Method and equipment for detecting perpendicularity of pile body in pile sinking based on machine vision and medium

Abstract

The invention discloses a method and equipment for detecting the perpendicularity of a pile body in pile sinking based on machine vision and a medium. The method comprises the following steps that a first camera and a second camera are arranged around a pile body; the view angle orientation of the first camera is a first orientation angle; the visual angle orientation of the second camera is a second orientation angle; in the pile sinking construction process, pile body pictures are shot through a first camera and a second camera correspondingly, and a first pile body image and a second pile body image are obtained; performing perpendicularity error identification on the first pile body image and the second pile body image to obtain a first angle error and a second angle error; and according to the first orientation angle, the second orientation angle, the first angle error and the second angle error, the current perpendicularity deviation angle of the pile body is obtained through calculation. The angle error of the pile body in the vertical direction is extracted in a machine vision mode, the accurate pile body perpendicularity deviation angle is obtained through calculation in combination with the orientation angle of the camera, and non-contact monitoring of the pile body perpendicularity in the construction process is achieved.

Description

Technical Field

[0001] This invention relates to the field of pile foundation construction technology, specifically to a method, equipment, and medium for detecting the verticality of piles during pile driving based on machine vision. Background Technology

[0002] During pile foundation construction, quality problems frequently occur due to various factors such as construction operation errors, weak foundation soil, pile foundation equipment compression, and soil squeezing effect between piles. Especially during the pile driving stage, eccentricity is quite common. These factors can all lead to deviations in the verticality of the pile body, thereby affecting the bearing capacity of the pile foundation and the overall project quality.

[0003] To improve project quality, effective monitoring of pile verticality is necessary during construction. Currently, the most mature pile verticality monitoring technology mainly involves installing sensors on the pile or pile driver, using tilt sensors or inertial measurement units (IMUs) to collect pile attitude data in real time.

[0004] However, these monitoring technologies require modifications to the piling machine or pile body for installation, and the sensors need to be in direct contact with the pile body or piling machine during the measurement process, which not only increases costs but may also interfere with pile driving. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, equipment and medium for detecting the verticality of piles during pile driving based on machine vision.

[0006] The first aspect of this invention provides a machine vision-based method for detecting the verticality of a driven pile, comprising the following steps: A first camera and a second camera are installed around the pile; the first camera's viewing angle is a first orientation angle; the second camera's viewing angle is a second orientation angle. During the pile driving process, images of the pile body are captured by the first camera and the second camera respectively, resulting in images of the first pile body and the second pile body. Verticality error is identified by analyzing the first pile image and the second pile image to obtain the first angle error and the second angle error. Based on the first orientation angle, the second orientation angle, the first angle error, and the second angle error, the current verticality deviation angle of the pile is calculated.

[0007] Furthermore, the angle difference between the first orientation angle and the second orientation angle is 90°.

[0008] Furthermore, the first camera and the second camera capture multiple images of the first pile and the second pile during the pile driving process according to a preset shooting frequency.

[0009] Furthermore, the verticality error identification of the first pile image and the second pile image is achieved through a machine vision model; the machine vision model performs verticality error identification through the following steps: Image segmentation is performed on the first pile image and the second pile image to identify the pile mask or contour in the image; Determine the angle between the main axis of the pile mask or contour and the vertical line to obtain the first angular error and the second angular error.

[0010] Furthermore, image segmentation is performed on the first pile image and the second pile image, specifically through a semantic segmentation model or an instance segmentation model; the angle between the principal axis direction of the pile mask or contour and the vertical line is determined specifically through an edge extraction algorithm or a principal axis direction centerline extraction algorithm.

[0011] Further, the step of calculating the current verticality deviation angle of the pile body based on the first orientation angle, the second orientation angle, the first angle error, and the second angle error specifically includes the following steps: Calculate the plane normal vectors of the first pile image and the second pile image respectively to obtain the first plane normal vector and the second plane normal vector; Calculate the cross product of the first normal vector and the second normal vector to obtain the pile direction vector; The pile body direction vector is processed by taking components to obtain a first direction component, a second direction component, and a third direction component; The current verticality deviation angle of the pile is calculated based on the first directional component, the second directional component, and the third directional component.

[0012] Furthermore, the coordinate system used for the first plane normal vector / second plane normal vector and the pile direction vector is a spatial rectangular coordinate system in which the z-axis is perpendicular to the horizontal plane and the x-axis and y-axis are parallel to the horizontal plane; The ratio of the first plane normal vector to the second plane normal vector is calculated using the following formula: ; The direction vector of the pile body is calculated using the following formula: ; The perpendicularity deviation angle is calculated using the following formula: ; in, n 1，2 Denotes the normal vectors of the first and second planes; θ 1，2 Indicates the first and second orientation angles; φ 1，2 Indicates the first and second angular errors. kThis represents the direction vector of the pile. k x，y，z Indicates the direction vector of the pile body in x , y , z Components of the axis, α This indicates the perpendicularity deviation angle.

[0013] Furthermore, after calculating the current verticality deviation angle of the pile, the following steps are also included: When the current verticality deviation angle of the pile is greater than the preset deviation angle threshold, an alarm message is output and the alarm message is displayed to the pile driving construction personnel. After the pile driving construction is completed, the pile verticality change trend is generated and displayed to the pile driving construction personnel based on the current verticality deviation angle of the pile at multiple times.

[0014] Another aspect of the present invention discloses an electronic device, including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the above-described machine vision-based method for detecting the verticality of piles during pile driving.

[0015] In another aspect, the present invention discloses a computer-readable storage medium storing a program that is executed by a processor to implement the above-described machine vision-based method for detecting the verticality of piles during pile driving.

[0016] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0017] The embodiments of the present invention have the following beneficial effects: The present invention provides a method, equipment, and medium for detecting the verticality of piles during pile driving based on machine vision. It extracts the angular error of the pile in the vertical direction using machine vision and calculates the accurate verticality deviation angle of the pile by combining the camera's orientation angle. This invention achieves non-contact monitoring of pile verticality during pile driving, can output the real-time calculation result of the pile verticality deviation angle, and does not interfere with the pile driving process. It is widely used in the field of pile foundation construction technology.

[0018] Additional aspects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description or may be learned by practice of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the steps of a machine vision-based method for detecting the verticality of a pile during pile driving according to the present invention. Figure 2 This is a schematic diagram of the camera arrangement in the pile verticality detection method of the present invention; Figure 3 This is a schematic diagram of the deviation angle calculation in the pile verticality detection method of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device according to the present invention; Figure 5 This is a schematic diagram of a computer-readable storage medium structure according to the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0022] like Figure 1 As shown, the first embodiment of the present invention discloses a method for detecting the verticality of a pile during pile driving based on machine vision, including the following steps: S1. Install a first camera and a second camera around the pile; the viewing angle of the first camera is a first orientation angle; the viewing angle of the second camera is a second orientation angle; S2. During the pile driving process, images of the pile body are captured by the first camera and the second camera respectively to obtain images of the first pile body and the second pile body; S3. Perform verticality error identification on the first pile image and the second pile image to obtain the first angle error and the second angle error; S4. Calculate the current verticality deviation angle of the pile body based on the first orientation angle, the second orientation angle, the first angle error, and the second angle error.

[0023] In this embodiment of the invention, the angular error of the pile in the vertical direction is extracted by machine vision, and the accurate verticality deviation angle of the pile is calculated by combining the orientation angle of the camera.

[0024] The implementation process of each step of this invention is described in detail below: S1. Install a first camera and a second camera around the pile.

[0025] A schematic diagram of the camera arrangement in an embodiment of the present invention is shown below. Figure 2 As shown, the first and second cameras are positioned around the pile to capture the pile's verticality from different angles. The distance between the cameras and the pile is controlled at approximately 10-20 meters. Too close a distance may result in a limited field of view, image distortion, or affect the pile driving construction; too far a distance will result in lower resolution images of the pile, affecting the accuracy of angle error extraction.

[0026] In this embodiment of the invention, the angle between the camera's orientation and due north is used as the orientation angle, and the orientation angles of the first camera and the second camera are determined as the first orientation angle and the second orientation angle. The error source of the orientation angle is the electronic compass error; the deviation angle of the pile measured from the two viewing angles has a significant impact on the total error. This deviation angle error comes from the camera roll angle error and the tilt algorithm error of the pile in the image (such as the error of perspective projection trapezoidal correction and the error of pile edge tilt recognition, etc.). Error analysis of the orientation angle shows that when the difference in orientation angles between the two cameras is 90°, the error caused by the orientation angle is minimal; the greater the deviation of the difference in orientation angles between the two cameras from 90°, the more the partial derivative of the orientation angle error may increase, thus amplifying the orientation angle error. Therefore, in this embodiment of the invention, the angle difference between the first camera and the second camera is preferably set to 90 degrees, forming an approximately perpendicular viewing angle of the pile, so that the cross product of the first normal vector and the second normal vector in the subsequent calculation can more accurately indicate the direction of the pile.

[0027] The camera used in this embodiment of the invention is preferably a self-leveling gimbal camera, which can automatically keep the camera horizontal relative to the ground plane to reduce angle errors in subsequent shooting.

[0028] In some embodiments, more cameras can be set up to photograph the pile from different angles, so that the number of cameras is not limited in the embodiments of the present invention.

[0029] S2. During the pile driving process, images of the pile body are captured by the first camera and the second camera respectively, resulting in images of the first pile body and the second pile body.

[0030] After the pile driving construction begins, this embodiment of the invention sets the shooting frequency of the first camera and the second camera respectively, continuously acquiring images of the first pile and the second pile during the pile driving process to perform real-time calculation of the pile verticality deviation angle. The shooting frequency can be set according to real-time time, such as shooting once every 30 seconds, or set in conjunction with the pile driving construction, such as shooting once every three hammer blows; to achieve periodic image signal acquisition.

[0031] S3. Perform verticality error identification on the first pile image and the second pile image to obtain the first angle error and the second angle error.

[0032] In step S3, verticality error identification is performed on the first and second pile images using a machine vision model. In this embodiment of the invention, the machine vision model is deployed in a cloud platform. The cloud platform receives the first and second pile images transmitted by the first and second cameras in real time through an edge gateway and performs real-time verticality error identification.

[0033] In step S3, the machine vision model identifies verticality errors through the following steps: S3-1. Perform image segmentation on the first and second pile images to identify the pile mask or contour in the images.

[0034] In step S3-1, image segmentation is performed on the first and second pile images, specifically using a semantic segmentation model or an instance segmentation model. This involves identifying the pile mask or contour in the image by recognizing the pixel location information of the pile. The semantic segmentation model is preferably a U-Net model or a Fast-SCNN model, which can quickly output the point set of the pile edge by recognizing the category of each pixel in the image, serving as the pile mask or contour. The instance segmentation model is preferably a CenterMask model or a Mask R-CNN model, which determines the location of the pile in the image as the region of interest and performs a binarization mask operation on this region to obtain the boundary of the pile, serving as the pile mask or contour.

[0035] S3-2. Determine the angle between the main axis direction of the pile mask or contour and the vertical line to obtain the first angle error and the second angle error.

[0036] After determining the pile mask or contour, this embodiment of the invention uses an edge extraction algorithm or a principal axis centerline extraction algorithm to determine the angle between the principal axis direction of the pile mask or contour and the vertical line. The edge extraction algorithm is more suitable for pile masks or contours identified by semantic segmentation models, while the principal axis centerline extraction algorithm is more suitable for pile masks or contours extracted by instance segmentation models.

[0037] In a preferred embodiment, the edge extraction algorithm is performed through the following steps: First, the left and right edges of the pile are detected; then, straight lines are fitted to the left and right edge points respectively, and the average value is taken as the average centerline of the pile; finally, the average centerline of the pile is compared with the vertical line to obtain the angle error value. The edge extraction algorithm can be performed using the Canny edge detection algorithm.

[0038] As a preferred embodiment, the centerline extraction algorithm along the main axis is performed through the following steps: First, the pile mask is skeletonized to obtain a centerline with a width of one pixel. Then, linear regression is used to fit the centerline of the pile. Finally, the centerline of the pile is compared with the vertical line to obtain the angle error value. The centerline extraction algorithm along the main axis can be performed using a skeletonization algorithm.

[0039] S4. Calculate the current verticality deviation angle of the pile body based on the first orientation angle, the second orientation angle, the first angle error, and the second angle error.

[0040] In step S4, the coordinate system used for the first plane normal vector / second plane normal vector and the pile direction vector is a spatial rectangular coordinate system with the z-axis perpendicular to the horizontal plane and the x-axis and y-axis parallel to the horizontal plane. Preferably, a right-handed coordinate system is used as the spatial rectangular coordinate system in step S4.

[0041] In step S4, the current verticality deviation angle of the pile is calculated based on the first orientation angle, the second orientation angle, the first angle error, and the second angle error. This specifically includes the following steps: S4-1. Calculate the plane normal vectors of the first pile image and the second pile image respectively to obtain the first plane normal vector and the second plane normal vector.

[0042] like Figure 3 As shown, the pile is considered as a cylinder. If the pile tilts, the direction of rotation is determined by the right-hand screw rule. The pile first rotates about the y-axis. φ If the y-axis points due north at this point, then the pile is deflected to either due west or due east by an angle; then rotate it around the z-axis. θ The tilt of the pile is modeled to the desired degree. The rotation of the pile at this point corresponds to the following two rotation matrices: ; .

[0043] After rotation, the normal vector of the inclined plane of the pile is:

[0044] Therefore, the plane normal vectors of the first pile image and the second pile image can be set as: .

[0045] S4-2. Calculate the cross product of the first normal vector and the second normal vector to obtain the pile direction vector; The cross product of the normal vectors yields the pile direction vector: .

[0046] S4-3. Componentize the pile direction vector to obtain the first direction component, the second direction component, and the third direction component.

[0047] The component analysis of the pile's direction vector yields the components of the three coordinate axes. k x , k y , k z : ; ; .

[0048] S4-4. Calculate the current verticality deviation angle of the pile body based on the first directional component, the second directional component, and the third directional component.

[0049] The perpendicularity deviation angle is calculated using the following formula: ; in, n 1，2 Denotes the normal vectors of the first and second planes; θ 1，2 Indicates the first and second orientation angles; φ 1，2 Indicates the first and second angular errors. k This represents the direction vector of the pile. k x，y，z Indicates the direction vector of the pile body in x , y , z Components of the axis, α This indicates the perpendicularity deviation angle.

[0050] In some embodiments, after calculating the current verticality deviation angle of the pile, the following steps are also included: S5. When the current verticality deviation angle of the pile is greater than the preset deviation angle threshold, an alarm message is output and the alarm message is sent to the pile driving construction personnel.

[0051] When the verticality deviation angle of the pile is greater than the preset deviation angle threshold, it indicates that the pile has deviated significantly during the pile driving process. In order to avoid construction accidents, it is necessary to promptly output alarm information to prompt the pile driving construction personnel to attempt to correct the pile deviation or pull out and redrive the pile.

[0052] S6. After the pile driving construction is completed, the pile verticality change trend is generated and displayed to the pile driving construction personnel based on the current verticality deviation angle of the pile at multiple times.

[0053] After the pile driving is completed, the construction quality of this pile driving project is determined by showing the pile driving construction personnel the trend of the pile verticality change, which facilitates the optimization of subsequent construction operations.

[0054] This invention enables non-contact monitoring of pile verticality during pile driving, and can output the real-time calculation results of the pile verticality deviation angle without interfering with the pile driving process. It is widely used in the field of pile foundation construction technology.

[0055] Figure 4 This is a schematic diagram of the electronic device proposed in the second embodiment of the present invention. The memory in this embodiment stores program instructions for implementing the machine vision-based pile verticality detection method of any of the above embodiments. The processor executes the program instructions stored in the memory to perform machine vision-based pile verticality detection. The processor can also be called a CPU (Central Processing Unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0056] The methods described in the first embodiment of the present invention are applicable to the embodiments of the present electronic device. The specific functions implemented by the embodiments of the present electronic device are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0057] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium according to the third embodiment of the present invention. The computer-readable storage medium of the fourth embodiment of the present invention stores program instructions capable of implementing the above-described machine vision-based method for detecting the verticality of piles during pile driving. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned computer-readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0058] The methods described in the first embodiment of the present invention are applicable to the computer-readable storage medium embodiment. The specific functions implemented by the computer-readable storage medium embodiment are the same as those in the above method embodiment, and the beneficial effects achieved are also the same as those achieved by the above method.

[0059] This embodiment also provides a computer program product. When the computer program product is run on a computer, it causes the computer to perform the above-mentioned related steps to realize the machine vision-based pile verticality detection method provided in the above embodiment.

[0060] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0061] Those skilled in the art will understand that modules in the device of the embodiments of the present invention can be adaptively modified and placed in one or more devices different from those embodiments. Modules, units, or components in the embodiments of the present invention can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the corresponding claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the corresponding claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0062] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0063] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0064] Furthermore, the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. In particular, for embodiments such as apparatus and devices, since they are basically similar to the method embodiments, the relevant parts can be referred to the description of the method embodiments. The apparatus, devices, and other embodiments described above are merely illustrative, and the modules, units, etc., described as separate components may or may not be physically separate, that is, they may be located in one place or distributed in multiple places, such as nodes in a system network. Specifically, some or all of the modules and units can be selected according to actual needs to achieve the purpose of the above-described embodiment solutions. Those skilled in the art can understand and implement this without creative effort.

[0065] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0066] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0067] Furthermore, the terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of technical features indicated in this embodiment. Therefore, features defined with terms such as "first" and "second" in the embodiments of this invention can explicitly or implicitly indicate that the embodiment includes at least one of those features. In the description of this invention, the word "multiple" means at least two or more, such as two, three, four, etc., unless otherwise explicitly specified in the embodiments.

[0068] In embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of the present invention may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0069] Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention. Other embodiments of the present invention will readily conceive of by considering the specification and practicing the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

Claims

1. A method for detecting the verticality of a pile body in pile sinking based on machine vision, characterized in that, The method comprises the following steps: A first camera and a second camera are arranged around the pile body; the visual angle direction of the first camera is a first direction angle; The visual angle direction of the second camera is a second direction angle; During the pile sinking construction, the first camera and the second camera respectively take pictures of the pile body to obtain a first pile body image and a second pile body image; The first pile body image and the second pile body image are subjected to verticality error identification to obtain a first angle error and a second angle error; According to the first direction angle, the second direction angle, the first angle error and the second angle error, the current verticality deviation angle of the pile body is calculated.

2. The method according to claim 1, wherein The angle difference between the first direction angle and the second direction angle is 90°. 3.The method of claim 1, wherein the method further comprises: The first camera and the second camera take multiple first pile body images and second pile body images during the pile sinking process according to a preset shooting frequency.

4. The method according to claim 1, wherein, The verticality error identification of the first pile body image and the second pile body image is realized by a machine vision model; the machine vision model realizes the verticality error identification through the following steps: The first pile body image and the second pile body image are subjected to image segmentation to identify the pile body mask or contour in the image; The included angle between the main shaft direction of the pile body mask or contour and the plumb line is determined to obtain the first angle error and the second angle error.

5. The method for detecting the verticality of a pile in a pile driving based on machine vision according to claim 1, characterized in that, The image segmentation of the first pile body image and the second pile body image is specifically realized by a semantic segmentation model or an instance segmentation model; the included angle between the main shaft direction of the pile body mask or contour and the plumb line is specifically obtained by an edge extraction algorithm or a main shaft direction center line extraction algorithm.

6. The method according to claim 1, wherein, The calculation of the current verticality deviation angle of the pile body according to the first direction angle, the second direction angle, the first angle error and the second angle error specifically comprises the following steps: The plane normal vectors of the first pile body image and the second pile body image are respectively calculated to obtain a first plane normal vector and a second plane normal vector; The cross product of the first normal vector and the second normal vector is calculated to obtain a pile body direction vector; The pile body direction vector is subjected to component processing to obtain a first direction component, a second direction component and a third direction component; According to the first direction component, the second direction component and the third direction component, the current verticality deviation angle of the pile body is calculated.

7. The method according to claim 6, wherein the method further comprises the steps of: determining the verticality of the pile body based on the image of the pile body. The coordinate system applied by the first plane normal vector / second plane normal vector and the pile body direction vector is a space rectangular coordinate system in which the z-axis is perpendicular to the horizontal plane and the x-axis and the y-axis are parallel to the horizontal plane; The first plane normal vector / second plane normal vector is calculated by the following formula: ； The pile body direction vector is calculated by the following formula: ； The verticality deviation angle is calculated by the following formula: ； wherein, n 1，2 denote first, second plane normal vectors; θ 1，2 denote first, second orientation angles; φ 1，2 denote first, second angle errors, k denote pile body direction vectors, k x，y，z denote components of the pile body direction vectors in x , y , z an axis, α denote plumb deviation angles.

8. The method for detecting the verticality of a pile body during pile driving based on machine vision according to claim 1, characterized in that, After the current verticality deviation angle of the pile body is calculated, the following steps are further included: When the current verticality deviation angle of the pile body is greater than a preset deviation angle threshold, an alarm information is output, and the alarm information is prompted to the pile sinking construction personnel; After the pile sinking construction is completed, a pile body verticality change trend is generated according to the current verticality deviation angles of the pile body at multiple time points and is displayed to the pile sinking construction personnel.

9. An electronic device, comprising: The device comprises a processor and a memory; The memory is used to store a program; The processor executes the program to implement the method for detecting the verticality of a pile body in pile sinking according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a program, and the program is executed by the processor to implement the method for detecting the verticality of a pile body in pile sinking according to any one of claims 1-8.

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