Angle measurement method and system for engineering equipment based on inclinometer

Abstract

The present invention discloses an engineering equipment angle measurement method and system based on an inclinometer, and relates to the technical field of engineering equipment angle measurement, including collecting point cloud data of engineering equipment and point cloud data of the surrounding environment, and performing preprocessing; using the preprocessed point cloud data to perform feature extraction, and obtaining the shape characteristics of the engineering equipment and the shape characteristics of the surrounding environment; analyzing the shape characteristics of the engineering equipment and the shape characteristics of the surrounding environment, obtaining the installation point position, and installing the inclinometer according to the installation point position; calibrating the installed inclinometer, and measuring the angle of the engineering equipment to obtain the inclination angle data in various directions; based on the inclination angle data in various directions and the point cloud data of the engineering equipment, calculating the three-dimensional posture of the engineering equipment. The present invention avoids the universal lock problem of Euler angles by fusing point cloud data and inclination angle data, and uses quaternions and rotation matrices to calculate the equipment posture, and provides a numerically stable posture representation.

Description

Technical Field

[0001] The invention relates to the technical field of engineering equipment angle measurement, and in particular to an engineering equipment angle measurement method and system based on an inclinometer. Background Art

[0002] Accurately measuring and monitoring the posture of large-scale engineering equipment (such as cranes, excavators, etc.) is essential to ensure safe operation, optimize workflow, diagnose faults, and improve efficiency. Traditional posture measurement methods include mechanical angle sensors, optical measurement systems, inertial measurement units (IMUs), and GPS positioning systems, but each has its limitations: mechanical sensors can only provide single-axis information; optical systems are highly accurate but complex to operate and are not suitable for real-time monitoring; IMUs will have drift errors after long-term use and are sensitive to vibration; GPS has limited ability to detect local micro-displacements, especially in indoor or blocked environments.

[0003] 3D laser scanners can quickly obtain high-precision point cloud data, providing rich information for analyzing the shape and structure of equipment and its surrounding environment. In recent years, with the development of 3D laser scanning technology and point cloud processing algorithms, it has become possible to apply these technologies to the posture measurement of engineering equipment. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an engineering equipment angle measurement method based on an inclinometer to solve the problems of complex motion with multiple degrees of freedom and insufficient precision.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an engineering equipment angle measurement method based on an inclinometer, which comprises:

[0008] Collect point cloud data of engineering equipment, physical information and point cloud data of the surrounding environment, and perform pre-processing;

[0009] Use the pre-processed point cloud data to extract features and obtain the shape features of the engineering equipment and the surrounding environment;

[0010] Analyze the shape characteristics of the engineering equipment and the shape characteristics of the surrounding environment, obtain the installation point location, and install the inclinometer according to the installation point location;

[0011] Calibrate the installed inclinometer and measure the angle of the engineering equipment to obtain the tilt angle data in all directions;

[0012] The three-dimensional posture of the engineering equipment is calculated based on the tilt angle data in all directions and the point cloud data of the engineering equipment.

[0013] As a preferred solution of the engineering equipment angle measurement method based on the inclinometer of the present invention, wherein:

[0014] As a preferred solution of the engineering equipment angle measurement method based on the inclinometer of the present invention, wherein: the point cloud data of the engineering equipment is data obtained by scanning the engineering equipment with a three-dimensional laser scanner;

[0015] The physical information of the engineering equipment is the elastic modulus and density of the engineering equipment material;

[0016] The point cloud data of the surrounding environment is data obtained by scanning the surrounding environment of the engineering equipment with a three-dimensional laser scanner;

[0017] The preprocessing includes data cleaning and data alignment.

[0018] As a preferred solution of the inclinometer-based engineering equipment angle measurement method of the present invention, the specific steps of data cleaning and data alignment are as follows:

[0019] Use filtering algorithms to clean point cloud data, remove noise points and abnormal points, and obtain clean point cloud data;

[0020] Use feature point matching algorithm to roughly align clean point cloud data to obtain preliminary aligned point cloud data;

[0021] The iterative closest point algorithm is used to finely align the preliminary aligned point cloud data to obtain high-precision aligned point cloud data, and the high-precision aligned point cloud data is used as the preprocessed point cloud data.

[0022] As a preferred solution of the inclinometer-based engineering equipment angle measurement method of the present invention, wherein: the pre-processed point cloud data is used for feature extraction to obtain the shape features of the engineering equipment and the shape features of the surrounding environment. The specific steps are as follows:

[0023] The boundary extraction algorithm is used to extract the boundary of the pre-processed point cloud data to obtain the contour features of the engineering equipment;

[0024] The principal component analysis method is used to simplify the contour features of engineering equipment and obtain the main directional features of engineering equipment;

[0025] The preprocessed point cloud data is analyzed using geometric analysis methods to obtain the shape characteristics of the surrounding environment.

[0026] As a preferred solution of the engineering equipment angle measurement method based on the inclinometer of the present invention, the shape characteristics of the engineering equipment and the shape characteristics of the surrounding environment are analyzed to obtain the installation point position, and the inclinometer is installed according to the installation point position. The specific steps are as follows:

[0027] The flatness, local curvature and geometric features of the surface of the engineering equipment are screened by geometric analysis methods to obtain potential installation areas;

[0028] A comprehensive calculation is performed based on the geometric stability, contact area and vibration conductivity of the potential installation area. The expression is as follows:

[0029] ;

[0030] in, is the stability score, is the number of points in the point cloud data within the potential installation area, is the index of the point cloud data point, is the total area of ​​the potential installation area, For the The tiny area corresponding to each point cloud data point, For the The three-dimensional coordinates of the point cloud data points, are the centroid coordinates of the potential installation area, is the elastic modulus of the engineering equipment material, is the density of the engineering equipment material, is the characteristic length of the installation area, is a natural constant;

[0031] Compare the stability scores of all potential installation areas, select the potential installation area with the highest score, and obtain the best installation point location;

[0032] Install the inclinometer based on the optimal mounting point location.

[0033] As a preferred solution of the engineering equipment angle measurement method based on the inclinometer of the present invention, the inclinometer after installation is calibrated, and the angle of the engineering equipment is measured to obtain the inclination angle data in various directions. The specific steps are as follows:

[0034] The installed inclinometer is preliminarily calibrated using the zero-point calibration method to obtain the inclinometer in the reference state;

[0035] The laboratory test data set is obtained by placing the inclinometer at a known inclination angle using standard equipment in a laboratory controlled environment, recording the output signal of the inclinometer, and measuring multiple known inclination angles and performing denoising and normalization.

[0036] The laboratory test data set is divided into training set, test set and validation set, and the ShuffleNet neural network model is trained to obtain the sensitivity correction model;

[0037] The inclinometer in the reference state is calibrated by using a sensitivity correction model to obtain a calibrated inclinometer;

[0038] The calibrated inclinometer is used to perform multi-directional measurements to obtain the tilt angle data in each direction.

[0039] As a preferred solution of the engineering equipment angle measurement method based on the inclinometer of the present invention, the three-dimensional posture of the engineering equipment is calculated based on the inclination angle data in each direction and the point cloud data of the engineering equipment. The specific steps are as follows:

[0040] The rotation quaternion is constructed using the precise tilt angle data, expressed as,

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] in, is the total rotation angle, is the tilt angle along the X axis, is the inclination angle along the Y axis, is the tilt angle along the Z axis, is the real part of the rotation quaternion, is the rotation component about the X axis, is the rotation component about the Y axis, is the rotation component about the Z axis, is the rotation quaternion;

[0048] Convert the rotation quaternion to a rotation matrix, expressed as,

[0049] ;

[0050] in, is the rotation matrix, is the rotation component about the X axis, is the rotation component about the Y axis, is the rotation component about the Z axis, is the real part of the rotation quaternion;

[0051] The rotation matrix is ​​applied to the preprocessed point cloud data to obtain the point cloud model after posture adjustment, and the point cloud model after posture adjustment is processed using three-dimensional visualization technology to obtain the three-dimensional posture image of the engineering equipment.

[0052] In a second aspect, the present invention provides an engineering equipment angle measurement system based on an inclinometer, comprising a data acquisition module, a feature extraction module, an installation module, a measurement module and a posture calculation module;

[0053] The data acquisition module is used to collect point cloud data of engineering equipment, physical information and point cloud data of the surrounding environment, and perform preprocessing;

[0054] The feature extraction module is used to extract features using the preprocessed point cloud data to obtain shape features of the engineering equipment and shape features of the surrounding environment;

[0055] The installation module analyzes the shape characteristics of the engineering equipment and the shape characteristics of the surrounding environment to obtain the installation point position, and installs the inclinometer according to the installation point position;

[0056] The measuring module calibrates the installed inclinometer and measures the angle of the engineering equipment to obtain the tilt angle data in various directions;

[0057] The posture calculation module calculates the three-dimensional posture of the engineering equipment based on the tilt angle data in various directions and the point cloud data of the engineering equipment.

[0058] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the engineering equipment angle measurement method based on an inclinometer as described in the first aspect of the present invention is implemented.

[0059] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the inclinometer-based engineering equipment angle measurement method as described in the first aspect of the present invention is implemented.

[0060] The beneficial effects of the present invention are as follows: by fusing the point cloud data obtained by the three-dimensional laser scanner with the tilt angle data provided by the inclinometer, the present invention provides a high-precision and reliable three-dimensional attitude measurement method for engineering equipment. This method not only overcomes the limitations of traditional measurement methods such as single-axis information of mechanical sensors, complex operation of optical systems that are not suitable for real-time monitoring, drift errors caused by long-term use of IMUs, and poor performance of GPS in indoor or shielded environments, but also significantly improves the accuracy and efficiency of measurements. Point cloud data is used for preprocessing, feature extraction, and selection of the best installation position to ensure the stable installation and accurate measurement of the inclinometer. At the same time, the use of quaternions and rotation matrices to calculate the device attitude avoids the universal lock problem of Euler angles and provides a numerically stable attitude representation. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 This is a flow chart of the engineering equipment angle measurement method based on the inclinometer in Example 1.

[0063] Figure 2 This is a module diagram of the engineering equipment angle measurement system based on the inclinometer in Example 1. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides an engineering equipment angle measurement method based on an inclinometer, comprising the following steps:

[0068] S1. Collect the point cloud data of the engineering equipment and the point cloud data of the surrounding environment, and perform preprocessing, including the following steps:

[0069] The point cloud data of engineering equipment is the data obtained by scanning the engineering equipment with a 3D laser scanner.

[0070] The physical information of the engineering equipment is the elastic modulus and density of the engineering equipment material.

[0071] The point cloud data of the surrounding environment is data obtained by scanning the surrounding environment of the engineering equipment with a 3D laser scanner.

[0072] Preprocessing includes data cleaning and data alignment.

[0073] The point cloud data is cleaned using filtering algorithms to remove noise points and abnormal points to obtain clean point cloud data.

[0074] The clean point cloud data is roughly aligned using the feature point matching algorithm to obtain preliminary aligned point cloud data.

[0075] The iterative closest point algorithm is used to finely align the preliminary aligned point cloud data to obtain high-precision aligned point cloud data, and the high-precision aligned point cloud data is used as the preprocessed point cloud data.

[0076] It should be noted that high-precision point cloud data is obtained through 3D laser scanners and preprocessed to ensure the basic data quality for subsequent analysis. The filtering algorithm effectively removes noise points and abnormal points, improving the purity of the point cloud data; the feature point matching algorithm achieves rough alignment, preliminarily ensuring the relative consistency between different scanning positions; the iterative closest point (ICP) algorithm further refines the alignment and obtains high-precision aligned point cloud data. These steps work together to not only improve the quality and consistency of the data, but also provide reliable data support for subsequent posture calculations.

[0077] S2. Using the pre-processed point cloud data to perform feature extraction, the shape features of the engineering equipment and the shape features of the surrounding environment are obtained, including the following steps:

[0078] The boundary extraction algorithm is used to extract the boundary of the preprocessed point cloud data to obtain the contour features of the engineering equipment. Specifically, the distance difference between each point and its neighboring points is calculated, and the boundary points and points in the smooth area are distinguished by setting a threshold for the Euclidean distance of the point cloud data points. Boundary points are usually manifested as locations with large local curvature or sudden changes in density. Next, curvature estimation methods such as moving least squares (MLS) or normal-based methods are applied to calculate the local curvature properties of each point. High curvature values ​​indicate possible boundary locations. Connectivity analysis is used to connect adjacent boundary points to form continuous boundary segments. This step can be implemented using depth-first search (DFS) or breadth-first search (BFS) algorithms. The extracted boundaries are smoothed to remove small-scale noise fluctuations. Techniques such as low-pass filters or polynomial fitting can be used to ensure the smoothness and continuity of boundary lines.

[0079] The principal component analysis method is used to simplify the contour features of engineering equipment and obtain the main directional features of engineering equipment. Specifically, a matrix containing the coordinates of all boundary points is constructed, and the covariance matrix of the matrix is ​​calculated. The covariance matrix is ​​decomposed by eigenvalues ​​to find the eigenvectors corresponding to the three principal components, which represent the directions of maximum variance in the X, Y, and Z axes, respectively. According to the eigenvalue size, the first two or three largest eigenvalues ​​and their corresponding eigenvectors are selected as the main directional features. These directions reflect the main extension directions of the equipment contour in three-dimensional space. The original point cloud is projected into a new coordinate system composed of the main directional features to simplify the subsequent posture calculation process while retaining the key information of the equipment shape.

[0080] The preprocessed point cloud data is analyzed using a geometric analysis method to obtain the shape characteristics of the surrounding environment. Specifically, multiple geometric shape models (such as planes, cylinders, spheres, etc.) are defined, and these geometric shape models are tried to fit into the point cloud data. For each geometric shape model, the error between it and the actual point cloud is calculated. A set of sample points are randomly extracted from the point cloud using the RANSAC (random sample consistency) algorithm, the geometric shape model is fitted, and the matching degree of the remaining points with the geometric shape model is evaluated. This process is repeated multiple times to select the best fitting model. For the identified geometric structures, they are further classified into different types of environmental elements such as ground, buildings, obstacles, etc. The shape characteristics of each type are described according to geometric parameters (such as normal vector, center point, radius, etc.). An environmental feature map is constructed to record the spatial distribution of different types of geometric structures, providing a reference for the subsequent selection of the inclinometer installation location.

[0081] It should be noted that by performing detailed boundary extraction, principal component analysis and geometric analysis on the pre-processed point cloud data, the present invention can accurately obtain the contour features and main directional features of the engineering equipment and fully understand the geometric shape features of the surrounding environment. Boundary extraction ensures the accuracy of the contour features, principal component analysis simplifies the complex shapes, and geometric analysis provides detailed information about the surrounding environment. These feature extraction steps lay a solid foundation for the selection of the optimal installation position of the inclinometer, while optimizing the input data required for the subsequent attitude calculation and improving the overall measurement accuracy.

[0082] S3, analyzing the shape characteristics of the engineering equipment and the shape characteristics of the surrounding environment, obtaining the installation point position, and installing the inclinometer according to the installation point position, including the following steps,

[0083] The flatness, local curvature and geometric features of the surface of the engineering equipment in the shape features of the engineering equipment are screened by geometric analysis methods to obtain potential installation areas. Specifically, the extracted main directional features are expanded to construct a bounding box or convex hull to determine the overall size range of the equipment. The flatness and smoothness of the equipment surface are analyzed, and the consistency of the normal vector of each local area is calculated. Flat areas with high consistency are selected as candidate areas for potential installation points. Apply curvature analysis to identify depressions, protrusions or other structural features on the surface of the equipment that may affect the stability of the inclinometer, and exclude locations that are not suitable for installation.

[0084] Comprehensive calculation is performed based on the point cloud data of the potential installation area, the three-dimensional coordinates of the point cloud data points, and the elastic modulus and density of the engineering equipment material. The expression is as follows:

[0085] ;

[0086] in, is the stability score, is the number of points in the point cloud data within the potential installation area, is the index of the point cloud data point, is the total area of ​​the potential installation area, For the The tiny area corresponding to each point cloud data point, For the The three-dimensional coordinates of the point cloud data points, are the centroid coordinates of the potential installation area, is the elastic modulus of the engineering equipment material, is the density of the engineering equipment material, is the characteristic length of the installation area, is a natural constant.

[0087] Compare the stability scores of all potential installation areas, select the potential installation area with the highest score, and obtain the best installation point location. Specifically, sort the normalized scores of all potential installation areas, and select the area with the highest score as the best installation point location. If the scores of multiple areas are similar (such as the difference is less than the preset threshold), further screening is performed based on geometric features (such as flatness, curvature, etc.).

[0088] Install the inclinometer based on the optimal installation point. Specifically, use professional tools to accurately install the inclinometer at the optimal position. Ensure that the surface of the equipment is not damaged during installation and keep the inclinometer horizontal or vertical. After installation, perform preliminary functional tests to check whether the inclinometer is working properly and whether the data transmission is stable and reliable. Record the specific coordinate information and related parameters of the installation location for reference during subsequent calibration and maintenance.

[0089] It should be noted that, through zero point calibration, sensitivity correction and multi-directional measurement, the present invention ensures high-precision output of the inclinometer in the entire measurement range. Zero point calibration eliminates the initial offset error, sensitivity correction corrects the nonlinear characteristics of the inclinometer, and multi-directional measurement verifies the stability of the inclinometer in different postures. The application of filtering algorithm further improves the accuracy and stability of the tilt angle data. These calibration and testing steps greatly improve the measurement accuracy of the inclinometer.

[0090] S4, calibrate the installed inclinometer, measure the angle of the engineering equipment, and obtain the tilt angle data in all directions, including the following steps:

[0091] Use the zero-point calibration method to perform a preliminary calibration on the installed inclinometer to obtain the inclinometer in the reference state. Specifically, place the device on a known horizontal or vertical reference plane to ensure that the inclinometer is in the ideal initial position. Record the angle value output by the inclinometer at this time as the zero-point offset and store it in the device's internal memory. Perform zero-point calibration in multiple directions to ensure that the inclinometer readings on all axes are accurate.

[0092] In a controlled laboratory environment, the inclinometer is placed at a known inclination using standard equipment, and the output signal of the inclinometer is recorded. At the same time, multiple known inclinations are measured and denoised and normalized to obtain a laboratory test data set. Specifically, a series of standard postures are designed to cover various tilt conditions that the device may encounter, such as pitch, roll, and yaw. In each standard posture, the inclinometer's tilt angle data on the three axes of X, Y, and Z are recorded to ensure comprehensive data coverage.

[0093] The laboratory test data set is divided into training set, test set and validation set, and the ShuffleNet neural network model (channel shuffle network) is trained to obtain the sensitivity correction model. Specifically, the laboratory test data set is divided into 70% training set, 15% test set and 15% validation set to ensure the generalization ability of the model. The training set is trained using the ShuffleNet neural network model, and the optimization goal is to minimize the error between the inclinometer output signal and the known inclination. The Adam optimizer is used in the training process, the learning rate is set to 0.001, and the batch size is 32.

[0094] The sensitivity correction model is used to calibrate the inclinometer in the reference state to obtain a calibrated inclinometer. Specifically, the trained sensitivity correction model is deployed to the embedded system of the inclinometer to correct the output signal of the inclinometer in real time.

[0095] The calibrated inclinometer is used to perform multi-directional measurements to obtain tilt angle data in various directions. Specifically, the engineering equipment is started, and operations such as movement and rotation of related components are performed, and the calibrated inclinometer is used to perform multi-directional measurements.

[0096] It should be noted that, through zero point calibration, sensitivity correction and multi-directional testing, the present invention ensures high-precision output of the inclinometer in the entire measuring range. Zero point calibration eliminates the initial offset error, and sensitivity correction corrects the nonlinear characteristics of the inclinometer. The ShuffleNet neural network model is used to perform sensitivity correction on the inclinometer, which significantly improves the calibration accuracy.

[0097] S5, based on the tilt angle data in various directions and the point cloud data of the engineering equipment, calculating the three-dimensional posture of the engineering equipment, including the following steps,

[0098] The rotation quaternion is constructed using the precise tilt angle data, expressed as,

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] in, is the total rotation angle, is the tilt angle along the X axis, is the inclination angle along the Y axis, is the tilt angle along the Z axis, is the real part of the rotation quaternion, is the rotation component about the X axis, is the rotation component about the Y axis, is the rotation component about the Z axis, is the rotation quaternion;

[0106] Convert the rotation quaternion to a rotation matrix, expressed as,

[0107] ;

[0108] in, is the rotation matrix, is the rotation component about the X axis, is the rotation component about the Y axis, is the rotation component about the Z axis, is the real part of the rotation quaternion;

[0109] Apply the rotation matrix to the preprocessed point cloud data to obtain the point cloud model after posture adjustment, and use 3D visualization technology to process the point cloud model after posture adjustment to obtain the 3D posture image of the engineering equipment. Specifically, apply the rotation matrix to the preprocessed point cloud data, and obtain the adjusted point cloud model through matrix multiplication, ensuring that each point cloud point undergoes the same rotation transformation to maintain the consistency of the entire point cloud model. Perform further visualization processing on the adjusted point cloud model, such as color coding, transparency setting, etc., to enhance the display effect.

[0110] It should be noted that by constructing a rotation quaternion and converting it into a rotation matrix, the Euler angle gimbal lock problem is avoided, and a numerically stable attitude representation is provided. The rotation matrix is ​​applied to the preprocessed point cloud data to achieve an accurate transformation from the original coordinate system to the new attitude coordinate system. The application of three-dimensional visualization technology enhances the display effect of the adjusted point cloud model, allowing users to intuitively understand the attitude changes of engineering equipment.

[0111] This embodiment also provides an engineering equipment angle measurement system based on an inclinometer, comprising: a data acquisition module, a feature extraction module, an installation module, a measurement module and a posture calculation module;

[0112] Data acquisition module, used to collect point cloud data of engineering equipment, physical information and point cloud data of the surrounding environment, and perform preprocessing;

[0113] A feature extraction module is used to extract features using the preprocessed point cloud data to obtain shape features of the engineering equipment and the shape features of the surrounding environment;

[0114] The installation module analyzes the shape characteristics of the engineering equipment and the shape characteristics of the surrounding environment, obtains the installation point position, and installs the inclinometer according to the installation point position;

[0115] The measurement module calibrates the installed inclinometer and measures the angle of the engineering equipment to obtain the tilt angle data in all directions;

[0116] The posture calculation module calculates the three-dimensional posture of the engineering equipment based on the tilt angle data in all directions and the point cloud data of the engineering equipment.

[0117] This embodiment also provides a computer device, which is suitable for the case of an engineering equipment angle measurement method based on an inclinometer, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the engineering equipment angle measurement method based on an inclinometer as proposed in the above embodiment.

[0118] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0119] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for measuring the angle of engineering equipment based on an inclinometer as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage device, a flash memory, a magnetic disk or an optical disk.

[0120] In summary, the present invention provides a high-precision and reliable three-dimensional attitude measurement method for engineering equipment by fusing the point cloud data obtained by a three-dimensional laser scanner with the tilt angle data provided by the inclinometer. This method not only overcomes the limitations of traditional measurement methods such as single-axis information of mechanical sensors, complex operation of optical systems that are not suitable for real-time monitoring, drift errors caused by long-term use of IMUs, and poor performance of GPS in indoor or blocked environments, but also significantly improves the accuracy and efficiency of measurements. Point cloud data is used for preprocessing, feature extraction, and selection of the best installation position to ensure the stable installation and accurate measurement of the inclinometer. At the same time, the use of quaternions and rotation matrices to calculate the device attitude avoids the universal lock problem of Euler angles and provides a numerically stable attitude representation.

[0121] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of an engineering equipment angle measurement method based on an inclinometer are provided.

[0122] In order to verify the effectiveness and superiority of the inclinometer-based angle measurement method for engineering equipment, a large crane was selected as the experimental object. The crane is located in a busy port with a complex surrounding environment, including multiple buildings, uneven ground, and other mechanical equipment. First, a 3D laser scanner was used to fully scan the crane and its surroundings to obtain high-precision point cloud data. During the scanning process, all key parts were covered, and special attention was paid to the crane's boom, base and other structural features.

[0123] Next, the collected point cloud data was imported into a specialized data processing software for preprocessing. Specifically, the filtering algorithm was applied to remove noise points and abnormal points to ensure the purity of the data; then the feature point matching algorithm was used to achieve preliminary alignment to ensure consistency between different scanning positions; finally, the iterative closest point (ICP) algorithm was used for fine alignment to obtain high-precision aligned point cloud data. This series of operations significantly improved the quality and consistency of the data, providing a solid foundation for subsequent analysis.

[0124] After the preprocessing is completed, the feature extraction stage begins. The contour features of the crane are identified from the point cloud data through the boundary extraction algorithm, and then the principal component analysis is used to simplify these features to obtain the main direction features. At the same time, the surrounding environment is analyzed using the geometric analysis method to construct a detailed environmental feature map. This step not only accurately describes the shape of the crane, but also fully considers the factors affecting its working environment, laying the foundation for the selection of the optimal installation position of the inclinometer.

[0125] After determining the best installation position, a customized fixture was designed and manufactured to ensure that the inclinometer could be firmly installed in the selected position. After installation, the inclinometer was set to its initial state according to the zero-point calibration method, and the nonlinear characteristics of the sensor were corrected by the sensitivity correction algorithm. Subsequently, the tilt angle data was recorded in multiple standard postures, and the filtering algorithm was applied to further improve the accuracy and stability of the data.

[0126] Based on the acquired tilt angle data and point cloud data, the rotation quaternion is constructed and converted into a rotation matrix, realizing the precise transformation from the original coordinate system to the new posture coordinate system. The adjusted point cloud model is processed by 3D visualization technology to generate a clear and intuitive 3D posture image, allowing users to intuitively understand the posture changes of the crane.

[0127] The details are shown in Table 1 below:

[0128] Table 1 Crane posture measurement experimental results

[0129] ,

[0130] By comparing and analyzing the contents of the above tables, it is clear that the content of the present invention is innovative and has advantages in practical applications. First, the traditional method can usually only provide angle information of a single axis, while the method of the present invention can not only obtain the tilt angle data in all directions, but also calculate the total rotation angle, thereby fully describing the posture change of the crane. For example, in the "Crane 1-Pitch 10°" state, the tilt angle of the X-axis is 9.8°, the Y-axis and Z-axis have only slight changes, and the total rotation angle is close to the expected 10°, which illustrates the sensitivity and accuracy of the system to single-axis changes.

[0131] The present invention uses a combination of quaternions and rotation matrices to represent attitudes, avoiding the universal lock problem that may occur in Euler angles and providing a numerically stable attitude representation. Taking "Crane 1-Compound Attitude 1" as an example, the three axes have different degrees of change, but the total rotation angle can still be accurately calculated as 12.8°, and the values ​​of the rotation components x, y, and z are also in line with expectations, demonstrating the stability and reliability of the system.

[0132] Although the traditional optical measurement system has high accuracy, it is complicated to operate in complex environments and is not suitable for real-time monitoring. In contrast, the present invention uses three-dimensional laser scanning technology and point cloud processing algorithms to obtain a large amount of high-quality spatial coordinate data in a short period of time, providing rich information support for subsequent analysis. Especially when faced with complex environments such as ports, the advantages of this method are particularly prominent. For example, the negative angle test results of "Crane 1-Compound Posture 2" show that even in the case of large angle changes in multiple axes, the system can still maintain high-precision output, reflecting its adaptability and robustness under harsh conditions.

[0133] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for measuring the angle of engineering equipment based on an inclinometer, characterized in that: include, Collect point cloud data of engineering equipment, physical information and point cloud data of the surrounding environment, and perform pre-processing; Use the pre-processed point cloud data to extract features and obtain the shape features of the engineering equipment and the surrounding environment; Analyze the shape characteristics of the engineering equipment and the shape characteristics of the surrounding environment, obtain the installation point location, and install the inclinometer according to the installation point location; Calibrate the installed inclinometer and measure the angle of the engineering equipment to obtain the tilt angle data in all directions; Calculate the three-dimensional posture of the engineering equipment based on the tilt angle data in all directions and the point cloud data of the engineering equipment; Analyze the shape characteristics of the engineering equipment and the surrounding environment to obtain the installation point location, including: The flatness, local curvature and geometric features of the surface of the engineering equipment are screened by geometric analysis methods to obtain potential installation areas; A stability score is obtained by performing a comprehensive calculation based on the point cloud data of the potential installation area, the three-dimensional coordinates of the point cloud data points, and the elastic modulus and density of the engineering equipment material; The stability scores of all potential installation areas are compared, and the potential installation area with the highest score is selected to obtain the optimal installation point location.

2. The engineering equipment angle measurement method based on the inclinometer according to claim 1, characterized in that: The point cloud data of the engineering equipment is data obtained by scanning the engineering equipment with a three-dimensional laser scanner; The physical information of the engineering equipment is the elastic modulus and density of the engineering equipment material; The point cloud data of the surrounding environment is data obtained by scanning the surrounding environment of the engineering equipment with a three-dimensional laser scanner; The preprocessing includes data cleaning and data alignment.

3. The engineering equipment angle measurement method based on the inclinometer according to claim 2, characterized in that: The specific steps of data cleaning and data alignment are as follows: Use filtering algorithms to clean point cloud data, remove noise points and abnormal points, and obtain clean point cloud data; Use feature point matching algorithm to roughly align clean point cloud data to obtain preliminary aligned point cloud data; The iterative closest point algorithm is used to finely align the preliminary aligned point cloud data to obtain high-precision aligned point cloud data, and the high-precision aligned point cloud data is used as the preprocessed point cloud data.

4. The engineering equipment angle measurement method based on the inclinometer according to claim 3, characterized in that: The preprocessed point cloud data is used for feature extraction to obtain the shape features of the engineering equipment and the shape features of the surrounding environment. The specific steps are as follows: The boundary extraction algorithm is used to extract the boundary of the pre-processed point cloud data to obtain the contour features of the engineering equipment; The principal component analysis method is used to simplify the contour features of engineering equipment and obtain the main directional features of engineering equipment; The preprocessed point cloud data is analyzed using geometric analysis methods to obtain the shape characteristics of the surrounding environment.

5. The engineering equipment angle measurement method based on the inclinometer according to claim 4, characterized in that: The stability score is calculated using the following formula, ； in, For the stability score, is the number of points in the point cloud data within the potential installation area, is the index of the point cloud data point, is the total area of ​​the potential installation area, For the The tiny area corresponding to each point cloud data point, For the The three-dimensional coordinates of the point cloud data points, are the centroid coordinates of the potential installation area, is the elastic modulus of the engineering equipment material, is the density of the engineering equipment material, is the characteristic length of the installation area, is a natural constant.

6. The engineering equipment angle measurement method based on the inclinometer according to claim 5, characterized in that: Calibrate the installed inclinometer and measure the angle of the engineering equipment to obtain the tilt angle data in all directions. The specific steps are as follows: The installed inclinometer is preliminarily calibrated using the zero-point calibration method to obtain the inclinometer in the reference state; The laboratory test data set is obtained by placing the inclinometer at a known inclination angle using standard equipment in a laboratory controlled environment, recording the output signal of the inclinometer, and measuring multiple known inclination angles and performing denoising and normalization. The laboratory test data set is divided into training set, test set and validation set, and the ShuffleNet neural network model is trained to obtain the sensitivity correction model; The inclinometer in the reference state is calibrated by using a sensitivity correction model to obtain a calibrated inclinometer; The calibrated inclinometer is used to perform multi-directional measurements to obtain the tilt angle data in each direction.

7. The engineering equipment angle measurement method based on the inclinometer according to claim 6, characterized in that: Based on the tilt angle data in all directions and the point cloud data of the engineering equipment, the three-dimensional posture of the engineering equipment is calculated. The specific steps are as follows: The rotation quaternion is constructed using the precise tilt angle data, expressed as, ； ； ； ； ； ； in, is the total rotation angle, is the tilt angle along the X axis, is the inclination angle along the Y axis, is the tilt angle along the Z axis, is the real part of the rotation quaternion, is the rotation component about the X axis, is the rotation component about the Y axis, is the rotation component about the Z axis, is the rotation quaternion; Convert the rotation quaternion to a rotation matrix, expressed as, ； in, is the rotation matrix, is the rotation component about the X axis, is the rotation component about the Y axis, is the rotation component about the Z axis, is the real part of the rotation quaternion; The rotation matrix is ​​applied to the preprocessed point cloud data to obtain the point cloud model after posture adjustment, and the point cloud model after posture adjustment is processed using three-dimensional visualization technology to obtain the three-dimensional posture image of the engineering equipment.

8. An engineering equipment angle measurement system based on an inclinometer, based on the engineering equipment angle measurement method based on an inclinometer according to any one of claims 1 to 7, characterized in that: Including data acquisition module, feature extraction module, installation module, measurement module and posture calculation module; The data acquisition module is used to collect point cloud data of engineering equipment, physical information and point cloud data of the surrounding environment, and perform preprocessing; The feature extraction module is used to extract features using the preprocessed point cloud data to obtain shape features of the engineering equipment and shape features of the surrounding environment; The installation module analyzes the shape characteristics of the engineering equipment and the shape characteristics of the surrounding environment to obtain the installation point position, and installs the inclinometer according to the installation point position; The measuring module calibrates the installed inclinometer and measures the angle of the engineering equipment to obtain the tilt angle data in various directions; The posture calculation module calculates the three-dimensional posture of the engineering equipment based on the tilt angle data in various directions and the point cloud data of the engineering equipment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the engineering equipment angle measurement method based on the inclinometer described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the engineering equipment angle measurement method based on an inclinometer as described in any one of claims 1 to 7 are implemented.

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