Static drift correction method and device based on data analysis

Abstract

The application provides a static drift correction method and device based on data analysis, and belongs to the field of satellite positioning. The method comprises the following steps: acquiring a plurality of continuous trajectory data within a preset time length to obtain sample points; when there is a static drift, filtering out points with an average distance difference greater than a preset condition from all sample points to obtain candidate sample points; filtering out points not on a road segment from the candidate sample points to obtain selected sample points; determining a center point according to the maximum latitude, minimum latitude, maximum longitude and minimum longitude of the selected sample points, and determining an actual point position of the static drift according to the center point. The method filters out points with an average distance difference greater than a preset condition from all sample points, and filters out points not on a road segment, thereby improving the accuracy of drift calibration. Meanwhile, the selected sample points are obtained by calculating and judging the actual point position, and compared with the current method of calculating the static drift according to the characteristic value in the regional range, the method is more simple and reliable.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite positioning, in particular to a static drift correction method and device based on data analysis. BACKGROUND

[0002] In today's GPS positioning technology, it will be interfered by satellite signals, weather, electrical electromagnetic and other factors, resulting in the phenomenon of static drift. Therefore, it is necessary to correct and aggregate the GPS signals that have generated static drift.

[0003] At present, the main processing method in the computing platform includes: using geohash algorithm to calculate the characteristic value of the positioning source data after analysis processing. However, these methods need to use other resources to judge the static drift, resulting in low calculation efficiency; on the other hand, the results of these calculation methods are not accurate and reliable, so that the final result has great difference with the actual situation. SUMMARY

[0004] In view of the problems existing in the prior art, the present application provides a static drift correction method and device based on data analysis.

[0005] The present application provides a static drift correction method based on data analysis, comprising: obtaining a plurality of continuous trajectory data within a preset time period to obtain sample points; filtering out points with an average distance difference greater than a preset condition from all sample points to obtain candidate sample points when there is a static drift condition; filtering out points not on the road segment from the candidate sample points to obtain selected sample points; determining a center point according to the maximum latitude, minimum latitude, maximum longitude and minimum longitude of the selected sample points, and determining the actual point position of the static drift according to the center point.

[0006] According to the static drift correction method based on data analysis provided by the present application, before filtering out the points with an average distance difference greater than a preset condition from all sample points, it further comprises: determining the static drift point according to the angle between the straight lines of the two continuous trajectories being less than a preset threshold; and determining that there is a static drift condition when the number of static drift points is greater than a preset proportion.

[0007] According to the static drift correction method based on data analysis provided by the present application, filtering out the sample points with an average distance difference greater than a standard deviation of 3 times from all sample points.

[0008] According to the static drift correction method based on data analysis provided by the present application, before determining the center point according to the maximum latitude, minimum latitude, maximum longitude and minimum longitude of the selected sample points, it further comprises: determining that the selected sample points meet the condition of distribution on both sides of the road segment.

[0009] According to the static drift correction method based on data analysis provided by the application, the determination of the situation that the selected sample point conforms to the distribution on both sides of the road segment comprises: fitting a straight line equation according to linear regression of the positioning points on the road segment; bringing the selected sample point into the straight line equation to obtain a number m greater than 0 and a number n less than 0; when the difference between m and n and the ratio of the sum of m and n are less than or equal to a preset threshold, it is determined that the selected sample point conforms to the distribution on both sides of the road segment.

[0010] According to the static drift correction method based on data analysis provided by the application, the determination of the situation that the selected sample point conforms to the distribution on both sides of the road segment comprises: fitting a straight line equation according to linear regression of the positioning points on the road segment; bringing the selected sample point into the straight line equation to obtain a number m greater than 0 and a number n less than 0; when the difference between m and n and the ratio of the sum of m and n are less than or equal to a preset threshold, it is determined that the selected sample point conforms to the distribution on both sides of the road segment.

[0011] The application further provides a static drift correction device based on data analysis, comprising: a trajectory sampling module for obtaining a plurality of continuous trajectory data in a preset time period to obtain sample points; a first filtering module for filtering out points with a distance difference greater than a preset condition from all sample points to obtain candidate sample points when there is a static drift; a second filtering module for filtering out points not on a road segment from the candidate sample points to obtain selected sample points; and a drift positioning module for determining a center point according to the maximum latitude, the minimum latitude, the maximum longitude and the minimum longitude of the selected sample points and determining an actual point of the static drift according to the center point.

[0012] The application further provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the static drift correction method based on data analysis according to any of the above when executing the program.

[0013] The application further provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the static drift correction method based on data analysis according to any of the above.

[0014] The application further provides a computer program product comprising a computer program, wherein the computer program is executable by a processor to implement the static drift correction method based on data analysis according to any of the above.

[0015] The static drift correction method and apparatus based on data analysis provided by this invention improves the accuracy of drift calibration by filtering out points whose average distance difference from all sample points is greater than a preset condition, and filtering out points among the candidate sample points that are not on road segments. At the same time, obtaining the selected sample points is based on the calculation and judgment of the actual point locations, which is simpler and more reliable than the current method of calculating static drift by feature values ​​based on regional range. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is one of the flowcharts of the static drift correction method based on data analysis provided by the present invention;

[0018] Figure 2 This is the second flowchart of the static drift correction method based on data analysis provided by the present invention;

[0019] Figure 3 This is a schematic diagram of the static drift simulation process provided by the present invention;

[0020] Figure 4 This is a schematic diagram of the static drift correction device based on data analysis provided by the present invention;

[0021] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] The following is combined Figures 1-5 This invention describes a static drift correction method and apparatus based on data analysis. Figure 1 This is one of the flowcharts of the static drift correction method based on data analysis provided by the present invention, such as... Figure 1 As shown, the present invention provides a static drift correction method based on data analysis, comprising:

[0024] 101. Obtain multiple continuous trajectory data within a preset time period to obtain sample points.

[0025] Continuous GPS trajectory data over a period of time is obtained through devices such as GPS positioning devices, such as points P1(lat1,lon1), P2(lat2,lon2), P3(lat3,lon3)...Pn(latn,lonn). The preset duration can be set according to requirements.

[0026] 102. In the case of static drift, filter out points whose average distance difference from all sample points is greater than the preset condition to obtain candidate sample points.

[0027] In this invention, points that exhibit static drift within a certain time period and whose distances differ significantly from the average distance in the sample are first filtered out. The distance between two points can then be calculated using their latitude and longitude as follows:

[0028]

[0029] Where r is the equatorial radius. The average distance between all points during this time is:

[0030]

[0031] 103. Filter out the candidate sample points that are not on the road segment to obtain the selected sample points.

[0032] This invention performs a second filtering on the samples obtained after the first filtering to identify points that are not on a certain road. For example, using the distance algorithm in step 102, the minimum distance between a point in the sample and all GPS points on a certain road is calculated. However, if this distance is greater than a certain parameter M3, the point is considered not to be on this road, thus filtering out points that are not on the road.

[0033] 104. Determine the center point based on the maximum latitude, minimum latitude, maximum longitude, and minimum longitude of the selected sample point, and determine the actual location of the static drift based on the center point.

[0034] Extract the maximum latitude, minimum latitude, maximum longitude, and minimum longitude from the sample, respectively, and obtain its center point:

[0035]

[0036] This determines the actual location of the final static drift.

[0037] The data analysis-based static drift correction method of the application improves the accuracy of drift calibration by filtering out points with a distance difference greater than a preset condition from all sample points and filtering out points not on a road segment from the candidate sample points.

[0038] In one embodiment, before the filtering out of the points with a distance difference greater than a preset condition from all sample points, the method further comprises: determining a static drift point according to an angle between two consecutive trajectory straight lines being less than a preset threshold; and determining that a static drift condition exists when the number of the static drift points is greater than a preset proportion.

[0039] A one-time function is satisfied between each two consecutive points:

[0040] lon=K*lat+C (K≠0)

[0041] When the angle between two consecutive trajectory straight lines is less than a certain coefficient M1, it can be considered that the two consecutive trajectories have a static drift phenomenon, i.e., the slopes of the one-time functions formed by the two consecutive trajectories are K1 and K2, respectively, which satisfy:

[0042]

[0043] Therefore, the total number of trajectories with a static drift in a period of time is N1, and when the ratio of N1 to the total number of all points N2 in the period of time is greater than a certain coefficient M2, it is considered that a static drift phenomenon occurs in the period of time.

[0044] The data analysis-based static drift correction method of the embodiment of the application realizes automatic judgment of the static drift phenomenon and triggers the correction process by judging the latitude and longitude trajectory angle of the positioning signal in a period of time, thereby greatly improving the processing efficiency of the computing platform for the static drift.

[0045] In one embodiment, the filtering out of the points with a distance difference greater than a preset condition from all sample points comprises: filtering out sample points with a distance difference greater than 3 times a standard deviation from all sample points.

[0046] The standard deviation of a certain sample point is:

[0047]

[0048] In the embodiment of the application, if the residual error of a certain point satisfies: the point is filtered out. In the embodiment of the application, the data with a large sample difference is filtered according to the 3σ criterion, so that the sample model is more accurate.

[0049] In one embodiment, before the center point is determined according to the maximum latitude, the minimum latitude, the maximum longitude and the minimum longitude of the selected sample points, the method further comprises: determining whether the selected sample points meet the condition of two-side distribution of the road segment.

[0050] Before step 104, the application determines whether the sample data obtained after the secondary filtering is based on the two-side distribution of the road, such as the north-south distribution, and then performs the static drift calibration.

[0051] In one embodiment, the determination of whether the selected sample points meet the condition of two-side distribution of the road segment comprises: fitting a straight line equation according to the positioning points on the road segment by linear regression; bringing the selected sample points into the straight line equation to obtain a number m greater than 0 and a number n less than 0; and when the ratio of the difference between m and n to the sum of m and n is less than or equal to a preset threshold, it is determined that the selected sample points meet the condition of two-side distribution of the road segment.

[0052] Specifically, the GPS signal on the road can be approximately obtained by linear regression to obtain a straight line equation: Ax+By+C=0. The data P(lat,lon) in the sample is substituted into the equation to count the number of Alat+Blon+C>0 as m and the number of Alat+Blon+C<0 as n. When m and n satisfy:

[0053]

[0054] It is considered that the sample meets the two-side distribution of the road, such as the north-south distribution.

[0055] In one embodiment, the determination of the actual point of the static drift according to the center point comprises: if the center point is on the road segment, the actual point of the static drift is the center point; and if the center point is not on the road segment, the actual point of the static drift is determined according to the foot of the perpendicular line from the center point to the road segment.

[0056] According to the distance algorithm in step 102, it is determined whether the point is on the road segment. If yes, the point PC is the actual point of the static drift. If not, the foot PV of the perpendicular line from the point to the road segment is calculated by multiplying the slope of the road segment by-1, and PV is the actual point of the static drift. Figure 2 is the second flowchart of the static drift correction method based on data analysis provided by the application, and the specific steps are as shown in Figure 2 .

[0057] Optionally, the above threshold values are M1=113, M2=0.85, M3=50 and M4=30%. Figure 3is a schematic diagram of a static drift simulation process provided by the present application, as shown in Figure 3 , wherein the common points are the point positions generating static drift before aggregation, the point 301 is the center point after aggregation, and the point 302 is the foot of the perpendicular line from the center point to the nearest line segment of the road.

[0058] The static drift correction device based on data analysis provided by the present application is described below, and the static drift correction device based on data analysis described below can be correspondingly referred to the static drift correction method based on data analysis described above.

[0059] Figure 4 is a structural schematic diagram of the static drift correction device based on data analysis provided by the present application, as shown in Figure 4 , the static drift correction device based on data analysis comprises a trajectory sampling module 401, a first filtering module 402, a second filtering module 403 and a drift positioning module 404. The trajectory sampling module 401 is used to obtain a plurality of continuous trajectory data in a preset time period to obtain sample points. The first filtering module 402 is used to filter out points with a distance difference greater than a preset condition from all sample points when there is static drift, to obtain candidate sample points. The second filtering module 403 is used to filter out points not on the road line segment from the candidate sample points to obtain selected sample points. The drift positioning module 404 is used to determine a center point according to the maximum latitude, the minimum latitude, the maximum longitude and the minimum longitude of the selected sample points, and to determine the actual point position of the static drift according to the center point.

[0060] The device embodiment provided by the embodiment of the present application is to realize the above-mentioned method embodiments, and the specific process and detailed content are referred to the above-mentioned method embodiments, which will not be described here.

[0061] The static drift correction device based on data analysis provided by the embodiment of the present application has the same implementation principle and technical effects as the above-mentioned static drift correction method based on data analysis, and for brief description, the part not mentioned in the static drift correction device based on data analysis can be referred to the corresponding content in the above-mentioned static drift correction method based on data analysis.

[0062] Figure 5 is a structural schematic diagram of the electronic device provided by the present application, as shown in Figure 5As shown, the electronic device can include a processor 501, a communications interface 502, a memory 503, and a communications bus 504, wherein the processor 501, the communications interface 502, and the memory 503 complete mutual communication through the communications bus 504. The processor 501 can invoke a logical instruction in the memory 503 to execute a static drift correction method based on data analysis, which includes: obtaining a plurality of continuous trajectory data within a preset time length to obtain sample points; in the presence of a static drift, filtering out points with an average distance difference greater than a preset condition from all sample points to obtain candidate sample points; filtering out points in the candidate sample points that are not on a road segment to obtain selected sample points; determining a center point according to the maximum latitude, the minimum latitude, the maximum longitude, and the minimum longitude of the selected sample points, and determining an actual point position of the static drift according to the center point.

[0063] In addition, the logical instruction in the memory 503 described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0064] On the other hand, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, and the computer can execute the static drift correction method based on data analysis provided by the above-mentioned method, which includes: obtaining a plurality of continuous trajectory data within a preset time length to obtain sample points; in the presence of a static drift, filtering out points with an average distance difference greater than a preset condition from all sample points to obtain candidate sample points; filtering out points in the candidate sample points that are not on a road segment to obtain selected sample points; determining a center point according to the maximum latitude, the minimum latitude, the maximum longitude, and the minimum longitude of the selected sample points, and determining an actual point position of the static drift according to the center point.

[0065] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the data analysis based static drift correction method provided by any of the above methods, the method comprising: obtaining a plurality of continuous trajectory data in a preset time period to obtain sample points; filtering out points having a distance difference greater than a preset condition from all sample points to obtain candidate sample points when there is a static drift; filtering out points not on a road segment from the candidate sample points to obtain selected sample points; determining a center point according to a maximum latitude, a minimum latitude, a maximum longitude and a minimum longitude of the selected sample points, and determining an actual point of the static drift according to the center point.

[0066] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0067] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary universal hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0068] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for static drift correction based on data analysis, characterized in that, The method comprises the following steps: acquiring a plurality of continuous trajectory data within a preset time length to obtain sample points; in the case of static drift, filtering out points with an average distance difference greater than a preset condition from all sample points to obtain candidate sample points; filtering out points not on the road segment from the candidate sample points to obtain selected sample points; fitting the positioning points on the road segment to obtain a straight line equation according to linear regression; bringing the selected sample points into the straight line equation to obtain a number m greater than 0 and a number n less than 0; when the difference between m and n, and the ratio of the sum of m and n are less than or equal to a preset threshold, it is determined that the selected sample points meet the distribution condition on both sides of the road segment; determining a center point according to the maximum latitude, minimum latitude, maximum longitude and minimum longitude of the selected sample points, and determining the actual point of the static drift according to the center point.

2. The method for static drift correction based on data analysis according to claim 1, wherein, Before the step of filtering out points with an average distance difference greater than a preset condition from all sample points, the method further comprises the following steps: determining a static drift point when the included angle between the straight lines of two continuous trajectories is less than a preset threshold; in the case that the number of static drift points is greater than a preset proportion, it is determined that there is a static drift.

3. The method for static drift correction based on data analysis according to claim 1, wherein, The step of filtering out points with an average distance difference greater than a preset condition from all sample points comprises the following step: filtering out sample points with an average distance difference greater than 3 times the standard deviation from all sample points.

4. The method for static drift correction based on data analysis according to claim 1, wherein, The step of determining the actual point of the static drift according to the center point comprises the following steps: if the center point is on the road segment, the actual point of the static drift is the center point; if the center point is not on the road segment, the actual point of the static drift is the foot of the perpendicular line from the center point to the road segment.

5. A static drift correction device based on data analysis, characterized by, The method comprises the following steps: a trajectory sampling module for acquiring a plurality of continuous trajectory data within a preset time length to obtain sample points; a first filtering module for filtering out points with an average distance difference greater than a preset condition from all sample points to obtain candidate sample points in the case of static drift; a second filtering module for filtering out points not on the road segment from the candidate sample points to obtain selected sample points; a drift positioning module for fitting the positioning points on the road segment to obtain a straight line equation according to linear regression; bringing the selected sample points into the straight line equation to obtain a number m greater than 0 and a number n less than 0; when the difference between m and n, and the ratio of the sum of m and n are less than or equal to a preset threshold, it is determined that the selected sample points meet the distribution condition on both sides of the road segment; determining a center point according to the maximum latitude, minimum latitude, maximum longitude and minimum longitude of the selected sample points, and determining the actual point of the static drift according to the center point.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the static drift correction method based on data analysis according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the static drift correction method based on data analysis according to any one of claims 1 to 4.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the static drift correction method based on data analysis according to any one of claims 1 to 4.

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