Curve standing book data checking method and device based on superelevation detection data

Abstract

The invention discloses a curve machine account data checking method and device based on superelevation detection data. The method comprises the following steps: acquiring superelevation detection data and track curve machine account data; the track curve standing book data comprises starting point information and ending point information; smoothing the superelevation detection data based on a preset sliding window length to obtain a processed superelevation detection data sequence, and drawing a corresponding rectangular wave map according to the superelevation detection data sequence; acquiring orbit curve data from the rectangular wave map; the track curve data comprises detection starting point information and detection ending point information; and comparing the detection starting point information with the starting point information in the track curve standing book data, comparing the detection termination point information with the termination point information in the track curve standing book data, and performing rechecking and proofreading on the track curve standing book data according to a comparison result. According to the method, the checking efficiency of the curve machine account data can be improved, and the manual rechecking cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of railway engineering, and in particular to a curve ledger data verification method and device based on superelevation detection data. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] With the continuous development of track geometry detection technology, the detection accuracy of track geometry has been greatly improved, and the positioning accuracy of track geometry detection data has become an important factor affecting the use of track geometry detection data to guide on-site maintenance decisions. Since the track curve structure has the characteristics of fixed position and obvious data characteristics, it is often used as an important reference for the mileage positioning of track geometry detection data.

[0004] The curve ledger records the curve position and structural parameters of the railway line, and is an important basis for engineering inspection data analysis, mileage correction, and data display. However, due to the large number of curve ledgers throughout the railway, the scattered management units, and the main reliance on manual sorting and review, the current curve ledgers have many quality problems such as "missing, wrong, and too much". The existing curve ledger data verification method has the following main defects: first, the curve ledger verification mainly relies on manual verification; second, the calculation amount is large, the calculation efficiency is low, and it is difficult to adapt to large-scale inspection data batch calculation. Therefore, there is an urgent need for a curve ledger data verification method to solve the above problems. Summary of the invention

[0005] The embodiment of the present invention provides a curve ledger data verification method based on superelevation detection data, which is used to improve the verification efficiency of curve ledger data and reduce labor costs. The method includes:

[0006] Obtain superelevation detection data and track curve ledger data; track curve ledger data includes starting point information and end point information;

[0007] Based on the preset sliding window length, the superelevation detection data is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence;

[0008] Acquire track curve data from the rectangular wave diagram; the track curve data includes detection start point information and detection end point information;

[0009] Compare the detection starting point information with the starting point information in the track curve ledger data, and compare the detection ending point information with the ending point information in the track curve ledger data. Review and proofread the track curve ledger data based on the comparison results.

[0010] The embodiment of the present invention further provides a curve ledger data verification device based on superelevation detection data, which is used to improve the verification efficiency of curve ledger data and reduce labor costs. The device includes:

[0011] The data acquisition module is used to obtain superelevation detection data and track curve ledger data; the track curve ledger data includes starting point information and end point information;

[0012] A rectangular wave graph drawing module is used to perform smoothing processing on the superelevation detection data based on a preset sliding window length to obtain a processed superelevation detection data sequence, and draw a corresponding rectangular wave graph according to the superelevation detection data sequence;

[0013] The track curve data acquisition module is used to acquire the track curve data from the rectangular wave diagram; the track curve data includes the detection start point information and the detection end point information;

[0014] The curve ledger data review and proofreading module is used to compare the detection starting point information with the starting point information in the track curve ledger data, and to compare the detection ending point information with the ending point information in the track curve ledger data, and to review and proofread the track curve ledger data based on the comparison results.

[0015] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned curve inventory data verification method based on superelevation detection data when executing the computer program.

[0016] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned curve inventory data verification method based on ultra-high detection data.

[0017] An embodiment of the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned curve inventory data verification method based on ultra-high detection data.

[0018] In an embodiment of the present invention, superelevation detection data and track curve ledger data are obtained; the track curve ledger data includes starting point information and ending point information; based on a preset sliding window length, the superelevation detection data is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence; the track curve data is obtained from the rectangular wave diagram; the track curve data includes detection starting point information and detection ending point information; the detection starting point information is compared with the starting point information in the track curve ledger data, and the detection ending point information is compared with the ending point information in the track curve ledger data, and the track curve ledger data is reviewed and proofread according to the comparison result. In the above process, in order to eliminate interference, the embodiment of the present invention smoothes the superelevation detection data based on a preset sliding window length; the superelevation detection data sequence is drawn into a rectangular wave diagram, and the rectangular wave diagram is used to facilitate the rapid comparison of the track curve ledger data with the track curve data, thereby improving the verification efficiency of the curve ledger data and reducing the manual review cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 Flow chart of a curve ledger data verification method based on ultra-high detection data in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the principle of removing abnormal ultra-high detection data in an embodiment of the present invention;

[0022] Figure 3 A flowchart of drawing a rectangular wave diagram in an embodiment of the present invention;

[0023] Figure 4 A specific example diagram of drawing a rectangular wave diagram in an embodiment of the present invention;

[0024] Figure 5 Schematic diagram of converting ultra-high detection data into rectangular pulse signals in an embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram showing that the curve account data of a certain line section [137.79625, 220.53875] is correct in an embodiment of the present invention;

[0026] Figure 7This is a schematic diagram of the invalidity of the curve account of a certain line section [1750, 1830] in an embodiment of the present invention;

[0027] Figure 8 This is a schematic diagram of missing curve records for a certain line section [640, 700] in an embodiment of the present invention;

[0028] Fig. 9 It is a schematic diagram of a curve ledger data verification device based on ultra-high detection data in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0030] Figure 1 Flow chart of a curve ledger data verification method based on ultra-high detection data in an embodiment of the present invention, the method comprising:

[0031] Step 101, obtaining superelevation detection data and track curve ledger data; the track curve ledger data includes starting point information and end point information;

[0032] Step 102, based on a preset sliding window length, smoothing the superelevation detection data to obtain a processed superelevation detection data sequence, and drawing a corresponding rectangular wave diagram according to the superelevation detection data sequence;

[0033] Step 103, obtaining track curve data from the rectangular wave diagram; the track curve data includes detection start point information and detection end point information;

[0034] Step 104, compare the detection starting point information with the starting point information in the track curve ledger data, compare the detection ending point information with the ending point information in the track curve ledger data, and review and proofread the track curve ledger data based on the comparison results.

[0035] Each step is described in detail below.

[0036] In step 101, superelevation detection data and track curve ledger data are obtained; the track curve ledger data includes starting point information and ending point information; in step 102, based on a preset sliding window length, the superelevation detection data is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence.

[0037] In one embodiment, before smoothing the superelevation detection data after elimination based on a preset sliding window length to obtain a processed superelevation detection data sequence, the method includes:

[0038] IQR (interquartile range) is used to eliminate abnormal data from ultra-high detection data;

[0039] Based on the preset sliding window length, the superelevation detection data is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence, including:

[0040] Based on the preset sliding window length, the superelevation detection data after elimination is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence.

[0041] In a specific embodiment, due to factors such as detection equipment or line conditions, the detection data is often mixed with abnormal superelevation detection data. In order to propose abnormal data, the superelevation detection data contains two dimensions, the horizontal axis is the mileage value, and the vertical axis is the superelevation value. First, all superelevation detection data are sorted from small to large according to the mileage value, and then the upper quartile, lower quartile and IQR interquartile range values ​​are obtained in turn. Since the mileage does not conform to the standard normal distribution, it is stipulated that if the mileage value is within the following valid range:

[0042] (Q1-0.75·IQR, Q2+0.75·IQR)

[0043] The superelevation detection data is considered valid, otherwise it is invalid, where Q1 is the upper quartile of the mileage data, Q2 is the lower quartile of the mileage data, and IQR = Q2-Q1. This allows for quick screening of valid data in superelevation detection data and elimination of abnormal data. Figure 2 4 is a schematic diagram of the principle of removing abnormal superelevation detection data in an embodiment of the present invention, wherein σ represents the standard deviation of the mileage value sequence in the superelevation detection data.

[0044] Figure 3 This is a flow chart of drawing a rectangular wave graph in an embodiment of the present invention. In one embodiment, based on a preset sliding window length, the superelevation detection data is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave graph is drawn according to the superelevation detection data sequence, including:

[0045] Step 301, calculating the average value and standard deviation of the superelevation detection data according to the superelevation detection data sequence;

[0046] Step 302, determining the rectangular wave sequence value corresponding to each data point in the superelevation detection data sequence according to the preset sliding window length, superelevation detection data average value and standard deviation;

[0047] Step 303: Draw a rectangular wave graph according to the rectangular wave sequence value corresponding to each data point in the ultra-high detection data sequence.

[0048] Figure 4 This is a specific example diagram of drawing a rectangular wave diagram in an embodiment of the present invention. In one embodiment, according to a preset sliding window length, an average value and a standard deviation of the ultra-elevation detection data, a rectangular wave sequence value corresponding to each data point in the ultra-elevation detection data sequence is determined, including:

[0049] Step 401, calculating the deviation value between each data point in the superelevation detection data sequence and the superelevation detection data average value and standard deviation according to the preset sliding window length, superelevation detection data average value and standard deviation;

[0050] Step 402, determining the rectangular wave sequence value corresponding to each data point in the superelevation detection data sequence according to the deviation value, the magnitude relationship between each data point in the superelevation detection data sequence and the superelevation detection data average value.

[0051] In a specific embodiment, in order to eliminate the burr interference of superelevation less than 10mm, a sliding window with a window of 10 is set to smooth the superelevation detection data, and the superelevation detection data sequence is converted into a regular rectangular wave. The main principle is to convert the superelevation detection data of the straight line segment into the value 0 in the rectangular wave, and convert the superelevation detection data of the curve part into the ±1 of the rectangular wave, so that its direction is consistent with the original superelevation detection data. Figure 5 The schematic diagram of converting superelevation detection data into a rectangular pulse signal in an embodiment of the present invention mainly includes two key points: one is to determine whether a superelevation detection data point is in a straight line segment or a curve segment, and the other is to determine the positive and negative directions of the rectangular wave.

[0052] The specific process is as follows:

[0053] Step 1: The superelevation detection data sequence after smoothing is:

[0054] X=[x′1,x′2,x′3,…,x′ n ]

[0055] Step 2: Calculate the average value m of superelevation detection data l and standard deviation σ l :

[0056]

[0057]

[0058] Where l is the length of the moving window, X j represents the jth data point.

[0059] Step 3: Calculate the i+1th data point x′ in the superelevation detection data sequence i+1 Compared with the average value of ultra-high detection data m l Deviation value W:

[0060]

[0061] If W is greater than the preset deviation threshold t hr , then we think x i ' +1 is on a curve segment, and if x i ' +1 The super-high detection data value x of the data point i+1 Greater than the average value of super-high detection data m l , then the value corresponding to the rectangular wave sequence is y i+1 =1; and if x i ' +1 The super-high detection data value x of the data point i+1 Less than or equal to the average value of super-high detection data m l , then the value corresponding to the rectangular wave sequence is y i+1 =-1.

[0062] If W is less than the threshold t hr , then the point is considered to be in a straight line segment, and the value corresponding to the rectangular wave sequence is y i+1 =0.

[0063] Step 5: Update the super-high detection data sequence. If W is greater than the threshold t hr Then we have:

[0064] X i+1 =k nf X i+1 +(1-k nf )m l ,

[0065] where k nf is the average value of the super-high detection data for the newly added value m l degree of impact.

[0066] Step 6: Obtain the updated average value m of the superelevation detection data l+1 and the updated standard deviation σ l+1 .

[0067] Step 7: Repeat steps 4 to 6 in sequence to obtain the rectangular wave sequence y.

[0068] In step 103, track curve data is obtained from the rectangular wave diagram; the track curve data includes detection start point information and detection end point information;

[0069] Step 104, compare the detection starting point information with the starting point information in the track curve ledger data, compare the detection ending point information with the ending point information in the track curve ledger data, and review and proofread the track curve ledger data based on the comparison results.

[0070] In one embodiment, before comparing the detection starting point information with the starting point information in the track curve ledger data, and comparing the detection ending point information with the ending point information in the track curve ledger data, the following steps are included:

[0071] Divide the track curve data and the track curve record data into multiple sections; the multiple sections include a starting slow and long section, an ending slow and long section, and a circumference section;

[0072] Compare the detection starting point information with the starting point information in the track curve ledger data, and compare the detection ending point information with the ending point information in the track curve ledger data, including:

[0073] In each section, the detection starting point information is compared with the starting point information in the track curve ledger data, and the detection ending point information is compared with the ending point information in the track curve ledger data to obtain the comparison results in each section.

[0074] In a specific embodiment, the starting mileage point P of the line is obtained from the track curve ledger data. s , End mileage point P e Since the track geometry of curved sections is significantly different from that of straight sections, the mileage positions of the characteristic points of each curve are determined based on the characteristic points of the curve, including the mileage position of the straight-slow point entering the curve, the mileage position of the slow-round point entering the circle, the mileage position of the slow-round point leaving the circle, and the mileage position of the slow-straight point leaving the curve.

[0075] The calculation formula for the mileage position M of each feature point is:

[0076] M ZH =P s

[0077] M HY =P s +L s

[0078] M YH =P e -L e

[0079] M HZ =P e

[0080] Among them, M ZH M is the mileage position of the straight and slow point entering the curve,HY M is the mileage position of the slow point entering the circle, YH M is the mileage position of the slow point leaving the circle, HZ is the mileage position of the straightening point leaving the curve, P s is the starting mileage of the curve, is the end mileage of the curve, L s L is the length of the relaxation line. e The final relaxation line length.

[0081] In a specific embodiment, the track curve data and the track curve ledger data are divided into multiple sections, including the following areas:

[0082] (1) Initial slow section

[0083]

[0084] L sk =P s +0.25*k,k=1,2,…N s

[0085] Gant sk =0.25*k*k s ,k=1,2,…,N s

[0086] Where: k s The slope of the initial slow section, N s is the number of interpolations required for the initial slow segment, L sk is the mileage point sequence value of the initial slow-length section, Gant sk It is the super high point sequence value of the initial slow length section.

[0087] (2) Termination of the slow and long section

[0088]

[0089] L ek =P e -0.25*k,k=1,2,…N e

[0090] Gant ek =-0.25*k*k s ,k=1,2,…,N s

[0091] Where: k e N is the slope of the termination slow section. e The number of interpolations required to terminate the slow segment, L ek Gant is the mileage point sequence value for terminating the slow and long section. ekIt is the super high point sequence value that terminates the slow and long section.

[0092] (3) Circumferential section

[0093]

[0094] L ck =M HY +0.25*k,k=1,2,…,N c

[0095] Gant ck =Gant,k=1,2,…,N s

[0096] Where: N c is the number of interpolation points required for the circular segment, L ck is the mileage point sequence value of the circular segment, Gant ck It is the superelevation point sequence value of the circular segment.

[0097] In one embodiment, the track curve ledger data is reviewed and proofread according to the comparison result, including:

[0098] Based on the comparison results within each section, the track curve inventory data of the corresponding section is reviewed and proofread.

[0099] In a specific embodiment, the generated track curve account data (corresponding to Figures 6 to 8 The data of the ledger in the Figures 6 to 8 The original super height in the drawing is compared, such as Figures 6 to 8 As shown, the quality problems of track curve inventory data can be clearly observed through this method. Figure 6 This is a schematic diagram showing that the curve account data of a certain line section [137.79625, 220.53875] is correct in an embodiment of the present invention. Figure 7 This is a schematic diagram of the invalidity of the curve account of a certain line section [1750, 1830] in an embodiment of the present invention. Figure 8 The following is a schematic diagram of missing curve records for a certain line section [640, 700] in an embodiment of the present invention. To improve the efficiency of record verification, in the process of visualization, the curves whose test data are consistent with the record are marked as dots (i.e., the record is correct), the curves that do not exist in the test data but exist in the record data are marked as triangles (i.e., the record is invalid), and the curves that exist in the test data but do not exist in the record data are marked as squares (i.e., the record is missing).

[0100] The embodiment of the present invention also provides a curve ledger data verification device based on superelevation detection data, as described in the following embodiment. Since the principle of solving the problem by the device is similar to the curve ledger data verification method based on superelevation detection data, the implementation of the device can refer to the implementation of the curve ledger data verification method based on superelevation detection data, and the repeated parts will not be repeated.

[0101] Fig. 9 Schematic diagram of a curve ledger data verification device based on superelevation detection data in an embodiment of the present invention, the device includes:

[0102] The data acquisition module 901 is used to acquire superelevation detection data and track curve ledger data; the track curve ledger data includes starting point information and end point information;

[0103] The rectangular wave graph drawing module 902 is used to perform smoothing processing on the superelevation detection data based on a preset sliding window length to obtain a processed superelevation detection data sequence, and draw a corresponding rectangular wave graph according to the superelevation detection data sequence;

[0104] The track curve data acquisition module 903 is used to acquire the track curve data from the rectangular wave diagram; the track curve data includes the detection start point information and the detection end point information;

[0105] The curve ledger data review and proofreading module 904 is used to compare the detection starting point information with the starting point information in the track curve ledger data, and to compare the detection ending point information with the ending point information in the track curve ledger data, and to review and proofread the track curve ledger data according to the comparison results.

[0106] In one embodiment, a data elimination module is further included, which is specifically used to:

[0107] The IQR interquartile range technology is used to eliminate abnormal data from ultra-high detection data;

[0108] Rectangular wave drawing module, specifically used for:

[0109] Based on the preset sliding window length, the superelevation detection data after elimination is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence.

[0110] In one embodiment, the rectangular wave graph drawing module 902 is specifically used to:

[0111] According to the superelevation detection data sequence, calculate the superelevation detection data mean value and standard deviation;

[0112] According to the preset sliding window length, the average value and standard deviation of the superelevation detection data, the rectangular wave sequence value corresponding to each data point in the superelevation detection data sequence is determined;

[0113] Draw a rectangular wave graph based on the rectangular wave sequence values ​​corresponding to each data point in the ultra-high detection data sequence.

[0114] In one embodiment, the rectangular wave graph drawing module 902 is specifically used to:

[0115] According to the preset sliding window length, the average value and standard deviation of the superelevation detection data, the deviation value between each data point in the superelevation detection data sequence and the average value of the superelevation detection data is calculated;

[0116] According to the deviation value, the relationship between each data point in the superelevation detection data sequence and the average value of the superelevation detection data, the rectangular wave sequence value corresponding to each data point in the superelevation detection data sequence is determined.

[0117] In one embodiment, a region division module is further included, which is specifically used to:

[0118] Before comparing the detection starting point information with the starting point information in the track curve ledger data, and comparing the detection ending point information with the ending point information in the track curve ledger data, the track curve data and the track curve ledger data are divided into a plurality of sections; the plurality of sections include a starting slow-long section, an ending slow-long section, and a circumferential section;

[0119] It also includes an information comparison module, which is specifically used for:

[0120] In each section, the detection starting point information is compared with the starting point information in the track curve ledger data, and the detection ending point information is compared with the ending point information in the track curve ledger data to obtain the comparison results in each section.

[0121] In one embodiment, the curve ledger data review and proofreading module 904 is specifically used to:

[0122] Based on the comparison results within each section, the track curve inventory data of the corresponding section is reviewed and proofread.

[0123] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned curve inventory data verification method based on superelevation detection data when executing the computer program.

[0124] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned curve inventory data verification method based on ultra-high detection data.

[0125] An embodiment of the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned curve inventory data verification method based on ultra-high detection data.

[0126] In an embodiment of the present invention, superelevation detection data and track curve ledger data are obtained; the track curve ledger data includes starting point information and ending point information; based on a preset sliding window length, the superelevation detection data is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence; the track curve data is obtained from the rectangular wave diagram; the track curve data includes detection starting point information and detection ending point information; the detection starting point information is compared with the starting point information in the track curve ledger data, and the detection ending point information is compared with the ending point information in the track curve ledger data, and the track curve ledger data is reviewed and proofread according to the comparison result. In the above process, in order to eliminate interference, the embodiment of the present invention smoothes the superelevation detection data based on a preset sliding window length; the superelevation detection data sequence is drawn into a rectangular wave diagram, and the rectangular wave diagram is used to facilitate the rapid comparison of the track curve ledger data with the track curve data, thereby improving the verification efficiency of the curve ledger data and reducing the manual review cost.

[0127] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0131] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A curve ledger data verification method based on ultra-high detection data, characterized in that: include: Obtain superelevation detection data and track curve ledger data; Track curve ledger data includes starting point information and end point information; Based on the preset sliding window length, the superelevation detection data is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence; Get track curve data from rectangular wave diagram; Track curve data includes detection start point information and detection end point information; Compare the detection starting point information with the starting point information in the track curve ledger data, and compare the detection ending point information with the ending point information in the track curve ledger data. Review and proofread the track curve ledger data based on the comparison results.

2. The method according to claim 1, characterized in that Before smoothing the superelevation detection data after elimination based on the preset sliding window length to obtain the processed superelevation detection data sequence, it includes: The IQR interquartile range technology is used to eliminate abnormal data from ultra-high detection data; Based on the preset sliding window length, the superelevation detection data is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence, including: Based on the preset sliding window length, the superelevation detection data after elimination is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence.

3. The method according to claim 1, characterized in that Based on the preset sliding window length, the superelevation detection data is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence, including: According to the superelevation detection data sequence, calculate the superelevation detection data mean value and standard deviation; According to the preset sliding window length, the average value and standard deviation of the superelevation detection data, the rectangular wave sequence value corresponding to each data point in the superelevation detection data sequence is determined; Draw a rectangular wave graph based on the rectangular wave sequence values ​​corresponding to each data point in the ultra-high detection data sequence.

4. The method according to claim 3, characterized in that According to the preset sliding window length, the average value and standard deviation of the superelevation detection data, the rectangular wave sequence value corresponding to each data point in the superelevation detection data sequence is determined, including: According to the preset sliding window length, the average value and standard deviation of the superelevation detection data, the deviation value between each data point in the superelevation detection data sequence and the average value of the superelevation detection data is calculated; According to the deviation value, the relationship between each data point in the superelevation detection data sequence and the average value of the superelevation detection data, the rectangular wave sequence value corresponding to each data point in the superelevation detection data sequence is determined.

5. The method according to claim 1, characterized in that Before comparing the detection starting point information with the starting point information in the track curve ledger data, and comparing the detection ending point information with the ending point information in the track curve ledger data, it includes: Divide the track curve data and the track curve record data into multiple sections; the multiple sections include a starting slow and long section, an ending slow and long section, and a circumference section; Compare the detection starting point information with the starting point information in the track curve ledger data, and compare the detection ending point information with the ending point information in the track curve ledger data, including: In each section, the detection starting point information is compared with the starting point information in the track curve ledger data, and the detection ending point information is compared with the ending point information in the track curve ledger data to obtain the comparison results in each section.

6. The method according to claim 5, characterized in that According to the comparison results, the track curve ledger data is reviewed and proofread, including: Based on the comparison results within each section, the track curve inventory data of the corresponding section is reviewed and proofread.

7. A curve ledger data verification device based on superelevation detection data, characterized in that: include: Data acquisition module, used to obtain superelevation detection data and track curve ledger data; Track curve ledger data includes starting point information and end point information; A rectangular wave graph drawing module is used to perform smoothing processing on the superelevation detection data based on a preset sliding window length to obtain a processed superelevation detection data sequence, and draw a corresponding rectangular wave graph according to the superelevation detection data sequence; A track curve data acquisition module is used to acquire track curve data from a rectangular wave diagram; Track curve data includes detection start point information and detection end point information; The curve ledger data review and proofreading module is used to compare the detection starting point information with the starting point information in the track curve ledger data, and to compare the detection ending point information with the ending point information in the track curve ledger data, and to review and proofread the track curve ledger data based on the comparison results.

8. The device according to claim 7, characterized in that It also includes a data removal module, which is specifically used to: The IQR interquartile range technology is used to eliminate abnormal data from ultra-high detection data; Rectangular wave drawing module, specifically used for: Based on the preset sliding window length, the superelevation detection data after elimination is smoothed to obtain a processed superelevation detection data sequence, and a corresponding rectangular wave diagram is drawn according to the superelevation detection data sequence.

9. The device according to claim 7, characterized in that Rectangular wave drawing module, specifically used for: According to the superelevation detection data sequence, calculate the superelevation detection data mean value and standard deviation; According to the preset sliding window length, the average value and standard deviation of the superelevation detection data, the rectangular wave sequence value corresponding to each data point in the superelevation detection data sequence is determined; Draw a rectangular wave graph based on the rectangular wave sequence values ​​corresponding to each data point in the ultra-high detection data sequence.

10. The device according to claim 9, characterized in that Rectangular wave drawing module, specifically used for: According to the preset sliding window length, the average value and standard deviation of the superelevation detection data, the deviation value between each data point in the superelevation detection data sequence and the average value of the superelevation detection data is calculated; According to the deviation value, the relationship between each data point in the superelevation detection data sequence and the average value of the superelevation detection data, the rectangular wave sequence value corresponding to each data point in the superelevation detection data sequence is determined.

11. The device according to claim 7, characterized in that It also includes a region partitioning module, which is specifically used to: Before comparing the detection starting point information with the starting point information in the track curve ledger data, and comparing the detection ending point information with the ending point information in the track curve ledger data, the track curve data and the track curve ledger data are divided into a plurality of sections; the plurality of sections include a starting slow-long section, an ending slow-long section, and a circumferential section; It also includes an information comparison module, which is specifically used for: In each section, the detection starting point information is compared with the starting point information in the track curve ledger data, and the detection ending point information is compared with the ending point information in the track curve ledger data to obtain the comparison results in each section.

12. The device according to claim 11, characterized in that The curve ledger data review and proofreading module is specifically used for: Based on the comparison results within each section, the track curve inventory data of the corresponding section is reviewed and proofread.

13. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

15. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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