A lightweight method and system for bridge BIM parameter model based on curvature optimization

Abstract

The present invention discloses a lightweight method and system for a bridge BIM parameter model based on curvature optimization, the method comprising: constructing a bridge parametric structure, and extracting information from the BIM model of the bridge parametric structure by using a digital-analog separation method to obtain a target OBJ file containing geometric space information and semantic attribute information; calculating the length of each edge in the BIM model of the bridge parametric structure and the collapse cost of the normal angle between the two sides of the edge; recalculating the edge collapse cost of the BIM model of the bridge parametric structure according to the potential influence of the model geometric structure and curvature distribution, and iteratively updating; smoothing the BIM model of the bridge parametric structure based on curvature and boundary conditions, and deriving a simplified target BIM model. The problem of the jamming and long loading time displayed when the BIM model data is too large when it is visualized on the website is solved, and intelligent management of engineering construction is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital data processing, and in particular relates to a method and system for lightweighting a bridge BIM parameter model based on curvature optimization. Background Art

[0002] With the rapid development and widespread application of Building Information Modeling (BIM) technology, BIM plays an increasingly important role in the full life cycle management of engineering projects. BIM technology greatly improves the efficiency and quality of design, construction, and operation and maintenance by providing an integrated, multi-dimensional view of building projects. For large-scale engineering projects, BIM models often contain a large amount of details and data, which leads to large model files and places high demands on computing resources and storage space. Technicians have an urgent need for methods that can achieve fast loading and lightweight processing of BIM models.

[0003] Bridge models usually contain a large number of details, which leads to large model files, difficult to transfer and load, and difficult to reasonably represent model details. Summary of the invention

[0004] The present invention provides a method and system for lightweighting a bridge BIM parameter model based on curvature optimization, which is used to solve the technical problem that a bridge model usually contains a large number of details, resulting in a large model file size and difficulty in transmission and loading.

[0005] In a first aspect, the present invention provides a lightweight method for a bridge BIM parameter model based on curvature optimization, comprising:

[0006] A parametric structure of the bridge is constructed according to a preset Dynamo plug-in, and a digital-analog separation method is used to extract information from a BIM model of the parametric structure of the bridge to obtain a target OBJ file containing geometric space information and semantic attribute information;

[0007] Parsing the file format of the target OBJ file, and calculating the first collapsed cost of the length of each edge and the second collapsed cost of the normal angle between two side surfaces of the edge in the BIM model of the parametric structure of the bridge;

[0008] Recalculating the first collapse cost and the second collapse cost of the BIM model of the parametric structure of the bridge according to the potential influence of the model geometric structure and curvature distribution, and performing iterative updates;

[0009] The BIM model of the parametric structure of the bridge is smoothed based on curvature and boundary conditions, the curvature continuity of the surface in the BIM model is maintained, and a simplified target BIM model is exported.

[0010] In a second aspect, the present invention provides a bridge BIM parameter model lightweight system based on curvature optimization, comprising:

[0011] An extraction module is configured to construct a bridge parametric structure according to a preset Dynamo plug-in, and extract information from a BIM model of the bridge parametric structure using a digital-analog separation method to obtain a target OBJ file containing geometric space information and semantic attribute information;

[0012] A calculation module is configured to parse the file format of the target OBJ file, and calculate a first collapsed cost of the length of each edge and a second collapsed cost of the normal angle between two side surfaces of the edge in the BIM model of the parametric structure of the bridge;

[0013] An updating module configured to recalculate the first collapse cost and the second collapse cost of the BIM model of the bridge parameterized structure according to potential influence of the model geometric structure and curvature distribution, and perform iterative updates;

[0014] The output module is configured to smooth the BIM model of the bridge parameterized structure based on curvature and boundary conditions, maintain the curvature continuity of the surface in the BIM model, and export a simplified target BIM model.

[0015] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the lightweight method of a bridge BIM parameter model based on curvature optimization according to any embodiment of the present invention.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program instructions are executed by a processor, the processor executes the steps of the method for lightweighting a bridge BIM parameter model based on curvature optimization according to any embodiment of the present invention.

[0017] The curvature-optimized bridge BIM parameter model lightweight method and system of the present application recalculates and iteratively updates the impact of geometric structure and curvature distribution on edge collapse cost, making the calculation results more accurate, while reducing the complexity of the model, maintaining the important geometric features of the model, making the simplified model closer to the visual texture and appearance of the original model, better reflecting the real situation of the model and retaining key features. The present invention not only considers the impact of curvature on smoothing, but also considers boundary conditions, so that the smoothed model is more in line with actual application requirements and reduces model distortion. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flow chart of a bridge BIM parameter model lightweight method based on curvature optimization provided by one embodiment of the present invention;

[0020] Figure 2 A structural block diagram of a bridge BIM parameter model lightweight system based on curvature optimization provided by an embodiment of the present invention;

[0021] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 , which shows a flow chart of a bridge BIM parameter model lightweight method based on curvature optimization of the present application.

[0024] like Figure 1 As shown in FIG. 1 , the lightweight method of bridge BIM parameter model based on curvature optimization specifically includes the following steps:

[0025] Step S101, constructing a parametric structure of a bridge according to a preset Dynamo plug-in, and extracting information from a BIM model of the parametric structure of the bridge using a digital-analog separation method to obtain a target OBJ file containing geometric space information and semantic attribute information.

[0026] In this step, according to the point projection node in the Dynamo plug-in, the coordinates of the bridge element are vertically projected onto the terrain surface to obtain the intersection point with the terrain;

[0027] Create adaptive component nodes based on the through points in the Dynamo plug-in, batch generate adaptive components in the bridge structure, export the vertical intersection coordinates of the bridge element and the terrain surface, extract the position coordinate information and cross-sectional parameter information of each component into an Excel table, and batch generate bridge structures;

[0028] Use the API in BIM to access and operate the bridge model, call a series of classes and methods to access and operate the BIM model, and identify the geometric information that needs to be exported and the parameter information that needs to be separated: geometric information, material properties, structural elements and connections and joints information, etc.;

[0029] Write the export logic code, export the model to OBJ format, check the texture and material usage in the exported model, remove duplicate or unnecessary textures, merge similar materials, and reduce the number of materials.

[0030] Step S102, parsing the file format of the target OBJ file, and calculating a first collapsed cost of the length of each edge in the BIM model of the parametric structure of the bridge and a second collapsed cost of the normal angle between two side surfaces of the edge.

[0031] In this step, the file format of the target OBJ file is parsed, and the vertex position, face information, normal and texture coordinates of the BIM model are extracted from the target OBJ file. The expression is:

[0032] ,

[0033] In the formula, is the vertex position of vertex i, is the normal of the vertex, is the face information of the i-th face, is the horizontal coordinate of vertex i, is the ordinate of vertex i, is the vertical coordinate of vertex i, is the component of the normal of the kth face on the X axis, is the component of the normal of the kth face on the Y axis, is the component of the normal of the kth face on the Z axis;

[0034] Use data structures to represent the connection relationship between vertices and the topological structure of the mesh;

[0035] Initialize the priority queue, add all the edges in the grid to the priority queue according to their collapse costs, and use the priority queue to manage the collapse order of the edges;

[0036] Calculate the length of each edge in the target OBJ file and the normal angle between the two sides of the edge, where the expression for calculating the length of each edge is:

[0037] ,

[0038] In the formula, The length of each side, is the horizontal coordinate of point j, is the horizontal coordinate of point i, is the ordinate of point j, is the ordinate of point i, is the vertical coordinate of point j, is the vertical coordinate of point i;

[0039] The expression for calculating the angle between the normal lines of the two side surfaces is:

[0040] ,

[0041] In the formula, is the normal of the ith face, is the normal of the jth face, is the component of the normal of the ith face on the X axis, is the component of the normal of the jth face on the X axis, is the component of the normal of the ith face on the Y axis, is the component of the normal of the jth face on the Y axis, is the component of the normal of the ith face on the Z axis, is the component of the normal of the jth face on the Z axis, is the angle between the normals of the two sides of the edge;

[0042] Calculate the comprehensive collapse cost of the edge length and the normal angle between the two sides of the edge, and use the priority queue to sort the comprehensive collapse cost. The expression for calculating the collapse cost is:

[0043] ,

[0044] In the formula, is the collapse cost of the length and the normal angle between the two sides of the edge, is the weight coefficient of the length of each edge, is the weight coefficient of the angle between the normal lines of the two side surfaces, For edge The set of all adjacent faces, is the normal vector of face f, is the average value of the difference between the normal vectors of the two sides of the edge;

[0045] Consider collapsing edges The cost of collapse , collapse cost Vertex and vertices The sum of the squares of the distances between adjacent faces is expressed as:

[0046] ,

[0047] In the formula, For vertical meaning, For the collection, is the vertical distance from the point to the surface, is the neighborhood of vertex j, is the neighborhood of vertex i;

[0048] New Vertex The optimal position of get, ,in, is the collapse cost weight coefficient of the edge length and the angle between the normal lines of the two sides of the edge, for The weight coefficient of

[0049] Solving equations get, is the QEM matrix of vertex i and vertex j, is the position of the new vertex, is the coefficient vector, For transposition, is a scalar.

[0050] Step S103: recalculating the first collapse cost and the second collapse cost of the BIM model of the bridge parameterized structure according to the potential influence of the model geometric structure and curvature distribution, and performing iterative updates.

[0051] In this step, according to the dispersion degree of the normal vector of each point, the neighborhood The starting point of the normal vector of each point in the neighborhood moves to the same point. Each point on the inner surface corresponds to a point on the unit sphere. The dispersion of the normal vector is expressed by the solid angle of the area composed of the points on the unit sphere relative to the center of the sphere. For the unit sphere, the solid angle is numerically equal to the area of ​​the area formed by the end points of the normal vector on the unit sphere. The expression is:

[0052] ,

[0053] In the formula, is the sum of the areas of the small regions, is the partial derivative of the normal vector n, For the entire surface area, For parameters The partial derivative of For parameters The partial derivative of is the coordinate describing the surface parameters, is the coordinate two describing the surface parameters, is the size of the ith small region, is the partial derivative of the surface Σ in the corresponding coordinate direction, is a point on the surface area;

[0054] Among them, point Q is the field Inside, when ,So , the more curved the surface is. Then the neighborhood The more dispersed the normal vectors of each point in the curve are, the more dispersed the normal vectors are, and the degree of dispersion of the normal vectors reflects the degree of curvature of the surface at that point.

[0055] Gaussian curvature K is used to describe the amount of local curvature of a surface at a certain point, and to measure the concavity and convexity of the surface in the BIM model. Specifically:

[0056] The first basic shape coefficient , is the first kind of basic quantity, is the mixing shape coefficient, is the second type of basic quantity, is the partial derivative with respect to x, is the partial derivative with respect to u, is the partial derivative with respect to y, is the partial derivative with respect to v;

[0057] The second basic shape factor , is the second-order continuous partial derivative of x, y, or xy, is the second-order partial derivative of x, is the second-order partial derivative of y;

[0058] in, ,and ;

[0059] The mean curvature H is defined as the average of the two principal curvatures at a point on the surface, which describes the average curvature of the surface at a point. The process of calculating the mean curvature H is as follows:

[0060] Compute the basic form coefficients:

[0061] ,

[0062] In the formula, is the curvature of the surface in the first principal direction, is the mixed term of the curvature of the surface along two main directions at a certain point, is the curvature of the surface along the second principal direction at a certain point;

[0063] Compute higher-order derivatives:

[0064] ,

[0065] In the formula, is the first coefficient of the second fundamental form of mean curvature, is the mixing coefficient of the second fundamental form of mean curvature, is the second coefficient of the second fundamental form of mean curvature;

[0066] Calculate the mean curvature H:

[0067] ;

[0068] Define a curvature-based edge collapse energy function during mesh simplification , when the normal vectors are relatively consistent, the edge collapse energy function mainly considers the average curvature. When the normal vectors are dispersed, the edge collapse energy function tends to consider the Gaussian curvature. The expression of the edge collapse energy function is:

[0069] ,

[0070] In the formula, To connect vertices and The edge, is the plane where edge ij lies, For edge The weight of is a constant that controls the shape of the function, Reason and The angle formed by the normal vectors indicates the degree of dispersion of the normal vectors. is the Gaussian curvature weight, is the mean curvature weight.

[0071] Step S104, smoothing the BIM model of the parametric structure of the bridge based on the curvature and boundary conditions, maintaining the curvature continuity of the surface in the BIM model, and deriving a simplified target BIM model.

[0072] In this step, for the vertex , define the vertex Local Adjacency Set , including Adjacent vertices;

[0073] For every pair of vertices in the adjacency set and Based on the idea of ​​local least squares smoothing, the distance between vertices is considered and the weight is calculated , and use the variance of the distance from each point in the vertex neighborhood to the average point to measure the diffusion degree of the neighborhood, the expression is:

[0074] ,

[0075] In the formula, It is the weight ratio of manual adjustment based on curvature adjustment, with a value range of 0-1. is the adjustment factor, Neighborhood the degree of diffusion;

[0076] Define an energy term that includes curvature, boundary conditions, and geometric constraints , the expression is:

[0077] ,

[0078] In the formula, is the geometric constraint, is the curvature constraint, is the weight of the curvature constraint, is the boundary condition constraint, is the weight of the boundary condition constraint, Vertex The curvature of is the boundary of the model, is the original position of the boundary vertex;

[0079] Construct an optimization objective function, combine weights and energy terms, and The position is updated until the smoothness requirement is met, that is, the displacement of each vertex in one iteration should not exceed the set threshold. The expression of the optimization objective function is:

[0080] ,

[0081] In the formula, Vertex Updated location, is the weight parameter for balancing the smoothness term and the energy term;

[0082] Export the simplified target BIM model and ensure that the exported target OBJ file contains the updated normal information and vertex information.

[0083] In summary, the method of the present application quickly arranges the parametric structure of the bridge based on the Dynamo plug-in in the BIM program, and uses the digital-analog separation method to extract the geometric space information and semantic attribute information required by the model in OBJ format; reads the OBJ file, calculates the length of each side of the parametric bridge model and the collapse cost of the normal angle of the two sides of the side; calculates the Gaussian curvature for the normal vector information of the model; recalculates the edge collapse cost of the model according to the potential influence of the model's geometric structure and curvature distribution, and iterates and updates; performs smoothing based on curvature and boundary conditions, maintains the curvature continuity of the surface in the model, and exports the simplified model. It solves the problem of BIM model data being too large, and the freeze and long loading time displayed when visualized on the website, and realizes the intelligent management of engineering construction.

[0084] See also Figure 2 , which shows a structural block diagram of a bridge BIM parameter model lightweight system based on curvature optimization in the present application.

[0085] like Figure 2 As shown, the bridge BIM parameter model lightweight system 200 includes an extraction module 210, a calculation module 220, an update module 230 and an output module 240.

[0086] The extraction module 210 is configured to construct a bridge parametric structure according to a preset Dynamo plug-in, and extract information from the BIM model of the bridge parametric structure using a digital-analog separation method to obtain a target OBJ file containing geometric space information and semantic attribute information;

[0087] The calculation module 220 is configured to parse the file format of the target OBJ file, and calculate the first collapsed cost of the length of each edge and the second collapsed cost of the normal angle between two side surfaces of the edge in the BIM model of the parametric structure of the bridge;

[0088] An updating module 230 is configured to recalculate the first collapse cost and the second collapse cost of the BIM model of the bridge parameterized structure according to the potential impact of the model geometry and curvature distribution, and perform iterative updates;

[0089] The output module 240 is configured to smooth the BIM model of the bridge parameterized structure based on the curvature and boundary conditions, maintain the curvature continuity of the surface in the BIM model, and export a simplified target BIM model.

[0090] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0091] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, and when the program instructions are executed by a processor, the processor is caused to execute the bridge BIM parameter model lightweight method based on curvature optimization in any of the above method embodiments;

[0092] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0093] A parametric structure of the bridge is constructed according to a preset Dynamo plug-in, and a digital-analog separation method is used to extract information from a BIM model of the parametric structure of the bridge to obtain a target OBJ file containing geometric space information and semantic attribute information;

[0094] Parsing the file format of the target OBJ file, and calculating the first collapsed cost of the length of each edge and the second collapsed cost of the normal angle between two side surfaces of the edge in the BIM model of the parametric structure of the bridge;

[0095] Recalculating the first collapse cost and the second collapse cost of the BIM model of the parametric structure of the bridge according to the potential influence of the model geometric structure and curvature distribution, and performing iterative updates;

[0096] The BIM model of the parametric structure of the bridge is smoothed based on curvature and boundary conditions, the curvature continuity of the surface in the BIM model is maintained, and a simplified target BIM model is exported.

[0097] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the bridge BIM parameter model lightweight system based on curvature optimization, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the bridge BIM parameter model lightweight system based on curvature optimization via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0098] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 In the example, the connection through the bus is taken as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the lightweight method of the bridge BIM parameter model based on curvature optimization of the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the lightweight system of the bridge BIM parameter model based on curvature optimization. The output device 340 may include display devices such as display screens.

[0099] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0100] As an implementation mode, the electronic device is applied to a bridge BIM parameter model lightweight system based on curvature optimization, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0101] A parametric structure of the bridge is constructed according to a preset Dynamo plug-in, and a digital-analog separation method is used to extract information from a BIM model of the parametric structure of the bridge to obtain a target OBJ file containing geometric space information and semantic attribute information;

[0102] Parsing the file format of the target OBJ file, and calculating the first collapsed cost of the length of each edge and the second collapsed cost of the normal angle between two side surfaces of the edge in the BIM model of the parametric structure of the bridge;

[0103] Recalculating the first collapse cost and the second collapse cost of the BIM model of the parametric structure of the bridge according to the potential influence of the model geometric structure and curvature distribution, and performing iterative updates;

[0104] The BIM model of the parametric structure of the bridge is smoothed based on curvature and boundary conditions, the curvature continuity of the surface in the BIM model is maintained, and a simplified target BIM model is exported.

[0105] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0106] Finally, 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 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. However, 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 invention.

Claims

1. A lightweight method for bridge BIM parameter model based on curvature optimization, characterized in that: include: A parametric structure of the bridge is constructed according to a preset Dynamo plug-in, and a digital-analog separation method is used to extract information from a BIM model of the parametric structure of the bridge to obtain a target OBJ file containing geometric space information and semantic attribute information; Parsing the file format of the target OBJ file, and calculating the first collapsed cost of the length of each edge and the second collapsed cost of the normal angle between two side surfaces of the edge in the BIM model of the parametric structure of the bridge; Recalculating the first collapse cost and the second collapse cost of the BIM model of the parametric structure of the bridge according to the potential influence of the model geometric structure and curvature distribution, and performing iterative updates; Smoothing the BIM model of the parametric structure of the bridge based on curvature and boundary conditions, maintaining the curvature continuity of the surface in the BIM model, and deriving a simplified target BIM model; The method of constructing a bridge parametric structure according to a preset Dynamo plug-in and extracting information from the BIM model of the bridge parametric structure using a digital-analog separation method to obtain a target OBJ file containing geometric space information and semantic attribute information includes: According to the point projection node in the Dynamo plug-in, the coordinates of the bridge element are vertically projected onto the terrain surface to obtain the intersection point with the terrain; Create adaptive component nodes based on the through points in the Dynamo plug-in, batch generate adaptive components in the bridge structure, export the vertical intersection coordinates of the bridge element and the terrain surface, extract the position coordinate information and cross-sectional parameter information of each component into an Excel table, and batch generate bridge structures; Use the API in BIM to access and operate the bridge model, identify the geometric information that needs to be exported and the parameter information that needs to be separated, and obtain the target OBJ file containing geometric space information and semantic attribute information; The step of parsing the file format of the target OBJ file and calculating the first collapsed cost of the length of each edge and the second collapsed cost of the normal angle between two side surfaces of the edge in the BIM model of the parametric structure of the bridge comprises: Parse the file format of the target OBJ file, and extract the vertex position, face information, normal and texture coordinates of the BIM model from the target OBJ file. The expression is: ， In the formula, is the vertex position of vertex i, is the normal of the vertex, is the face information of the i-th face, is the horizontal coordinate of vertex i, is the ordinate of vertex i, is the vertical coordinate of vertex i, is the component of the normal of the kth face on the X axis, is the component of the normal of the kth face on the Y axis, is the component of the normal of the kth face on the Z axis; Use data structures to represent the connection relationship between vertices and the topological structure of the mesh; Initialize the priority queue, add all the edges in the grid to the priority queue according to their collapse costs, and use the priority queue to manage the collapse order of the edges; Calculate the length of each edge in the target OBJ file and the normal angle between the two sides of the edge, where the expression for calculating the length of each edge is: ， In the formula, The length of each side, is the horizontal coordinate of point j, is the horizontal coordinate of point i, is the ordinate of point j, is the ordinate of point i, is the vertical coordinate of point j, is the vertical coordinate of point i; The expression for calculating the angle between the normal lines of the two side surfaces is: ， In the formula, is the normal of the ith face, is the normal of the jth face, is the component of the normal of the ith face on the X axis, is the component of the normal of the jth face on the X axis, is the component of the normal of the ith face on the Y axis, is the component of the normal of the jth face on the Y axis, is the component of the normal of the ith face on the Z axis, is the component of the normal of the jth face on the Z axis, is the angle between the normals of the two sides of the edge; Calculate the comprehensive collapse cost of the edge length and the normal angle between the two sides of the edge, and use the priority queue to sort the comprehensive collapse cost. The expression for calculating the collapse cost is: ， In the formula, is the collapse cost of the length and the normal angle between the two sides of the edge, is the weight coefficient of the length of each edge, is the weight coefficient of the angle between the normal lines of the two side surfaces, is the set of all faces adjacent to the edge, is the normal vector of face f, is the average value of the difference between the normal vectors of the two sides of the edge; Consider collapsing edges The cost of collapse , collapse cost Vertex and vertices The sum of the squares of the distances between adjacent faces is expressed as: ， In the formula, For vertical meaning, For the collection, is the vertical distance from the point to the surface, is the neighborhood of vertex j, is the neighborhood of vertex i; New Vertex The optimal position of get, , where is the collapse cost weight coefficient of the edge length and the angle between the normal lines of the two sides of the edge, The weight coefficient of Solving equations get, is the QEM matrix of vertex i and vertex j, is the position of the new vertex, is the coefficient vector, For transposition, is a scalar; The recalculating the first collapse cost and the second collapse cost of the BIM model of the parametric structure of the bridge according to the potential influence of the model geometric structure and curvature distribution, and performing iterative updating includes: According to the dispersion degree of the normal vector of each point, the neighborhood The starting point of the normal vector of each point in the neighborhood moves to the same point. Each point on the inner surface corresponds to a point on the unit sphere. The dispersion of the normal vector is expressed by the solid angle of the area composed of the points on the unit sphere relative to the center of the sphere. For the unit sphere, the solid angle is numerically equal to the area of ​​the area formed by the end points of the normal vector on the unit sphere. The expression is: ， In the formula, is the sum of the areas of the small regions, is the partial derivative of the normal vector n, For the entire surface area, is the partial derivative of the parameter, is the partial derivative of the parameter, is the coordinate describing the surface parameters, is the coordinate two describing the surface parameters, is the size of the ith small region, is the partial derivative of the surface Σ in the corresponding coordinate direction, is a point on the surface area; Gaussian curvature K is used to describe the amount of local curvature of a surface at a certain point, and to measure the concavity and convexity of the surface in the BIM model. Specifically: The first basic shape coefficient , is the first kind of basic quantity, is the mixing shape coefficient, is the second type of basic quantity, is the partial derivative with respect to x, is the partial derivative with respect to u, is the partial derivative with respect to y, is the partial derivative with respect to v; The second basic shape factor , is the second-order continuous partial derivative of x, y, or xy, is the second-order partial derivative of x, is the second-order partial derivative of y; in, ,and ; The mean curvature H is defined as the average of the two principal curvatures at a point on the surface, which describes the average curvature of the surface at a point. The process of calculating the mean curvature H is as follows: Compute the basic form coefficients: ， In the formula, is the curvature of the surface in the first principal direction, is the mixed term of the curvature of the surface along the two main directions at a certain point, is the curvature of the surface along the second principal direction at a certain point; Compute higher-order derivatives: ， In the formula, is the first coefficient of the second fundamental form of mean curvature, is the mixing coefficient of the second fundamental form of mean curvature, is the second coefficient of the second fundamental form of mean curvature; Calculate the mean curvature H: ； Define a curvature-based edge collapse energy function during mesh simplification , when the normal vectors are relatively consistent, the edge collapse energy function mainly considers the average curvature. When the normal vectors are dispersed, the edge collapse energy function tends to consider the Gaussian curvature. The expression of the edge collapse energy function is: ， In the formula, For the edges connecting vertices and , is the plane where edge ij lies, is the edge weight, is a constant that controls the shape of the function, is the angle formed by and the normal vector, indicating the dispersion of the normal vector, is the Gaussian curvature weight, is the mean curvature weight; The method of smoothing the BIM model of the bridge parameterized structure based on the curvature and boundary conditions, maintaining the curvature continuity of the surface in the BIM model, and deriving a simplified target BIM model includes: For Vertex , define the vertex Local Adjacency Set , including Adjacent vertices; For every pair of vertices in the adjacency set and Based on the idea of ​​local least squares smoothing, the distance between vertices is considered and the weight is calculated , and use the variance of the distance from each point in the vertex neighborhood to the average point to measure the diffusion degree of the neighborhood, the expression is: ， In the formula, It is the weight ratio of manual adjustment based on curvature adjustment, with a value range of 0-1. is the adjustment factor, Neighborhood the degree of diffusion; Define an energy term that includes curvature, boundary conditions, and geometric constraints , the expression is: ， In the formula, is the geometric constraint, is the curvature constraint, is the weight of the curvature constraint, is the boundary condition constraint, is the weight of the boundary condition constraint, is the curvature at the vertex, is the boundary of the model, is the original position of the boundary vertex; Construct an optimization objective function, combine weights and energy terms, and The position is updated until the smoothness requirement is met, that is, the displacement of each vertex in one iteration should not exceed the set threshold. The expression of the optimization objective function is: ， In the formula, is the updated position of the vertex, is the weight parameter for balancing the smoothness term and the energy term; Export the simplified target BIM model and ensure that the exported target OBJ file contains the updated normal information and vertex information.

2. A bridge BIM parameter model lightweight system based on curvature optimization according to the method of claim 1, characterized in that: include: An extraction module is configured to construct a bridge parametric structure according to a preset Dynamo plug-in, and extract information from a BIM model of the bridge parametric structure using a digital-analog separation method to obtain a target OBJ file containing geometric space information and semantic attribute information; A calculation module is configured to parse the file format of the target OBJ file, and calculate a first collapsed cost of the length of each edge and a second collapsed cost of the normal angle between two side surfaces of the edge in the BIM model of the parametric structure of the bridge; An updating module configured to recalculate the first collapse cost and the second collapse cost of the BIM model of the bridge parameterized structure according to potential influence of the model geometric structure and curvature distribution, and perform iterative updates; The output module is configured to smooth the BIM model of the bridge parameterized structure based on curvature and boundary conditions, maintain the curvature continuity of the surface in the BIM model, and export a simplified target BIM model.

3. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 1 is implemented.

Images