Transmission tower bolt loosening detection method and device

Abstract

The present invention provides a method and device for detecting loose bolts of a transmission tower. The method includes: in response to a drone flying along a preset route until it scans any tower sign, obtaining the detection coordinates corresponding to the tower sign, and controlling the drone to fly to a preset detection point; retrieving the initial virtual tower based on the tower sign, and determining the detection route based on the route formulation strategy; the drone flies to the preset detection point, and controls the drone to fly around the transmission tower based on the detection route, and collects images of each actual detection area passed through to obtain each detection image; determines the loose bolt prediction coefficient of each actual detection area, and determines each predicted risk level of each actual detection area based on each bolt loose prediction coefficient; based on each predicted risk level, multi-dimensionally marks the area portion corresponding to each actual detection area in the initial virtual tower, and obtains the current virtual tower. The present invention at least improves the detection efficiency.

Description

Technical Field

[0001] The invention relates to data processing technology, and in particular to a method and a device for detecting loose bolts of a transmission tower. Background Art

[0002] The transmission tower is the supporting point of the overhead line. If one circuit is installed on the transmission tower, it is a single-circuit transmission tower, and if two circuits are installed on the transmission tower, it is a double-circuit transmission tower. Bolts are important connecting components in the transmission tower, and their tightening state directly affects the safety performance of the transmission tower.

[0003] The inventors found in their research that the existing methods for detecting loose bolts on transmission towers are mostly manual inspections, which are inefficient and cannot guarantee the accuracy of the detection results. Summary of the invention

[0004] Based on the above problems, the present invention is proposed to provide a method and device for detecting loose bolts of a transmission tower, which overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for detecting loose bolts of a transmission tower is provided, comprising the following steps:

[0006] In response to the drone flying along a preset route until it scans any tower sign, the detection coordinates corresponding to the tower sign are acquired, and the drone is controlled to fly toward a preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates;

[0007] Retrieving an initial virtual tower corresponding to the transmission tower based on the tower sign, and determining a detection route that takes the preset detection point as a detection starting point and passes through each virtual detection area corresponding to the initial virtual tower based on a route formulation strategy;

[0008] In response to the drone flying to the preset detection point, the drone is controlled to fly around the transmission tower based on the detection route, and images are collected for each real detection area corresponding to each virtual detection area along the way to obtain each detection image;

[0009] Determine each bolt loosening prediction coefficient corresponding to each actual detection area based on each detection image, and determine each prediction risk level corresponding to each actual detection area based on each bolt loosening prediction coefficient;

[0010] Based on each predicted risk level, multi-dimensional area marking is performed on the area parts located in the initial virtual tower and corresponding to each actual detection area, so as to obtain the current virtual tower.

[0011] Optionally, in the method according to the present invention, in response to the drone flying along a preset route until scanning any tower sign, obtaining the detection coordinates corresponding to the tower sign, and controlling the drone to fly toward a preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates, including:

[0012] Coordinate processing is performed on the preset route, and the preset route is divided into points based on the distribution of towers to obtain scanning points corresponding to different point coordinates;

[0013] Acquire the GPS positioning coordinates corresponding to the drone in real time, and trigger the opening of the scanning detection unit preset on the drone in response to the GPS positioning coordinates coinciding with the point coordinates corresponding to any scanning point;

[0014] In response to the scanning and detecting unit scanning a tower tag located on any tower sign, acquiring a detection coordinate having a link relationship with the tower tag, and generating an initial navigation route to reach the detection coordinate;

[0015] Determine the geo-fence information corresponding to the transmission tower, and update the initial navigation route based on the geo-fence information to obtain a current navigation route;

[0016] The drone is controlled to fly toward the preset detection point based on the current navigation route.

[0017] Optionally, in the method according to the present invention, the preset route is processed into coordinates, and the preset route is divided into points based on the distribution of towers to obtain scanning points corresponding to different point coordinates, including:

[0018] In the preset route, tower identification points and starting points corresponding to different transmission towers arranged in the detection order are respectively determined, and route segments reaching each transmission tower are respectively determined in the detection order with the starting point of the preset route as the starting point;

[0019] The preset route is processed into coordinates to determine the starting coordinates and the identification coordinates corresponding to the starting point and the identification points of each tower, and based on the starting coordinates and the identification coordinates, a difference calculation based on adjacent positions is performed to obtain the route distances corresponding to the route segments;

[0020] Retrieving a preset division table, wherein the preset division table includes different distance intervals and the number of segments corresponding to the different distance intervals;

[0021] Traversing the preset division table, determining each distance interval corresponding to each route distance, and determining each number of segments corresponding to each route segment based on each distance interval;

[0022] Divide each route segment into segments based on the corresponding number of segments to obtain segment sub-points corresponding to each route segment, determine the one of the segment sub-points closest to the corresponding tower identification point as the scanning point, and determine the point coordinates of the scanning point based on the identification coordinates of the tower identification point to obtain scanning points corresponding to different point coordinates.

[0023] Optionally, in the method according to the present invention, determining the geo-fence information corresponding to the transmission tower, and updating the initial navigation route based on the geo-fence information to obtain the current navigation route includes:

[0024] In response to the scanning and detecting unit scanning a tower tag located on any tower sign, each fence point corresponding to the transmission tower is acquired, and point connections are performed based on each fence point to obtain a virtual geographic fence surrounding the transmission tower;

[0025] Determine whether there is interference between the initial navigation route and the virtual geo-fence, and divide the initial navigation route if there is interference to obtain a first sub-segment including the scanning point, a second sub-segment including the interference part, and a third sub-segment including the preset detection point;

[0026] Determine the endpoints of the first sub-segment other than the corresponding scanning points as the starting endpoints, determine the endpoints of the third sub-segment other than the corresponding preset detection points as the ending endpoints, and generate an updated line segment around the fence edge of the virtual geographic fence based on the shortest line segment generation principle with the starting endpoint as the starting point and the ending endpoint as the end point;

[0027] The first sub-segment, the updated segment and the third sub-segment are combined to obtain a current navigation route.

[0028] Optionally, in the method according to the present invention, an initial virtual tower corresponding to the transmission tower is retrieved based on the tower sign, and a detection route with the preset detection point as the detection starting point and passing through each virtual detection area corresponding to the initial virtual tower is determined based on a route formulation strategy, including:

[0029] In response to the scanning and detecting unit scanning a tower tag located on any tower sign, retrieving an initial virtual tower corresponding to the transmission tower;

[0030] Determining the structure of the initial virtual tower and determining the facades of each tower corresponding to the initial virtual tower;

[0031] Determine the straight-line distances between each tower facade and the preset detection point respectively, determine the tower facade corresponding to the shortest straight-line distance as the starting facade, and determine the corresponding detection sequence based on the positional relationship between all remaining tower facades and the starting facade;

[0032] Determine the virtual detection areas corresponding to the facades of each tower based on a preset area determination strategy, and obtain a first area sequence arranged from top to bottom corresponding to the facade of each tower;

[0033] Based on the detection sequence, reversely modify the first area sequence of all tower facades corresponding to even numbers to obtain a second area sequence;

[0034] Based on the detection sequence and each of the area sequences, a detection route of each virtual detection area corresponding to the initial virtual tower with the preset detection point as the detection starting point is determined.

[0035] Optionally, in the method according to the present invention, determining the virtual detection areas corresponding to the facades of each tower respectively based on a preset area determination strategy includes:

[0036] Determine the vertical height of the initial virtual iron tower based on the vertical dimension, determine the maximum horizontal width and the minimum horizontal width of each iron tower facade based on the horizontal dimension, and calculate the mean of the maximum horizontal width and the minimum horizontal width to obtain the horizontal mean corresponding to each iron tower facade;

[0037] Determine a height evaluation coefficient based on the vertical height, determine each lateral evaluation coefficient based on each lateral mean value, and determine each division coefficient corresponding to each tower facade based on the height evaluation coefficient and each lateral evaluation coefficient;

[0038] Calculate the ratios of the division coefficients and the retrieved reference coefficients respectively, and multiply the obtained ratio coefficients by the retrieved coefficient weights to obtain the adjusted values;

[0039] The adjusted values ​​are multiplied by the retrieved reference division numbers corresponding to the reference coefficients to obtain initial division numbers, and integer adjustments are made based on the initial division numbers to obtain current division numbers corresponding to the facades of the towers;

[0040] Based on the current division numbers, the outer facades of each tower are divided into regions from bottom to top to obtain virtual detection regions corresponding to the outer surfaces of each tower.

[0041] Optionally, in the method according to the present invention, controlling the drone to fly around the transmission tower based on the detection route, and collecting images of each real detection area corresponding to each virtual detection area along the way to obtain each detection image, includes:

[0042] Determine the detection center points corresponding to the actual detection areas, establish reference collection circles with the detection center points as circles, and obtain the detection heights corresponding to the detection center points;

[0043] Controlling the UAV to fly around the transmission tower based on the detection route, and acquiring the flight altitude in real time;

[0044] In response to the drone flying to a flight altitude that is the same as any detection altitude, the drone is controlled to hover for a preset time, and an image acquisition unit preset on the drone is triggered to be turned on;

[0045] When the detection center point does not exist in the image acquisition frame corresponding to the image acquisition unit, controlling the image acquisition frame of the image acquisition unit to adjust its posture;

[0046] When the detection center point exists in the image acquisition frame corresponding to the image acquisition unit, the connecting line segment between the acquisition center point of the image acquisition frame and the detection center point is obtained, and the image acquisition frame of the image acquisition unit is adjusted in position based on the extension direction of the connecting line segment until the acquisition center point is located in the reference acquisition circle.

[0047] Optionally, in the method according to the present invention, determining each bolt loosening prediction coefficient corresponding to each actual detection area based on each detection image, and determining each predicted risk level corresponding to each actual detection area based on each bolt loosening prediction coefficient, including:

[0048] Performing image recognition on each detection image to determine bolt images corresponding to different bolt structures in the detection image;

[0049] Based on each bolt image, all nut structures having threaded connection relationships with each corresponding bolt structure are determined as nut corresponding groups, and the positional relationship between each nut structure and the corresponding bolt structure in each nut corresponding group is determined respectively;

[0050] Based on the positional relationship, the looseness coefficients of the bolts corresponding to the bolt images are determined respectively, the looseness coefficients of the bolts are averaged, and the ratios of the average results and the detection heights corresponding to the detection images are calculated to obtain the looseness prediction coefficients of the bolts corresponding to the actual detection areas;

[0051] The predicted risk levels corresponding to the actual detection areas are determined based on the prediction coefficients of the loosening of the bolts.

[0052] Optionally, in the method according to the present invention, determining the looseness coefficients of the bolts corresponding to the bolt images based on the positional relationship respectively includes:

[0053] When any of the nut corresponding groups includes a nut structure, obtaining a bolt distance between the nut structure and the corresponding bolt structure, and determining a bolt loosening coefficient corresponding to the bolt image based on the bolt distance;

[0054] When any of the nut corresponding groups includes multiple nut structures, the multiple nut structures are sorted based on the proximity to the corresponding bolt structure to obtain different nut serial numbers corresponding to the multiple nut structures, and different serial number coefficients are allocated to the multiple nut structures corresponding to different nut serial numbers based on the allocation principle from large to small;

[0055] Obtaining bolt distances between each nut structure and the corresponding bolt structure respectively, and determining initial looseness coefficients corresponding to each nut structure respectively based on the bolt distances;

[0056] Calculate the product of each initial loosening coefficient and the corresponding coefficient with different serial numbers to obtain the bolt loosening coefficient corresponding to the bolt image;

[0057] When any of the nut corresponding groups does not include a nut structure, the retrieved reference looseness coefficient is determined as the bolt looseness coefficient corresponding to the bolt image.

[0058] According to another aspect of the present invention, there is provided a transmission tower bolt loosening detection device, comprising:

[0059] The flight module is configured to obtain detection coordinates corresponding to the tower sign in response to the drone flying along a preset route until it scans any tower sign, and control the drone to fly toward a preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates;

[0060] A calling module is configured to call an initial virtual tower corresponding to the transmission tower based on the tower sign, and determine a detection route with the preset detection point as the detection starting point and passing through each virtual detection area corresponding to the initial virtual tower based on a route formulation strategy;

[0061] The acquisition module is configured to control the drone to fly around the transmission tower based on the detection route in response to the drone flying to the preset detection point, and to acquire images of each real detection area corresponding to each virtual detection area passed by, so as to obtain each detection image;

[0062] A prediction module is configured to determine each bolt loosening prediction coefficient corresponding to each actual detection area based on each detection image, and determine each prediction risk level corresponding to each actual detection area based on each bolt loosening prediction coefficient;

[0063] The marking module is configured to perform multi-dimensional regional marking on the area parts corresponding to each actual detection area in the initial virtual tower based on each predicted risk level to obtain the current virtual tower.

[0064] According to the solution of the present invention, when the drone flies along a preset route and scans any tower sign, the server will obtain the detection coordinates corresponding to the tower sign, and control the drone to fly toward the preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates; then, based on the tower sign, the initial virtual tower corresponding to the transmission tower is retrieved, and based on the route formulation strategy, a detection route passing through each virtual detection area corresponding to the initial virtual tower with the preset detection point as the detection starting point is determined; then, in response to the drone flying to the preset detection point, the server will control the drone to fly around the transmission tower based on the detection route, and collect images of each real detection area corresponding to each virtual detection area passed by, thereby obtaining each detection image; then, the prediction coefficient of each bolt loosening of each real detection area is determined, and the prediction risk level of each real detection area is determined; then, based on each predicted risk level, the area portion corresponding to each real detection area in the initial virtual tower is multi-dimensionally marked to obtain the current virtual tower. The present invention can quickly predict the risk level of bolt loosening, improves detection efficiency, and has a certain degree of accuracy, which can provide corresponding reference for management personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of a method for detecting loose bolts of a transmission tower according to an embodiment of the present invention is shown;

[0066] Figure 2 A structural block diagram of a transmission tower bolt loosening detection device according to another embodiment of the present invention is shown;

[0067] Figure 3 A schematic diagram of the exterior elevation of a transmission tower is shown;

[0068] Figure 4 A diagram showing the positional relationship between the bolt structure and the nut structure in the bolt image. DETAILED DESCRIPTION

[0069] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0070] The transmission tower is the supporting point of the overhead line. If one circuit is installed on the transmission tower, it is a single-circuit transmission tower, and if two circuits are installed on the transmission tower, it is a double-circuit transmission tower. Bolts are important connecting components in the transmission tower, and their tightening state directly affects the safety performance of the transmission tower.

[0071] The inventors found in their research that the existing methods for detecting loose bolts on transmission towers are mostly manual inspections, which are inefficient and cannot guarantee the accuracy of the detection results.

[0072] In order to solve the above problems in the prior art, the inventor proposes the solution of the present invention. An embodiment of the present invention provides a method for detecting loose bolts of a transmission tower, which can be executed in a computing device.

[0073] Figure 1 A flow chart of a method for detecting loose bolts on a transmission tower according to an embodiment of the present invention is shown. The method is suitable for execution in a computing device, wherein the computing device can be understood as a terminal with data processing capabilities, such as a mobile phone or a computer.

[0074] like Figure 1 As shown, the purpose of this embodiment is to implement a method for detecting loose bolts of a transmission tower, which starts at step S102. In step S102, the following contents are included:

[0075] In response to the drone flying along a preset route until it scans any tower sign, the detection coordinates corresponding to the tower sign are obtained, and the drone is controlled to fly toward a preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates.

[0076] For example, in this embodiment, the server will pre-set the flight route of the drone according to the arrangement order of all the transmission towers that need to be inspected for bolt loosening, that is, the preset route, and a sign with a number is set in front of each transmission tower. Then, when the drone scans any tower sign while flying along the preset route, the server can obtain the detection coordinates of the tower sign, and each detection coordinate corresponds to a preset detection point of the transmission tower, and then the server will control the drone to fly toward the preset detection point of the transmission tower corresponding to the tower sign according to the detection coordinates.

[0077] Furthermore, the above-mentioned “in response to the drone flying along a preset route until it scans any tower sign, obtaining the detection coordinates corresponding to the tower sign, and controlling the drone to fly toward the preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates” also includes the following steps:

[0078] Coordinate processing is performed on the preset route, and the preset route is divided into points based on the distribution of towers to obtain scanning points corresponding to different point coordinates;

[0079] Acquire the GPS positioning coordinates corresponding to the drone in real time, and trigger the opening of the scanning detection unit preset on the drone in response to the GPS positioning coordinates coinciding with the point coordinates corresponding to any scanning point;

[0080] In response to the scanning and detecting unit scanning a tower tag located on any tower sign, acquiring a detection coordinate having a link relationship with the tower tag, and generating an initial navigation route to reach the detection coordinate;

[0081] Determine the geo-fence information corresponding to the transmission tower, and update the initial navigation route based on the geo-fence information to obtain a current navigation route;

[0082] The drone is controlled to fly toward the preset detection point based on the current navigation route.

[0083] For example, in this embodiment, the server will first coordinate the preset route to obtain the point coordinates of each point on the preset route, and then divide the preset route into points based on the distribution of the towers, so as to obtain the scanning points corresponding to different point coordinates. During the flight of the drone, the server will obtain the GPS positioning coordinates of the drone in real time, and when the GPS positioning coordinates coincide with the point coordinates corresponding to any scanning point, it means that the drone has flown to the area near the transmission tower, and at this time the server will turn on the scanning detection unit preset on the drone.

[0084] When the scanning detection unit scans the tower tag on any tower sign, the server will obtain the detection coordinates that are linked to the tower tag and generate an initial navigation route to the detection coordinates. Since the drone cannot fly through the tower body, but needs to fly around the transmission tower, the server will determine the geographic fence information of the transmission tower and update the initial navigation route based on the geographic fence information to obtain the current navigation route, and then control the drone to fly toward the preset detection point according to the current navigation route.

[0085] Furthermore, the above-mentioned "processing the preset route into coordinates, and dividing the preset route into points based on the distribution of towers to obtain scanning points corresponding to different point coordinates" also includes the following steps:

[0086] In the preset route, tower identification points and starting points corresponding to different transmission towers arranged in the detection order are respectively determined, and route segments reaching each transmission tower are respectively determined in the detection order with the starting point of the preset route as the starting point;

[0087] The preset route is processed into coordinates to determine the starting coordinates and the identification coordinates corresponding to the starting point and the identification points of each tower, and based on the starting coordinates and the identification coordinates, a difference calculation based on adjacent positions is performed to obtain the route distances corresponding to the route segments;

[0088] Retrieving a preset division table, wherein the preset division table includes different distance intervals and the number of segments corresponding to the different distance intervals;

[0089] Traversing the preset division table, determining each distance interval corresponding to each route distance, and determining each number of segments corresponding to each route segment based on each distance interval;

[0090] Divide each route segment into segments based on the corresponding number of segments to obtain segment sub-points corresponding to each route segment, determine the one of the segment sub-points closest to the corresponding tower identification point as the scanning point, and determine the point coordinates of the scanning point based on the identification coordinates of the tower identification point to obtain scanning points corresponding to different point coordinates.

[0091] For example, in this embodiment, since a preset route includes multiple transmission towers, the server will first determine the tower identification points and starting points of the transmission towers arranged in the detection order in the preset route, and use the starting point of the preset route as the starting point and determine the route segments reaching each transmission tower according to the detection order.

[0092] Then, the server will coordinate the preset route, determine the starting coordinates of the starting point and the identification coordinates of each tower identification point, and calculate the difference between adjacent coordinate values ​​to obtain the route distance of each route segment after calculation.

[0093] A partition table, i.e., a preset partition table, is pre-set in the server, which includes different distance intervals and the number of segments corresponding to the different distance intervals. After obtaining the distances of each route, the server will retrieve the preset partition table and determine the distance intervals corresponding to each route distance and the number of segments in the preset partition table. Then, the server will perform segment division on each route segment according to the number of segments to obtain each segment sub-point corresponding to each route segment. For example, if the route distance of the route segment is 300, and the number of segments corresponding to the distance interval [200, 350] in the preset partition table is 3, the server will determine two segment sub-points on the route segment, thereby dividing the route segment into three segments. Furthermore, the server will determine the segment sub-point closest to the tower identification point among each segment sub-point as the scanning point, and determine the identification coordinates of the tower identification point as the point coordinates of the scanning point, thereby obtaining each scanning point corresponding to different point coordinates.

[0094] In this embodiment, the server determines the line segment sub-point closest to the tower identification point among each line segment sub-point as the scanning point, which can ensure that the scanning detection unit is turned on in time when the drone arrives near the transmission tower, thereby reducing the corresponding resource consumption of the equipment and saving certain costs.

[0095] Furthermore, the above-mentioned “determining the geographic fence information corresponding to the transmission tower, and updating the initial navigation route based on the geographic fence information to obtain the current navigation route” also includes the following steps:

[0096] In response to the scanning and detecting unit scanning a tower tag located on any tower sign, each fence point corresponding to the transmission tower is acquired, and point connections are performed based on each fence point to obtain a virtual geographic fence surrounding the transmission tower;

[0097] Determine whether there is interference between the initial navigation route and the virtual geo-fence, and divide the initial navigation route if there is interference to obtain a first sub-segment including the scanning point, a second sub-segment including the interference part, and a third sub-segment including the preset detection point;

[0098] Determine the endpoints of the first sub-segment other than the corresponding scanning points as the starting endpoints, determine the endpoints of the third sub-segment other than the corresponding preset detection points as the ending endpoints, and generate an updated line segment around the fence edge of the virtual geographic fence based on the shortest line segment generation principle with the starting endpoint as the starting point and the ending endpoint as the end point;

[0099] The first sub-segment, the updated segment and the third sub-segment are combined to obtain a current navigation route.

[0100] For example, in this embodiment, when the scanning detection unit scans a tower tag located on any tower sign, the server can obtain the fence points of the transmission tower and connect the fence points to obtain a virtual geographic fence surrounding the transmission tower.

[0101] Furthermore, the server can determine whether there is interference between the initial navigation route and the virtual geo-fence, and divide the initial navigation route if there is interference, thereby obtaining a first line segment including the scanning point, a second line segment including the interference part, and a third line segment including the preset detection point. Then, the server will determine the endpoints of the first line segment other than the scanning point as the starting endpoint, and the endpoints of the third line segment other than the preset detection point as the ending endpoint, and based on the principle of shortest line segment generation, generate an updated line segment around the edge of the fence of the virtual geo-fence with the starting endpoint as the starting point and the ending endpoint as the end point, and then combine the first line segment, the updated line segment, and the third line segment to obtain the current navigation route.

[0102] This embodiment can adjust the initial navigation route when there is interference between the initial navigation route and the virtual geographic fence, so that the adjusted current navigation route surrounds the transmission tower instead of passing through the transmission tower, thereby ensuring a certain degree of safety.

[0103] In step S104, the following contents are included:

[0104] Based on the tower sign, an initial virtual tower corresponding to the transmission tower is retrieved, and based on a route formulation strategy, a detection route is determined that takes the preset detection point as the detection starting point and passes through each virtual detection area corresponding to the initial virtual tower.

[0105] For example, in this embodiment, the server will establish an initial virtual tower corresponding to the transmission tower based on the digital twin space, and can retrieve the initial virtual tower corresponding to the transmission tower according to the tower sign, and determine the inspection route of each virtual inspection area passing through the initial virtual tower with the preset inspection point as the inspection starting point based on the route formulation strategy.

[0106] Furthermore, the above-mentioned "retrieving the initial virtual tower corresponding to the transmission tower based on the tower sign, and determining the detection route with the preset detection point as the detection starting point and passing through each virtual detection area corresponding to the initial virtual tower based on the route formulation strategy" also includes the following steps:

[0107] In response to the scanning and detecting unit scanning a tower tag located on any tower sign, retrieving an initial virtual tower corresponding to the transmission tower;

[0108] Determining the structure of the initial virtual tower and determining the facades of each tower corresponding to the initial virtual tower;

[0109] Determine the straight-line distances between each tower facade and the preset detection point respectively, determine the tower facade corresponding to the shortest straight-line distance as the starting facade, and determine the corresponding detection sequence based on the positional relationship between all remaining tower facades and the starting facade;

[0110] Determine the virtual detection areas corresponding to the facades of each tower based on a preset area determination strategy, and obtain a first area sequence arranged from top to bottom corresponding to the facade of each tower;

[0111] Based on the detection sequence, reversely modify the first area sequence of all tower facades corresponding to even numbers to obtain a second area sequence;

[0112] Based on the detection sequence and each of the area sequences, a detection route of each virtual detection area corresponding to the initial virtual tower with the preset detection point as the detection starting point is determined.

[0113] For example, in this embodiment, when the scanning detection unit scans a tower tag located on any tower sign, the server will retrieve the initial virtual tower corresponding to the transmission tower and perform structural determination on the initial virtual tower, thereby determining the tower facades of the initial virtual tower, such as Figure 3 As shown, the straight-line distances between each tower facade and the preset detection points are determined respectively, and the tower facade corresponding to the shortest straight-line distance is determined as the starting facade, and then the corresponding detection sequence is determined based on the positional relationship between all the remaining tower facades and the starting facade. For example, an initial virtual tower has four tower facades, and the detection sequence of the starting facade in the initial virtual tower is 1. In this way, the drone can start detection from the nearest tower facade, thereby saving a certain amount of working time.

[0114] Then, the server will determine the virtual detection areas corresponding to the facades of each tower based on the preset area determination strategy, thereby obtaining a first area sequence arranged from top to bottom corresponding to each tower facade. Then, based on the detection sequence, the first area sequence corresponding to all even-numbered tower facades is reversely modified to obtain a second area sequence. Finally, based on the detection sequence and each area sequence, the detection route of each virtual detection area corresponding to the initial virtual tower with the preset detection point as the detection starting point is determined.

[0115] This embodiment enables the drone to inspect the facades of each tower along an S-shaped route, which can save a certain amount of inspection time and improve corresponding work efficiency.

[0116] Furthermore, the above-mentioned “determining the virtual detection areas corresponding to the facades of each tower based on the preset area determination strategy” further includes the following steps:

[0117] Determine the vertical height of the initial virtual iron tower based on the vertical dimension, determine the maximum horizontal width and the minimum horizontal width of each iron tower facade based on the horizontal dimension, and calculate the mean of the maximum horizontal width and the minimum horizontal width to obtain the horizontal mean corresponding to each iron tower facade;

[0118] Determine a height evaluation coefficient based on the vertical height, determine each lateral evaluation coefficient based on each lateral mean value, and determine each division coefficient corresponding to each tower facade based on the height evaluation coefficient and each lateral evaluation coefficient;

[0119] Calculate the ratios of the division coefficients and the retrieved reference coefficients respectively, and multiply the obtained ratio coefficients by the retrieved coefficient weights to obtain the adjusted values;

[0120] The adjusted values ​​are multiplied by the retrieved reference division numbers corresponding to the reference coefficients to obtain initial division numbers, and integer adjustments are made based on the initial division numbers to obtain current division numbers corresponding to the facades of the towers;

[0121] Based on the current division numbers, the outer facades of each tower are divided into regions from bottom to top to obtain virtual detection regions corresponding to the outer surfaces of each tower.

[0122] For example, in this embodiment, after determining the detection sequence, the server will determine the vertical height of the initial virtual tower based on the vertical dimension, and determine the maximum horizontal width and the minimum horizontal width of the facade of each tower based on the horizontal dimension, and calculate the average of the maximum horizontal width and the minimum horizontal width of the facade of each tower, and obtain the horizontal mean of the facade of each tower after calculation.

[0123] The server is pre-set with a reference coefficient and a corresponding reference division number, for example, the reference coefficient is 1.5 and the reference division number is 10. The server determines the height evaluation coefficient according to the vertical height, and then determines each horizontal evaluation coefficient according to each horizontal mean, and determines each division coefficient of each tower facade based on the height evaluation coefficient and each horizontal evaluation coefficient.

[0124] Since the types of each transmission tower that needs to be inspected may be different, different coefficient weights will be set in advance for different types of transmission towers in the server. Then, the server will calculate the ratio of each division coefficient and the retrieved benchmark coefficient, and multiply the obtained ratio coefficients by the retrieved coefficient weights to obtain each adjustment value. Then, each adjustment value is multiplied by the benchmark division number of the corresponding benchmark coefficient to obtain each initial division number. Since the initial division number obtained may not be an integer, the server will round off the digit after the decimal point to adjust the initial division number to an integer, thereby obtaining the current division numbers corresponding to the facades of each tower.

[0125] This embodiment can determine the division coefficients of each tower facade according to the height evaluation coefficient and each lateral evaluation coefficient, so that the sizes of each detection area after division are similar, which can improve a certain detection effect.

[0126] In step S106, the following contents are included:

[0127] In response to the drone flying to the preset detection point, the drone is controlled to fly around the transmission tower based on the detection route, so as to ensure that the subsequent server can detect all the bolts on the transmission tower, and collect images of the actual detection areas corresponding to the virtual detection areas along the way to obtain various detection images.

[0128] For example, in this embodiment, when the drone flies to a preset detection point, the server will control the drone to fly around the transmission tower according to the detection route, and collect images of each real detection area corresponding to each virtual detection area along the way, thereby obtaining each detection image.

[0129] Furthermore, the above-mentioned “controlling the drone to fly around the transmission tower based on the detection route, and collecting images of each real detection area corresponding to each virtual detection area along the way, to obtain each detection image” also includes the following steps:

[0130] Determine the detection center points corresponding to the actual detection areas, establish reference collection circles with the detection center points as circles, and obtain the detection heights corresponding to the detection center points;

[0131] Controlling the UAV to fly around the transmission tower based on the detection route, and acquiring the flight altitude in real time;

[0132] In response to the drone flying to a flight altitude that is the same as any detection altitude, the drone is controlled to hover for a preset time, and an image acquisition unit preset on the drone is triggered to be turned on;

[0133] When the detection center point does not exist in the image acquisition frame corresponding to the image acquisition unit, controlling the image acquisition frame of the image acquisition unit to adjust its posture;

[0134] When the detection center point exists in the image acquisition frame corresponding to the image acquisition unit, the connecting line segment between the acquisition center point of the image acquisition frame and the detection center point is obtained, and the image acquisition frame of the image acquisition unit is adjusted in position based on the extension direction of the connecting line segment until the acquisition center point is located in the reference acquisition circle.

[0135] For example, in this embodiment, when the drone flies to the preset detection point, the server will determine the detection center points of each actual detection area, and establish a reference collection circle with each detection center point as a circle, and then obtain the detection heights of each detection center point.

[0136] The server will then control the drone to fly around the transmission tower according to the detection route and obtain the drone's flight altitude in real time. When the drone's flight altitude is the same as any detection altitude, the server will control the drone to hover for a preset time and turn on the image acquisition unit preset on the drone.

[0137] When the detection center point does not exist in the image acquisition frame corresponding to the image acquisition unit, it means that the image acquisition unit cannot completely acquire the actual detection area. Since the image acquisition unit is a camera that can be rotated, the server can control the image acquisition unit to rotate, that is, control the image acquisition frame of the image acquisition unit to adjust the posture until the detection center point exists in the image acquisition frame of the image acquisition unit. When the detection center point exists in the image acquisition frame of the image acquisition unit, the server will further ensure that the acquisition center point of the image acquisition frame is located in the reference acquisition circle, so that the image acquisition unit can acquire the complete actual detection area. At this time, the server will obtain the connecting line segment between the acquisition center point of the image acquisition frame and the detection center point, and adjust the posture of the image acquisition frame of the image acquisition unit in the extension direction of the connecting line segment until the acquisition center point is located in the reference acquisition circle. This not only ensures that the image acquisition unit can acquire the complete actual detection area, but also reduces the amount of data calculation.

[0138] In step S108, the following contents are included:

[0139] The bolt loosening prediction coefficients corresponding to the actual detection areas are determined based on the detection images, and the prediction risk levels corresponding to the actual detection areas are determined based on the bolt loosening prediction coefficients.

[0140] For example, in this embodiment, after obtaining each detection image, the server will determine the bolt loosening prediction coefficient corresponding to each actual detection area, and determine the predicted risk level corresponding to each actual detection area based on the bolt loosening prediction coefficient, so as to determine the risk situation of the transmission tower.

[0141] Furthermore, the above-mentioned “determining the bolt loosening prediction coefficients corresponding to the actual detection areas based on the detection images, and determining the predicted risk levels corresponding to the actual detection areas based on the bolt loosening prediction coefficients” also includes the following steps:

[0142] Performing image recognition on each detection image to determine bolt images corresponding to different bolt structures in the detection image;

[0143] Based on each bolt image, all nut structures having threaded connection relationships with each corresponding bolt structure are determined as nut corresponding groups, and the positional relationship between each nut structure and the corresponding bolt structure in each nut corresponding group is determined respectively;

[0144] Based on the positional relationship, the looseness coefficients of the bolts corresponding to the bolt images are determined respectively, the looseness coefficients of the bolts are averaged, and the ratios of the average results and the detection heights corresponding to the detection images are calculated to obtain the looseness prediction coefficients of the bolts corresponding to the actual detection areas;

[0145] The predicted risk levels corresponding to the actual detection areas are determined based on the prediction coefficients of the loosening of the bolts.

[0146] For example, in this embodiment, after obtaining each detection image, the server will perform image recognition on each detection image through neural network learning and other methods. Since a detection area may contain multiple bolt structures, the server can determine the bolt images in the detection image that correspond to different bolt structures after performing image recognition on the detection image.

[0147] Since one or more nut structures may be installed on a bolt structure, that is, there may be one or more nut structures that have a threaded connection relationship with a bolt structure, the server will determine all nut structures that have a threaded connection relationship with each corresponding bolt structure as a nut corresponding group according to each bolt image, and determine the positional relationship between each nut structure and the corresponding bolt structure in each nut corresponding group. Then, the server can determine the bolt loosening coefficient of each bolt image according to the positional relationship, and calculate the average of each bolt loosening coefficient.

[0148] Since each transmission tower has a certain height, the risk of loosening of the bolt structure at the lower part of the transmission tower will be higher than the risk of loosening of the bolt structure at the higher part. Therefore, the server will calculate the ratio of each mean result with each detection height corresponding to each detection image, so as to obtain the bolt loosening prediction coefficient corresponding to each actual detection area. Finally, the server will determine the predicted risk level corresponding to each actual detection area based on the bolt loosening prediction coefficient.

[0149] It should be noted that the correspondence between the predicted risk level and the bolt loosening prediction coefficient can be set in advance. For example, when the predicted risk level is level 1, the corresponding coefficient interval is [8.10], that is, when the bolt loosening prediction coefficient is in the corresponding coefficient interval, it can be determined as having a predicted risk level of level 1; for example, when the predicted risk level is level 2, the corresponding coefficient interval is [5.7], that is, when the bolt loosening prediction coefficient is in the corresponding coefficient interval, it can be determined as having a predicted risk level of level 2; here, the correspondence between the predicted risk level and the bolt loosening prediction coefficient can be adjusted at any time based on user needs, and the above values ​​are for example only.

[0150] Furthermore, the above-mentioned “determining the looseness coefficient of each bolt corresponding to each bolt image based on the position relationship” further includes the following steps:

[0151] When any of the nut corresponding groups includes a nut structure, obtaining a bolt distance between the nut structure and the corresponding bolt structure, and determining a bolt loosening coefficient corresponding to the bolt image based on the bolt distance;

[0152] When any of the nut corresponding groups includes multiple nut structures, the multiple nut structures are sorted based on the proximity to the corresponding bolt structure to obtain different nut serial numbers corresponding to the multiple nut structures, and different serial number coefficients are allocated to the multiple nut structures corresponding to different nut serial numbers based on the allocation principle from large to small;

[0153] Obtaining bolt distances between each nut structure and the corresponding bolt structure respectively, and determining initial looseness coefficients corresponding to each nut structure respectively based on the bolt distances;

[0154] Calculate the product of each initial loosening coefficient and the corresponding coefficient with different serial numbers to obtain the bolt loosening coefficient corresponding to the bolt image;

[0155] When any of the nut corresponding groups does not include a nut structure, the retrieved reference looseness coefficient is determined as the bolt looseness coefficient corresponding to the bolt image.

[0156] For example, in this embodiment, after the server determines the positional relationship between each nut structure and the corresponding bolt structure in each nut corresponding group, when any nut corresponding group includes a nut structure, the server will obtain the bolt distance between the nut structure and the corresponding bolt structure, and compare the bolt distance with the preset distance, so as to determine the bolt loosening coefficient corresponding to the bolt image.

[0157] When any nut corresponding group includes multiple nut structures, the server will sort the multiple nut structures according to the degree of proximity to the corresponding bolt structure, so as to obtain different nut serial numbers corresponding to the multiple nut structures. Since when the nut structure closest to the bolt structure is loose, it indicates that the vibration is more severe and the risk level is higher, the server will allocate different serial number coefficients to the multiple nut structures corresponding to different nut serial numbers based on the principle of allocation from large to small. For example, if there are three nut structures, the nut serial number closest to the bolt structure is 1, and its serial number coefficient is 2; the nut serial number in the middle is 2, and its serial number coefficient is 1.5; the nut serial number farthest from the bolt is 3, and its serial number coefficient is 1.2. Then, the server will obtain the distance between each bolt structure and the corresponding bolt structure, and compare the bolt distance with the preset distance, so as to determine the initial loosening coefficients corresponding to each nut structure, and then multiply each initial loosening coefficient with the corresponding different serial number coefficients to obtain the bolt loosening coefficient corresponding to the bolt image.

[0158] When any nut corresponding group does not include a nut structure, it indicates that the nut structure in the current nut corresponding group has been separated from the bolt due to vibration. At this time, the server will determine the retrieved reference looseness coefficient as the bolt looseness coefficient of the bolt image.

[0159] This embodiment can identify different situations where the nut corresponding group includes nut structures, such as Figure 4 As shown, the bolt loosening coefficient is determined according to different situations, which has a certain reference value.

[0160] In step S110, the following contents are included:

[0161] Based on each predicted risk level, multi-dimensional area marking is performed on the area parts located in the initial virtual tower and corresponding to each actual detection area, so as to obtain the current virtual tower.

[0162] For example, in this embodiment, after determining each predicted risk level, the server will perform multi-dimensional area marking on the area parts in the initial virtual tower corresponding to each actual detection area according to each predicted risk level to obtain the current virtual tower, which can enhance a certain degree of identification and facilitate subsequent viewing by management personnel.

[0163] It should be noted that, here, multi-dimensional area marking can be understood as marking the corresponding area parts based on color dimension, size dimension or other dimensions. For example, in the case of color dimension, different marking colors can be set based on the different predicted risk levels. For example, when the predicted risk levels include level 1, level 2 and level 3, level 1 can be configured to be red, level 2 can be configured to be yellow, and level 3 can be configured to be green, so that the corresponding area marking can be performed according to the conspicuousness of the color; for another example, in the case of size dimension, different marking sizes can be set based on the different predicted risk levels. For example, when the predicted risk levels include level 1, level 2 and level 3, the area outline of the area part corresponding to level 1 can be set to a first size, the area outline of the area part corresponding to level 2 can be set to a second size, and the area outline of the area part corresponding to level 3 can be set to a third size, wherein the first size is larger than the second size, and the second size is larger than the third size; through the above-mentioned multi-dimensional area marking, it is possible to realize visual area marking of different predicted risk levels, thereby improving the corresponding display effect, and can help users quickly locate the area parts with higher predicted risk levels, thereby improving the corresponding user experience.

[0164] To sum up, in this embodiment, when the drone flies along a preset route and scans any tower sign, the server will obtain the detection coordinates corresponding to the tower sign, and control the drone to fly toward the preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates; then, based on the tower sign, the initial virtual tower corresponding to the transmission tower is retrieved, and based on the route formulation strategy, a detection route is determined that takes the preset detection point as the detection starting point and passes through each virtual detection area corresponding to the initial virtual tower; then, in response to the drone flying to the preset detection point, the server will control the drone to fly around the transmission tower based on the detection route, and collect images of each real detection area corresponding to each virtual detection area passed by, thereby obtaining each detection image; then, the loosening prediction coefficient of each bolt in each real detection area is determined, and each predicted risk level of each real detection area is determined; then, based on each predicted risk level, the area portion corresponding to each real detection area in the initial virtual tower is multi-dimensionally marked to obtain the current virtual tower. The present invention can quickly predict the risk level of bolt loosening, improves detection efficiency, and has a certain degree of accuracy, which can provide corresponding reference for management personnel.

[0165] Another embodiment of the present invention provides a transmission tower bolt loosening detection device, Figure 2 The corresponding device block diagram is as follows:

[0166] The flight module is configured to obtain detection coordinates corresponding to the tower sign in response to the drone flying along a preset route until it scans any tower sign, and control the drone to fly toward a preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates;

[0167] A calling module is configured to call an initial virtual tower corresponding to the transmission tower based on the tower sign, and determine a detection route that takes the preset detection point as a detection starting point and passes through each virtual detection area corresponding to the initial virtual tower based on a route formulation strategy;

[0168] The acquisition module is configured to control the drone to fly around the transmission tower based on the detection route in response to the drone flying to the preset detection point, and to acquire images of each real detection area corresponding to each virtual detection area passed by, so as to obtain each detection image;

[0169] A prediction module is configured to determine each bolt loosening prediction coefficient corresponding to each actual detection area based on each detection image, and determine each prediction risk level corresponding to each actual detection area based on each bolt loosening prediction coefficient;

[0170] The marking module is configured to perform multi-dimensional regional marking on the area parts corresponding to each actual detection area in the initial virtual tower based on each predicted risk level to obtain the current virtual tower.

[0171] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the preferred embodiment of the present invention.

[0172] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0173] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0174] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into multiple submodules.

[0175] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of submodules or subunits or subcomponents.

[0176] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present invention and to form different embodiments.

[0177] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions. Therefore, a processor with necessary instructions for implementing the method or method elements forms a device for implementing the method or method elements. In addition, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.

[0178] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved, and is not intended to imply that the objects so described must have a given order in time, space, order, or in any other manner.

[0179] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is primarily selected for readability and instructional purposes, rather than for explaining or limiting the subject matter of the present invention.

Claims

1. A method for detecting loose bolts on a transmission tower, characterized in that: The following steps are involved: In response to the drone flying along a preset route until it scans any tower sign, the detection coordinates corresponding to the tower sign are acquired, and the drone is controlled to fly toward a preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates; Retrieving an initial virtual iron tower corresponding to the transmission iron tower based on the iron tower sign, and determining a detection route with the preset detection point as the detection starting point and passing through each virtual detection area corresponding to the initial virtual iron tower based on a route formulation strategy; In response to the drone flying to the preset detection point, the drone is controlled to fly around the transmission tower based on the detection route, and images are collected for each real detection area corresponding to each virtual detection area along the way to obtain each detection image; Determine each bolt loosening prediction coefficient corresponding to each actual detection area based on each detection image, and determine each prediction risk level corresponding to each actual detection area based on each bolt loosening prediction coefficient; Based on each predicted risk level, a multi-dimensional area marking is performed on the area portions corresponding to each actual detection area in the initial virtual tower to obtain a current virtual tower; In response to the drone flying along a preset route until it scans any tower sign, obtaining the detection coordinates corresponding to the tower sign, and controlling the drone to fly toward the preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates, including: Coordinate processing is performed on the preset route, and the preset route is divided into points based on the distribution of towers to obtain scanning points corresponding to different point coordinates; Acquire the GPS positioning coordinates corresponding to the drone in real time, and trigger the opening of the scanning detection unit preset on the drone in response to the GPS positioning coordinates coinciding with the point coordinates corresponding to any scanning point; In response to the scanning and detecting unit scanning a tower tag located on any tower sign, acquiring a detection coordinate having a link relationship with the tower tag, and generating an initial navigation route to reach the detection coordinate; Determine the geo-fence information corresponding to the transmission tower, and update the initial navigation route based on the geo-fence information to obtain a current navigation route; The drone is controlled to fly toward the preset detection point based on the current navigation route.

2. The method for detecting loose bolts of a transmission tower according to claim 1, characterized in that: The preset route is processed into coordinates, and the preset route is divided into points based on the distribution of towers to obtain scanning points corresponding to different point coordinates, including: In the preset route, tower identification points and starting points corresponding to different transmission towers arranged in the detection order are respectively determined, and route segments reaching each transmission tower are respectively determined in the detection order with the starting point of the preset route as the starting point; The preset route is processed into coordinates to determine the starting coordinates and the identification coordinates corresponding to the starting point and the identification points of each tower, and based on the starting coordinates and the identification coordinates, a difference calculation based on adjacent positions is performed to obtain the route distances corresponding to the route segments; Retrieving a preset division table, wherein the preset division table includes different distance intervals and the number of segments corresponding to the different distance intervals; Traversing the preset division table, determining each distance interval corresponding to each route distance, and determining each number of segments corresponding to each route segment based on each distance interval; Divide each route segment into segments based on the corresponding number of segments to obtain segment sub-points corresponding to each route segment, determine the one of the segment sub-points closest to the corresponding tower identification point as the scanning point, and determine the point coordinates of the scanning point based on the identification coordinates of the tower identification point to obtain scanning points corresponding to different point coordinates.

3. The method for detecting loose bolts of a transmission tower according to claim 2, characterized in that: Determining the geo-fence information corresponding to the transmission tower, and updating the initial navigation route based on the geo-fence information to obtain a current navigation route, including: In response to the scanning and detecting unit scanning a tower tag located on any tower sign, each fence point corresponding to the transmission tower is acquired, and point connections are performed based on each fence point to obtain a virtual geographic fence surrounding the transmission tower; Determine whether there is interference between the initial navigation route and the virtual geo-fence, and divide the initial navigation route if there is interference to obtain a first sub-segment including the scanning point, a second sub-segment including the interference part, and a third sub-segment including the preset detection point; Determine the endpoints of the first sub-segment other than the corresponding scanning points as the starting endpoints, determine the endpoints of the third sub-segment other than the corresponding preset detection points as the ending endpoints, and generate an updated line segment around the fence edge of the virtual geographic fence based on the shortest line segment generation principle with the starting endpoint as the starting point and the ending endpoint as the end point; The first sub-segment, the updated segment and the third sub-segment are combined to obtain a current navigation route.

4. The method for detecting loose bolts of a transmission tower according to claim 1, characterized in that: Based on the tower sign, an initial virtual tower corresponding to the transmission tower is retrieved, and based on a route formulation strategy, a detection route is determined that takes the preset detection point as a detection starting point and passes through each virtual detection area corresponding to the initial virtual tower, including: In response to the scanning and detecting unit scanning a tower tag located on any tower sign, retrieving an initial virtual tower corresponding to the transmission tower; Determining the structure of the initial virtual tower and determining the facades of each tower corresponding to the initial virtual tower; Determine the straight-line distances between each tower facade and the preset detection point respectively, determine the tower facade corresponding to the shortest straight-line distance as the starting facade, and determine the corresponding detection sequence based on the positional relationship between all remaining tower facades and the starting facade; Determine the virtual detection areas corresponding to the facades of each tower based on a preset area determination strategy, and obtain a first area sequence arranged from top to bottom corresponding to the facade of each tower; Based on the detection sequence, reversely modify the first area sequence of all tower facades corresponding to even numbers to obtain a second area sequence; Based on the detection sequence and each of the area sequences, a detection route of each virtual detection area corresponding to the initial virtual tower with the preset detection point as the detection starting point is determined.

5. The method for detecting loose bolts of a transmission tower according to claim 4, characterized in that: The virtual detection areas corresponding to the facades of each tower are determined based on the preset area determination strategy, including: Determine the vertical height of the initial virtual iron tower based on the vertical dimension, determine the maximum horizontal width and the minimum horizontal width of each iron tower facade based on the horizontal dimension, and calculate the mean of the maximum horizontal width and the minimum horizontal width to obtain the horizontal mean corresponding to each iron tower facade; Determine a height evaluation coefficient based on the vertical height, determine each lateral evaluation coefficient based on each lateral mean value, and determine each division coefficient corresponding to each tower facade based on the height evaluation coefficient and each lateral evaluation coefficient; Calculate the ratios of the division coefficients and the retrieved reference coefficients respectively, and multiply the obtained ratio coefficients by the retrieved coefficient weights to obtain the adjusted values; The adjusted values ​​are multiplied by the retrieved reference division numbers corresponding to the reference coefficients to obtain initial division numbers, and integer adjustments are made based on the initial division numbers to obtain current division numbers corresponding to the facades of the towers; Based on the current division numbers, the outer facades of each tower are divided into regions from bottom to top to obtain virtual detection regions corresponding to the outer surfaces of each tower.

6. The method for detecting loose bolts of a transmission tower according to claim 1, characterized in that: The drone is controlled to fly around the transmission tower based on the detection route, and images are collected from each real detection area corresponding to each virtual detection area along the way to obtain each detection image, including: Determine the detection center points corresponding to the actual detection areas, establish reference collection circles with the detection center points as circles, and obtain the detection heights corresponding to the detection center points; Controlling the UAV to fly around the transmission tower based on the detection route, and acquiring the flight altitude in real time; In response to the drone flying to a flight altitude that is the same as any detection altitude, the drone is controlled to hover for a preset time, and an image acquisition unit preset on the drone is triggered to be turned on; When the detection center point does not exist in the image acquisition frame corresponding to the image acquisition unit, controlling the image acquisition frame of the image acquisition unit to adjust its posture; When the detection center point exists in the image acquisition frame corresponding to the image acquisition unit, the connecting line segment between the acquisition center point of the image acquisition frame and the detection center point is obtained, and the image acquisition frame of the image acquisition unit is adjusted in position based on the extension direction of the connecting line segment until the acquisition center point is located in the reference acquisition circle.

7. The method for detecting loose bolts of a transmission tower according to claim 6, characterized in that: Determine each bolt loosening prediction coefficient corresponding to each actual detection area based on each detection image, and determine each prediction risk level corresponding to each actual detection area based on each bolt loosening prediction coefficient, including: Performing image recognition on each detection image to determine bolt images corresponding to different bolt structures in the detection image; Based on each bolt image, all nut structures having threaded connection relationships with each corresponding bolt structure are determined as nut corresponding groups, and the positional relationship between each nut structure and the corresponding bolt structure in each nut corresponding group is determined respectively; Based on the positional relationship, the looseness coefficients of the bolts corresponding to the bolt images are determined respectively, the looseness coefficients of the bolts are averaged, and the ratios of the average results and the detection heights corresponding to the detection images are calculated to obtain the looseness prediction coefficients of the bolts corresponding to the actual detection areas; The predicted risk levels corresponding to the actual detection areas are determined based on the prediction coefficients of the loosening of the bolts.

8. The method for detecting loose bolts of a transmission tower according to claim 7, characterized in that: The looseness coefficients of the bolts corresponding to the bolt images are determined based on the positional relationship, including: When any of the nut corresponding groups includes a nut structure, obtaining a bolt distance between the nut structure and the corresponding bolt structure, and determining a bolt loosening coefficient corresponding to the bolt image based on the bolt distance; When any of the nut corresponding groups includes multiple nut structures, the multiple nut structures are sorted based on the proximity to the corresponding bolt structure to obtain different nut serial numbers corresponding to the multiple nut structures, and different serial number coefficients are allocated to the multiple nut structures corresponding to different nut serial numbers based on the allocation principle from large to small; Obtaining bolt distances between each nut structure and the corresponding bolt structure respectively, and determining initial looseness coefficients corresponding to each nut structure respectively based on the bolt distances; Calculate the product of each initial loosening coefficient and the corresponding coefficient with different serial numbers to obtain the bolt loosening coefficient corresponding to the bolt image; When any of the nut corresponding groups does not include a nut structure, the retrieved reference looseness coefficient is determined as the bolt looseness coefficient corresponding to the bolt image.

9. A transmission tower bolt loosening detection device, characterized in that: include: The flight module is configured to obtain detection coordinates corresponding to the tower sign in response to the drone flying along a preset route until it scans any tower sign, and control the drone to fly toward a preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates; A calling module is configured to call an initial virtual tower corresponding to the transmission tower based on the tower sign, and determine a detection route with the preset detection point as the detection starting point and passing through each virtual detection area corresponding to the initial virtual tower based on a route formulation strategy; The acquisition module is configured to control the drone to fly around the transmission tower based on the detection route in response to the drone flying to the preset detection point, and to acquire images of each real detection area corresponding to each virtual detection area passed by, so as to obtain each detection image; A prediction module is configured to determine each bolt loosening prediction coefficient corresponding to each actual detection area based on each detection image, and determine each prediction risk level corresponding to each actual detection area based on each bolt loosening prediction coefficient; A marking module is configured to perform multi-dimensional regional marking on the regional parts corresponding to the actual detection areas respectively located in the initial virtual tower based on the predicted risk levels to obtain a current virtual tower; In response to the drone flying along a preset route until it scans any tower sign, obtaining the detection coordinates corresponding to the tower sign, and controlling the drone to fly toward the preset detection point of the transmission tower corresponding to the tower sign based on the detection coordinates, including: Coordinate processing is performed on the preset route, and the preset route is divided into points based on the distribution of towers to obtain scanning points corresponding to different point coordinates; Acquire the GPS positioning coordinates corresponding to the drone in real time, and trigger the opening of the scanning detection unit preset on the drone in response to the GPS positioning coordinates coinciding with the point coordinates corresponding to any scanning point; In response to the scanning and detecting unit scanning a tower tag located on any tower sign, acquiring a detection coordinate having a link relationship with the tower tag, and generating an initial navigation route to reach the detection coordinate; Determine the geo-fence information corresponding to the transmission tower, and update the initial navigation route based on the geo-fence information to obtain a current navigation route; The drone is controlled to fly toward the preset detection point based on the current navigation route.

Images