Existing line state comprehensive analysis method and system based on three-dimensional mobile scanning system

Abstract

The application discloses a method for comprehensive analysis of existing line state based on a three-dimensional mobile scanning system, which comprises the following steps: fine scanning of a steel rail and rapid acquisition of full-line data of the existing line based on a high-precision three-dimensional mobile laser scanning system; acquisition of field data; obtaining of local point cloud data of the steel rail and point cloud results of a full-section large scene based on preprocessing and fusion registration calculation of original point cloud data, inertial navigation data, GNSS data and encoder mileage data; establishment of a line full-line mileage calibration account based on the point cloud results; performance of multiple detections based on the line full-line mileage calibration account and scanning data, and segmented output of an existing line comprehensive detection diagram and comprehensive evaluation of the steel rail state; the multiple detections comprise linear design adjustment detection, track geometry state detection, fastener detection, rail surface disease detection, profile wear detection, gauge detection, power and electrical equipment detection and existing line linear fine design detection. Corresponding systems, electronic devices and computer readable storage media are also disclosed.

Description

Technical Field

[0001] The present invention relates to the technical field of track geometry service status detection and analysis and railway operation and maintenance management, and in particular to a method and system for comprehensive analysis of existing track status based on a three-dimensional mobile scanning system. Background Art

[0002] By the end of 2023, my country's railway operating mileage reached 159,000 kilometers, including 45,000 kilometers of high-speed rail. As the operating time and mileage of existing lines increase, track equipment defects are becoming increasingly severe and diverse, posing a significant threat to train safety and continued operation. Maintaining good track geometry is a prerequisite for safe train operation. Currently, railway authorities primarily rely on manual visual inspection, track gauges, and track inspection / measuring instruments to determine track geometry. Manual inspections are inefficient and inaccurate, and track equipment inspections continue to face significant challenges.

[0003] 1. Current measurement methods for existing lines:

[0004] (1) Traditional measurement method: manual inspection is used, using simple tools such as rulers, hammers, and rulers.

[0005] (2) The current existing line status analysis method mainly processes independent analysis items of the existing line, such as the rail surface, fasteners, track plate, ballast volume, rail geometry, etc. Therefore, separate detection equipment is developed for each detection item of the existing line:

[0006] a: Design of existing line shape. The current mainstream equipment for this test includes inertial navigation vehicles, total stations, three-dimensional mobile scanning systems, and ground station scanners. They can measure existing lines and calculate the current line shape data through the accompanying software.

[0007] b: Rail surface disease detection: The current mainstream detection equipment includes rail surface detectors based on linear array cameras (which can detect rail surface damage and diseases) and rail surface disease detectors based on line structured light (which can not only detect rail surface damage and diseases, but also calculate their depth and other information).

[0008] c: Fastener defect detection: The current mainstream detection technologies or equipment include line-based light-based fastener detectors and image-based fastener detectors.

[0009] d: Profile wear detection: The current mainstream ones are static contact profile detectors, low-speed non-contact profile detectors, and high-speed non-contact profile detectors.

[0010] e: Sleeper identification and status detection: There are image-based detection equipment and 3D laser scanner-based detection equipment.

[0011] f: Limit detection: The current mainstream equipment includes total stations, ground station laser scanners, 3D mobile scanning systems and other equipment

[0012] g: Electrical equipment testing: The current mainstream testing technologies include: total station testing, ground station scanner, and 3D laser scanner.

[0013] 2. Objective shortcomings of existing technologies:

[0014] (1) Field work efficiency is slow.

[0015] (2) The existing equipment is unable to establish a set of records corresponding to the site and is completely dependent on the equipment’s own encoder.

[0016] (3) It is not possible to complete the comprehensive status assessment and testing of the entire existing line using one set of equipment, that is, it is not possible to conduct a comprehensive analysis and testing of the status of the existing line.

[0017] (4) The inspection results of existing lines cannot be integrated and synchronized into a related design.

[0018] (5) The existing line shape design cannot be adjusted and updated according to the fastener type and pad thickness (high-speed rail), bridge eccentricity, engineering limits, and electrical equipment inspection results, and the design is not sufficiently correlated with the site. Summary of the Invention

[0019] The purpose of the present invention is to address the defects of the existing technology and to construct a comprehensive analysis method and system for the status of existing lines based on a three-dimensional mobile scanning system. The system is equipped with a track detection and measurement platform with a high-precision three-dimensional mobile laser scanning system, which can quickly collect fine track point cloud data and full-section spatiotemporal point cloud data along the railway. Based on the scanning data of the equipment, a mileage calibration ledger for the entire line can be established (the ledger information includes mileage, sleepers, contact network poles, and GNSS position). This method can be used to detect the scanning data of all sections of the line's roadbed, bridges, and tunnels at one time, and a comprehensive assessment of the status of the detected line can be made. One device can perform eight tests at one time, including linear design, track geometry detection, fastener status detection, sleeper identification, profile wear detection, rail surface disease identification, limit detection, and electrical equipment detection. Based on the equipment, fine design of existing lines can be carried out. Based on the detection results of each component, the status of the existing line can be automatically evaluated in real time and the output line maintenance plan can be calculated.

[0020] A first aspect of the present invention provides a method for comprehensive analysis of existing line status based on a three-dimensional mobile scanning system, comprising:

[0021] S1, based on a high-precision 3D mobile laser scanning system, performs precise scanning of rails and rapidly collects data on the entire existing line;

[0022] S2, collects field data through field collection;

[0023] S3, obtaining rail local point cloud data and full-section large-scene point cloud results based on data preprocessing and fusion registration solution of the original point cloud data, inertial navigation data, GNSS data, and encoder mileage data;

[0024] S4, establishing a full-line mileage calibration ledger based on the local rail point cloud data and the full-section large-scene point cloud results, wherein the ledger information included in the full-line mileage calibration ledger includes one or more of mileage, sleepers, contact network poles, and GNSS positions;

[0025] S5, based on the mileage calibration ledger of the entire line and the scanning data, multiple tests are performed, and based on the multiple tests, a comprehensive test map of the existing line and a comprehensive assessment of the rail status are output in sections; the multiple tests include line design adjustment test, track geometry test, fastener test, rail surface disease test, profile wear test, limit test, electrical equipment test and existing line line fine design test.

[0026] Preferably, the S1 includes:

[0027] S11, establish a track detection and measurement platform based on a high-precision 3D mobile laser scanning system;

[0028] S12, based on the track detection and measurement platform, the rails are finely scanned and the data of the entire existing line are quickly collected; wherein the data of the entire existing line includes the fine point cloud data of the track and the spatiotemporal point cloud data of the entire section along the railway.

[0029] Preferably, the high-precision three-dimensional mobile laser scanning system includes a cross-sectional scanner, a line structured light scanner, a laser inertial navigation or inertial measurement unit, a measurable imaging device, a GNSS board, a synchronization board and an industrial computer; wherein the industrial computer is used to control the cross-sectional scanner, the structured light scanner and the laser inertial navigation or inertial measurement unit to perform fine scanning of the rails and quickly collect data on the entire existing line; the cross-sectional scanner and the line structured light scanner are both used to obtain original point cloud data; the laser inertial navigation or inertial measurement unit is used to obtain inertial navigation data and GNSS data; and the measurable imaging device is used to obtain encoder mileage data.

[0030] Preferably, the S2 includes:

[0031] S21, conducting a field survey and on-site data collection to collect field data; the collected field data includes encoder data, GNSS data, inertial navigation data, full-section laser point cloud data, and rail structured light point cloud data;

[0032] S22, copying the field data;

[0033] By dividing field work collection into three steps: field investigation, on-site data collection, and data copying, the smooth progress of field work can be ensured.

[0034] Preferably, the S3 includes:

[0035] S31, based on the data preprocessing subsystem of the on-board lidar system, preprocess and solve the inertial navigation data (IMU), GNSS data, and encoder mileage data (DMI) to obtain POS data; the POS data includes GNSS data and IMU data, namely, the exterior orientation elements in oblique photogrammetry: latitude, longitude, elevation, heading angle, pitch angle, and roll angle; wherein the GNSS data is represented by X, Y, and Z, representing the geographic location at the time of exposure; the IMU data mainly includes heading angle, pitch angle, and roll angle;

[0036] S32, based on the fusion registration solution algorithm, the POS data, the factory calibration data, the original point cloud data of the cross-section scanner and the original point cloud data of the line structured light scanner are multi-scale fused to obtain the local point cloud data of the rail with absolute coordinates and the point cloud results of the full-section large scene.

[0037] Preferably, the S4 includes:

[0038] S41, establish mileage centerline, including:

[0039] (1) Obtaining the pre-data required for the mileage centerline, the pre-data including: structured light original point cloud data; POS data obtained by fusion and solution of inertial navigation data IMU, GNSS data and encoder mileage data DMI; and structured light and inertial navigation calibration data;

[0040] (2) Obtain four original structured light point cloud data for each frame, splice the four original structured light point cloud data into left and right track data according to the calibration parameters, and record the current frame GNSS time as T;

[0041] (3) Filter the left and right rails, calculate the highest points of the left and right rails, and extract the inner gauge points 16 mm below the highest points of the left and right rails respectively;

[0042] (4) Based on the X coordinate of the inner gauge point and the current rail width D, the highest point of the left rail in the region [-1mm, 1mm] at the X-1 / 2D position is taken as the center of the left rail, and the highest point of the right rail in the region [-1mm, 1mm] at the X+1 / 2D position is taken as the center of the right rail;

[0043] (5) Determine the corresponding POS data based on the current frame GNSS time T, the POS data including position X, Y, Z and attitude data Yaw, Pitch, Roll, construct the rotation matrix M1 with the POS data, combine the M matrix composed of the structured light and inertial navigation calibration data, and convert the two-dimensional coordinates of the X coordinate of the inner track gauge point into three-dimensional coordinates, wherein the line connecting the center points of the left track center and the right track center constitutes the mileage center line of the track;

[0044] (6) Obtain the center point of the track of each frame in sequence until the acquisition ends. All the center points extracted in the whole process are connected into a line, which is the mileage center line of the track;

[0045] S42, extract mileage piles, including:

[0046] (1) Using manual interaction, extract each hundred-meter mark and kilometer mark in the three-dimensional laser point cloud;

[0047] (2) calculating relative mileage information and three-dimensional coordinate data based on each of the hundred-meter and kilometer markers;

[0048] (3) inputting the absolute mileage information of each hundred-meter mark and kilometer mark to calibrate the relative mileage of the mileage centerline of the track;

[0049] (4) obtaining the GNSS position information of the point based on the three-dimensional coordinates and the POS data;

[0050] S43, automatic sleeper identification, including:

[0051] (1) Detect the relevant information of the sleepers and fasteners. First, filter out the point cloud near the rail head based on the highest point, and only retain the point cloud of the sleeper fasteners.

[0052] (2) Set the line baseline height h and set any point in the point cloud as pt(x,y);

[0053] (3) traverse the point cloud frame by frame starting from the starting frame of the structured light original point cloud data shown;

[0054] (4) Count the number of point clouds nPt that satisfy the following formula (1) within the range where the fastener is located; if nPt is greater than the set threshold, the frame is a frame where the suspected fastener is located;

[0055]

[0056] In formula (1), x1, x2 (x1>x2) are the x-coordinates of the set fastener area; h is the line baseline height;

[0057] (5) Starting from the frame where the suspected fastener is found, count whether the condition of nPt being greater than the set threshold is met within 50 mm after the frame where the suspected fastener is found. If so, the frame is the sleeper starting frame F1;

[0058] (6) Starting from the start frame of the current sleeper, assuming that the first frame position that does not meet the condition is the end position of the sleeper, starting from this position, if all subsequent frames within 100 mm do not meet the condition, then this frame is the end position F2 of the current sleeper, then the sleeper start frame F1 and the end position F2 of the current sleeper are the start frame and end frame of the current sleeper, obtain the start frame time T1 and the end frame time T2, find the latest POS coordinate data P1 and P2 from the POS file according to the time, project the latest POS coordinate data P1 and P2 onto the mileage center line of the track, obtain the start mileage M1 and the end mileage M2, and at the same time, obtain the GNSS position information from the latest POS coordinate data P1 and P2 according to the start frame time T1 and the end frame time T2;

[0059] (7) Starting from the end frame F2, continue to traverse frame by frame in the order of (3)-(6), record the start and end frames that meet the conditions, and set them as the sleeper positions until the end position of the point cloud;

[0060] S44, obtaining data related to multiple contact network poles, including:

[0061] (1) extracting the center point of each contact network pole from the three-dimensional laser point cloud by manual interaction;

[0062] (2) calculating relative mileage calibration data and three-dimensional coordinate data of each contact network pole based on the center point of each contact network pole;

[0063] (3) obtaining absolute mileage calibration data based on the relative mileage calibration data;

[0064] (4) Obtaining the GNSS position information of the point based on the three-dimensional coordinates and POS data;

[0065] S45, preparing a ledger, including: preparing full-line ledger data based on the relative mileage calibration data, the absolute mileage calibration data, the sleeper data and the contact network pole data, associating the on-site mileage, contact network poles, sleepers and GNSS position information, and positioning the existing line detection data based on the on-site mileage, contact network poles, sleepers and GNSS position information.

[0066] Preferably, the linear design adjustment detection includes: flat curve design detection, longitudinal curve design detection and linear adjustment detection; the track geometry detection includes: geometric parameter detection of geometric shape, size and spatial position, which is described by the coordinate position of the track feature points, specifically including gauge, height, track direction, superelevation, level and triangular pit, and adopts non-contact measurement inertial reference method to finally generate a three-dimensional point cloud of the track structure for detection; the fastener detection includes: fastener identification, fastener performance detection, the fastener performance includes: spring bar gap value detection, spring bar skew detection and fastener disease detection; the rail surface disease detection includes: rail surface disease detection based on the collected rail line structure laser data; the profile wear detection includes: according to the specified spacing Obtain four structured light raw data of specified frame numbers. According to the calibration parameters, the four structured light data are spliced ​​into left and right rail data. Then, the left and right rail data are analyzed separately. The point cloud data of the top of the rail is filtered and smoothed based on the Savitzky-Golay smoothing method. The single-side rail point cloud is segmented into the rail head point cloud and the rail waist and rail bottom point cloud based on threshold segmentation. The rail waist and rail bottom point cloud are used as the starting point set P to match the standard point set Q of the designed rail. The ICP matching algorithm is used to automatically calculate the rotation and translation matrix. Based on the calculated translation matrix and rotation matrix, the same rotation and translation transformation is applied to the rail head point cloud. The transformed point cloud is matched with the rail head point cloud of the standard profile. The total wear is calculated as vertical wear + 1 / 2 side wear.

[0067] The limit detection includes:

[0068] (1) Importing and loading the track centerline L that needs to be checked for clearance, including: if it is determined that the clearance check is for track design, importing and loading the mileage centerline of the track; if it is determined that the clearance check is for track fine design, importing and loading the adjusted track design centerline;

[0069] (2) Based on the track center line L, the L line is divided into equal intervals of 4 mm to obtain a series of points P i (x i ,y i ,z i )i∈[0,n), n is the number of vertices after segmentation;

[0070] (3) Taking each vertex P as the origin and the next point and the current point as the direction vector, a custom coordinate system is constructed, which is specifically described as the matrix M;

[0071] (IV) Set the section thickness T, section length D, and section height H. In the custom coordinate system, the bounding box is described as follows:

[0072] x∈[-D-0.3,-D+0.1]

[0073] y∈[-T / 2.0,T / 2.0]

[0074] z∈[-H-0.5,-H+0.5](13);

[0075] Convert all point clouds into a custom coordinate system and filter out the point cloud data within the bounding box;

[0076] (5) Match the cross-section point cloud data and the bounding box data according to the track center to unify the two data coordinate systems;

[0077] (6) Using the point-in-polygon algorithm, if there are consecutive points falling within the bounding box, the area is considered to be in violation of the limit;

[0078] The electrical equipment inspection includes:

[0079] (1) Using manual interaction, obtain the center coordinates P of the key auxiliary settings in the three-dimensional laser point cloud data, calculate the perpendicular intersection point P1 of point P and the mileage centerline, and then obtain the mileage of this point;

[0080] (2) The plane distance between the center coordinate P of the key auxiliary device and the perpendicular intersection point P1 of the mileage center line is the horizontal distance of the feature point, and the absolute value of the Z coordinate difference between the center coordinate P of the key auxiliary device and the perpendicular intersection point P1 of the mileage center line is the vertical distance of the feature point;

[0081] (3) Obtain the mileage, horizontal distance and vertical distance of all key auxiliary facilities along the entire inspection route in sequence;

[0082] The existing line alignment fine design inspection includes:

[0083] (1) Calculating a track quality index based on the track geometry detection data, determining which track section on the current line requires design and maintenance according to specification requirements, and designing only the track that requires design adjustment;

[0084] (2) Design the track that needs to be designed and maintained, and import the horizontal curve data and longitudinal curve data adjusted according to the design, recalculate the horizontal distance and vertical distance of the electrical equipment, find and record the situation where the horizontal distance and vertical distance of the electrical equipment exceed the design value, and then update the horizontal curve and longitudinal curve of the current location of the electrical equipment that exceeds the limit;

[0085] (3) Perform limit detection on the detection data according to the limit detection method, find out the current limit violation position, and if there is no limit violation, there is no need to adjust the design line shape; if there is a limit violation at the top, update the horizontal curve after adjustment; if there is a limit violation on the left or right, update the adjusted longitudinal curve;

[0086] (4) performing steps (1) to (3) on each track section to obtain final design adjustment data for the entire line;

[0087] The outputting of the comprehensive inspection map of the existing line and the comprehensive evaluation of the rail status based on the multiple inspection segments includes:

[0088] (1) obtaining, based on calculations, ledger data, track design adjustment data, and various detection and defect data; the track design adjustment data and various detection and defect data include fastener detection and defect data, rail surface detection and defect data, profile wear abnormality data, limit intrusion data, and electrical equipment data;

[0089] (2) Vectorize the line design adjustment data and various detection disease data, and overlay the vectorized data with the point cloud data for management;

[0090] (3) The entire line data is divided into 2.4km segments and the station area is mapped separately;

[0091] (4) Each segmented data is scored and managed according to each type of detected disease data, and ultimately a status score for each 2.4km existing line data is obtained to guide maintenance management.

[0092] A second aspect of the present invention is to provide a comprehensive analysis system for existing line status based on a three-dimensional mobile scanning system, which is used to implement the method of the first aspect, including:

[0093] A data rapid acquisition module (101) is used to perform fine scanning of rails and rapid acquisition of data on the entire existing line based on a high-precision three-dimensional mobile laser scanning system;

[0094] A field data collection module (102) is used to collect field data by field collection;

[0095] A fusion registration and solution module (103) is used to obtain rail local point cloud data and full-section large-scene point cloud results based on data preprocessing and fusion registration and solution of the original point cloud data, inertial navigation data, GNSS data and encoder mileage data;

[0096] A mileage calibration ledger establishment module (104) is used to establish a line-wide mileage calibration ledger based on the rail local point cloud data and the full-section large-scene point cloud results, wherein the ledger information contained in the line-wide mileage calibration ledger includes one or more of mileage, sleepers, contact network poles, and GNSS positions;

[0097] The detection and evaluation module (105) is used to perform multiple detections based on the mileage calibration ledger of the entire line and the scan data, and output the existing line comprehensive detection map and comprehensive evaluation of the rail status based on the multiple detections; the multiple detections include alignment design adjustment detection, track geometry detection, fastener detection, rail surface disease detection, profile wear detection, limit detection, electrical equipment detection and existing line alignment fine design detection.

[0098] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method described in the first aspect.

[0099] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method described in the first aspect.

[0100] Beneficial effects of the method and system of the present invention:

[0101] This method and system proposes a comprehensive analysis method for existing line status based on a 3D laser scanning system, which mainly includes:

[0102] 1. The present invention designs a track detection and measurement platform equipped with a high-precision mobile 3D laser scanning system, which can quickly collect fine track point cloud data and full-section spatiotemporal point cloud data along the railway.

[0103] 2. Based on the scanning data of the equipment, a mileage calibration ledger for the entire line can be established (the ledger information includes mileage, sleepers, contact network poles, and GNSS positions).

[0104] 3. This method can be used to scan data of all sections of the roadbed, bridges, and tunnels of the detection line at one time, and conduct a comprehensive assessment of the status of the detection line.

[0105] 4. One device can perform eight tests at one time, including line design, track geometry detection, fastener status detection, sleeper identification, profile wear detection, rail surface disease identification, limit detection, and electrical equipment detection.

[0106] 5. Based on this equipment, detailed design of existing lines can be carried out.

[0107] 6. Based on the test results of each component, the existing line status can be automatically evaluated in real time and the output line maintenance plan can be calculated.

[0108] The main advantages of this method and system are:

[0109] 1) High detection efficiency. Using electric rail vehicles, no manual operation is required, and the field operation scanning efficiency is high (more than 20km / skylight);

[0110] 2) High degree of 3D scene restoration. Non-contact measurement collects data on the track and the entire section along the track, providing a good spatial reference for comprehensive inspection and evaluation of the existing line status;

[0111] 3) Automated extraction process. Utilizing efficient and high-precision roadbed section point cloud fusion, point cloud classification, point cloud matching, feature extraction, line design, automatic fastener identification and detection, automatic measurement of track geometry, and automatic calculation of profile wear, various inspection parameters of existing lines can be efficiently acquired.

[0112] 4) High-precision results. Utilizing high-precision and high-resolution point clouds, the system calculates and outputs safe and reliable comprehensive inspection results for existing lines, comprehensively assessing the status of existing lines and guiding line maintenance operations.

[0113] 5) The detection scenario is complete. This method can detect all the detection data related to the line at one time. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0115] Figure 1 A flow chart of a comprehensive analysis method for existing line status based on a three-dimensional mobile scanning system according to an embodiment of the present invention;

[0116] Figure 2 A schematic structural diagram of a high-precision three-dimensional mobile laser scanning system according to an embodiment of the present invention;

[0117] Figure 3 A schematic diagram of point cloud data of a single sleeper provided according to an embodiment of the present invention;

[0118] Figure 4 A schematic diagram of a point cloud after elimination provided according to an embodiment of the present invention;

[0119] Figure 5 A schematic diagram of traversing a point cloud frame by frame starting from a starting frame of structured light point cloud data according to an embodiment of the present invention;

[0120] Figure 6 A schematic diagram of a three-dimensional point cloud of a track structure ultimately generated by adopting a non-contact measurement inertial reference method according to an embodiment of the present invention;

[0121] Figure 7Schematic diagram of track geometry parameter calculation according to an embodiment of the present invention; wherein Figure 7 (a) Indicates track gauge, track direction and height; Figure 7 (b) indicates an over-elevation condition;

[0122] Figure 8 A type 7 fastener template provided according to an embodiment of the present invention, Figure 8 (1) is the grayscale image template of the 7-type fastener, and the image stores the height information of the corresponding three-dimensional point pt(x, y); Figure 8 (2) High information display effect; Figure 8 (3) is the grayscale image template of type 8 fastener; Figure 8 (4) High information display effect;

[0123] Figure 9 This is an architecture diagram of an existing line state comprehensive analysis system based on a three-dimensional mobile scanning system according to an embodiment of the present invention;

[0124] Figure 10 A structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0125] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0126] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0127] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0128] This embodiment utilizes the track detection and measurement platform of a high-precision three-dimensional mobile laser scanning system to perform fine scanning of the rails and quickly collect data on the entire existing line. It does not rely on ledgers to quickly and accurately perform linear design, track geometry detection, fastener disease detection, profile wear detection, automatic sleeper identification and status detection, rail surface disease detection, limit detection, and electrical equipment detection (roadbed, bridge, tunnel). At the same time, a set of ledger data associated with the site is established based on the measurement data (the ledger data includes mileage, sleeper number, GNSS position information, GNSS time, and contact network pole number). Combined with the ledger data and detection results, the line design data is fine-tuned and designed, the status of every 2.4 km of existing lines is comprehensively evaluated, and a comprehensive evaluation status drawing of the existing line is output to guide the fine maintenance management of the existing lines.

[0129] Example 1

[0130] like Figure 1 As shown, this embodiment provides a comprehensive analysis method of existing line status based on a three-dimensional mobile scanning system, including:

[0131] S1, based on a high-precision 3D mobile laser scanning system, performs precise scanning of rails and rapidly collects data on the entire existing line;

[0132] As a preferred embodiment, the S1 includes:

[0133] S11, establish a track detection and measurement platform based on a high-precision 3D mobile laser scanning system;

[0134] S12, based on the track detection and measurement platform, the rails are finely scanned and the data of the entire existing line are quickly collected; wherein the data of the entire existing line includes the fine point cloud data of the track and the spatiotemporal point cloud data of the entire section along the railway.

[0135] In this embodiment, the high-precision three-dimensional mobile laser scanning system is as follows Figure 2As shown, it includes a cross-sectional scanner, a line structured light scanner, a laser inertial navigation or inertial measurement unit, a measurable image (DMI) device, a GNSS board, a synchronization board and an industrial computer; wherein the industrial computer is used to control the cross-sectional scanner, the structured light scanner and the laser inertial navigation or inertial measurement unit (IMU) to perform fine scanning of the rails and quickly collect data on the entire existing line; the cross-sectional scanner and the line structured light scanner are both used to obtain raw point cloud data; the laser inertial navigation or inertial measurement unit (IMU) is used to obtain inertial navigation data and GNSS data; the measurable image (DMI) device is used to obtain encoder mileage data. An inertial measurement unit (IMU) is a device that measures an object's three-axis attitude angle and acceleration. Determined Measurable Imagery (DMI) is an emerging ground-based stereo imagery product that contains information on absolute exterior orientation elements in a temporal and spatial sequence. It supports direct viewing of the real environment, relative measurement of target object height, width, area, and other information, as well as applications such as absolute position resolution measurement and target attribute information mining.

[0136] Using a series of sensors for 3D mobile scanning, it can simultaneously capture high-density, high-precision rail point cloud data and 360-degree, full-section, spatiotemporal point cloud data of existing lines. The high-precision 3D mobile laser scanning system can be mounted on a cart or electric vehicle platform and operated at speeds of 0-15 km / h.

[0137] S2, collects field data through field collection;

[0138] As a preferred embodiment, the S2 includes:

[0139] S21, conducting a field survey and on-site data collection to collect field data; the collected field data includes encoder data, GNSS data, inertial navigation data, full-section laser point cloud data, and rail structured light point cloud data;

[0140] S22, copying the field data;

[0141] By dividing field work collection into three steps: field investigation, on-site data collection, and data copying, the smooth progress of field work can be ensured.

[0142] S3, obtaining rail local point cloud data and full-section large-scene point cloud results based on data preprocessing and fusion registration solution of the original point cloud data, inertial navigation data, GNSS data, and encoder mileage data;

[0143] As a preferred embodiment, the S3 includes:

[0144] S31, based on the data preprocessing subsystem of the vehicle-mounted lidar system, the inertial navigation data IMU, GNSS data and encoder mileage data DMI are preprocessed and solved to obtain POS data; the POS data includes GNSS data and IMU data, that is, the external orientation elements in oblique photogrammetry: (latitude, longitude, elevation, heading angle (Phi), pitch angle (Omega) and roll angle (Kappa)), among which GNSS data is represented by X, Y, and Z, representing the geographical location at the moment of exposure; IMU data mainly includes three data: heading angle (Phi), pitch angle (Omega) and roll angle (Kappa).

[0145] S32, based on the fusion registration solution algorithm, the POS data, the factory calibration data, the original point cloud data of the cross-section scanner and the original point cloud data of the line structured light scanner are multi-scale fused to obtain the local point cloud data of the rail with absolute coordinates and the point cloud results of the full-section large scene.

[0146] S4, establishing a full-line mileage calibration ledger based on the local rail point cloud data and the full-section large-scene point cloud results, wherein the ledger information included in the full-line mileage calibration ledger includes one or more of mileage, sleepers, contact network poles, and GNSS positions;

[0147] As a preferred embodiment, the S4 includes:

[0148] S41, establish mileage centerline, including:

[0149] (1) Obtaining the pre-data required for the mileage centerline, the pre-data including: structured light original point cloud data; POS data obtained by fusion and solution of inertial navigation data IMU, GNSS data and encoder mileage data DMI; and structured light and inertial navigation calibration data;

[0150] (2) Obtain four original structured light point cloud data for each frame, splice the four original structured light point cloud data into left and right track data according to the calibration parameters, and record the current frame GNSS time as T;

[0151] (3) Filter the left and right rails, calculate the highest points of the left and right rails, and extract the inner gauge points 16 mm below the highest points of the left and right rails respectively;

[0152] (4) Based on the X coordinate of the inner gauge point and the current rail width D, the highest point of the left rail in the region [-1mm, 1mm] at the X-1 / 2D position is taken as the center of the left rail, and the highest point of the right rail in the region [-1mm, 1mm] at the X+1 / 2D position is taken as the center of the right rail;

[0153] (5) Determine the corresponding POS data based on the current frame GNSS time T, the POS data including position X, Y, Z and attitude data Yaw, Pitch, Roll, construct the rotation matrix M1 with the POS data, combine the M matrix composed of the structured light and inertial navigation calibration data, and convert the two-dimensional coordinates of the X coordinate of the inner track gauge point into three-dimensional coordinates, wherein the line connecting the center points of the left track center and the right track center constitutes the mileage center line of the track;

[0154] (6) The center point of the track of each frame is obtained in sequence until the acquisition ends. All the center points extracted in the whole process are connected into a line, which is the mileage center line of the track.

[0155] S42, extract mileage piles, including:

[0156] (1) Using manual interaction, extract each hundred-meter mark and kilometer mark in the three-dimensional laser point cloud;

[0157] (2) calculating relative mileage information and three-dimensional coordinate data based on each of the hundred-meter and kilometer markers;

[0158] (3) inputting the absolute mileage information of each hundred-meter mark and kilometer mark to calibrate the relative mileage of the mileage centerline of the track;

[0159] (4) Obtain the GNSS position information of the point based on the three-dimensional coordinates and the POS data.

[0160] S43, automatic sleeper identification, including:

[0161] (1) Detect the relevant information of sleepers and fasteners. First, filter out the point cloud near the rail head according to the highest point, and only retain the point cloud of the sleeper fastener. The point cloud data of a single sleeper is as follows: Figure 3 As shown, the point cloud after elimination is as follows Figure 4 As shown;

[0162] (2) Set the line baseline height h and set any point in the point cloud as pt(x,y);

[0163] (3) Starting from the starting frame of the structured light original point cloud data shown, the point cloud is traversed frame by frame; as follows Figure 5 Shown is a single frame point cloud;

[0164] (4) Count the number of point clouds nPt that satisfy the following formula (1) within the range where the fastener is located; if nPt is greater than the set threshold, the frame is a frame where the suspected fastener is located;

[0165]

[0166] In formula (1), x1, x2 (x1>x2) are the x-coordinates of the set fastener area; h is the line baseline height;

[0167] (5) Starting from the frame where the suspected fastener is found, count whether the condition of nPt being greater than the set threshold is met within 50 mm after the frame where the suspected fastener is found. If so, the frame is the sleeper start frame F1;

[0168] (6) Starting from the start frame of the current sleeper, assuming that the first frame position that does not meet the condition is the end position of the sleeper, starting from this position, if all subsequent frames within 100 mm do not meet the condition, then this frame is the end position F2 of the current sleeper, then the sleeper start frame F1 and the end position F2 of the current sleeper are the start frame and end frame of the current sleeper, obtain the start frame time T1 and the end frame time T2, find the latest POS coordinate data P1 and P2 from the POS file according to the time, project the latest POS coordinate data P1 and P2 onto the mileage center line of the track, obtain the start mileage M1 and the end mileage M2, and at the same time, obtain the GNSS position information from the latest POS coordinate data P1 and P2 according to the start frame time T1 and the end frame time T2;

[0169] (7) Starting from the end frame F2, continue to traverse frame by frame in the order of (3)-(6), record the start and end frames that meet the conditions, and set them as the sleeper positions until the end position of the point cloud.

[0170] S44, obtaining data related to multiple contact network poles, including:

[0171] (1) extracting the center point of each contact network pole from the three-dimensional laser point cloud by manual interaction;

[0172] (2) calculating relative mileage calibration data and three-dimensional coordinate data of each contact network pole based on the center point of each contact network pole;

[0173] (3) obtaining absolute mileage calibration data based on the relative mileage calibration data;

[0174] (4) Obtain the GNSS position information of the point based on the three-dimensional coordinates and POS data.

[0175] S45, preparing a ledger, including: preparing full-line ledger data based on the relative mileage calibration data, the absolute mileage calibration data, the sleeper data and the contact network pole data, associating the on-site mileage, contact network poles, sleepers and GNSS position information, and locating the existing line detection data based on the on-site mileage, contact network poles, sleepers and GNSS position information.

[0176] S5: Perform multiple tests based on the full-line mileage calibration ledger and scan data, and output a comprehensive inspection map of the existing line and a comprehensive assessment of the rail condition based on the multiple tests; the multiple tests include alignment design adjustment inspection, track geometry inspection, fastener inspection, rail surface defect inspection, profile wear inspection, clearance inspection, electrical equipment inspection, and existing line alignment fine design inspection;

[0177] As a preferred embodiment, the linear design adjustment detection includes:

[0178] (1) Flat curve design inspection

[0179] Flat curves are mainly composed of straight lines, transition curves, and circular curves. The starting and ending positions of all straight lines, transition curves, and circular curves are calculated based on the extracted track centerline and the extracted curve boundary points. The specific steps include:

[0180] 1. Calculating the azimuth of the track centerline vertex using the measured track centerline vertex coordinates;

[0181] 2. Calculating the curvature of the track centerline vertex from the azimuth angle of the track centerline vertex;

[0182] 3. Use filtering algorithm to smooth the azimuth and curvature of the extracted track centerline vertex;

[0183] 4. Draw and display the curvature of each vertex to assist manual review and determine the specific curve and straight line segmentation points of each vertex;

[0184] 5. Fitting a straight line and a circular curve using the least squares method to obtain a transition curve, wherein the transition curve is a fitted flat curve; the fitting method of the transition curve is as follows:

[0185] Assume that the parameter equation of the transition curve is =x 3 / 6R*l0, where l0 is the length of the transition curve and R is the curvature. The cubic polynomial fitting is used to fit the transition curve parameter equation, and the least squares fitting is performed on each coordinate point (xi, yi). The fitting equation is formula (1):

[0186] y=ax 3 +bx 2 +cx+d(1)

[0187] According to the least squares method, by substituting all the orbit center points in the interval, the point values ​​can be calculated to calculate the four parameters a, b, c, and d.

[0188] (2) Longitudinal curve design inspection

[0189] The longitudinal curve is mainly composed of straight lines and circular curves. The starting and ending positions of all straight lines and circular curves are calculated based on the extracted track centerline and the extracted curve dividing points. The specific steps are as follows:

[0190] 1. With the mileage of the track center point as the X-axis and the elevation Z coordinate direction as the Y-axis, a coordinate system is constructed and all the vertices of the track centerline are converted into longitudinal section points of the coordinate system.

[0191] 2. Calculating the azimuth of the longitudinal section point through the longitudinal section point, and calculating the slope of the longitudinal section point from the azimuth;

[0192] 3. Since measurement errors may affect the calculation, the sliding average method is used to smooth the azimuth and curvature of the longitudinal section points;

[0193] 4. Using the characteristic that the curvature of a straight section is zero, the longitudinal section line formed by the longitudinal section points is preliminarily divided into straight sections and curved sections;

[0194] 5. Set the lateral measurement error as a constraint condition and perform iterative fitting on the identified road section according to the corresponding model to determine the starting point, end point and related descriptive parameters of the two alignments, and obtain the longitudinal section alignment representation of the track, which is the fitted longitudinal curve.

[0195] (3) Linear adjustment detection

[0196] 1. For the longitudinal curve, the difference between the measured section point and the fitted longitudinal curve point at each mileage is calculated based on the measured track longitudinal section line and the fitted longitudinal curve. The deviation is adjusted based on manual experience to obtain the final adjusted design longitudinal curve.

[0197] 2. For the flat curve, calculate the difference between the measured center plane point and the fitted flat curve point at each mileage based on the measured track center plane line and the fitted flat curve. Manually review and adjust the local area on the basis of ensuring the smoothness of the flat curve of the entire track to obtain the final adjusted design flat curve.

[0198] As a preferred embodiment, the track geometry detection includes:

[0199] Track geometry mainly refers to geometric parameters such as geometric shape, size and spatial position, which are mainly described by the coordinate position of track feature points, including track gauge, height, track direction, superelevation, level and triangular pits. Using non-contact measurement inertial reference method, the final generated track structure 3D point cloud is as follows: Figure 6 shown.

[0200] Because this point cloud is a high-precision point cloud containing absolute positions, it can truly reflect the on-site situation. Based on this point cloud and the definition of track geometry parameters, key points are extracted or fitted to calculate the track geometry parameters. Figure 7 Schematic diagram for calculation of track geometric parameters.

[0201] The track gauge refers to the shortest distance between two rails, 16 mm below the top of the rail on the inner side of the rail. The standard track gauge for straight sections of my country's railways is 1435 mm. The deviation between the actual track gauge value and the standard value is the track gauge deviation (G), which is calculated as formula (2):

[0202] G=DD b (2);

[0203] In formula (2), D represents the actual measured track gauge value; D b Indicates the standard track gauge value, generally 1435mm.

[0204] Track irregularity refers to the lateral irregularity along the length of the inner rail. Lateral irregularity is differentiated between left and right rails and is usually inconsistent. The average of the left and right rail directional irregularities is used as the directional deviation of the track centerline. Directional irregularity increases lateral forces on the wheels, causing lateral vibration and sway, which can accelerate component damage. Directly extracting the horizontal coordinates of the gauge points directly calculates track irregularity. Figure 7 The calculation formula for the track irregularity a at B1 in (a) is formula (3):

[0205]

[0206] In formula (3): Represents the area of ​​△A1B1C1.

[0207] Height refers to the unevenness of the rail top surface in the vertical direction along the rail centerline. Height unevenness is divided into left and right rails and can be represented by sine waves of different chord lengths and spatial curves of different wavelength ranges. Height unevenness (H) can cause excessive vertical forces between the wheel and rail, causing the train to float and nod violently. Its calculation formula is (4):

[0208]

[0209] In formula (4): They represent the elevations of points A2, B2, and C2 respectively.

[0210] Track level refers to the difference in elevation between the tops of the left and right rails on the same track cross section relative to the horizontal plane, excluding superelevation on circular curves and superelevation along the slope on transition curves. Superelevation (S) refers to the difference in design horizontal height between the top of the outer rail and the top of the inner rail in a curved section. It is calculated using Equation (5):

[0211] S=S o -S i (5);

[0212] In formula (5): S o 、S i Respectively represent the horizontal heights of the outer and inner rail top surfaces.

[0213] As a preferred embodiment, the fastener detection includes:

[0214] (1) Fastener identification

[0215] like Figure 8 As shown in Figure 2, fastener template matching is to match the point cloud collected by the equipment with the template. The matching method is the normalized correlation coefficient matching method, as shown in formula (6):

[0216]

[0217] In formula (6), T represents the template image as shown in Figure WJ_7.tiff, and I represents the image to be matched, which is obtained by converting the point cloud data. The closer the calculation result R is to 1, the better the matching effect is. Different templates are used to match the fastener point cloud, and the best result is taken as the fastener recognition result.

[0218] (2) Fastener performance testing, including:

[0219] 1. Detection of spring bar gap value, including:

[0220] (1) The tongue area information pre-set in the fastener template is mapped to the matched fastener point cloud to obtain the extracted tongue.

[0221] (2) Given that the diameter of the spring bar is 1, obtain the highest point P(x, y, h) of the spring tongue area and obtain the height parameter Ph;

[0222] (3) Fit the pad plane M and obtain its elevation H;

[0223] (4) Then the fastener gap value t is: t = Ph-H-1;

[0224] 2. Spring bar skew detection, including:

[0225] Using the pre-set spring bar area information in the fastener template, the equation of the straight line outside the spring bar is fitted to obtain its angle α. If the angle is greater than the set threshold θ, the fastener is judged to be in a skewed state; otherwise, it is in a normal state.

[0226] 3. Fastener defect detection, including:

[0227] The fastener point cloud is automatically extracted to generate a grayscale image, and then the fastener missing defects such as fastener missing, fastener skewness and protective bolt missing are identified through automatic recognition methods.

[0228] As a preferred embodiment, the rail surface defect detection includes: performing rail surface defect detection based on collected rail line structure laser data, including:

[0229] (1) Extracting track surface point cloud;

[0230] (2) projecting the rail surface point cloud into a grayscale orthophoto according to the rail plane coordinates and reflection intensity;

[0231] (3) Automatically identifying rail surface defects such as welds, corrugations, and surface damage in the grayscale orthophoto based on a deep learning model and deep learning methods.

[0232] The construction of the deep learning model begins with the collection of massive rail image data, which covers a wide variety of normal rail conditions as well as various types and degrees of defects such as abrasions, welds, and corrugation.

[0233] The deep learning method is based on a convolutional neural network (CNN) to identify rail defects. The convolutional layer of the CNN effectively extracts image features through the mechanism of local receptive field and weight sharing. Taking a simple two-dimensional convolution operation as an example, its formula can be expressed as formula (7):

[0234]

[0235] Among them (7), represents the output of the jth neuron in the i-th layer; f represents the activation function (such as the ReLU function), which introduces nonlinear characteristics and helps the model learn complex patterns; M j represents the set of input feature maps, Represents the convolution kernel, which determines the filtering operation of the input features. represents the bias term.

[0236] Rail abrasion damage typically manifests as localized surface wear and irregular texture variations. By training on a large number of images containing abrasions, the deep learning model captures these subtle but crucial differences. For example, a scuffed area may differ in color brightness from a normal area, or have distinct differences in texture roughness and directionality.

[0237] In order to improve the performance and generalization ability of the model, this embodiment adopts auxiliary technical means, including: increasing data diversity through data augmentation (including random rotation, cropping, flipping images, etc.), and using regularization methods (such as L1 and L2 regularization) to prevent overfitting.

[0238] For example, in data augmentation, if the original image is I, a new image I can be obtained by randomly rotating it by an angle θ. rotated =R θ I where R θ Represents a rotation matrix.

[0239] Through continuous training and optimization, the deep learning model can quickly and accurately process large amounts of rail image data, thereby efficiently identifying various defects.

[0240] As a preferred embodiment, the profile wear detection includes:

[0241] (1) Obtain four structured light raw data of a specified frame number at a specified interval, splice the four structured light data into left and right rail data according to the calibration parameters, and then analyze the left and right rail data separately.

[0242] (2) Based on the Savitzky-Golay smoothing method (abbreviated as SG smoothing), the rail top point cloud data is filtered and smoothed. The Savitzky-Golay smoothing method is a smoothing method based on local polynomial fitting. For the curve sequence {(x i ,y i )|i=1,...,n}, take 2p+1 consecutive points each time, and use formula (8) to solve the smoothing result of the center point (x′ i ,y′ i ).

[0243]

[0244] In formula (8), k represents the position of the point in the interval, k∈(-p,p); h k Denotes the smoothing coefficient, let H = {h k}, then H can be solved by polynomial fitting based on the least squares principle; for zp+1 consecutive data points x k , use the N-order polynomial of formula (9) for fitting.

[0245]

[0246] In formula (9), j is the order of each term of the polynomial, j = 0, 1, ... N; a j is the coefficient of the j-order polynomial; let the residual ε of the least squares fit be N Minimum, as shown in the residual formula (10).

[0247]

[0248] Find the partial derivative of the residual formula (10) and let b jk=k j ,B={b jk}, we can get formula (11):

[0249] H=(B T B) -1 B T E (11);

[0250] The Savitzky-Golay smoothing method is to calculate the convolution of the coefficient vector H and the data point sequence. H can be called the convolution smoothing coefficient. The smoothing formula (8) can be rewritten as formula (12):

[0251] x'=H*x,y'=H*y (12);

[0252] (3) Based on threshold segmentation, the single-side rail point cloud is segmented into the rail head point cloud and the rail waist and rail base point cloud. The rail waist and rail base point cloud is used as the starting point set P to match the standard point set Q of the design rail. The ICP matching algorithm is used to automatically calculate the rotation and translation matrix. Based on the calculated translation and rotation matrices, the same rotation and translation transformation is applied to the rail head point cloud, and the transformed point cloud is matched with the rail head point cloud of the standard profile.

[0253] (4) Calculation of wear, including:

[0254] 1. Measure vertical wear from the highest point of the rail head;

[0255] 2. Measure the side wear 16mm below the rail tread (according to the standard section);

[0256] 3. Calculate total wear = vertical wear + 1 / 2 side wear.

[0257] As a preferred embodiment, the limit detection includes:

[0258] (1) Importing and loading the track centerline L that needs to be checked for clearance, including: if it is determined that the clearance check is for track design, importing and loading the mileage centerline of the track; if it is determined that the clearance check is for track fine design, importing and loading the adjusted track design centerline;

[0259] (2) Based on the track center line L, the L line is divided into equal intervals of 4 mm to obtain a series of points P i (x i ,y i ,z i )i∈[0,n), n is the number of vertices after segmentation;

[0260] (3) Taking each vertex P as the origin and the next point and the current point as the direction vector, a custom coordinate system is constructed, which is specifically described as the matrix M;

[0261] (IV) Set the section thickness T, section length D, and section height H. In the custom coordinate system, the bounding box is described as follows:

[0262] x∈[-D-0.3,-D+0.1]

[0263] y∈[-T / 2.0,T / 2.0]

[0264] z∈[-H-0.5,-H+0.5](13);

[0265] Convert all point clouds into a custom coordinate system and filter out the point cloud data within the bounding box;

[0266] (5) Match the cross-section point cloud data and the bounding box data (standard bounding box defined by the specification) according to the track center to unify the two data coordinate systems;

[0267] (6) Through the point-in-polygon algorithm, if there are continuous points falling within the limit box, it is considered that the place is in violation of the limit.

[0268] As a preferred embodiment, the electrical equipment detection includes:

[0269] (1) Using manual interaction, obtain the center coordinates P of the key auxiliary settings in the 3D laser point cloud data, calculate the perpendicular intersection point P1 of point P and the mileage centerline, and then obtain the mileage of this point.

[0270] (2) The plane distance between the center coordinate P of the key auxiliary device and the perpendicular intersection point P1 of the mileage center line is the horizontal distance of the feature point, and the absolute value of the Z coordinate difference between the center coordinate P of the key auxiliary device and the perpendicular intersection point P1 of the mileage center line is the vertical distance of the feature point;

[0271] (3) Obtain the mileage, horizontal distance and vertical distance of all key ancillary facilities along the entire inspection route in sequence.

[0272] As a preferred embodiment, the existing line fine design detection includes:

[0273] Existing line design generally uses track centerline data collected by equipment for design, relying entirely on optimal mathematical models or relying on records to define straight and curved segments. This completely fails to achieve the purpose of fine design and maintenance. This method can perform detailed design of the existing line shape based on track geometry data, fastener identification and detection data, sleeper identification and detection data, limit detection, and electrical equipment detection data. The specific steps are as follows:

[0274] (1) Calculating a track quality index (TQI) based on the track geometry detection data, determining which track sections on the current line require design and maintenance in accordance with regulatory requirements, and designing only those sections that require design adjustments;

[0275] (2) Design the track that needs to be designed and maintained, and import the horizontal curve data and longitudinal curve data adjusted according to the design, recalculate the horizontal distance and vertical distance of the electrical equipment, find and record the situation where the horizontal distance and vertical distance of the electrical equipment exceed the design value, and then update the horizontal curve and longitudinal curve of the current location of the electrical equipment that exceeds the limit;

[0276] (3) Perform limit detection on the detection data according to the limit detection method, find out the current limit violation position, and if there is no limit violation, there is no need to adjust the design line shape; if there is a limit violation at the top, update the horizontal curve after adjustment; if there is a limit violation on the left or right, update the adjusted longitudinal curve;

[0277] (4) Perform steps (1) to (3) on each section of track to obtain the final design adjustment data for the entire line.

[0278] As a preferred embodiment, the segmented output of the comprehensive inspection map of the existing line and the comprehensive assessment of the rail condition based on the multiple inspections include comprehensively assessing the operating status of the existing line based on the track geometry inspection, fastener inspection, rail surface inspection, rail profile inspection, clearance inspection, and electrical equipment inspection data, to guide subsequent refined maintenance management. The specific steps are as follows:

[0279] (1) obtaining, based on calculations, ledger data, track design adjustment data, and various detection and defect data; the track design adjustment data and various detection and defect data include fastener detection and defect data, rail surface detection and defect data, profile wear abnormality data, limit intrusion data, and electrical equipment data;

[0280] (2) Vectorize the line design adjustment data and various detection disease data, and overlay the vectorized data with the point cloud data for management;

[0281] (3) The entire line data will be divided into sections of 2.4 km each and the station area will be mapped separately.

[0282] (4) Each segmented data is scored and managed according to each type of detected disease data, and ultimately a status score for each 2.4km existing line data is obtained to guide maintenance management.

[0283] Example 2

[0284] like Figure 9 As shown, the present embodiment provides a comprehensive analysis system for existing line status based on a three-dimensional mobile scanning system, which is used to implement the method of the first aspect, including:

[0285] The data rapid acquisition module 101 is used to perform precise scanning of rails and rapid acquisition of data on the entire existing line based on a high-precision three-dimensional mobile laser scanning system;

[0286] The field data collection module 102 is used to collect field data by field collection;

[0287] The fusion registration and solution module 103 is used to obtain the local point cloud data of the rail and the point cloud results of the entire cross-section scene based on the data preprocessing and fusion registration solution of the original point cloud data, inertial navigation data, GNSS data and encoder mileage data;

[0288] A mileage calibration ledger establishing module 104 is configured to establish a line-wide mileage calibration ledger based on the rail local point cloud data and the full-section large-scale point cloud results, wherein the ledger information included in the line-wide mileage calibration ledger includes one or more of mileage, sleepers, contact network poles, and GNSS positions;

[0289] The inspection and evaluation module 105 is used to perform multiple inspections based on the mileage calibration ledger of the entire line and the scanning data, and output the existing line comprehensive inspection map and comprehensive evaluation of the rail status based on the multiple inspections; the multiple inspections include alignment design adjustment inspection, track geometry inspection, fastener inspection, rail surface disease inspection, profile wear inspection, limit inspection, electrical equipment inspection and existing line alignment fine design inspection.

[0290] The present invention also provides a memory storing a plurality of instructions, wherein the instructions are used to implement the method as in the first embodiment.

[0291] like Figure 10 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, the memory 302 stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the method as in embodiment 1.

[0292] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive analysis method for existing line status based on a three-dimensional mobile scanning system, characterized in that: include: S1, based on a high-precision 3D mobile laser scanning system, performs precise scanning of rails and rapidly collects data on the entire existing line; S2, collects field data through field collection; S3, based on data preprocessing and fusion registration solution of raw point cloud data, inertial navigation data, GNSS data and encoder mileage data, obtains local point cloud data of rail and point cloud results of full-section large scene; S4, establishing a full-line mileage calibration ledger based on the local rail point cloud data and the full-section large-scene point cloud results, wherein the ledger information included in the full-line mileage calibration ledger includes one or more of mileage, sleepers, contact network poles, and GNSS positions; S5: Perform multiple tests based on the full-line mileage calibration ledger and scan data, and output a comprehensive inspection map of the existing line and a comprehensive assessment of the rail condition based on the multiple tests; the multiple tests include alignment design adjustment inspection, track geometry inspection, fastener inspection, rail surface defect inspection, profile wear inspection, clearance inspection, electrical equipment inspection, and existing line alignment fine design inspection; Said S1 comprises: S11, establish a track detection and measurement platform based on a high-precision 3D mobile laser scanning system; S12, performing fine scanning of the rails and rapid collection of full-line data of the existing line based on the track detection and measurement platform, wherein the full-line data of the existing line includes fine track point cloud data and full-section spatiotemporal point cloud data along the railway line; The S2 includes: S21, conducting a field survey and on-site data collection to collect field data; the collected field data includes encoder data, GNSS data, inertial navigation data, full-section laser point cloud data, and rail structured light point cloud data; S22, copying the field data; By dividing field work collection into three steps: field investigation, on-site data collection, and data copying, the smooth progress of field work can be ensured; The S3 includes: S31, based on the data preprocessing subsystem of the on-board lidar system, preprocess and solve the inertial navigation data (IMU), GNSS data, and encoder mileage data (DMI) to obtain POS data; the POS data includes GNSS data and IMU data, namely, the exterior orientation elements in oblique photogrammetry: latitude, longitude, elevation, heading angle, pitch angle, and roll angle; wherein the GNSS data is represented by X, Y, and Z, representing the geographic location at the time of exposure; the IMU data mainly includes heading angle, pitch angle, and roll angle; S32, performing multi-scale fusion of the POS data, factory calibration data, raw point cloud data from the cross-section scanner, and raw point cloud data from the line structured light scanner based on a fusion registration solution algorithm, and obtaining local point cloud data of the rail with absolute coordinates and point cloud results of the full cross-section scene; The S4 includes: S41, establish mileage centerline, including: (1) Obtaining the pre-data required for the mileage centerline, the pre-data includes: structured light original point cloud data; POS data obtained by fusion and solution of inertial navigation data IMU, GNSS data and encoder mileage data DMI; and structured light and inertial navigation calibration data; (2) Obtain four original structured light point cloud data for each frame, splice the four original structured light point cloud data into left and right track data according to the calibration parameters, and record the current frame GNSS time as T; (3) Filter the left and right rails, calculate the highest points of the left and right rails, and extract the inner gauge points 16 mm below the highest points of the left and right rails respectively; (4) According to the X coordinate of the inner gauge point and the current rail width D, the left rail is located at the highest point in the region [-1mm, 1mm] at the X-1 / 2D position, and the right rail is located at the highest point in the region [-1mm, 1mm] at the X+1 / 2D position, and the right rail is located at the highest point in the region [-1mm, 1mm] at the X+1 / 2D position; (5) Determine the corresponding POS data based on the current frame GNSS time T, the POS data including position X, Y, Z and attitude data Yaw, Pitch, Roll, and construct the rotation matrix M1 with the POS data. Combined with the M matrix composed of the structured light and inertial navigation calibration data, the two-dimensional coordinate of the X coordinate of the inner track gauge point is converted into a three-dimensional coordinate, wherein the line connecting the center points of the left track center and the right track center constitutes the mileage center line of the track; (6) Obtain the center point of the track of each frame in sequence until the acquisition ends. All the center points extracted in the whole process are connected into a line, which is the mileage center line of the track; S42, extract mileage piles, including: (1) Extract each 100-meter mark and kilometer mark from the 3D laser point cloud using manual interaction; (2) Calculating relative mileage information and three-dimensional coordinate data based on each of the hundred-meter and kilometer markers; (3) Inputting the absolute mileage information of each hundred-meter mark and kilometer mark to calibrate the relative mileage of the mileage centerline of the track; (4) Obtaining the GNSS position information of the point based on the three-dimensional coordinates and the POS data; S43, automatic sleeper identification, including: (1) Detect information related to sleepers and fasteners. First, filter out the point cloud near the rail head based on the highest point, and only retain the point cloud of the sleeper fasteners. (2) Set the line baseline height h and set any point in the point cloud as pt(x,y); (3) Traverse the point cloud frame by frame starting from the starting frame of the structured light original point cloud data shown; (4) Count the number of point clouds nPt that satisfy the following formula (1) within the range where the fastener is located; if nPt is greater than the set threshold, the frame is a frame where the suspected fastener is located; （1）； In formula (1), x1, x2 (x1>x2) are the x-coordinates of the set fastener area; h is the line baseline height; (5) Starting from the frame where the suspected fastener is found, count whether the condition of nPt being greater than the set threshold is met within 50 mm after the frame where the suspected fastener is found. If so, the frame is the sleeper starting frame F1; (6) Starting from the start frame of the current sleeper, assuming that the first frame position that does not meet the condition is the end position of the sleeper, starting from this position, if all subsequent frames within 100 mm do not meet the condition, then this frame is the end position F2 of the current sleeper, then the sleeper start frame F1 and the end position F2 of the current sleeper are the start frame and end frame of the current sleeper, obtain the start frame time T1 and the end frame time T2, find the latest POS coordinate data P1 and P2 from the POS file according to the time, project the latest POS coordinate data P1 and P2 onto the mileage center line of the track, obtain the start mileage M1 and the end mileage M2, and at the same time, obtain the GNSS position information from the latest POS coordinate data P1 and P2 according to the start frame time T1 and the end frame time T2; (7) Starting from the end frame F2, continue to traverse frame by frame in the order of (3)-(6), record the start and end frames that meet the conditions, and set them as the sleeper positions until the end position of the point cloud; S44, obtaining data related to multiple contact network poles, including: (1) extracting the center point of each contact network pole from the three-dimensional laser point cloud by manual interaction; (2) calculating relative mileage calibration data and three-dimensional coordinate data of each contact network pole based on the center point of each contact network pole; (3) Obtaining absolute mileage calibration data based on the relative mileage calibration data; (4) Obtain the GNSS position information of the point based on the three-dimensional coordinates and POS data; S45, preparing a ledger, including: preparing full-line ledger data based on the relative mileage calibration data, the absolute mileage calibration data, the sleeper data and the contact network pole data, associating the on-site mileage, contact network poles, sleepers and GNSS position information, and locating the existing line detection data based on the on-site mileage, contact network poles, sleepers and GNSS position information.

2. The method for comprehensive analysis of existing line status based on a three-dimensional mobile scanning system according to claim 1, characterized in that: The high-precision three-dimensional mobile laser scanning system includes a cross-sectional scanner, a line structured light scanner, a laser inertial navigation or inertial measurement unit, a measurable imaging device, a GNSS board, a synchronization board and an industrial computer; wherein the industrial computer is used to control the cross-sectional scanner, the structured light scanner and the laser inertial navigation or inertial measurement unit to perform fine scanning of the rails and quickly collect data on the entire existing line; the cross-sectional scanner and the line structured light scanner are both used to obtain raw point cloud data; the laser inertial navigation or inertial measurement unit is used to obtain inertial navigation data and GNSS data; and the measurable imaging device is used to obtain encoder mileage data.

3. The method for comprehensive analysis of existing line status based on a three-dimensional mobile scanning system according to claim 2, characterized in that: The linear design adjustment detection includes: horizontal curve design detection, longitudinal curve design detection and linear adjustment detection; the track geometry detection includes: geometric parameter detection of geometric shape, size and spatial position, which is described by the coordinate position of the track feature points, specifically including gauge, height, track direction, superelevation, level and triangular pit, and adopts non-contact measurement inertial reference method to finally generate the three-dimensional point cloud of the track structure for detection; the fastener detection includes: fastener identification, fastener performance detection, the fastener performance includes: spring bar gap value detection, spring bar skew detection and fastener disease detection; the rail surface disease detection includes: rail surface disease detection based on the collected rail line structure laser data; the profile wear detection includes: obtaining the specified spacing according to the specified spacing. Specify the frame number of four structured light raw data, and splice the four structured light data into left and right rail data according to the calibration parameters. Then analyze the left and right rail data separately, filter and smooth the rail top point cloud data based on the Savitzky-Golay smoothing method, segment the single-side rail point cloud into the rail head point cloud and the rail waist and rail bottom point cloud based on threshold segmentation, use the rail waist and rail bottom point cloud as the starting point set P to match the standard point set Q of the designed rail, and use the ICP matching algorithm to automatically calculate the rotation and translation matrix. Based on the calculated translation matrix and rotation matrix, apply the same rotation and translation transformation to the rail head point cloud, match the transformed point cloud with the rail head point cloud of the standard profile, and calculate the total wear = vertical wear + 1 / 2 side wear; The limit detection includes: (1) Importing and loading the track centerline L that requires clearance inspection, including: if it is determined that the clearance inspection is for track pre-design, importing and loading the mileage centerline of the track; if it is determined that the clearance inspection is for track fine design, importing and loading the adjusted track design centerline; (2) Based on the track center line L, divide the L line into equal intervals of 4 mm to obtain a series of points , n is the number of vertices after segmentation; (3) Taking each vertex P as the origin and the next point and the current point as the direction vector, a custom coordinate system is constructed, which is specifically described as the matrix M; (IV) Set the section thickness T, section length D, and section height H. In the custom coordinate system, the bounding box is described as: （13）； Convert all point clouds into a custom coordinate system and filter out the point cloud data within the bounding box; (5) Match the cross-section point cloud data and bounding box data according to the track center to unify the two data coordinate systems; (6) Using the point-in-polygon algorithm, if there are consecutive points falling within the bounding box, the area is considered to be in violation of the limit; The electrical equipment inspection includes: (1) Using manual interaction, obtain the center coordinates P of the key auxiliary settings in the 3D laser point cloud data, calculate the perpendicular intersection point P1 of point P and the mileage centerline, and then obtain the mileage of this point; (2) The plane distance between the center coordinate P of the key auxiliary device and the perpendicular intersection point P1 of the mileage center line is the horizontal distance of the feature point, and the absolute value of the Z coordinate difference between the center coordinate P of the key auxiliary device and the perpendicular intersection point P1 of the mileage center line is the vertical distance of the feature point; (3) Obtain the mileage, horizontal distance and vertical distance of all key auxiliary facilities along the entire inspection route in sequence; The existing line alignment fine design inspection includes: (1) Calculating the track quality index based on the track geometry test data, determining which track sections on the current line require design and maintenance in accordance with regulatory requirements, and designing only those sections that require design adjustments; (2) Design the track that needs design maintenance, import the horizontal curve data and vertical curve data adjusted according to the design, recalculate the horizontal distance and vertical distance of the electrical equipment, find and record the situation where the horizontal distance and vertical distance of the electrical equipment exceed the design value, and then update the horizontal curve and vertical curve of the current location of the electrical equipment that exceeds the limit; (3) Perform limit detection on the test data according to the limit detection method, find out the current limit violation position, and if there is no limit violation, there is no need to adjust the design line shape; if there is a limit violation at the top, update the horizontal curve after adjustment; if there is a limit violation on the left or right, update the adjusted longitudinal curve; (4) Performing steps (1) to (3) on each track section to obtain the final design adjustment data for the entire line; The outputting of the comprehensive inspection map of the existing line and the comprehensive evaluation of the rail status based on the multiple inspection segments includes: (1) Based on the calculation, the ledger data, track design adjustment data, and various detection and defect data are obtained; the track design adjustment data and various detection and defect data include fastener detection and defect data, rail surface detection and defect data, profile wear abnormality data, limit intrusion data, and electrical equipment data; (2) Vectorize the line design adjustment data and various detection disease data, and overlay the vectorized data with the point cloud data for management; (3) The entire line data is divided into sections every 2.4 km and the station area is mapped separately; (IV) Each segmented data set is scored and managed according to each type of detected disease data, and ultimately a status score for each 2.4km of existing line data is obtained to guide maintenance management.

4. A comprehensive analysis system for existing line status based on a three-dimensional mobile scanning system, used to implement the method according to any one of claims 1 to 3, characterized in that: include: A data rapid acquisition module (101) is used for performing fine scanning of rails and rapid acquisition of data on the entire existing line based on a high-precision three-dimensional mobile laser scanning system; A field data collection module (102) is used to collect field data by field collection; A fusion registration and solution module (103) is used to obtain rail local point cloud data and full-section large-scene point cloud results based on data preprocessing and fusion registration and solution of the original point cloud data, inertial navigation data, GNSS data and encoder mileage data; A mileage calibration ledger establishment module (104) is used to establish a line-wide mileage calibration ledger based on the rail local point cloud data and the full-section large-scene point cloud results, wherein the ledger information contained in the line-wide mileage calibration ledger includes one or more of mileage, sleepers, contact network poles, and GNSS positions; The inspection and evaluation module (105) is used to perform multiple inspections based on the mileage calibration ledger of the entire line and the scan data, and output the existing line comprehensive inspection map and comprehensive evaluation of the rail status based on the multiple inspections; the multiple inspections include alignment design adjustment inspection, track geometry inspection, fastener inspection, rail surface disease inspection, profile wear inspection, limit inspection, electrical equipment inspection and existing line alignment fine design inspection.

5. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method according to any one of claims 1 to 3.

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