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

Abstract

The invention discloses an existing line state comprehensive analysis method based on a three-dimensional mobile scanning system. The method comprises the steps that fine scanning is conducted on a steel rail based on a high-precision three-dimensional mobile laser scanning system, and whole-line data of an existing line are rapidly collected; collecting field data; local point cloud data of the steel rail and a point cloud result of a full-section large scene are obtained based on preprocessing and fusion registration calculation of original point cloud data, inertial navigation data, GNSS data and encoder mileage data; establishing a whole line mileage calibration ledger based on the point cloud result; performing multi-term detection based on the whole line mileage calibration ledger and the scanning data, and outputting an existing line comprehensive detection graph and comprehensively evaluating the steel rail state in a segmented manner; the multiple detection items comprise linear design adjustment detection, track geometric state detection, fastener detection, track surface disease detection, profile abrasion detection, limit detection, industrial and electrical equipment detection and existing line linear fine design detection. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

Description

Technical Field

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

[0002] By the end of 2023, my country's railway operating mileage will reach 159,000 kilometers, of which high-speed railways will reach 45,000 kilometers. With the increase in the operating time and mileage of existing lines, the defects of line equipment have become more serious and diverse, posing a huge threat to the safety and continuous operation of trains. A good track geometry service state is a prerequisite for ensuring the safe operation of trains. At present, the railway department mainly uses manual visual inspection, track gauge, track inspection / measuring instrument and other methods to obtain track geometry. Manual inspection methods are inefficient and have poor accuracy, and line equipment inspection work still faces huge challenges.

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

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

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

[0006] a: Alignment design of existing lines. The current mainstream equipment for this inspection 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 attached 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 detect rail surface damage and diseases and calculate their depth and other information).

[0008] c: Fastener defect detection: The current mainstream detection technologies or equipment include linear 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 station, ground station laser scanner, 3D mobile scanning system and other equipment

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

[0013] 2. Objective shortcomings of the prior art:

[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 use one set of equipment to complete the comprehensive status assessment and testing of the entire existing line, 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 construct a comprehensive analysis method and system for the status of existing lines based on a three-dimensional mobile scanning system in response to the defects of the prior art. A track detection and measurement platform equipped with a high-precision three-dimensional mobile laser scanning system 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 positions); using this method, the scanning data of all sections of the roadbed, bridges, and tunnels of the line can be detected at one time, and the status of the detection line can be comprehensively evaluated; 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, a fine design of existing lines can be performed; 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 comprehensive analysis method of existing line status based on a three-dimensional mobile scanning system, comprising:

[0021] S1, based on the high-precision 3D mobile laser scanning system, conducts detailed scanning of rails and quickly collects data on the entire existing line;

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

[0023] S3, obtaining the local point cloud data of the rail and the point cloud results of the whole section large 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;

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

[0025] S5, performing multiple tests based on the line mileage calibration ledger and scanning data, and outputting the existing line comprehensive test map and comprehensive assessment of rail status in segments based on the multiple tests; the multiple tests include linear design adjustment test, track geometry test, fastener test, rail surface disease test, profile wear test, limit test, electrical and mechanical equipment test, and existing line fine design test.

[0026] Preferably, the S1 comprises:

[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 full-line data of the existing line is quickly collected; wherein the full-line data of the existing line includes the fine point cloud data of the track and the full-section spatiotemporal point cloud data 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 to quickly collect data of 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, S2 includes:

[0031] S21, conducting field survey and on-site data collection to collect field data; the collected field data includes encoder data, GNSS data, inertial navigation data, comprehensive cross-section laser point cloud data, and rail structure 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, S3 includes:

[0035] S31, based on the data preprocessing subsystem of the vehicle-mounted laser radar 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 the 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 geographical location at the exposure point; the IMU data mainly includes three data of 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, S4 includes:

[0038] S41, establish mileage centre lines, including:

[0039] (1) Acquiring 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 calibration parameters, and record the current frame GNSS time as T;

[0041] (3) Filter the left and right rails, count 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) According to the X coordinate of the inner gauge point and the current rail type width D, the highest point of the left rail in the area [-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 area [-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 a 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 100-meter mark and kilometer mark in the 3D laser point cloud;

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

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

[0049] (4) obtaining the GNSS position information of the point according to 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 according to 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 that nPt is greater than the set threshold is satisfied within 50 mm after the frame where the suspected fastener is found. If so, the frame is the sleeper start 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, and project the latest POS coordinate data P1 and P2 onto the mileage center line of the track to 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 multiple contact network pole related data, including:

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

[0062] (2) calculating the 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 according to 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 locating 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: 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 pits, 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 Get four structured light raw data of the specified frame number, 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 respectively, filter and smooth the point cloud data of the top of the rail 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 the 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, use the ICP matching algorithm to automatically calculate the rotation and translation matrix, apply the same rotation and translation transformation to the point cloud of the rail head part according to the calculated translation matrix and rotation matrix, 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;

[0067] The limit detection includes:

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

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

[0070] (iii) 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] (V) 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] (vi) Through the point-in-polygon algorithm, if there are continuous points falling within the bounding box, then the place is considered to be in violation of the limit;

[0078] The electrical equipment inspection includes:

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

[0080] (ii) the plane distance between the center coordinate P of the key auxiliary setting and the vertical 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 setting and the vertical 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 of the entire inspection line in sequence;

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

[0083] (1) Based on the detection data of the track geometry detection, the track quality index is calculated, and according to the specification requirements, it is determined whether a section of the track on the current line needs to be designed and maintained, and only the track that needs to be designed and adjusted is designed;

[0084] (ii) Design the track that needs to be designed and maintained, 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 simultaneously update the horizontal curve and longitudinal curve of the current location of the electrical equipment that exceeds the limit;

[0085] (iii) According to the limit detection method, the detection data is tested for limit, and the current limit violation position is found. 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, the horizontal curve is updated and adjusted; if there is a limit violation on the left or right, the adjusted longitudinal curve is updated;

[0086] (iv) performing steps (i) to (iii) 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 comprises:

[0088] (i) based on the calculation, the account data, the line design adjustment data and various detection and disease data are obtained; the line design adjustment data and various detection and disease data include fastener detection and disease data, rail surface detection and disease data, profile wear abnormality data, limit intrusion data and electrical equipment data;

[0089] (ii) Vectorize the line design adjustment data and various detection disease data, and manage the vectorized data by superimposing it with the point cloud data;

[0090] (III) The whole line data is divided into sections every 2.4 km and the station area is mapped separately;

[0091] (IV) 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] The second aspect of the present invention is to provide an existing line state comprehensive analysis system 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), used for collecting field data by field collection;

[0095] A fusion registration and solution module (103) is used to obtain local point cloud data of the rail and point cloud results of the entire cross-section large scene 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 account establishment module (104) is used to establish a full-line mileage calibration account based on the local point cloud data of the rail and the point cloud results of the full-section large scene, wherein the account information contained in the full-line mileage calibration account 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 scanning data, and output the existing line comprehensive detection map and comprehensively evaluate the rail status based on the multiple detections; the multiple detections include linear design adjustment detection, track geometry detection, fastener detection, rail surface disease detection, profile wear detection, limit detection, electrical equipment detection and existing line linear 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 and execute the method described in the first aspect.

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

[0101] The present method and system propose a comprehensive analysis method of existing line status based on a three-dimensional laser scanning system, which mainly includes:

[0102] 1. The present invention designs a track detection and measurement platform equipped with a high-precision mobile three-dimensional 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 position).

[0104] 3. This method can be used to scan the 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 linear 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 work is required, and the field operation scanning efficiency is high (more than 20km / skylight);

[0110] 2) High degree of restoration of three-dimensional scenes. 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) Automation of the extraction process. Using efficient and high-precision roadbed section point cloud fusion, point cloud classification, point cloud matching, feature extraction, linear design, automatic fastener identification and detection, automatic measurement of track geometry, automatic calculation of profile wear and other algorithms, the existing line detection parameters can be efficiently obtained;

[0112] 4) High precision of results. Use high-precision and high-resolution point clouds to calculate and output safe and reliable comprehensive inspection results of existing lines, comprehensively evaluate the status of existing lines, and guide line maintenance operations.

[0113] 5) The detection scene 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 the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. 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 creative work.

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

[0116] Figure 2 A schematic diagram of the structure 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 finally generated by adopting a non-contact measurement inertial reference method according to an embodiment of the present invention;

[0121] Figure 7A schematic 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-height 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 type 7 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] Fig. 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] Fig.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 described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0127] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0128] This embodiment uses the track detection and measurement platform of the 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 the test results, the line design data is finely adjusted and designed, the status of every 2.4km of existing lines is comprehensively evaluated, and the comprehensive evaluation status drawings of the existing lines are output to guide the fine maintenance management of the existing lines.

[0129] Embodiment 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 the high-precision 3D mobile laser scanning system, conducts detailed scanning of rails and quickly 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 full-line data of the existing line is quickly collected; wherein the full-line data of the existing line includes the fine point cloud data of the track and the full-section spatiotemporal point cloud data 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 of 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 (IMU) is used to obtain inertial navigation data and GNSS data; the measurable image (DMI) device is used to obtain encoder mileage data. Inertial measurement unit (IMU) is a device for measuring the three-axis attitude angle and acceleration of an object; Depth Measurable Image (DMI) is an emerging ground stereo image information product that contains absolute exterior orientation element information in a time-space sequence. It can support direct browsing of the real environment, relative measurement of target height, width, area and other information, as well as absolute position analysis measurement and target attribute information mining.

[0136] The three-dimensional mobile scanning is performed by using a series of sensors, which can simultaneously scan high-density and high-precision rail point cloud data and 360-degree full-section spatiotemporal point cloud data of existing lines. The high-precision three-dimensional mobile laser scanning system can be loaded onto a trolley or an electric vehicle platform for operation, with an operating speed of 0-15Km / h.

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

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

[0139] S21, conducting field survey and on-site data collection to collect field data; the collected field data includes encoder data, GNSS data, inertial navigation data, comprehensive cross-section laser point cloud data, and rail structure 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 the local point cloud data of the rail and the point cloud results of the whole section large 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;

[0143] As a preferred implementation, 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, namely, the external orientation elements in oblique photogrammetry: (latitude, longitude, elevation, heading angle (Phi), pitch angle (Omega) and roll angle (Kappa)), wherein the GNSS data is represented by X, Y, and Z, representing the geographical location at the moment of exposure; the 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 account based on the local point cloud data of the rail and the point cloud results of the full-section large scene, wherein the account information contained in the full-line mileage calibration account includes one or more of mileage, sleepers, contact network poles and GNSS positions;

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

[0148] S41, establish mileage centre lines, including:

[0149] (1) Acquiring 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 calibration parameters, and record the current frame GNSS time as T;

[0151] (3) Filter the left and right rails, count 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) According to the X coordinate of the inner gauge point and the current rail type width D, the highest point of the left rail in the area [-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 area [-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 a 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 during 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 100-meter mark and kilometer mark in the 3D laser point cloud;

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

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

[0159] (4) Obtaining 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 that nPt is greater than the set threshold is satisfied 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, and project the latest POS coordinate data P1 and P2 onto the mileage center line of the track to 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 multiple contact network pole related data, including:

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

[0172] (2) calculating the 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 according to the relative mileage calibration data;

[0174] (4) Obtain the GNSS position information of the point based on the 3D 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, performing multiple tests based on the mileage calibration ledger of the entire line and the scanning data, and outputting the comprehensive test map of the existing line and comprehensively evaluating the rail status in sections based on the multiple tests; the multiple tests include linear design adjustment test, track geometry test, fastener test, rail surface disease test, profile wear test, limit test, electrical equipment test and existing line fine design test;

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

[0178] (I) 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. Calculate the azimuth of the vertex of the track centerline by using the measured vertex coordinates of the track centerline;

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

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

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

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

[0185] Assume 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 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 orbital center points in the interval, the point values ​​can be calculated to calculate the four parameters a, b, c, and d.

[0188] (II) 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 boundary 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 center lines are converted into longitudinal section points of the coordinate system.

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

[0192] 3. Since measurement errors will 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 description parameters of the two alignments, and obtain the longitudinal section alignment representation of the track, which is the fitted longitudinal curve.

[0195] (III) 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, and the deviation is adjusted in combination with manual experience to obtain the final adjusted designed longitudinal curve.

[0197] 2. For the flat curve, the difference between the measured center plane point and the fitted flat curve point at each mileage is calculated based on the measured track center plane line and the fitted flat curve. On the basis of ensuring the smoothness of the flat curve of the entire track, manual review and adjustment are performed on the local part to obtain the final adjusted designed flat curve.

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

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

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

[0201] The track gauge refers to the shortest distance between two rails at a point 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 using the 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 direction refers to the lateral unevenness of the inner side of the rail along the length direction. The lateral unevenness is divided into left and right rails, and is usually inconsistent. The average value of the directional unevenness of the left and right rails is used as the directional deviation value of the track centerline. The existence of directional unevenness will increase the lateral force on the wheel, causing lateral vibration and left and right swing, and accelerating component damage. The horizontal coordinates at the gauge point can be directly extracted to directly calculate the track direction unevenness. Figure 7 The calculation formula for the track irregularity a at B1 in (a) is as follows:

[0205]

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

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

[0208]

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

[0210] Track level refers to the elevation difference between the tops of the left and right rails on the same cross section of the track relative to the horizontal plane, excluding the superelevation set on the circular curve and the superelevation along the slope on the transition curve. 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 the curve section, and its calculation formula is formula (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] (I) Fastener identification

[0215] like Figure 8 As shown, the 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] (ii) Fastener performance testing, including:

[0219] 1. Detection of the 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 gap value t of the fastener 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 outer line of 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 the collected rail line structure laser data, including:

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

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

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

[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 damage 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 convolutional neural network (CNN) effectively extracts image features through a 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 on the input features. Represents the bias term.

[0236] Scratches on rails usually manifest as localized surface wear and irregular texture changes. The deep learning model captures these subtle but critical feature differences by learning from a large number of images containing scratches. For example, the scratched area may differ from the normal area in color brightness, or there may be obvious 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 the diversity of data through data enhancement (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 enhancement, if the original image is I, by randomly rotating the image by an angle θ, a new image I can be obtained. 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] (i) Obtain four structured light raw data of specified frame numbers at a specified interval, splice the four structured light data into left and right rail data according to calibration parameters, and then analyze the left and right rail data respectively.

[0242] (ii) Filtering and smoothing the rail top point cloud data based on the Savitzky-Golay smoothing method (SG smoothing for short). The Savitzky-Golay smoothing method is a smoothing method based on local polynomial fitting. i ,y i )|i=1,...,n}, take 2p+1 consecutive points each time, and use formula (8) to solve the smoothing result (x′) of the center point 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] Take the partial derivative of the residual formula (10) and let b jk=k j ,B={b jk}, and then 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] (III) Based on threshold segmentation, the single-side rail point cloud is segmented into rail head point cloud and rail waist and rail bottom point cloud. The rail waist and rail bottom point cloud is used as the starting point set P to match the standard point set Q of the designed rail, and the ICP matching algorithm is used to automatically calculate the rotation and translation matrix. According to the calculated translation matrix and rotation matrix, 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] (iv) Calculation of wear and tear, 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 implementation, the limit detection includes:

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

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

[0260] (iii) 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] (V) Match the cross-section point cloud data and the bounding box (standard bounding box defined by the specification) data according to the track center to unify the two data coordinate systems;

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

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

[0269] (i) Using manual interaction, obtain the center coordinates P of the key auxiliary settings in the three-dimensional laser point cloud data, calculate the vertical intersection point P1 of point P and the mileage centerline, and then obtain the mileage of the point.

[0270] (ii) the plane distance between the center coordinate P of the key auxiliary setting and the vertical 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 setting and the vertical intersection point P1 of the mileage center line is the vertical distance of the feature point;

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

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

[0273] The design of existing lines is generally carried out directly using the track centerline data collected by the equipment, which completely relies on the optimal mathematical model or relies on the ledger to define the straight and curved segments. This completely fails to achieve the purpose of fine design and maintenance. This method can be used to carry out detailed design of the existing line shape based on the track geometry status 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) based on the detection data of the track geometry detection, calculating the track quality index (TQI), judging which section of the track on the current line needs to be designed and maintained according to the specification requirements, and only designing the track that needs to be designed and adjusted;

[0275] (ii) Design the track that needs to be designed and maintained, 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 simultaneously update the horizontal curve and longitudinal curve of the current location of the electrical equipment that exceeds the limit;

[0276] (iii) According to the limit detection method, the detection data is tested for limit, and the current limit violation position is found. 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, the horizontal curve is updated and adjusted; if there is a limit violation on the left or right, the adjusted longitudinal curve is updated;

[0277] (iv) Performing steps (i) to (iii) on each track section to obtain final design adjustment data for the entire line.

[0278] As a preferred implementation, the output of the comprehensive inspection map of the existing line and the comprehensive evaluation of the rail status based on the multiple inspection segments include an overall evaluation of the operating status of the existing line based on the track geometry inspection, fastener inspection, rail surface inspection, rail profile inspection, limit inspection and electrical equipment inspection data, and guides the subsequent refined maintenance management. The specific steps are as follows:

[0279] (i) based on the calculation, the account data, the line design adjustment data and various detection and disease data are obtained; the line design adjustment data and various detection and disease data include fastener detection and disease data, rail surface detection and disease data, profile wear abnormality data, limit intrusion data and electrical equipment data;

[0280] (ii) Vectorize the line design adjustment data and various detection disease data, and manage the vectorized data by superimposing it with the point cloud data;

[0281] (III) The whole line data is divided into sections every 2.4 km and the station area is mapped separately.

[0282] (IV) 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] Embodiment 2

[0284] like Fig. 9 As shown, the present embodiment provides a comprehensive analysis system of 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 fine scanning of the rails and rapid acquisition of data of 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 solving module 103 is used to obtain the local point cloud data of the rail and the point cloud results of the whole cross-section large scene based on the data preprocessing and fusion registration and solving of the original point cloud data, inertial navigation data, GNSS data and encoder mileage data;

[0288] The mileage calibration account establishment module 104 is used to establish a mileage calibration account for the entire line based on the local point cloud data of the rail and the point cloud results of the full-section large scene, and the account information contained in the mileage calibration account for the entire line includes one or more of mileage, sleepers, contact network poles and GNSS positions;

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

[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 Fig.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 a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the method as in the first embodiment.

[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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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 of existing line status based on a three-dimensional mobile scanning system, characterized in that: include: S1, based on the high-precision 3D mobile laser scanning system, conducts detailed scanning of rails and quickly collects data on the entire existing line; S2, collecting field data through field collection; S3, obtaining the local point cloud data of the rail and the point cloud results of the whole section large 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; S4, establishing a full-line mileage calibration account based on the local point cloud data of the rail and the point cloud results of the full-section large scene, wherein the account information contained in the full-line mileage calibration account includes one or more of mileage, sleepers, contact network poles and GNSS positions; S5, performing multiple tests based on the line mileage calibration ledger and scanning data, and outputting the existing line comprehensive test map and comprehensive assessment of rail status in segments based on the multiple tests; the multiple tests include linear design adjustment test, track geometry test, fastener test, rail surface disease test, profile wear test, limit test, electrical and mechanical equipment test, and existing line fine design test.

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 S1 includes: S11, establish a track detection and measurement platform based on a high-precision 3D mobile laser scanning system; S12, based on the track detection and measurement platform, the rails are finely scanned and the full-line data of the existing line is quickly collected; wherein the full-line data of the existing line includes the fine point cloud data of the track and the full-section spatiotemporal point cloud data along the railway.

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 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 to quickly collect data of 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; the measurable imaging device is used to obtain encoder mileage data.

4. The method for comprehensive analysis of existing line status based on a three-dimensional mobile scanning system according to claim 3, characterized in that: The S2 includes: S21, conducting field survey and on-site data collection to collect field data; the collected field data includes encoder data, GNSS data, inertial navigation data, comprehensive cross-section laser point cloud data, and rail structure 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.

5. The method for comprehensive analysis of existing line status based on a three-dimensional mobile scanning system according to claim 4, characterized in that: The S3 includes: S31, based on the data preprocessing subsystem of the vehicle-mounted laser radar 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 the 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 geographical location at the exposure point; the IMU data mainly includes three data of heading angle, pitch angle and roll angle; 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.

6. The method for comprehensive analysis of existing line status based on a three-dimensional mobile scanning system according to claim 5, characterized in that: The S4 includes: S41, establish mileage centre lines, including: (1) Acquiring 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; (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 calibration parameters, and record the current frame GNSS time as T; (3) Filter the left and right rails, count 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 type width D, the highest point of the left rail in the area [-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 area [-1mm, 1mm] at the X+1 / 2D position is taken as the center of the right rail; (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 l, construct a 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 where the X coordinate of the inner track gauge point is located 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; (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) Using manual interaction, extract each 100-meter mark and kilometer mark in the 3D laser point cloud; (2) calculating relative mileage information and three-dimensional coordinate data based on each of the hundred-meter mark and kilometer mark; (3) inputting the absolute mileage information of each of the hundred-meter and kilometer markers to calibrate the relative mileage of the mileage centerline of the track; (4) obtaining GNSS position information of the point according to the three-dimensional coordinates and the POS data; S43, automatic sleeper identification, including: (1) Detect the relevant information of the 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 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; 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 that nPt is greater than the set threshold is satisfied within 50 mm after the frame where the suspected fastener is found. If so, the frame is the sleeper start 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, and project the latest POS coordinate data P1 and P2 onto the mileage center line of the track to 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 multiple contact network pole related data, including: (1) extracting the center point of each contact network pole in the three-dimensional laser point cloud by manual interaction; (2) calculating the 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 according to the relative mileage calibration data; (4) Obtaining 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.

7. The method for comprehensive analysis of existing line status based on a three-dimensional mobile scanning system according to claim 6, 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 pits, 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 and fastener performance detection, and 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, 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 respectively, filter and smooth the rail top point cloud data based on the Savitzky-Golay smoothing method, segment the single-side rail point cloud into rail head point cloud and 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, use the ICP matching algorithm to automatically calculate the rotation and translation matrix, apply the same rotation and translation transformation to the rail head point cloud according to the calculated translation matrix and rotation matrix, 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: (i) importing and loading the track center line L that needs to be checked for clearance, including: if it is determined that the clearance check is before track design, importing and loading the mileage center line of the track; if it is determined that the clearance check is for track fine design, importing and loading the adjusted track design center line; (ii) Based on the track center line L, the L line is divided at equal intervals of 4 mm to obtain a series of points P i (x i ,y i ,z i )i∈[0,n), where n is the number of vertices after segmentation; (iii) 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 follows: x∈[-D-0.3,-D+0.1] y∈[-T / 2.0,T / 2.0] z∈[-H-0.5,-H+0.5] (13); Convert all point clouds into a custom coordinate system and filter out the point cloud data within the bounding box; (V) Match the cross-section point cloud data and the bounding box data according to the track center to unify the two data coordinate systems; (vi) Through the point-in-polygon algorithm, if there are continuous points falling within the bounding box, then the place is considered to be in violation of the limit; The electrical equipment inspection includes: (i) Using manual interaction, obtain the center coordinates P of the key auxiliary settings in the three-dimensional laser point cloud data, calculate the vertical intersection point P1 of point P and the mileage centerline, and then obtain the mileage of the point; (ii) the plane distance between the center coordinate P of the key auxiliary setting and the vertical 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 setting and the vertical 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 of the entire inspection line in sequence; The existing line alignment fine design inspection includes: (1) Based on the detection data of the track geometry detection, the track quality index is calculated, and according to the specification requirements, it is determined whether a section of the track on the current line needs to be designed and maintained, and only the track that needs to be designed and adjusted is designed; (ii) Design the track that needs to be designed and maintained, 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 simultaneously update the horizontal curve and longitudinal curve of the current location of the electrical equipment that exceeds the limit; (iii) According to the limit detection method, the detection data is tested for limit, and the current limit violation position is found. 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, the horizontal curve is updated and adjusted; if there is a limit violation on the left or right, the adjusted longitudinal curve is updated; (iv) performing steps (i) to (iii) on each track section to obtain 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 comprises: (i) based on the calculation, the account data, the line design adjustment data and various detection and disease data are obtained; the line design adjustment data and various detection and disease data include fastener detection and disease data, rail surface detection and disease data, profile wear abnormality data, limit intrusion data and electrical equipment data; (ii) Vectorize the line design adjustment data and various detection disease data, and manage the vectorized data by superimposing it with the point cloud data; (III) The whole line data is divided into sections every 2.4 km and the station area is mapped separately; (IV) 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.

8. A comprehensive analysis system for existing line status based on a three-dimensional mobile scanning system, used to implement any method of claims 1-7, characterized in that: include: 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; A field data collection module (102), used for collecting field data by field collection; A fusion registration and solution module (103) is used to obtain local point cloud data of the rail and point cloud results of the entire cross-section large scene 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 account establishment module (104) is used to establish a full-line mileage calibration account based on the local point cloud data of the rail and the point cloud results of the full-section large scene, wherein the account information contained in the full-line mileage calibration account includes one or more of mileage, sleepers, contact network poles and GNSS positions; The detection and evaluation module (105) is used to perform multiple detections based on the mileage calibration ledger of the entire line and the scanning data, and output the existing line comprehensive detection map and comprehensively evaluate the rail status based on the multiple detections; the multiple detections include linear design adjustment detection, track geometry detection, fastener detection, rail surface disease detection, profile wear detection, limit detection, electrical equipment detection and existing line linear fine design detection.

9. 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 used to read the instructions and execute the method according to any one of claims 1 to 7.

10. 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 7.

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