Mountain area bridge temperature field modeling visualization method based on space-time interpolation fusion

Abstract

The invention relates to a mountainous area bridge temperature field modeling visualization method based on space-time interpolation fusion, and belongs to the technical field of geographic information systems and three-dimensional space modeling. The method comprises the following steps: analyzing heat transfer, heat exchange and heat radiation laws of a mountainous area bridge temperature field, and establishing a temperature field numerical simulation model of the mountainous area bridge to obtain a temperature field simulation result; performing consistency registration on a temperature field simulation result and a bridge voxel model, and modeling a mountainous area bridge temperature field by adopting spatio-temporal interpolation fusion according to physical characteristics of internal and external temperature differences of the bridge; and volume rendering high-efficiency visualization of the mountainous area bridge temperature field is realized based on ray casting. According to the method, the temperature field simulation result can be effectively complemented, the space-time distribution and change rule of the temperature field simulation result can be accurately described, and key data support can be provided for subsequent digital twinning driven mountainous area bridge intelligent construction and fine management.

Description

Technical Field

[0001] The invention relates to the technical field of geographic information system and three-dimensional space modeling, in particular to a visualization method for modeling temperature field of mountain bridges based on time-space interpolation fusion. Background Art

[0002] Bridges are key nodes and hub projects that span rivers and lakes, connect canyons and deep gullies, and realize the interconnection of transportation facilities. With the continuous progress of my country's infrastructure construction, especially the implementation of the new medium- and long-term railway plan and the construction of the Sichuan-Tibet Railway, more and more complex bridge projects will be put into construction and operation in difficult mountainous areas, facing a more rugged and changeable construction environment. Temperature, as an important form of load in the process of bridge construction, significantly affects the performance of bridge structures. In particular, mountainous environments have the characteristics of large day-night temperature differences and rapid temperature changes. Bridges often show rapid surface temperature changes, while internal temperature changes are small or even unchanged. This significant internal and external temperature difference and uneven distribution will cause the bridge structure to shift or deform. Therefore, how to model and visualize the temperature field of mountain bridges and intuitively reflect the temporal and spatial distribution and change laws of the temperature field is of great significance to improving the level of refined management of bridge construction.

[0003] There are two main studies on bridge temperature fields. One is to install sensors at specific locations on the bridge to continuously monitor temperature changes. However, this method has high costs for sensor equipment and data acquisition, and can only obtain temperature data at limited locations on the bridge surface, making it difficult to detect the temperature distribution inside the bridge and across the entire bridge. The other method is to perform numerical simulation of the bridge temperature field through finite element analysis, but due to the geometric irregularities of the structures of the various components of the bridge, an unstructured network is usually used to divide the bridge structure. This method results in sparse temperature field simulation results and uneven distribution in time and space, and lacks fusion with geographic scenes, making it difficult to accurately describe the temperature changes of the bridge. Summary of the invention

[0004] In order to solve the above-mentioned problems of the prior art, a visualization method of temperature field modeling of mountain bridges based on time-space interpolation fusion was proposed based on the numerical simulation model of temperature field of mountain bridges and taking into account the physical characteristics of temperature difference between the inside and outside of the bridge. This method solves the problem of uneven temporal and spatial distribution of temperature simulation results due to geometric irregularities of bridge component structures.

[0005] A visualization method for temperature field modeling of mountain bridges based on spatiotemporal interpolation fusion includes the following steps:

[0006] Step 1: Analyze the heat transfer, heat exchange and heat radiation laws of the temperature field of mountain bridges, establish a numerical simulation model of the temperature field of mountain bridges, and obtain the temperature field simulation results;

[0007] Step 2: The temperature field simulation results are aligned with the bridge voxel model, and the temperature field of the mountain bridge is modeled by using spatiotemporal interpolation fusion according to the physical characteristics of the temperature difference between the inside and outside of the bridge;

[0008] Step 3: Implement efficient visualization of the temperature field of bridges in mountainous areas through volume rendering based on ray casting.

[0009] Furthermore, the step 1 specifically includes:

[0010] Step 101, setting basic assumptions: a. The material density and specific heat capacity of bridge components are uniform and do not change with changes in external conditions; b. The exterior of bridge components is only affected by solar radiation heat transfer and air convection heat transfer, and the internal temperature is affected by heat transfer;

[0011] Step 102, according to the basic laws of computational fluid dynamics (including the law of conservation of mass, the law of conservation of momentum and the law of conservation of energy, etc.), establish the basic control equations for the simulation of the temperature field of the mountain bridge;

[0012] Step 103, setting initial parameters and boundary conditions of the simulation;

[0013] Step 104, meshing the bridge geometric model, and using the resulting generated volume mesh model as input data for simulation calculation;

[0014] Step 105, adopting a numerical method to iteratively solve the discrete equation and outputting the numerical simulation result of the temperature field of the mountain bridge.

[0015] Furthermore, the step 2 specifically includes:

[0016] Step 201, voxelizing the bridge geometric model to obtain a bridge voxel model;

[0017] Step 202, mapping the temperature field simulation value to the bridge voxel model through point cloud registration;

[0018] Step 203, using the data on the consistency of the temporal and spatial relationships in the same area as metadata for time dimension interpolation and space dimension interpolation, and processing the aligned temperature field simulation data in two ways, time first then space and space first then time, respectively. Finally, the two interpolation results are fused to obtain a comprehensive temperature field model.

[0019] Furthermore, the voxelization process of the bridge geometric model in step 201 is specifically as follows:

[0020] Axis-aligned bounding box acquisition: The axis-aligned bounding box of the bridge geometric model is represented by the maximum-minimum value representation method. By traversing the vertex coordinates of the triangular mesh of the bridge geometric model, the minimum and maximum values ​​of the model on the x, y, and z coordinate axes are obtained;

[0021] Seed scanning line scanning: a straight line is formed with a unit cube as a seed, which is parallel to the coordinate axis. The internal and external positional relationship between the voxels on the path and the bridge geometric model is determined by scanning; when the unit cube seed and the bridge geometric model are in an intersecting or containing relationship, the position information of the unit cube seed is recorded;

[0022] Voxel judgment: When the seed scanning line passes through the bridge geometric model, it has three positional relationships with the bridge geometric model: the unit cube seed just enters the model, completely enters the model, and just exits the model; the positional relationship is used to perform voxel judgment. The seed coordinates that have just entered the model and just exited the model represent the boundary voxels, and the seed coordinates that have completely entered the model represent the voxels inside the model.

[0023] Furthermore, the step 202 is specifically as follows: using a four-point consistent set algorithm to roughly align the temperature simulation data with the bridge voxel data, and then using an iterative closest point algorithm to precisely align the point cloud.

[0024] Furthermore, the specific process of using the iterative closest point algorithm to accurately align the point cloud is as follows:

[0025] Step 2021, determine the corresponding point pair set; take the temperature simulation data as the source point cloud P, and the bridge voxel data as the target point cloud Q, for all the points in the source point cloud P Search for the closest corresponding point in the target point cloud Q Thus forming a point pair set n is the number of point pairs, i is the number of point clouds, and k is the number of iterations;

[0026] Step 2022, according to the formula

[0027] Establish the target error function and calculate the rotation matrix R k and the translation matrix T k ;

[0028] Step 2023, update the source point cloud coordinates; use the rotation matrix R calculated in step 2022 k and the translation matrix T k , for the source point cloud The coordinates are updated to obtain the updated point cloud The update formula is:

[0029]

[0030] Step 2024, determine whether to continue iteration; according to the formula

[0031]

[0032] Calculate the mean square error σ of the iterative transformation k, if the number of iterations reaches the maximum number k max or |σ k+1 -σ k |<ε, ε is the iterative mean square error threshold, then the iterative calculation is stopped, otherwise k=k+1 is set, and the iterative process is returned to step 2021;

[0033] Step 2025, align the point cloud; use the best rotation matrix R calculated last time k and the translation matrix T k , change the point cloud coordinates, complete the point cloud registration, and further map the temperature simulation value to the bridge voxel point cloud.

[0034] Furthermore, the time dimension interpolation adopts cubic spline function for time interpolation; the space dimension interpolation adopts inverse distance weighted method to complete the data of mountain bridge temperature field model in space dimension, and reduces the amount of data used for a single interpolation point by limiting the data influence range.

[0035] Furthermore, the fusion process of the interpolation results is:

[0036] T=λ1T1+λ2T2

[0037] Among them, T is the comprehensive temperature field model, T1 is the temperature field model obtained by interpolating time first and then space, T2 is the temperature field model obtained by interpolating space first and then time, λ1 represents the weight coefficient of T1, and λ2 represents the weight coefficient of T2.

[0038] Furthermore, the step 3 is specifically as follows:

[0039] Step 301, constructing a volume texture and a volume bounding box in the CPU based on the bridge temperature field modeling result v=(d, t, c) in step 2; wherein v is a comprehensive temperature field voxel model, d represents the spatial domain, t represents the time domain, and c represents the temperature value;

[0040] Step 302, the constructed volume texture and volume bounding box data are transmitted to the GPU, and the GPU is used for ray casting. Through the optimized rendering method of parallel acceleration, early termination of rays, and blank voxel jumping, the real-time and efficient rendering of the spatiotemporal data of the temperature field of mountain bridges is achieved.

[0041] Beneficial effects of the invention: The invention proposes a three-dimensional modeling method of the temperature field of mountain bridges that couples numerical simulation with spatiotemporal interpolation. Through the registration of temperature simulation results with bridge voxel models, spatiotemporal interpolation fusion modeling of the temperature field, and temperature field volume rendering based on ray projection, the precise characterization of the spatiotemporal distribution of the temperature field of mountain bridges and efficient visualization rendering are achieved. It can effectively complete the temperature field simulation results, accurately characterize its spatiotemporal distribution and change laws, and can provide key data support for the subsequent intelligent construction and refined management of mountain bridges driven by digital twins. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is the overall flow chart of the three-dimensional modeling method of bridge temperature field;

[0043] Figure 2 It is the digital simulation calculation process of the temperature field of mountain bridges;

[0044] Figure 3 It is the voxelization process of the bridge geometry model;

[0045] Figure 4 It is a flow chart of spatiotemporal interpolation fusion modeling of mountain bridge temperature field;

[0046] Figure 5 It is a flow chart of temperature field volume rendering based on ray casting. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0048] The "Notice on Comprehensively Promoting the Construction of Realistic 3D China" issued by the General Office of the Ministry of Natural Resources in February 2022 clearly pointed out that real-life 3D is divided into terrain level, city level and component level according to the expression content and level. Among them, component-level real-life 3D focuses on the realistic description and refined expression of city-level real-life 3D, mainly including high-precision geometric modeling, texture directional mapping, perceptual information fusion and attribute behavior simulation, so as to serve the on-demand customization of personalized applications. With regard to the modeling of temperature fields of mountain bridges, on the one hand, the refinement of component-level real-life 3D models of bridges can effectively improve the accuracy of numerical simulation of temperature fields. On the other hand, the results of spatiotemporal modeling of temperature fields can further enrich the semantic information of real-life 3D models of bridges, and have important reference opinions on the realistic expression of component-level real-life 3D environmental parameters, thereby exerting greater benefits in the construction of digital bridges with multi-professional collaboration.

[0049] Based on this, this embodiment takes a mountain bridge as an example and proposes a visualization method for temperature field modeling of a mountain bridge based on spatiotemporal interpolation fusion. Figure 1 As shown, the method comprises the following steps:

[0050] Step 1: Analyze the heat transfer, heat exchange and heat radiation laws of the temperature field of mountain bridges, and establish a numerical simulation model of the solar radiation temperature field of mountain bridges;

[0051] Step 2: The temperature simulation results are aligned and corrected with the bridge voxel model in terms of spatial position, spatial attitude and spatial scale, and a 3D modeling method for mountain bridge temperature scenes based on spatiotemporal interpolation fusion is proposed, taking into account the physical characteristics of the temperature difference between the inside and outside of the bridge.

[0052] Step three, based on ray casting, efficient visualization of mountain bridge temperature volume rendering was achieved.

[0053] 1. Numerical simulation of temperature field of mountain bridges

[0054] This example performs numerical simulation of the temperature field for a steel truss suspension bridge in a mountainous area, and its basic assumptions are as follows:

[0055] (1) The material density and specific heat capacity of bridge components are uniform and do not change with changes in external conditions;

[0056] (2) The exterior of bridge components is only affected by solar radiation heat transfer and air convection heat transfer, while the internal temperature is affected by heat transfer;

[0057] The digital simulation calculation process of mountain bridge temperature field is as follows: Figure 2 As shown in the figure. First, according to the basic laws of computational fluid dynamics such as the law of conservation of mass, the law of conservation of momentum and the law of conservation of energy, the basic control equations for the simulation of the temperature field of mountain bridges are established; secondly, the initial parameters and boundary conditions of the simulation are set, such as the thermodynamic coefficient, the inlet and outlet wind speed and temperature, etc.; then, the bridge geometric model is meshed to generate a volume mesh model as the input data for the simulation calculation; finally, the numerical method is used to iteratively solve the discrete equations and output the numerical simulation results of the temperature field of mountain bridges.

[0058] 2. Temperature field modeling based on spatiotemporal interpolation fusion

[0059] 2.1 Spatial consistency registration of simulation results

[0060] Although most of the temperature simulation results of mountain bridges exist in the form of scattered points, due to differences in geometric division and data organization, the simulation results are inconsistent with the bridge model in terms of spatial position, spatial attitude and spatial scale, making it difficult to directly use them for spatiotemporal interpolation and fusion modeling of the temperature field of mountain bridges. In order to solve this problem, the idea adopted in this paper is to voxelize the bridge geometric model and use it as a benchmark, and then map the temperature simulation value to the bridge voxel model through point cloud registration as the basis for spatiotemporal interpolation and fusion modeling of the temperature field.

[0061] 2.1.1 Voxelization of bridge geometry model:

[0062] The voxelization of the bridge geometric model mainly includes axis-aligned bounding box acquisition, seed scanning line scanning and voxel judgment. The specific process is as follows: Figure 3 shown.

[0063] 2.1.2 Simulation model and voxel model registration

[0064] In this embodiment, the four-point consistent set algorithm is used to roughly align the temperature simulation data with the bridge voxel data, and then the robust iterative closest point algorithm is used to precisely align the point cloud. Let the temperature simulation data be the source point cloud, denoted as P = {p1, p2, ... p m}, m is the number of point clouds, and the bridge voxel data is the target point cloud, denoted as Q = {q1, q2, …q n}, n is the number of point clouds, the number of iterations is k, and the mean square error threshold of the iteration is ε , the fine registration process is as follows:

[0065] (1) Determine the set of corresponding point pairs. For all points in the source point cloud P Search for the closest corresponding point in the target point cloud Q Thus forming a point pair set n is the number of point pairs;

[0066] (2) According to the formula Establish the target error function and calculate the rotation matrix R k and the translation matrix T k ;

[0067] (3) Update the source point cloud coordinates. The rotation matrix R calculated in step (2) is k and the translation matrix T k , for the source point cloud The coordinates are updated to obtain the updated point cloud The update formula is:

[0068]

[0069] (4) Determine whether to continue iterating. According to the formula

[0070]

[0071] Calculate the mean square error σ of the iterative transformation k , if the number of iterations reaches the maximum number k max or |σ k+1 -σ k |<ε, ε is the iterative mean square error threshold, then the iterative calculation is stopped, otherwise k=k+1 is set and the process returns to step (1) and iterates again;

[0072] (5) Align the point cloud. The best rotation matrix R calculated last time is used. k and the translation matrix T k , change the point cloud coordinates, complete the point cloud registration, and further map the temperature simulation value to the bridge voxel point cloud.

[0073] 2.2 Temporal and spatial interpolation fusion modeling of mountain bridge temperature field

[0074] This embodiment adopts the idea of ​​reduction to perform spatiotemporal interpolation fusion modeling on the temperature field of mountain bridges. The basic idea is to use the data with good temporal consistency and spatial relationship consistency in the same area as the metadata of time dimension interpolation and space dimension interpolation, and then process the temperature data in two ways: time first and then space, and space first and then time. Finally, the two interpolation results are fused to form a mountain bridge temperature field model and perform accuracy evaluation, such as Figure 4 shown.

[0075] 2.2.1 Temperature field time dimension interpolation

[0076] In the time dimension, considering the accuracy of interpolation smoothing, this embodiment uses cubic spline function for time interpolation, which has the characteristics of high accuracy, stable numerical calculation, freely adjustable endpoint conditions, and small amount of calculation. Assume that there is a set of temperature field time series data (x i ,y i ),i∈[1,n],x i is the timestamp, y i is the temperature value, then the interval [x i ,x i+1 The interpolation polynomial of ] is: S i (x) = a i (xx i ) 3 +b i (xx i ) 2 +c i (xx i )+d i ,

[0077] S i (x) The following three conditions are met:

[0078] (1)S i (x i )=y i , i∈[1,n];

[0079] (2)S i (x i ) in each interval [x i ,x i+1 ] is a cubic polynomial;

[0080] (3)S i (x i ) The entire interval [x i ,x n ] has continuous first and second order derivatives, and S'(x i )=S'(x i+1 ), S”(x i )=S”(x i+1 );

[0081] This embodiment selects the "free boundary" constraint, S" (x0) = S" (x n )=0, then the unknown parameter a can be solved i , b i , c i and d i , substitute into the polynomial S i The time interpolation curve of the entire temperature field can be obtained in (x).

[0082] 2.2.2 Temperature field spatial dimension interpolation

[0083] In the spatial dimension, this embodiment uses the inverse distance weighted method to complete the data of the mountain bridge temperature field model in the spatial dimension. The formula is as follows:

[0084]

[0085] In the formula, λ j Indicates the weight value of point j to point i in the temperature field, d ij Represents the distance from point j to point i, and the power value ≥ is usually 2;

[0086] In this embodiment, in order to improve the efficiency of spatial interpolation calculation, the amount of data used for a single interpolation point is reduced by limiting the data influence range. The temperature distribution depth of the bridge tower affected by the sunlight temperature in the mountainous area is approximately 0.9m, but the temperature variation within the depth of 0-0.6m is much greater than that in the range of 0.6-0.9m, and the steel structure influence depth is 1.0m. Therefore, the temperature data influence ranges of the left and right bridge towers and steel truss units are set to 0.6m and 1.0m respectively.

[0087] 2.2.3 Spatiotemporal interpolation fusion modeling

[0088] The temperature field model T1 obtained by interpolating in time first and then in space is fused with the temperature field model T2 obtained by interpolating in space first and then in time to obtain the comprehensive temperature field model T. The calculation formula is:

[0089] T=λ1T1+λ2T2

[0090] Wherein, λ1 and λ2 represent the weight coefficient of T1 and the weight coefficient of T2 respectively. In this embodiment, the values ​​are both 0.5.

[0091] 3. Temperature field volume rendering based on ray casting

[0092] In this embodiment, in order to improve the visualization efficiency of the temperature field of mountain bridges, a ray casting algorithm is used to render the temperature field. The process is as follows: Figure 5 First, based on the above-mentioned spatiotemporal voxel model of temperature field v = (d, t, c), d represents the spatial domain, t represents the time domain, and c represents the temperature value, the volume texture and volume bounding box are constructed, and this process is completed in the CPU; secondly, the data of the constructed volume texture and volume bounding box are transmitted to the GPU, and the GPU is used for ray casting, and the parallel acceleration, early termination of rays, blank voxel jumping and other optimized rendering methods are used to realize the real-time and efficient rendering of the spatiotemporal data of the temperature field of mountain bridges.

[0093] The main innovation of the present invention is that it is based on the numerical simulation model of the temperature field of mountain bridges, takes into account the physical characteristics of the temperature difference between the inside and outside of the bridge, and proposes a three-dimensional modeling method of mountain bridge temperature scene with spatiotemporal interpolation fusion, which solves the problem of uneven spatiotemporal distribution of temperature simulation results due to the geometric irregularity of bridge component structures. In addition, the ray casting algorithm is used in the geographical scene to efficiently render the modeling results of the bridge temperature field. On the one hand, it can provide key data support for the multi-field coupling modeling of mountain bridges driven by digital twins, and serve the intelligent construction and refined management of bridges, effectively improving the construction quality of bridges in complex and dangerous mountainous areas. On the other hand, it can further enrich the semantic information of the real-scene three-dimensional model of the bridge, and has important reference opinions on the realistic expression of the real-scene three-dimensional environmental parameters at the component level, so as to play a greater role in the digital bridge construction with multi-professional collaboration.

[0094] In the description of the embodiments of the present invention, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", and "fourth" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0095] In the description of the embodiments of the present invention, it is to be understood that "-" and "~" represent the range between two values, and the range includes the endpoints. For example: "AB" represents a range greater than or equal to A and less than or equal to B. "A~B" represents a range greater than or equal to A and less than or equal to B.

[0096] In the description of the embodiments of the present invention, the term "and / or" in this embodiment is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this embodiment generally indicates that the associated objects before and after are in an "or" relationship.

[0097] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A visualization method for modeling temperature field of mountain bridges based on spatiotemporal interpolation fusion, characterized in that: The following steps are involved: Step 1: Analyze the heat transfer, heat exchange and heat radiation laws of the temperature field of mountain bridges, establish a numerical simulation model of the temperature field of mountain bridges, and obtain the temperature field simulation results; Step 2: The temperature field simulation results are aligned with the bridge voxel model, and the temperature field of the mountain bridge is modeled by using spatiotemporal interpolation fusion according to the physical characteristics of the temperature difference between the inside and outside of the bridge; Step 3: Implement efficient visualization of the temperature field of bridges in mountainous areas through volume rendering based on ray casting.

2. According to claim 1, a method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion is characterized in that: The step 1 specifically includes: Step 101, setting basic assumptions: a. The material density and specific heat capacity of bridge components are uniform and do not change with changes in external conditions; b. The exterior of bridge components is only affected by solar radiation heat transfer and air convection heat transfer, and the internal temperature is affected by heat transfer; Step 102, establishing basic control equations for simulating the temperature field of mountain bridges according to the basic laws of computational fluid dynamics; Step 103, setting initial parameters and boundary conditions of the simulation; Step 104, meshing the bridge geometric model, and using the resulting generated volume mesh model as input data for simulation calculation; Step 105, adopting a numerical method to iteratively solve the discrete equation and outputting the numerical simulation result of the temperature field of the mountain bridge.

3. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion according to claim 1 is characterized in that: The step 2 specifically includes: Step 201, voxelizing the bridge geometric model to obtain a bridge voxel model; Step 202, mapping the temperature field simulation value to the bridge voxel model through point cloud registration; Step 203, using the data on the consistency of the temporal and spatial relationships in the same area as metadata for time dimension interpolation and space dimension interpolation, and processing the aligned temperature field simulation data in two ways, time first then space and space first then time, respectively. Finally, the two interpolation results are fused to obtain a comprehensive temperature field model.

4. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion according to claim 3 is characterized in that: The specific process of voxelizing the bridge geometric model in step 201 is as follows: Axis-aligned bounding box acquisition: The axis-aligned bounding box of the bridge geometric model is represented by the maximum-minimum value representation method. By traversing the vertex coordinates of the triangular mesh of the bridge geometric model, the minimum and maximum values ​​of the model on the x, y, and z coordinate axes are obtained; Seed scanning line scanning: a straight line is formed with a unit cube as a seed, which is parallel to the coordinate axis. The internal and external position relationship between the voxels on the path and the bridge geometric model is determined by scanning; When the unit cube seed and the bridge geometric model are in an intersecting or containing relationship, the position information of the unit cube seed is recorded; Voxel judgment: When the seed scanning line passes through the bridge geometric model, it has three positional relationships with the bridge geometric model: the unit cube seed just enters the model, completely enters the model, and just exits the model; the positional relationship is used to perform voxel judgment. The seed coordinates when just entering the model and just exiting the model represent the boundary voxels, and the seed coordinates when completely entering the model represent the voxels inside the model.

5. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion according to claim 3 is characterized in that: The step 202 specifically includes: using a four-point consistent set algorithm to roughly align the temperature simulation data with the bridge voxel data, and then using an iterative closest point algorithm to finely align the point cloud.

6. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion according to claim 5 is characterized in that: The specific process of using the iterative closest point algorithm to accurately align the point cloud is as follows: Step 2021, determine the corresponding point pair set; take the temperature simulation data as the source point cloud P, and the bridge voxel data as the target point cloud Q, for all the points in the source point cloud P Search for the closest corresponding point in the target point cloud Q Thus forming a point pair set n is the number of point pairs, i is the number of point clouds, and k is the number of iterations; Step 2022, according to the formula Establish the target error function and calculate the rotation matrix R k and the translation matrix T k ; Step 2023, update the source point cloud coordinates; use the rotation matrix R calculated in step 2022 k and the translation matrix T k , for the source point cloud The coordinates are updated to obtain the updated point cloud The update formula is: Step 2024, determine whether to continue iteration; according to the formula Calculate the mean square error σ of the iterative transformation k , if the number of iterations reaches the maximum number k max or |σ k+1 -σ k |<ε, ε is the iterative mean square error threshold, then the iterative calculation is stopped, otherwise k=k+1 is set, and the iterative process is returned to step 2021; Step 2025, align the point cloud; use the best rotation matrix R calculated last time k and the translation matrix T k , change the point cloud coordinates, complete the point cloud registration, and further map the temperature simulation value to the bridge voxel point cloud.

7. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion according to claim 3 is characterized in that: The time dimension interpolation adopts cubic spline function to perform time interpolation; the space dimension interpolation adopts inverse distance weighted method to complete the data of mountain bridge temperature field model in space dimension, and reduces the amount of data used for a single interpolation point by limiting the data influence range.

8. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion according to claim 3 is characterized in that: The fusion process of the interpolation results is: T=λ1T1+λ2T2 Among them, T is the comprehensive temperature field model, T1 is the temperature field model obtained by interpolation of time first and space later, T2 is the temperature field model obtained by interpolation of space first and time later, λ1 represents the weight coefficient of T1, λ2 represents T 2Weight coefficient.

9. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion according to claim 1 is characterized in that: The step 3 is specifically as follows: Step 301, constructing a volume texture and a volume bounding box in the CPU based on the result v=(d, t, c) of the bridge temperature field modeling in step 2; wherein v is a comprehensive temperature field voxel model, d represents the spatial domain, t represents the time domain, and c represents the temperature value; Step 302, the constructed volume texture and volume bounding box data are transmitted to the GPU, and the GPU is used for ray casting. Through the optimized rendering method of parallel acceleration, early termination of rays, and blank voxel jumping, the real-time and efficient rendering of the spatiotemporal data of the temperature field of mountain bridges is achieved.

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