Method and system for automatically generating CAD drawing of road pavement disease distribution map

Abstract

The invention belongs to the technical field of road pavement maintenance, and discloses a method and system for automatically generating a CAD drawing of a road pavement disease distribution map. The method comprises the following steps: identifying a disease legend and associating disease data; the stake number, the disease offset and the length and width parameters are converted into a drawing contour; reusing filling attributes of the disease legend to enable the disease drawing graph to keep a drawing style the same as that of the legend; drawing a graph according to the disease after attribute filling, and automatically filling drawing attributes by adopting an attribute text technology; and finally, importing an Excel table, selecting basic information of the road grade and the road surface width, and then drawing. The method can automatically draw the disease distribution diagram, saves the labor cost, is short in time consumption, and is high in efficiency of drawing the disease distribution diagram. And construction drawings can be issued quickly, and implementation of construction opportunities is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of road pavement maintenance, and in particular relates to a method and system for automatically generating a CAD drawing of a road pavement disease distribution map. Background Art

[0002] With the completion of the highway network, the tension in the use of major transportation channels has been significantly improved, and the transportation capacity has been greatly improved. However, large-scale construction will inevitably bring heavy maintenance tasks, and highway maintenance has transitioned from the traditional "emergency repair era" to the "comprehensive maintenance era."

[0003] Highway maintenance projects involve testing, design, construction, and post-evaluation. Highway maintenance design involves surveying and measuring existing roads, re-repairing and designing old roads through investigation and evaluation, disease diagnosis and maintenance strategy selection, technical design, and construction drawing design, so as to restore their good service functions. As the first threshold for controlling construction quality, it is particularly important in all links. The quality of maintenance design documents often determines the quality of construction. Among them, the treatment of diseases is more important. Whether the disease treatment is thorough determines the length of service life of the road after maintenance.

[0004] Generally, the pavement damage distribution map will be given in the construction drawing design of maintenance projects. The traditional pavement damage distribution map needs to be drawn manually. For maintenance design projects with a long mileage, drawing the damage distribution map is labor-intensive and time-consuming, and the efficiency of drawing the damage distribution map is low. On the other hand, when the design cycle is tight, it may lead to a time lag in issuing construction drawings, and even delay the construction opportunity. Therefore, it is very necessary to develop a software or method for automatically exporting the CAD map of the damage distribution drawing. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the disclosed embodiment of the present invention provides a method and system for automatically generating a CAD drawing of a road pavement defect distribution map.

[0006] The technical solution is as follows: A method for automatically generating a CAD drawing of a road pavement disease distribution map comprises:

[0007] S1, identify disease legends and associate disease data;

[0008] S2, combined with the legend, convert the pile number, disease offset, length and width parameters into drawing outlines;

[0009] S3, using the fill attribute of the disease legend to make the disease drawing graphics maintain the same drawing style as the legend; according to the legend information of the disease data and the location information of the disease data, using the cloning technology to reuse the drawing style of the legend to the disease graphics;

[0010] S4, draw the graphics according to the defects after filling in the attributes, and use the attribute text technology to automatically fill in the drawing attributes; import the Excel table, select the basic information of road grade and road width, and then draw the map.

[0011] In step S1, identifying the disease legend and associating the disease data includes:

[0012] S101, extracting text, polylines, and fill data in the template image according to a preset template range mark; establishing associations between the text, polylines, and fills according to relative position relationships; establishing a set of associated graphics, storing them with the text as a key value as a temporary legend, and the polylines as the fill legend boundary;

[0013] S102, read the data in the Excel table, store the Json string according to the pile number as the key value; parse the name and offset value in the Json string as temporary disease data;

[0014] S103, for temporary legends and temporary disease data, match the legend key value and the disease data name, and store a group of data with the same key value and data name as disease data;

[0015] S104, issuing a warning prompt to the temporary legend that cannot be associated with the disease data, and issuing a warning prompt to the disease data that cannot be associated with the temporary legend.

[0016] In step S2, the pile number, the disease offset, and the length and width parameters are converted into a drawing outline, including:

[0017] S201, according to the pile number data of the disease data, the starting pile number and the ending pile number of each lane are calculated; according to the pile number of the lane, the total starting pile number and the ending pile number of the disease data are summarized; in combination with the drawing size and drawing scale of the template, the number of drawing sheets is calculated; in combination with the drawing size and drawing scale of the template, according to the total distance between the total starting pile number and the ending pile number of the disease data, the number of drawing sheets is obtained, and the pile numbers of all the disease data are displayed;

[0018] S202, using the drawing start and end pile numbers assigned to each map sheet, converting the pile numbers of the disease data in the pile number range into drawing coordinate reference points; using the lane width, disease offset, and disease length and width attributes, converting to obtain the disease data drawing outline.

[0019] In step S202, lane width, defect offset, and defect length and width attributes are used to convert the defect data drawing contour, including:

[0020] The first step is to analyze and determine whether the starting point of the disease data drawing conversion instruction is the upper left corner point. If the starting point is the upper left corner point, set q=0; otherwise, set q=1;

[0021] The second step is to record the attribute node length of the disease data drawing conversion instruction. When drawing the disease data drawing conversion instruction, the attribute node length is 3-9, and at least 3 points need to be connected; the letter g represents the actual attribute node length of the disease data drawing conversion instruction, and the letter g' represents the calculated minimum attribute node length. If the attribute node length is less than or equal to 7, set g'=0; otherwise g'=g-7

[0022] In the third step, when drawing the graphic conversion instruction, i is used to represent the number of intersections; in the influence of the number of intersections on the accuracy of the conversion instruction, the accuracy meter is calculated using min(i,9);

[0023] Step 4: When three consecutive points in the graphic conversion instruction are not on a straight line and form an angle different from 180 degrees, it is considered that the direction has changed; when the number of direction changes h in the graphic conversion instruction is greater than or equal to 9, let h'=2; when the number of direction changes is 6 and 8, h'=1; otherwise h'=0; h' is the calculated value of the precision meter of the number of direction changes in the graphic conversion instruction;

[0024] Step 5: In the disease data drawing conversion instruction, if the number of times the node is passed is greater than or equal to 6 times, let f = 2; if the number of times the node is passed is 4 times, then f = 1; otherwise, f = 0; f is the disease data drawing conversion instruction value;

[0025] Step 6: When the selected graphic conversion instruction is a lane width conversion instruction, a defect offset conversion instruction, a defect length and width conversion instruction, or the reverse order of the conversion instruction, let qe=0; if the conversion instruction selected by the user is not the above conversion instruction but includes these conversion instructions, let qe=1; if the conversion instruction selected by the user is not a character shape and does not include the above character shape, let qe=2; qe is the selected graphic conversion instruction value;

[0026] Step 7: The accuracy score is recorded as defen. The conversion instruction accuracy score is divided into three levels: high, medium, and low; where q is the starting point instruction value of the graphic conversion instruction, and g' is the minimum attribute node length calculated; when 0≤defen≤0.27, the accuracy level is low; when 0.28≤defen≤0.63, the accuracy level is medium; when 0.64≤defen≤1.00, the accuracy level is high;

[0027] In the eighth step, the accuracy of the conversion instruction selected by the user is displayed to the user in the form of a conversion instruction strength progress bar, or the accuracy of the conversion instruction selected by the user is displayed to the user in the form of a text description.

[0028] Furthermore, the lane width, the defect offset, and the defect length and width attributes are used to convert the defect data drawing contour, which further includes:

[0029] (1) Check whether the starting point of the graphic conversion instruction is the upper left corner point 1. The starting point of the graphic conversion instruction is 1, and q=0;

[0030] (2) Calculate the actual attribute node length of the graphic conversion instruction. The actual attribute node length of the graphic conversion instruction is 9, g'=9-7=2;

[0031] (3) Calculate the number of intersections of the graphic conversion instruction. The number of intersections is 0, i = 0, so min(i,9) = 0;

[0032] (4) Count the number of direction changes in the disease data drawing conversion instructions. There are 8 direction changes, so h'=1;

[0033] (5) Analyze the number of overlapping nodes in the disease data drawing conversion instructions to see if any nodes are passed through multiple times. If there are no overlapping nodes in the graphic conversion instructions, f = 0;

[0034] (6) analyzing whether the defect data drawing conversion instruction is a character shape or contains a character shape, and the graphic conversion instruction contains the lane width, qe=1;

[0035] (7) Based on the above analysis, the accuracy score is calculated as The accuracy level of the graphics conversion instruction is determined to be low.

[0036] In step S3, the drawing style of the legend is reused in the disease graph using cloning technology, including:

[0037] Step 1: using at least one disease data location detection unit to measure the disease physical state information of the pavement disease object at the installation location point, and using the communication system to package the acquired disease physical state data and the disease data location detection unit installation information and send them to the data transmission module;

[0038] Step 2: The data transmission module calculates and obtains the original disease physical state perception information data, and the virtual clone module establishes a three-dimensional model that is the same as the pavement disease object geometry model;

[0039] Step three: the virtual clone module provides geometric information of any point of the pavement disease object based on the established three-dimensional model, fuses the perception information with the geometric information to obtain a real-time clone data set, and uses the clone data to drive the virtual-real interaction module to perform synchronous evolution of the virtual clone and the pavement disease object; the disease data position detection unit measures and feedbacks the physical state information of the pavement disease object that changes with time in the local coordinate system where the disease data position detection unit is installed, and obtains the disease physical state data of the installation position point of the disease data position detection unit in the physical coordinate system and the disease physical state information of the center of mass position point of the pavement disease object through transformation from the local coordinate system to the physical coordinate system of the pavement disease object and Euler angle and quaternion conversion; the disease physical state information of the center of mass position point is data-fused with the geometric information of any point of the pavement disease object of the virtual clone of the pavement disease object to obtain a real-time clone data set of the disease physical state information of the pavement disease object; the physical coordinate system of the pavement disease object is used to define the evolution state of the virtual clone of the pavement disease object, and the rotation center position of the pavement disease object rotation evolution is taken as the origin O d , x d The positive direction of the axis points to the bow, Z d The positive direction of the axis points to the port side, y d The positive direction of the axis is vertically upward, and the entity coordinate system of the virtual clone of the pavement disease object is established. Each point on the virtual clone has a corresponding entity coordinate value; the clone data is used to drive the real-time evolution of the virtual clone of the pavement disease object in the virtual scene, and the coordinates of the physical state of the virtual clone in different frames are set according to the time series of the clone data. The discrete clone data sequence is converted into a continuous simulation evolution data sequence through the 3D engine interpolation algorithm, so that the virtual clone of the pavement disease object has the same evolution law as the pavement disease object, and the synchronous evolution of the virtual clone and the pavement disease object is realized;

[0040] Step 4: Use the virtual disease data position detection unit of the virtual-reality interaction module to measure and obtain the real-time disease physical state information of any position point of the pavement disease object. At the same time, feed back the expected disease physical state information of the calibrated disease data position detection unit to the pavement disease object, feed back the disease physical state calibration data, correct the calibrated disease data position detection unit, and finally obtain the road pavement disease graphics drawn in the drawing style of the legend.

[0041] In step 3, the geometric information of any point of the pavement damage object is provided based on the established 3D model, including:

[0042] (1) Use the disease data location detection unit A to measure and obtain the physical state information D of the road disease object in the local coordinate system {a} where the installation point is located a =[D 1a , D 2a ] F=[J a , K a , L a ,y a , z a , b a ] F , where D 1a =[J a , K a , L a ] F Describe the linear velocity of translation change in three directions J, K, L, D 2a =[y a , z a , b a ] F are the angular rates y, z, and b of rotation in three directions; F is the order, Ja is the linear rate of the horizontal translation change, and K a is the linear velocity of the longitudinal translation change, L a is the linear velocity of the translational change in the spatial direction, y a is the angular velocity of the lateral rotation, z a is the angular velocity of the longitudinal rotation, b a is the angular velocity of rotation in the spatial direction;

[0043] (2) The virtual clone of the pavement disease object obtains the disease data through the three-dimensional model. The location of the detection unit installation point A is relative to the pavement disease object with the center of mass O d , the radial direction b in the solid coordinate system {d} with the rotation center as the origin a ; According to the spatial position relationship between the local coordinate system {a} of the disease data location detection unit and the physical coordinate system {b}, the coordinate transformation matrix of the local coordinate system {a} to the physical coordinate system {d} is obtained Obtain the evolution state information of the center of mass of the pavement disease object in the entity coordinate system {d};

[0044] (3) Obtain the center of mass O of any point i of the pavement defect object relative to the pavement defect object through 3D model measurement d Radius b in the solid coordinate system {d} i , the fused data is used to obtain the clone data expressing the real-time physical state of the pavement disease object at any point i:

[0045]

[0046] Where, C is the physical state data of the physical pavement disease object;

[0047] (4) Install n sets of disease data location detection units on the pavement disease object, and the clone data obtained by each set of disease data location detection unit is:

[0048]

[0049] Where an is the disease data location detection unit installed on the pavement disease object, and n sets of disease data location detection units include lane width, disease offset, disease length and width attribute disease data location detection submodules, respectively. i For the direction; The linear velocity of the longitudinal translation change of the center of mass of the pavement disease object after cloning of the disease data location detection unit installed for the pavement disease object;

[0050] (5) According to the accuracy level, observation noise and measurement environment conditions of each set of disease data location detection unit in different measurement ranges, the weight coefficient L of each set of disease data location detection unit is obtained. an =c, c includes the measurement range, observation noise, and environmental conditions;

[0051] The data of multiple disease data location detection units are integrated to obtain more accurate estimation of the physical state information of the pavement disease object. After integration, the real-time clone data of any position of the pavement disease object is:

[0052]

[0053] In the formula, K i The real-time clone data value of any position of the fused pavement disease object, L sj is the weight coefficient of the disease data location detection unit at any point j of the attribute node length and a certain range, observation noise, and environmental condition physical state s, K i，aj The linear velocity of the longitudinal translation change of any point j of the defect data location detection unit object installed for the pavement defect object after cloning.

[0054] In step 4, the calibrated disease data position detection unit is corrected, including:

[0055] (1) Obtaining the evolution state clone data of any position point of the pavement disease object based on the high-precision measurement disease data position detection unit A1;

[0056] (2) Select the installation location of the calibrated disease data position detection unit a2 on the virtual clone of the pavement disease object, and calculate the clone data information K of the location point. a2 Reversely solve the expected observed physical state information of the pavement defect object at that location

[0057] (3) The acquired A2 disease data location detection unit expects the disease physical state information Actual measurement information with A2 disease data location detection unit By comparison, the measurement compensation value of the calibrated disease data position detection unit A2 is obtained.

[0058] In step S4, attribute text technology is used to automatically fill in the drawing attributes, including:

[0059] S401, for the texts such as the starting pile number, the ending pile number, the first sheet, and the number of sheets in total of the drawing, attribute texts are set in advance in the template, and the program replaces them according to the sheet attributes during drawing; the graphics of the fixed part are in an open form, and are processed based on the existing disease distribution map drawings to form a standard drawing template;

[0060] S402, loading the disease output plug-in program through the plug-in program, and after starting the CAD software, dragging and dropping the command file in the plug-in program directory into the CAD software window;

[0061] S403, finally import the prepared Excel spreadsheet, select the basic information of road grade and road width, and then automatically generate the map.

[0062] Another object of the present invention is to provide a system for automatically generating a CAD drawing of a road pavement disease distribution map, the system implementing the method for automatically generating a CAD drawing of a road pavement disease distribution map, the system comprising:

[0063] Disease data association module, used to identify disease legends and associate disease data;

[0064] The drawing outline conversion module is used to convert the pile number, disease offset, length and width parameters into drawing outlines in combination with the legend;

[0065] The fill attribute module is used to use the fill attribute of the disease legend to make the disease drawing graphics maintain the same drawing style as the legend; according to the legend information of the disease data and the location information of the disease data, the drawing style of the legend is reused in the disease graphics by using the cloning technology;

[0066] The drawing attribute automatic filling module is used to draw graphics according to the defects after filling in the attributes, and automatically fill in the drawing attributes using attribute text technology; import Excel tables, select basic information such as road grade and road width, and then draw the drawing.

[0067] In combination with all the above-mentioned technical solutions, the beneficial effects possessed by the present invention are as follows: the present invention prepares a drawing template, and the graphics of the disease distribution map are divided into two parts, one part is a fixed graphic, such as an outer frame, an inner frame, a legend, design unit and other information; the other part is a variable graphic, such as a disease distribution graphic, start and end pile number mileage, page number, etc. The variable graphics are divided into three categories: disease drawing outline, disease filling, and drawing attributes, and these three types of variable graphics are drawn into CAD. Through the above-mentioned technical features, the disease distribution map can be automatically drawn, which saves labor costs, is time-saving, and has high efficiency in drawing the disease distribution map. The time for issuing construction drawings is fast, which ensures the implementation of construction timing. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0069] Figure 1 It is a flow chart of a method for automatically generating a CAD drawing of a road pavement disease distribution map provided by an embodiment of the present invention;

[0070] Figure 2 It is a schematic diagram of a system for automatically generating a CAD drawing of a road pavement disease distribution map provided by an embodiment of the present invention;

[0071] Figure 3 The embodiment of the present invention provides a method for converting text entity content into an interface effect diagram of "Page &PAGE&";

[0072] Figure 4 It is a configuration effect diagram that the plug-in program provided by the embodiment of the present invention replaces this attribute text with the actual page number of the drawing when drawing a graphic;

[0073] Figure 5 The plug-in program provided in the embodiment of the present invention automatically prompts the information interface effect diagram in the CAD program command line;

[0074] Figure 6 It is a rendering of the startup program provided in an embodiment of the present invention searching the system registry for the installation path of the CAD2023 version program;

[0075] Figure 7 The plug-in program provided in the embodiment of the present invention reads the lane width, schematic median strip width and other parameters saved in the configuration file, reads the template path and data file path selected last time, and pops up a window effect diagram;

[0076] Figure 8 It is an effect diagram of selecting a template file in dwg format provided by an embodiment of the present invention;

[0077] Fig. 9 The correct template file is selected according to the embodiment of the present invention, and the command line will prompt an information effect diagram;

[0078] Fig.10 A data file selection button provided in an embodiment of the present invention pops up a file selection dialog box effect diagram;

[0079] Fig.11 It is an information effect diagram showing that the selected data file information provided by the embodiment of the present invention will be displayed in the plug-in program window;

[0080] In the figure: 1. Disease data association module; 2. Drawing contour conversion module; 3. Fill attribute module; 4. Drawing attribute automatic filling module. DETAILED DESCRIPTION

[0081] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0082] Embodiment 1, the method for automatically generating a CAD drawing of a road pavement disease distribution map provided by the embodiment of the present invention is divided according to the disease type classification of the road technical condition assessment standard, by classifying the collected disease types and sizes, placing them in an Excel table, and then reading the Excel table data into the computer memory through programming, identifying and associating the legend in the disease distribution map template, and associating the successfully associated disease data and disease legend as valid disease data. For the valid disease data, fault tolerance processing can be performed, expanded by coefficient, and merged according to rules. According to the disease data pile number and the size of the disease distribution map template, the number of map sheets is calculated, and then the various types of disease data corresponding to the pile number are offset by the pile number and the center of the disease, and the disease drawing coordinates are calculated (the pile number data is converted into the CAD coordinates of the relative map frame), and the filling graphics of the template legend are reused to draw the disease data in the frame. (The basic data obtained by the existing lightweight road disease detection equipment is imported into the programming plug-in, and the instructions are selected according to the step flow) to realize the automatic export of the disease distribution map.

[0083] like Figure 1 As shown, specifically including:

[0084] S1, identify disease legends and associate disease data; including:

[0085] S101, extracting text, polylines, and fill data in the template image according to a preset template range mark; establishing associations between the text, polylines, and fills according to relative position relationships; establishing a set of associated graphics, storing them with the text as a key value as a temporary legend, and the polylines as the fill legend boundary;

[0086] S102, read the data in the Excel table, store the Json string according to the pile number as the key value; parse the name and offset value in the Json string as temporary disease data;

[0087] S103, for temporary legends and temporary disease data, match the legend key value and the disease data name, and store a group of data with the same key value and data name as disease data;

[0088] S104, issuing a warning prompt to the temporary legend that cannot be associated with the disease data, and issuing a warning prompt to the disease data that cannot be associated with the temporary legend.

[0089] S2, combined with the legend, convert the pile number, disease offset, length and width parameters into drawing outlines; including:

[0090] S201, according to the pile number data of the disease data, the starting pile number and the ending pile number of each lane are calculated; according to the pile number of the lane, the total starting pile number and the ending pile number of the disease data are summarized; in combination with the drawing size and drawing scale of the template, the number of drawing sheets is calculated; in combination with the drawing size and drawing scale of the template, according to the total distance between the total starting pile number and the ending pile number of the disease data, the number of drawing sheets is obtained, and the pile numbers of all the disease data are displayed;

[0091] S202, using the drawing start and end pile numbers assigned to each map sheet, converting the pile numbers of the disease data in the pile number range into drawing coordinate reference points; using the lane width, disease offset, and disease length and width attributes, converting to obtain the disease data drawing outline.

[0092] In step S202, lane width, defect offset, and defect length and width attributes are used to convert the defect data drawing contour, including:

[0093] The first step is to analyze and determine whether the starting point of the disease data drawing conversion instruction is the upper left corner point. If the starting point is the upper left corner point, set q=0; otherwise, set q=1;

[0094] The second step is to record the attribute node length of the disease data drawing conversion instruction. When drawing the disease data drawing conversion instruction, the attribute node length is 3-9, and at least 3 points need to be connected; the letter g represents the actual attribute node length of the disease data drawing conversion instruction, and the letter g′ represents the calculated minimum attribute node length. If the attribute node length is less than or equal to 7, set g′=0; otherwise g′=g-7

[0095] Step 3: When drawing the graphic conversion instruction, i is used to represent the number of intersections; in the influence of the number of intersections on the accuracy of the conversion instruction, the accuracy meter is calculated using min(i, 9);

[0096] Step 4: When three consecutive points in the graphic conversion instruction are not on a straight line and form an angle different from 180 degrees, it is considered that the direction has changed; when the number of direction changes h in the graphic conversion instruction is greater than or equal to 9, let h′=2; when the number of direction changes is 6 and 8, h′=1; otherwise h′=0; h′ is the calculated value of the precision meter of the number of direction changes in the graphic conversion instruction;

[0097] Step 5: In the disease data drawing conversion instruction, if the number of times the node is passed is greater than or equal to 6 times, let f = 2; if the number of times the node is passed is 4 times, then f = 1; otherwise, f = 0; f is the disease data drawing conversion instruction value;

[0098] Step 6: When the selected graphic conversion instruction is a lane width conversion instruction, a defect offset conversion instruction, a defect length and width conversion instruction, or the reverse order of the conversion instruction, let qe=0; if the conversion instruction selected by the user is not the above conversion instruction but includes these conversion instructions, let qe=1; if the conversion instruction selected by the user is not a character shape and does not include the above character shape, let qe=2; qe is the selected graphic conversion instruction value;

[0099] Step 7: The accuracy score is recorded as defen. The conversion instruction accuracy score is divided into three levels: high, medium, and low; where q is the starting point instruction value of the graphic conversion instruction, and g′ is the calculated minimum attribute node length; when 0≤defen≤0.27, the accuracy level is low; when 0.28≤defen≤0.63, the accuracy level is medium; when 0.64≤defen≤1.00, the accuracy level is high;

[0100] In the eighth step, the accuracy of the conversion instruction selected by the user is displayed to the user in the form of a conversion instruction strength progress bar, or the accuracy of the conversion instruction selected by the user is displayed to the user in the form of a text description.

[0101] Further including:

[0102] (1) Check whether the starting point of the graphic conversion instruction is the upper left corner point 1. The starting point of the graphic conversion instruction is 1, and q=0;

[0103] (2) Calculate the actual attribute node length of the graphic conversion instruction. The actual attribute node length of the graphic conversion instruction is 9, g′=9-7=2;

[0104] (3) Calculate the number of intersections of the graphic conversion instruction. The number of intersections is 0, i = 0, so min(i, 9) = 0;

[0105] (4) Count the number of direction changes in the disease data drawing conversion instructions. There are 8 direction changes, so h′=1;

[0106] (5) Analyze the number of overlapping nodes in the disease data drawing conversion instructions to see if any nodes are passed through multiple times. If there are no overlapping nodes in the graphic conversion instructions, f = 0;

[0107] (6) analyzing whether the defect data drawing conversion instruction is a character shape or contains a character shape, and the graphic conversion instruction contains the lane width, qe=1;

[0108] (7) Based on the above analysis, the accuracy score is calculated as The accuracy level of the graphics conversion instruction is determined to be low.

[0109] It can be seen from the above embodiments that in the present invention, the conversion instruction accuracy meter means that when a user inputs a conversion instruction, the system evaluates the accuracy of the conversion instruction selected by the user and displays to the user in some form whether the conversion instruction is a strong, medium or weak conversion instruction.

[0110] The disease data drawing conversion instruction accuracy meter proposed by the present invention takes into account whether there are conversion instructions with character shapes. When character shapes appear in the conversion instructions, the conversion instruction accuracy evaluation score will decrease. This evaluation method can measure the accuracy of the conversion instructions more accurately.

[0111] S3, using the fill attribute of the disease legend to make the disease drawing graphics maintain the same drawing style as the legend; according to the legend information of the disease data and the location information of the disease data, using the cloning technology to reuse the drawing style of the legend to the disease graphics;

[0112] Step 1: using at least one disease data location detection unit to measure the disease physical state information of the pavement disease object at the installation location point, and using the communication system to package the acquired disease physical state data and the disease data location detection unit installation information and send them to the data transmission module;

[0113] Step 2: The data transmission module calculates and obtains the original disease physical state perception information data, and the virtual clone module establishes a three-dimensional model that is the same as the pavement disease object geometry model;

[0114] Step three: the virtual clone module provides geometric information of any point of the pavement disease object based on the established three-dimensional model, fuses the perception information with the geometric information to obtain a real-time clone data set, and uses the clone data to drive the virtual-real interaction module to perform synchronous evolution of the virtual clone and the pavement disease object; the disease data position detection unit measures and feedbacks the physical state information of the pavement disease object that changes with time in the local coordinate system where the disease data position detection unit is installed, and obtains the disease physical state data of the installation position point of the disease data position detection unit in the physical coordinate system and the disease physical state information of the center of mass position point of the pavement disease object through transformation from the local coordinate system to the physical coordinate system of the pavement disease object and Euler angle and quaternion conversion; the disease physical state information of the center of mass position point is data-fused with the geometric information of any point of the pavement disease object of the virtual clone of the pavement disease object to obtain a real-time clone data set of the disease physical state information of the pavement disease object; the physical coordinate system of the pavement disease object is used to define the evolution state of the virtual clone of the pavement disease object, and the rotation center position of the pavement disease object rotation evolution is taken as the origin O d , x d The positive direction of the axis points to the bow, z d The positive direction of the axis points to the port side, y d The positive direction of the axis is vertically upward, and the entity coordinate system of the virtual clone of the pavement disease object is established. Each point on the virtual clone has a corresponding entity coordinate value; the clone data is used to drive the real-time evolution of the virtual clone of the pavement disease object in the virtual scene, and the coordinates of the physical state of the virtual clone in different frames are set according to the time series of the clone data. The discrete clone data sequence is converted into a continuous simulation evolution data sequence through the 3D engine interpolation algorithm, so that the virtual clone of the pavement disease object has the same evolution law as the pavement disease object, and the synchronous evolution of the virtual clone and the pavement disease object is realized;

[0115] Step 4: Use the virtual disease data position detection unit of the virtual-reality interaction module to measure and obtain the real-time disease physical state information of any position point of the pavement disease object. At the same time, feed back the expected disease physical state information of the calibrated disease data position detection unit to the pavement disease object, feed back the disease physical state calibration data, correct the calibrated disease data position detection unit, and finally obtain the road pavement disease graphics drawn in the drawing style of the legend.

[0116] In step 3, the geometric information of any point of the pavement damage object is provided based on the established 3D model, including:

[0117] (1) Use the disease data location detection unit A to measure and obtain the physical state information D of the road disease object in the local coordinate system {a} where the installation point is located a =[D 1a , D 2a ] F=[J a , K a , L a ,y a , z a , b a ] F , where D 1a =[J a , K a , L a ] F Describe the linear velocity of translation change in three directions J, K, L, D 2a =[y a , z a , b a ] F are the angular rates y, z, b of rotation in three directions; F is the order, J a K is the linear velocity of the horizontal translation change, a is the linear velocity of the longitudinal translation change, L a is the linear velocity of the translational change in the spatial direction, y a is the angular velocity of the lateral rotation, z a is the angular velocity of the longitudinal rotation, b a is the angular velocity of rotation in the spatial direction;

[0118] (2) The virtual clone of the pavement disease object obtains the disease data through the three-dimensional model. The location of the detection unit installation point A is relative to the pavement disease object with the center of mass O d , the radial direction b in the solid coordinate system {d} with the rotation center as the origin a ; According to the spatial position relationship between the local coordinate system {a} of the disease data location detection unit and the physical coordinate system {b}, the coordinate transformation matrix of the local coordinate system {a} to the physical coordinate system {d} is obtained Obtain the evolution state information of the center of mass of the pavement disease object in the entity coordinate system {d};

[0119] (3) Obtain the center of mass O of any point i of the pavement defect object relative to the pavement defect object through 3D model measurement d Radius b in the solid coordinate system {d} i , the fused data is used to obtain the clone data expressing the real-time physical state of the pavement disease object at any point i:

[0120]

[0121] Where, C is the physical state data of the physical pavement disease object;

[0122] (4) Install n sets of disease data location detection units on the pavement disease object, and the clone data obtained by each set of disease data location detection unit is:

[0123]

[0124] Where an is the disease data location detection unit installed on the pavement disease object, and n sets of disease data location detection units include lane width, disease offset, disease length and width attribute disease data location detection submodules, respectively. i For the direction; The linear velocity of the longitudinal translation change of the center of mass of the pavement disease object after cloning of the disease data location detection unit installed for the pavement disease object;

[0125] (5) According to the accuracy level, observation noise and measurement environment conditions of each set of disease data location detection unit in different measurement ranges, the weight coefficient L of each set of disease data location detection unit is obtained. an =c, c includes the measurement range, observation noise, and environmental conditions;

[0126] The data of multiple disease data location detection units are integrated to obtain more accurate estimation of the physical state information of the pavement disease object. After integration, the real-time clone data of any position of the pavement disease object is:

[0127]

[0128] In the formula, K i The real-time clone data value of any position of the fused pavement disease object, L sj is the weight coefficient of the disease data location detection unit at any point j of the attribute node length and a certain range, observation noise, and environmental condition physical state s, K i，aj The linear velocity of the longitudinal translation change of any point j of the defect data location detection unit object installed for the pavement defect object after cloning.

[0129] In step 4, the calibrated disease data position detection unit is corrected, including:

[0130] (1) Obtaining the evolution state clone data of any position point of the pavement disease object based on the high-precision measurement disease data position detection unit A1;

[0131] (2) Select the installation location of the calibrated disease data position detection unit a2 on the virtual clone of the pavement disease object, and calculate the clone data information K of the location point. a2 Reversely solve the expected observed physical state information of the pavement defect object at that location

[0132] (3) The acquired A2 disease data location detection unit expects the disease physical state information Actual measurement information with A2 disease data location detection unit By comparison, the measurement compensation value of the calibrated disease data position detection unit A2 is obtained.

[0133] S4, draw the graphics according to the defects after filling in the attributes, and use the attribute text technology to automatically fill in the drawing attributes; import the Excel table, select the basic information of road grade and road width, and then draw the map.

[0134] According to the legend information of the disease data and the location information of the disease data, the drawing style of the legend is reused in the disease graphics using cloning technology.

[0135] Automatically fill in drawing properties using attribute text technology, including:

[0136] S401, for the texts such as the starting pile number, the ending pile number, the first sheet, and the number of sheets in total of the drawing, attribute texts are set in advance in the template, and the program replaces them according to the sheet attributes during drawing; the graphics of the fixed part are in an open form, and are processed based on the existing disease distribution map drawings to form a standard drawing template;

[0137] S402, loading the disease output plug-in program through the plug-in program, and after starting the CAD software, dragging and dropping the command file in the plug-in program directory into the CAD software window;

[0138] S403, finally import the prepared Excel spreadsheet, select the basic information of road grade and road width, and then automatically generate the map.

[0139] Through the above embodiments, it can be known that the present invention constructs a virtual clone of a pavement disease object and a virtual environment scene in the information space with the same physical properties as the pavement disease object and the external environment, and uses the evolution state information perceived by the pavement disease object disease data position detection unit to drive the virtual clone to evolve synchronously with the pavement disease object in the information space, and then fuses the real evolution state data with the geometric information data of the virtual clone to generate the pavement disease object evolution state clone data. Based on the clone data, the evolution state of any position point of the pavement disease object can be obtained, and the real-time rate information of any point can be obtained from the disease physical state information of a single disease data position detection unit.

[0140] The present invention proposes a real-time, cross-domain, spatiotemporal fusion method for the time series data of the disease data position detection unit of pavement disease objects with different layout positions and different measurement ranges in physical space and the three-dimensional model space data of virtual clones in information space, thereby realizing the observation and reliable estimation of the physical state of the disease at any measuring point of the pavement disease object by utilizing the virtual-real fusion and the complementarity of the data of multiple disease data position detection units.

[0141] The present invention establishes a new method for calibrating a pavement disease object disease data position detection unit based on cloned data. The high-precision measurement disease data position detection unit and the calibrated disease data position detection unit can be respectively arranged at any position of the pavement disease object. The calibration work can be completed in the information space with the help of cloned data, which simplifies the complexity of the calibration of the pavement disease object disease data position detection unit.

[0142] Embodiment 2, as Figure 2 As shown, the system for automatically generating a CAD drawing of a road pavement defect distribution map provided by an embodiment of the present invention includes:

[0143] Disease data association module 1, used to identify disease legends and associate disease data;

[0144] Drawing outline conversion module 2 is used to convert the pile number, disease offset, length and width parameters into drawing outline in combination with the legend;

[0145] Filling attribute module 3 is used to use the filling attribute of the disease legend to make the disease drawing graphics maintain the same drawing style as the legend; according to the legend information of the disease data and the location information of the disease data, the drawing style of the legend is reused in the disease graphics by using cloning technology;

[0146] The drawing attribute automatic filling module 4 is used to draw graphics according to the defects after filling in the attributes, and automatically fill in the drawing attributes using attribute text technology; import the Excel table, select the basic information of road grade and road width, and then draw the drawing.

[0147] Example 3: To further illustrate the related technology of the present invention, the following description is given.

[0148] With illustrations.

[0149] 1. Prepare the drawing template

[0150] 1.1 Open a dwg file containing a disease distribution map drawing;

[0151] 1.2 Use the CAD window selection function to select the changed graphics, delete them, and keep the fixed graphics;

[0152] 1.3 Use polyline entities to interactively draw two rectangular boxes using CAD commands. In CAD, rectangular boxes are entities like drawing frames and are visible graphics. Note that the layers of the two rectangular boxes must be "AdTK". The larger rectangular box overlaps with the outer frame of the drawing to assist the plug-in program in identifying the drawing boundary; the smaller rectangular box marks the boundary of the defect graphic drawing. Among them, the CAD command is used in the CAD template to build in special graphic entity markers, which is a technical means to facilitate subsequent plug-in program recognition.

[0153] 1.4 Produce standard drawing attributes text, including starting and ending stake numbers, date, scale of this drawing, and page number. For example, if you delete the number 1 in the text "Page 1", and replace it with &PAGE&, the text entity will become "Page &PAGE&"; as Figure 3 shown.

[0154] Mark the attribute text with the & symbol. For example, "&PAGE&" represents the current page number. When the plug-in program draws the graph, it will replace this attribute text with the actual page number of the drawing. See the configuration effect in Figure 4 .

[0155] Now the plug-in program supports four attribute texts: page number, total number of pages, starting stake number of the drawing, and ending stake number of the drawing. For example: when the plug-in program draws the 10th drawing, find the text entity "Page &PAGE&", replace the "&PAGE&" in it with 10, so that the text entity becomes "Page 10", and then save this text entity. Note: The attribute text can appear at any position within the inner frame of the drawing.

[0156] 2. Prepare the drawing template.

[0157] After starting the CAD software, drag the zrx file in the plug-in program directory into the CAD software window. After successful loading, the plug-in program will automatically prompt information in the CAD program command line as Figure 5 shown.

[0158] Quick start: You can double-click LoadCAD software 2022.exe to start the program, open the operating system registry, find the installation path of the CAD2022 version program, obtain the complete path of the CAD program exe main program, and use the operating system API function to start the CAD software 2022 by creating a child process; similarly, double-click LoadCAD software 2023.exe, and the startup program will find the installation path of the CAD2023 version program in the system registry. See Figure 6 shown.

[0159] 3. Start drawing.

[0160] 3.1 According to the CAD command line prompt, first open the pre-made standard drawing template;

[0161] 3.2 Then, in the command line window, enter the drawdistress command and execute it. The plug-in program reads the parameters such as lane width and schematic median strip width saved in the configuration file, reads the template path and data file path selected last time, and pops up a window as Figure 7 shown.

[0162] 3.3 Enter the road width parameter; enter the schematic median strip width parameter; confirm and modify the name of the disease data Sheet;

[0163] 3.4 Check and modify the expansion coefficient of the length of the disease attribute node and the expansion coefficient of the disease area (the length of the disease attribute node is expanded for longitudinal joints and transverse joints; the disease area is expanded for strip repairs, block repairs and other diseases, and the width of the strip repair remains unchanged). For example: the expansion coefficient is changed to 1.3, and there are several key steps to apply the expansion coefficient to the longitudinal joint disease data:

[0164] The center stake of the longitudinal joint remains unchanged, the longitudinal joint attribute node length*1.3, and the bottom offset distance is subtracted from the longitudinal joint attribute node length*1.3*0.5, thus achieving the effect of keeping the longitudinal joint center unchanged and expanding it to 1.3 times;

[0165] 3.5 Check and modify the vertical interval merging threshold of the disease and the horizontal interval merging threshold of the disease. For example, to merge two block repair diseases, there are several key steps:

[0166] 3.5.1 Sort the disease data by disease data type, lane, and pile number, so that the disease data of the same disease data type are sorted from left to right by lane and from small to large by pile number, and the front, back, left, and right adjacent relationship of the disease data is constructed in the memory.

[0167] 3.5.2 Cycle block repair defect data, compare two by two as follows: Determine the distance from the end pile number of the previous defect to the start pile number of the next defect (this is the longitudinal interval between the two defects). If the distance is greater than the merge threshold, it means that the two defects do not meet the longitudinal interval merge threshold, and continue to cycle other defect data; if the distance is less than or equal to the merge threshold, continue to calculate and try to merge. Determine the lateral interval of the two defects. The calculation method is: take the minimum and maximum lateral offsets of the two defects, take the lane pile number direction as the reference direction, and check the relative relationship between the minimum offset value of the left defect and the maximum offset value of the right defect. If the left smaller is smaller than the right larger, it means that the two defects partially overlap in the horizontal direction. If the minimum is greater than the right larger, it means that the two defects are separated in the horizontal direction. The distance between these two values ​​is the horizontal interval. Determine the horizontal interval. If it is less than the horizontal interval merge threshold, the two defects can be merged.

[0168] 3.6 Click the "Select Template File..." button, a dialog box will pop up to select a template file in dwg format; Figure 8 shown.

[0169] If the correct template file is selected, the command line will prompt the following information: Fig. 9 shown.

[0170] 3.7 Click the "Select Data File..." button, and a dialog box for selecting files will pop up. You can select multiple files, such as Fig.10 shown.

[0171] 3.8 Select the data file to be exported and click the "Open" button;

[0172] 3.9 The selected data file information will be displayed in the plug-in window. The information displayed in the list needs to be checked and confirmed; Fig.11 shown.

[0173] Note: Lane numbers are used to control the order in which lane data is drawn on the drawing. The numbering of the uplink (left lane) uses positive numbers to correspond to lanes 1 / 2 / 3 / 4; the numbering of the downlink (right lane) uses negative numbers to correspond to lanes 1 / 2 / 3 / 4; Lane number 0 is reserved for the median strip, which can be regarded as a lane and can be modified in the list.

[0174] Note: After selecting the data file, the plug-in will compare and check the name of the disease legend with the disease type name in the data file. The comparison process is as follows:

[0175] According to the preset template range mark, extract the text, polyline, and fill data in the template image; try to establish associations between text, polyline (fill legend boundary), and fill according to the relative position relationship. A group of graphics that are successfully associated are stored with text as the key value as a temporary legend.

[0176] Read the data in the Excel table, store the Json string with the pile number as the key value; parse the name and offset value in the Json string as temporary disease data.

[0177] For temporary legends and temporary disease data, match the legend key value and disease data name, and store a group of data with the same key value and data name as disease data.

[0178] Finally, a warning will be given for the temporary legend that cannot be associated with the disease data, and an alarm will be given for the disease data that cannot be associated with the temporary legend. When inconsistency occurs, an alarm will be given.

[0179] 3.10 Click the “Print” button to complete the printing of the disease distribution map.

[0180] Below the template diagram, multiple disease distribution diagrams are displayed in row and column order, and the command line prompts:

[0181] The disease data of 6 lanes were read, with a mileage range of 499641.0 to 530088.0.

[0182] One drawing can display 200.0 meters of data, with each strip being 100.0 meters long, for a total of 2 strips.

[0183] The mileage range of the 6 lane disease data is 499641.0~530088.0. After taking the whole picture, the mileage range of the drawing is 499600.0~530200.0, and a total of 153 drawings are drawn.

[0184] Command:*Cancel*.

[0185] Note: To facilitate the verification of the enlarged and merged results of the disease, the plug-in now draws three layers, namely the original size disease, the enlarged disease, and the merged disease. The superposition of the three layers can simplify the verification process. The verification process includes the following main steps:

[0186] (1) Draw the original size of the disease to achieve what you see is what you get in the Excel file. The disease data can be restored in a graphical way. Through the boundaries such as lane lines, median dividing lines, and 100-meter stakes, it can be intuitively judged whether the disease data crosses the lane line and whether the disease data overlaps.

[0187] (2) When the original size layer and the enlarged layer are displayed at the same time, it is possible to visually determine whether each disease has been enlarged according to the set parameters. The CAD measurement command can be used to spot check the length of the enlarged graphic attribute node and verify the enlargement effect of the plug-in program.

[0188] (3) When the expanded layer and the merged layer are displayed at the same time, the effect before and after the merge can be intuitively judged. The "what you see is what you get" graphical comparison can assist in judging whether the automatic merge result is consistent with the specification and experience. If it does not meet the specification or experience, the final merged disease can be adjusted interactively by referring to the original disease and the expanded disease. This makes the judgment more intuitive and the adjustment more based on evidence.

[0189] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0190] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for automatically generating a CAD drawing of a road pavement disease distribution map, characterized in that: The method includes: S1, identify disease legends and associate disease data; S2, combined with the legend, convert the pile number, disease offset, length and width parameters into drawing outlines; S3, using the fill attribute of the disease legend to make the disease drawing graphics maintain the same drawing style as the legend; according to the legend information of the disease data and the location information of the disease data, using the cloning technology to reuse the drawing style of the legend to the disease graphics; S4, draw the graphics according to the defects after filling in the attributes, and use the attribute text technology to automatically fill in the drawing attributes; import the Excel table, select the basic information of road grade and road width, and then draw the map.

2. The method for automatically generating a CAD drawing of a road pavement disease distribution map according to claim 1, characterized in that: In step S1, identifying the disease legend and associating the disease data includes: S101, extracting text, polylines, and fill data in the template image according to a preset template range mark; establishing associations between the text, polylines, and fills according to relative position relationships; establishing a set of associated graphics, storing them with the text as a key value as a temporary legend, and the polylines as the fill legend boundary; S102, read the data in the Excel table, store the Json string according to the pile number as the key value; parse the name and offset value in the Json string as temporary disease data; S103, for temporary legends and temporary disease data, match the legend key value and the disease data name, and store a group of data with the same key value and data name as disease data; S104, issuing a warning prompt to the temporary legend that cannot be associated with the disease data, and issuing a warning prompt to the disease data that cannot be associated with the temporary legend.

3. The method for automatically generating a CAD drawing of a road pavement disease distribution map according to claim 1, characterized in that: In step S2, the pile number, the disease offset, and the length and width parameters are converted into a drawing outline, including: S201, according to the pile number data of the disease data, the starting pile number and the ending pile number of each lane are calculated; according to the pile number of the lane, the total starting pile number and the ending pile number of the disease data are summarized; in combination with the drawing size and drawing scale of the template, the number of drawing sheets is calculated; in combination with the drawing size and drawing scale of the template, according to the total distance between the total starting pile number and the ending pile number of the disease data, the number of drawing sheets is obtained, and the pile numbers of all the disease data are displayed; S202, using the drawing start and end pile numbers assigned to each map sheet, converting the pile numbers of the disease data in the pile number range into drawing coordinate reference points; using the lane width, disease offset, and disease length and width attributes, converting to obtain the disease data drawing outline.

4. The method for automatically generating a CAD drawing of a road pavement disease distribution map according to claim 3, characterized in that: In step S202, lane width, defect offset, and defect length and width attributes are used to convert the defect data drawing contour, including: The first step is to analyze and determine whether the starting point of the disease data drawing conversion instruction is the upper left corner point. If the starting point is the upper left corner point, set q=0; otherwise, set q=1; The second step is to record the attribute node length of the disease data drawing conversion instruction. When drawing the disease data drawing conversion instruction, the attribute node length is 3-9, and at least 3 points need to be connected; the letter g represents the actual attribute node length of the disease data drawing conversion instruction, and the letter g' represents the calculated minimum attribute node length. If the attribute node length is less than or equal to 7, set g'=0; otherwise, g'=g-7; In the third step, when drawing the graphic conversion instruction, i is used to represent the number of intersections; in the influence of the number of intersections on the accuracy of the conversion instruction, the accuracy meter is calculated using min(i,9); Step 4: When three consecutive points in the graphic conversion instruction are not on a straight line and form an angle different from 180 degrees, it is considered that the direction has changed; when the number of direction changes h in the graphic conversion instruction is greater than or equal to 9, let h'=2; when the number of direction changes is 6 and 8, h'=1; otherwise h'=0; h' is the calculated value of the precision meter of the number of direction changes in the graphic conversion instruction; Step 5: In the disease data drawing conversion instruction, if the number of times the node is passed is greater than or equal to 6 times, let f = 2; if the number of times the node is passed is 4 times, then f = 1; otherwise, f = 0; f is the disease data drawing conversion instruction value; Step 6: When the selected graphic conversion instruction is a lane width conversion instruction, a defect offset conversion instruction, a defect length and width conversion instruction, or the reverse order of the conversion instruction, let qe=0; if the conversion instruction selected by the user is not the above conversion instruction but includes these conversion instructions, let qe=1; if the conversion instruction selected by the user is not a character shape and does not include the above character shape, let qe=2; qe is the selected graphic conversion instruction value; Step 7: The accuracy score is recorded as defen. The conversion instruction accuracy score is divided into three levels: high, medium, and low; where q is the starting point instruction value of the graphic conversion instruction, and g' is the minimum attribute node length calculated; when 0≤defen≤0.27, the accuracy level is low; when 0.28≤defen≤0.63, the accuracy level is medium; when 0.64≤defen≤1.00, the accuracy level is high; In the eighth step, the accuracy of the conversion instruction selected by the user is displayed to the user in the form of a conversion instruction strength progress bar, or the accuracy of the conversion instruction selected by the user is displayed to the user in the form of a text description.

5. The method for automatically generating a CAD drawing of a road pavement disease distribution map according to claim 4, characterized in that: Using lane width, defect offset, and defect length and width attributes, the defect data drawing outline is converted to further include: (1) Check whether the starting point of the graphic conversion instruction is the upper left corner point 1. The starting point of the graphic conversion instruction is 1, and q=0; (2) Calculate the actual attribute node length of the graphic conversion instruction. The actual attribute node length of the graphic conversion instruction is 9, g'=9-7=2; (3) Calculate the number of intersections of the graphic conversion instruction. The number of intersections is 0, i = 0, so min(i,9) = 0; (4) Count the number of direction changes in the disease data drawing conversion instructions. There are 8 direction changes, so h'=1; (5) Analyze the number of overlapping nodes in the disease data drawing conversion instructions to see if any nodes are passed through multiple times. If there are no overlapping nodes in the graphic conversion instructions, f = 0; (6) analyzing whether the defect data drawing conversion instruction is a character shape or contains a character shape, and the graphic conversion instruction contains the lane width, qe=1; (7) Based on the above analysis, the accuracy score is calculated as The accuracy level of the graphics conversion instruction is determined to be low.

6. The method for automatically generating a CAD drawing of a road pavement disease distribution map according to claim 1, characterized in that: In step S3, the drawing style of the legend is reused in the disease graph using cloning technology, including: Step 1: using at least one disease data location detection unit to measure the disease physical state information of the pavement disease object at the installation location point, and using the communication system to package the acquired disease physical state data and the disease data location detection unit installation information and send them to the data transmission module; Step 2: The data transmission module calculates and obtains the original disease physical state perception information data, and the virtual clone module establishes a three-dimensional model that is the same as the pavement disease object geometry model; Step three: the virtual clone module provides geometric information of any point of the pavement disease object based on the established three-dimensional model, fuses the perception information with the geometric information to obtain a real-time clone data set, and uses the clone data to drive the virtual-real interaction module to perform synchronous evolution of the virtual clone and the pavement disease object; the disease data position detection unit measures and feedbacks the physical state information of the pavement disease object that changes with time in the local coordinate system where the disease data position detection unit is installed, and obtains the disease physical state data of the installation position point of the disease data position detection unit in the physical coordinate system and the disease physical state information of the center of mass position point of the pavement disease object through transformation from the local coordinate system to the physical coordinate system of the pavement disease object and Euler angle and quaternion conversion; the disease physical state information of the center of mass position point is data-fused with the geometric information of any point of the pavement disease object of the virtual clone of the pavement disease object to obtain a real-time clone data set of the disease physical state information of the pavement disease object; the physical coordinate system of the pavement disease object is used to define the evolution state of the virtual clone of the pavement disease object, and the rotation center position of the pavement disease object rotation evolution is taken as the origin O d , x d The positive direction of the axis points to the bow, z d The positive direction of the axis points to the port side, y d The positive direction of the axis is vertically upward, and the entity coordinate system of the virtual clone of the pavement disease object is established. Each point on the virtual clone has a corresponding entity coordinate value; the clone data is used to drive the real-time evolution of the virtual clone of the pavement disease object in the virtual scene, and the coordinates of the physical state of the virtual clone in different frames are set according to the time series of the clone data. The discrete clone data sequence is converted into a continuous simulation evolution data sequence through the 3D engine interpolation algorithm, so that the virtual clone of the pavement disease object has the same evolution law as the pavement disease object, and the synchronous evolution of the virtual clone and the pavement disease object is realized; Step 4: Use the virtual disease data position detection unit of the virtual-reality interaction module to measure and obtain the real-time disease physical state information of any position point of the pavement disease object. At the same time, feed back the expected disease physical state information of the calibrated disease data position detection unit to the pavement disease object, feed back the disease physical state calibration data, correct the calibrated disease data position detection unit, and finally obtain the road pavement disease graphics drawn in the drawing style of the legend.

7. The method for automatically generating a CAD drawing of a road pavement disease distribution map according to claim 6, characterized in that: In step 3, the geometric information of any point of the pavement damage object is provided based on the established 3D model, including: (1) Use the disease data location detection unit A to measure and obtain the physical state information D of the road disease object in the local coordinate system {a} where the installation point is located a =[D 1a ,D 2a ] F =[J a ,K a ,L a ,y a ,z a ,b a ] F , where D 1a =[J a ,K a ,L a ] F Describe the linear velocity of translational changes in three directions J, K, L, D 2a =[y a ,z a ,b a ] F is the angular velocity y, z, b of the rotation in three directions; F is the order, J a K is the linear velocity of the horizontal translation change, a is the linear velocity of the longitudinal translation change, L a is the linear velocity of the translational change in the spatial direction, y a is the angular velocity of the lateral rotation, z a is the angular velocity of the longitudinal rotation, b a is the angular velocity of rotation in the spatial direction; (2) The virtual clone of the pavement disease object obtains the disease data through the three-dimensional model. The location of the detection unit installation point A is relative to the pavement disease object with the center of mass O d , the radial direction b in the solid coordinate system {d} with the rotation center as the origin a ; According to the spatial position relationship between the local coordinate system {a} of the disease data location detection unit and the physical coordinate system {b}, the coordinate transformation matrix of the local coordinate system {a} to the physical coordinate system {d} is obtained Obtain the evolution state information of the center of mass of the pavement disease object in the entity coordinate system {d}; (3) Obtain the center of mass O of any point i of the pavement defect object relative to the pavement defect object through 3D model measurement d Radius b in the solid coordinate system {d} i , the fused data is used to obtain the clone data expressing the real-time physical state of the pavement disease object at any point i: Where, C is the physical state data of the physical pavement disease object; (4) Install n sets of disease data location detection units on the pavement disease object, and the clone data obtained by each set of disease data location detection unit is: Where an is the disease data location detection unit installed on the pavement disease object, and n sets of disease data location detection units include lane width, disease offset, disease length and width attribute disease data location detection submodules, respectively. i For the diameter, The linear velocity of the longitudinal translation change of the center of mass of the pavement disease object after cloning of the disease data location detection unit installed for the pavement disease object; (5) According to the accuracy level, observation noise and measurement environment conditions of each set of disease data location detection unit in different measurement ranges, the weight coefficient L of each set of disease data location detection unit is obtained. an =c, c includes the measurement range, observation noise, and environmental conditions; The data of multiple disease data location detection units are integrated to obtain more accurate estimation of the physical state information of the pavement disease object. After integration, the real-time clone data of any position of the pavement disease object is: In the formula, K i The real-time clone data value of any position of the fused pavement disease object, L sj K is the weight coefficient of the disease data location detection unit at any point k of the attribute node length and a certain range, observation noise, and environmental condition physical state s. i,aj The linear velocity of the longitudinal translation change of any point j of the defect data location detection unit object installed for the pavement defect object after cloning.

8. The method for automatically generating a CAD drawing of a road pavement disease distribution map according to claim 6, characterized in that: In step 4, the calibrated disease data position detection unit is corrected, including: (1) Obtaining the evolution state clone data of any position point of the pavement disease object based on the high-precision measurement disease data position detection unit A1; (2) Select the installation location of the calibrated disease data position detection unit a2 on the virtual clone of the pavement disease object, and calculate the clone data information K of the location point. a2 Reverse solve the expected observed physical state information of the pavement defect object at that location (3) The acquired A2 disease data location detection unit expects the disease physical state information Actual measurement information with A2 disease data location detection unit By comparison, the measurement compensation value of the calibrated disease data position detection unit A2 is obtained.

9. The method for automatically generating a CAD drawing of a road pavement disease distribution map according to claim 1, characterized in that: In step S4, attribute text technology is used to automatically fill in the drawing attributes, including: S401, for the texts such as the starting pile number, the ending pile number, the first sheet, and the number of sheets in total of the drawing, attribute texts are set in advance in the template, and the program replaces them according to the sheet attributes during drawing; the graphics of the fixed part are in an open form, and are processed based on the existing disease distribution map drawings to form a standard drawing template; S402, loading the disease drawing plug-in program through the plug-in program, and after starting the CAD software, dragging and dropping the command file in the plug-in program directory into the CAD software window; S403, finally import the prepared Excel spreadsheet, select the basic information of road grade and road width, and then automatically generate the map.

10. A system for automatically generating CAD drawings of road pavement disease distribution maps, characterized in that: The system implements the method for automatically generating a CAD drawing of a road pavement disease distribution map as claimed in any one of claims 1 to 9, and the system comprises: A disease data association module (1), used for identifying disease legends and associating disease data; A drawing outline conversion module (2) is used to convert the pile number, disease offset, length and width parameters into a drawing outline in combination with the legend; A fill attribute module (3) is used to use the fill attribute of the disease legend to make the disease drawing graphics maintain the same drawing style as the legend; according to the legend information of the disease data and the location information of the disease data, the drawing style of the legend is reused in the disease graphics by using cloning technology; The drawing attribute automatic filling module (4) is used to draw graphics according to the defects after the attributes are filled, and automatically fill in the drawing attributes using attribute text technology; import the Excel table, select the basic information of road grade and road width, and then draw the drawing.

Images