Rail transit station building drawing matrix compression method and system

Abstract

The application discloses a rail transit station building drawing matrix compression method and system, and the method comprises the following steps: extracting line segment information of building walls and structural columns in a station building drawing, wherein the line segment information comprises straight line segment information and arc line segment information; converting all arc line segments into corresponding straight line segments, and calculating the segment length according to the column radius of the structural column; extracting the head and tail points of all line segments, obtaining the X and Y coordinates of each head and tail point, sorting the X and Y coordinates, and constructing an image matrix, with the X and Y coordinates serving as the horizontal and vertical axes of the image matrix; filling the geometric information and attribute information of building elements in the image matrix, and filling negative numbers in the edge of the image matrix through an edge filling technique, to represent the peripheral contour information of the rail transit station.

Description

Technical Field

[0001] This invention belongs to the field of architectural drawing processing technology, and more specifically, relates to a matrix compression method and system for rail transit station architectural drawings. Background Technology

[0002] With the rapid development of artificial intelligence technology, technologies such as deep learning and image recognition have shown enormous application potential in various fields. Particularly in the rail transit sector, stations, as a crucial component of urban transportation, require intelligent processing of their design and construction information for improving operational efficiency and optimizing resource allocation. Traditional station design relies on tools such as CAD or BIM. While these tools can provide accurate architectural drawings, the data they generate is often unstructured and difficult to directly utilize for intelligent algorithms such as deep learning.

[0003] Currently, design drawings for rail transit stations are primarily generated using CAD or BIM software, which can create vector graphics containing rich architectural elements. However, converting this graphic data into a matrix format suitable for deep learning presents challenges. Because the coordinate information of architectural elements uses different units of precision, such as millimeters, centimeters, and meters, directly converting the drawings into an image matrix results in an extremely large sparse matrix. This matrix not only consumes enormous computational resources but also struggles to effectively represent all the design details within the drawings.

[0004] While CAD and BIM technologies offer unparalleled advantages in architectural design, such as high precision, flexibility, and the ability to express rich design elements, they also have limitations in data structuring and intelligent applications. Key drawbacks include the difficulty in directly converting data into formats suitable for machine learning algorithms, and the excessive computational resource requirements when processing large-scale data. Furthermore, the mixed use of different precision units increases the complexity of data preprocessing.

[0005] In conclusion, while existing CAD and BIM technologies offer significant advantages in architectural design, challenges remain in intelligent processing and deep learning applications. Therefore, a new approach is needed that maintains the integrity of station element information, adapts to the characteristics of different unit sizes, and reduces computational resource requirements, thereby achieving efficient and intelligent processing of station drawing information. Summary of the Invention

[0006] To address the above technical problems, this invention proposes a matrix compression method for rail transit station architectural drawings, comprising:

[0007] Extract line segment information of building walls and structural columns from station architectural drawings, wherein the line segment information includes straight line segment information and curved line segment information;

[0008] Convert all arc segments into corresponding straight line segments, and calculate the segment length based on the column radius of the structural column;

[0009] Extract the first and last points of all line segments, obtain the X and Y coordinates of each first and last point, sort the X and Y coordinates, and construct an image matrix, using the X and Y coordinates as the horizontal and vertical axes of the image matrix.

[0010] The geometric and attribute information of building elements is filled into the image matrix, and negative numbers are filled at the edges of the image matrix using edge filling technology to represent the surrounding contour information of the rail transit station.

[0011] Furthermore, converting all arc segments into corresponding straight line segments involves adjusting the number of straight line segments according to accuracy requirements.

[0012] Furthermore, this includes dividing the arc segment of each structural column into 16 straight segments.

[0013] Furthermore, the method includes: the horizontal and vertical axes of the image matrix are arranged according to the sorted X and Y coordinate numbers, and the actual physical coordinates of the points in the image matrix are recovered by the correspondence between the numbers and the actual coordinates.

[0014] Furthermore, it includes filling the edges of the image matrix with negative numbers and coloring them with the connection relationship between negative numbers and 0 values ​​to represent the surrounding contour information of the rail transit station.

[0015] This invention also proposes a matrix compression system for rail transit station architectural drawings, comprising:

[0016] The information extraction module is used to extract line segment information of building walls and structural columns in station architectural drawings, wherein the line segment information includes straight line segment information and arc segment information;

[0017] The line segment conversion module is used to convert all arc segments into corresponding straight line segments and calculate the segment length based on the column radius of the structural column;

[0018] The matrix generation module is used to extract the start and end points of all line segments, obtain the X and Y coordinates of each start and end point, sort the X and Y coordinates, and construct an image matrix, using the X and Y coordinates as the horizontal and vertical axes of the image matrix.

[0019] The filling module is used to fill the geometric and attribute information of building elements in the image matrix, and to fill the edges of the image matrix with negative numbers through edge filling technology to represent the surrounding contour information of the rail transit station.

[0020] Furthermore, converting all arc segments into corresponding straight line segments involves adjusting the number of straight line segments according to accuracy requirements.

[0021] Furthermore, this includes dividing the arc segment of each structural column into 16 straight segments.

[0022] Furthermore, the method includes: the horizontal and vertical axes of the image matrix are arranged according to the sorted X and Y coordinate numbers, and the actual physical coordinates of the points in the image matrix are recovered by the correspondence between the numbers and the actual coordinates.

[0023] Furthermore, it includes filling the edges of the image matrix with negative numbers and coloring them with the connection relationship between negative numbers and 0 values ​​to represent the surrounding contour information of the rail transit station.

[0024] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0025] The technical solution proposed in this invention converts the architectural drawings of rail transit stations into an efficient and high-precision matrix representation through an innovative method, achieving the following significant technical effects:

[0026] 1. Significant increase in productivity:

[0027] Automated data conversion processes reduce the time spent on manual drafting and data processing, significantly improving the efficiency of converting architectural drawings into data matrices. This not only speeds up the design and planning phases but also provides a rapid response capability for subsequent construction and operation management.

[0028] 2. Improvement in quality and precision:

[0029] This invention ensures high-precision conversion of architectural drawing information through accurate coordinate point extraction and matrix construction. Compared with traditional methods, this invention can more accurately reflect the spatial relationships and dimensional information of architectural elements, thereby improving design quality and construction accuracy.

[0030] 3. Simplification of intelligent design process:

[0031] Converting complex architectural drawings into structured matrix data simplifies subsequent data processing and analysis. This simplification not only reduces complexity in the design and construction process but also lowers the likelihood of errors, thereby improving the overall reliability of the project.

[0032] 4. The beneficial expansion of the information value of drawings:

[0033] The matrix data of this invention not only includes the geometric information of architectural elements, but also represents the attribute information of the elements, such as wall type and column type, through different numerical values. This rich information representation provides more useful features for deep learning and image recognition, enabling machines to better understand and analyze architectural drawings.

[0034] In summary, the technical solution of the present invention demonstrates significant beneficial effects in terms of improving productivity, ensuring quality and accuracy, simplifying intelligent design processes, expanding information value, showcasing innovation and foresight, and improving economic efficiency. Attached Figure Description

[0035] Figure 1 Flowchart of the method in Embodiment 1 of the present invention;

[0036] Figure 2 System structure diagram of Embodiment 2 of the present invention. Detailed Implementation

[0037] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0038] The method provided by this invention can be implemented in a terminal environment that may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0039] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0040] Storage media can include random access memory (RAM) or read-only memory (ROM). Storage media can be used to store instructions, programs, code, code sets, or instructions.

[0041] The display screen is used to show the user interface of each application.

[0042] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0043] Example 1

[0044] like Figure 1 As shown, this embodiment of the invention provides a matrix compression method for rail transit station architectural drawings, including:

[0045] Extract line segment information of building walls and structural columns from station architectural drawings, wherein the line segment information includes straight line segment information and curved line segment information;

[0046] Convert all arc segments into corresponding straight line segments, and calculate the segment length based on the column radius of the structural column;

[0047] Extract the first and last points of all line segments, obtain the X and Y coordinates of each first and last point, sort the X and Y coordinates, and construct an image matrix, using the X and Y coordinates as the horizontal and vertical axes of the image matrix.

[0048] The geometric and attribute information of building elements is filled into the image matrix, and negative numbers are filled at the edges of the image matrix using edge filling technology to represent the surrounding contour information of the rail transit station.

[0049] Specifically, converting all arc segments into corresponding straight line segments involves adjusting the number of straight line segments according to accuracy requirements.

[0050] Specifically, this includes dividing the arc segment of each structural column into 16 straight segments.

[0051] Specifically, this includes: the horizontal and vertical axes of the image matrix are arranged according to the sorted X and Y coordinate numbers, and the actual physical coordinates of the points in the image matrix are recovered through the correspondence between the numbers and the actual coordinates.

[0052] Specifically, this includes filling the edges of the image matrix with negative numbers and coloring them with the connection between negative numbers and 0 values ​​to represent the surrounding contour information of the rail transit station.

[0053] Specifically, it also includes setting a coordinate point compression model, and further compressing the image matrix using the coordinate point compression model to reduce the complexity of the image matrix. The coordinate point compression model includes:

[0054]

[0055] Where C(x, y) is a new image matrix containing all points (x, y), N is the number of compression layers, each layer compresses different architectural elements, g is a multi-dimensional adaptive compression function used to increase the capture of details of architectural drawing elements, ρ is a dynamic weight adjustment function used to adaptively adjust compression according to position changes, and L k This is a connection constraint function for multi-level architectural elements at the k-th level. It is used to make non-linear adjustments for the compression of each level of architectural drawing elements, while ensuring that the geometric distance and complexity relationships between elements are effectively compressed.

[0056] The multidimensional adaptive compression function g is:

[0057]

[0058] Where, x i Let y be the x-coordinate of the i-th point. i Let θ be the ordinate of the i-th point, ∈ be the first adaptive adjustment factor, α be the second adaptive adjustment factor, θ be the third adaptive adjustment factor, λ be the fourth adaptive adjustment factor, and μ be the fifth adaptive adjustment factor.

[0059] The dynamic weight adjustment function ρ is:

[0060]

[0061] Where ζ is the first adjustment factor for the dynamic weights, x j Let y be the x-coordinate of the j-th point. j Let be the ordinate of the j-th point, and η be the second adjustment factor of the dynamic weight.

[0062] The multi-level building element connection constraint function L at layer k k for:

[0063]

[0064] Where, γ k n is the first adjustment factor of the k-th layer. k β is the second adjustment factor for the k-th layer. k It is the third adjustment factor of the k-th layer.

[0065] To make this embodiment clearer, Embodiment 1 will be described in detail below:

[0066] The architectural drawings of rail transit stations consist of building walls and structural columns, which in turn are composed of line segments and arcs. We obtain all the line segments and arcs (generally complete circles) belonging to the building walls and structural columns through the CAD interface.

[0067] Since curved segments are generally used for the load-bearing structural columns of stations, which are essentially complete circles, we need to convert all curved segments into corresponding straight segments. The length of each segment is calculated based on the column radius. For general accuracy, dividing the required arc of each column into approximately 16 segments is sufficient for the conversion. The number of segments can be adjusted according to actual needs; a higher number of segments results in higher accuracy. We sample the curved segment based on the number of segments, with the number of segments matching the number of sampling points. Moving clockwise, each adjacent sampling point constitutes a line segment, thus achieving the conversion of the curved segment into a straight segment.

[0068] At this point, we have obtained all the line segments representing the architectural drawings. Since each line segment consists of two points, the first and the last, we extract the points of all the line segments to obtain the X and Y coordinates of each point.

[0069] If we were to directly construct a matrix based on all X and Y coordinates, filling in the relevant information of the matrix with each point and representing the position of the point in the matrix as 1, our method would be to divide the matrix into equal parts. If, to ensure accuracy, we express the precision as 10mm, and considering that a typical station has a single-layer length of 200 meters and a width of 25 meters, the matrix would be 200,000 x 25,000 elements, containing a large amount of useless data, making it difficult to obtain practical calculations.

[0070] Therefore, we improved the traditional method by no longer directly representing the matrix using the X and Y coordinates of points, but instead constructing the matrix through the relative relationships of coordinate points. Generally, the concourse level of a railway station is the most complex and contains the most information, with approximately 3,000 line segments constituting the architectural elements. After extracting the first and second points of each line segment and removing duplicate points (the connection point between the two ends of a line segment is actually a single point), the actual number of points is approximately 2,000.

[0071] We extract and sort the X and Y axis data of all points separately, rounding them to the nearest integer or the remainder with a precision of 10mm (precision can be freely set). The sorted indices of all points on the X-axis are arranged to form the horizontal axis of the matrix, and the sorted indices of all points on the Y-axis are arranged to form the vertical axis. The matrix is ​​constructed by initially representing all positions with 0 and the positions where the sorted indices of points exist with 1. For example, in 1D data, considering only the X-axis, we have three points: 0, 10, and 100. The traditional method results in a sequence of size 11, containing [1,1,0,0,0,0,0,0,0,0,1]. The improved method results in a sequence of size 3, containing [1,1,1], representing the order of [0,10,100] as 1, 2, and 3 respectively.

[0072] When we arrange the points according to their sorted indices as the horizontal and vertical axes of a matrix, we can greatly reduce the size of the matrix. However, the matrix only represents the sorting information of that point among all the data, not the actual physical coordinate information. Therefore, we need to design a method to recover the actual coordinate position of the point from the matrix. Continuing with the example from the previous step, if the element indices of [1,1,1] are 0, 1, and 2 respectively, and we want to obtain the actual data (i.e., 100) with index 2 in [1,1,1], we know that the target index is 2. At the same time, because we have sorted the actual data on the X-axis, we have also stored the sorted queue [0, 10, 100] (with indices 0, 1, and 2 respectively). Now, we take the data with index 2 from the sorted queue of the actual data on the X-axis, which is 100, and that is the actual data represented by the target object.

[0073] This method can also be used in reverse. When we want to find the position of the data 100 in the matrix, we can obtain its index by finding the position of 100 in the sequence and then substitute that index into the matrix position.

[0074] Thus, through the transformation of the serial numbers, we have represented the high-precision matrix information of the station building map using a matrix and two sorted sequences. Typically, the matrix size is 600x300 elements, with 600 elements on the X-axis and 300 elements on the Y-axis, significantly reducing the matrix size for station building element information. The matrix size is primarily determined by the relative connections of the building element line segments and is less affected by precision.

[0075] Since we store the connection relationships between all points of line segments, our current matrix only contains point-related information and does not include line segment information. Therefore, we need to deduce the horizontal and vertical axis indices of the relevant points in the matrix from the actual positions of the line segment points, and then fill the data between connected points in the matrix with 1s to represent line segments. For example, in 1D data, the data is [0,1,0,0,1,0]. Once we know that the points in sequence 1 and sequence 4 form a line segment, we modify the sequence content to [0,1,1,1,1,0]. Thus, we represent the high-precision data of the station using the optimized dynamic matrix and two sequences.

[0076] Although we have represented the high-precision data of the station using a matrix through the above construction, we still need to optimize the matrix in order to use it for subsequent applications such as spatial contour recognition, image detection, and data classification.

[0077] Optimization 1 - Spatial Contour Recognition: To ensure that the matrix can represent the spatial contour of a building, if the matrices are closely arranged, such as in a simple rectangular room, the matrix might become [[1,1],[1,1]], meaning the space is dynamically optimized to 0 area, and the matrix loses some spatial information. Therefore, we adjust the relationship between the sequence number and the matrix number, multiplying the correspondence by a factor. For example, the original one-to-one correspondence between the data points 0, 10, and 100 was [1,1,1]. By multiplying the corresponding ordinal numbers by a factor of 2, the transformed sequence becomes [1,0,1,0,1], maintaining a certain distance between adjacent points. This allows us to enlarge the matrix size to avoid the loss of spatial information. The matrix enlargement factor is related to the corresponding coefficient. Currently, the dynamically optimized matrix for stations is generally 1200x600 elements, which is still perfectly usable in feature recognition, spatial contour recognition, and image detection. For example, the rectangular room mentioned earlier has its matrix changed to [[1,1,1],[1,0,1],[1,1,1]], preserving its spatial information.

[0078] Optimization 2 - Representation of Attribute Information: Previously, we simply used 1 and 0 to represent building element information on the matrix. However, through the line segments we obtain in CAD, we can know whether the line segment belongs to a wall or a column layer, as well as other information such as wall type and column type. This information can be represented in CAD by line segment layers, colors, linearity, etc. Therefore, we can define the relevant building element attributes that we are interested in and need, and represent building element information on the matrix through different values ​​such as 1, 2, 3, or different levels such as 1, 100, 1000, etc., so that the matrix not only stores the geometric information of building elements, but also stores non-geometric information.

[0079] Optimization 3 - Edge Information Filling: Following the previous method, the matrix edges represent the information of the outermost contour line of the building, but lack information about the outer contour line and its spatial relationship. Therefore, referencing convolutional neural networks, we expand the matrix edges using filling techniques and fill them with negative numbers such as -1. For example, the original matrix [[1,0],[1,1]] becomes [-1,-1,-1,-1],[-1,1,0,-1],[-1,1,1,-1],[-1,-1,-1,-1]. Then, using a coloring method, all 0 values ​​connected to negative numbers are changed to -1, resulting in the matrix becoming [-1,-1,-1,-1],[-1,1,-1,-1],[-1,1,1,-1],[-1,-1,-1,-1]. In this way, negative numbers can represent all the relevant information about the building's surrounding contour. Furthermore, because the surrounding contour information is filled, the matrix can be directly used for graph convolutional deep learning. The size of the fill, i.e. the amount of edge fill, can be adjusted according to the actual project use. Generally, a fill size of 2 is sufficient for use.

[0080] Example 2

[0081] like Figure 2 As shown, this embodiment of the invention also provides a matrix compression system for rail transit station architectural drawings, including:

[0082] The information extraction module is used to extract line segment information of building walls and structural columns in station architectural drawings, wherein the line segment information includes straight line segment information and arc segment information;

[0083] The line segment conversion module is used to convert all arc segments into corresponding straight line segments and calculate the segment length based on the column radius of the structural column;

[0084] The matrix generation module is used to extract the start and end points of all line segments, obtain the X and Y coordinates of each start and end point, sort the X and Y coordinates, and construct an image matrix, using the X and Y coordinates as the horizontal and vertical axes of the image matrix.

[0085] The filling module is used to fill the geometric and attribute information of building elements in the image matrix, and to fill the edges of the image matrix with negative numbers through edge filling technology to represent the surrounding contour information of the rail transit station.

[0086] Specifically, converting all arc segments into corresponding straight line segments involves adjusting the number of straight line segments according to accuracy requirements.

[0087] Specifically, this includes dividing the arc segment of each structural column into 16 straight segments.

[0088] Specifically, this includes: the horizontal and vertical axes of the image matrix are arranged according to the sorted X and Y coordinate numbers, and the actual physical coordinates of the points in the image matrix are recovered through the correspondence between the numbers and the actual coordinates.

[0089] Specifically, this includes filling the edges of the image matrix with negative numbers and coloring them with the connection between negative numbers and 0 values ​​to represent the surrounding contour information of the rail transit station.

[0090] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0091] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0092] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of units or modules, and may be electrical or other forms.

[0093] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0094] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0096] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for matrix-based compression of architectural drawings for rail transit stations, characterized in that, include: Extract line segment information of building walls and structural columns from station architectural drawings, wherein the line segment information includes straight line segment information and curved line segment information; Convert all arc segments into corresponding straight line segments, and calculate the segment length based on the column radius of the structural column; Extract the first and last points of all line segments, obtain the X and Y coordinates of each first and last point, sort the X and Y coordinates, and construct an image matrix, using the X and Y coordinates as the horizontal and vertical axes of the image matrix. The geometric and attribute information of building elements is filled into the image matrix, and negative numbers are filled at the edges of the image matrix using edge filling technology to represent the surrounding contour information of the rail transit station.

2. The matrix compression method for rail transit station architectural drawings as described in claim 1, characterized in that, Convert all arc segments into corresponding straight line segments, including adjusting the number of straight line segments according to accuracy requirements.

3. The matrix compression method for rail transit station architectural drawings as described in claim 2, characterized in that, include: The curved segment of each structural column is divided into 16 straight segments.

4. The matrix compression method for rail transit station architectural drawings as described in claim 1, characterized in that, include: The horizontal and vertical axes of the image matrix are arranged according to the sorted X and Y coordinate numbers, and the actual physical coordinates of the points in the image matrix are recovered by the correspondence between the numbers and the actual coordinates.

5. The matrix compression method for rail transit station architectural drawings as described in claim 1, characterized in that, include: Negative numbers are filled at the edges of the image matrix, and the surrounding contour information of the rail transit station is represented by the connection relationship between negative numbers and 0 values.

6. A matrix compression system for rail transit station architectural drawings, characterized in that, include: The information extraction module is used to extract line segment information of building walls and structural columns in station architectural drawings, wherein the line segment information includes straight line segment information and arc segment information; The line segment conversion module is used to convert all arc segments into corresponding straight line segments and calculate the segment length based on the column radius of the structural column; The matrix generation module is used to extract the start and end points of all line segments, obtain the X and Y coordinates of each start and end point, sort the X and Y coordinates, and construct an image matrix, using the X and Y coordinates as the horizontal and vertical axes of the image matrix. The filling module is used to fill the geometric and attribute information of building elements in the image matrix, and to fill the edges of the image matrix with negative numbers through edge filling technology to represent the surrounding contour information of the rail transit station.

7. A matrix compression system for rail transit station architectural drawings as described in claim 6, characterized in that, Convert all arc segments into corresponding straight line segments, including adjusting the number of straight line segments according to accuracy requirements.

8. A matrix compression system for rail transit station architectural drawings as described in claim 7, characterized in that, include: The curved segment of each structural column is divided into 16 straight segments.

9. A matrix compression system for rail transit station architectural drawings as described in claim 6, characterized in that, include: The horizontal and vertical axes of the image matrix are arranged according to the sorted X and Y coordinate numbers, and the actual physical coordinates of the points in the image matrix are recovered by the correspondence between the numbers and the actual coordinates.

10. A matrix compression system for rail transit station architectural drawings as described in claim 6, characterized in that, include: Negative numbers are filled at the edges of the image matrix, and the surrounding contour information of the rail transit station is represented by the connection relationship between negative numbers and 0 values.

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