CN109492071B - Railway high-precision map data processing method and system - Google Patents
Railway high-precision map data processing method and system Download PDFInfo
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- CN109492071B CN109492071B CN201811338805.1A CN201811338805A CN109492071B CN 109492071 B CN109492071 B CN 109492071B CN 201811338805 A CN201811338805 A CN 201811338805A CN 109492071 B CN109492071 B CN 109492071B
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Abstract
The invention discloses a method and a system for acquiring, cleaning, formatting, marking, verifying, generating and maintaining high-precision map data of a railway. The problem that the existing railway digital map cannot support judgment of refined service requirements and cannot be updated quickly to display the current situation of the latest line and station yard equipment is solved.
Description
Technical Field
The invention relates to the field of electronic maps, in particular to a railway high-precision map data processing method and system.
Background
At present, a common map is combined with field positioning equipment, so that the real-time display and monitoring of equipment coordinates on the map can be realized; however, it is necessary to have a high-precision map for professional service in the railway industry to be able to finally realize a series of fine business requirements in the railway industry, such as:
which line (up or down) the on-site personnel, equipment or train is located on, which station track (inner river station track 3 or inner river station track 4) of the station yard; the mileage point near which the field personnel, equipment or train is located; the field personnel, equipment or train is located near which characteristic point of which switch in the station yard (stock rail joint, switch before switch, straight switch after switch, curved switch after switch);
currently, existing maps for the railway industry fall into several categories:
1. railway lines, station yard equipment and the like in the map are only positioned on the base map, can only be used for displaying the effect and cannot support the judgment of the refined service requirement.
2. The railway lines and the station yard equipment in the map have insufficient precision, and the judgment of refined service requirements cannot be supported.
3. The railway lines and station yard equipment in the map only comprise parts, for example, one station yard may comprise a plurality of tracks, even dozens of tracks, but only 1 and 2 tracks on the map are displayed, and the judgment of the requirement of the fine service cannot be supported.
4. After the actual railway line and station equipment are changed, the railway line and the station equipment are put into operation, such as new line and new station construction; the line and station are transformed, and the map cannot be updated quickly to display the current situation of the latest line and station equipment.
Disclosure of Invention
The invention aims to: the method and the system for processing the high-precision map data of the railway are provided, and the problems that the existing railway digital map cannot support judgment of refined service requirements and cannot be quickly updated to display the current situation of the latest line and station yard equipment are solved.
The technical scheme adopted by the invention is as follows:
a railway high-precision map data processing method comprises the following steps:
s1, collecting geographic coordinate data of a railway site;
s2, processing the geographic coordinate data acquired in S1 to obtain complete high-precision geographic coordinate data;
s3, performing mileage formatting, characteristic point and characteristic area identification on the complete high-precision geographical coordinate data obtained in the S2 to obtain railway high-precision map data;
s4, verifying the high-precision railway map data obtained in the S3;
and S5, continuously maintaining the high-precision railway map data.
Further, the geographic coordinate data includes: latitude, longitude, altitude, and current data accuracy of the location point;
the method for collecting the geographic coordinate data of the railway site in the step S1 comprises the following steps:
s101, installing data acquisition equipment on a train, and acquiring field data by utilizing the running process of the train on a line and in a station yard
S102, mounting the data acquisition equipment on a patrol inspection machine, and acquiring field data by utilizing the process of the machine running on a line and in a station yard
And S103, manually acquiring geographical coordinate data in lines and stations by carrying data acquisition equipment by workers.
Further, the method for processing the collected geographic coordinate data in step S2 includes:
s201, analyzing the collected data: judging which are the geographic data related to the field device and which are the geographic data irrelevant to the field device, and rejecting the geographic data irrelevant to the field device;
s202, cleaning the acquired data: removing repeated or redundant geographic coordinate data caused by stopping or moving back and forth;
and S203, merging the data acquired for many times, and replacing low-precision data with high-precision data.
Further, the method for processing the collected geographic coordinate data in step S2 includes a method for deriving the collected geographic coordinate data, which uses basic data of the railway LKJ for reference, and the method includes:
s204, derivation of a straight line: when a section of high-precision road section is known, geographic coordinate data of other unknown position points on the straight line are deduced according to basic data of the railway LKJ;
s205, derivation of at least two lines having a parallel relationship: knowing a high-precision road section of one of the lines, and deducing geographic coordinate data of an unknown position point on a road section with a parallel relation corresponding to the other line according to the LKJ basic data of the railway;
s206, derivation of a circular curve: when a high-precision road section of one section of circular curve is known, geographic coordinate data of other unknown position points on the circular curve are deduced according to basic data of the railway LKJ;
s207, derivation of an axisymmetric or centrosymmetric curve line: geographic coordinate data of unknown position points on a symmetrical curve road section corresponding to a known curve road section are deduced according to railway LKJ basic data;
s208, derivation of lines and switches with the same geometric shapes: and when the high-precision road section of one of the lines and the turnouts is known, the geographic coordinate data of the unknown position points on the corresponding line and turnout with the same geometry on the other line are deduced according to the LKJ basic data of the railway.
Further, the step S3 is to perform mileage formatting, feature point and feature area identification on the complete high-precision geographical coordinate data obtained in the step S2, and the method for obtaining the high-precision railway map data includes:
s301, mileage formatting: formatting the geographical coordinate data of the line and station tracks into a sequence of equidistant position points, and endowing each position point with a corresponding mileage value according to the definition of basic data of the railway LKJ;
s302, characteristic point identification: marking turnout characteristic points, signal machine characteristic points, insulation joint characteristic points, grade change point characteristic points and passage door characteristic points according to railway LKJ basic data;
s303 feature region identification: according to the LKJ basic data of the railway, a station yard characteristic region, a station platform characteristic region, a main line characteristic region, a station track characteristic region, a turnout characteristic region, a left rail and right rail characteristic region of the line, a road shoulder characteristic region, a tunnel characteristic region, a bridge characteristic region, a culvert and channel characteristic region, a line speed characteristic region, a curve characteristic region, a slope characteristic region, a roadbed characteristic region and a flood control dangerous section characteristic region.
Further, the method for verifying the railway high-precision map data obtained in the step S3 according to the railway LKJ basic data in the step S4 includes the following steps:
s401, length verification: verifying whether an error exceeding a threshold exists between the length of a section of straight line or curve line defined in the railway LKJ basic data and the length of the straight line or curve line in the railway high-precision map data;
s402, curve parameter verification: verifying whether errors exceeding a threshold exist in parameters of a section of curve line defined in basic data of the railway LKJ and parameters of the curve line in high-precision map data of the railway;
s403, mileage verification: verifying whether the mileage of the identification point in the basic data of the railway LKJ and the mileage in the high-precision map data of the railway have errors exceeding a threshold value;
s404, line spacing verification: verifying whether the line spacing of the tracks in the basic data of the LKJ and the line spacing of the corresponding tracks in the high-precision map data of the railway have errors exceeding a threshold value or not;
s405, shortest distance verification: verifying the shortest line distance between adjacent lines or tracks in the high-precision railway map data and whether a place exceeding a threshold exists;
s406, switch verification: verifying whether the error exceeding a threshold exists in the turnout model and the parameter in the LKJ basic data of the railway and the corresponding turnout model and parameter in the high-precision map data of the railway;
s407, feature verification: and verifying whether the error exceeding the threshold exists in the slope characteristic area in the railway LKJ basic data and the corresponding slope characteristic area in the railway high-precision map data.
Further, the method for continuously maintaining the high-precision map data of the railway at step S5 performs step S4 at a fixed cycle, and repeats steps S1 to S4 when the verification fails.
Further, the railway high-precision map data system further comprises a collecting terminal for collecting the geographic coordinate data of the railway site and a processor for executing the steps S1-S5.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. the invention relates to a railway high-precision map data processing method and a railway high-precision map data processing system, which solve the problems that the existing railway digital map cannot support the judgment of refined service requirements and cannot be quickly updated to display the current situation of the latest line and station yard equipment;
2. according to the railway high-precision map data processing method and system, the data acquired on site are combined with the railway LKJ basic data, and the obtained map is high in reliability.
Drawings
The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a schematic flow diagram of the present invention;
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
The present invention will be described in detail with reference to fig. 1.
Example 1
A railway high-precision map data processing method comprises the following steps:
s1, collecting geographic coordinate data of a railway site;
s2, processing the geographic coordinate data acquired in S1 to obtain complete high-precision geographic coordinate data;
s3, performing mileage formatting, characteristic point and characteristic area identification on the complete high-precision geographical coordinate data obtained in the S2 to obtain railway high-precision map data;
s4, verifying the high-precision railway map data obtained in the S3;
and S5, continuously maintaining the high-precision railway map data.
Example 2
The present embodiment is different from embodiment 1 in that the geographic coordinate data includes: latitude, longitude, altitude, and current data accuracy of the location point;
the method for collecting the geographic coordinate data of the railway site in the step S1 comprises the following steps:
s101, installing data acquisition equipment on a train, and acquiring field data by utilizing the running process of the train on a line and in a station yard
S102, mounting the data acquisition equipment on a patrol inspection machine, and acquiring field data by utilizing the process of the machine running on a line and in a station yard
And S103, manually acquiring geographical coordinate data in lines and stations by carrying data acquisition equipment by workers.
Further, the method for processing the collected geographic coordinate data in step S2 includes:
s201, analyzing the collected data: judging which are the geographic data related to the field device and which are the geographic data irrelevant to the field device, and rejecting the geographic data irrelevant to the field device;
s202, cleaning the acquired data: removing repeated or redundant geographic coordinate data caused by stopping or moving back and forth;
and S203, merging the data acquired for many times, and replacing low-precision data with high-precision data.
Further, the method for processing the collected geographic coordinate data in step S2 includes a method for deriving the collected geographic coordinate data, which uses basic data of the railway LKJ for reference, and the method includes:
s204, derivation of a straight line: when a section of high-precision road section is known, geographic coordinate data of other unknown position points on the straight line are deduced according to basic data of the railway LKJ;
s205, derivation of at least two lines having a parallel relationship: knowing a high-precision road section of one of the lines, and deducing geographic coordinate data of an unknown position point on a road section with a parallel relation corresponding to the other line according to the LKJ basic data of the railway;
s206, derivation of a circular curve: when a high-precision road section of one section of circular curve is known, geographic coordinate data of other unknown position points on the circular curve are deduced according to basic data of the railway LKJ;
s207, derivation of an axisymmetric or centrosymmetric curve line: geographic coordinate data of unknown position points on a symmetrical curve road section corresponding to a known curve road section are deduced according to railway LKJ basic data;
s208, derivation of lines and switches with the same geometric shapes: and when the high-precision road section of one of the lines and the turnouts is known, the geographic coordinate data of the unknown position points on the corresponding line and turnout with the same geometry on the other line are deduced according to the LKJ basic data of the railway.
Further, the step S3 is to perform mileage formatting, feature point and feature area identification on the complete high-precision geographical coordinate data obtained in the step S2, and the method for obtaining the high-precision railway map data includes:
s301, mileage formatting: formatting the geographical coordinate data of the line and station tracks into a sequence of equidistant position points, and endowing each position point with a corresponding mileage value according to the definition of basic data of the railway LKJ;
s302, characteristic point identification: marking turnout characteristic points, signal machine characteristic points, insulation joint characteristic points, grade change point characteristic points and passage door characteristic points according to railway LKJ basic data;
s303 feature region identification: according to the LKJ basic data of the railway, a station yard characteristic region, a station platform characteristic region, a main line characteristic region, a station track characteristic region, a turnout characteristic region, a left rail and right rail characteristic region of the line, a road shoulder characteristic region, a tunnel characteristic region, a bridge characteristic region, a culvert and channel characteristic region, a line speed characteristic region, a curve characteristic region, a slope characteristic region, a roadbed characteristic region and a flood control dangerous section characteristic region.
Further, the method for verifying the railway high-precision map data obtained in the step S3 according to the railway LKJ basic data in the step S4 includes the following steps:
s401, length verification: verifying whether an error exceeding a threshold exists between the length of a section of straight line or curve line defined in the railway LKJ basic data and the length of the straight line or curve line in the railway high-precision map data;
s402, curve parameter verification: verifying whether errors exceeding a threshold exist in parameters of a section of curve line defined in basic data of the railway LKJ and parameters of the curve line in high-precision map data of the railway;
s403, mileage verification: verifying whether the mileage of the identification point in the basic data of the railway LKJ and the mileage in the high-precision map data of the railway have errors exceeding a threshold value;
s404, line spacing verification: verifying whether the line spacing of the tracks in the basic data of the LKJ and the line spacing of the corresponding tracks in the high-precision map data of the railway have errors exceeding a threshold value or not;
s405, shortest distance verification: verifying the shortest line distance between adjacent lines or tracks in the high-precision railway map data and whether a place exceeding a threshold exists;
s406, switch verification: verifying whether the error exceeding a threshold exists in the turnout model and the parameter in the LKJ basic data of the railway and the corresponding turnout model and parameter in the high-precision map data of the railway;
s407, feature verification: and verifying whether the error exceeding the threshold exists in the slope characteristic area in the railway LKJ basic data and the corresponding slope characteristic area in the railway high-precision map data.
Further, the method for continuously maintaining the high-precision map data of the railway at step S5 performs step S4 at a fixed cycle, and repeats steps S1 to S4 when the verification fails.
Example 3
A railway high-precision map data system further comprises a collecting terminal for collecting geographic coordinate data of a railway site and a processor for executing steps S1-S5.
Example 4
The embodiment provides a method for acquiring geographic coordinate data of a railway site. Specifically, as shown in fig. 1, the method may include the steps of:
s101: and the on-site data acquisition is carried out by utilizing the running process of the train on the line and in the station yard.
S10101, firstly, selecting satellite positioning equipment with decimeter level, centimeter level or higher accuracy;
s10102, arranging a satellite antenna of the device in an open and unshaded area on the train, such as the position of the top of the train;
s10103, placing the equipment main body in a train as much as possible, and supplying power by self or by the train;
s10104, when the train is about to start to run along the line, starting equipment, continuously capturing geographic coordinate data and automatically storing the geographic coordinate data;
and S10105, after the train stops, closing the equipment, and ending the data acquisition work.
S10106, in addition, the equipment can be set to automatically start to work after being powered on, and automatically stop working after being powered off, so that operation links are reduced.
The captured geographic coordinate data includes: longitude, latitude, altitude, and current location accuracy.
S102: and the process of running the inspection tool on the line and in the station yard is utilized to carry out field data acquisition.
S10201, firstly, selecting satellite positioning equipment with decimeter level, centimeter level or higher accuracy;
s10202, arranging a satellite antenna of the equipment in an open and unshaded area on the equipment, such as the top part right above the equipment;
s10203, when the tool is placed on a line and is about to run along the line, starting the equipment, continuously capturing geographic coordinate data and automatically storing the geographic coordinate data;
and S10204, when the machine finishes working and is moved away from the line, closing the equipment and finishing the data acquisition work.
The captured geographic coordinate data includes: longitude, latitude, altitude, and current location accuracy.
S103: personnel carry data acquisition equipment to manually acquire geographical coordinate data in lines and stations.
S10301, firstly, selecting satellite positioning equipment with decimeter level, centimeter level or higher accuracy;
and S10302, carrying the equipment by people.
S10303, before the personnel start to walk on the line or station equipment, the equipment is started, and description information related to the collected data can be selectively recorded or recorded, such as: an upward K57.600 start curve is achieved.
S10304, the personnel with the equipment start to walk on the line or the station yard equipment, and the satellite antenna is kept in an open and unobstructed area. The device continuously and automatically captures and automatically stores the geographic coordinate data.
And after the personnel finish walking, closing or stopping the equipment and finishing the data acquisition work.
The captured geographic coordinate data includes: longitude, latitude, altitude, and current location accuracy.
S201: and analyzing the collected geographic coordinate data.
And S20101, analyzing and determining the overall situation of the geographic coordinate data acquired by the data acquisition activity.
The collected integral data set can be imported into a certain satellite map, such as Google Earth, and the integral condition of the collected data can be checked.
S20102, distinguishing low-precision data and high-precision data in the data set.
According to the positioning accuracy in the collected geographic coordinate data, the data with the positioning accuracy being a fixed solution is high-accuracy data, and the data with the positioning accuracy not being a fixed solution is low-accuracy data.
And S20103, distinguishing the low-precision road section and the high-precision road section in the data set.
According to the predetermined longest distance between 2 points, such as 50 meters, if at least 1 high-precision position point is included in the longest distance, the distance is considered as a high-precision road section; if the distance does not contain any high-precision position point, the distance is considered as a low-precision road section, and the high-precision position point needs to be collected or supplemented in the road section again.
And traversing the whole data set to obtain all low-precision road sections and high-precision road sections.
And S20104, distinguishing invalid data in the data set.
Two methods of manual differentiation and automatic differentiation may be employed.
The manual distinction is that the lines formed by looking up the data sets one by one in the map are mainly checked, and the lines are obviously not smooth lines, and the railway lines are certainly smooth transition lines, and the lines which are not smooth are probably invalid data.
Automatic differentiation may employ an algorithm that automatically analyzes data in a data set, marks out sections of data that may be invalid, and manually confirms them. Possible algorithms are: calculating an included angle ABC between line segments AB and BC formed by the geographic positions of continuous 3 data points A, B and C one by one, if the angle is smaller than a preset numerical value, such as 100 degrees, considering that the three points A, B and C are not in smooth transition and possibly are invalid data, marking the invalid data, and then sequentially analyzing the points B, C and subsequent points D until the traversal of all data in the data set is completed.
S20105, distinguishing the repeated data in the data set.
When continuous automatic data acquisition is performed, a large amount of repeated data of the position may be recorded at a certain position point due to the static state or slow movement. The duplicate data can be tagged by two methods, manual differentiation and automatic differentiation.
Manual differentiation is distinguished by looking at data points in the data set in the map, manually analyzing and labeling areas where a large number of data points are clustered.
Automatic differentiation may employ an algorithm to automatically analyze data in the dataset, tag out duplicate data segments, and then manually confirm. Possible algorithms are: calculating a line segment AB formed by the geographic positions of 2 continuous data points A and B one by one, if the length of the line segment AB is less than a preset numerical value, such as 0.05 m, considering that the two points A and B are too close to each other, regarding the two points A and B as coincidence, marking the point B as a repetition point, and then checking a point C behind the points A and B; if the length of the AB line segment is larger than a preset value, the points A and B are not repeated points, and the points B and C behind the points B can be continuously checked until the traversal of all data in the data set is completed.
S20106, distinguishing reverse direction data in the data set.
In the data acquisition process, the train, the machine tool and the acquisition personnel may not always keep the walking state in the same direction, so that the finally acquired data are concentrated and a plurality of data in the opposite direction may exist. These reverse direction data need to be marked.
S202: and cleaning the collected data.
And S20201, deleting low-precision data in the collected data set.
And S20202, deleting invalid data in the collected data set.
S20203, deleting repeated data in the collected data set.
And S20204, deleting the reverse direction data in the collected data set.
S203: and merging the collected data.
S20301, evaluating high-precision road sections and low-precision road sections in each data set in a plurality of different acquisition activities for the same line and station yard.
S20302, combining the high-precision road sections in each data set to obtain the only final data set of the same line or station yard, wherein the data set comprises the sum of the high-precision road section data in a plurality of different acquisition activities.
And deducing the collected lines and part of the road sections in the station yard.
S204: derivation of a straight line section:
s20401, a road segment is known to be a straight line segment, and at least 2 high accuracy data points in the straight line segment are known.
S20402, establishing a corresponding rectangular coordinate system or spherical coordinate system according to the known geographical coordinate data of the high-precision data points of the straight line section.
S20403, establishing a linear equation according to the coordinates of the known linear points in the rectangular coordinate system or the spherical coordinate system.
And S20404, deducing other unknown straight line points in the straight line section according to the straight line equation.
And S20405, reversely converting the coordinate of the derived unknown straight-line point in the rectangular coordinate system or the spherical coordinate system into the longitude and the latitude of the geographic coordinate.
S205: derivation of at least two lines with parallel relationship:
s20501, knowing that 2 adjacent lines or tracks are in parallel, knowing that a high-precision road section of one line or track and at least one high-precision data point of the other parallel line or track.
S20502, establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting the high-precision road data point of the known line or track and one high-precision data point of the parallel line or track into a point in the rectangular coordinate system or the spherical coordinate system.
And S20503, traversing the data points of the high-precision road section of the known line or track, and deducing other data points of the parallel line or track one by one according to the known high-precision data points of the parallel line or track.
And S20503, reversely converting the coordinate of the derived parallel line or the derived track in the rectangular coordinate system or the spherical coordinate system into the longitude and the latitude in the geographic coordinate system.
S206: and (3) derivation of a circular curve section:
and S20601, knowing that one section of the route is a circular curve section and knowing that a part of the section is a high-precision section.
S20602, establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting the data points of the high-precision road sections in the known circular curve road sections into points in the rectangular coordinate system or the spherical coordinate system.
And S20603, copying the data points of the unknown circular curve section in the rectangular coordinate system or the spherical coordinate system according to the data points of the known circular curve section.
And S20604, reversely converting the coordinates in the deduced unknown circular curve segment into longitude and latitude in a geographic coordinate system.
S207: derivation of axisymmetric or centrosymmetric curve line:
and S20701, knowing that a section of line is a curve section, knowing that a part of the section of line has high precision, and knowing the position of the curve point of the curve section.
And S20702, establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting the data points of the high-precision road sections in the known curve road sections into points in the rectangular coordinate system or the spherical coordinate system.
And S20703, copying the data points of the known high-precision road section of the curve into the data points of the curve road section on the other side of the curved point in a mirror image mode based on the position of the curved point in a rectangular coordinate system or a spherical coordinate system.
And S20704, reversely converting the coordinates in the derived curve section into longitude and latitude in a geographic coordinate system.
S208: derivation of lines and switches with the same geometry:
and S20801, knowing that the geometries of the 2 lines are the same, knowing the high-precision data set of one line, and knowing at least 2 high-precision known data points of the other line.
S20802, establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting the data points of the high-precision road sections in the known 2 road sections into points in the rectangular coordinate system or the spherical coordinate system.
S20803, in a rectangular coordinate system or a spherical coordinate system, rotating and mirroring the road sections of the known line, and then superposing the road sections of the known line on the known data points of another line.
And S20804, reversely converting the coordinate of the point of the other derived line into longitude and latitude in a geographic coordinate system.
The method for deducing the railway LKJ basic data further comprises the following steps:
a. the railway LKJ basic data corresponding to a road section is known, and certain high-precision known points in the road section are known.
b. And establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting the high-precision data points of the known road section into points in the rectangular coordinate system or the spherical coordinate system.
c. And establishing a mathematical model of the road section in a rectangular coordinate system or a spherical coordinate system according to basic data of the railway LKJ.
d. Coordinates of other data points in the road segment are derived based on the mathematical model of the road segment and the known data points.
e. The coordinates of the derived points are inversely translated into longitude and latitude in a geographic coordinate system.
S3: mileage formatting, feature points, and feature area identification.
S301: and determining the marked mileage points in the line based on the basic data of the railway LKJ.
S30101, a switch point before a switch of a main line switch in the station yard can be selected as a marked mileage point.
S30102, tunnel entrances and exits can be selected as the marked mileage points.
S30103, specially collected marked mileage points during site manual point collection can be selected.
And S30104, according to each 2 marked mileage points, deducing equidistant mileage points between the two lines.
S302: and (3) characteristic point identification:
according to the LKJ basic data of the railway, turnout characteristic points, signal machine characteristic points, insulation joint characteristic points, slope changing point characteristic points, access door characteristic points and the like are marked. :
s303: and (3) characteristic area identification:
according to LKJ basic data of the railway, station yard characteristic areas, station platform characteristic areas, main line characteristic areas, station track characteristic areas, turnout characteristic areas, left rail and right rail characteristic areas of the railway, shoulder characteristic areas, tunnel characteristic areas, bridge characteristic areas, culvert and canal characteristic areas, speed characteristic areas of the railway, curve characteristic areas, slope characteristic areas such as uphill slopes, downhill slopes and flat slopes, roadbed characteristic areas such as embankments, cutting and flood control dangerous area characteristic areas are marked.
S4: and verifying the high-precision map data of the railway.
S401, verifying whether a large error exists between the length of a section of straight line or curve line defined in the railway LKJ basic data and the length of the straight line or curve line in the railway high-precision map data.
S402, verifying parameters of a section of curve line defined in basic data of railway LKJ, such as: and (3) whether large errors exist between the slow curve length, the round curve radius and the parameters of the curve line in the high-precision map data of the railway.
And S403, verifying whether the mileage of the identification point in the basic data of the railway LKJ has a large error with the mileage in the high-precision map data of the railway.
S404, verifying whether the line spacing of the station tracks in the basic data of the railway LKJ has a large error with the line spacing of the corresponding station tracks in the high-precision map data of the railway.
S405, verifying whether the shortest line distance between adjacent lines or tracks in the high-precision railway map data exists in an unreasonable place or not.
S406, verifying whether the turnout model and the parameters in the basic data of the LKJ of the railway have larger errors with the corresponding turnout model and the parameters in the high-precision map data of the railway.
S407, verifying whether a large error exists between the slope characteristic area in the railway LKJ basic data and the corresponding slope characteristic area in the railway high-precision map data.
S5: and continuously maintaining the high-precision map data of the railway.
S501, the train and the inspection tool are periodically checked, and manually collected line and station yard data are obtained.
And S502, if the newly added or changed line and station yard parts exist, repeating the steps to obtain the updated high-precision railway map data.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus (device), or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be made by those skilled in the art without inventive work within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope defined by the claims.
Claims (7)
1. A railway high-precision map data processing method is characterized by comprising the following steps: the method comprises the following steps:
s1, collecting geographic coordinate data of a railway site;
s2, processing the geographic coordinate data acquired in S1 to obtain complete high-precision geographic coordinate data;
s3, performing mileage formatting, characteristic point and characteristic area identification on the complete high-precision geographical coordinate data obtained in the S2 to obtain railway high-precision map data;
s4, verifying the high-precision railway map data obtained in the S3;
s5, continuously maintaining the high-precision railway map data;
the method for processing the collected geographic coordinate data in step S2 includes referring to basic data of the railway LKJ, and deriving the collected geographic coordinate data, and the method includes:
s204, derivation of a straight line: when a section of high-precision road section is known, geographic coordinate data of other unknown position points on the straight line are deduced according to basic data of the railway LKJ;
s205, derivation of at least two lines having a parallel relationship: knowing a high-precision road section of one of the lines, and deducing geographic coordinate data of an unknown position point on a road section with a parallel relation corresponding to the other line according to the LKJ basic data of the railway;
s206, derivation of a circular curve: when a high-precision road section of one section of circular curve is known, geographic coordinate data of other unknown position points on the circular curve are deduced according to basic data of the railway LKJ;
s207, derivation of an axisymmetric or centrosymmetric curve line: geographic coordinate data of unknown position points on a symmetrical curve road section corresponding to a known curve road section are deduced according to railway LKJ basic data;
s208, derivation of lines and switches with the same geometric shapes: knowing a high-precision road section of one of the lines and the turnouts, and deducing geographic coordinate data of unknown position points on the corresponding line and turnout with the same geometric shape on the other line according to LKJ basic data of the railway;
wherein, the derivation step of the straight line segment in step S204 includes:
s20401, knowing that a road section is a straight line section and knowing at least 2 high-precision data points in the straight line section;
s20402, establishing a corresponding rectangular coordinate system or spherical coordinate system according to the known geographical coordinate data of the high-precision data points of the straight line section;
s20403, establishing a linear equation according to the coordinates of the known linear points in a rectangular coordinate system or a spherical coordinate system;
s20404, deducing other unknown straight line points in the straight line section according to a straight line equation;
s20405, reversely converting the coordinate of the deduced unknown straight line point in a rectangular coordinate system or a spherical coordinate system into longitude and latitude of a geographic coordinate;
the step of deriving at least two lines having a parallel relationship in step S205 includes:
s20501, knowing that 2 adjacent lines or tracks are in parallel, knowing that a high-precision road section of one line or track and at least one high-precision data point of the other parallel line or track are known;
s20502, establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting a high-precision road data point of a known line or a track and a high-precision data point of a parallel line or a track into points in the rectangular coordinate system or the spherical coordinate system;
s20503, traversing data points of the high-precision road section of the known line or track, and deducing other data points of the parallel line or track one by one according to the known high-precision data points of the parallel line or track;
s20503, reversely converting the coordinate of the derived parallel line or station track in a rectangular coordinate system or a spherical coordinate system into longitude and latitude in a geographic coordinate system;
the derivation step of the circular curve segment in step S206 includes:
s20601, knowing that a section of line is a circular curve section, and knowing that a part of the section of line is a high-precision section;
s20602, establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting data points of high-precision road sections in the known circular curve road sections into points in the rectangular coordinate system or the spherical coordinate system;
s20603, copying to obtain data points of unknown circular curve sections in a rectangular coordinate system or a spherical coordinate system according to the data points of the known circular curve sections;
s20604, reversely converting the coordinate in the deduced unknown circular curve section into longitude and latitude in a geographic coordinate system;
the deriving step of the axisymmetric or centrosymmetric curve line in step S207 includes:
s20701, knowing that a section of line is a curve section, knowing that one part of the section of line is a high-precision section, and knowing the position of a curve point of the curve section;
s20702, establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting data points of the high-precision road sections in the known curve road sections into points in the rectangular coordinate system or the spherical coordinate system;
s20703, copying the data points of the known high-precision road section of the curve into the data points of the curve road section on the other side of the curved point in a rectangular coordinate system or a spherical coordinate system based on the position of the curved point in a mirror image manner;
s20704, reversely converting the coordinates in the derived curve section into longitude and latitude in a geographic coordinate system;
the step of deriving the routes and switches with the same geometry in S208 includes:
s20801, knowing that the geometric shapes of the 2 lines are the same, knowing a high-precision data set of one line, and knowing at least 2 high-precision known data points of the other line;
s20802, establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting data points of high-precision road sections in the known 2 road sections into points in the rectangular coordinate system or the spherical coordinate system;
s20803, rotating and mirroring the road sections of the known line in a rectangular coordinate system or a spherical coordinate system, and then overlapping the road sections of the known line on the known data points of another line;
s20804, reversely converting the coordinate of the point of the other line into longitude and latitude in a geographic coordinate system;
the method for deducing the railway LKJ basic data further comprises the following steps:
a. knowing railway LKJ basic data corresponding to a road section, and knowing certain high-precision known points in the road section;
b. establishing a rectangular coordinate system or a spherical coordinate system corresponding to the geographic coordinate system, and converting high-precision data points of the known road section into points in the rectangular coordinate system or the spherical coordinate system;
c. in a rectangular coordinate system or a spherical coordinate system, establishing a mathematical model of a road section according to basic data of the railway LKJ;
d. deriving coordinates of other data points in the road segment based on the mathematical model of the road segment and the known data points;
e. the coordinates of the derived points are inversely translated into longitude and latitude in a geographic coordinate system.
2. The railway high-precision map data processing method according to claim 1, characterized in that: the geographic coordinate data includes: latitude, longitude, altitude, and current data accuracy of the location point;
the method for collecting the geographic coordinate data of the railway site in the step S1 comprises the following steps:
s101, installing data acquisition equipment on a train, and acquiring field data by utilizing the running process of the train on a line and in a station yard
S102, mounting the data acquisition equipment on a patrol inspection machine, and acquiring field data by utilizing the process of the machine running on a line and in a station yard
And S103, manually acquiring geographical coordinate data in lines and stations by carrying data acquisition equipment by workers.
3. The railway high-precision map data processing method according to claim 1, characterized in that: the method for processing the collected geographic coordinate data in step S2 includes:
s201, analyzing the collected data: judging which are the geographic data related to the field device and which are the geographic data irrelevant to the field device, and rejecting the geographic data irrelevant to the field device;
s202, cleaning the acquired data: removing repeated or redundant geographic coordinate data caused by stopping or moving back and forth;
and S203, merging the data acquired for many times, and replacing low-precision data with high-precision data.
4. The railway high-precision map data processing method according to claim 1, characterized in that: in the step S3, the mileage formatting, the feature point and the feature area identification are performed on the complete high-precision geographical coordinate data obtained in the step S2, and the method for obtaining the high-precision railway map data includes:
s301, mileage formatting: formatting the geographical coordinate data of the line and station tracks into a sequence of equidistant position points, and endowing each position point with a corresponding mileage value according to the definition of basic data of the railway LKJ;
s302, characteristic point identification: marking turnout characteristic points, signal machine characteristic points, insulation joint characteristic points, grade change point characteristic points and passage door characteristic points according to railway LKJ basic data;
s303, characteristic region identification: according to the LKJ basic data of the railway, a station yard characteristic region, a station platform characteristic region, a main line characteristic region, a station track characteristic region, a turnout characteristic region, a left rail and right rail characteristic region of the line, a road shoulder characteristic region, a tunnel characteristic region, a bridge characteristic region, a culvert and channel characteristic region, a line speed characteristic region, a curve characteristic region, a slope characteristic region, a roadbed characteristic region and a flood control dangerous section characteristic region.
5. The railway high-precision map data processing method according to claim 1, characterized in that: the method for verifying the railway high-precision map data obtained in the step S3 according to the railway LKJ basic data in the step S4 comprises the following steps:
s401, length verification: verifying whether an error exceeding a threshold exists between the length of a section of straight line or curve line defined in the railway LKJ basic data and the length of the straight line or curve line in the railway high-precision map data;
s402, curve parameter verification: verifying whether errors exceeding a threshold exist in parameters of a section of curve line defined in basic data of the railway LKJ and parameters of the curve line in high-precision map data of the railway;
s403, mileage verification: verifying whether the mileage of the identification point in the basic data of the railway LKJ and the mileage in the high-precision map data of the railway have errors exceeding a threshold value;
s404, line spacing verification: verifying whether the line spacing of the tracks in the basic data of the LKJ and the line spacing of the corresponding tracks in the high-precision map data of the railway have errors exceeding a threshold value or not;
s405, shortest distance verification: verifying the shortest line distance between adjacent lines or tracks in the high-precision railway map data and whether a place exceeding a threshold exists;
s406, switch verification: verifying whether the error exceeding a threshold exists in the turnout model and the parameter in the LKJ basic data of the railway and the corresponding turnout model and parameter in the high-precision map data of the railway;
s407, feature verification: and verifying whether the error exceeding the threshold exists in the slope characteristic area in the railway LKJ basic data and the corresponding slope characteristic area in the railway high-precision map data.
6. The railway high-precision map data processing method according to claim 1, characterized in that: the method for continuously maintaining the high-precision map data of the railway by the step S5 performs the step S4 with a fixed period, and repeats the steps S1 to S4 when the verification fails.
7. A railway high-precision map data system is characterized in that: includes a collecting terminal for collecting geographical coordinate data of a railway site and a processor for performing steps S1-S5 of the railway high precision map data processing method as claimed in any one of claims 1 to 6.
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