A UAV aerial photography method and configuration system based on river management

Abstract

The present invention relates to the technical field of river channel management, and specifically to a drone aerial photography method and configuration system based on river channel management, comprising: based on river channel image data collected by a drone aerial photography configuration system based on river channel management, extracting drone reflection area image data in each group of river channel image data, identifying the integrity of each group of drone reflection area image data, segmenting each group of drone reflection area image data based on the drone reflection area image data integrity identification result, and extracting the area where the drone reflection is located in the drone reflection area image data. The present invention collects river channel image data to make real-time judgment on the pollution degree of the river channel. In the river channel image data collection stage, it is configured with specific logic to make targeted collection of feature areas with a higher probability of pollution in the river channel, provide the necessary refinement processing for the river channel pollution degree judgment result, and finally realize the river channel pollution degree judgment and the sniffing of the polluted area.

Description

Technical Field

[0001] The present invention relates to the technical field of river management, and in particular to an unmanned aerial vehicle (UAV) aerial photography method and a configuration system based on river management. Background Art

[0002] River management is an important environmental protection work, its purpose is to protect and improve the river ecosystem and ensure people's life and production safety;

[0003] River pollution in urban areas usually comes from garbage or sewage discharged by people in their daily lives. When a large amount of garbage accumulates in the river, the self-purification ability of the river can no longer "metabolize" the garbage in the river, and the water quality of the river will gradually decline, thereby affecting the ecological environment around the river.

[0004] To this end, river managers usually use civilized persuasion and manual salvage to reduce garbage in the river, so as to maintain the self-purification ability of the river and bring protection effects to the ecological environment of the river and its surrounding areas. However, this method is less targeted and efficient. For the process of salvaging garbage from the river, the entire river needs to be observed to determine the area where garbage salvage is required, and it is impossible to quickly "treat" the polluted river. Summary of the invention

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention provides a UAV aerial photography method and configuration system based on river management, which solves the technical problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] In the first aspect, a drone aerial photography method based on river management includes:

[0008] Based on the river channel image data collected by a drone aerial photography configuration system based on river channel management, the drone reflection area image data in each group of river channel image data is extracted, the integrity of each group of drone reflection area image data is identified, and based on the drone reflection area image data integrity identification result, each group of drone reflection area image data is segmented, and the area where the drone reflection is located in the drone reflection area image data is extracted, and the image data of each group of drone reflection areas is reorganized based on the corresponding image of the area where the drone reflection is located;

[0009] Set up the river pollution degree analysis logic, and use the reorganized drone reflection area image data combined with the river pollution degree analysis logic to determine the river pollution degree;

[0010] The river pollution degree determination logic is expressed as follows:

[0011] ;

[0012] Where: is the image complexity; is the total amount of image data of the drone reflection area; , are the rows and columns of the image; Pixels in the image The gradient value at ; is the average gradient of the image data of the qth group of drone reflection areas, is the information volume of the image data of the q groups of drone reflection areas;

[0013] Among them, the image complexity The larger the value, the more serious the river pollution is; conversely, the smaller the value, the lighter the river pollution is.

[0014] Obtain the execution results of the river pollution degree determination logic, and sniff the polluted river section in the river based on the execution results.

[0015] Furthermore, the integrity recognition logic of the image data of the drone reflection area is expressed as:

[0016] The UAV has a honeycomb-shaped evenly installed ranging modules on one side of the river surface. The ranging modules measure the distance from themselves to the river surface. The integrity recognition result of the image data of the UAV reflection area is:

[0017] Traverse the ranging results of each group of ranging modules, take the group of ranging results with the largest number of identical results as qualified ranging results, obtain unqualified ranging results from the ranging results, and determine the drone reflection missing area in the drone reflection area image data based on the unqualified ranging results;

[0018] Among them, when the drone flies over the river to collect river image data, it is always level with the river surface.

[0019] Furthermore, the extraction logic of the area where the drone reflection is located in the drone reflection area image data is expressed as:

[0020] ;

[0021] Where: The area where the drone is reflected; It is the image data of the drone reflection area; It is the area where the drone reflection is missing in the drone reflection area image data; A collection of ranging modules that are sources of unqualified ranging results; The area in the image data of the reflection area of ​​the drone is a closed figure formed by the adjacent ranging modules of the i-th group of ranging modules;

[0022] Among them, when the ranging module that is the source of the unqualified ranging result is the edge ranging module installed on the drone, the adjacent ranging modules form a closed figure, in which: the adjacent ranging modules are connected into a line segment, and the image data of the drone reflection area is separated and divided based on the position of each group of ranging modules, and the image data of the sub-drone reflection area where the ranging modules are connected into a line segment is obtained in several groups of regular hexagonal sub-drone reflection area image data. The image data is replaced by the sub-drone reflection area image data where the ranging modules are connected into a line segment.

[0023] Furthermore, in the stage of reorganizing the image data of the drone reflection area, the areas where the reflections of each group of drones are located are traversed, and the areas where the reflections of each group of drones are located are recorded as: , When reconstructing the image data of the drone reflection area, The area where the drone reflection is missing The scope is The image block is obtained by segmenting the corresponding position in the image block and filling it with the obtained image block The area where the drone reflection is missing , The area where the drone reflection is missing and The area where the drone reflection is missing When there is overlap, Image block filling obtained by mid-segmentation The area where the drone reflection is missing , The area where the drone reflection is missing and The area where the drone reflection is missing When there is overlap, Image block filling obtained by mid-segmentation The area where the drone reflection is missing , and so on, The image data of the drone's reflection area are reassembled and restored.

[0024] Furthermore, the information amount of the image data of the drone reflection area is obtained by the following formula:

[0025] D = <m> lim < / m> log [ − ∑ y = 1 N ( r ) p y log p y ] log r ;

[0026] Where: is the number of partial areas into which the image data of the drone reflection area is divided based on the image resolution r; is the proportion of the yth part at resolution r;

[0027] in, The larger the value, the more information there is in the image data of the drone reflection area. Conversely, the smaller the value, the less information there is in the image data of the drone reflection area.

[0028] Furthermore, the setting of the river pollution degree analysis logic is to collect the image data of the drone reflection area. Settings;

[0029] All reconstructed image data of the drone reflection area are recorded as: ,and The sorting of the image data of the reflection area of ​​the drone is based on the shooting order of the river image data. When the river pollution degree judgment logic is executed for the first time, As , calculate the complexity of the image , the second execution is As , calculate the complexity of the image , three times when executed As , calculate the complexity of the image , and so on, so that all the image data of the drone reflection area are included in the image complexity Calculation.

[0030] Furthermore, the operation of obtaining the river pollution degree determination logic execution result, that is, obtaining the complexity of each group of images Operation of calculation results, complexity of each group of images After the calculation results are obtained, the image complexity Source: drone reflection area image data mark as the horizontal axis, with image complexity The value of is used as the vertical axis to plot the complexity of each group of images The line graph of the value captures the complexity of the graph where all the endpoints of the rising trend line segments are located in the line graph. , the complexity of each group of images captured The image data of the reflection area of ​​the source drone corresponds to the river section, that is, the sniffing result of the polluted river section in the river.

[0031] In the second aspect, a UAV aerial photography configuration system based on river management includes: a control terminal, an analysis layer and a configuration layer;

[0032] The analysis layer includes a collection module and a division module. The collection module is used to collect the location information of buildings adjacent to the river channel. The division module is used to receive the location information of buildings around the river channel collected by the collection module, and divide the river channel based on the location information of buildings around the river channel.

[0033] The configuration layer includes a logic module and a drone module. The logic module is used to set the aerial photography logic. The drone module is used to receive the aerial photography logic set in the logic module and execute the aerial photography task of river channel image data based on the aerial photography logic.

[0034] Among them, each sub-river channel segment obtained by the division module is recorded as an aerial photography river channel segment and a non-aerial photography river channel segment. When the division module divides the river channel, when the interval between two adjacent groups of buildings on both sides of the river channel is greater than thirty meters, the two adjacent groups of buildings are determined as division points. A group of division points on one side of the river channel and adjacent division points on the other side of the river channel are connected to form a line segment recorded as a division line. The river channel is divided based on each group of division lines. The river channel segments without adjacent buildings in each group of sub-rivers obtained by division are recorded as non-aerial photography river channel segments. Otherwise, they are recorded as aerial photography river channel segments. The aerial photography logic set in the logic module includes: the shooting position of the aerial photography river channel segment river image data, the number of aerial photography river channel segment river image data shooting, and the shooting height and angle of the river channel image data during aerial photography.

[0035] Furthermore, the shooting height and angle of the river channel image data in the aerial photography logic set by the logic module are manually set by the system end user, and the number of river channel image data shot by the aerial photography of the river channel section is subject to:

[0036] ;

[0037] Where: is the number of river channel image data taken for the aerial photography of river channel section h; is the number of buildings adjacent to the aerial photographed river section h; The number of river channel image data taken; is the current year; The completion year of the earliest building among the buildings adjacent to the aerial photographed river section h;

[0038] in, The value is rounded up, the base number of river channel image data shooting The value of follows: 10 ≥ ≥1, the greater the flow of people in the area adjacent to the buildings in the aerial river section, the greater the number of river image data shooting The larger the value, the smaller the value. After the number of river channel image data shooting of the aerial photographed river section is obtained, the same number of evenly distributed position coordinates are selected in the corresponding river section as the river channel image data shooting positions of the aerial photographed river section, and the area where the river channel image data shooting positions of each group of aerial photographed river section are located shall not be less than four-fifths of the area of ​​the aerial photographed river section.

[0039] Furthermore, the acquisition module is interactively connected to the division module via a wireless network, the division module is interactively connected to the logic module via a wireless network, and the logic module is interactively connected to the drone module via a wireless network;

[0040] The drone module feeds back the current location information to the control terminal in real time based on the wireless network, and the system end user reads the real-time location information of the drone in the control terminal.

[0041] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0042] The present invention provides a drone aerial photography method based on river management. During the execution of the method, the river pollution degree is determined in real time by collecting river image data. In the river image data collection stage, a specific logic is configured to enable targeted collection of characteristic areas with a higher probability of pollution in the river, thereby providing necessary refinement processing for the river pollution degree determination result. In the process of analyzing the river image data to determine the river pollution degree, the drone reflection area image is extracted from the river image data, and the drone reflection area image is segmented and reorganized, so that the drone reflection area image can fully display the river pollution situation, thereby enabling the method to accurately make the final river pollution degree determination and sniff the pollution area. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 It is a flow chart of a UAV aerial photography method based on river management;

[0045] Figure 2 This is a structural diagram of a drone aerial photography configuration system based on river management;

[0046] Figure 3 This is a schematic diagram of the structure of the drone module in the present invention;

[0047] Figure 4 This is a logical diagram of the reorganization of the image data of the reflection area of ​​the drone in the present invention;

[0048] Figure 5 In the present invention, the complexity of each group of images is represented by Line chart of values;

[0049] The numbers in the figure represent: 1. UAV body; 2. Camera; 3. Distance measurement module. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0051] The present invention will be further described below in conjunction with the embodiments.

[0052] Embodiment 1:

[0053] A drone aerial photography method based on river management in this embodiment, such as Figure 1 As shown, including:

[0054] Based on the river channel image data collected by a drone aerial photography configuration system based on river channel management, the drone reflection area image data in each group of river channel image data is extracted, the integrity of each group of drone reflection area image data is identified, and based on the drone reflection area image data integrity identification result, each group of drone reflection area image data is segmented, and the area where the drone reflection is located in the drone reflection area image data is extracted, and the image data of each group of drone reflection areas is reorganized based on the corresponding image of the area where the drone reflection is located;

[0055] Set up the river pollution degree analysis logic, and use the reorganized drone reflection area image data combined with the river pollution degree analysis logic to determine the river pollution degree;

[0056] The logic for determining the degree of river pollution is expressed as;

[0057] ;

[0058] Where: is the image complexity; is the total amount of image data of the drone reflection area; , are the rows and columns of the image; Pixels in the image The gradient value at ; is the average gradient of the image data of the qth group of drone reflection areas, is the information volume of the image data of the q groups of drone reflection areas;

[0059] Among them, the image complexity The larger the value, the more serious the river pollution is; conversely, the smaller the value, the lighter the river pollution is.

[0060] Obtain the execution result of the river pollution degree determination logic, and sniff the polluted river section in the river based on the execution result;

[0061] The integrity recognition logic of the drone reflection area image data is expressed as:

[0062] The UAV has a honeycomb-shaped evenly installed ranging modules on one side of the river surface. The ranging modules measure the distance from themselves to the river surface. The integrity recognition result of the image data of the UAV reflection area is:

[0063] Traverse the ranging results of each group of ranging modules, take the group of ranging results with the largest number of identical results as qualified ranging results, obtain unqualified ranging results from the ranging results, and determine the drone reflection missing area in the drone reflection area image data based on the unqualified ranging results;

[0064] Among them, when the UAV flies over the river to collect river image data, it is always horizontal to the river surface;

[0065] The extraction logic of the drone reflection area in the drone reflection area image data is expressed as:

[0066] ;

[0067] Where: The area where the drone is reflected; It is the image data of the drone reflection area; It is the area where the drone reflection is missing in the drone reflection area image data; A collection of ranging modules that are sources of unqualified ranging results; The area in the image data of the reflection area of ​​the drone is a closed figure formed by the adjacent ranging modules of the i-th group of ranging modules;

[0068] Among them, when the ranging module that is the source of the unqualified ranging result is the edge ranging module installed on the drone, the adjacent ranging modules form a closed figure, in which: the adjacent ranging modules are connected into a line segment, and the image data of the drone reflection area is separated and divided based on the position of each group of ranging modules, and the image data of the sub-drone reflection area where the ranging modules are connected into a line segment is obtained in several groups of regular hexagonal sub-drone reflection area image data. The image data is replaced by the sub-drone reflection area image data where the ranging modules are connected into a line segment.

[0069] In this embodiment, through the execution of the above method, a drone aerial photography configuration system based on river management is used as a source of river image data collection, and the collected river image data is used to conduct targeted pollution degree analysis and judgment on the river, so that the process of river pollution supervision can bring intelligent supervision to the river in a faster, more targeted and lower labor cost way, thereby ensuring the safety of the river ecological environment;

[0070] See also Figure 3 As shown, the figure further shows the deployment positions of the drone body 1, the camera 2, and the ranging module 3 relative to each other;

[0071] The drone body 1 carries a camera 3 to collect river channel image data. When the river channel image data is converted into drone reflection area image data, the ranging module 3 provides conditions for extraction and subsequent segmentation of the drone reflection area image data, ensuring that the method in the embodiment can obtain each group of drone reflection area image data based on the corresponding image recombined in the area where each group of drone reflections are located;

[0072] See also Figure 4 As shown, based on the arrow indication, the sufficient process of the image data of the above-mentioned drone reflection area is further demonstrated. Figure 4 The two groups in the middle and upper parts are the images corresponding to the area where the drone reflection is located, which are obtained by segmenting based on the ranging results of the ranging module. The images in the lower part of column (a) are the reconstructed images obtained by extracting some area images from column (b) and filling the gaps in the upper part of column (a). The same is true for the images in the lower part of column (b).

[0073] See also Figure 5 As shown in the figure, the complexity of the image is further demonstrated. Source: drone reflection area image data mark as the horizontal axis, with image complexity The value of is used as the vertical axis to plot the complexity of each group of images The black dots in the figure represent the complexity of the image where all the endpoints of the upward trending line segments are captured in the line graph. , the complexity of each group of images captured Source: UAV reflection area image data corresponding to the river section.

[0074] Embodiment 2:

[0075] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The drone aerial photography method based on river management in Example 1 is further described in detail:

[0076] In the stage of reorganizing the image data of the drone reflection area, the areas where the reflections of each group of drones are located are traversed, and the areas where the reflections of each group of drones are located are recorded as: , When reconstructing the image data of the drone reflection area, The area where the drone reflection is missing The scope is The image block is obtained by segmenting the corresponding position in the image block and filling it with the obtained image block The area where the drone reflection is missing , The area where the drone reflection is missing and The area where the drone reflection is missing When there is overlap, Image block filling obtained by mid-segmentation The area where the drone reflection is missing , The area where the drone reflection is missing and The area where the drone reflection is missing When there is overlap, Image block filling obtained by mid-segmentation The area where the drone reflection is missing , and so on, The image data of the drone's reflection area are reassembled and restored.

[0077] Through the above settings, the reconstruction logic of the image data of the drone reflection area is further limited.

[0078] like Figure 1 As shown in the figure, the information amount of the image data of the drone reflection area is obtained by the following formula:

[0079] D = <m> lim < / m> log [ − ∑ y = 1 N ( r ) p y log p y ] log r ;

[0080] Where: is the number of partial areas into which the image data of the drone reflection area is divided based on the image resolution r; is the proportion of the yth part at resolution r;

[0081] in, The larger the value, the more information there is in the image data of the drone reflection area. Conversely, the smaller the value, the less information there is in the image data of the drone reflection area.

[0082] The above calculation provides necessary operating parameter support for the logic of river pollution degree determination.

[0083] like Figure 1 As shown in the figure, the setting of the river pollution degree analysis logic is the collection of drone reflection area image data. Settings;

[0084] All reconstructed image data of the drone reflection area are recorded as: ,and The sorting of the image data of the reflection area of ​​the drone is based on the shooting order of the river image data. When the river pollution degree judgment logic is executed for the first time, As , calculate the complexity of the image , the second execution is As , calculate the complexity of the image , three times when executed As , calculate the complexity of the image , and so on, so that all the image data of the drone reflection area are included in the image complexity Calculation of

[0085] The operation of obtaining the results of the river pollution degree judgment logic execution, that is, obtaining the complexity of each group of images Operation of calculation results, complexity of each group of images After the calculation results are obtained, the image complexity Source: drone reflection area image data mark as the horizontal axis, with image complexity The value of is used as the vertical axis to plot the complexity of each group of images The line graph of the value captures the complexity of the graph where all the endpoints of the rising trend line segments are located in the line graph. , the complexity of each group of images captured The image data of the reflection area of ​​the source drone corresponds to the river section, that is, the sniffing result of the polluted river section in the river.

[0086] Through the above settings, the logic of river pollution degree analysis is further limited.

[0087] Embodiment 3:

[0088] In terms of specific implementation, based on Example 1, this example refers to Figure 2 The drone aerial photography method based on river management in Example 1 is further described in detail:

[0089] A UAV aerial photography configuration system based on river management, comprising: a control terminal, an analysis layer and a configuration layer;

[0090] The analysis layer includes a collection module and a division module. The collection module is used to collect the location information of buildings adjacent to the river channel. The division module is used to receive the location information of buildings around the river channel collected by the collection module, and divide the river channel based on the location information of buildings around the river channel.

[0091] The configuration layer includes a logic module and a drone module. The logic module is used to set the aerial photography logic. The drone module is used to receive the aerial photography logic set in the logic module and execute the aerial photography task of river channel image data based on the aerial photography logic.

[0092] Among them, each sub-river channel segment obtained by the division module is recorded as an aerial photography river channel segment and a non-aerial photography river channel segment. When the division module divides the river channel, when the interval between two adjacent groups of buildings on both sides of the river channel is greater than 30 meters, the two adjacent groups of buildings are determined as division points. A group of division points on one side of the river channel and adjacent division points on the other side of the river channel are connected to form a line segment recorded as a division line. The river channel is divided based on each group of division lines. The river channel segments without adjacent buildings in each group of sub-rivers obtained by division are recorded as non-aerial photography river channel segments. Otherwise, they are recorded as aerial photography river channel segments. The aerial photography logic set in the logic module includes: the shooting position of the river channel image data of the aerial photography river channel segment, the number of river channel image data shot by the aerial photography river channel segment, and the shooting height and angle of the river channel image data when the aerial photography is performed;

[0093] The shooting height and angle of the river channel image data in the aerial photography logic set by the logic module are manually set by the system end user. The number of river channel image data shot by the aerial photography river section follows:

[0094] ;

[0095] Where: is the number of river channel image data taken for the aerial photography of river channel section h; is the number of buildings adjacent to the aerial photographed river section h; The number of river channel image data taken; is the current year; The completion year of the earliest building among the buildings adjacent to the aerial photographed river section h;

[0096] in, The value is rounded up, the base number of river channel image data shooting The value of follows: 10 ≥ ≥1, the greater the flow of people in the area adjacent to the buildings in the aerial river section, the greater the number of river image data shooting The larger the value, the smaller the value. After the number of river channel image data shooting of the aerial photographed river channel section is obtained, the same number of evenly distributed position coordinates are selected in the corresponding river channel section as the river channel image data shooting positions of the aerial photographed river channel section, and the area where the river channel image data shooting positions of each group of aerial photographed river channel sections are located shall not be less than four fifths of the area of ​​the aerial photographed river channel section.

[0097] The acquisition module is interactively connected to the division module via a wireless network, the division module is interactively connected to the logic module via a wireless network, and the logic module is interactively connected to the drone module via a wireless network;

[0098] The drone module feeds back the current location information to the control terminal in real time based on the wireless network, and the system end user reads the real-time location information of the drone in the control terminal.

[0099] In this embodiment, the control terminal controls the operation of the control system, the acquisition module operates to collect the location information of the buildings adjacent to the river channel, the division module synchronously receives the location information of the buildings around the river channel collected in the acquisition module, and divides the river channel based on the location information of the buildings around the river channel. The logic module post-operates to set the aerial photography logic, and then the drone module receives the aerial photography logic set in the logic module, and executes the aerial photography task of the river channel image data based on the aerial photography logic. After the drone module operates and receives, several groups of river channel image data are collected, and the river channel image data are provided for the execution of a drone aerial photography method based on river channel management.

[0100] In summary, during the execution of the method in the above embodiment, the degree of river pollution is determined in real time by collecting river image data. In the river image data collection stage, specific logic is configured to enable targeted collection of feature areas with a higher probability of pollution in the river, which provides the necessary refinement processing for the river pollution degree determination result. In the process of analyzing the river image data to determine the river pollution degree, the drone reflection area image is extracted from the river image data, and the drone reflection area image is segmented and reorganized, so that the drone reflection area image can fully display the river pollution situation, so that the method can accurately make the final river pollution degree determination and sniff the pollution area.

[0101] 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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A drone aerial photography method based on river management, characterized in that: include: Based on the river channel image data collected by a drone aerial photography configuration system based on river channel management, the drone reflection area image data in each group of river channel image data is extracted, the integrity of each group of drone reflection area image data is identified, and based on the drone reflection area image data integrity identification result, each group of drone reflection area image data is segmented, and the area where the drone reflection is located in the drone reflection area image data is extracted, and the image data of each group of drone reflection areas is reorganized based on the corresponding image of the area where the drone reflection is located; Set up the river pollution degree analysis logic, and use the reorganized drone reflection area image data combined with the river pollution degree analysis logic to determine the river pollution degree; The river pollution degree determination logic is expressed as follows: ； Where: is the image complexity; is the total amount of image data of the drone reflection area; , are the rows and columns of the image; Pixels in the image The gradient value at ; is the average gradient of the image data of the qth group of drone reflection areas, is the information volume of the image data of the q groups of drone reflection areas; Among them, the image complexity The larger the value, the more serious the river pollution is; conversely, the smaller the value, the lighter the river pollution is. Obtain the execution result of the river pollution degree determination logic, and sniff the polluted river section in the river based on the execution result; The integrity recognition logic of the image data of the drone reflection area is expressed as: The UAV has a honeycomb-shaped evenly installed ranging modules on one side of the river surface. The ranging modules measure the distance from themselves to the river surface. The integrity recognition result of the image data of the UAV reflection area is: Traverse the ranging results of each group of ranging modules, take the group of ranging results with the largest number of identical results as qualified ranging results, obtain unqualified ranging results from the ranging results, and determine the drone reflection missing area in the drone reflection area image data based on the unqualified ranging results; Among them, when the UAV flies over the river to collect river image data, it is always horizontal to the river surface; The extraction logic of the area where the drone reflection is located in the drone reflection area image data is expressed as follows: ； Where: The area where the drone is reflected; It is the image data of the drone reflection area; It is the area where the drone reflection is missing in the drone reflection area image data; A collection of ranging modules that are sources of unqualified ranging results; The area in the image data of the reflection area of ​​the drone is a closed figure formed by the adjacent ranging modules of the i-th group of ranging modules; Among them, when the ranging module that is the source of the unqualified ranging result is the edge ranging module installed on the drone, the adjacent ranging modules form a closed figure, in which: the adjacent ranging modules are connected into a line segment, and the image data of the drone reflection area is separated and divided based on the position of each group of ranging modules, and the image data of the sub-drone reflection area where the ranging modules are connected into a line segment is obtained in several groups of regular hexagonal sub-drone reflection area image data. The image data is replaced by the sub-drone reflection area image data where the ranging modules are connected into a line segment.

2. The method for aerial photography based on river management by using a drone according to claim 1, characterized in that: In the drone reflection area image data reorganization stage, the areas where the drone reflections of each group are located are traversed, and the areas where the drone reflections of each group are located are recorded as: , When reconstructing the image data of the drone reflection area, The area where the drone reflection is missing The scope is The image block is obtained by segmenting the corresponding position in the image block and filling it with the obtained image block The area where the drone reflection is missing , The area where the drone reflection is missing and The area where the drone reflection is missing When there is overlap, Image block filling obtained by mid-segmentation The area where the drone reflection is missing , The area where the drone reflection is missing and The area where the drone reflection is missing When there is overlap, Image block filling obtained by mid-segmentation The area where the drone reflection is missing , and so on, The image data of the drone's reflection area are reassembled and restored.

3. The method for aerial photography based on river management by using a drone according to claim 1, characterized in that: The information amount of the image data of the drone reflection area is obtained by the following formula: ； Where: is the number of partial areas into which the image data of the drone reflection area is divided based on the image resolution r; is the proportion of the yth part at resolution r; in, The larger the value, the more information there is in the image data of the drone reflection area. Conversely, the smaller the value, the less information there is in the image data of the drone reflection area.

4. The method for aerial photography based on river management by using a drone according to claim 1, characterized in that: The setting of the river pollution degree analysis logic, that is, the collection of drone reflection area image data Settings; All reconstructed image data of the drone reflection area are recorded as: ,and The order of the image data of the reflection area of ​​the drone is subject to the shooting order of the river image data. When the river pollution degree judgment logic is executed for the first time, As , calculate the complexity of the image , the second execution is As , calculate the complexity of the image , three times when executed As , calculate the complexity of the image , and so on, so that all the image data of the drone reflection area are included in the image complexity Calculation.

5. The method for aerial photography based on river management by using an unmanned aerial vehicle according to claim 4, characterized in that: The operation of obtaining the river pollution degree judgment logic execution result, that is, obtaining the complexity of each group of images Operation of calculation results, complexity of each group of images After the calculation results are obtained, the image complexity Source: drone reflection area image data mark as the horizontal axis, with image complexity The value of is used as the vertical axis to plot the complexity of each group of images The line graph of the value captures the complexity of the graph where all the endpoints of the upward trending line segments are located. , the complexity of each group of images captured The image data of the reflection area of ​​the source drone corresponds to the river section, that is, the sniffing result of the polluted river section in the river.

6. A drone aerial photography configuration system based on river management, the method is an implementation system of a drone aerial photography method based on river management as claimed in any one of claims 1 to 5, characterized in that: include: Control terminal, analysis layer and configuration layer; The analysis layer includes a collection module and a division module. The collection module is used to collect the location information of buildings adjacent to the river channel. The division module is used to receive the location information of buildings around the river channel collected by the collection module, and divide the river channel based on the location information of buildings around the river channel. The configuration layer includes a logic module and a drone module. The logic module is used to set the aerial photography logic. The drone module is used to receive the aerial photography logic set in the logic module and execute the aerial photography task of river channel image data based on the aerial photography logic. Among them, each sub-river channel segment obtained by the division module is recorded as an aerial photography river channel segment and a non-aerial photography river channel segment. When the division module divides the river channel, when the interval between two adjacent groups of buildings on both sides of the river channel is greater than thirty meters, the two adjacent groups of buildings are determined as division points. A group of division points on one side of the river channel and adjacent division points on the other side of the river channel are connected to form a line segment recorded as a division line. The river channel is divided based on each group of division lines. The river channel segments without adjacent buildings in each group of sub-rivers obtained by division are recorded as non-aerial photography river channel segments. Otherwise, they are recorded as aerial photography river channel segments. The aerial photography logic set in the logic module includes: the shooting position of the aerial photography river channel segment river image data, the number of aerial photography river channel segment river image data shooting, and the shooting height and angle of the river channel image data during aerial photography.

7. The method for drone aerial photography based on river management according to claim 6 is characterized in that: The shooting height and angle of the river channel image data in the aerial photography logic set by the logic module are manually set by the system end user, and the number of river channel image data shot by the aerial photography of the river channel section is subject to: ； Where: is the number of river channel image data taken for the aerial photography of river channel section h; is the number of buildings adjacent to the aerial photographed river section h; The number of river channel image data taken; is the current year; The completion year of the earliest building among the buildings adjacent to the aerial photographed river section h; in, The value is rounded up, the base number of river channel image data shooting The value of follows: 10 ≥ ≥1, the greater the flow of people in the area adjacent to the buildings in the aerial river section, the greater the number of river image data shooting The larger the value, the smaller the value. After the number of river channel image data shooting of the aerial photographed river section is obtained, the same number of evenly distributed position coordinates are selected in the corresponding river section as the river channel image data shooting positions of the aerial photographed river section, and the area where the river channel image data shooting positions of each group of aerial photographed river section are located shall not be less than four-fifths of the area of ​​the aerial photographed river section.

8. The method for aerial photography using a drone based on river management according to claim 6, characterized in that: The acquisition module is interactively connected to the division module via a wireless network, the division module is interactively connected to the logic module via a wireless network, and the logic module is interactively connected to the drone module via a wireless network; The drone module feeds back the current location information to the control terminal in real time based on the wireless network, and the system end user reads the real-time location information of the drone in the control terminal.

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