BIM-based Traffic Engineering Construction Schedule Management Method and System

Abstract

The present invention relates to the technical field of data processing, and specifically relates to a traffic engineering construction progress management method and system based on BIM, including: equally dividing the asphalt pavement area to be analyzed into several sub-block pavement areas; obtaining the compaction degree of each sub-block pavement area; constructing a compaction degree matrix according to the compaction degrees of all sub-block pavement areas in the asphalt pavement area to be analyzed; obtaining several final similar subgrade matrices; obtaining the similarity of the compaction degrees in the final similar subgrade matrices; determining the final sub-blocks of the final similar subgrade matrices according to the similarity; and respectively performing discrete cosine transform compression storage on all the final similar subgrade matrices according to the sizes of the final sub-blocks of the final similar subgrade matrices. The present invention improves the effect of compaction degree data compression storage of the asphalt pavement area during the traffic engineering construction progress based on BIM.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for managing the construction progress of a traffic engineering project based on BIM. Background Art

[0002] In the construction of transportation engineering projects based on BIM (Building Information Modeling), as the project progresses, especially for the collection and storage of compaction data of asphalt pavement construction, the continuous increase in data volume is a challenge that cannot be ignored. The amount of data increases significantly with the expansion of the construction scope. How to efficiently manage and store this data has become a problem that needs to be solved in construction management.

[0003] When compressing the compaction data of the asphalt pavement area during the construction progress of traffic engineering, discrete cosine transform (DCT) compression is often used. When using discrete cosine transform to compress data, larger blocks can improve compression efficiency, but data integrity will be sacrificed to a certain extent. Smaller blocks can effectively ensure data integrity, but the compression efficiency is low. In the existing compression, the compaction matrix data is compressed only through fixed-size blocks. However, the roadbed soil in the asphalt pavement area will change. If a single block method is used for discrete cosine transform compression storage during the data compression process without analyzing the changes in the data, the compression storage effect will be poor. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a BIM-based transportation engineering construction progress management method and system.

[0005] The BIM-based traffic engineering construction progress management method and system of the present invention adopts the following technical solutions:

[0006] An embodiment of the present invention provides a method for managing the construction progress of a traffic engineering project based on BIM, the method comprising the following steps:

[0007] The asphalt pavement area to be analyzed is equally divided into a number of sub-block pavement areas; the compaction degree of each sub-block pavement area is obtained; and a compaction degree matrix is ​​constructed according to the compaction degrees of all sub-block pavement areas in the asphalt pavement area to be analyzed;

[0008] According to the change of the compactness in each column matrix of the compactness matrix, the compactness non-uniformity of the asphalt pavement area corresponding to each column matrix in the compactness matrix is obtained; according to the difference in the compactness non-uniformity of the asphalt pavement areas corresponding to two adjacent column matrices in the compactness matrix and the difference in the compactness of the corresponding order in the two adjacent column matrices, the compactness difference of the asphalt pavement areas corresponding to two adjacent column matrices in the compactness matrix is obtained; according to the magnitude of the compactness difference, two adjacent column matrices in the compactness matrix are merged to obtain several initial similar subgrade matrices;

[0009] Obtain the similarity factor of two adjacent initial similar subgrade matrices; according to the compactness difference, the similarity factor and the difference in the compactness in two adjacent initial similar subgrade matrices, the similarity of two adjacent initial similar subgrade matrices is obtained; according to the magnitude of the similarity, two adjacent initial similar subgrade matrices are merged to obtain several final similar subgrade matrices;

[0010] Cluster the compactness in the final similar subgrade matrices to obtain the normal class clusters and several abnormal class clusters of each final similar subgrade matrix; according to the normal class clusters and abnormal class clusters of the final similar subgrade matrices and the similarity of two adjacent initial similar subgrade matrices, the similarity degree of the compactness in each final similar subgrade matrix is obtained; according to the similarity degree, adjust the side length of the preset initial block of each final similar subgrade matrix to obtain the side length of the final block of each final similar subgrade matrix; according to the side length of the final block of each final similar subgrade matrix, determine the final block of each final similar subgrade matrix, and the final block is a square block; according to the size of the final block of the final similar subgrade matrix, perform discrete cosine transform compression storage on all the final similar subgrade matrices respectively.

[0011] Further, the step of obtaining the compactness non-uniformity of the asphalt pavement area corresponding to each column matrix in the compactness matrix according to the change of the compactness in each column matrix of the compactness matrix specifically includes the following steps:

[0012]

[0013] In the formula, is the average value of the absolute differences of all adjacent compactness in the th column matrix in the compactness matrix; is the number of compactness in the th column matrix in the compactness matrix; is the th compactness in the th column matrix in the compactness matrix; is the preset compactness reference value; is to take the absolute value; is the A column matrix corresponds to the unevenness of the compaction degree of the asphalt pavement area.

[0014] Furthermore, obtaining the compaction degree difference between the asphalt pavement areas corresponding to two adjacent column matrices in the compaction degree matrix based on the difference in the unevenness of the compaction degree of the asphalt pavement areas corresponding to two adjacent column matrices in the compaction degree matrix and the difference in the compaction degree of the corresponding order within two adjacent column matrices includes the following specific steps:

[0015]

[0016] In the formula, is the absolute difference between the unevenness of the compaction degree of the asphalt pavement areas corresponding to the th column matrix and the th column matrix in the compaction degree matrix; is the number of compaction degrees in the th column matrix in the compaction degree matrix; is the th compaction degree in the th column matrix in the compaction degree matrix; is the th compaction degree in the th column matrix in the compaction degree matrix; is to take the absolute value; is the linear normalization function; is the compaction degree difference between the asphalt pavement areas corresponding to the th column matrix and the th column matrix in the compaction degree matrix.

[0017] Furthermore, merging two adjacent column matrices in the compaction degree matrix according to the magnitude of the compaction degree difference to obtain several initial similar subgrade matrices includes the following specific steps:

[0018] Preset a compaction degree difference threshold. If the compaction degree difference between the asphalt pavement areas corresponding to the th column matrix and the th column matrix in the compaction degree matrix is less than the compaction degree difference threshold, merge the th column matrix and the th column matrix in the compaction degree matrix into one matrix, denoted as the initial similar subgrade matrix, and continue to calculate the compaction degree difference between the asphalt pavement areas corresponding to the th column matrix and the th column matrix, the compaction degree difference between the asphalt pavement areas corresponding to the th column matrix and the Merge and judge the compaction degree differences of the asphalt pavement areas corresponding to each column matrix. If the compaction degree difference between the asphalt pavement areas corresponding to two adjacent column matrices in the compaction degree matrix is less than the compaction degree difference threshold, merge the column matrix with the compaction degree difference less than the threshold into the initial similar subgrade matrix until the compaction degree difference between the asphalt pavement areas corresponding to two adjacent column matrices in the compaction degree matrix is greater than or equal to the compaction degree difference threshold, and then stop the merging; perform the merging judgment on the compaction degree differences of the asphalt pavement areas corresponding to all adjacent two column matrices in the compaction degree matrix to obtain several initial similar subgrade matrices.

[0019] Further, the specific method for obtaining the similarity factor between two adjacent initial similar subgrade matrices is as follows:

[0020] Take the initial similar subgrade matrix with the least number of columns among the th and the th initial similar subgrade matrices as the first initial similar subgrade matrix; take the initial similar subgrade matrix with the most number of columns among the th and the th initial similar subgrade matrices as the second initial similar subgrade matrix; obtain all submatrices with the same number of columns as the first initial similar subgrade matrix in the second initial similar subgrade matrix, and all are denoted as submatrices to be analyzed; obtain the Euclidean distance between the first initial similar subgrade matrix and each submatrix to be analyzed, and take the reciprocal of the average of the Euclidean distances between the first initial similar subgrade matrix and all submatrices to be analyzed as the similarity factor between the th and the th initial similar subgrade matrices. If the th and the th initial similar subgrade matrices have the same number of columns, take the reciprocal of the Euclidean distance between the th and the th initial similar subgrade matrices as the similarity factor between the th and the th initial similar subgrade matrices.

[0021] Further, obtaining the similarity between two adjacent initial similar subgrade matrices based on the compaction degree difference, similarity factor, and the difference in compaction degree between two adjacent initial similar subgrade matrices includes the following specific steps:

[0022]

[0023] In the formula, is the average value of the compaction degree differences of the asphalt pavement areas corresponding to all adjacent two column matrices in the th initial similar subgrade matrix; is the average value of the compaction degree differences of the asphalt pavement areas corresponding to all adjacent two column matrices in the th initial similar subgrade matrix; is the similarity factor between the th initial similar subgrade matrix and the th initial similar subgrade matrix; is the average value of all compaction degrees in the th initial similar subgrade matrix; is the average value of all compaction degrees in the th initial similar subgrade matrix; is to take the absolute value; is the exponential function with the natural constant as the base; is the linear normalization function; is the similarity between the th initial similar subgrade matrix and the th initial similar subgrade matrix.

[0024] Furthermore, merging adjacent two initial similar subgrade matrices according to the magnitude of the similarity to obtain several final similar subgrade matrices includes the following specific steps:

[0025] Preset a similarity threshold. If the similarity between the th initial similar subgrade matrix and the th initial similar subgrade matrix is greater than or equal to the similarity threshold, merge the th initial similar subgrade matrix and the th initial similar subgrade matrix into one matrix, denoted as the final similar subgrade matrix, and continue to judge the similarity between the th initial similar subgrade matrix and the th initial similar subgrade matrix, and the similarity between the th initial similar subgrade matrix and the th initial similar subgrade matrix. If the similarity between adjacent two initial similar subgrade matrices is greater than or equal to the similarity threshold, merge the initial similar subgrade matrices greater than or equal to the similarity threshold into the final similar subgrade matrix until the similarity between adjacent two initial similar subgrade matrices is less than the similarity threshold, then stop merging; judge the similarity of all adjacent two initial similar subgrade matrices to obtain several final similar subgrade matrices.

[0026] Furthermore, obtaining the similarity of the compaction degree in each final similar subgrade matrix according to the normal class cluster and abnormal class cluster of the final similar subgrade matrix and the similarity between adjacent two initial similar subgrade matrices includes the following specific steps:

[0027]

[0028] In the formula, is the average of the similarities of all adjacent initial similar subgrade matrices in the -th final similar subgrade matrix; is the number of compaction degrees in the normal class cluster of the -th final similar subgrade matrix; is the number of compaction degrees in the -th final similar subgrade matrix; is the number of abnormal class clusters of the -th final similar subgrade matrix; is the average distance between the centroids of all abnormal class clusters and the centroid of the normal class cluster of the -th final similar subgrade matrix; is the exponential function with the natural constant as the base; is the linear normalization function; is the similarity of the compaction degree in the -th final similar subgrade matrix.

[0029] Furthermore, adjusting the side length of the initial block preset for the final similar subgrade matrix according to the similarity to obtain the side length of the final block of each final similar subgrade matrix includes the following specific steps:

[0030]

[0031] In the formula, is the side length of the initial block preset for all final similar subgrade matrices, and the initial block is a square block; is the similarity of the compaction degree in the -th final similar subgrade matrix; is the preset basic parameter; is the ceiling function; is the side length of the final block of the -th final similar subgrade matrix.

[0032] The present invention also proposes a traffic engineering construction progress management system based on BIM, including a memory and a processor, and the processor executes the computer program stored in the memory to implement the steps of the foregoing method.

[0033] The beneficial effects of the technical solution of the present invention are as follows: According to the present invention, when the subgrade soil quality in the asphalt pavement area changes, the difference in compaction degree caused by the change in subgrade soil quality can be accurately analyzed, the similar data in the compaction degree matrix can be divided to obtain similar data, that is, the final similar subgrade matrix, and the side length of the preset initial block of each final similar subgrade matrix can be adjusted according to the similarity of the compaction degree in the final similar subgrade matrix, and the appropriate block size of each final similar subgrade matrix can be adaptively determined. Furthermore, the discrete cosine transform is combined to compress and store all the final similar subgrade matrices, improving the compression and storage effect of the compaction degree data in the asphalt pavement area during the traffic engineering construction progress, ensuring both the relative integrity of the data and the compression efficiency during the compression process. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 It is a flowchart of the steps of the BIM-based traffic engineering construction progress management method provided by an embodiment of the present invention;

[0036] Figure 2 It is a schematic diagram of obtaining a compaction degree matrix provided by an embodiment of the present invention;

[0037] Among them, 1, 2, 3, and 4 respectively represent four different horizontal lanes, a1, b1, c1, d1, e1, and f1 respectively represent 6 different block pavement areas of the first lane, a2, b2, c2, d2, e2, and f2 respectively represent 6 different block pavement areas of the second lane, a3, b3, c3, d3, e3, and f3 respectively represent 6 different block pavement areas of the third lane, a4, b4, c4, d4, e4, and f4 respectively represent 6 different block pavement areas of the fourth lane, and a, b, c, d, e, and f respectively represent the position identifiers of different block pavement areas on the horizontal lane. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of the BIM-based traffic engineering construction progress management method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs.

[0040] The following specifically describes the specific solutions of the BIM-based traffic engineering construction progress management method and system provided by the present invention in conjunction with the accompanying drawings.

[0041] Please refer to Figure 1 , which shows a flowchart of the steps of the BIM-based traffic engineering construction progress management method provided by an embodiment of the present invention. The method includes the following steps:

[0042] Step S001: Evenly divide the asphalt pavement area to be analyzed into several sub-pavement areas; obtain the compaction degree of each sub-pavement area; construct a compaction degree matrix based on the compaction degrees of all sub-pavement areas in the asphalt pavement area to be analyzed.

[0043] It should be noted that a large amount of compaction degree data of the asphalt pavement area needs to be stored in the BIM-based traffic engineering construction progress. As the traffic engineering construction progress advances, the compaction degree data of the asphalt pavement area is also increasing. The main purpose of this embodiment is to efficiently compress and store the compaction degree data of the asphalt pavement area during the traffic engineering construction progress. The compression method used is discrete cosine transform (DCT) compression. Before starting the compression analysis, first collect the compaction degree data of the asphalt pavement area to be analyzed.

[0044] Specifically, evenly divide the asphalt pavement area to be analyzed into several sub-pavement areas as follows:

[0045] Divide each lane in the asphalt pavement area to be analyzed into a sub-pavement area at intervals of 10 meters. Divide all lanes in the asphalt pavement area to be analyzed at intervals of 10 meters to obtain several sub-pavement areas of the asphalt pavement area to be analyzed.

[0046] It should be noted that in this embodiment, each lane is divided into a sub-pavement area at intervals of 10 meters, and specific implementation can be adjusted according to the actual situation.

[0047] Further, obtain the compaction degree of each sub-block pavement area as follows:

[0048] Measure the compaction degree of the center point of each sub-block pavement area through a nuclear densitometer, and take the compaction degree measured at the center point as the compaction degree of the sub-block pavement area. It should be noted that the specific compaction degree measurement is an existing method and will not be elaborated in this embodiment.

[0049] Further, construct a compaction degree matrix according to the compaction degrees of all sub-block pavement areas in the asphalt pavement area to be analyzed as follows:

[0050] The asphalt pavement area to be analyzed contains multiple parallel lanes. Each lane contains several sub-block pavement areas. The number of sub-block pavement areas contained in different lanes is the same. Each sub-block pavement area corresponds to a unique compaction degree. Construct a matrix with all the compaction degrees according to the positions of the corresponding sub-block pavement areas, denoted as the compaction degree matrix.

[0051] It should be noted that please refer to Figure 2 , Figure 2 which is a schematic diagram for obtaining the compaction degree matrix in this embodiment. Figure 2 In it, there are 4 horizontal lanes, that is Figure 2 in which 1, 2, 3, and 4 are the numbers of the horizontal lanes, representing 4 different horizontal lanes respectively. Each lane is divided into 6 sub-block pavement areas, that is Figure 2 in which a, b, c, d, e, and f respectively represent the position identifiers of different sub-block pavement areas on the horizontal lane. Specifically for each lane, a1, b1, c1, d1, e1, and f1 respectively represent 6 different sub-block pavement areas of the first lane, a2, b2, c2, d2, e2, and f2 respectively represent 6 different sub-block pavement areas of the second lane, a3, b3, c3, d3, e3, and f3 respectively represent 6 different sub-block pavement areas of the third lane, and a4, b4, c4, d4, e4, and f4 respectively represent 6 different sub-block pavement areas of the fourth lane. Fill all the compaction degrees according to the positions of the corresponding sub-block pavement areas to construct a 4-row and 6-column matrix, that is, the compaction degree matrix.

[0052] Thus, the compaction degree matrix is obtained.

[0053] Step S002: According to the change of the compactness in each column matrix of the compactness matrix, obtain the compactness non-uniformity of the asphalt pavement area corresponding to each column matrix in the compactness matrix; according to the difference in the compactness non-uniformity of the asphalt pavement areas corresponding to two adjacent column matrices in the compactness matrix and the difference in the compactness of the corresponding order in the two adjacent column matrices, obtain the compactness difference of the asphalt pavement areas corresponding to two adjacent column matrices in the compactness matrix; merge two adjacent column matrices in the compactness matrix according to the magnitude of the compactness difference to obtain several initial similar subgrade matrices.

[0054] It should be noted that for asphalt pavements, there are certain requirement standards for the detection of their compactness. However, in different areas of the pavement, different subgrade soil types (such as clay, sand, etc.) have different physical and mechanical properties, which will all have a certain impact on the compaction effect of the pavement. Therefore, it is necessary to distinguish the data of different subgrade soil types according to the compactness of each divided pavement area, and classify and compressively store the pavement compactness data of different soil types after distinction.

[0055] Furthermore, it should be noted that the compaction of asphalt pavements is mainly carried out horizontally, that is, the compaction machinery compacts all lanes simultaneously along the width direction of the pavement. Therefore, in the compactness matrix constructed by the compactness of all the above-mentioned divided pavement areas, the compaction operation is completed repetitively in one column. Therefore, it is first necessary to analyze the difference in the compactness of the asphalt pavement areas corresponding to two adjacent column matrices in the compactness matrix, and judge whether there is a sudden change in the soil quality of the subgrade between the asphalt pavement areas corresponding to two adjacent column matrices, such as the transition of the subgrade from clay to sand, and then merge two adjacent column matrices in the compactness matrix to improve the effect of compressive storage.

[0056] It should be noted that during the same compaction operation, the materials used are usually the same. However, during the compaction process, due to the existence of local rocks or hard interlayers in the subgrade, the compactness of different divided pavement areas may be different, resulting in non-uniform compactness during the same compaction operation. Therefore, it is necessary to analyze the compactness non-uniformity of the asphalt pavement area corresponding to each column matrix in the compactness matrix.

[0057] Specifically, according to the change of the compactness in each column matrix of the compactness matrix, obtain the compactness non-uniformity of the asphalt pavement area corresponding to each column matrix in the compactness matrix, as follows:

[0058]

[0059] In the formula, is the mean value of the absolute differences of all adjacent compactness in the th column matrix in the compactness matrix; is the The number of compaction degrees within a column matrix; is the th column matrix in the compaction degree matrix, and is the th compaction degree within the th column matrix; is a preset compaction degree reference value. In this embodiment, the average value of all compaction degrees in the compaction degree matrix is used as the compaction degree reference value; denotes taking the absolute value;

[0060] It should be noted that the larger is, the more uneven the compaction degrees of adjacent sub - pavement areas in the asphalt pavement area corresponding to the th column matrix in the compaction degree matrix are. The larger is, the greater the difference between the compaction degrees of all sub - pavement areas in the asphalt pavement area corresponding to the

[0061] th column matrix and the compaction degree reference value, and the more uneven the compaction degree of the asphalt pavement area corresponding to the

[0062] th column matrix is. It should be noted that the above analyzes the unevenness of the compaction degree of the asphalt pavement area corresponding to each column matrix in the compaction degree matrix. During different compaction operations, normally, the overall compaction degrees corresponding to similar subgrade soil qualities are similar, that is, the difference in compaction degree unevenness is small, and the difference in compaction degrees between adjacent two compaction operations is small, indicating that the difference in compaction degree data of the two corresponding column matrices in the compaction degree matrix is small. The adjacent two column matrices should be merged to improve the compression storage effect.

[0063]

[0064] In the formula, is the absolute difference between the unevenness of the compaction degree of the asphalt pavement area corresponding to the th column matrix and the th column matrix in the compaction degree matrix; is the number of compaction degrees within the th column matrix in the compaction degree matrix; is the th compaction degree within the th column matrix in the compaction degree matrix; is the the degree of compaction within a column matrix; is to take the absolute value; is a linear normalization function for normalization processing; is the difference in the degree of compaction of the asphalt pavement area corresponding to the th column matrix and the th column matrix in the compaction degree matrix.

[0065] It should be noted that represents the difference in the non-uniformity of the degree of compaction of the asphalt pavement area corresponding to two adjacent column matrices in the compaction degree matrix. The larger it is, the greater the difference in the non-uniformity of the degree of compaction of the asphalt pavement area corresponding to the th column matrix and the th column matrix in the compaction degree matrix. represents the difference in the degree of compaction of the corresponding order within two adjacent column matrices. The larger it is, the greater the difference in the degree of compaction of the two elements in the same row of two adjacent column matrices in the compaction degree matrix corresponding to the segmented pavement area. Calculate the cumulative difference in the degree of compaction. The larger the cumulative difference in the degree of compaction, the greater the difference in the degree of compaction of the asphalt pavement area corresponding to the th column matrix and the th column matrix in the compaction degree matrix.

[0066] It should be noted that the above analyzes the difference in the degree of compaction of the asphalt pavement area corresponding to two adjacent column matrices in the compaction degree matrix. Next, two adjacent column matrices in the compaction degree matrix are merged according to the size of the compaction degree difference to improve the compression storage effect of the compaction degree data of the asphalt pavement area.

[0067] Furthermore, two adjacent column matrices in the compaction degree matrix are merged according to the size of the compaction degree difference to obtain several initial similar subgrade matrices, specifically as follows:

[0068] Preset a compaction degree difference threshold. In this embodiment, the compaction degree difference threshold is described as 0.3. If the difference in the degree of compaction of the asphalt pavement area corresponding to the th column matrix and the th column matrix in the compaction degree matrix is less than the compaction degree difference threshold, the th column matrix and the th column matrix in the compaction degree matrix are merged into one matrix, denoted as the initial similar subgrade matrix, and continue to calculate the difference in the degree of compaction of the asphalt pavement area corresponding to the th column matrix and the th column matrix, the difference in the degree of compaction of the asphalt pavement area corresponding to the th column matrix and the The compaction degree differences of the asphalt pavement areas corresponding to adjacent column matrices in the compaction degree matrix are merged and judged. If the compaction degree difference of the asphalt pavement areas corresponding to two adjacent column matrices in the compaction degree matrix is less than the compaction degree difference threshold, the column matrices with compaction degree differences less than the threshold are merged into the initial similar subgrade matrix until there are two adjacent column matrices in the compaction degree matrix whose corresponding asphalt pavement areas have a compaction degree difference greater than or equal to the compaction degree difference threshold, and then the merging stops; the compaction degree differences of the asphalt pavement areas corresponding to all adjacent column matrices in the compaction degree matrix are merged and judged to obtain several initial similar subgrade matrices.

[0069] It should be noted that the smaller the compaction degree difference of the asphalt pavement areas corresponding to two adjacent column matrices in the compaction degree matrix, the closer the subgrade soil qualities of the asphalt pavement areas corresponding to the two adjacent column matrices are. Therefore, in this embodiment, by setting the compaction degree difference threshold, two adjacent column matrices in the compaction degree matrix are merged to obtain the initial similar subgrade matrix for subsequent analysis and improve the effect of compressed storage.

[0070] So far, several initial similar subgrade matrices are obtained.

[0071] Step S003: Obtain the similarity factors of two adjacent initial similar subgrade matrices; according to the compaction degree difference, the similarity factors, and the difference in compaction degree between two adjacent initial similar subgrade matrices, obtain the similarity between two adjacent initial similar subgrade matrices; merge two adjacent initial similar subgrade matrices according to the magnitude of the similarity to obtain several final similar subgrade matrices.

[0072] It should be noted that under normal circumstances, the compaction degrees corresponding to the soil qualities of similar subgrades are generally similar. However, due to problems in the construction operation during the compaction process, the compaction degree of a certain section of the road surface may mutate under similar subgrade soil qualities, causing the data that should originally be connected to be split. For example, the asphalt pavement areas corresponding to columns to in the compaction degree matrix are all asphalt pavement areas with similar subgrade soil qualities. If an operation error occurs during the compaction process in column , causing the compaction degree of this column to mutate, then the data from column to will be divided into two matrices. Columns to form an initial similar subgrade matrix, and columns to form an initial similar subgrade matrix. Therefore, it is necessary to compare the similarity of the compaction degrees under different similar subgrade soil qualities, merge the initial similar subgrade matrices with higher similarity, and determine the final similar subgrade matrix corresponding to the similar subgrade soil quality. First, analyze the similarity factors of two adjacent initial similar subgrade matrices.

[0073] Specifically, the similarity factor of two adjacent initial similar subgrade matrices is obtained as follows:

[0074] Take the initial similar subgrade matrix with the least number of columns among the -th and the -th initial similar subgrade matrices as the first initial similar subgrade matrix; take the initial similar subgrade matrix with the most number of columns among the -th and the -th initial similar subgrade matrices as the second initial similar subgrade matrix; obtain all submatrices in the second initial similar subgrade matrix that have the same number of columns as the first initial similar subgrade matrix, and all of them are denoted as submatrices to be analyzed; obtain the Euclidean distance between the first initial similar subgrade matrix and each submatrix to be analyzed, and take the reciprocal of the average of the Euclidean distances between the first initial similar subgrade matrix and all submatrices to be analyzed as the similarity factor between the -th and the -th initial similar subgrade matrices. In this embodiment, the inverse proportional relationship is presented by the model, and is the input of the model. It should be particularly noted that if the -th and the -th initial similar subgrade matrices have the same number of columns, take the reciprocal of the Euclidean distance between the -th and the -th initial similar subgrade matrices as the similarity factor between the -th and the -th initial similar subgrade matrices.

[0075] It should be noted that since each submatrix to be analyzed and the first initial similar subgrade matrix are matrices of the same size, the Euclidean distance between the two matrices can be analyzed to analyze the similarity between the two matrices. The smaller the Euclidean distance between the two matrices, the more similar the two matrices are; by analyzing the Euclidean distances between the first initial similar subgrade matrix and all submatrices to be analyzed, the similarity factor between the -th and the -th initial similar subgrade matrices is obtained.

[0076] It should be noted that the similarity factor of two adjacent initial similar subgrade matrices is analyzed above. Next, in combination with the compaction degree difference, the similarity factor, and the difference in compaction degree between two adjacent initial similar subgrade matrices, the similarity between two adjacent initial similar subgrade matrices is accurately judged.

[0077] Further, according to the compaction degree difference, similarity factor, and the difference in compaction degree between two adjacent initial similar subgrade matrices, the similarity between two adjacent initial similar subgrade matrices is obtained as follows:

[0078]

[0079] In the formula, is the average value of the compaction degree differences of the asphalt pavement areas corresponding to all adjacent two-column matrices in the th initial similar subgrade matrix; is the average value of the compaction degree differences of the asphalt pavement areas corresponding to all adjacent two-column matrices in the th initial similar subgrade matrix; is the similarity factor between the th initial similar subgrade matrix and the th initial similar subgrade matrix; is the average value of all compaction degrees in the th initial similar subgrade matrix; is the average value of all compaction degrees in the th initial similar subgrade matrix; represents taking the absolute value; is the exponential function with the natural constant as the base. In this embodiment, the model is used to present the inverse proportional relationship, is the input of the model; is the linear normalization function for normalization processing; is the similarity between the th initial similar subgrade matrix and the th initial similar subgrade matrix.

[0080] It should be noted that represents the magnitude of the compaction degree difference corresponding to the th initial similar subgrade matrix and the th initial similar subgrade matrix. The smaller is, the smaller the compaction degree difference corresponding to the th initial similar subgrade matrix and the th initial similar subgrade matrix, and the more similar the th initial similar subgrade matrix and the th initial similar subgrade matrix; represents the difference in compaction degree between the th initial similar subgrade matrix and the th initial similar subgrade matrix. The smaller is, the more similar the th initial similar subgrade matrix and the th initial similar subgrade matrix; is the similarity factor. The larger the similarity factor, the more similar the th initial similar subgrade matrix and the th initial similar subgrade matrix are. By combining the comprehensive analysis of the three, the similarity of the th initial similar subgrade matrix and the th initial similar subgrade matrix is used to improve the accuracy of similarity judgment.

[0081] It should be noted that the similarity of two adjacent initial similar subgrade matrices is analyzed above. Next, the two adjacent initial similar subgrade matrices are merged according to the size of the similarity to obtain the final similar subgrade matrix.

[0082] Furthermore, the two adjacent initial similar subgrade matrices are merged according to the size of the similarity to obtain several final similar subgrade matrices, as follows:

[0083] A similarity threshold is preset. In this embodiment, the similarity threshold is 0.8 for description. If the similarity between the th initial similar subgrade matrix and the th initial similar subgrade matrix is greater than or equal to the similarity threshold, the th initial similar subgrade matrix and the th initial similar subgrade matrix are merged into one matrix, denoted as the final similar subgrade matrix, and the similarity between the th initial similar subgrade matrix and the th initial similar subgrade matrix, and the similarity between the th initial similar subgrade matrix and the th initial similar subgrade matrix are continuously merged and judged. If the similarity between two adjacent initial similar subgrade matrices is greater than or equal to the similarity threshold, the initial similar subgrade matrices greater than or equal to the similarity threshold are merged into the final similar subgrade matrix until the similarity between two adjacent initial similar subgrade matrices is less than the similarity threshold, and the merging stops; the similarity of all adjacent two initial similar subgrade matrices is merged and judged to obtain several final similar subgrade matrices.

[0084] It should be noted that the greater the similarity between two adjacent initial similar subgrade matrices, the closer the subgrade soil quality of the asphalt pavement areas corresponding to the two adjacent initial similar subgrade matrices. Therefore, in this embodiment, by setting the similarity threshold, the two adjacent initial similar subgrade matrices are merged to obtain the final similar subgrade matrix for subsequent analysis to improve the effect of compression storage.

[0085] So far, several final similar subgrade matrices are obtained.

[0086] Step S004: Cluster the compaction degrees in the final similar subgrade matrices to obtain the normal clusters and several abnormal clusters for each final similar subgrade matrix; Based on the normal clusters and abnormal clusters of the final similar subgrade matrices and the similarity between two adjacent initial similar subgrade matrices, obtain the similarity degree of the compaction degree in each final similar subgrade matrix; Adjust the side length of the preset initial block of each final similar subgrade matrix according to the similarity degree to obtain the side length of the final block of each final similar subgrade matrix; Determine the final block of each final similar subgrade matrix according to the side length of the final block of each final similar subgrade matrix, where the final block is a square block; Compress and store all the final similar subgrade matrices respectively according to the size of the final block of the final similar subgrade matrix by using discrete cosine transform.

[0087] It should be noted that when using discrete cosine transform (DCT) compression, smaller blocks can better capture the details of the image because there are fewer changes within each block, and the coefficients after DCT transformation are more concentrated, which helps to retain the high-frequency details of the image, such as edges and textures. At the same time, smaller blocks will also result in more computational and storage overhead. Larger blocks can improve the compression effect, but may sacrifice the image quality, especially at the boundaries of the blocks, where artifacts may appear. Therefore, it is necessary to adjust the size of the blocks according to the specific situation of the image. Correspondingly, in the compression of compaction degree matrix data, smaller blocks help to more accurately capture data changes and avoid mixing different data. Larger blocks can improve the compression effect and reduce the amount of operations, and the size of the block depends on the similarity of the data in the matrix. When the similarity of the data in the matrix is high, it means that the data changes in the matrix are small, and larger blocks can be used. For the situation where the similarity of the data in the matrix is low, it means that its data changes more, and it needs to be focused on, so smaller blocks are required. Therefore, it is necessary to analyze the similarity of the compaction degree in the final similar subgrade matrix, and then determine the appropriate block size for each final similar subgrade matrix, and complete efficient compression in combination with discrete cosine transform.

[0088] Specifically, clustering the compaction degrees in the final similar subgrade matrices to obtain the normal clusters and several abnormal clusters for each final similar subgrade matrix is as follows:

[0089] For the th final similar subgrade matrix, perform K-means clustering on all the compaction degrees to obtain several clusters of the th final similar subgrade matrix. The distance metric for K-means clustering uses the absolute difference of the compaction degrees between element positions, and the value of K for K-means clustering is determined by the elbow method; Denote the cluster with the largest area among the several clusters of the th final similar subgrade matrix as the The normal class clusters of the final similar subgrade matrices. Denote the remaining clusters among several clusters of the th final similar subgrade matrix, except for the cluster with the largest area, as the th abnormal class clusters of the final similar subgrade matrix.

[0090] It should be noted that the above analyzes the normal class clusters and several abnormal class clusters of each final similar subgrade matrix. Next, combined with the normal class clusters and abnormal class clusters of the final similar subgrade matrix and the similarity between two adjacent initial similar subgrade matrices, accurately analyze the similarity of the compaction degree in each final similar subgrade matrix.

[0091] Furthermore, according to the normal class clusters and abnormal class clusters of the final similar subgrade matrix and the similarity between two adjacent initial similar subgrade matrices, obtain the similarity of the compaction degree in each final similar subgrade matrix, as follows:

[0092]

[0093] In the formula, is the average value of the similarities between all adjacent two initial similar subgrade matrices in the th final similar subgrade matrix; is the number of compaction degrees in the normal class clusters of the th final similar subgrade matrix; is the number of compaction degrees in the th final similar subgrade matrix; is the number of abnormal class clusters of the th final similar subgrade matrix; is the average value of the distances between the centroids of all abnormal class clusters and the centroids of the normal class clusters of the th final similar subgrade matrix; is the exponential function with the natural constant as the base. In this embodiment, the model is used to present the inverse proportional relationship, is the input of the model; is the linear normalization function for normalization processing; is the similarity of the compaction degree in the th final similar subgrade matrix.

[0094] It should be noted that the larger the average value of the similarities between all adjacent two initial similar subgrade matrices in the th final similar subgrade matrix, the higher the similarity between the adjacent initial similar subgrade matrices in the th final similar subgrade matrix, and the higher the similarity of the compaction degree in the final similar subgrade matrix should be; represents the normal class clusters of the th final similar subgrade matrix in the The area proportion of the final similar subgrade matrix. The larger the area proportion, it indicates that in the th final similar subgrade matrix, there are more compaction degrees that are close to each other because they are all in the normal cluster. In the th final similar subgrade matrix, the similarity of the compaction degree is also higher; The smaller it is, it indicates that in the th final similar subgrade matrix, there are fewer abnormal clusters, and the distance between the abnormal cluster and the normal cluster is closer. In the th final similar subgrade matrix, the distribution of the compaction degree is more uniform, and in the th final similar subgrade matrix, the similarity of the compaction degree is higher.

[0095] It should be noted that when the similarity of the compaction degree in the final similar subgrade matrix is relatively high, the compaction degrees in the final similar subgrade matrix are closer. When performing discrete cosine transform compression, the block size of the final similar subgrade matrix should be increased to improve the compression effect. On the contrary, the block size needs to be reduced. Therefore, next, the side length of the final block of each final similar subgrade matrix is determined in combination with the similarity.

[0096] Furthermore, the side length of the initial block preset for the final similar subgrade matrix is adjusted according to the similarity to obtain the side length of the final block of each final similar subgrade matrix, specifically as follows:

[0097]

[0098] In the formula, is the side length of the initial block preset for all final similar subgrade matrices. The initial block is a square block. In this embodiment, the side length of the initial block is described as 5, and the unit is pixel, that is, the initial block is a square block; is the similarity of the compaction degree in the th final similar subgrade matrix; is a preset basic parameter. In this embodiment, it is described with ; is the ceiling function; is the side length of the final block of the th final similar subgrade matrix.

[0099] It should be noted that the higher the similarity of the compaction degree in the th final similar subgrade matrix, it indicates that the compaction degrees in the th final similar subgrade matrix are closer. When performing discrete cosine transform (Discrete Cosine Transform, DCT) compression, in the The block size of each final similar subgrade matrix should be increased, that is, the side length of the final block should be increased. Larger blocks generally mean higher compression ratios to improve the compression effect on the compaction data of the th final similar subgrade matrix. Conversely, it is necessary to reduce the compression effect on the compaction data of the th final similar subgrade matrix.

[0100] Furthermore, according to the side length of the final block of each final similar subgrade matrix, the final block of each final similar subgrade matrix is determined, and the final block is a square block.

[0101] Furthermore, according to the size of the final block of the final similar subgrade matrix, discrete cosine transform compression storage is performed on all final similar subgrade matrices respectively.

[0102] It should be noted that after determining the size of the final block of each final similar subgrade matrix, compression storage according to the size of the final block and discrete cosine transform is an existing method, which will not be elaborated in this embodiment.

[0103] Through the above steps, the BIM-based traffic engineering construction progress management method is completed.

[0104] Another embodiment of the present invention provides a BIM-based traffic engineering construction progress management system. The system includes a memory and a processor. When the processor executes the computer program stored in the memory, the following operations are performed:

[0105] The asphalt pavement area to be analyzed is evenly divided into several sub - pavement areas; the compactness of each sub - pavement area is obtained; a compactness matrix is constructed according to the compactness of all sub - pavement areas in the asphalt pavement area to be analyzed; according to the change of compactness within each column matrix in the compactness matrix, the compactness non - uniformity of the asphalt pavement area corresponding to each column matrix in the compactness matrix is obtained; according to the difference in compactness non - uniformity between the asphalt pavement areas corresponding to two adjacent column matrices in the compactness matrix and the difference in compactness of the corresponding order within the two adjacent column matrices, the compactness difference between the asphalt pavement areas corresponding to two adjacent column matrices in the compactness matrix is obtained; the two adjacent column matrices in the compactness matrix are merged according to the magnitude of the compactness difference to obtain several initial similar subgrade matrices; the similarity factor between two adjacent initial similar subgrade matrices is obtained; according to the compactness difference, the similarity factor, and the difference in compactness between two adjacent initial similar subgrade matrices, the similarity between two adjacent initial similar subgrade matrices is obtained; two adjacent initial similar subgrade matrices are merged according to the magnitude of the similarity to obtain several final similar subgrade matrices; the compactness in the final similar subgrade matrices is clustered to obtain the normal class clusters and several abnormal class clusters of each final similar subgrade matrix; according to the normal class clusters and abnormal class clusters of the final similar subgrade matrices and the similarity between two adjacent initial similar subgrade matrices, the similarity degree of the compactness in each final similar subgrade matrix is obtained; according to the similarity degree, the side length of the initial sub - division preset for the final similar subgrade matrix is adjusted to obtain the side length of the final sub - division of each final similar subgrade matrix; according to the side length of the final sub - division of each final similar subgrade matrix, the final sub - divisions of each final similar subgrade matrix are determined, and the final sub - divisions are square sub - divisions; according to the size of the final sub - divisions of the final similar subgrade matrices, discrete cosine transform compression storage is performed on all final similar subgrade matrices respectively.

[0106] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. The BIM-based traffic engineering construction progress management method is characterized by: The method comprises the following steps: The asphalt pavement area to be analyzed is equally divided into a number of sub-block pavement areas; the compaction degree of each sub-block pavement area is obtained; and a compaction degree matrix is ​​constructed according to the compaction degrees of all sub-block pavement areas in the asphalt pavement area to be analyzed; According to the change of compaction degree in each column matrix in the compaction degree matrix, the compaction degree non-uniformity of the asphalt pavement area corresponding to each column matrix in the compaction degree matrix is ​​obtained; according to the difference of compaction degree non-uniformity of the asphalt pavement area corresponding to two adjacent column matrices in the compaction degree matrix and the compaction degree difference of the corresponding order in two adjacent column matrices, the compaction degree difference of the asphalt pavement area corresponding to two adjacent column matrices in the compaction degree matrix is ​​obtained; according to the size of the compaction degree difference, two adjacent column matrices in the compaction degree matrix are merged to obtain several initial similar roadbed matrices; Obtaining the similarity factor of two adjacent initial similar roadbed matrices; obtaining the similarity of two adjacent initial similar roadbed matrices according to the difference in compaction degree, the similarity factor and the difference in compaction degree in the two adjacent initial similar roadbed matrices; merging the two adjacent initial similar roadbed matrices according to the size of the similarity to obtain several final similar roadbed matrices; The compaction degree in the final similar roadbed matrix is ​​clustered to obtain a normal cluster and several abnormal clusters of each final similar roadbed matrix; the similarity of the compaction degree in each final similar roadbed matrix is ​​obtained based on the normal cluster and abnormal cluster of the final similar roadbed matrix and the similarity of two adjacent initial similar roadbed matrices; the side length of the initial block preset in the final similar roadbed matrix is ​​adjusted according to the similarity to obtain the side length of the final block of each final similar roadbed matrix; the final block of each final similar roadbed matrix is ​​determined according to the side length of the final block of each final similar roadbed matrix, and the final block is a square block; according to the size of the final block of the final similar roadbed matrix, all final similar roadbed matrices are respectively subjected to discrete cosine transformation compression and storage.

2. The BIM-based traffic engineering construction progress management method according to claim 1 is characterized in that: The method of obtaining the compaction unevenness of the asphalt pavement area corresponding to each column matrix in the compaction matrix according to the compaction change in each column matrix in the compaction matrix includes the following specific steps: In the formula, is the first The mean of the absolute differences of all adjacent compaction degrees in the column matrix; is the first The number of compactions within the column matrix; is the first The first column in the matrix degree of compaction; is the preset compaction reference value; To take the absolute value; is the first The column matrix corresponds to the compaction non-uniformity of the asphalt pavement area.

3. The BIM-based traffic engineering construction progress management method according to claim 1 is characterized in that: The method of obtaining the compaction difference of the asphalt pavement area corresponding to two adjacent column matrices in the compaction matrix according to the compaction unevenness difference of the asphalt pavement area corresponding to two adjacent column matrices in the compaction matrix and the compaction difference of the corresponding order in the two adjacent column matrices includes the following specific steps: In the formula, is the first The column matrix and The columns of the matrix correspond to the absolute differences between the compaction non-uniformity of the asphalt pavement area; is the first The number of compactions within the column matrix; is the first The first column in the matrix degree of compaction; is the first The first column in the matrix degree of compaction; To take the absolute value; is a linear normalization function; is the first The column matrix and The columns of the matrix correspond to the differences in compaction of the asphalt pavement area.

4. The BIM-based traffic engineering construction progress management method according to claim 1 is characterized in that: The method of merging two adjacent column matrices in the compaction degree matrix according to the compaction degree difference to obtain a plurality of initial similar roadbed matrices includes the following specific steps: A compaction difference threshold is preset. If the first The column matrix and The compaction difference of the asphalt pavement area corresponding to the column matrix is ​​less than the compaction difference threshold, and the first column in the compaction matrix is The column matrix and The column matrices are merged into one matrix, recorded as the initial similar roadbed matrix, and the The column matrix and The column matrix corresponds to the compaction difference of the asphalt pavement area, The column matrix and The compaction differences of the asphalt pavement areas corresponding to the column matrices are merged and judged. If the compaction difference of the asphalt pavement areas corresponding to the two adjacent column matrices in the compaction matrix is ​​less than the compaction difference threshold, the column matrices less than the compaction difference threshold are merged into the initial similar roadbed matrix until the compaction difference of the asphalt pavement areas corresponding to the two adjacent column matrices in the compaction matrix is ​​greater than or equal to the compaction difference threshold, and the merging is stopped; the compaction differences of the asphalt pavement areas corresponding to all the adjacent two column matrices in the compaction matrix are merged and judged to obtain several initial similar roadbed matrices.

5. The BIM-based traffic engineering construction progress management method according to claim 1 is characterized in that: The specific method for obtaining the similarity factor of two adjacent initial similar roadbed matrices is as follows: The first The initial similarity roadbed matrix and the The initial similar roadbed matrix with the least number of columns among the initial similar roadbed matrices is recorded as the first initial similar roadbed matrix; The initial similarity roadbed matrix and the The initial similar roadbed matrix with the largest number of columns in the initial similar roadbed matrices is recorded as the second initial similar roadbed matrix; all sub-matrices with the same number of columns as the first initial similar roadbed matrix are obtained in the second initial similar roadbed matrix, and are all recorded as sub-matrices to be analyzed; the Euclidean distance between the first initial similar roadbed matrix and each sub-matrix to be analyzed is obtained, and the inverse proportion of the average value of the Euclidean distance between the first initial similar roadbed matrix and all sub-matrices to be analyzed is taken as the first The initial similarity roadbed matrix and the The similarity factor of the initial similar roadbed matrix is The initial similarity roadbed matrix and the The number of columns of the initial similar roadbed matrices is the same. The initial similarity roadbed matrix and the The inverse proportion of the Euclidean distance of the initial similar roadbed matrix is ​​taken as the The initial similarity roadbed matrix and the The similarity factor of the initial similar roadbed matrix.

6. The BIM-based traffic engineering construction progress management method according to claim 1 is characterized in that: The similarity of two adjacent initial similar roadbed matrices is obtained according to the compaction difference, the similarity factor and the compaction difference in two adjacent initial similar roadbed matrices, and the specific steps include the following: In the formula, For the The average value of the compaction difference of the asphalt pavement area corresponding to all two adjacent column matrices in the initial similar roadbed matrix; For the The average value of the compaction difference of the asphalt pavement area corresponding to all two adjacent column matrices in the initial similar roadbed matrix; For the The initial similarity roadbed matrix and the The similarity factor of the initial similar roadbed matrix; For the The average value of all compaction degrees in the initial similar roadbed matrix; For the The average value of all compaction degrees in the initial similar roadbed matrix; To take the absolute value; is an exponential function with a natural constant as base; is a linear normalization function; For the The initial similarity roadbed matrix and the The similarity of the initial similar roadbed matrices.

7. The BIM-based traffic engineering construction progress management method according to claim 1 is characterized in that: The method of merging two adjacent initial similar roadbed matrices according to the size of similarity to obtain a plurality of final similar roadbed matrices includes the following specific steps: Preset a similarity threshold. The initial similarity roadbed matrix and the If the similarity of the initial similar roadbed matrices is greater than or equal to the similarity threshold, the first The initial similarity roadbed matrix and the The initial similar roadbed matrices are merged into one matrix, recorded as the final similar roadbed matrix, and the The initial similarity roadbed matrix and the The similarity of the initial similar roadbed matrix, The initial similarity roadbed matrix and the The similarity of the initial similar roadbed matrices is merged and judged. If the similarity of two adjacent initial similar roadbed matrices is greater than or equal to the similarity threshold, the initial similar roadbed matrices greater than or equal to the similarity threshold are merged into the final similar roadbed matrix until the similarity of two adjacent initial similar roadbed matrices is less than the similarity threshold, and the merging is stopped; The similarity of all two adjacent initial similar roadbed matrices is combined and judged to obtain several final similar roadbed matrices.

8. The method for managing the construction progress of a traffic engineering project based on BIM according to claim 1 is characterized in that: The similarity of the compaction degree in each final similar roadbed matrix is ​​obtained according to the normal clusters and abnormal clusters of the final similar roadbed matrix and the similarity of two adjacent initial similar roadbed matrices, and the specific steps include the following: In the formula, For the The average value of the similarities of all two adjacent initial similar roadbed matrices in the final similar roadbed matrix; For the The number of compaction levels in the normal clusters of the final similar roadbed matrix; For the The number of compaction levels in the final similar roadbed matrix; For the The number of abnormal clusters in the final similarity roadbed matrix; For the The average distance between the centroid of all abnormal clusters and the centroid of the normal cluster in the final similar roadbed matrix; is an exponential function with a natural constant as base; is a linear normalization function; For the The similarity of compaction degree in the final similar roadbed matrix.

9. The BIM-based traffic engineering construction progress management method according to claim 1 is characterized in that: The step of adjusting the side length of the initial block preset in the final similar roadbed matrix according to the similarity to obtain the side length of the final block of each final similar roadbed matrix includes the following specific steps: In the formula, The side length of the initial block preset for all final similar roadbed matrices, wherein the initial block is a square block; For the The similarity of compaction degree in the final similar roadbed matrix; are the preset basic parameters; is the ceiling rounding function; For the The side length of the final block of the final similarity roadbed matrix.

10. A BIM-based traffic engineering construction progress management system, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the BIM-based transportation engineering construction progress management method as described in any one of claims 1 to 9 are implemented.

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