CN116933355A - Method and device for identifying rough difference points in railway line measurement data - Google Patents
Method and device for identifying rough difference points in railway line measurement data Download PDFInfo
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Abstract
A method and apparatus for identifying rough differences in railway line measurement data, the method comprising: according to the measurement data of each measuring point in the measurement data of the railway line, obtaining the plane deviation and the elevation deviation of each measuring point; carrying out Gaussian filter processing on the plane deviation and the elevation deviation of each measuring point respectively, and calculating the worse plane deviation and the worse elevation deviation of each measuring point before and after the filtering; and when the absolute value of the worse plane deviation of the measuring point is larger than the plane recognition threshold value, or when the absolute value of the worse elevation deviation of the measuring point is larger than the elevation recognition threshold value, judging the measuring point as a rough difference point. The method and the device provided by the embodiment of the invention provide a method for identifying the rough difference point in the railway line measurement data, and can realize the rapid and accurate identification of the rough difference point in the line measurement data, thereby providing a guarantee for the subsequent line flat vertical section reconstruction design work.
Description
Technical Field
The invention relates to the technical field of railway engineering, in particular to a method and a device for identifying rough difference points in railway line measurement data.
Background
In recent years, as the operation of the common speed railways in China continuously carries out speed-increasing and energy-expanding work, stricter requirements are put forward on the service quality of the lines. The design parameters of the horizontal and vertical sections of the line are taken as important basis for line maintenance and repair operation of the railway service department, and the rationality and applicability of the design parameters directly influence the quality of the line maintenance operation and the operation safety of the train. However, for most of operation common speed railways in China, because maintenance technical means mainly comprising relatively smooth front and back of the line are mainly adopted in the daily maintenance process, the absolute position of the line is greatly deviated, and the actual flat vertical section parameters of the line are obviously different from the original design parameters. Therefore, in order to further improve the quality of line maintenance operation, the actual condition of the railway line flat vertical section is required to be measured, and the line flat vertical section is subjected to reconstruction design according to the measured data, so that the flat vertical section parameters which meet the standard requirements and are attached to the current actual condition of the line are obtained.
The railway line measurement is used as a precondition for the reconstruction design of the horizontal and vertical section of the line, and three-dimensional coordinates of the line are mainly and rapidly obtained through various technical means, such as satellite positioning, laser positioning, inertial combined measurement, three-dimensional laser scanning and the like. However, the measurement process is affected by satellite signal jump, instrument and equipment rapid vibration, short sensor failure and other factors, so that the data quality of partial measurement points is poor, namely the whole line measurement data contains rough difference points. Because the quality of the measured data directly influences the correctness of the reconstruction design result of the subsequent line flat and longitudinal section, the three-dimensional coordinate data of each measuring point needs to be analyzed in advance, coarse difference points in the measured data are identified and removed, and the normal development of the subsequent reconstruction design work can be ensured.
Disclosure of Invention
In view of the above, the invention provides a method and a device for identifying rough difference points in railway line measurement data, so as to solve the problem of rapid and accurate identification of the rough difference points in the railway line measurement data in the related technology.
In a first aspect, embodiments of the present invention provide a method for identifying rough differences in railway line measurement data, the method comprising: according to the measurement data of each measuring point in the measurement data of the railway line, obtaining the plane deviation and the elevation deviation of each measuring point; carrying out Gaussian filtering treatment on the plane deviation and the elevation deviation of each measuring point respectively, and calculating the worse plane deviation and the worse elevation deviation of each measuring point before and after filtering; and when the absolute value of the worse plane deviation of the measuring point is larger than the plane recognition threshold value, or when the absolute value of the worse elevation deviation of the measuring point is larger than the elevation recognition threshold value, judging the measuring point as a rough difference point.
Further, each measuring point measuring data includes each measuring point plane measuring data and each measuring point elevation measuring data, and plane deviation and elevation deviation of each measuring point are obtained according to each measuring point measuring data in the railway line measuring data, including: obtaining the design mileage and plane deviation of each measuring point according to the plane measurement data of each measuring point; and obtaining the elevation deviation of each measuring point according to the design mileage of each measuring point and the elevation measurement data of each measuring point.
Further, according to the plane measurement data of each measuring point, obtaining the design mileage and plane deviation of each measuring point includes: calculating the plane curvature of each measuring point according to the plane measurement data of each measuring point, and obtaining a plane curvature graph; according to the plane curvature map, carrying out segmentation processing on plane measurement data; carrying out reconstruction design on the segmented plane measurement data by adopting an orthogonal least square fitting method to obtain design plane data; and obtaining the design mileage and plane deviation of each measuring point according to the plane measurement data and the design plane data of each measuring point.
Further, calculating the plane curvature of each measuring point according to the plane measurement data of each measuring point comprises: the plane curvature of each measuring point is obtained by adopting the following steps:
；
wherein ,x i ,y i is the first i Plane coordinate measurement data at the position of the measuring point,iis a positive integer.
Further, the processing of the plane measurement data in a segmentation way comprises: the plane measurement data is divided into straight line segment measurement data, gentle curve segment measurement data and circular curve segment measurement data.
Further, according to the design mileage of each measuring point and the measuring data of the elevation of each measuring point, the method for obtaining the elevation deviation of each measuring point comprises the following steps: calculating the curvature of the longitudinal section of each measuring point according to the design mileage of each measuring point and the height measurement data of each measuring point, and obtaining a curvature graph of the longitudinal section; according to the longitudinal section curvature map, carrying out sectional processing on the elevation measurement data; carrying out reconstruction design on the elevation measurement data after the segmentation processing by adopting an orthogonal least square fitting method to obtain design elevation data; and obtaining the elevation deviation of each measuring point according to the elevation measurement data and the design elevation data of each measuring point.
Further, according to the design mileage of each measuring point and the height measurement data of each measuring point, calculating the curvature of the vertical section of each measuring point comprises the following steps: the curvature of the longitudinal section of each measuring point is obtained by adopting the following steps:
；
wherein ,l i ,h i is the first i Mileage and elevation measurement data are designed at the measuring point positions,iis a positive integer.
Further, the step of processing the elevation measurement data in a segmentation way comprises the following steps: the elevation measurement data is divided into a plurality of slope segment measurement data.
Further, performing gaussian filtering processing on the plane deviation and the elevation deviation of each measuring point, including: and respectively processing the plane deviation and the elevation deviation of each measuring point by adopting the following formula to obtain the plane deviation and the elevation deviation of each measuring point after filtering:
；
wherein ,pc j is the firstjPlane or elevation deviations at the site locations,jis a positive integer;GLis a gaussian filter template.
Further, calculating the worse plane deviation and elevation deviation of each measuring point before and after filtering comprises the following steps: the difference between the plane deviation after filtering and the plane deviation before filtering of each measuring point is calculated to obtain the plane deviation of each measuring point to be poor, and the difference between the elevation deviation after filtering and the elevation deviation before filtering of each measuring point is calculated to obtain the elevation deviation of each measuring point to be poor.
Further, the plane recognition threshold is a plurality of times of standard deviation with poor plane deviation, and the elevation recognition threshold is a plurality of times of standard deviation with poor elevation deviation.
In a second aspect, embodiments of the present invention also provide an apparatus for identifying rough difference points in railway line measurement data, the apparatus comprising: the deviation calculation unit is used for obtaining plane deviation and elevation deviation of each measuring point according to the measuring data of each measuring point in the railway line measuring data; the poor calculation unit is used for carrying out Gaussian filter processing on the plane deviation and the elevation deviation of each measuring point and calculating the poor of the plane deviation and the elevation deviation of each measuring point before and after the filtering; and the identification unit is used for judging the rough difference point when the absolute value of the worse plane deviation of the measuring point is larger than the plane identification threshold value or when the absolute value of the worse elevation deviation of the measuring point is larger than the elevation identification threshold value.
In a third aspect, embodiments of the present invention further provide a computer readable storage medium having a computer program stored thereon, where the program when executed by a processor implements the method provided by the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention further provides an electronic device, including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method provided by the embodiments of the present invention.
According to the method and the device for identifying the rough difference point in the railway line measurement data, the plane deviation and the elevation deviation of each measuring point are obtained according to the measurement data of each measuring point in the railway line measurement data, gaussian filter processing is conducted on the plane deviation and the elevation deviation of each measuring point, the plane deviation and the elevation deviation of each measuring point before and after filtering are calculated, when the plane deviation of each measuring point is poor or the absolute value of the elevation deviation is poor is larger than the identification threshold value, the rough difference point is judged, the rapid and accurate identification of the rough difference point in the line measurement data can be achieved, and accordingly guarantee is provided for the follow-up line flat longitudinal section reconstruction design work.
Drawings
FIG. 1 is a flow chart of a method for identifying rough differential points in railroad line measurement data provided in an exemplary embodiment of the present invention;
FIG. 2 is a schematic diagram of planar curvature calculation and planar measurement data segmentation provided by an exemplary embodiment of the present invention;
FIG. 3 is a schematic illustration of a slice curvature calculation and elevation measurement data segmentation provided in accordance with an exemplary embodiment of the present invention;
FIG. 4 is a schematic diagram of Gaussian filter template generation provided by an exemplary embodiment of the invention;
FIG. 5 is a schematic diagram of a Gaussian filter process according to an exemplary embodiment of the invention;
FIG. 6 is a schematic diagram of a poor bias calculation provided by an exemplary embodiment of the present invention;
FIG. 7 is a schematic diagram of rough difference point identification provided by an exemplary embodiment of the present invention;
FIG. 8 is a schematic diagram of an apparatus for identifying rough points in railroad line measurement data provided in accordance with an exemplary embodiment of the present invention;
fig. 9 is a block diagram of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the examples described herein, which are provided to fully and completely disclose the present invention and fully convey the scope of the invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, like elements/components are referred to by like reference numerals.
Unless otherwise indicated, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, it will be understood that terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 is a flow chart of a method for identifying rough differential points in railroad line measurement data provided in an exemplary embodiment of the invention. The execution subject of the embodiment of the invention is computer equipment. Optionally, the computer device is a terminal, and the terminal is a portable, pocket, hand-held terminal of various types, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, and the like. Optionally, the execution body of the embodiment of the present invention is a server, where the server is an independent physical server, or a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server that provides a basic cloud computing service.
As shown in fig. 1, the method includes:
step S101: and obtaining the plane deviation and the elevation deviation of each measuring point according to the measuring data of each measuring point in the railway line measuring data.
Further, each measuring point measurement data includes each measuring point plane measurement data and each measuring point elevation measurement data, and step S101 includes:
obtaining the design mileage and plane deviation of each measuring point according to the plane measurement data of each measuring point;
and obtaining the elevation deviation of each measuring point according to the design mileage of each measuring point and the elevation measurement data of each measuring point.
Further, according to the plane measurement data of each measuring point, obtaining the design mileage and plane deviation of each measuring point includes:
calculating the plane curvature of each measuring point according to the plane measurement data of each measuring point, and obtaining a plane curvature graph;
according to the plane curvature graph, carrying out segmentation processing on plane measurement data;
carrying out reconstruction design on the segmented plane measurement data by adopting an orthogonal least square fitting method to obtain design plane data;
and obtaining the design mileage and plane deviation of each measuring point according to the plane measurement data and the design plane data of each measuring point.
Further, calculating the plane curvature of each measuring point according to the plane measurement data of each measuring point comprises:
the plane curvature of each measuring point is obtained by adopting the following steps:
；
wherein ,x i ,y i is the first i Plane coordinate measurement data at the position of the measuring point,iis a positive integer.
Further, the processing of the plane measurement data in a segmentation way comprises:
the plane measurement data is divided into straight line segment measurement data, gentle curve segment measurement data and circular curve segment measurement data.
FIG. 2 is a schematic diagram of planar curvature calculation and planar measurement data segmentation according to an exemplary embodiment of the present invention. As shown in fig. 2, a line plane curvature map is calculated according to plane measurement data of each measuring point, the plane measurement data is segmented according to a curvature map result, the line plane curvature map is divided into straight line segment measurement data, gentle curve segment measurement data and circular curve segment measurement data, a line plane is reconstructed and designed according to a plane data segmentation result by adopting an orthogonal least square fitting method, and design mileage and plane deviation of each measuring point are calculated according to line actual measurement plane coordinates and a reconstruction design result. In the plane preliminary reconstruction design process, curve parameters (radius, moderation curve length) can be consistent with the original standing account, and the curve parameters can be properly changed according to the deviation minimum principle without considering plane limiting factors such as bridges, tunnels, contact networks and the like.
Further, according to the design mileage of each measuring point and the measuring data of the elevation of each measuring point, the elevation deviation of each measuring point is obtained, which comprises the following steps:
calculating the curvature of the longitudinal section of each measuring point according to the design mileage of each measuring point and the height measurement data of each measuring point, and obtaining a curvature graph of the longitudinal section;
according to the curvature graph of the longitudinal section, carrying out sectional processing on the elevation measurement data;
carrying out reconstruction design on the elevation measurement data after the segmentation processing by adopting an orthogonal least square fitting method to obtain design elevation data;
and obtaining the elevation deviation of each measuring point according to the elevation measurement data and the design elevation data of each measuring point.
Further, according to the design mileage of each measuring point and the height measurement data of each measuring point, calculating the curvature of the vertical section of each measuring point comprises the following steps:
the curvature of the longitudinal section of each measuring point is obtained by adopting the following steps:
；
wherein ,l i ,h i is the first i Mileage and elevation measurement data are designed at the measuring point positions,iis a positive integer.
Further, the step of processing the elevation measurement data in a segmentation way comprises the following steps:
the elevation measurement data is divided into a plurality of slope segment measurement data.
FIG. 3 is a schematic illustration of a slice curvature calculation and elevation measurement data segmentation, according to an exemplary embodiment of the present invention. As shown in fig. 3, a curvature map of a longitudinal section of the line is calculated according to design mileage and elevation measurement data of each measuring point, the elevation measurement data is segmented according to a curvature map result, and is divided into first slope section measurement data, second slope section measurement data, … and N slope section measurement data, the longitudinal section of the line is reconstructed and designed according to a longitudinal section data segmentation result by adopting an orthogonal least square fitting method, and elevation deviation of each measuring point is calculated according to line implementation elevation coordinates and a longitudinal section reconstruction design result. In the preliminary reconstruction design process of the vertical section, design parameters can be selected according to actual conditions of measured data.
Step S102: and carrying out Gaussian filtering treatment on the plane deviation and the elevation deviation of each measuring point respectively, and calculating the worse plane deviation and the worse elevation deviation of each measuring point before and after filtering.
Further, the Gaussian filtering processing is performed on the plane deviation and the elevation deviation of each measuring point, and the Gaussian filtering processing comprises the following steps:
the plane deviation and the elevation deviation of each measuring point are processed by adopting the following steps to obtain the plane deviation and the elevation deviation of each measuring point after filtering:
；
wherein ,pc j is the firstjPlane or elevation deviations at the site locations,jis a positive integer;GLis a gaussian filter template.
Fig. 4 is a schematic diagram of gaussian filter template generation according to an exemplary embodiment of the present invention. As shown in fig. 4, a gaussian filtering template is calculated according to the one-dimensional gaussian function characteristic, and the formula is as follows:
；
wherein ,q i the weight is calculated by the following formula:
；
rdetermining the size of a Gaussian filtering template for positive integers; preferably, the first and second channels are arranged in a row,rthe value range is 3-5;
σthe smaller the standard deviation is, the larger the center weight of the Gaussian filter template is, and the smaller the surrounding weight is; preferably, the first and second channels are arranged in a row,σ=1。
fig. 5 is a schematic diagram of a gaussian filtering process according to an exemplary embodiment of the present invention. As shown in fig. 5, the plane deviation and the elevation deviation are respectively filtered by using a gaussian filter template, so as to obtain a plane deviation trend line and an elevation deviation trend line.
Further, calculating the worse plane deviation and elevation deviation of each measuring point before and after filtering comprises the following steps:
the difference between the plane deviation after filtering and the plane deviation before filtering of each measuring point is calculated to obtain the plane deviation of each measuring point to be poor, and the difference between the elevation deviation after filtering and the elevation deviation before filtering of each measuring point is calculated to obtain the elevation deviation of each measuring point to be poor.
FIG. 6 is a schematic diagram of a poor bias calculation provided by an exemplary embodiment of the present invention. As shown in fig. 6, the plane deviation and the elevation deviation of each measuring point are obtained by respectively using the following formulas:
；
wherein ,pc j is the first to filterjPlane or elevation deviations at the site locations,is the post-filtering firstjPlane or elevation deviations at the site locations,jis a positive integer.
Step S103: and when the absolute value of the worse plane deviation of the measuring point is larger than the plane recognition threshold value, or when the absolute value of the worse elevation deviation of the measuring point is larger than the elevation recognition threshold value, judging the measuring point as a rough difference point.
Further, the plane recognition threshold is a plurality of times of standard deviation with poor plane deviation, and the elevation recognition threshold is a plurality of times of standard deviation with poor elevation deviation.
The standard deviation with poor plane deviation and the standard deviation with poor elevation deviation are respectively calculated, and the formulas are as follows:
；
wherein ,the average value of the plane deviation is poor, or the average value of the elevation deviation is poor.
To be deviated by worse standard deviationkThe multiple is taken as the rough difference point identification threshold,kmay be an integer or a decimal. Preferably, the first and second channels are arranged in a row,kthe value range is 2-5. Wherein the plane recognition threshold corresponds to a multiplek Plane surface Multiple corresponding to elevation recognition thresholdk Elevation May be the same or different.
Fig. 7 is a schematic diagram of rough difference point recognition according to an exemplary embodiment of the present invention. As shown in fig. 7, when the bit deviation of a certain measuring point is worse and meets the following conditions, the data at the point is judged to be normal, otherwise, the rough difference point is measured and needs to be removed:
。
according to the embodiment, the plane deviation and the elevation deviation of each measuring point are obtained according to the measuring data of each measuring point in the railway line measuring data, gaussian filtering processing is conducted on the plane deviation and the elevation deviation of each measuring point, the plane deviation and the elevation deviation of each measuring point before and after filtering are calculated, when the plane deviation of each measuring point is poor or the absolute value of the elevation deviation is poor is larger than the respective identification threshold value, the rough difference point is judged, the rapid and accurate identification of the rough difference point in the line measuring data can be realized, and therefore guarantee is provided for the follow-up line flat longitudinal section reconstruction design work.
Fig. 8 is a schematic structural view of an apparatus for identifying rough points in railway line measurement data according to an exemplary embodiment of the present invention.
As shown in fig. 8, the apparatus includes:
a deviation calculation unit 801, configured to obtain a plane deviation and an elevation deviation of each measurement point according to measurement data of each measurement point in measurement data of a railway line;
a poor calculating unit 802, configured to perform gaussian filtering processing on the plane deviation and the elevation deviation of each measuring point, and calculate the poor of the plane deviation and the elevation deviation of each measuring point before and after filtering;
and the identifying unit 803 is configured to determine that the difference point is a rough difference point when the absolute value of the worse planar deviation of the measurement point is greater than the planar identifying threshold value or when the absolute value of the worse elevation deviation of the measurement point is greater than the elevation identifying threshold value.
Further, each measuring point measurement data includes each measuring point plane measurement data and each measuring point elevation measurement data, and the deviation calculation unit 801 is further configured to:
obtaining the design mileage and plane deviation of each measuring point according to the plane measurement data of each measuring point;
and obtaining the elevation deviation of each measuring point according to the design mileage of each measuring point and the elevation measurement data of each measuring point.
Further, according to the plane measurement data of each measuring point, obtaining the design mileage and plane deviation of each measuring point includes:
calculating the plane curvature of each measuring point according to the plane measurement data of each measuring point, and obtaining a plane curvature graph;
according to the plane curvature graph, carrying out segmentation processing on plane measurement data;
carrying out reconstruction design on the segmented plane measurement data by adopting an orthogonal least square fitting method to obtain design plane data;
and obtaining the design mileage and plane deviation of each measuring point according to the plane measurement data and the design plane data of each measuring point.
Further, calculating the plane curvature of each measuring point according to the plane measurement data of each measuring point comprises:
the plane curvature of each measuring point is obtained by adopting the following steps:
；
wherein ,x i ,y i is the first i Plane coordinate measurement data at the position of the measuring point,iis a positive integer.
Further, the processing of the plane measurement data in a segmentation way comprises:
the plane measurement data is divided into straight line segment measurement data, gentle curve segment measurement data and circular curve segment measurement data.
Further, according to the design mileage of each measuring point and the measuring data of the elevation of each measuring point, the elevation deviation of each measuring point is obtained, which comprises the following steps:
calculating the curvature of the longitudinal section of each measuring point according to the design mileage of each measuring point and the height measurement data of each measuring point, and obtaining a curvature graph of the longitudinal section;
according to the curvature graph of the longitudinal section, carrying out sectional processing on the elevation measurement data;
carrying out reconstruction design on the elevation measurement data after the segmentation processing by adopting an orthogonal least square fitting method to obtain design elevation data;
and obtaining the elevation deviation of each measuring point according to the elevation measurement data and the design elevation data of each measuring point.
Further, according to the design mileage of each measuring point and the height measurement data of each measuring point, calculating the curvature of the vertical section of each measuring point comprises the following steps:
the curvature of the longitudinal section of each measuring point is obtained by adopting the following steps:
；
wherein ,l i ,h i is the first i Mileage and elevation measurement data are designed at the measuring point positions,iis a positive integer.
Further, the step of processing the elevation measurement data in a segmentation way comprises the following steps:
the elevation measurement data is divided into a plurality of slope segment measurement data.
Further, the Gaussian filtering processing is performed on the plane deviation and the elevation deviation of each measuring point, and the Gaussian filtering processing comprises the following steps:
the plane deviation and the elevation deviation of each measuring point are processed by adopting the following steps to obtain the plane deviation and the elevation deviation of each measuring point after filtering:
；
wherein ,pc j is the firstjPlane or elevation deviations at the site locations,jis a positive integer;GLis a gaussian filter template.
Further, calculating the worse plane deviation and elevation deviation of each measuring point before and after filtering comprises the following steps:
the difference between the plane deviation after filtering and the plane deviation before filtering of each measuring point is calculated to obtain the plane deviation of each measuring point to be poor, and the difference between the elevation deviation after filtering and the elevation deviation before filtering of each measuring point is calculated to obtain the elevation deviation of each measuring point to be poor.
Further, the plane recognition threshold is a plurality of times of the standard deviation of the poor plane deviation, and the elevation recognition threshold is a plurality of times of the standard deviation of the poor elevation deviation.
According to the embodiment, the plane deviation and the elevation deviation of each measuring point are obtained according to the measuring data of each measuring point in the railway line measuring data, gaussian filtering processing is conducted on the plane deviation and the elevation deviation of each measuring point, the plane deviation and the elevation deviation of each measuring point before and after filtering are calculated, when the plane deviation of each measuring point is poor or the absolute value of the elevation deviation is poor is larger than the respective identification threshold value, the rough difference point is judged, the rapid and accurate identification of the rough difference point in the line measuring data can be realized, and therefore guarantee is provided for the follow-up line flat longitudinal section reconstruction design work.
It should be noted that, when the apparatus provided in the foregoing embodiment performs the functions thereof, only the division of the foregoing functional modules is used as an example, in practical application, the foregoing functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to perform all or part of the functions described above. In addition, the apparatus and the method embodiments provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the apparatus and the method embodiments are detailed in the method embodiments and are not repeated herein.
Fig. 9 is a block diagram of an electronic device according to an exemplary embodiment of the present invention. As shown in fig. 9, the electronic device includes one or more processors 910 and memory 920.
The processor 910 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions.
Memory 920 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or nonvolatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 910 to implement the methods for identifying rough differences in railroad line measurement data and/or other desired functions of the software program of the various embodiments of the present invention described above. In one example, the electronic device may further include: an input device 930, and an output device 940, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
In addition, the input device 930 may also include, for example, a keyboard, a mouse, and the like.
The output device 940 may output various information to the outside. The output device 940 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device relevant to the present invention are shown in fig. 9 for simplicity, components such as buses, input/output interfaces, etc. being omitted. In addition, the electronic device may include any other suitable components depending on the particular application.
Exemplary computer program product and computer readable storage medium in addition to the methods and apparatus described above, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the method for identifying rough spots in railway line measurement data according to the various embodiments of the invention described in the "exemplary methods" section of this specification.
The computer program product may write program code for performing operations of embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium, having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps in a method for identifying rough differences in railway line measurement data according to the various embodiments of the present invention described in the "exemplary methods" section of the present specification.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present invention. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the invention is not necessarily limited to practice with the above described specific details.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, apparatuses, devices, systems referred to in the present invention are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
The method and apparatus of the present invention may be implemented in a number of ways. For example, the methods and apparatus of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
It is also noted that in the apparatus, devices and methods of the present invention, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the invention to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (14)
1. A method for identifying rough differential points in railway line measurement data, the method comprising:
according to the measurement data of each measuring point in the measurement data of the railway line, obtaining the plane deviation and the elevation deviation of each measuring point;
carrying out Gaussian filtering treatment on the plane deviation and the elevation deviation of each measuring point respectively, and calculating the worse plane deviation and the worse elevation deviation of each measuring point before and after filtering;
and when the absolute value of the worse plane deviation of the measuring point is larger than the plane recognition threshold value, or when the absolute value of the worse elevation deviation of the measuring point is larger than the elevation recognition threshold value, judging the measuring point as a rough difference point.
2. The method of claim 1, wherein each site measurement data includes each site plane measurement data and each site elevation measurement data, and deriving a plane deviation and an elevation deviation for each site from each site measurement data in the rail line measurement data comprises:
obtaining the design mileage and plane deviation of each measuring point according to the plane measurement data of each measuring point;
and obtaining the elevation deviation of each measuring point according to the design mileage of each measuring point and the elevation measurement data of each measuring point.
3. The method of claim 2, wherein deriving the design mileage and plane deviation for each station from the station plane measurement data comprises:
calculating the plane curvature of each measuring point according to the plane measurement data of each measuring point, and obtaining a plane curvature graph;
according to the plane curvature map, carrying out segmentation processing on plane measurement data;
carrying out reconstruction design on the segmented plane measurement data by adopting an orthogonal least square fitting method to obtain design plane data;
and obtaining the design mileage and plane deviation of each measuring point according to the plane measurement data and the design plane data of each measuring point.
4. A method according to claim 3, wherein calculating the planar curvature of each station from the station planar measurement data comprises:
the plane curvature of each measuring point is obtained by adopting the following steps:
；
wherein ,x i ,y i is the first i Plane coordinate measurement data at the position of the measuring point,iis a positive integer.
5. A method according to claim 3, wherein the segmentation of the planar measurement data comprises:
the plane measurement data is divided into straight line segment measurement data, gentle curve segment measurement data and circular curve segment measurement data.
6. The method of claim 2, wherein obtaining the elevation deviation of each station based on the design mileage of each station and the elevation measurement data of each station comprises:
calculating the curvature of the longitudinal section of each measuring point according to the design mileage of each measuring point and the height measurement data of each measuring point, and obtaining a curvature graph of the longitudinal section;
according to the longitudinal section curvature map, carrying out sectional processing on the elevation measurement data;
carrying out reconstruction design on the elevation measurement data after the segmentation processing by adopting an orthogonal least square fitting method to obtain design elevation data;
and obtaining the elevation deviation of each measuring point according to the elevation measurement data and the design elevation data of each measuring point.
7. The method of claim 6, wherein calculating the curvature of the profile at each station based on the design mileage at each station and the elevation measurement data at each station comprises:
the curvature of the longitudinal section of each measuring point is obtained by adopting the following steps:
；
wherein ,l i ,h i is the first i Mileage and elevation measurement data are designed at the measuring point positions,iis a positive integer.
8. The method of claim 6, wherein segmenting the elevation measurement data comprises:
the elevation measurement data is divided into a plurality of slope segment measurement data.
9. The method according to claim 1, wherein the gaussian filtering of the plane deviation and the elevation deviation of each measuring point comprises:
and respectively processing the plane deviation and the elevation deviation of each measuring point by adopting the following formula to obtain the plane deviation and the elevation deviation of each measuring point after filtering:
；
wherein ,pc j is the firstjPlane or elevation deviations at the site locations,jis a positive integer;GLis a gaussian filter template.
10. The method of claim 1, wherein calculating the worse of the plane deviation and the elevation deviation of each measurement point before and after filtering comprises:
the difference between the plane deviation after filtering and the plane deviation before filtering of each measuring point is calculated to obtain the plane deviation of each measuring point to be poor, and the difference between the elevation deviation after filtering and the elevation deviation before filtering of each measuring point is calculated to obtain the elevation deviation of each measuring point to be poor.
11. The method of claim 1, wherein the planar recognition threshold is a multiple of the standard deviation of poor planar deviation and the elevation recognition threshold is a multiple of the standard deviation of poor elevation deviation.
12. An apparatus for identifying rough differences in railway line measurement data, the apparatus comprising:
the deviation calculation unit is used for obtaining plane deviation and elevation deviation of each measuring point according to the measuring data of each measuring point in the railway line measuring data;
the poor calculation unit is used for carrying out Gaussian filter processing on the plane deviation and the elevation deviation of each measuring point and calculating the poor of the plane deviation and the elevation deviation of each measuring point before and after the filtering;
and the identification unit is used for judging the rough difference point when the absolute value of the worse plane deviation of the measuring point is larger than the plane identification threshold value or when the absolute value of the worse elevation deviation of the measuring point is larger than the elevation identification threshold value.
13. A computer readable storage medium, characterized in that the storage medium stores a computer program for executing the method of any one of claims 1-11.
14. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1-11.
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