BIM-based traffic engineering construction progress management method and system

Abstract

The invention relates to the technical field of data processing, in particular to a BIM-based traffic engineering construction progress management method and system, and the method comprises the steps: equally dividing a to-be-analyzed asphalt pavement region into a plurality of block pavement regions; the compaction degree of each block pavement area is obtained; constructing a compactness matrix according to the compactness of all block pavement areas in the to-be-analyzed asphalt pavement area; obtaining a plurality of final similar roadbed matrixes; obtaining the similarity of the compactness in the final similar roadbed matrix; determining a final block of the final similar roadbed matrix according to the similarity; and according to the sizes of the final blocks of the final similar roadbed matrixes, discrete cosine transform compression storage is carried out on all the final similar roadbed matrixes. According to the method, the compaction degree data compression and storage effect of the asphalt pavement area in the BIM-based traffic engineering construction progress process is improved.

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: 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: 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.

[0006] Furthermore, the compaction degree unevenness of the asphalt pavement area corresponding to each column matrix in the compaction degree matrix is ​​obtained according to the compaction degree change in each column matrix in the compaction degree matrix, and the specific steps include the following: 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.

[0007] Furthermore, 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.

[0008] Furthermore, the step 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.

[0009] Furthermore, 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.

[0010] Furthermore, 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.

[0011] Furthermore, the step 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 merged and judged to obtain several final similar roadbed matrices.

[0012] Furthermore, 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.

[0013] Furthermore, 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, including 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.

[0014] The present invention also proposes a BIM-based traffic engineering construction progress management system, including a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the steps of the aforementioned method.

[0015] The beneficial effect of the technical solution of the present invention is: according to the present invention, when the roadbed soil in the asphalt pavement area changes, the difference in compaction caused by the change in the roadbed soil can be accurately analyzed, and similar data in the compaction matrix can be divided to obtain similar data, that is, the final similar roadbed matrix, and the side length of the initial block preset in the final similar roadbed matrix is ​​adjusted according to the similarity of the compaction in the final similar roadbed matrix, and the appropriate block size of each final similar roadbed matrix is ​​adaptively determined, and then all the final similar roadbed matrices are compressed and stored in combination with discrete cosine transform, thereby improving the effect of compression and storage of compaction data on the asphalt pavement area during the construction progress of the traffic engineering project, so that in the compression process, both the relative integrity of the data and the efficiency of compression can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of a method for managing the construction progress of a traffic project based on BIM provided by an embodiment of the present invention; Figure 2 A schematic diagram of obtaining a compaction degree matrix provided by an embodiment of the present invention; Among them, 1, 2, 3 and 4 represent four different lateral lanes respectively, a1, b1, c1, d1, e1 and f1 represent six different block road areas of the first lane respectively, a2, b2, c2, d2, e2 and f2 represent six different block road areas of the second lane respectively, a3, b3, c3, d3, e3 and f3 represent six different block road areas of the third lane respectively, a4, b4, c4, d4, e4 and f4 represent six different block road areas of the fourth lane respectively, a, b, c, d, e and f represent the location marks of different block road areas on the lateral lanes respectively. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the BIM-based traffic engineering construction progress management method and system proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of the BIM-based transportation engineering construction progress management method and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of the steps of a BIM-based traffic engineering construction progress management method provided by an embodiment of the present invention, the method comprising the following steps: Step S001, equally divide the asphalt pavement area to be analyzed into a number of sub-block pavement areas; obtain the compaction degree of each sub-block pavement area; and construct a compaction degree matrix according to the compaction degrees of all sub-block pavement areas in the asphalt pavement area to be analyzed.

[0022] It should be noted that a large amount of compaction data on asphalt pavement areas needs to be stored in the BIM-based traffic engineering construction progress. As the traffic engineering construction progress progresses, the compaction data on asphalt pavement areas is also increasing. The main purpose of this embodiment is to efficiently compress and store the compaction data on asphalt pavement areas 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 data on the asphalt pavement area to be analyzed.

[0023] Specifically, the asphalt pavement area to be analyzed is equally divided into several block pavement areas, as follows: Each lane in the asphalt pavement area to be analyzed is divided into a block pavement area at intervals of 10 meters, and all lanes in the asphalt pavement area to be analyzed are divided at intervals of 10 meters to obtain several block pavement areas of the asphalt pavement area to be analyzed.

[0024] It should be noted that, in the present embodiment, each lane is divided into a road surface area at intervals of 10 meters, which can be adjusted according to the implementation situation.

[0025] Furthermore, the compaction degree of each block road area is obtained as follows: The compaction degree of the center point of each block road area is measured by a nuclear density meter, and the compaction degree measured at the center point is used as the compaction degree of the block road area. It should be noted that the specific compaction degree measurement is an existing method and will not be repeated in this embodiment.

[0026] Furthermore, a compaction matrix is ​​constructed according to the compaction of all the block pavement areas in the asphalt pavement area to be analyzed, as follows: The asphalt pavement area to be analyzed contains multiple parallel lanes, each lane contains several block pavement areas, and different lanes contain the same number of block pavement areas. Each block pavement area corresponds to a unique compaction degree. All compaction degrees are constructed into a matrix according to the position of the corresponding block pavement area, which is recorded as the compaction degree matrix.

[0027] Please refer to Figure 2 , Figure 2 This is a schematic diagram of obtaining a compaction degree matrix in this embodiment. Figure 2 Including 4 transverse lanes, that is Figure 2 1, 2, 3 and 4 are the numbers of the transverse lanes, representing four different transverse lanes. Each lane is divided into six road surface areas, that is, Figure 2 Where a, b, c, d, e and f respectively represent the position identification of different block road areas on the transverse lane. Specifically for each lane, a1, b1, c1, d1, e1 and f1 respectively represent the 6 different block road areas of the first lane, a2, b2, c2, d2, e2 and f2 respectively represent the 6 different block road areas of the second lane, a3, b3, c3, d3, e3 and f3 respectively represent the 6 different block road areas of the third lane, a4, b4, c4, d4, e4 and f4 respectively represent the 6 different block road areas of the fourth lane. By filling all the compaction degrees according to the positions of the corresponding block road areas, a matrix with 4 rows and 6 columns can be constructed, namely the compaction degree matrix.

[0028] At this point, the compaction degree matrix is ​​obtained.

[0029] Step S002: 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 the 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.

[0030] It should be noted that there are certain requirements and standards for the compaction detection of asphalt pavement. However, in different areas of the pavement, different roadbed soils (such as clay, sand, etc.) have different physical and mechanical properties, which will have a certain impact on the compaction effect of the pavement. Therefore, it is necessary to distinguish the data of different roadbed soils according to the compaction of each block pavement area, and classify and compress the pavement compaction data of different soils after distinction.

[0031] It should be further explained that the compaction of asphalt pavement is mainly carried out horizontally, that is, the compaction machinery compacts all lanes simultaneously along the width direction of the pavement. Therefore, in the compaction degree matrix constructed by the compaction degree of all the above-mentioned block pavement areas, the compaction operation is completed repeatedly in one column. Therefore, it is necessary to first analyze the difference in compaction degree between the two adjacent column matrices corresponding to the asphalt pavement area in the compaction degree matrix, and determine whether there is a sudden change in the soil quality of the roadbed between the two adjacent column matrices corresponding to the asphalt pavement area, such as the transition of the roadbed from clay to sand, and then merge the two adjacent column matrices in the compaction degree matrix to improve the compression storage effect.

[0032] It should be noted that during the same compaction operation, the materials used are usually the same. However, during the compaction process, the compaction degree of different road pavement areas may be different due to the presence of local rocks or hard interlayers in the roadbed, resulting in uneven compaction during the same compaction operation. Therefore, it is necessary to analyze the uneven compaction degree of the asphalt pavement area corresponding to each column matrix in the compaction matrix.

[0033] Specifically, 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, as follows: 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 a preset compaction reference value. In this embodiment, the average of all compactions in the compaction matrix is ​​used as the 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.

[0034] It should be noted that The larger the value, the greater the compactness matrix. The compaction degree of adjacent block pavement areas in the asphalt pavement area corresponding to the column matrix is ​​uneven. The larger the value, the greater the compactness matrix. The compaction degree of all the block pavement areas in the asphalt pavement area corresponding to the column matrix is ​​quite different from the compaction degree reference value. The more uneven the compaction of the asphalt pavement area corresponds to, the smaller the column matrix.

[0035] It should be noted that the above analysis shows the compaction unevenness of the asphalt pavement area corresponding to each column matrix in the compaction matrix. During different compaction operations, under normal circumstances, the compaction corresponding to the soil of similar roadbed is similar as a whole, that is, the difference in compaction unevenness is small, and the difference in compaction between two adjacent compaction operations is small, indicating that the difference in compaction data corresponding to the two column matrices in the compaction matrix is ​​small, and the two adjacent column matrices should be merged to improve the effect of compression storage.

[0036] Furthermore, 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, the compaction difference of the asphalt pavement area corresponding to two adjacent column matrices in the compaction matrix is ​​obtained, which is as follows: 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 used for normalization processing; is the first The column matrix and The columns of the matrix correspond to the differences in compaction of the asphalt pavement area.

[0037] It should be noted that It represents the difference in compaction unevenness between the two adjacent column matrices in the compaction matrix corresponding to the asphalt pavement area. The larger the value, the greater the compactness matrix. The column matrix and The greater the difference in the compaction unevenness of the asphalt pavement area corresponding to the column matrix, It represents the difference in compaction degree between the corresponding orders in two adjacent column matrices. The larger the value, the greater the difference in compaction degree between the two elements in the same row of the two adjacent columns in the compaction matrix. The cumulative compaction degree difference is calculated. The greater the cumulative compaction degree difference, the greater the difference in compaction degree. The column matrix and The greater the difference in compaction degree of the asphalt pavement area corresponding to the column matrix.

[0038] It should be noted that the above analysis shows the compaction difference between two adjacent column matrices in the compaction matrix corresponding to the asphalt pavement area. Next, the two adjacent column matrices in the compaction matrix are merged according to the size of the compaction difference to improve the compression storage effect of the compaction data of the asphalt pavement area.

[0039] 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 roadbed matrices, as follows: A compaction difference threshold is preset. In this embodiment, the compaction difference threshold is 0.3. 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.

[0040] It should be noted that the smaller the difference in compaction between the asphalt pavement areas corresponding to the two adjacent column matrices in the compaction matrix, the more similar the roadbed soil in the asphalt pavement areas corresponding to the two adjacent column matrices is. Therefore, this embodiment sets a compaction difference threshold to merge the two adjacent column matrices in the compaction matrix to obtain an initial similar roadbed matrix for subsequent analysis, thereby improving the effect of compression storage.

[0041] At this point, several initial similar roadbed matrices are obtained.

[0042] Step S003, obtaining the similarity factor of two adjacent initial similar roadbed matrices; obtaining the similarity of the two adjacent initial similar roadbed matrices according to the compaction difference, the similarity factor and the compaction difference 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 a number of final similar roadbed matrices.

[0043] It should be noted that, under normal circumstances, the compaction degree corresponding to similar roadbed soil is similar overall. However, due to problems in construction operations during the compaction process, the compaction degree of a certain section of the road surface under similar roadbed soil may suddenly change, causing the data that should have been connected to be separated. For example, the compaction degree matrix arrive The asphalt pavement areas corresponding to the columns are all asphalt pavement areas with similar roadbed soil. If an operation error occurs during the compaction process, causing a sudden change in the compaction degree of the column, then arrive The data in the columns will be divided into two matrices, arrive is listed as an initial similar roadbed matrix, arrive It is listed as an initial similar roadbed matrix. Therefore, it is necessary to compare the similarity of compaction degree under different similar roadbed soils, merge the initial similar roadbed matrices with higher similarity, and determine the final similar roadbed matrix corresponding to the similar roadbed soil. First, the similarity factors of two adjacent initial similar roadbed matrices are analyzed.

[0044] Specifically, the similarity factor of two adjacent initial similar roadbed matrices is obtained 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 ​​calculated by The model shows the inverse proportional relationship. is the input of the model. It should be noted that if 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.

[0045] It should be noted that, since each submatrix to be analyzed and the first initial similar roadbed matrix are matrices of the same size, the Euclidean distance between the two matrices can be analyzed to analyze the similarity of the two matrices. The smaller the Euclidean distance between the two matrices, the more similar the two matrices are. By analyzing the Euclidean distance between the first initial similar roadbed matrix and all submatrices to be analyzed, the first The initial similarity roadbed matrix and the The similarity factor of the initial similar roadbed matrix.

[0046] It should be noted that the above analysis is about the similarity factor of two adjacent initial similar roadbed matrices. Next, the compaction difference, similarity factor and compaction difference between two adjacent initial similar roadbed matrices are combined to accurately judge the similarity between two adjacent initial similar roadbed matrices.

[0047] Furthermore, according to the difference in compaction degree, similarity factor and the difference in compaction degree between two adjacent initial similar roadbed matrices, the similarity between two adjacent initial similar roadbed matrices is obtained, as follows: 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 the base. The model shows the inverse proportional relationship. is the input of the model; is a linear normalization function used for normalization processing; For the The initial similarity roadbed matrix and the The similarity of the initial similar roadbed matrices.

[0048] It should be noted that Indicates The initial similar roadbed matrix and the The size of the corresponding compaction difference in the initial similar roadbed matrix is The smaller the The initial similar roadbed matrix and the The smaller the difference in compaction degree between the initial similar roadbed matrices, the The initial similarity roadbed matrix and the The more similar the initial similar roadbed matrices are; Indicates The initial similar roadbed matrix and the The difference in compaction degree in the initial similar roadbed matrix, The smaller it is, the The initial similarity roadbed matrix and the The more similar the initial similar roadbed matrices are; is the similarity factor. The larger the similarity factor, the The initial similarity roadbed matrix and the The more similar the initial similar roadbed matrices are, the more similar the The initial similarity roadbed matrix and the The similarity of the initial similar roadbed matrices is calculated to improve the accuracy of similarity judgment.

[0049] It should be noted that the above analysis is about the similarity of two adjacent initial similar roadbed matrices. Next, the two adjacent initial similar roadbed matrices are merged according to the size of the similarity to obtain the final similar roadbed matrix.

[0050] Furthermore, two adjacent initial similar roadbed matrices are merged according to the size of the similarity to obtain several final similar roadbed matrices, as follows: A similarity threshold is preset. In this embodiment, the similarity threshold is 0.8. 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 merged and judged to obtain several final similar roadbed matrices.

[0051] It should be noted that the greater the similarity between two adjacent initial similar roadbed matrices, the more similar the roadbed soil in the asphalt pavement area corresponding to the two adjacent initial similar roadbed matrices is. Therefore, this embodiment sets a similarity threshold to merge the two adjacent initial similar roadbed matrices to obtain the final similar roadbed matrix for subsequent analysis, thereby improving the effect of compressed storage.

[0052] At this point, several final similar roadbed matrices are obtained.

[0053] Step S004, clustering the compaction degree in the final similar roadbed matrix to obtain a normal cluster and several abnormal clusters for each final similar roadbed matrix; obtaining the similarity of the compaction degree in each final similar roadbed matrix based on the normal cluster and abnormal cluster of the final similar roadbed matrix and the similarity of two adjacent initial similar roadbed matrices; adjusting the side length of the initial block preset in the final similar roadbed matrix based on the similarity to obtain the side length of the final block of each final similar roadbed matrix; determining the final block of each final similar roadbed matrix based on the side length of the final block of each final similar roadbed matrix, wherein the final block is a square block; performing discrete cosine transform compression and storage on all final similar roadbed matrices based on the size of the final block of the final similar roadbed matrix.

[0054] It should be noted that when using Discrete Cosine Transform (DCT) compression, smaller blocks can better capture image details 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. Although larger blocks can improve compression effects, they may sacrifice image quality, especially at the boundaries of blocks, where artifacts may appear. Therefore, the block size needs to be adjusted according to the specific circumstances of the image. Corresponding to the compression of compaction matrix data, the same is true. Smaller blocks help to capture data changes more accurately and avoid mixing different data. Larger blocks can improve compression effects and reduce the amount of operations. 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 case where the similarity of the data in the matrix is ​​low, it means that the data changes more and needs to be paid special attention, so smaller blocks are needed. Therefore, it is necessary to analyze the similarity of the compaction degree in the final similar roadbed matrix, and then determine the appropriate block size of each final similar roadbed matrix, and combine discrete cosine transform to achieve efficient compression.

[0055] Specifically, 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, as follows: For K-means clustering is performed on all compaction degrees in the final similar roadbed matrix to obtain the The final similar roadbed matrix is ​​several clusters. The distance metric of K-means clustering adopts the absolute difference of compaction degree between element positions. The K value of K-means clustering is determined by the elbow method. The cluster with the largest area among several clusters of the final similar roadbed matrix is ​​recorded as The normal cluster of the final similar roadbed matrix is Among the clusters of the final similar roadbed matrix, except for the cluster with the largest area, the remaining clusters are all recorded as The abnormal clusters of the final similar roadbed matrix.

[0056] It should be noted that the above analysis analyzes the normal clusters and several abnormal clusters of each final similar roadbed matrix. Next, the similarity of the compaction degree in each final similar roadbed matrix is ​​accurately analyzed by combining the normal clusters and abnormal clusters of the final similar roadbed matrix and the similarity of the two adjacent initial similar roadbed matrices.

[0057] Furthermore, according to the normal clusters and abnormal clusters of the final similar roadbed matrix and the similarity of two adjacent initial similar roadbed matrices, the similarity of the compaction degree in each final similar roadbed matrix is ​​obtained, as follows: 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 the base. The model shows the inverse proportional relationship. is the input of the model; is a linear normalization function used for normalization processing; For the The similarity of compaction degree in the final similar roadbed matrix.

[0058] It should be noted that The larger the average value of the similarities of all two adjacent initial similar roadbed matrices in the final similar roadbed matrix, the The higher the similarity of adjacent initial similar roadbed matrices in a final similar roadbed matrix, the higher the similarity of compaction degree in the final similar roadbed matrix should be. Indicates The normal clusters of the final similar roadbed matrix are The area ratio of the final similar roadbed matrix is ​​larger. There are many compaction degrees in the final similar roadbed matrix that are close to each other because they are all in the normal cluster. The higher the similarity of compaction degree in the final similar roadbed matrix; The smaller the The fewer the abnormal clusters in the final similar roadbed matrix, the closer the distance between the abnormal clusters and the normal clusters. The more uniform the compaction degree distribution is in the final similar roadbed matrix, the The higher the similarity of compaction degree in the final similar roadbed matrix, the higher the similarity of compaction degree in the final similar roadbed matrix.

[0059] It should be noted that when the similarity of the compaction degree in the final similar roadbed matrix is ​​high, the compaction degree in the final similar roadbed matrix is ​​closer. When performing discrete cosine transform compression, the block size of the final similar roadbed matrix should be increased to improve the compression effect. Conversely, the block size needs to be reduced. Therefore, the side length of the final block of each final similar roadbed matrix is ​​determined based on the similarity.

[0060] Furthermore, 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, as follows: In the formula, The side length of the initial block preset for all final similar roadbed matrices is a square block. This embodiment is described with the side length of the initial block being 5, in pixels, that is, the initial block is Square blocks; For the The similarity of compaction degree in the final similar roadbed matrix; is the preset basic parameter. to give a narrative; is the ceiling rounding function; For the The side length of the final block of the final similarity roadbed matrix.

[0061] It should be noted that The higher the similarity of compaction degree in the final similar roadbed matrix, the The closer the compaction degree is in the final similar roadbed matrix, the closer the compaction degree is in the discrete cosine transform (DCT) compression. The block size of the final similar roadbed matrix should be increased, that is, the side length of the final block should be increased. Larger blocks usually mean higher compression rates to improve the compression of the first The compression effect of the compaction degree data of the final similar roadbed matrix is ​​reduced. The compression effect of the compaction data of the final similar roadbed matrix.

[0062] Furthermore, the final blocks of each final similar roadbed matrix are determined according to the side length of the final blocks of each final similar roadbed matrix, and the final blocks are square blocks.

[0063] Furthermore, according to the size of the final block of the final similar roadbed matrix, all the final similar roadbed matrices are respectively subjected to discrete cosine transformation compression and storage.

[0064] It should be noted that after determining the size of the final block of each final similar roadbed matrix, compression and storage are performed according to the size of the final block and discrete cosine transform as an existing method, which will not be described in detail in this embodiment.

[0065] Through the above steps, the BIM-based transportation engineering construction progress management method is completed.

[0066] Another embodiment of the present invention provides a BIM-based traffic engineering construction progress management system, the system comprising a memory and a processor, and when the processor executes a computer program stored in the memory, the processor performs the following operations: The asphalt pavement area to be analyzed is equally divided into several sub-block pavement areas; the compaction degree of each sub-block pavement area is obtained; 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 unevenness of the asphalt pavement area corresponding to each column matrix in the compaction degree matrix is ​​obtained; according to the difference in compaction degree unevenness 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 the 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; the similarity factor of two adjacent initial similar roadbed matrices is obtained; according to the compaction degree difference, the similarity factor and the difference in compaction degree in two adjacent initial similar roadbed matrices, The similarity of two adjacent initial similar roadbed matrices; merging two adjacent initial similar roadbed matrices according to the size of the similarity to obtain several final similar roadbed matrices; clustering the compaction degree in the final similar roadbed matrix to obtain a normal cluster and several abnormal clusters of each final similar roadbed matrix; obtaining the similarity of the compaction degree in each final similar roadbed matrix according to the normal cluster and abnormal cluster of the final similar roadbed matrix and the similarity of two adjacent initial similar roadbed matrices; 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; determining the final block of each final similar roadbed matrix according to the side length of the final block of each final similar roadbed matrix, wherein the final block is a square block; performing discrete cosine transform compression and storage on all final similar roadbed matrices according to the size of the final block of the final similar roadbed matrix.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in 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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