development of UAV technology has provided new solutions for geological surveying and mapping. UAVs have the advantages of high flexibility, low cost, and adaptability to complex environments, and can quickly obtain high-resolution geological data. However, the operating capacity of a single UAV is limited, and it is difficult to cover large areas or complete complex tasks. Therefore, collaborative surveying and mapping technology based on multiple UAVs has become a research hotspot.

[0004] The existing UAV collaborative mapping technology still has some defects. For example, in a complex geological environment, when multiple UAVs are collecting geological information according to a set flight path, once the UAVs themselves fail or the environment changes suddenly, it is difficult for them to adjust their tasks and paths in real time, resulting in the inability to complete the scheduled mapping tasks in a timely and effective manner, affecting the overall mapping accuracy of the exploration area, that is, there is a problem of poor adaptability to dynamic environments. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles, so as to solve the technical problem that in complex geological environments, it is difficult for unmanned aerial vehicles to adjust tasks and paths in real time, thus affecting the surveying and mapping accuracy of the exploration area.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles comprises the following steps:

[0008] S1. Collect environmental dynamic data of the geological survey area in real time through the sensor groups carried by multiple drones, and build a dynamically updated three-dimensional environmental model;

[0009] S2. Based on the preset mapping mission objectives, the initial mapping sub-areas are allocated to each UAV and a conflict-free flight path is planned;

[0010] S3, real-time monitoring of the flight status and environmental dynamic data of the drone, and analysis and processing to obtain the terrain mutation index, meteorological disturbance index and drone health index; if any of the following trigger conditions is detected:

[0011] When at least one of the terrain mutation index, meteorological disturbance index, and UAV health index exceeds their respective preset threshold ranges, dynamic task reassignment and path optimization are triggered.

[0012] As a further technical solution, the method further includes:

[0013] S4. According to the real-time updated three-dimensional environment model and the remaining UAV status, re-divide the unfinished mapping sub-regions and generate a new flight path.

[0014] As a further technical solution, the terrain mutation index T tu The calculation formula is:

[0015]

[0016] Among them, ρ1 and ρ2 are weight coefficients, ΔH max Is the maximum elevation change of the current mapping sub-region within a unit time, Is the average elevation of the current mapping sub-region within a unit time, Δσ k Is the terrain standard deviation change of the current mapping sub-region within a unit time, σ 0 Is the reference terrain standard deviation, and F is the historical trend coefficient.

[0017] As a further technical solution, the expression of the historical trend coefficient F is:

[0018]

[0019] Among them, Ph is the mean value of ΔH within the past n unit times max Pσ is the mean value of Δσ within the past n unit times k ΔH maxi Is the maximum elevation change within the i-th unit time, Δσ ki Is the terrain standard deviation change within the i-th unit time, and γ is the historical trend influence coefficient.

[0020] As a further technical solution, the meteorological disturbance index W rao The calculation formula is:

[0021]

[0022] Among them, Is the weight coefficient of the j-th meteorological parameter, m is the total number of meteorological parameters, G j Is the deviation value of the j-th meteorological parameter, t a t a+1 Are the start and end times of the monitoring period respectively, gj (t) is the actual value of the j-th meteorological parameter, g j0 (t) is the threshold of the meteorological parameter for safe flight of the drone for the j-th item.

[0023] As a further technical solution, the drone health index K health The calculation formula is:

[0024]

[0025] Among them, E bat is the battery power of the drone, E full is the full battery power of the drone, S sensor is the sensor performance score, S max is the maximum score of the sensor performance, C comm is the communication status score, C max is the maximum score of the communication status, θ1, θ2, θ3, θ4 are preset proportionality coefficients, W yu is the maximum allowable threshold of the meteorological disturbance index.

[0026] As a further technical solution, the steps of dynamic task reallocation in the S4 step are:

[0027] Based on the geological risk level M in the real-time three-dimensional environment model, sort the unfinished survey sub-areas in descending order according to the geological risk level M from high to low;

[0028] Among them, M = δ1 * T tu + δ2 * W rao ; δ1, δ2 are weighting factors;

[0029] Sort each drone in descending order according to the drone health index K health from high to low, and allocate the drones with high drone health index to the survey sub-areas with high task geological risk levels.

[0030] As a further technical solution, the generation of the new flight path is realized through the path re-planning algorithm.

[0031] A geological collaborative survey operation control system based on drones, the system includes:

[0032] An environmental dynamic data acquisition module, which uses the sensor groups carried by multiple drones to collect the environmental dynamic data of the geological survey area in real time and constructs a dynamically updated three-dimensional environment model;

[0033] A flight path planning module, based on the preset survey task objectives, allocates initial survey sub-areas to each drone and plans conflict-free flight paths;

[0034] A monitoring and analysis module is used to monitor the flight status of the UAV and environmental dynamic data in real time, and analyze and process them to obtain a terrain mutation index, a meteorological disturbance index, and a UAV health index. If any of the following triggering conditions are detected:

[0035] When at least one of the terrain mutation index, the meteorological disturbance index, and the UAV health index exceeds their respective preset threshold ranges, dynamic task reallocation and path optimization are triggered.

[0036] Advantages of the present invention:

[0037] (1) By using multiple UAVs equipped with sensor groups to collect environmental dynamic data in real time and construct a dynamically updated three-dimensional environmental model, the actual situation of the geological survey area can be reflected in a timely manner, providing more accurate basic data for subsequent survey tasks, avoiding survey errors caused by data lag, and thus effectively improving the survey accuracy.

[0038] (2) Monitor the flight status of the UAV and environmental dynamic data in real time, calculate the terrain mutation index, the meteorological disturbance index, and the UAV health index. Once these indexes exceed the preset threshold range, dynamic task reallocation and path optimization are triggered, enabling the UAV to quickly adapt to complex and changeable geological environments, sudden meteorological changes, and its own faults, etc., ensuring the continuous and efficient progress of the survey task, and solving the problem that traditional UAVs are difficult to adjust tasks and paths in real time in complex environments.

[0039] (3) Based on the preset survey task objectives, initial survey sub-areas are allocated to each UAV and conflict-free flight paths are planned, realizing the collaborative operation of multiple UAVs and reducing the mutual interference between UAVs; in case of emergencies, the uncompleted survey sub-areas can be re-divided and new flight paths can be generated according to the real-time updated three-dimensional environmental model and the remaining UAV status, making full use of the remaining resources, avoiding the stagnation of the entire survey task due to problems with some UAVs, and effectively improving the survey efficiency. Description of the Drawings

[0040] The present invention will be further described below with reference to the accompanying drawings.

[0041] Figure 1 It is a flowchart of the method steps of the present invention. Detailed Embodiments

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0043] Please refer to Figure 1 As shown, the present invention is a method for controlling geological collaborative mapping operations based on unmanned aerial vehicles, comprising the following steps:

[0044] S1. Real-time collect environmental dynamic data of the geological mapping area through sensor groups carried by multiple unmanned aerial vehicles, and construct a dynamically updated three-dimensional environmental model; the sensor groups include cameras, lidars, and inertial measurement units; environmental dynamic data: refers to the dynamic information collected in real time by the unmanned aerial vehicles during the mapping process, reflecting changes in geological and meteorological conditions, including but not limited to elevation change data, terrain complexity data, wind speed, rainfall, and visibility;

[0045] S2. Based on the preset mapping task objectives, allocate initial mapping sub-areas to each unmanned aerial vehicle and plan non-conflicting flight paths;

[0046] S3. Real-time monitor the flight status of the unmanned aerial vehicles and environmental dynamic data, and analyze and process to obtain terrain mutation index, meteorological disturbance index, and unmanned aerial vehicle health index; if any of the following trigger conditions are detected:

[0047] When at least one of the terrain mutation index, meteorological disturbance index, and unmanned aerial vehicle health index exceeds their respective preset threshold ranges, dynamic task reallocation and path optimization are triggered.

[0048] The method further includes:

[0049] S4. According to the real-time updated three-dimensional environmental model and the remaining unmanned aerial vehicle status, re-divide the unfinished mapping sub-areas and generate new flight paths.

[0050] In this embodiment, various sensors such as cameras, lidars, and inertial measurement units are used to perform real-time environmental perception on the geological mapping area, enabling the collection of multi-source data and the construction of a real-time three-dimensional environmental model; thus, it is possible to comprehensively and accurately understand information such as the topography, obstacle distribution, and meteorological conditions of the mapping area, providing a reliable basis for subsequent task allocation and path planning; through initial task allocation and path planning, the mapping work can be carried out in an orderly manner, promoting the efficient collaborative coverage of the mapping area, and improving the overall mapping efficiency on the premise of meeting the mapping accuracy; finally, through continuous monitoring and feedback control mechanisms, tasks and paths can be adjusted in a timely manner when the environment changes or the unmanned aerial vehicles show abnormalities, improving the adaptability and mapping accuracy of the system in complex and changeable geological environments; finally, step S4 improves the entire mapping operation control process. After encountering unexpected situations, the areas can be re-divided and paths can be re-planned according to the real-time model and the status of the unmanned aerial vehicles, ensuring that the unfinished mapping tasks can continue to be efficiently promoted, further improving the mapping efficiency and the reliability of task completion.

[0051] The terrain mutation index Ttu The calculation formula is as follows:

[0052]

[0053] where ρ1 and ρ2 are weight coefficients determined based on regression analysis of historical disaster data, and ΔH max is the maximum elevation change of the current surveyed sub-region within a unit time, is the average elevation of the current surveyed sub-region within a unit time, and Δσ k is the change amount of terrain standard deviation of the current surveyed sub-region within a unit time, which is used to reflect the fluctuation of surface roughness and is calculated based on the standard deviation of terrain point cloud, and σ 0 is the reference terrain standard deviation, and F is the historical trend coefficient.

[0054] The expression of the historical trend coefficient F is:

[0055]

[0056] where, Ph is the mean value of ΔH within the past n unit times max , which reflects the historical elevation change trend, Pσ is the mean value of Δσ within the past n unit times k , which reflects the historical terrain complexity trend, ΔH maxi is the maximum elevation change amount within the i-th unit time, and Δσ ki is the change amount of terrain standard deviation within the i-th unit time, and γ is the historical trend influence coefficient, which adjusts the sensitivity of historical data to the terrain mutation index.

[0057] In this embodiment, the terrain mutation index T is calculated through and

[0058] ; tu ;

[0059] Obviously, it can be seen that through the product coupling of the previous terrain mutation reference term and the historical trend deviation term , when the historical data is abnormal, the mutation index is significantly amplified; the historical trend influence coefficient γ controls the intensity of historical influence to adapt to the requirements of different geological scenarios. At the same time, the standardized deviation of historical data is introduced, which can effectively distinguish normal fluctuations from abnormal mutations; by accurately calculating the terrain mutation index, it provides a quantitative basis for judging the impact of terrain changes on surveying and mapping operations, can detect abnormal terrain changes in a timely manner, and triggers an adjustment mechanism when the index exceeds the threshold to ensure the surveying and mapping safety and accuracy of drones in areas with complex and changeable terrain.

[0060] Through the above technical solutions, the calculation of the terrain mutation index can better combine historical data and more accurately reflect the terrain change trend; considering historical data when judging terrain mutations can improve the accuracy and reliability of predictions, avoid misjudgments due to short-term abnormal fluctuations, and provide a more scientific basis for dynamic adjustment.

[0061] The meteorological disturbance index W rao is calculated by the formula:

[0062]

[0063] where is the weight coefficient of the j-th meteorological parameter, m is the total number of meteorological parameters, G j is the deviation value of the j-th meteorological parameter, t a and t a+1 are the start and end times of the monitoring period respectively, g j (t) is the actual value of the j-th meteorological parameter, and g j0 (t) is the threshold of the j-th meteorological parameter that satisfies the safe flight of the UAV; meteorological parameters include but are not limited to wind speed, rainfall, and visibility.

[0064] In this embodiment, a calculation method for the meteorological disturbance index is provided. Through

[0065] and it is calculated. Obviously, the above formula can quantify the interference degree of meteorological factors on UAV mapping operations. Through the meteorological disturbance index, the impact of adverse meteorological conditions on mapping can be detected in a timely manner, so as to adjust tasks and paths in a timely manner and ensure the flight safety and mapping accuracy of the UAV.

[0066] The UAV health index K health is calculated by the formula:

[0067]

[0068] where E bat is the battery power of the UAV, E full is the full battery power of the UAV, S sensor is the sensor performance score, which is calculated by the weighted average method of the number of point clouds of the lidar, the image clarity of the camera, and the attitude angle error of the inertial measurement unit. S max is the maximum score of the sensor performance, C comm is the communication status score, which is calculated by the weighted average method of signal strength, packet loss rate, and delay. C max is the maximum score of the communication status, and θ1, θ2, θ3, θ4 are preset proportional coefficients, which are determined comprehensively based on historical data and empirical data. W yuis the maximum allowable threshold of the meteorological disturbance index.

[0069] In this embodiment, a calculation method for the UAV health index is given. Through the formula it is calculated. Obviously, through the above formula, the impacts of the UAV's battery power, sensor performance, communication status, and meteorological conditions on the UAV health index can be comprehensively evaluated, the UAV health status can be grasped in real time, and the task can be adjusted in time when the index is abnormal, avoiding the impact of UAV failures on the mapping task and improving the stability and reliability of the mapping operation.

[0070] As a further technical solution, the steps of dynamic task reallocation in the S4 step are as follows:

[0071] Based on the geological risk level M in the real-time three-dimensional environment model, the uncompleted mapping sub-regions are sorted in descending order according to the geological risk level M from high to low;

[0072] where M = δ1*T tu +δ2*Wrao; δ1 and δ2 are weighting factors determined based on historical data analysis;

[0073] For each UAV, according to the UAV health index K health they are sorted in descending order from high to low, and the UAVs with high UAV health indices are assigned to the mapping sub-regions with high task geological risk levels.

[0074] As a further technical solution, the generation of the new flight path is realized through a path re-planning algorithm, and the path re-planning algorithm adopts the A* algorithm, Dijkstra algorithm or particle swarm optimization algorithm.

[0075] A geological collaborative mapping operation control system based on UAVs, the system includes:

[0076] An environmental dynamic data acquisition module, which uses the sensor groups carried by multiple UAVs to collect the environmental dynamic data of the geological mapping area in real time and constructs a dynamically updated three-dimensional environment model;

[0077] A flight path planning module, based on the preset mapping task objectives, assigns initial mapping sub-regions to each UAV and plans conflict-free flight paths;

[0078] A monitoring and analysis module, which is used to monitor the flight status of the UAVs and the environmental dynamic data in real time, and analyze and process to obtain the terrain mutation index, meteorological disturbance index and UAV health index; if any of the following trigger conditions are detected:

[0079] When at least one of the terrain mutation index, meteorological disturbance index and UAV health index exceeds their respective preset threshold ranges, dynamic task reallocation and path optimization are triggered.

[0080] It should be noted that: the calculation formulas and each parameter participating in the operation in the present invention have been dimensionless processed in advance, and the process of dimensionless processing is well known in the industry and will not be described herein.

[0081] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles, characterized in that: The following steps are involved: S1. Collect environmental dynamic data of the geological survey area in real time through the sensor groups carried by multiple drones, and build a dynamically updated three-dimensional environmental model; S2. Based on the preset mapping mission objectives, the initial mapping sub-areas are allocated to each UAV and a conflict-free flight path is planned; S3, real-time monitoring of the flight status and environmental dynamic data of the drone, and analysis and processing to obtain the terrain mutation index, meteorological disturbance index and drone health index; if any of the following trigger conditions is detected: When at least one of the terrain mutation index, meteorological disturbance index and drone health index exceeds the corresponding preset threshold range, dynamic task reallocation and path optimization are triggered.

2. The method for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles according to claim 1 is characterized in that: The method further comprises: S4. Re-divide the unfinished mapping sub-area and generate a new flight path based on the real-time updated 3D environment model and the remaining UAV status.

3. The method for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles according to claim 2 is characterized in that: The terrain mutation index T tu The calculation formula is: Among them, ρ1 and ρ2 are weight coefficients, ΔH max is the maximum elevation change of the current surveying sub-area within a unit time, is the average elevation of the current surveying sub-area in a unit time, Δσ k is the change in the terrain standard deviation of the current surveying sub-area within a unit time, σ0 is the standard deviation of the benchmark terrain, and F is the historical trend coefficient.

4. The method for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles according to claim 3 is characterized in that: The expression of the historical trend coefficient F is: in, Ph is ΔH in the past n unit time max The mean of Pσ is Δσ in the past n unit time. k The mean value, ΔH maxi is the maximum elevation change in the i-th unit time, Δσ ki is the change in terrain standard deviation within the i-th unit time, and γ is the historical trend influence coefficient.

5. The method for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles according to claim 2 is characterized in that: The meteorological disturbance index W rao The calculation formula is: in, is the weight coefficient of the jth meteorological parameter, m is the total number of meteorological parameters, G j is the deviation value of the jth meteorological parameter, t a ,t a+1 are the starting and ending time of the monitoring period, respectively. j (t) is the actual value of the jth meteorological parameter, g j0 (t) is the jth meteorological parameter threshold that satisfies the safe flight of the UAV.

6. The method for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles according to claim 2, characterized in that: The drone health index K health The calculation formula is: Among them, E bat is the battery power of the drone, E full The drone battery is fully charged, S sensor Score the sensor performance, S max is the maximum rating of the sensor performance, C comm Score the communication status, C max is the maximum score of the communication status, θ1, θ2, θ3, θ4 are the preset proportional coefficients, W yu is the maximum allowable threshold of the meteorological disturbance index.

7. The method for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles according to claim 1, characterized in that: The steps of dynamic task reallocation in step S4 are: Based on the geological risk level M in the real-time 3D environment model, the unfinished surveying and mapping sub-areas are sorted in descending order from high to low according to the geological risk level M; Where M = δ1*T tu +δ2*W rao ; δ1, δ2 are weight factors; For each drone, the drone health index K health Arrange them in descending order from high to low, and assign drones with high drone health index to surveying sub-areas with high mission geological risk levels.

8. The method for controlling geological collaborative surveying and mapping operations based on unmanned aerial vehicles according to claim 1, characterized in that: The generation of a new flight path is achieved through a path replanning algorithm.

9. A UAV-based geological collaborative surveying and mapping operation control system, the system is used to implement the UAV-based geological collaborative surveying and mapping operation control method described in claim 1, characterized in that: include: The environmental dynamic data acquisition module collects environmental dynamic data of the geological survey area in real time through the sensor groups carried by multiple drones, and builds a dynamically updated three-dimensional environmental model; The flight path planning module allocates initial mapping sub-areas to each UAV and plans a conflict-free flight path based on the preset mapping mission objectives; The monitoring and analysis module is used to monitor the flight status and environmental dynamic data of the drone in real time, and analyze and process the terrain mutation index, meteorological disturbance index and drone health index; if any of the following trigger conditions is detected: When at least one of the terrain mutation index, meteorological disturbance index and drone health index exceeds the corresponding preset threshold range, dynamic task reallocation and path optimization are triggered.

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