A building crack detection device

Abstract

The present invention relates to the field of engineering detection technology, and specifically to a building crack detection device, including an unmanned aerial vehicle, a wall climbing vehicle, an environment recognition module, a crack recognition module and a controller, wherein the wall climbing vehicle is arranged on the top of the unmanned aerial vehicle, the environment recognition module is used for the environmental parameters around the bridge, the crack recognition module is used to obtain bridge crack data, and the controller is configured to: if the environmental parameters around the bridge belong to the first target type, then the detection is performed in a first detection mode; if the environmental parameters around the bridge belong to the second target type, then the detection is performed in a second detection mode, wherein the environmental parameters around the bridge at least include weather information. The building crack detection device provided by the present invention can reasonably select the detection mode according to the weather information, so as to improve the safety of the unmanned aerial vehicle detection while ensuring the accuracy of the detection result as much as possible.

Description

Technical Field

[0001] The invention relates to the technical field of engineering detection, and in particular to a building crack detection device. Background Art

[0002] With the continuous development of my country's highway transportation industry, the number of bridges is also increasing. During the service life of bridges, they will inevitably encounter the influence of various traffic loads and environmental factors, such as random traffic loads, strong winds, rain, snow, earthquakes, freezing and other environmental factors. At the same time, they may suffer from heavy traffic, even overloading and damage caused by human factors such as impact damage. These damages manifest in various forms, such as concrete carbonization, breakage and cracking, steel corrosion, support hollowing and deformation, etc. Among them, most of the damage to bridges is caused by cracks. Therefore, in the health detection of bridge structures, the detection, identification and extraction of bridge cracks and the analysis and prediction of crack expansion to ensure the safe operation and maintenance of the structure are particularly important.

[0003] Since most bridges are suspended, there are many areas that need to be inspected that cannot be directly reached by personnel or inspection equipment, or they can reach but cannot get close for inspection. Drones are mobile and flexible, can operate at high altitudes, and can obtain image information beyond human line of sight. Therefore, with the rapid development of drone technology, drones are widely used in bridge inspection. Using drones for bridge crack detection can not only improve work efficiency during inspection, but also ensure the safety of inspection personnel.

[0004] In the related technology, for example, the reference document with authorization announcement number CN108051450B discloses a drone-based bridge health detection system and method. The drone-based bridge health detection system uses the detection crack data collected by the drone to compare with the historical data in the database to obtain the crack change trend, thereby facilitating the staff to predict the future development of the cracks and perform maintenance work in a timely manner, thereby improving the efficiency of the maintenance work and ensuring the normal use of the bridge to be tested.

[0005] When a bridge is built in a valley, it is often covered by fog due to the special geographical environment of the valley. As a result, when the bridge is inspected for cracks by a drone, the presence of fog will interfere with the drone's detection results. Summary of the invention

[0006] Based on this, it is necessary to provide a building crack detection device to address the problem of inaccurate detection results in the current bridge crack detection process.

[0007] The above purpose is achieved through the following technical solutions:

[0008] A building crack detection device, comprising:

[0009] Drones;

[0010] A wall climbing vehicle, arranged on top of the drone;

[0011] An environmental recognition module is used to obtain environmental parameters around the bridge;

[0012] Crack identification module, used to obtain bridge crack data;

[0013] The controller is configured as:

[0014] If the environmental parameters around the bridge belong to the first target type, detection is performed in a first detection mode;

[0015] If the environmental parameters around the bridge belong to the second target type, detection is performed in the second detection mode;

[0016] The environmental parameters around the bridge include at least weather information;

[0017] The weather information includes fog; the environment identification module is also used to identify the type of fog;

[0018] The controller is configured to perform detection in a first detection mode if the environmental parameter around the bridge belongs to the first target type, specifically including:

[0019] When the first target type is foggy, and part of the area to be detected is in the foggy area, detection is performed according to the first detection procedure according to the type of fog;

[0020] When the area to be detected is not in the fog area, detection is performed according to the second detection procedure;

[0021] The types of fog include at least uphill fog, valley fog, advection fog and evaporation fog;

[0022] The controller is configured to perform detection according to a first detection procedure according to the type of fog when part of the area to be detected is in the fog area, specifically comprising:

[0023] When the type of fog is uphill fog, the drone is started and moved to the bottom of the fog area, and when the wall climbing vehicle contacts the bridge wall, the wall climbing vehicle is started and moves on the bridge wall according to a first preset path, and the cracks on the bridge are identified by the crack identification module;

[0024] When the type of the fog is advection fog / evaporation fog, the drone is started and moved to the top of the fog area, and when the wall climbing vehicle contacts the bridge wall, the wall climbing vehicle is started and moves on the bridge wall according to a second preset path, and the cracks on the bridge are identified by the crack identification module;

[0025] When the type of fog is valley fog, the drone is started and moved to a non-fog area; when the wall climbing vehicle contacts the bridge wall, the wall climbing vehicle is started and moves on the bridge wall according to a third preset path, and cracks on the bridge are identified by the crack identification module;

[0026] The environment recognition module is also used to identify the type of the bridge wall surface, and the type of the bridge wall surface includes at least one of a straight surface, an inclined surface, and a curved surface, or a combination of at least two of them;

[0027] The controller is configured to perform detection in a second detection mode if the environmental parameter around the bridge belongs to the second target type, specifically including:

[0028] When the type of the bridge wall surface is a straight surface, starting the drone and flying along a sixth preset path, while identifying cracks on the bridge through the crack identification module;

[0029] When the type of the bridge wall is a slope, a curved surface, or a combination of at least two of a straight surface, a slope, and a curved surface, the drone is started and moved to the area to be detected. When the wall-climbing vehicle contacts the bridge wall, the wall-climbing vehicle is started and moves on the bridge wall according to the seventh preset path, and the cracks on the bridge are identified by the crack identification module.

[0030] Furthermore, the environment recognition module is also used to identify the type of the bridge wall surface, and the type of the bridge wall surface includes at least one of a straight surface, an inclined surface, and a curved surface, or a combination of at least two of them;

[0031] The controller is configured to perform detection according to a second detection procedure when the area to be detected is not in the fog area, specifically including:

[0032] When the type of the bridge wall surface is a straight surface, starting the drone and flying along a fourth preset path, while identifying cracks on the bridge through the crack identification module;

[0033] When the type of the bridge wall is a slope, a curved surface, or a combination of at least two of a straight surface, a slope, and a curved surface, the drone is started and moved to the area to be inspected; when the wall-climbing vehicle contacts the bridge wall, the wall-climbing vehicle is started and moves on the bridge wall along a fifth preset path, and at the same time, the cracks on the bridge are identified by the crack identification module.

[0034] Furthermore, the area to be inspected includes the side wall surface, the bottom surface and the surface of the pier of the abutment.

[0035] Furthermore, the building crack detection device further includes a first acquisition module, and the first acquisition module is used to acquire the concentration of fog;

[0036] The controller is further configured to:

[0037] When the wall-climbing vehicle travels on the bridge, the rotation speed of the rotor on the UAV is adjusted in direct proportion to the concentration of the fog.

[0038] Further, the crack data includes the length and width of the crack;

[0039] The controller is further configured to:

[0040] When the length of the crack is greater than or equal to a preset length, and the width of the crack is greater than or equal to a first preset width, marking the crack as a first repair echelon;

[0041] When the length of the crack is greater than or equal to the preset length and the width of the crack is less than the first preset width, or when the length of the crack is less than the preset length and the width of the crack is greater than or equal to the first preset width, the crack is marked as a second repair echelon;

[0042] When the length of the crack is less than the preset length and the width of the crack is less than the first preset width, the crack is marked as a third repair echelon.

[0043] Furthermore, the controller is also configured to:

[0044] When the crack belongs to the first repair echelon, the second repair echelon, or the third repair echelon, and the width of the crack is less than a second preset width, marking the crack as needing to be widened;

[0045] The second preset width is smaller than the first preset width.

[0046] Furthermore, the crack data also includes the type of crack;

[0047] The controller is further configured to:

[0048] When the crack belongs to the first repair echelon, the second repair echelon, or the third repair echelon, and the type of the crack is an active crack, marking the active crack as being repaired using a flexible material;

[0049] When the crack belongs to the first repair echelon, the second repair echelon, or the third repair echelon, and the type of the crack is a static crack, the active crack is marked as being repaired using a rigid material.

[0050] The beneficial effects of the present invention are:

[0051] When a building crack detection device provided by the present invention is used, the environmental parameters of the surroundings of the bridge are first obtained through an environmental recognition module. When the environmental parameters of the surroundings of the bridge belong to the first target type, the drone and the wall-climbing vehicle are controlled by the controller to perform detection in the first detection mode. When the environmental parameters of the surroundings of the bridge belong to the second target type, the drone and the wall-climbing vehicle are controlled by the controller to perform detection in the second detection mode. In this way, the detection mode can be reasonably selected according to weather information, so that the safety of drone detection is improved while ensuring the accuracy of the detection results as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of the three-dimensional structure of a building crack detection device provided by an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of the structure of the bridge provided by the present invention erected in a valley;

[0054] Figure 3 A flow chart of a control method for a building crack detection device provided by an embodiment of the present invention;

[0055] Figure 4 This is a flow chart of a control method for a building crack detection device provided by an embodiment of the present invention.

[0056] in:

[0057] 1. Drones;

[0058] 2. Wall climbing vehicle;

[0059] 3. Bridge abutment;

[0060] 4. Bridge pier; 41. Straight surface; 42. Inclined surface; 43. Curved surface. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned herein, unless otherwise specified, include direct and indirect connections (couplings). In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0063] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0064] like Figure 1 As shown, a building crack detection device provided by an embodiment of the present invention is configured to include a drone 1, a wall climbing vehicle 2, an environment recognition module, a crack recognition module and a controller, wherein the wall climbing vehicle 2 is arranged on the top of the drone 1; the environment recognition module is used to obtain environmental parameters around the bridge; the crack recognition module is used to obtain bridge crack data; the controller is configured as follows: Figure 3 As shown, if the environmental parameters around the bridge belong to the first target type, detection is performed in the first detection mode; if the environmental parameters around the bridge belong to the second target type, detection is performed in the second detection mode; wherein the environmental parameters around the bridge include at least weather information.

[0065] Specifically in this embodiment, the environment recognition module is configured to include a first camera and a first processor with a wired connection, and both are arranged on the drone 1, so that when in use, the first camera can be used to obtain real-time images of the surrounding environment of the bridge, and the image data can be transmitted to the first processor while guiding the drone 1 to fly. After the first processor processes the images of the surrounding environment of the bridge obtained by the first camera, the environmental parameters of the surrounding environment of the bridge are obtained; the crack recognition module can be configured to include a second camera and a second processor with a wired connection, and both are arranged on the wall-climbing vehicle 2, so that when in use, the second camera can be used to obtain real-time images of the bridge, and then input them into the second processor by wire. After the second processor processes the images of the bridge obtained by the second camera, the crack data on the bridge is obtained.

[0066] Exemplarily, both the first camera and the second camera may adopt the Vision Imaging MV-VD500SM / SC, with a resolution of 1280×1024, a pixel size of 2.2 μm×2.2 μm, and a sampling frequency set to 3s / time, that is, a sample is taken every 3s.

[0067] Exemplarily, both the first processor and the second processor may adopt Mali-C71 model ARM microprocessor, which can not only recognize the image of the bridge surrounding environment, but also perform grayscale correction and filtering denoising on the bridge image, and identify cracks.

[0068] It is to be understood that the first processor and the second processor may also be configured as the same processor.

[0069] More specifically, the controller refers to a device that can generate an operation control signal according to an instruction operation code and a timing signal. For example, the controller can be set as a central processing unit (CPU), a general-purpose processor network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD) or any combination thereof. The controller can also be other devices with processing functions, such as circuits, devices or software modules.

[0070] More specifically, the weather information is set to include fog and no fog.

[0071] During use, first start the drone 1 and operate the drone 1 to fly to a preset height, then obtain an image of the surrounding area of ​​the bridge through the first camera, and then process the image of the surrounding environment of the bridge obtained by the first camera through the first processor to obtain environmental parameters of the surrounding area of ​​the bridge. When the environmental parameters of the surrounding area of ​​the bridge belong to the first target type, the drone 1 and the wall-climbing vehicle 2 are controlled by the controller to perform detection in the first detection mode. When the environmental parameters of the surrounding area of ​​the bridge belong to the second target type, the drone 1 and the wall-climbing vehicle 2 are controlled by the controller to perform detection in the second detection mode, and at the same time, the bridge image is obtained through the second camera, and then the bridge image obtained by the second camera is processed by the second processor to obtain crack data on the bridge; thereby, the detection mode can be reasonably selected according to weather information, so that the safety of the drone 1 during detection is improved while ensuring the accuracy of the detection result as much as possible.

[0072] In some embodiments, Figure 3 As shown, the weather information includes fog; the environment recognition module is also used to obtain the type of fog;

[0073] The controller is configured to perform detection in a first detection mode if the environmental parameter around the bridge belongs to the first target type, specifically including:

[0074] The first target type is foggy. When part of the area to be detected is in the foggy area, detection is performed according to the first detection procedure based on the type of fog;

[0075] When the area to be inspected is not in the fog area, the inspection is performed according to the second inspection procedure;

[0076] Among them, the types of fog include at least uphill fog, valley fog, advection fog and evaporation fog.

[0077] Specifically in this embodiment, after the first camera obtains the image data of the bridge's surrounding environment, it is input into the first processor via a wired manner. After the first processor processes the image of the bridge's surrounding environment obtained by the first camera, the environmental parameters of the bridge's surroundings and the type of fog are obtained.

[0078] More specifically, when there is fog in the environment around the bridge, since different types of fog cover different ranges and have different impacts on the flight trajectory of the drone 1, in order to ensure the accuracy of the detection results, it is set to perform detection according to the type of fog and in accordance with the first detection procedure; among them, uphill fog is caused by the condensation of water vapor due to the cooling of humid air rising along the hillside, and generally formed at the top of the valley; valley fog specifically refers to fog formed in the valley, usually at night or in the early morning, when the air in the valley cools, it is formed by condensation of water vapor, and its location is uncertain; advection fog is formed when warm and humid air flows over cold ground and the air cools to below the dew point temperature, generally appearing in the lower part of the valley; evaporative fog is formed when warm air passes over cold water, the temperature drops, and the air near the water surface quickly becomes saturated, generally appearing in the lower part of the valley; when there is no fog in the environment around the bridge, in order to ensure the safety of the drone 1 and the efficiency of detection, it is set to perform detection according to the second detection procedure.

[0079] In a further embodiment, Figure 3 As shown, the controller is configured to perform detection according to the first detection procedure according to the type of fog when part of the area to be detected is in the fog area, specifically including:

[0080] When the fog type is uphill fog, the drone 1 is started and moved to the bottom of the fog area. When the wall climbing vehicle 2 contacts the bridge wall, the wall climbing vehicle 2 is started and moves on the bridge wall according to the first preset path, and the cracks on the bridge are identified by the crack identification module.

[0081] When the fog type is advection fog / evaporation fog, the drone 1 is started and moved to the top of the fog area. When the wall climbing vehicle 2 contacts the bridge wall, the wall climbing vehicle 2 is started and moves on the bridge wall according to the second preset path, and the cracks on the bridge are identified by the crack identification module.

[0082] When the type of fog is valley fog, start the drone 1 and move it to a non-fog area. When the wall-climbing vehicle 2 contacts the bridge wall, start the wall-climbing vehicle 2 and move on the bridge wall according to a third preset path. At the same time, the cracks on the bridge are identified through the crack recognition module.

[0083] Specifically in this embodiment, when the type of fog is uphill fog, since it is mainly formed at the height of the valley, when part of the area to be detected is in the fog area, the uphill fog mainly covers the upper end of the area to be detected. In order to improve the accuracy of the landing point of the drone 1 on the bridge wall and avoid collision accidents, it is set to start the drone 1, and drive the wall climbing vehicle 2 to move to the bottom of the fog area through the drone 1, and then adjust the posture of the drone 1 so that the drone 1 can drive the wall climbing vehicle 2 to move in the direction close to the bridge wall. When the wall climbing vehicle 2 contacts the bridge wall, the wall climbing vehicle 2 is started and the first prediction is followed. A path is assumed to travel on the wall of the bridge, and cracks on the bridge are identified by a crack recognition module; during the movement of the wall-climbing vehicle 2, the lift generated by the drone 1 enables the wall-climbing vehicle 2 to be pushed against the wall of the bridge with a certain pressure, thereby generating a friction force between the wall-climbing vehicle 2 and the wall of the bridge to balance the gravity of the drone 1 and the wall-climbing vehicle 2, so that the wall-climbing vehicle 2 can move stably on the wall of the bridge, and at the same time, the suction force generated by the rotation of the rotor of the drone 1 can suck away the fog between the wall-climbing vehicle 2 and the wall of the bridge, so that the second camera can obtain a clear image.

[0084] It can be understood that the first preset path can be set to be an S-shaped track that goes up first and then down, or down first and then up, or left then right, or right then left, so that the area to be detected can be fully detected.

[0085] When the type of fog is advection fog / evaporation fog, since it is mainly formed in the lower part of the valley, when part of the area to be detected is in the fog area, the advection fog / evaporation fog mainly covers the lower end of the area to be detected. In order to improve the accuracy of the landing point of the drone 1 on the bridge wall and avoid collision accidents, it is set to start the drone 1, and drive the wall-climbing vehicle 2 to move to the top of the fog area through the drone 1, and then adjust the posture of the drone 1 so that the drone 1 can drive the wall-climbing vehicle 2 to move in the direction close to the bridge wall. When the wall-climbing vehicle 2 contacts the bridge wall, the wall-climbing vehicle 2 is started and moves on the bridge wall according to the second preset path, and at the same time, the cracks on the bridge are identified by the crack recognition module.

[0086] It can be understood that the second preset path can be set to be an S-shaped track that goes up first and then down, or down first and then up, or left then right, or right then left, so that the area to be detected can be fully detected.

[0087] When the type of fog is valley fog, due to its uncertain formation position, when part of the area to be detected is in the foggy area, in order to improve the accuracy of the landing point of the drone 1 on the bridge wall and avoid collision accidents, it is set to start the drone 1, and use the drone 1 to drive the wall-climbing vehicle 2 to move to the non-fog area, and then adjust the posture of the drone 1 so that the drone 1 can drive the wall-climbing vehicle 2 to move in the direction close to the bridge wall. When the wall-climbing vehicle 2 contacts the bridge wall, the wall-climbing vehicle 2 is started and moves on the bridge wall according to the second preset path, and at the same time, the cracks on the bridge are identified by the crack recognition module.

[0088] It can be understood that the third preset path can be set to be an S-shaped track that goes up first and then down, or down first and then up, or left then right, or right then left, so that the area to be detected can be fully detected.

[0089] In other embodiments, Figure 3 As shown, the environment recognition module is also used to identify the type of bridge wall, such as Figure 2 As shown, the types of the bridge wall surface include at least one of a straight surface 41, an inclined surface 42, and a curved surface 43, or a combination of at least two of them;

[0090] The controller is configured to perform detection according to a second detection procedure when the area to be detected is not in the fog area, specifically including:

[0091] When the type of the bridge wall is a straight surface 41, the drone 1 is started and flies along a fourth preset path, and cracks on the bridge are identified by the crack identification module;

[0092] When the type of the bridge wall is a slope 42, a curved surface 43 or a combination of at least two of the straight surface 41, the slope 42, and the curved surface 43, the drone 1 is started and moved to the area to be inspected. When the wall-climbing vehicle 2 contacts the bridge wall, the wall-climbing vehicle 2 is started and moves on the bridge wall according to the fifth preset path, and the cracks on the bridge are identified by the crack recognition module.

[0093] Specifically in this embodiment, when the type of the bridge wall is a straight surface 41, the distance between the second camera and the bridge wall can be kept constant by simply controlling the drone 1. This can effectively avoid the reduction in sampling accuracy caused by small cracks being easily ignored when the sampling range is too large, and on the other hand, it can effectively avoid the reduction in detection efficiency caused by the sampling range being too small.

[0094] It can be understood that the second preset path can be set to be an S-shaped track that goes up first and then down, or down first and then up, so that the area to be inspected can be fully inspected.

[0095] More specifically, when the type of the bridge wall is a slope 42, a curved surface 43 or a combination of at least two of the straight surface 41, the slope 42, and the curved surface 43, in order to keep the distance between the second camera and the bridge wall unchanged, the flight trajectory of the drone 1 is relatively complicated, which not only makes it easy to miss and repeat inspections, but also makes it easy to cause collision accidents. In order to avoid the above situation, it is set to start the drone 1, and use the drone 1 to drive the wall-climbing vehicle 2 to move to the area to be inspected, and then adjust the posture of the drone 1 so that the drone 1 can drive the wall-climbing vehicle 2 to move in the direction close to the bridge wall. When the wall-climbing vehicle 2 contacts the bridge wall, the wall-climbing vehicle 2 is started and moves on the bridge wall according to the fifth preset path, and at the same time, the cracks on the bridge are identified by the crack recognition module.

[0096] It can be understood that the fifth preset path can be set to be an S-shaped track that goes up first and then down, or down first and then up, or left then right, or right then left, so that the area to be detected can be fully detected.

[0097] In a further embodiment, the area to be inspected is set to include the side wall surface, the bottom surface of the abutment 3 and the surface of the pier 4 .

[0098] In other embodiments, when the rotor speed of the drone 1 remains unchanged and the fog concentration increases, on the one hand, the suction force generated by the rotor of the drone 1 is limited, resulting in the inability to completely suck out the fog between the wall-climbing vehicle 2 and the bridge wall, so that the second camera cannot obtain a clear bridge image, affecting the accuracy of the crack data; on the other hand, the bridge wall will become more moist, making the wall-climbing vehicle 2 prone to slipping during travel, affecting the detection efficiency; to solve this problem, as Figure 3 As shown, the building crack detection device is configured to further include a first acquisition module, and the first acquisition module is used to acquire the concentration of fog;

[0099] The controller is also configured to:

[0100] When the wall-climbing vehicle 2 is traveling on the bridge, the rotation speed of the rotor on the UAV 1 is adjusted in direct proportion to the concentration of the fog.

[0101] Specifically in this embodiment, the first acquisition module can be set as a haze meter and connected to the controller by wire. The haze meter measures optical transmittance. The lower the optical transmittance, the thicker the fog and the lower the visibility. When the concentration of the fog represented by the haze meter increases, the rotation speed of the rotor on the drone 1 is increased proportionally through the controller. On the one hand, the rotor of the drone 1 can generate sufficient suction to suck away the fog between the wall-climbing vehicle 2 and the bridge wall to avoid affecting the clarity of the bridge image obtained by the second camera. On the other hand, the contact force between the wall-climbing vehicle 2 and the bridge wall is greater to avoid slipping.

[0102] In other embodiments, Figure 3 As shown, the environment recognition module is also used to identify the type of the bridge wall surface, and the type of the bridge wall surface includes at least one of a straight surface 41, an inclined surface 42, and a curved surface 43, or a combination of at least two of them;

[0103] The controller is configured to perform detection in a second detection mode if the environmental parameter around the bridge belongs to the second target type, specifically including:

[0104] When the type of the bridge wall surface is a straight surface 41, the drone 1 is started and flies according to the sixth preset path, and the cracks on the bridge are identified by the crack identification module;

[0105] When the type of the bridge wall is a slope 42, a curved surface 43 or a combination of at least two of the straight surface 41, the slope 42, and the curved surface 43, the drone 1 is started and moved to the area to be detected. When the wall-climbing vehicle 2 contacts the bridge wall, the wall-climbing vehicle 2 is started and moves on the bridge wall according to the seventh preset path, and the cracks on the bridge are identified by the crack recognition module.

[0106] Specifically in this embodiment, when the type of the bridge wall is a straight surface 41, the distance between the second camera and the bridge wall can be kept constant by simply controlling the drone 1. This can effectively avoid the reduction in sampling accuracy caused by small cracks being easily ignored when the sampling range is too large, and on the other hand, it can effectively avoid the reduction in detection efficiency caused by the sampling range being too small.

[0107] It can be understood that the sixth preset path can be set to be an S-shaped track that goes up first and then down, or down first and then up, so that the area to be inspected can be fully inspected.

[0108] More specifically, when the type of the bridge wall is a slope 42, a curved surface 43 or a combination of at least two of the straight surface 41, the slope 42, and the curved surface 43, in order to keep the distance between the second camera and the bridge wall unchanged, the flight trajectory of the drone 1 is relatively complicated, which not only makes it easy to miss and repeat inspections, but also makes it easy to cause collision accidents. Therefore, in order to avoid the above situation, it is set to start the drone 1, and use the drone 1 to drive the wall-climbing vehicle 2 to move to the area to be inspected, and then adjust the posture of the drone 1 so that the drone 1 can drive the wall-climbing vehicle 2 to move in the direction close to the bridge wall. When the wall-climbing vehicle 2 contacts the bridge wall, the wall-climbing vehicle 2 is started and moves on the bridge wall according to the seventh preset path, and at the same time, the cracks on the bridge are identified by the crack recognition module.

[0109] It can be understood that the seventh preset path can be set to be an S-shaped track that goes up first and then down, or down first and then up, or left then right, or right then left, so that the area to be detected can be fully detected.

[0110] In other embodiments, Figure 4 As shown, the crack data is set to include the length and width of the crack;

[0111] The controller is also configured to:

[0112] When the length of the crack is greater than or equal to the preset length, and the width of the crack is greater than or equal to the first preset width, the crack is marked as the first repair echelon;

[0113] When the length of the crack is greater than or equal to the preset length and the width of the crack is less than the first preset width, or the length of the crack is less than the preset length and the width of the crack is greater than or equal to the first preset width, the crack is marked as the second repair echelon;

[0114] When the length of the crack is less than the preset length and the width of the crack is less than the first preset width, the crack is marked as the third repair echelon.

[0115] Specifically in this embodiment, the preset length and the first preset width are both set widths, which can be changed according to actual needs; illustratively, the preset length can be set to 50cm, and the first preset width can be set to 5mm. When the length of the crack is greater than or equal to 50cm, and the width of the crack is greater than or equal to 5mm, it means that the situation of the crack is very serious. In order to avoid further expansion of the crack due to its own factors and environmental factors, the crack is marked as the first repair echelon; when the length of the crack is greater than or equal to 50cm and the width of the crack is less than 5mm, or the length of the crack is less than 50cm and the width of the crack is greater than or equal to 5mm, it means that the situation of the crack is relatively serious. In order to avoid further expansion of the crack due to its own factors and environmental factors, the crack is marked as the second repair echelon; when the length of the crack is less than 50cm and the width of the crack is less than 5mm, it means that the situation of the crack is general. In order to avoid further expansion of the crack due to its own factors and environmental factors, the crack is marked as the third repair echelon; after marking, maintenance personnel can promptly repair the cracks belonging to the first repair echelon, the second repair echelon and the third repair echelon according to priority from high to low.

[0116] In a further embodiment, Figure 4 As shown, the controller is also configured as:

[0117] When the crack belongs to the first repair echelon, the second repair echelon, or the third repair echelon, and the width of the crack is less than the second preset width, the crack is marked as needing to be widened;

[0118] The second preset width is smaller than the first preset width.

[0119] Specifically in this embodiment, the second preset width is set to a set width, which can be changed according to actual needs; illustratively, the second preset width can be set to 2 mm. When the width of the crack is less than 2 mm, it is difficult to fill the filler into the crack. In order to avoid affecting the repair work, it is set to mark the crack during the detection stage, so that in subsequent maintenance, the widening tools can be brought in advance to avoid affecting the maintenance efficiency.

[0120] In other embodiments, Figure 4 As shown, the crack data is set to also include the type of crack;

[0121] The controller is also configured to:

[0122] When the crack belongs to the first repair echelon, the second repair echelon, or the third repair echelon, and the type of the crack is an active crack, the active crack is marked as being repaired with a flexible material;

[0123] When the crack belongs to the first repair echelon, the second repair echelon, or the third repair echelon, and the type of the crack is a static crack, the active crack is marked as being repaired using a rigid material.

[0124] Specifically in this embodiment, it can be determined whether the current crack is an active crack or a static crack by comparing with historical data; when it is an active crack, since it may continue to expand, in order to avoid affecting the repair effect, it is set to mark the crack in the detection stage, so that during subsequent maintenance, flexible materials can be prepared in advance to adapt to the deformation of the structure, wherein the flexible material can be set to include polyurethane foam or elastic epoxy resin; when it is a static crack, in order to avoid affecting the repair effect, it is set to mark the crack in the detection stage, so that during subsequent maintenance, rigid materials can be prepared in advance, wherein the rigid material can be set to include epoxy resin or cement mortar.

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A building crack detection device, characterized in that: include: Drones; A wall-climbing vehicle, arranged on top of the drone; An environmental recognition module is used to obtain environmental parameters around the bridge; Crack identification module, used to obtain bridge crack data; The controller is configured as: If the environmental parameters around the bridge belong to the first target type, detection is performed in a first detection mode; If the environmental parameters around the bridge belong to the second target type, detection is performed in the second detection mode; The environmental parameters around the bridge include at least weather information; The weather information includes fog; the environment identification module is also used to identify the type of fog; The controller is configured to perform detection in a first detection mode if the environmental parameter around the bridge belongs to the first target type, specifically including: When the first target type is foggy, and part of the area to be detected is in the foggy area, detection is performed according to the first detection procedure based on the type of fog; When the area to be detected is not in the fog area, detection is performed according to the second detection procedure; The types of fog include at least uphill fog, valley fog, advection fog and evaporation fog; The controller is configured to perform detection according to a first detection procedure according to the type of fog when part of the area to be detected is in the fog area, specifically comprising: When the type of fog is uphill fog, the drone is started and moved to the bottom of the fog area, and when the wall climbing vehicle contacts the bridge wall, the wall climbing vehicle is started and moves on the bridge wall according to a first preset path, and the cracks on the bridge are identified by the crack identification module; When the type of the fog is advection fog / evaporation fog, the drone is started and moved to the top of the fog area, and when the wall climbing vehicle contacts the bridge wall, the wall climbing vehicle is started and moves on the bridge wall according to a second preset path, and the cracks on the bridge are identified by the crack identification module; When the type of fog is valley fog, the drone is started and moved to a non-fog area; when the wall climbing vehicle contacts the bridge wall, the wall climbing vehicle is started and moves on the bridge wall according to a third preset path, and cracks on the bridge are identified by the crack identification module; The environment recognition module is also used to identify the type of the bridge wall surface, and the type of the bridge wall surface includes one of a straight surface, an inclined surface, and a curved surface, or a combination of any two or three of them; The controller is configured to perform detection in a second detection mode if the environmental parameter around the bridge belongs to the second target type, specifically including: When the type of the bridge wall surface is a straight surface, starting the drone and flying along a sixth preset path, while identifying cracks on the bridge through the crack identification module; When the type of the bridge wall is a slope, a curved surface, or a combination of at least two of a straight surface, a slope, and a curved surface, the drone is started and moved to the area to be detected. When the wall-climbing vehicle contacts the bridge wall, the wall-climbing vehicle is started and moves on the bridge wall according to the seventh preset path, and the cracks on the bridge are identified by the crack identification module.

2. The building crack detection device according to claim 1, characterized in that: The environment recognition module is also used to identify the type of the bridge wall surface, and the type of the bridge wall surface includes one of a straight surface, an inclined surface, and a curved surface, or a combination of any two or three of them; The controller is configured to perform detection according to a second detection procedure when the area to be detected is not in the fog area, specifically including: When the type of the bridge wall surface is a straight surface, starting the drone and flying along a fourth preset path, while identifying cracks on the bridge through the crack identification module; When the type of the bridge wall is a slope, a curved surface, or a combination of at least two of a straight surface, a slope, and a curved surface, the drone is started and moved to the area to be inspected; when the wall-climbing vehicle contacts the bridge wall, the wall-climbing vehicle is started and moves on the bridge wall along a fifth preset path, and at the same time, the cracks on the bridge are identified by the crack identification module.

3. The building crack detection device according to claim 2, characterized in that: The area to be inspected includes the side wall surface, the bottom surface and the surface of the pier of the abutment.

4. The building crack detection device according to claim 1 or 2, characterized in that: The building crack detection device further includes a first acquisition module, the first acquisition module being used to acquire the concentration of fog; The controller is further configured to: When the wall-climbing vehicle travels on the bridge, the rotation speed of the rotor on the UAV is adjusted in direct proportion to the concentration of the fog.

5. The building crack detection device according to claim 1, characterized in that: The crack data includes the length and width of the crack; The controller is further configured to: When the length of the crack is greater than or equal to a preset length, and the width of the crack is greater than or equal to a first preset width, marking the crack as a first repair echelon; When the length of the crack is greater than or equal to the preset length and the width of the crack is less than the first preset width, or when the length of the crack is less than the preset length and the width of the crack is greater than or equal to the first preset width, the crack is marked as a second repair echelon; When the length of the crack is less than the preset length and the width of the crack is less than the first preset width, the crack is marked as a third repair echelon.

6. The building crack detection device according to claim 5, characterized in that: The controller is further configured to: When the crack belongs to the first repair echelon, the second repair echelon, or the third repair echelon, and the width of the crack is less than a second preset width, marking the crack as needing to be widened; The second preset width is smaller than the first preset width.

7. The building crack detection device according to claim 5, characterized in that: The crack data also includes the type of crack; The controller is further configured to: When the crack belongs to the first repair echelon, the second repair echelon, or the third repair echelon, and the type of the crack is an active crack, marking the active crack as being repaired using a flexible material; When the crack belongs to the first repair echelon, the second repair echelon, or the third repair echelon, and the type of the crack is a static crack, the active crack is marked as being repaired using a rigid material.

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