Fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones

Abstract

The present invention belongs to the field of remote sensing and discloses a fully automatic low-altitude remote sensing and non-sensing patrol monitoring system based on an unmanned aerial vehicle (UAV), comprising a route planning device, a remote control device, and a remote sensing UAV; the route planning device is used to cut a remote sensing area into multiple local areas, and is used to plan a remote sensing route for each local area based on the terrain characteristics of each local area; the remote control device is used to control the remote sensing UAV based on the remote sensing route, so that the remote sensing UAV flies along the remote sensing route; the remote sensing UAV is used to perform remote sensing at waypoints on the remote sensing route to obtain remote sensing images. The present invention cuts the remote sensing area so that the terrain in each cut area is as flat as possible, so that the route overlap rate and the side overlap rate in the planned route can adaptively change with changes in terrain characteristics, thereby ensuring remote sensing efficiency while ensuring that the remote sensing results of different local areas meet coverage requirements.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing, and in particular to a fully automatic low-altitude remote sensing and non-sensing patrol monitoring system based on an unmanned aerial vehicle (UAV). Background Art

[0002] In the prior art, when planning the route for a drone to conduct low-altitude remote sensing, the entire remote sensing area is generally determined first, and then parameters such as the route overlap rate and the lateral overlap rate are set. This setting method has certain disadvantages. Since the terrain conditions in different local areas of the remote sensing area are different, if the same shooting parameters are set for all local areas, the images captured will not meet the accuracy requirements. For example, for local area A and local area B, local area A and local area B are both part of the remote sensing area. If the terrain in local area A is relatively flat, while the terrain in local area B is relatively complex and rugged. For local area A, using a larger route overlap rate and lateral overlap rate can also obtain remote sensing results with coverage that meets the requirements. The coverage rate here refers to the ratio between the total area of ​​the blind spot in the local area and the total area of ​​the local area. The higher the coverage rate, the better. However, if the same parameters as for region A are used for route planning and then remote sensing is performed on region B, the rugged terrain of region B will result in a large number of blind spots, making it impossible to obtain a remote sensing result that meets the required coverage. This means that the remote sensing image will contain a large number of blind spots. Furthermore, if a low route overlap and lateral overlap ratio are used for the entire remote sensing area, remote sensing efficiency will inevitably be low due to the excessive number of aerial photography points.

[0003] Therefore, how to ensure the efficiency of remote sensing while making the remote sensing results of different local areas meet the coverage requirements becomes a technical problem that needs to be solved. Summary of the Invention

[0004] The purpose of the present invention is to disclose a fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones, so as to solve the problem of how to ensure the efficiency of remote sensing while making the remote sensing results of different local areas meet the coverage requirements in the process of using drones for low-altitude remote sensing.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides a fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on a UAV, comprising a route planning device, a remote control device and a remote sensing UAV;

[0007] The route planning device is used for cutting the remote sensing area into a plurality of local areas, and for planning a remote sensing route for each local area based on the terrain characteristics of each local area;

[0008] The remote control device is used to control the remote sensing UAV based on the remote sensing route, so that the remote sensing UAV flies according to the remote sensing route;

[0009] Remote sensing drones are used to perform remote sensing at waypoints on remote sensing routes and obtain remote sensing images.

[0010] Optionally, the route planning device includes an input module, an acquisition module, a partitioning module, and a route planning module;

[0011] The input module is used by remote sensing workers to input the set of longitude and latitude of the edge of the remote sensing area;

[0012] The acquisition module is used to intercept the satellite map according to the set of longitude and latitude of the edge of the remote sensing area to obtain the satellite map corresponding to the remote sensing area, and is used to intercept the contour map according to the set of longitude and latitude of the edge of the remote sensing area to obtain the contour map corresponding to the remote sensing area;

[0013] The partitioning module is used to cut the remote sensing area into multiple local areas based on the satellite map and contour map of the remote sensing area;

[0014] The route planning module is used to plan a remote sensing route for each local area based on the terrain features of each local area.

[0015] Optionally, the remote sensing area is segmented based on the satellite map and contour map of the remote sensing area, and the remote sensing area is segmented into multiple local areas, including:

[0016] S1, randomly select a point A from the edge of the remote sensing area as the coordinate origin, establish a rectangular coordinate system, and place the area of ​​the remote sensing area except point A in the first quadrant of the rectangular coordinate system;

[0017] S2, xa and xi represent the maximum and minimum values ​​of the X-axis coordinate of the remote sensing area, and ya and yi represent the maximum and minimum values ​​of the Y-axis coordinate of the remote sensing area;

[0018] S3, determining the cutting area cutarea, the value range of the X-axis coordinate of cutarea is [xi,xa]; the value range of the Y-axis coordinate in cutarea is [yi,ya];

[0019] S4, cutting the cutarea based on the satellite map and contour map of the remote sensing area, and cutting the remote sensing area into multiple local areas.

[0020] Optionally, cutarea is cut based on the satellite map and contour map of the remote sensing area, and the remote sensing area is cut into multiple local areas, including:

[0021] The first step is to cut the cutarea into N local areas with the same area, and save the obtained local areas to the next cutting set;

[0022] The second step is to cut each local area in the next cutting set into N local areas with the same area, and save the obtained local areas into the judgment set;

[0023] The third step is to calculate the cutting probability coefficient of each local area in the judgment set respectively;

[0024] The fourth step is to save the local areas whose cutting probability coefficient is less than or equal to the cutting probability coefficient threshold to the output set;

[0025] Step 5: Delete all local regions in the next cutting set to obtain the updated next cutting set;

[0026] Step 6: determine whether the number of local areas whose cutting probability coefficient is greater than the cutting probability coefficient threshold is greater than or equal to 1. If so, proceed to step 7; if not, proceed to step 8.

[0027] Step 7: Save the local area whose cutting probability coefficient is greater than the cutting probability coefficient threshold to the next cutting set and enter the second step;

[0028] In the eighth step, further calculation is performed on the local area in the output set to obtain the final local area.

[0029] Optionally, the calculation formula for the cutting probability coefficient is:

[0030]

[0031] cutpro a Represents the cutting probability coefficient of the local area a, numctr a Indicates the number of contour lines of different heights in the contour map corresponding to the local area a, numctr std Indicates the number of presets, numedge a Represents the grayscale image imggray corresponding to the satellite map of local area a a The number of edge pixels in, numpixe a Indicates imggray a The total number of pixels in grayvar a Indicates imggray a The information amount parameter of the pixel in, maxgray means imggray a The maximum gray value of the pixel point in areasub aIt represents the area of ​​local area a in the rectangular coordinate system, rmtare represents the area of ​​remote sensing area in the rectangular coordinate system, weight1, weight2, weight3, and weight4 represent the weight of the number of contour lines, the weight of the number of edge pixels, the weight of the information parameter, and the weight of the area, respectively;

[0032] grayvar a The calculation formula is:

[0033]

[0034] pixeu a for imggray a The collection of pixels in pixgray i is the grayscale value of pixel i.

[0035] Optionally, the process of obtaining the number of contour lines of different heights in the contour map corresponding to the local area a includes:

[0036] Determine whether there is an overlapping area between the local area a and the remote sensing area. If so, perform the following calculation:

[0037] Get the latitude and longitude set of the edge points of the overlapping area latltd a ;

[0038] In the contour map, according to latltd a Get the corresponding area ctr a ;

[0039] Get ctr a The number of contour lines of different heights in the ;

[0040] If not, the number of contour lines of different heights in the contour map corresponding to the local area a is set to 0.

[0041] Optionally, the grayscale image imggray corresponding to the satellite map of the local area a a The acquisition process includes:

[0042] Determine whether there is an overlapping area between the local area a and the remote sensing area. If so, perform the following calculation:

[0043] Get the latitude and longitude set of the edge points of the overlapping area latltd a ;

[0044] In the satellite map, according to latltd a Get the corresponding area satmap a ;

[0045] satmap a Perform grayscale processing to obtain a grayscale image imggray a .

[0046] Optionally, further calculations are performed on the local regions in the output set to obtain the final local regions, including:

[0047] Obtain the overlapping area between each local area and the remote sensing area respectively;

[0048] All overlapping areas are taken as the final local areas.

[0049] Optionally, the terrain features of the local area include the number of contour lines of different heights in a contour map corresponding to the local area and the area variance of regions in different height ranges.

[0050] Optionally, the remote sensing image includes at least one of a visible light remote sensing image, a panchromatic remote sensing image, a multispectral remote sensing image, an infrared remote sensing image, a Lidar remote sensing image and a synthetic aperture radar remote sensing image.

[0051] Compared with the existing technology, the present invention cuts the remote sensing area in the process of route planning for the remote sensing area so that the terrain in each cut area is as flat as possible, so that corresponding route planning can be carried out according to the terrain characteristics of each cut area respectively, so that in the planned route, the route overlap rate and the lateral overlap rate can adaptively change with the change of terrain characteristics, thereby ensuring the efficiency of remote sensing while ensuring that the remote sensing results of different local areas can meet the coverage requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 The figure is a schematic diagram of the fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on UAV of the present invention.

[0054] Figure 2 This is another schematic diagram of the fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on a drone of the present invention. DETAILED DESCRIPTION

[0055] 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 any creative efforts are within the scope of protection of the present invention.

[0056] like Figure 1 In one embodiment shown, the present invention provides a fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on a UAV, including a route planning device, a remote control device and a remote sensing UAV.

[0057] In one embodiment, the route planning device is used to divide the remote sensing area into multiple local areas, and to plan a remote sensing route for each local area based on the terrain features of each local area.

[0058] Specifically, by dividing the remote sensing area into multiple local areas with smaller terrain changes, the route overlap rate and lateral overlap rate can be adaptively calculated in the subsequent remote sensing route planning process. Generally, the flatter the area, the higher the set route overlap rate and lateral overlap rate, and the fewer waypoints in the remote sensing route, thereby effectively improving the efficiency of remote sensing.

[0059] A waypoint is a point on a remote sensing route where you stop to take photos.

[0060] Furthermore, the route planning device includes an input module, an acquisition module, a partitioning module, and a route planning module;

[0061] The input module is used by remote sensing workers to input the set of longitude and latitude of the edge of the remote sensing area;

[0062] The acquisition module is used to intercept the satellite map according to the set of longitude and latitude of the edge of the remote sensing area to obtain the satellite map corresponding to the remote sensing area, and is used to intercept the contour map according to the set of longitude and latitude of the edge of the remote sensing area to obtain the contour map corresponding to the remote sensing area;

[0063] The partitioning module is used to cut the remote sensing area into multiple local areas based on the satellite map and contour map of the remote sensing area;

[0064] The route planning module is used to plan a remote sensing route for each local area based on the terrain features of each local area.

[0065] Specifically, the remote sensing area needs to be specified manually in advance. This can be done by selecting multiple points on the edge of the remote sensing area and forming a set with the longitude and latitude of these points. Then, when the remote sensing area needs to be analyzed, it is only necessary to connect the points corresponding to the longitude and latitude in the set to obtain the corresponding remote sensing area.

[0066] Furthermore, the remote sensing area is segmented based on the satellite map and contour map of the remote sensing area, and the remote sensing area is segmented into multiple local areas, including:

[0067] S1, randomly select a point A from the edge of the remote sensing area as the coordinate origin, establish a rectangular coordinate system, and place the area of ​​the remote sensing area except point A in the first quadrant of the rectangular coordinate system;

[0068] S2, xa and xi represent the maximum and minimum values ​​of the X-axis coordinate of the remote sensing area, and ya and yi represent the maximum and minimum values ​​of the Y-axis coordinate of the remote sensing area;

[0069] S3, determining the cutting area cutarea, the value range of the X-axis coordinate of cutarea is [xi,xa]; the value range of the Y-axis coordinate in cutarea is [yi,ya];

[0070] S4, cutting the cutarea based on the satellite map and contour map of the remote sensing area, and cutting the remote sensing area into multiple local areas.

[0071] Specifically, in this step, the main task is to realize the coordinateization of the remote sensing area. By placing the remote sensing area in a rectangular coordinate system, various graphic calculations can be easily performed on the remote sensing area, such as cutting.

[0072] Furthermore, the cutarea is cut based on the satellite map and contour map of the remote sensing area, and the remote sensing area is cut into multiple local areas, including:

[0073] The first step is to cut the cutarea into N local areas with the same area, and save the obtained local areas to the next cutting set;

[0074] The second step is to cut each local area in the next cutting set into N local areas with the same area, and save the obtained local areas into the judgment set;

[0075] The third step is to calculate the cutting probability coefficient of each local area in the judgment set;

[0076] The fourth step is to save the local areas whose cutting probability coefficient is less than or equal to the cutting probability coefficient threshold to the output set;

[0077] Step 5: Delete all local regions in the next cutting set to obtain the updated next cutting set;

[0078] Step 6: determine whether the number of local areas whose cutting probability coefficient is greater than the cutting probability coefficient threshold is greater than or equal to 1. If so, proceed to step 7; if not, proceed to step 8.

[0079] Step 7: Save the local area whose cutting probability coefficient is greater than the cutting probability coefficient threshold to the next cutting set and enter the second step;

[0080] In the eighth step, further calculation is performed on the local area in the output set to obtain the final local area.

[0081] Specifically, in the above-mentioned cutting process, by continuously calculating the cutting probability coefficient of the local area obtained by cutting, the terrain changes in the local area are made smoother, so that after the route planning of the local area is carried out using a single route overlap rate and lateral overlap rate, the remote sensing image obtained can also meet the coverage requirements, thereby ensuring the accuracy of remote sensing.

[0082] Furthermore, the value of N is 4.

[0083] Further, the cutting probability coefficient threshold may be one quarter of the limit of the cutting probability coefficient.

[0084] Furthermore, the calculation formula of the cutting probability coefficient is:

[0085]

[0086] cutpro a Represents the cutting probability coefficient of the local area a, numctr a Indicates the number of contour lines of different heights in the contour map corresponding to the local area a, numctr std Indicates the number of presets, numedge a Represents the grayscale image imggray corresponding to the satellite map of local area a a The number of edge pixels in, numpixe a Indicates imggray a The total number of pixels in grayvar a Indicates imggray a The information amount parameter of the pixel in, maxgray means imggray a The maximum gray value of the pixel point in areasub aIt represents the area of ​​local area a in the rectangular coordinate system, rmtare represents the area of ​​remote sensing area in the rectangular coordinate system, weight1, weight2, weight3, and weight4 represent the weight of the number of contour lines, the weight of the number of edge pixels, the weight of the information parameter, and the weight of the area, respectively;

[0087] grayvar a The calculation formula is:

[0088]

[0089] pixeu a for imggray a The collection of pixels in pixgray i is the grayscale value of pixel i.

[0090] Specifically, the cutting probability coefficient comprehensively represents the local area from multiple different aspects, including the number of contour lines, the number of edge pixels, information parameter and area, so that the cutting probability coefficient can more accurately represent the terrain characteristics of the local area.

[0091] When the number of contour lines is greater, the number of edge pixels is greater, the information parameter is greater, and the area is larger, the terrain of the local area is more uneven and diverse. At this time, the cutting probability coefficient will be greater than the cutting probability coefficient threshold set in advance, so that the local area enters the next cutting process, so that the purpose of the present invention of obtaining a local area with a gentle terrain change is achieved;

[0092] The fewer the number of contour lines, the fewer the number of edge pixels, the smaller the information parameter, and the smaller the area, the flatter the terrain of the local area is, and there is no need to enter the next cutting process.

[0093] Therefore, the cutting probability coefficient of the present invention can make the terrain changes in the local area obtained by the present invention sufficiently smooth, and since the area parameter is set, it can also avoid obtaining a local area that is too small, which requires planning too many different remote sensing routes and affects the efficiency of remote sensing.

[0094] In a local area, since the amount of information is calculated based on the grayscale image of the satellite image, the greater the difference in the grayscale values ​​of the pixels in the local area, that is, the greater the amount of information, the more uneven the terrain in the area.

[0095] Furthermore, the values ​​of the contour line quantity weight, edge pixel quantity weight, information parameter weight, and area weight can be 0.2, 0.3, 0.3, and 0.2, respectively.

[0096] Furthermore, the preset number is 50. The preset number can be set according to the area of ​​the remote sensing range and the interval of the contour lines. The larger the area of ​​the remote sensing range and the smaller the interval of the contour lines, the larger the preset number.

[0097] Furthermore, the process of obtaining the number of contour lines of different heights in the contour map corresponding to the local area a includes:

[0098] Determine whether there is an overlapping area between the local area a and the remote sensing area. If so, perform the following calculation:

[0099] Get the latitude and longitude set of the edge points of the overlapping area latltd a ;

[0100] In the contour map, according to latltd a Get the corresponding area ctr a ;

[0101] Get ctr a The number of contour lines of different heights in the ;

[0102] If not, the number of contour lines of different heights in the contour map corresponding to the local area a is set to 0.

[0103] Specifically, in the contour map, mark the set latltd a The points corresponding to the longitude and latitude in the , and then connect these points, the area enclosed is ctr a .

[0104] Specifically, the intervals between the contour lines may be 100 meters, 200 meters, etc. The specific intervals may be set according to the area of ​​the remote sensing region. The larger the area, the larger the intervals.

[0105] Furthermore, the grayscale image imggray corresponding to the satellite map of the local area a a The acquisition process includes:

[0106] Determine whether there is an overlapping area between the local area a and the remote sensing area. If so, perform the following calculation:

[0107] Get the latitude and longitude set of the edge points of the overlapping area latltd a ;

[0108] In the satellite map, according to latltd a Get the corresponding area satmap a ;

[0109] satmap a Perform grayscale processing to obtain a grayscale image imggraya .

[0110] Specifically, on the satellite map, mark the collection latltd a The points corresponding to the longitude and latitude in the image are then connected, and the area enclosed is the satmap. a .

[0111] Furthermore, in the present invention, the satellite image is a visible light image.

[0112] Optionally, further calculations are performed on the local regions in the output set to obtain the final local regions, including:

[0113] Obtain the overlapping area between each local area and the remote sensing area respectively;

[0114] All overlapping areas are taken as the final local areas.

[0115] Specifically, since cutarea includes a portion that does not belong to the remote sensing area, the present invention needs to perform further calculations on the local area and only retain the portion of the local area that belongs to the remote sensing area.

[0116] Furthermore, the terrain characteristics of the local area include the number of contour lines of different heights in the contour map corresponding to the local area and the area variance of regions in different height ranges.

[0117] Furthermore, a remote sensing route is planned for each local area based on the terrain characteristics of each local area, including:

[0118] For the local area b, use numctr b Indicates the number of contour lines of different heights in the contour map corresponding to the local area b, using areavar b Represents the area variance of regions with different height ranges in the contour map corresponding to the local area b;

[0119] areavar b The calculation formula is:

[0120]

[0121] numhei represents the number of regions with different height ranges in the contour map corresponding to the local area b, and areaahei represents the set of all height ranges in the contour map corresponding to the local area b. j represents the area of ​​the height range j in the contour map corresponding to the local area b, and avearea represents the mean area of ​​all height ranges in areaahei;

[0122] The following formula is used to calculate the route overlap rate when remote sensing is performed on a local area b:

[0123]

[0124] rutovl b Indicates the route overlap rate when remote sensing is performed on a local area b, rutovl std Indicates the preset route overlap rate, areavar max Indicates the maximum value of the area variance of regions with different height ranges in all local areas, α represents the first weight, and δ represents the second weight;

[0125] The following formula is used to calculate the side overlap rate when remote sensing is performed on a local area b:

[0126]

[0127] latovl b Indicates the lateral overlap rate when remote sensing is performed on a local area b, latovl std Indicates the preset lateral overlap ratio;

[0128] The route overlap rate and the side overlap rate are input into the route planning software to obtain the remote sensing route of the local area b.

[0129] Specifically, when performing route planning, the present invention can adaptively change the route overlap rate and the lateral overlap rate as the number of contour lines at different heights and the area variance of regions with different height ranges change, thereby enabling the use of larger route overlap rates and lateral overlap rates in areas with flat terrain to improve remote sensing efficiency while ensuring coverage. In areas with complex terrain, smaller route overlap rates and lateral overlap rates are used to ensure coverage.

[0130] In a local area, if the number of contour lines at different heights is greater, it means that the height changes in the local area are more complex and less flat. In addition, the present invention also characterizes whether the area is flat based on the variance of the areas of regions in different height ranges. Specifically, if the variance of the areas of regions in different height ranges is greater, it means that the area difference between regions in different height ranges in the area is greater, and the probability of complex terrain occurring is greater.

[0131] Therefore, when remote sensing photography is performed on all local areas, the present invention can take into account both remote sensing efficiency and coverage requirements.

[0132] The following is a specific example to illustrate the areas with different height ranges:

[0133] Assume that local area b contains contour lines at four heights: 150, 200, 250, and 300. Then the number of regions with different height ranges is 5, and the height ranges are less than or equal to 150, greater than 150 and less than or equal to 200, greater than 200 and less than or equal to 250, greater than 250 and less than or equal to 300, and greater than 300. In the contour map corresponding to the local area, the areas of the regions with different height ranges can be obtained by calculating the areas enclosed by the contour lines and the areas enclosed by the contour lines and the edge of the map.

[0134] Furthermore, the preset route overlap rate is 30%, and the preset lateral overlap rate is 30%.

[0135] Specifically, the range of values ​​for both the route overlap ratio and the lateral overlap ratio is [10%, 90%.] If the calculated route overlap ratio exceeds the range, the endpoint of the range is used as the final route overlap ratio. If the calculated lateral overlap ratio exceeds the range, the endpoint of the range is used as the final lateral overlap ratio.

[0136] Specifically, if the calculated route overlap rate is 95%, the final route overlap rate is 90%, and if the calculated route overlap rate is 5%, the final route overlap rate is 10%.

[0137] Specifically, if the calculated lateral overlap ratio is 95%, the final lateral overlap ratio is 90%, and if the calculated lateral overlap ratio is 5%, the final lateral overlap ratio is 10%.

[0138] Specifically, when using route planning software, in addition to the route overlap ratio and lateral overlap ratio, parameters such as the gimbal's pitch angle and shooting height must also be set. This is prior art and will not be further described in this disclosure. For example, DJI Maps can be used for route planning.

[0139] Furthermore, the values ​​of the first weight and the second weight are 0.6 and 0.4 respectively.

[0140] Furthermore, the remote sensing image includes at least one of a visible light remote sensing image, a panchromatic remote sensing image, a multispectral remote sensing image, an infrared remote sensing image, a Lidar remote sensing image, and a synthetic aperture radar remote sensing image.

[0141] In one embodiment, the remote control device is used to control the remote sensing UAV based on the remote sensing route, so that the remote sensing UAV flies according to the remote sensing route.

[0142] The remote control device can control the remote sensing drone to stay at each waypoint for a preset time, such as 5 seconds. During the stay period, the remote sensing drone can perform remote sensing below.

[0143] In one embodiment, the remote sensing drone is used to perform remote sensing at waypoints on a remote sensing route to obtain remote sensing images.

[0144] Remote sensing drones carry lenses for remote sensing, and a gimbal is used to connect the lens to the drone body, thereby improving anti-shake performance.

[0145] In one embodiment, Figure 2 As shown, it also includes a remote sensing image analysis module;

[0146] The remote sensing image analysis module is used to identify remote sensing images and obtain recognition results of remote sensing images, thereby realizing inspection of remote sensing areas.

[0147] Specifically, the recognition of remote sensing images includes area calculation of the region of interest, type recognition of the region of interest, etc.

[0148] For example, in the field of forestry remote sensing, the region of interest is the area where trees are located. In urban remote sensing, the region of interest can be buildings, and the type identification of the region of interest is the identification of the type of building.

[0149] In the process of route planning for the remote sensing area, the remote sensing area is cut so that the terrain in each cut area is as flat as possible, so that corresponding route planning can be carried out according to the terrain characteristics of each cut area. In the planned route, the route overlap rate and the lateral overlap rate can change adaptively with the changes in terrain characteristics, thereby ensuring the efficiency of remote sensing while ensuring that the remote sensing results of different local areas can meet the coverage requirements.

[0150] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0151] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0152] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0153] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope parameters of the technical solutions of the various embodiments of the present invention.

Claims

1. The fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones is characterized by: including route planning devices, remote control devices and remote sensing drones; The route planning device is used to divide the remote sensing area into multiple local areas, and to plan a remote sensing route for each local area based on the terrain characteristics of each local area, including: Determine the cutting area ; Satellite map and contour map based on remote sensing area Cut the remote sensing area into multiple local areas, including: The first step is to Cut into N local regions with the same area, and save the obtained local regions to the next cutting set; The second step is to cut each local area in the next cutting set into N local areas with the same area, and save the obtained local areas into the judgment set; The third step is to calculate the cutting probability coefficient of each local area in the judgment set respectively; The fourth step is to save the local areas whose cutting probability coefficient is less than or equal to the cutting probability coefficient threshold to the output set; Step 5: Delete all local regions in the next cutting set to obtain the updated next cutting set; Step 6: determine whether the number of local areas whose cutting probability coefficient is greater than the cutting probability coefficient threshold is greater than or equal to 1. If so, proceed to step 7; if not, proceed to step 8. Step 7: Save the local area whose cutting probability coefficient is greater than the cutting probability coefficient threshold to the next cutting set and enter the second step; Step 8: Further calculate the local area in the output set to obtain the final local area; The calculation formula of the cutting probability coefficient is: Represents a local area The cutting probability coefficient, Represents a local area The number of contour lines of different heights in the corresponding contour map, Indicates the number of presets, Represents a local area Grayscale image corresponding to the satellite map The number of edge pixels in express The total number of pixels in express The information amount parameter of the pixel in express The maximum gray value of the pixel in Represents a local area The area of ​​​​in the rectangular coordinate system, Represents the area of ​​the remote sensing region in the rectangular coordinate system, 、 、 、 They represent the weight of the number of contour lines, the weight of the number of edge pixels, the weight of the information parameter, and the area weight respectively; The calculation formula is: for The set of pixels in is the grayscale value of pixel i; The remote control device is used to control the remote sensing UAV based on the remote sensing route, so that the remote sensing UAV flies according to the remote sensing route; Remote sensing drones are used to perform remote sensing at waypoints on remote sensing routes and obtain remote sensing images.

2. The fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones according to claim 1 is characterized in that: The route planning device includes an input module, an acquisition module, a partitioning module and a route planning module; The input module is used by remote sensing workers to input the set of longitude and latitude of the edge of the remote sensing area; The acquisition module is used to intercept the satellite map according to the set of longitude and latitude of the edge of the remote sensing area to obtain the satellite map corresponding to the remote sensing area, and is used to intercept the contour map according to the set of longitude and latitude of the edge of the remote sensing area to obtain the contour map corresponding to the remote sensing area; The partitioning module is used to cut the remote sensing area into multiple local areas based on the satellite map and contour map of the remote sensing area; The route planning module is used to plan a remote sensing route for each local area based on the terrain features of each local area.

3. The fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones according to claim 2 is characterized in that: The remote sensing area is cut based on the satellite map and contour map of the remote sensing area, and the remote sensing area is cut into multiple local areas, including: S1, randomly select a point A from the edge of the remote sensing area as the coordinate origin, establish a rectangular coordinate system, and place the area of ​​the remote sensing area except point A in the first quadrant of the rectangular coordinate system; S2, respectively and Indicates the maximum and minimum values ​​of the X-axis coordinates of the remote sensing area, respectively. and Indicates the maximum and minimum values ​​of the Y-axis coordinate of the remote sensing area; S3, determine the cutting area , The value range of the X-axis coordinate is ; The value range of the Y-axis coordinate in is ; S4, satellite map and contour map based on remote sensing area Cutting is performed to divide the remote sensing area into multiple local areas.

4. The fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones according to claim 1 is characterized in that: local area The process of obtaining the number of contour lines at different heights in the corresponding contour map includes: Determine local area Is there any overlapping area with the remote sensing area? If so, perform the following calculation: Get the set of latitude and longitude points of the edge of the overlapping area ; In a contour map, according to Get the corresponding area ; Get The number of contour lines of different heights in the ; If not, the local area The number of contour lines of different heights in the corresponding contour map is set to 0.

5. The fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones according to claim 1 is characterized in that: local area Grayscale image corresponding to the satellite map The acquisition process includes: Determine local area Is there any overlapping area with the remote sensing area? If so, perform the following calculation: Get the set of latitude and longitude points of the edge of the overlapping area ; In the satellite map, according to Get the corresponding area ; right Perform grayscale processing to obtain a grayscale image .

6. The fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones according to claim 1 is characterized in that: Further calculations are performed on the local regions in the output set to obtain the final local regions, including: Obtain the overlapping area between each local area and the remote sensing area respectively; All overlapping areas are taken as the final local areas.

7. The fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones according to claim 2 is characterized in that: The terrain characteristics of the local area include the number of contour lines of different heights in the contour map corresponding to the local area and the area variance of regions with different height ranges.

8. The fully automatic low-altitude remote sensing and non-sensing inspection and monitoring system based on drones according to claim 2 is characterized in that: The remote sensing image includes at least one of a visible light remote sensing image, a panchromatic remote sensing image, a multispectral remote sensing image, an infrared remote sensing image, a Lidar remote sensing image, and a synthetic aperture radar remote sensing image.

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