Structural space form and position monitoring method and device, electronic equipment and storage medium

Abstract

The invention provides a structure space form and position monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring original point cloud data of a target structure at a first moment and a second moment; performing structured processing on the original point cloud data to obtain initial point cloud data corresponding to a first moment and registered point cloud data corresponding to a second moment; performing curved surface fitting on the initial point cloud data and the registration point cloud data according to the coordinates, the color and the reflection intensity to obtain an initial curved surface model and a registration curved surface model; selecting a first preset number of first registration points from the initial curved surface model, and searching second registration points corresponding to the first registration points in the registration curved surface model; generating an initial updating model according to the first registration point; generating a registration updating model according to the second registration point; and determining the spatial form and position of the target structure based on the initial update model and the registration update model. The real displacement of the material points on the surface of the structure can be determined, and the overall state of the structure is reflected.

Description

Technical Field

[0001] The present application relates to the technical field of structural deformation monitoring, and in particular to a method, device, electronic equipment and storage medium for monitoring the spatial shape and position of a structure. Background Art

[0002] The spatial shape and position of the structure (i.e., the shape and displacement of the surface of the structure) is an important monitoring indicator in structural health monitoring. Related monitoring methods such as displacement meters, strain gauges, total stations, levels, laser rangefinders, and global navigation satellite systems can only obtain local shape changes and displacement information of the structure, which is difficult to reflect the overall state of the structure; the development of image recognition technology (such as digital image correlation method) and radar interferometry technology has made it possible to obtain full-field information on the spatial shape and position of the structure, but due to the limitations of the measurement principle, image acquisition or radar stations need to remain constant during the monitoring process, which to a certain extent limits their application in the field of structural health monitoring.

[0003] In related technologies, point cloud information of the surface of an object can be obtained through laser 3D scanning, without relying on fixed measuring stations, which greatly facilitates data collection. However, due to the discreteness, unorganized data structure and non-uniform distribution characteristics of point clouds, it is difficult to use point clouds to obtain the true displacement of material points on the surface of the structure. Summary of the invention

[0004] The embodiments of the present application provide a method, device, electronic device and storage medium for monitoring the spatial shape and position of a structure to determine the actual displacement of material points on the surface of the structure and reflect the overall state of the structure.

[0005] In a first aspect, an embodiment of the present application provides a method for monitoring the shape and position of a structure, including:

[0006] Acquire original point cloud data of the target structure at a first moment and a second moment; wherein the original point cloud data includes coordinates, color, and reflection intensity;

[0007] Performing structured processing on the original point cloud data to obtain initial point cloud data corresponding to the first moment and registration point cloud data corresponding to the second moment;

[0008] Performing surface fitting on the initial point cloud data and the registration point cloud data according to coordinates, colors and reflection intensities to obtain an initial surface model and a registration surface model;

[0009] Selecting a first preset number of first registration points from the initial surface model, and searching the registration surface model for second registration points corresponding to the first registration points;

[0010] generating an initial updated model according to the first registration point; generating a registration updated model according to the second registration point;

[0011] Based on the initial updated model and the registered updated model, the spatial shape and position of the target structure are determined.

[0012] In a possible implementation, performing surface fitting on the initial point cloud data and the registered point cloud data according to coordinates, colors, and reflection intensities to obtain an initial surface model and a registered surface model includes:

[0013] Determining geometric feature information of the target structure at a first moment and a second moment respectively according to the coordinates in the original point cloud data;

[0014] Perform B-spline surface fitting according to the coordinates in the initial point cloud data, and add geometric feature information, color and reflection intensity corresponding to the initial point cloud data during the fitting process to obtain an initial surface model corresponding to the first moment;

[0015] B-spline surface fitting is performed according to the coordinates in the registration point cloud data, and geometric feature information, color and reflection intensity corresponding to the registration point cloud data are added during the fitting process to obtain a registration surface model corresponding to the second moment.

[0016] In a possible implementation, performing structured processing on the original point cloud data to obtain initial point cloud data corresponding to the first moment and registered point cloud data corresponding to the second moment includes:

[0017] Determining a characteristic section according to at least two characteristic points preset in the target structure;

[0018] Taking the characteristic section as a reference, dividing the section in the original point cloud data at the first moment and the second moment respectively, to obtain a plurality of initial section in the original point cloud data at the first moment and a plurality of registration section in the original point cloud data at the second moment;

[0019] For each target section, in the original point cloud data corresponding to the target section, a point whose length is within a preset length of the target section is projected onto the target section to obtain a first projection point of the target section, and the first projection point of the target section is sampled to obtain a structural point of the target section; wherein the target section includes a plurality of initial sections and a plurality of registration sections;

[0020] For each structural point, in the original point cloud data corresponding to the structural point, determine a second preset number of nearby points that are closest to the structural point; and determine the structural point cloud data of the structural point based on the nearby points;

[0021] Based on the multiple structural points on the initial section and the corresponding structural point cloud data, the initial point cloud data corresponding to the first moment is determined; based on the multiple structural points on the registration section and the corresponding structural point cloud data, the registration point cloud data corresponding to the second moment is determined.

[0022] In a possible implementation manner, the second preset number is three;

[0023] Determining the structural point cloud data of the structural point according to the nearby points includes:

[0024] According to the nearby points, a projection plane of the structure point is formed;

[0025] Projecting the structure point onto the projection plane to obtain a second projection point corresponding to the structure point on the projection plane;

[0026] For each nearby point, determine the area of ​​a triangle formed by the second projection point and the other two nearby points;

[0027] Determine the weight corresponding to each nearby point according to the triangle area corresponding to each nearby point;

[0028] Based on the original point cloud data corresponding to each nearby point and the weight corresponding to each nearby point, the structural point cloud data of the structural point corresponding to the second projection point is determined.

[0029] In a possible implementation, selecting a first preset number of first registration points from the initial surface model, and searching the registration surface model for second registration points corresponding to the first registration points, includes:

[0030] Randomly selecting a first preset number of first registration points from the initial surface model;

[0031] For each first registration point, determining an initial mapping coordinate of the first registration point on the initial surface model;

[0032] Determine, according to the initial mapping coordinates, a registration range of the first registration point in the initial surface model, and determine a search range of the first registration point in the registration surface model;

[0033] According to the geometric feature information, color and reflection intensity corresponding to the registration range, and the geometric feature information, color and reflection intensity corresponding to the search range, searching within the search range for an area that is most similar to the registration range;

[0034] The registration mapping coordinates of the first registration point on the registration surface model are determined in an area most similar to the registration range, and a second registration point corresponding to the first registration point is obtained.

[0035] In a possible implementation, determining the spatial shape and position of the target structure based on the initial updated model and the registered updated model includes:

[0036] Determining the surface shape of the target structure at the first moment and the second moment according to the initial updated model and the registered updated model;

[0037] Determining a structural displacement function of the target structure from a first moment to a second moment according to the initial update model and the registration update model;

[0038] Determining the displacement of each material point on the surface of the target structure from a first moment to a second moment according to the structural displacement function;

[0039] Based on the surface shape and the displacement, the spatial position of the target structure is determined.

[0040] In a possible implementation manner, after determining the structural displacement function of the target structure from the first moment to the second moment according to the initial update model and the registration update model, the method further includes:

[0041] The strain data of the surface of the target structure is determined according to the structural displacement function.

[0042] In a second aspect, an embodiment of the present application provides a structure space shape and position monitoring device, comprising:

[0043] An acquisition module, used to acquire original point cloud data of the target structure at a first moment and a second moment; wherein the original point cloud data includes coordinates, color and reflection intensity;

[0044] A structuring module, used to perform structured processing on the original point cloud data to obtain initial point cloud data corresponding to the first moment and registered point cloud data corresponding to the second moment;

[0045] A fitting module, used for performing surface fitting on the initial point cloud data and the registration point cloud data according to coordinates, colors and reflection intensity, to obtain an initial surface model and a registration surface model;

[0046] A registration module, configured to select a first preset number of first registration points from the initial surface model, and search the registration surface model for second registration points corresponding to the first registration points;

[0047] An updating module, configured to generate an initial update model according to the first registration point; and generate a registration update model according to the second registration point;

[0048] A determination module is used to determine the spatial shape and position of the target structure based on the initial update model and the registration update model.

[0049] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0050] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0051] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device executes the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0052] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0053] In the embodiment of the present application, the original point cloud data is structured to obtain the initial point cloud data corresponding to the first moment and the registered point cloud data corresponding to the second moment, so that the original point cloud data can be converted into structured data, which is convenient for subsequent matching and processing; the surfaces in the initial point cloud data and the registered point cloud data are fitted by coordinates, colors and reflection intensity respectively to obtain the initial surface model and the registered surface model, and the coordinates and other information of the point cloud can be fitted into the surface model; then a first preset number of first registration points are selected from the initial surface model, and the second registration points corresponding to the first registration points are searched in the registered surface model, and the coordinates and other information of the point cloud can be used during the registration to reduce the probability of falling into local missimilarity, improve the positioning efficiency and accuracy of the registration, and realize the accurate registration of the initial surface model and the registered surface model; an initial update model is generated according to the first registration point, and a registration update model is generated according to the second registration point, so that the initial update model and the registration update model can correspond to each other, so that the spatial shape and position of the target structure, that is, the surface shape change and displacement, can be obtained according to the initial update model and the registration update model, reflecting the overall state of the target structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 is a flow chart for implementing the structural space shape and position monitoring method provided in the embodiment of the present application;

[0056] Figure 2 It is a flow chart of a method for monitoring the structure space shape and position provided in an embodiment of the present application;

[0057] Figure 3 is a schematic diagram of the structure of a structural space shape and position monitoring device provided in an embodiment of the present application;

[0058] Figure 4 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0060] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0061] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0062] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0063] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0064] In addition, the “plurality” mentioned in the embodiments of the present application should be interpreted as two or more.

[0065] The inventors have found that when performing structural health monitoring on large structures such as bridges, tunnel linings, slopes and dams, it is usually necessary to accurately obtain the spatial shape and position of the structure. The relevant technology mainly monitors the spatial shape and position through displacement meters, strain gauges, total stations, levels, laser rangefinders and global navigation satellite systems. However, the above-mentioned monitoring methods can only obtain the local spatial shape and position of the structure, and it is difficult to reflect the overall status of the structure.

[0066] Another way is to obtain point cloud information on the surface of an object through laser three-dimensional scanning. However, the point cloud information obtained is discrete, unorganized and uneven, and can only be used to compare geometric morphology. It is difficult to obtain the actual displacement of material points on the surface of the structure by comparing point cloud information at different times.

[0067] In order to determine the real displacement of material points on the surface of the structure and reflect the overall state of the structure, in the implementation mode of the present application, the surface fitting of the point cloud data is performed by using the coordinates, color and reflection intensity in the point cloud data. The coordinates and other information in the point cloud data can be fitted into the surface model, so as to facilitate the registration of the surface models at different times; by selecting a first registration point from the initial surface model corresponding to the first time, and searching for a second registration point corresponding to the first registration point in the registration surface model corresponding to the second time, the coordinates and other information of the point cloud can be used to perform point registration on the initial surface model and the registration surface model to achieve registration between the two surface models; and by updating the two surface models to obtain the initial updated model and the registration updated model, the registration between the two surface models can be achieved, so that the spatial shape and position of the structure can be accurately obtained, which can reflect the overall state of the structure at different times as a whole.

[0068] In order to make the purpose, technical solutions and advantages of the present application clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0069] See also Figure 1 The implementation flow chart of the structure space shape and position monitoring method shown in Figure 2 The schematic diagram of the flow chart of the structural space shape and position monitoring method shown is described in detail as follows:

[0070] Step 101, obtaining original point cloud data of a target structure at a first moment and a second moment; wherein the original point cloud data includes coordinates, color, and reflection intensity.

[0071] In this embodiment, the original point cloud data may be obtained by scanning with a laser device, and the coordinates here may be coordinates in a Cartesian coordinate system.

[0072] The reflection intensity can be the echo intensity collected by the receiving unit of the laser device. The reflection intensity is generally related to the surface material, roughness, incident angle direction of the target structure, and the emission energy and laser wavelength of the laser device.

[0073] The laser device may be provided with a photographic unit, such as a camera or a video camera, etc. After acquiring an image through the photographic unit, the color information of the pixels at the corresponding positions in the image may be assigned to the corresponding points in the point cloud, so that the point cloud data may contain color information. Here, the image may be a red, green, and blue (RGB) image.

[0074] Optionally, the collected RGB image may be processed to extract features of the color information to reduce the amount of data to be processed. For example, the RGB image may be grayscaled or information of a color channel of the RGB image may be selected.

[0075] Correspondingly, the color in the original point cloud data can be grayscale or the data of one channel in RGB, for example, the data of the red channel.

[0076] Step 102 , structurally processing the original point cloud data to obtain initial point cloud data corresponding to the first moment and registered point cloud data corresponding to the second moment.

[0077] In this embodiment, by performing structured processing on the original point cloud data, the collected original point cloud data showing discreteness, unorganized data structure, and non-uniform distribution characteristics can be standardized, and the original point cloud data at the first moment and the original point cloud data at the second moment can be unified into the same Cartesian coordinate framework, so as to facilitate subsequent comparison and alignment.

[0078] Here, the original point cloud data at the first moment is structured to obtain initial point cloud data, and the original point cloud data at the second moment is structured to obtain registration point cloud data.

[0079] Step 103 , performing surface fitting on the initial point cloud data and the registered point cloud data according to the coordinates, colors and reflection intensity, respectively, to obtain an initial surface model and a registered surface model.

[0080] In this embodiment, the coordinates, color and reflection intensity in the initial point cloud data can be used to perform surface fitting to obtain an initial surface model. Similarly, the coordinates, color and reflection intensity in the registration point cloud data can be used to perform surface fitting to obtain a registration surface model.

[0081] By performing the above surface fitting, the structured point cloud information can be converted into a surface model, so that the surface model can be used for registration in subsequent steps.

[0082] Optionally, surface fitting is performed on the initial point cloud data and the registered point cloud data according to the coordinates, colors and reflection intensities to obtain the initial surface model and the registered surface model, which can be:

[0083] First, according to the coordinates in the original point cloud data, the geometric feature information of the target structure at the first moment and the second moment is determined respectively.

[0084] Here, the geometric feature information may include Gaussian curvature, mean curvature, roughness, and the like.

[0085] In addition, the calculation of geometric feature information can be performed before the original point cloud data is structured, and the geometric feature information corresponding to each point in the original point cloud data is assigned to the corresponding point, so that the original point cloud data can contain the geometric feature information. Correspondingly, when the original point cloud data is structured, the geometric feature information of each point in the original point cloud data is also processed.

[0086] Then, B-spline surface fitting is performed according to the coordinates in the initial point cloud data, and the geometric feature information, color and reflection intensity corresponding to the initial point cloud data are added during the fitting process to obtain the initial surface model corresponding to the first moment.

[0087] Finally, B-spline surface fitting is performed according to the coordinates in the registration point cloud data, and the geometric feature information, color and reflection intensity corresponding to the registration point cloud data are added during the fitting process to obtain the registration surface model corresponding to the second moment.

[0088] See also Figure 2 In the figure, the B-spline surface obtained by fitting is shown, that is, the initial surface model or the registration surface model obtained. Here, the least square method can be used to fit the B-spline surface. In the fitting process, in addition to using the coordinates (x-coordinate, y-coordinate and z-coordinate) in the point cloud data, the geometric feature information, color and reflection intensity in the point cloud data can also be mapped to obtain a generalized B-spline surface model, that is, the initial surface model and the registration surface model.

[0089] Among them, the expression of the B-spline surface model can be:

[0090] In the formula, S(u,v) represents the B-spline surface model, N k,p (u) and N l,q (v) represents the basis function of the B-spline surface model, u and v represent the two-dimensional space mapping coordinates of the B-spline surface model, 0≤u≤1, 0≤v≤1, represents the control point, Among them, x k,l ,yk,l ,z k,l represents the Cartesian coordinates of the control point, f 1,k,l ,f 2,k,l ,f 3,k,l ,…represents the color, geometric feature information and reflection intensity of the control point, and k and l represent the coordinate values ​​of the control point mapped in the two-dimensional space.

[0091] In addition, X={x, y, z, f1, f2, f3, ...} can be used to represent the information of a point in the point cloud data, where x, y, z represent the Cartesian coordinates of the point, and f1, f2, f3, ... represent the color, geometric feature information, and reflection intensity of the control point. For example, f1 represents grayscale, f2 represents curvature, and f3 represents reflection intensity.

[0092] Step 104 : Select a first preset number of first registration points from the initial surface model, and search for second registration points corresponding to the first registration points in the registration surface model.

[0093] In this embodiment, see Figure 2 In C, by searching for the second registration point corresponding to the first registration point in the registration surface model, the initial surface model and the registration surface model can be registered through the first registration point and the second registration point, which is convenient for subsequent comparison. The number of first registration points can be determined according to the registration accuracy. For example, if the registration accuracy is high, more first registration points can be selected; if the registration accuracy is low, fewer first registration points can be selected.

[0094] Here, the coordinates, geometric feature information, color, reflection intensity and other information of the first registration point and the second registration point may be used for searching and registration to achieve accurate registration of the registration points.

[0095] Step 105: Generate an initial update model according to the first registration point; and generate a registration update model according to the second registration point.

[0096] In this embodiment, see Figure 2 In D, the first registration point and the second registration point are matched points. The B-spline surface is regenerated by the first registration point to obtain the initial update model, and the B-spline surface is regenerated by the second registration point to obtain the registration update model. The initial update model and the registration update model can be uniformly mapped at the model level, so that the spatial position of the target structure at different times can be obtained by comparing the two models.

[0097] Optionally, a difference fitting method can be used to generate an initial update model using the coordinates of the first registration point in the initial surface model. The expression can be: Among them, X(u,v) represents the initial update model, X={x,y,z}, and the first registration point Si,j The coordinates of {x i,j ,y i,j ,z i,j}, the number of the first registration points is (n+1)(m+1), N 1k,p (u i ) and N 1l,q (v j ) represents the basis function of the initial update model, P k,l represents the control points of the initial update model obtained by interpolation fitting, and its number is (n+1)(m+1), P k,l ={x k,l ,y k,l ,z k,l}, n+1 represents the number of the first registration points in the u direction, and m+1 represents the number of the first registration points in the v direction.

[0098] Accordingly, the difference fitting method can be used to generate the registration update model using the coordinates of the second registration point in the registration surface model. The expression can be: in, represents the registration update model, Second registration point The coordinates of The number of the second registration points is (n+1)(m+1), N 2k,p (u i ) and N 2l,q (v j ) represents the basis function of the registration update model, represents the control points of the registration update model obtained by interpolation fitting, and its number is (n+1)(m+1), n+1 represents the number of second registration points in the u direction, and m+1 represents the number of second registration points in the v direction.

[0099] Step 106: Determine the spatial shape and position of the target structure based on the initial updated model and the registered updated model.

[0100] In this embodiment, the basis functions and node information in the initial update model and the registration update model are consistent, and only the control points are different. Therefore, by comparing the initial update model and the registration update model, the displacement of any material point on the surface of the structure can be clarified, thereby obtaining the changes in the shape and displacement of the surface of the structure.

[0101] Optionally, in this embodiment, initial point cloud data at multiple different times may be acquired, so as to compare the target structures at different times and determine the spatial shapes and positions at different times.

[0102] For example, initial point cloud data of the target structure at times T0, T1, T2, and T3 may be obtained.

[0103] When performing registration to determine the spatial shape and position of the target structure, one of the moments, for example, T0, can be used as a reference, and the target structures at T1, T2, and T3 can be compared with the target structure at T0 to obtain changes in the spatial shape and position of the target structure at different moments.

[0104] It is also possible to compare the target structure at time T1 with the target structure at time T0, the target structure at time T2 with the target structure at time T1, and the target structure at time T3 with the target structure at time T2, based on the previous moment, to obtain changes in the spatial shape and position of the target structure at different moments.

[0105] Here, when determining the spatial position of the target structure in a short period of time, the first method can be used to obtain the changes of the target structure at different times based on one moment. When determining the spatial position of the target structure in a long period of time, since the target structure may have changed significantly, the second method can be used to obtain the changes of the target structure at different times based on the spatial position of the previous moment.

[0106] In the embodiment of the present application, the original point cloud data is structured to obtain initial point cloud data corresponding to the first moment and registered point cloud data corresponding to the second moment, so that the original point cloud data can be converted into structured data, which is convenient for subsequent matching and processing; the initial point cloud data and the registered point cloud data are respectively surface fitted by coordinates, colors and reflection intensity to obtain an initial surface model and a registered surface model, and the coordinates and other information of the point cloud can be fitted into the surface model; then a first preset number of first registration points are selected from the initial surface model, and the second registration points corresponding to the first registration points are searched in the registered surface model, and the coordinates and other information of the point cloud can be used during the registration to reduce the probability of falling into local missimilarity, improve the positioning efficiency and accuracy of the registration, and realize accurate registration of the initial surface model and the registered surface model; an initial update model is generated according to the first registration point, and a registration update model is generated according to the second registration point, so that the initial update model and the registration update model can correspond to each other, so that the spatial shape and position of the target structure, that is, the surface shape change and displacement, can be obtained according to the initial update model and the registration update model, reflecting the overall state of the target structure.

[0107] In some embodiments, the Gaussian curvature and the mean curvature in the geometric feature information may be determined in the following manner.

[0108] Here, we set the curvature calculation radius R, and find all points {p1,p2,…,p k}, take p as the origin, and determine two orthogonal vector bases {E1, E2} according to the normal vector N of this point, and N is orthogonal to E1 and E2, and calculate the point {p1, p2,…, p k The local coordinate projection of all points in {E1, E2} forms the first projection matrix, which is expressed as: A = Δp T [E1 E2], where Δp = [(p1-p)(p2-p)…(p k -p)].

[0109] Calculate the projection of the normal vector difference of the above k adjacent points on {E1, E2}, and form the second projection matrix matrix with the projection of all normal vector differences, which is expressed as: B = ΔN T [E1 E2], where ΔN = [(N1-N)(N2-N)…(N k -N)].

[0110] Using the relationship B = AG, calculate G = (A T A) -1 A T B, and further calculate the determinant value det(-G) and trace trace(-G), then the Gauss curvature C of the point g (r) and the mean curvature C m (r) is: C g (r) = det(-G),

[0111] In the above implementation process, the resolution of the curvature information can be controlled by setting the curvature calculation radius R, which plays a filtering role.

[0112] In some embodiments, the original point cloud data is structured to obtain the initial point cloud data corresponding to the first moment and the registered point cloud data corresponding to the second moment, which can be:

[0113] Step 1: Determine a feature section according to at least two feature points preset in the target structure.

[0114] Here, the feature points are points with obvious features in the target structure, such as bolts and nails on a bridge, gaps in a tunnel lining, and points that can exist stably, such as trees on a slope.

[0115] By selecting the above-mentioned feature points with obvious characteristics and stable existence and determining the feature sections, corresponding feature points can be found in the original point cloud data at the first moment and the second moment, so that in the process of structuring, the parts of the original point cloud data at the first moment and the second moment that can match each other are retained to ensure the accuracy and efficiency of matching.

[0116] Step 2: Based on the characteristic section, the original point cloud data at the first moment and the second moment are divided into sections to obtain multiple initial sections in the original point cloud data at the first moment and multiple registration sections in the original point cloud data at the second moment.

[0117] In this embodiment, the position of the feature section can be determined in the original point cloud data at the first moment, and the position of the feature section in the original point cloud data at the first moment is used as the first initial section. Based on this, a new section is formed at a preset distance in the normal direction of the section, thereby dividing the original point cloud data at the first moment into multiple sections, forming multiple initial sections in the original point cloud data at the first moment.

[0118] Correspondingly, for the original point cloud data at the second moment, the position of the feature section in the original point cloud data at the second moment is also used as the first registration section. Based on this, a new section is formed at a preset distance in the normal direction of the section, thereby dividing the original point cloud data at the second moment into multiple sections, forming multiple registration sections in the original point cloud data at the second moment.

[0119] The preset distance between the original point cloud data at the second moment is the same as the preset distance between the original point cloud data at the first moment, so as to ensure that the structured initial point cloud data and the registered point cloud data have similar features and can still achieve registration. Here, the preset distance between the points can be determined with reference to the average spacing of the original point cloud data.

[0120] Step three, for each target section, in the original point cloud data corresponding to the target section, project the points whose lengths are within a preset length of the target section onto the target section to obtain the first projection point of the target section, sample the first projection point of the target section to obtain the structural point of the target section; wherein the target section includes multiple initial sections and multiple registration sections.

[0121] In this embodiment, any one of all the initial sections and all the registered sections is taken as a target section, and the same operation is performed on each target section.

[0122] By projecting all points within a preset length from the target section onto the target section, all points in the original point cloud data can be mapped onto the section. By sampling the points on the target section, the points can be structured and the discreteness and non-uniformity of the points can be reduced.

[0123] Here, when sampling the first projection point of the target section, the target section may be uniformly sampled using a two-point method.

[0124] For example, the first projection points on the target section are sorted to form a polyline, and the spacing between the first projection points and the total length l of the polyline are calculated. Set the number of samples on the target section to N u +1, then the sampling interval can be The two-point method is used to achieve uniform sampling of the point cloud data on the target section, and N u +1 structured point cloud sequence.

[0125] Among them, the sampling number can be determined with reference to the spacing between the first projection points, the preset length can be determined with reference to the average spacing between the point clouds, and the preset distance can be twice the preset length, that is, the preset distance is evenly divided into two parts, one part is projected onto the section surface on the adjacent side, and the other part is projected onto the section surface on the other side.

[0126] Step 4: for each structural point, determine a second preset number of nearby points that are closest to the structural point in the original point cloud data corresponding to the structural point; and determine the structural point cloud data of the structural point based on the nearby points.

[0127] In this embodiment, step three determines the position of the structure point, that is, the coordinates, and step four is used to determine the data corresponding to each structure point, which may include color, geometric feature information, reflection intensity, etc.

[0128] Here, considering that the structure point is formed by projecting nearby points onto the target section, multiple nearby points closest to the structure point can be determined from the original point cloud data corresponding to the structure point, and the color, geometric feature information, reflection intensity and other data of the structure point can be determined through the color, geometric feature information, reflection intensity and other data of the multiple nearby points, so that the structure point reflects the data corresponding to the real points nearby.

[0129] Step 5: Based on the multiple structural points on the initial section and the corresponding structural point cloud data, determine the initial point cloud data corresponding to the first moment; based on the multiple structural points on the registration section and the corresponding structural point cloud data, determine the registration point cloud data corresponding to the second moment.

[0130] In this embodiment, the structural processing of the original point cloud data at the first moment and the second moment is completed by matching the structural points with the corresponding structural point cloud data.

[0131] Here, when determining the initial point cloud data and the registration point cloud data, the coordinates of each structural point can be transformed based on the determined feature points to achieve the unification of the coordinate frames of the initial point cloud data and the registration point cloud data, thereby achieving preliminary registration of the point clouds at the first moment and the second moment.

[0132] Furthermore, the geometric feature information may be calculated before the original point cloud data is structured, and the geometric feature information may be added to the original point cloud data. Accordingly, the structured initial point cloud data and the registered point cloud data may also include the geometric feature information.

[0133] Optionally, in this embodiment, the second preset number is three, that is, three nearby points with the closest distance are selected for each structural point.

[0134] In this embodiment, the structural point cloud data of the structural point is determined according to the nearby points, which can be: first, a projection plane of the structural point is formed according to the nearby points; the structural point is projected onto the projection plane to obtain a second projection point corresponding to the structural point on the projection plane; then, for each nearby point, the area of ​​the triangle formed by the second projection point and the other two nearby points is determined; according to the area of ​​the triangle corresponding to each nearby point, the weight corresponding to each nearby point is determined; finally, based on the original point cloud data corresponding to each nearby point and the weight corresponding to each nearby point, the structural point cloud data of the structural point corresponding to the second projection point is determined.

[0135] In this embodiment, the weight is determined by the area of ​​the triangle, and the original point cloud data corresponding to each nearby point is mapped to the structure point, so that the geometric feature information, color, reflection intensity and other information of the structure point can be obtained, that is, the structure point cloud data.

[0136] For example, see Figure 2 A in FIG. 1 shows a schematic diagram of projecting a structure point onto a triangular plane formed by three nearby points. Three nearby points near the structure point P are searched in the original point cloud data, namely, P1, P2, and P3. P1, P2, and P3 can form a triangular plane. The structure point P is projected onto the triangular plane, which corresponds to the second projection point. Then the geometric feature information, color, reflection intensity and other information of the structural point P can be determined by the following formula: f(P)=f1L1+f2L2+f3L3, where f(P) represents the structural point cloud data obtained by the projection of the structural point P, f1 represents the original point cloud data of the nearby point P1, f2 represents the original point cloud data of the nearby point P2, f3 represents the original point cloud data of the nearby point P3, L1 represents the weight of the nearby point P1, L2 represents the weight of the nearby point P2, and L3 represents the weight of the nearby point P3.

[0137] in, A=A1+A2+A3, where A1 represents a triangle The area of ​​the triangle is represented by A2. The area of ​​a triangle is A3. The area of ​​a triangle is and The sum of the areas of .

[0138] Here, by bringing data such as color, geometric feature information, and reflection intensity into the above formula, the color, geometric feature information, and reflection intensity data corresponding to the structure point can be calculated, thereby obtaining the structure point cloud data of the structure point.

[0139] In addition, the second preset number can also be two, four, five, etc., without limitation. Among them, when the second preset number is two, the weight corresponding to each nearby point can be determined by the length of the line segment. When the second preset number is four or five, any three points can be selected to form a projection plane, thereby forming multiple projection planes, and the distance between the structural point and the projection plane is used as the weight of each projection plane, and then the weight of the nearby points on each projection plane is determined; the preset number of nearby points can also be formed into a spatial shape, such as four nearby points forming a spatial quadrilateral, each nearby point and its nearest nearby point, and the second projection point form a triangle, and the weight corresponding to each nearby point is determined according to the area of ​​all triangles, such as, it can be proportionally determined by the inverse of the area of ​​the triangle determined by each nearby point.

[0140] In some embodiments, selecting a first preset number of first registration points from the initial surface model and searching for second registration points corresponding to the first registration points in the registration surface model may be:

[0141] Step 1: randomly select a first preset number of first registration points from the initial surface model.

[0142] In this embodiment, the first registration points may be randomly selected, for example, n+1 points are selected in one direction and m+1 points are selected in another direction to form the first registration points.

[0143] Step 2: for each first registration point, determine the initial mapping coordinates of the first registration point on the initial surface model.

[0144] In this embodiment, the first registration point S i,j The initial mapping coordinates are (u i ,v j ), i = 0, 1, ..., n, j = 0, 1, ..., m, the corresponding coordinates, geometric feature information, color and reflection intensity information can be obtained through the initial surface model mapping, which can be expressed as {x i,j ,y i,j ,z i,j ,f 1,i,j ,f 2,i,j ,f 3,i,j ,…}, where x i,j ,y i,j ,z i,j represents the first registration point S i,j The Cartesian coordinates of1,i,j ,f 2,i,j ,f 3,i,j ,… represents the first registration point S i,j The geometric feature information, color and reflection intensity of the point cloud are shown in Figure 2. It should be noted that the initial mapping coordinates here are not the Cartesian coordinates in the original point cloud data, but the two-dimensional space mapping coordinates of the B-spline surface model.

[0145] Step three: determine the registration range of the first registration point in the initial surface model according to the initial mapping coordinates, and determine the search range of the first registration point in the registration surface model.

[0146] Here, the first registration point S i,j The registration range set in the initial surface model can be: i -Δu:u i +Δu,v j -Δv:v j +Δv), the search range set in the registration surface model can be: in,

[0147] Step 4: Search the search range for the area most similar to the registration range based on the geometric feature information, color and reflection intensity corresponding to the registration range and the geometric feature information, color and reflection intensity corresponding to the search range.

[0148] Step 5: Determine the registration mapping coordinates of the first registration point on the registration surface model in the area most similar to the registration range, and obtain the second registration point corresponding to the first registration point.

[0149] In this embodiment, when searching and registering, the first registration point S can be obtained through multiple iterations using the digital correlation method based on geometric feature information, color, reflection intensity and other information. i,j Registration Mapping Coordinates in Registration Surface Models The second registration point in the registration surface model

[0150] The coordinates in the registration surface model obtained above can be verified with each other through multivariate information such as geometric feature information, color and reflection intensity.

[0151] Will Bring it into the registration surface model to obtain the second registration point The Cartesian coordinates of

[0152]

[0153] In some embodiments, determining the spatial shape and position of the target structure based on the initial update model and the registration update model may be: determining the surface shape of the target structure at the first moment and the second moment based on the initial update model and the registration update model; determining the structural displacement function of the target structure from the first moment to the second moment based on the initial update model and the registration update model; determining the displacement of each material point on the surface of the target structure from the first moment to the second moment based on the structural displacement function; determining the spatial shape and position of the target structure based on the surface shape and the displacement.

[0154] In this embodiment, since the basis functions and node information in the initial update model and the registration update model are consistent, unified mapping is achieved, and the difference between the two B-spline surfaces (initial update model and registration update model) is the displacement of any material point on the surface of the structure, thereby realizing global monitoring of the spatial shape and position of the structure.

[0155] Here, the structural displacement function can be:

[0156]

[0157] Where ΔX represents the structural displacement function.

[0158] In some embodiments, after determining the structural displacement function of the target structure from the first moment to the second moment according to the initial update model and the registration update model, strain data of the surface of the target structure may also be determined according to the structural displacement function.

[0159] In this embodiment, the Green-Lagrangian strain of the surface can be further evaluated based on the structural displacement function. Here, the strain data of the target structure surface can be obtained by deriving the structural displacement function. The relevant formula is as follows:

[0160]

[0161] in,

[0162] In the formula, ε uu represents the line strain of the target structure surface along the u coordinate direction, ε vv represents the line strain of the target structure surface along the v coordinate direction, ε uv Represents the shear strain on the surface of the target structure in the u and v coordinates.

[0163] The embodiment of the present application performs structured processing on the original point cloud data to obtain initial point cloud data corresponding to the first moment and registration point cloud data corresponding to the second moment, so that the original point cloud data can be converted into structured data, which is convenient for subsequent matching and processing; the initial point cloud data and the registration point cloud data are respectively subjected to surface fitting by coordinates, colors and reflection intensity to obtain an initial surface model and a registration surface model, and the coordinates and other information of the point cloud can be fitted into the surface model to achieve preliminary registration of the point cloud; then a first preset number of first registration points are selected from the initial surface model, and in the registration By searching for the second registration point corresponding to the first registration point in the surface model, the coordinates of the point cloud and other information can be used during registration to reduce the probability of falling into local missimilarity, improve the positioning efficiency and accuracy of the registration, and achieve accurate registration of the initial surface model and the registration surface model; by generating an initial update model according to the first registration point and generating a registration update model according to the second registration point, the initial update model and the registration update model can be made to correspond to each other, so that the spatial shape and position of the target structure, that is, the surface shape change and displacement, can be obtained based on the initial update model and the registration update model, reflecting the overall state of the target structure.

[0164] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0165] The following is an embodiment of the device of the present application. For details not described in detail, please refer to the corresponding method embodiment described above.

[0166] Figure 3 The schematic diagram of the structure of the structure space shape and position monitoring device provided by the embodiment of the present application is shown. For the convenience of explanation, only the part related to the embodiment of the present application is shown, which is described in detail as follows:

[0167] like Figure 3 As shown, the structure space shape and position monitoring device 30 includes:

[0168] An acquisition module 31 is used to acquire original point cloud data of the target structure at a first moment and a second moment; wherein the original point cloud data includes coordinates, color and reflection intensity;

[0169] A structuring module 32 is used to perform structural processing on the original point cloud data to obtain initial point cloud data corresponding to the first moment and registration point cloud data corresponding to the second moment;

[0170] A fitting module 33 is used to perform surface fitting on the initial point cloud data and the registration point cloud data according to the coordinates, colors and reflection intensity to obtain an initial surface model and a registration surface model;

[0171] A registration module 34, configured to select a first preset number of first registration points from the initial surface model, and search for second registration points corresponding to the first registration points in the registration surface model;

[0172] An updating module 35, configured to generate an initial update model according to the first registration point; and generate a registration update model according to the second registration point;

[0173] The determination module 36 is used to determine the spatial shape and position of the target structure based on the initial update model and the registration update model.

[0174] In a possible implementation, the fitting module 33 is specifically used for:

[0175] According to the coordinates in the original point cloud data, the geometric feature information of the target structure at the first moment and the second moment is determined respectively;

[0176] Perform B-spline surface fitting according to the coordinates in the initial point cloud data, and add geometric feature information, color and reflection intensity corresponding to the initial point cloud data during the fitting process to obtain an initial surface model corresponding to the first moment;

[0177] B-spline surface fitting is performed according to the coordinates in the registration point cloud data, and the geometric feature information, color and reflection intensity corresponding to the registration point cloud data are added during the fitting process to obtain the registration surface model corresponding to the second moment.

[0178] In a possible implementation, the structuring module 32 is specifically used for:

[0179] Determine a characteristic section according to at least two characteristic points preset in the target structure;

[0180] Based on the characteristic section, the original point cloud data at the first moment and the second moment are divided into sections to obtain a plurality of initial sections in the original point cloud data at the first moment and a plurality of registration sections in the original point cloud data at the second moment;

[0181] For each target section, in the original point cloud data corresponding to the target section, a point whose length is within a preset length of the target section is projected onto the target section to obtain a first projection point of the target section, and the first projection point of the target section is sampled to obtain a structural point of the target section; wherein the target section includes a plurality of initial sections and a plurality of registration sections;

[0182] For each structural point, in the original point cloud data corresponding to the structural point, determine a second preset number of nearby points that are closest to the structural point; and determine the structural point cloud data of the structural point based on the nearby points;

[0183] Based on the multiple structural points on the initial section and the corresponding structural point cloud data, the initial point cloud data corresponding to the first moment is determined; based on the multiple structural points on the registration section and the corresponding structural point cloud data, the registration point cloud data corresponding to the second moment is determined.

[0184] In a possible implementation, the second preset number is three;

[0185] The structured module 32 is specifically used for:

[0186] Based on nearby points, a projection plane of the structure point is formed;

[0187] Projecting the structure point onto the projection plane to obtain a second projection point corresponding to the structure point on the projection plane;

[0188] For each nearby point, determine the area of ​​the triangle formed by the second projection point and the other two nearby points;

[0189] Determine the weight corresponding to each nearby point according to the triangle area corresponding to each nearby point;

[0190] Based on the original point cloud data corresponding to each nearby point and the weight corresponding to each nearby point, the structural point cloud data of the structural point corresponding to the second projection point is determined.

[0191] In a possible implementation, the registration module 34 is specifically used for:

[0192] Randomly selecting a first preset number of first registration points from the initial surface model;

[0193] For each first registration point, determine the initial mapping coordinates of the first registration point on the initial surface model;

[0194] Determine the registration range of the first registration point in the initial surface model according to the initial mapping coordinates, and determine the search range of the first registration point in the registration surface model;

[0195] According to the geometric feature information, color and reflection intensity corresponding to the registration range, and the geometric feature information, color and reflection intensity corresponding to the search range, searching for the area most similar to the registration range in the search range;

[0196] The registration mapping coordinates of the first registration point on the registration surface model are determined in the area most similar to the registration range, and a second registration point corresponding to the first registration point is obtained.

[0197] In a possible implementation, the determination module 36 is specifically configured to:

[0198] Determine the surface shape of the target structure at a first moment and a second moment according to the initial updated model and the registered updated model;

[0199] Determine a structural displacement function of the target structure from a first moment to a second moment according to the initial update model and the registration update model;

[0200] According to the structural displacement function, the displacement of each material point on the surface of the target structure from the first moment to the second moment is determined;

[0201] Based on the surface shape and displacement, the spatial position of the target structure is determined.

[0202] In a possible implementation, the determination module 36 is further configured to:

[0203] According to the structural displacement function, the strain data of the target structure surface is determined.

[0204] Figure 4 Schematic diagram of an electronic device provided in an embodiment of the present application. Figure 4 As shown, the electronic device 40 of this embodiment includes: a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable on the processor 41. When the processor 41 executes the computer program 43, the steps in the above-mentioned various structural space shape and position monitoring method embodiments are implemented, such as Figure 1 Alternatively, when the processor 41 executes the computer program 43, the functions of each module in the above-mentioned device embodiments are realized, for example Figure 3 The functions of the modules 31 to 36 are shown.

[0205] Exemplarily, the computer program 43 may be divided into one or more modules / units, one or more modules / units are stored in the memory 42 and executed by the processor 41 to complete the present application. The one or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program 43 in the electronic device 40. For example, the computer program 43 may be divided into Figure 3 Modules 31 to 36 are shown.

[0206] The electronic device 40 may include, but is not limited to, a processor 41 and a memory 42. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 40 and does not constitute a limitation of the electronic device 40. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0207] The processor 41 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0208] The memory 42 may be an internal storage unit of the electronic device 40, such as a hard disk or memory of the electronic device 40. The memory 42 may also be an external storage device of the electronic device 40, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 40. Further, the memory 42 may also include both an internal storage unit of the electronic device 40 and an external storage device. The memory 42 is used to store computer programs and other programs and data required by the electronic device. The memory 42 may also be used to temporarily store data that has been output or is to be output.

[0209] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0210] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0211] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0212] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0213] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0214] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0215] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0216] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for monitoring the shape and position of a structure space, characterized in that: include: Acquire original point cloud data of the target structure at a first moment and a second moment; wherein the original point cloud data includes coordinates, color, and reflection intensity; Performing structured processing on the original point cloud data to obtain initial point cloud data corresponding to the first moment and registration point cloud data corresponding to the second moment; Performing surface fitting on the initial point cloud data and the registration point cloud data according to coordinates, colors and reflection intensities to obtain an initial surface model and a registration surface model; Selecting a first preset number of first registration points from the initial surface model, and searching the registration surface model for second registration points corresponding to the first registration points; generating an initial updated model according to the first registration point; generating a registration updated model according to the second registration point; Based on the initial updated model and the registered updated model, the spatial shape and position of the target structure are determined.

2. The structural space shape and position monitoring method according to claim 1 is characterized in that: The performing surface fitting on the initial point cloud data and the registration point cloud data according to the coordinates, colors and reflection intensities to obtain an initial surface model and a registration surface model comprises: Determining geometric feature information of the target structure at a first moment and a second moment respectively according to the coordinates in the original point cloud data; Perform B-spline surface fitting according to the coordinates in the initial point cloud data, and add geometric feature information, color and reflection intensity corresponding to the initial point cloud data during the fitting process to obtain an initial surface model corresponding to the first moment; B-spline surface fitting is performed according to the coordinates in the registration point cloud data, and geometric feature information, color and reflection intensity corresponding to the registration point cloud data are added during the fitting process to obtain a registration surface model corresponding to the second moment.

3. The structural space shape and position monitoring method according to claim 2 is characterized in that: The original point cloud data is subjected to structural processing to obtain initial point cloud data corresponding to the first moment and registered point cloud data corresponding to the second moment, including: Determining a characteristic section according to at least two characteristic points preset in the target structure; Taking the characteristic section as a reference, dividing the section in the original point cloud data at the first moment and the second moment respectively, to obtain a plurality of initial section in the original point cloud data at the first moment and a plurality of registration section in the original point cloud data at the second moment; For each target section, in the original point cloud data corresponding to the target section, a point whose length is within a preset length of the target section is projected onto the target section to obtain a first projection point of the target section, and the first projection point of the target section is sampled to obtain a structural point of the target section; wherein the target section includes a plurality of initial sections and a plurality of registration sections; For each structural point, in the original point cloud data corresponding to the structural point, determine a second preset number of nearby points that are closest to the structural point; and determine the structural point cloud data of the structural point based on the nearby points; Based on the multiple structural points on the initial section and the corresponding structural point cloud data, the initial point cloud data corresponding to the first moment is determined; based on the multiple structural points on the registration section and the corresponding structural point cloud data, the registration point cloud data corresponding to the second moment is determined.

4. The structural space shape and position monitoring method according to claim 3 is characterized in that: The second preset number is three; Determining the structural point cloud data of the structural point according to the nearby points includes: According to the nearby points, a projection plane of the structure point is formed; Projecting the structure point onto the projection plane to obtain a second projection point corresponding to the structure point on the projection plane; For each nearby point, determine the area of ​​a triangle formed by the second projection point and the other two nearby points; Determine the weight corresponding to each nearby point according to the triangle area corresponding to each nearby point; Based on the original point cloud data corresponding to each nearby point and the weight corresponding to each nearby point, the structural point cloud data of the structural point corresponding to the second projection point is determined.

5. The structural space shape and position monitoring method according to claim 2, characterized in that: Selecting a first preset number of first registration points from the initial surface model, and searching the registration surface model for second registration points corresponding to the first registration points, comprising: Randomly selecting a first preset number of first registration points from the initial surface model; For each first registration point, determining an initial mapping coordinate of the first registration point on the initial surface model; Determining a registration range of the first registration point in the initial surface model according to the initial mapping coordinates, and determining a search range of the first registration point in the registration surface model; According to the geometric feature information, color and reflection intensity corresponding to the registration range, and the geometric feature information, color and reflection intensity corresponding to the search range, searching within the search range for an area that is most similar to the registration range; The registration mapping coordinates of the first registration point on the registration surface model are determined in an area most similar to the registration range, and a second registration point corresponding to the first registration point is obtained.

6. The structural space shape and position monitoring method according to any one of claims 1 to 5, characterized in that: Determining the spatial shape and position of the target structure based on the initial update model and the registration update model includes: Determining the surface shape of the target structure at the first moment and the second moment according to the initial updated model and the registered updated model; Determining a structural displacement function of the target structure from a first moment to a second moment according to the initial update model and the registration update model; Determining the displacement of each material point on the surface of the target structure from a first moment to a second moment according to the structural displacement function; Based on the surface shape and the displacement, the spatial position of the target structure is determined.

7. The structural space shape and position monitoring method according to claim 6 is characterized in that: After determining the structural displacement function of the target structure from the first moment to the second moment according to the initial update model and the registration update model, the method further includes: The strain data of the surface of the target structure is determined according to the structural displacement function.

8. A structural space shape and position monitoring device, characterized in that: include: An acquisition module, used to acquire original point cloud data of the target structure at a first moment and a second moment; wherein the original point cloud data includes coordinates, color and reflection intensity; A structuring module, used to perform structured processing on the original point cloud data to obtain initial point cloud data corresponding to the first moment and registered point cloud data corresponding to the second moment; A fitting module, used for performing surface fitting on the initial point cloud data and the registration point cloud data according to coordinates, colors and reflection intensity, to obtain an initial surface model and a registration surface model; A registration module, configured to select a first preset number of first registration points from the initial surface model, and search the registration surface model for second registration points corresponding to the first registration points; An updating module, configured to generate an initial update model according to the first registration point; and generate a registration update model according to the second registration point; A determination module is used to determine the spatial shape and position of the target structure based on the initial update model and the registration update model.

9. An electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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