A method for coordinating geological surveying and mapping of a beidou patrol unmanned aerial vehicle

Abstract

The application discloses a kind of geological surveying and mapping coordination operation methods for Beidou patrol unmanned plane, it is related to image optimization technology, for improving the problem of low image acquisition accuracy of single orbit flight unmanned plane, including setting multiple unmanned plane flight height, and collecting the flight environment data of each group unmanned plane at different height, according to the attribute data of each group unmanned plane, the precision of the region to be measured detected by each group unmanned plane is analyzed by selecting different flight environment data combination, and the flight height of unmanned plane is screened and marked, set retrieval scoring mechanism is used to retrieve and score unmanned plane, select geological surveying and mapping unmanned plane in each marked group to carry out geological surveying and mapping, real-time detect the real-time power of unmanned plane and image transmission time stamp interval, analyze the image effect of the geological surveying and mapping unmanned plane of each marked group, record the surveying and mapping time of the geological surveying and mapping unmanned plane and divide into multiple surveying and mapping time periods, select the surveying and mapping image of each marked group in each surveying and mapping time period according to the image effect analysis result.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) control technology, and more specifically, to a geological surveying and mapping coordination operation method for a Beidou patrol UAV. Background Art

[0002] UAV control technology aims to improve the performance, stability and safety of UAVs through a series of methods and technologies. The application of UAV control technology and the coordinated geological mapping operations of patrol UAVs can improve the efficiency of surveying and mapping tasks and provide a data basis for analyzing the area to be surveyed.

[0003] The existing technology has the following deficiencies:

[0004] When using patrol drones to survey the geology of the area to be surveyed, the flight altitude and flight trajectory are pre-determined by analyzing environmental factors. The drones then fly at the set altitude and flight trajectory and record the geological conditions. However, some areas to be surveyed require extremely long exploration times, and the image acquisition accuracy during this period is low, which can easily lead to misjudgments in surveying and analysis. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a geological surveying and mapping coordination operation method for Beidou patrol drones. By setting multiple groups of altitudes, the image acquisition accuracy of each group is evaluated based on the environmental data and drone data of each group of altitudes, and then the detection time of the drone flight is segmented. The surveying and mapping image accuracy of each group of drones in the segment is judged, and the best image is selected from each group of surveying and mapping images in each segment to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for coordinated geological mapping operations using a Beidou patrol drone includes the following steps:

[0008] Step S1: Set the boundaries of the area to be measured, import the drone navigation system, set the flight altitudes of multiple groups of drones, and collect flight environment data of each group of drones at different altitudes;

[0009] Step S2: calling the attribute data of each group of drones, selecting different flight environment data combinations based on the attribute data of each group of drones, and performing accuracy analysis on the areas to be tested detected by each group of drones;

[0010] Step S3: Screen and mark the flight altitudes of the UAVs according to the accuracy analysis results, set a retrieval scoring mechanism, search and score the UAVs in each marked group through the retrieval scoring mechanism, and select one UAV from each marked group as the geological mapping UAV of the corresponding marked group according to the retrieval scoring results;

[0011] Step S4: Use the geological mapping drones of each marking group to perform geological mapping operations, detect the real-time power of the drone and the incoming timestamp interval of the image sensor in real time, analyze the image effect of the geological mapping drones of each marking group based on the real-time detection results, record the mapping time of the geological mapping drone and divide it into multiple mapping time periods, and select the mapping image of each marking group within each mapping time period based on the image effect analysis results.

[0012] In a preferred embodiment, in step S1, the flight environment data includes sunlight angle, airflow velocity and vegetation coverage; the flight altitude range is divided into multiple groups, each group is set with a different flight altitude, and the flight environment data of each group of drones at different altitudes are collected.

[0013] In a preferred embodiment, in step S1, the vegetation coverage range is identified by using vegetation images of different bands of the drone and analyzing the reflectivity of different bands to identify the area blocked by vegetation and the total area, where the vegetation blocking rate is the ratio of the area blocked by vegetation to the total area.

[0014] In a preferred embodiment, in step S2, the drone attribute data includes the drone's mass, shading treatment area, and detection range; before the drone is deployed, the attribute data of each drone is entered into a database and associated with the corresponding drone number or identification, and is called when scoring the environmental adaptability of the drone attribute data.

[0015] In a preferred embodiment, in step S2, when using drone attribute data to score environmental adaptability, a quantitative scoring formula is used:

[0016]

[0017] in, is the weight coefficient, the drone mass is m, and the maximum drone mass in the drone mass task is , the hood size is , the shading area is The detection range is , the maximum nominal detection distance is , the quantitative score of the UAV attributes’ ability to adapt to the environment is .

[0018] In a preferred embodiment, in step S2, a comparison table of the impact of each flight environment data on the acquisition accuracy of the altitude environment image is obtained. The acquisition accuracy impact comparison table contains the impact scores of each environmental factor on the image acquisition accuracy of different drone flight altitudes. The maximum and second largest flight environment data scores corresponding to different drone flight altitudes are called and summed to obtain the environmental impact score of the corresponding drone flight altitude, which is marked as .

[0019] In a preferred embodiment, in step S2, the comprehensive score of the environmental accuracy analysis at different flight altitudes is the sum of the environmental impact score at the corresponding flight altitude and the quantitative score of the UAV attribute's ability to adapt to the environment.

[0020] In a preferred embodiment, in step S3, the candidate drones of each altitude group are sorted in descending order according to the comprehensive scores, and the drones with the highest scores are selected to enter the next round of comparison; after the drones with the highest comprehensive scores are screened out, the quantitative scores of the drone attributes on the environmental adaptability are refined and screened; the quantitative scores of the drone attributes on the environmental adaptability are recalculated based on the change in the environmental influencing factors due to the preset altitude, and the drones with the highest quantitative scores in each altitude group are selected as the final mapping drones for the corresponding altitude group.

[0021] In a preferred embodiment, in step S4, when analyzing the image effects of the geological mapping drone of each marking group according to the real-time detection results, the specific steps are as follows:

[0022] Power monitoring: collects the remaining power of the drone in real time and predicts the available operating time through a preset power consumption model;

[0023] Timestamp recording: During the flight, the precise timestamp of each image acquisition is recorded, and the time interval between adjacent images is obtained by subtracting the adjacent precise timestamps;

[0024] Stability score: The timestamp interval stability score is calculated using the variance of the time interval;

[0025] Basic score setting: According to the core environmental impact factors of different height groups, a basic score is set for each height group;

[0026] Dynamically evaluate the collection timing: Based on the real-time power and timestamp scores, a weighted sum is calculated to obtain a real-time accuracy index, which is used to evaluate the optimal collection timing.

[0027] Data selection: At the end of each timestamp, the real-time accuracy indexes of all drones in the same area are compared, and the image collected by the drone with the highest value is selected as the image of that timestamp.

[0028] The technical effects and advantages of the geological surveying and mapping coordination operation method for Beidou patrol drones of the present invention are as follows:

[0029] The present invention sets the boundary of the area to be measured and imports the drone navigation system, sets the flight altitude of multiple groups of drones, collects flight environment data of each group of drones at different altitudes, calls the attribute data of each group of drones, selects different flight environment data combinations according to the attribute data of each group of drones, performs precision analysis on the area to be measured detected by each group of drones, screens and marks the drone flight altitude according to the precision analysis result, reduces the impact of the environmental difference at altitude on the reduction of collection accuracy, sets a retrieval scoring mechanism to search and score the drones, screens out geological surveying drones from each marked group according to the retrieval scoring result to perform geological surveying operations, detects the real-time power of the drones and the time interval of image input timestamps in real time, analyzes the image effect of the geological surveying drones of each marked group, performs secondary detection on the detection accuracy of the drone according to the drone image effect and increases the improvement effect, records the surveying time of the geological surveying drone and divides it into multiple surveying time periods, selects the surveying image of each marked group in each surveying time period according to the image effect analysis result, thereby improving the surveying accuracy of the area to be measured. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic diagram of a geological surveying and mapping coordination operation method for a Beidou patrol drone according to the present invention.

[0031] Figure 2 This is a scoring diagram of the impact of high environmental factors in the present invention.

[0032] Figure 3 This is a screening diagram for image acquisition accuracy of each height group in the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] The present invention sets the boundary of the area to be measured and imports the drone navigation system, sets the flight altitude of multiple groups of drones, collects the flight environment data of each group of drones at different altitudes, calls the attribute data of each group of drones, selects different flight environment data combinations according to the attribute data of each group of drones, performs precision analysis on the area to be measured detected by each group of drones, screens and marks the drone flight altitude according to the precision analysis result, sets a retrieval scoring mechanism to search and score the drones, screens out geological mapping drones from each marked group according to the retrieval scoring result to perform geological mapping operations, detects the real-time power of the drones and the image input timestamp interval in real time, analyzes the image effect of the geological mapping drones of each marked group, records the mapping time of the geological mapping drones and divides it into multiple mapping time periods, selects the mapping images of each marked group in each mapping time period according to the image effect analysis result, thereby improving the mapping accuracy of the area to be measured.

[0035] Embodiment, a geological surveying and mapping coordination operation method for Beidou patrol drone, such as Figure 1 As shown, the following steps are included:

[0036] Step S1: Set the boundaries of the area to be measured, import the drone navigation system, set the flight altitudes of multiple groups of drones, and collect flight environment data of each group of drones at different altitudes;

[0037] Step S2: calling the attribute data of each group of drones, selecting different flight environment data combinations based on the attribute data of each group of drones, and performing accuracy analysis on the areas to be tested detected by each group of drones;

[0038] Step S3: Screen and mark the flight altitudes of the UAVs according to the accuracy analysis results, set a retrieval scoring mechanism, search and score the UAVs in each marked group through the retrieval scoring mechanism, and select one UAV from each marked group as the geological mapping UAV of the corresponding marked group according to the retrieval scoring results;

[0039] Step S4: Use the geological mapping drones of each marking group to perform geological mapping operations, detect the real-time power of the drone and the incoming timestamp interval of the image sensor in real time, analyze the image effect of the geological mapping drones of each marking group based on the real-time detection results, record the mapping time of the geological mapping drone and divide it into multiple mapping time periods, and select the mapping image of each marking group within each mapping time period based on the image effect analysis results.

[0040] The specific implementation is as follows:

[0041] In step S1, a satellite map or geographic information system software is used to obtain a map of the geological area to be measured. The map of the geological area includes the longitude and latitude of the boundary of the area to be measured. The longitude and latitude of the boundary are converted into universal transverse Mercator projection coordinates. The universal transverse Mercator projection coordinate formula is as follows:

[0042]

[0043] in is the scale factor, R is the radius of the earth, e is the eccentricity of the ellipsoid, is the central meridian, is the easting false offset, It is a false northing offset. is the longitude, is the latitude, is the preset standard parallel.

[0044] It should be explained that the preset standard parallels are used to correct the deviation in the latitude direction in the Mercator projection. The specific values ​​are set by professional surveyors based on the location of the actual survey area.

[0045] The Universal Transverse Mercator projection coordinates of the boundary of the area to be surveyed must be converted into a format supported by the UAV navigation system, such as the boundary of the area to be surveyed in CSV or JSON format, and imported into the UAV system. The imported converted CSV / JSON format boundary of the area to be surveyed must be configured in the UAV navigation system, and the boundary of the area to be surveyed must be associated with the UAV's flight mission to ensure that the UAV can fly in accordance with the boundary requirements during flight.

[0046] The flight altitude range is divided into multiple groups, and each group is set to a different flight altitude. The flight altitude range can be divided into three altitude groups: low, medium, and high. The flight altitude interval of each altitude group can be adjusted according to the actual situation to ensure that geographical information of different altitude layers can be obtained. For example, the flight altitudes of three groups of drones are set to 50m, 100m, 150m, etc., which will not be analyzed in detail again.

[0047] Collect flight environment data from each group of drones at different altitudes; flight environment data includes sunlight angle, airflow speed, and vegetation coverage;

[0048] Sunlight Angle Collection: The drone's sunlight sensor captures real-time sunlight angles. The sensor converts light intensity and direction information into electrical signals, which are then processed by the data processing unit to calculate the sunlight angle. Airflow sensors on the drone, including anemometers and wind direction sensors, collect airflow speed and direction information. These sensors convert airflow speed into electrical signals, which are then transmitted to the data processing unit for analysis and analysis to determine airflow height.

[0049] The multispectral cameras and lidar equipment carried on drones can capture vegetation images in different bands, measure the height and three-dimensional structure of vegetation, etc. By analyzing the reflectivity of different bands and identifying the vegetation type and coverage range, the area blocked by vegetation and the total area can be obtained. The vegetation occlusion rate is the ratio of the area blocked by vegetation to the total area.

[0050] Use multispectral cameras or lidar equipment to collect vegetation occlusion information. The multispectral camera is used to capture vegetation images in different bands, and the lidar emits laser pulses to obtain the three-dimensional structure of vegetation. The vegetation occlusion rate is calculated through image processing and analysis.

[0051] It should be noted that in the coordinated operation scenario of UAV geological mapping, the data processing unit is the core component responsible for processing, analyzing and storing the data collected by the sensors; if the UAV is equipped with a dedicated onboard computer, it has strong computing and data processing capabilities; this onboard computer can run complex algorithms and process the electrical signals from the sensors in real time.

[0052] In step S2, the drone attribute data includes the drone's mass, shading treatment area, and detection range; a dedicated drone attribute database is established, which can be a relational database MySQL; before the drone is deployed, the attribute data of each drone is entered into the database and associated with the corresponding drone number or identification; when accuracy analysis is required, the required drone attribute data is called by writing a database query statement.

[0053] The quantitative scoring formula for calculating the adaptability of drone attributes to the environment is as follows:

[0054]

[0055] in is the weight coefficient, the drone mass is m, and the maximum drone mass allowed in the drone mass task is , the hood size is , the shading area is The detection range is , the maximum nominal detection distance of similar sensors is , the quantitative score of the UAV attributes’ ability to adapt to the environment is .

[0056] It should be noted that the weight coefficients can be set according to the actual situation. In this example, the default values ​​of the weight coefficients are set to 0.4, 0.3, and 0.3 respectively, and the sum of the weight coefficients must be 1. The value is set to 20kg, Set the value to 0.5 , The value is set to 150m.

[0057] Next, call the required drone attribute data, such as drone 1, mass 9.8kg, shading area 0.25m², detection range 120m; substitute the data into the quantitative score of the drone attribute's environmental adaptability =0.586, which can be mapped to 5.86 according to the ten-point conversion system.

[0058] The impact of drone flight altitude on image accuracy varies. At low altitude, the order of environmental data impact on image accuracy is sunlight angle > vegetation occlusion rate > airflow. At mid-altitude, the order is airflow > sunlight angle > vegetation occlusion rate. At high altitude, the order is airflow > sunlight angle > vegetation occlusion rate. When calculating the impact score of drone flight altitude on image accuracy, only the most influential and second most influential environmental factors are used.

[0059] like Figure 2 As shown, the geological surveying and mapping specification document is called to obtain a comparison table of the impact of various environmental factors on the acquisition accuracy of high-altitude environmental images. The acquisition accuracy impact comparison table contains the impact scores of various environmental factors on the image acquisition accuracy of different drone flight altitudes. At different drone flight altitudes, the image acquisition accuracy impact scores corresponding to the environmental factors with the largest and second largest impact levels are called and summed to obtain the environmental impact score of the corresponding drone flight altitude. , as shown in the following table:

[0060]

[0061] The calculation method of the comprehensive score of environmental accuracy analysis at different flight altitudes is to reflect the final effect by the sum of the environmental impact score and the quantitative score of the UAV attribute's ability to adapt to the environment: Comprehensive score = Environmental Impact Score + Quantitative scoring of drone attributes on environmental adaptability , we can get the accuracy of the environmental precision at different flight altitudes.

[0062] Assume that the scoring range is defined as follows: Excellent (25): minimal environmental interference, fully adapted. Good (21-25): environmental interference is controllable, suitable for operation. Fair (16-20): flight parameters need to be optimized or the drone needs to be replaced. Poor (0-15): not recommended for operation. For example, in the above example, the quantitative score of drone 1's environmental adaptability is 5.86. Then, the comprehensive score at low altitude is 15 + 5.86 = 20.86, which means that the adaptability of this drone in low-altitude environment is fair.

[0063] In step S3, after completing the comprehensive scoring of the image acquisition accuracy analysis at different flight altitudes in step 2, the system will screen and label the drones based on the comprehensive scores. First, the system will organize the data and extract drones from the drone attribute database for different flight altitude groups, including each drone's number and comprehensive score. All candidate drones will be grouped by flight altitude to form labeled groups. For example, the low-altitude group, medium-altitude group, and high-altitude group will be labeled as Group A, Group B, and Group C, respectively.

[0064]

[0065] Sort the comprehensive scores of each altitude group classification in descending order. Select the drones with the highest comprehensive scores to enter the next round of comparison.

[0066] After selecting the drones with the highest overall scores, to ensure optimal performance at a specific altitude, the selected drones need to be refined by combining the drone attributes with the quantitative scores of their environmental adaptability. Based on the core environmental influencing factors at each altitude level, the weighting priority of the drone attributes for the quantitative scores of their environmental adaptability is dynamically adjusted. The corresponding drone attribute weighting priority table is as follows:

[0067]

[0068] The quantitative scores of the UAV attributes’ adaptability to the environment are recalculated based on the impact of the preset altitude on the changes in environmental factors, and the UAV with the highest quantitative score in each altitude group is selected as the final mapping UAV for the corresponding altitude group.

[0069] In step 4, the drones in the above steps have been selected by altitude group to form the final mapping drone. The drone's built-in battery management system (BMS) collects the remaining power in real time and uses the preset power consumption model to predict the available operating time. When the drone is fully charged, it will fly stably. The real-time battery health score formula is set as:

[0070]

[0071] It should be noted that Score the battery health score. 、 、 、 The score is an index value of the battery health score and is set by professionals according to specific circumstances. For example, if the battery , then Set the value of ,like Battery , then Set the value of ,like Battery , then The value of If the power , then The value of .

[0072] The high-precision clock chip in the BeiDou positioning system installed in the drone ensures that the image sensor's timestamp is synchronized with the BeiDou timing signal. During flight, the system records the precise timestamp of each image captured by the image sensor. The difference between the timestamps of adjacent images is calculated to obtain a time interval sequence: , calculate its average value: , the time interval variance is used to measure the degree of fluctuation of the time interval. The smaller the variance, the higher the stability. The time variance formula is:

[0073]

[0074] Where, is the time variance, is the time interval, is the average value of the time interval, and n is the total number of time intervals;

[0075] The formula for calculating the timestamp interval stability score is:

[0076]

[0077] in, The unit is seconds, Score the timestamp interval stability, 、 、 The index value for the timestamp interval stability score is set according to the range of time variance. For example, if , then The value of ,like , then The value of ,like , then The value of ; 、 、 The scoring value is set by professionals based on specific circumstances.

[0078] According to the core environmental impact factors of different altitude groups in step 3, assign basic scores to each altitude group according to actual conditions. For example, the low altitude group is easily affected by light interference, but has high resolution, so =8; The hollow group needs to balance light and airflow, so =7; The high altitude group has significant airflow and low resolution, so =6.

[0079] It should be noted that the Beidou positioning system is capable of positioning and high-precision timing functions; it sends precise time signals to the ground through satellites, and these signals contain precise time information; after the receiving device on the drone receives the signal from the Beidou satellite, it can obtain this high-precision time reference.

[0080] like Figure 3 As shown in the figure, based on the high-precision timing function of the BeiDou system, the total operation time is divided into fixed-interval timestamps, and all drones collect images within the same timestamp. The impact of real-time power consumption and timestamp intervals on image accuracy at different altitudes is quantified, and the optimal collection time is dynamically evaluated.

[0081] The real-time accuracy index is obtained by weighting the height, power, and timestamp scores. For example, when ≥8, it is a high-quality collection period, and the data in this period is recorded as a high-confidence data set; when 6≤ < 8, it is a medium collection period, and the mark needs to be verified later; when If the time is less than 6 o'clock, it is a low-quality acquisition period, and the acquisition should be suspended and the flight parameters should be adjusted. At the end of each timestamp, the system compares the real-time accuracy index of all altitude groups of drones in the same area. , select the drone image with the highest value as the timestamp image.

[0082] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0083] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0084] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0085] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0086] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for geological surveying and mapping coordination using a Beidou patrol drone, characterized in that: The following steps are included: Step S1: Set the boundaries of the area to be measured, import the drone navigation system, set the flight altitudes of multiple groups of drones, and collect flight environment data of each group of drones at different altitudes; In step S1, the flight environment data includes sunlight angle, airflow speed and vegetation coverage; the flight altitude range is divided into multiple groups, each group is set with a different flight altitude, and the flight environment data of each group of drones at different altitudes is collected; Step S2: calling each group of drone attribute data, selecting different flight environment data combinations based on each group of drone attribute data, and performing accuracy analysis on the test area detected by each group of drones; In step S2, the drone attribute data includes the drone's mass, shading area, and detection range; before the drone is deployed, each drone's attribute data is entered into a database and associated with the corresponding drone number or identifier, which is then used when scoring the drone's environmental adaptability. In step S2, when using drone attribute data to score environmental adaptability, the quantitative scoring formula is used: ， in, is the weight coefficient, the drone mass is m, and the maximum drone mass in the drone mass task is , the hood size is , the shading area is The detection range is , the maximum nominal detection distance is , the quantitative score of the UAV attributes’ ability to adapt to the environment is ; In step S2, a comparison table of the impact of each flight environment data on the acquisition accuracy of the altitude environment image is obtained. The acquisition accuracy impact comparison table contains the impact scores of each environmental factor on the image acquisition accuracy of different drone flight altitudes. The maximum and second largest flight environment data scores corresponding to different drone flight altitudes are called and summed to obtain the environmental impact score of the corresponding drone flight altitude, which is marked as ; In step S2, the comprehensive score of the environmental accuracy analysis at different flight altitudes is the sum of the environmental impact score at the corresponding flight altitude and the quantitative score of the UAV's attribute adaptability to the environment; Step S3: Screen and mark the flight altitudes of the UAVs according to the accuracy analysis results, set a retrieval scoring mechanism, search and score the UAVs in each marked group through the retrieval scoring mechanism, and select one UAV from each marked group as the geological mapping UAV of the corresponding marked group according to the retrieval scoring results; In step S3, the candidate drones in each altitude group are sorted in descending order based on their comprehensive scores, and the drones with the highest scores are selected for the next round of comparison. After the drones with the highest comprehensive scores are screened, the quantitative scores of the drone attributes on their environmental adaptability are refined and screened. The quantitative scores of the drone attributes on their environmental adaptability are recalculated based on the changes in the environmental influencing factors due to the preset altitude, and the drones with the highest quantitative scores in each altitude group are selected as the final mapping drones for the corresponding altitude group. Step S4: Use the geological mapping drones of each marking group to perform geological mapping operations, detect the real-time power of the drone and the incoming timestamp interval of the image sensor in real time, analyze the image effect of the geological mapping drones of each marking group based on the real-time detection results, record the mapping time of the geological mapping drone and divide it into multiple mapping time periods, and select the mapping image of each marking group within each mapping time period based on the image effect analysis results.

2. The method for coordinated geological surveying and mapping using a Beidou inspection drone according to claim 1, characterized in that: In step S1, the vegetation coverage range is identified by analyzing the reflectivity of different bands of vegetation images taken by the drone to identify the area blocked by vegetation and the total area, where the vegetation blocking rate is the ratio of the area blocked by vegetation to the total area.

3. The method for coordinated geological surveying and mapping using a Beidou inspection drone according to claim 1, characterized in that: In step S4, when analyzing the image effects of the geological mapping drone of each marking group based on the real-time detection results, the specific steps are as follows: Power monitoring: collects the remaining power of the drone in real time and predicts the available operating time through a preset power consumption model; Timestamp recording: During the flight, the precise timestamp of each image acquisition is recorded, and the time interval between adjacent images is obtained by subtracting the adjacent precise timestamps; Stability score: The timestamp interval stability score is calculated using the variance of the time interval; Basic score setting: According to the core environmental impact factors of different height groups, a basic score is set for each height group; Dynamically evaluate the collection timing: Based on the real-time power and timestamp scores, a weighted sum is calculated to obtain a real-time accuracy index, which is used to evaluate the optimal collection timing. Data selection: At the end of each timestamp, the real-time accuracy indexes of all drones in the same area are compared, and the image collected by the drone with the highest value is selected as the image of that timestamp.

Images