CN113223049B - Track data processing method and device - Google Patents
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Abstract
The invention provides a track data processing method and a track data processing device, wherein the track data processing method comprises the following steps: acquiring track data to be processed; based on a Targelas-Puck algorithm, performing thinning treatment on track data to be processed, and determining a first track point; screening the track data to be processed based on target attribute characteristics, and determining a second track point, wherein the target attribute characteristics are used for representing target service attributes corresponding to each track point in the track data to be processed; the target track data is determined based on the first track point and the second track point. According to the track data processing method and device, the first track point is obtained through thinning processing according to the Target Laplace-Prak algorithm, and the second track point is obtained through screening processing according to the target attribute characteristics, so that the target track data is obtained according to the first track point and the second track point, the data quantity can be reduced, the space occupied by the track data is reduced, the accuracy of information reflected by the track data is ensured, and the utilization efficiency of the track data is improved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a track data processing method and apparatus.
Background
In the intelligent traffic field, track data is generated by moving objects such as vehicles, ships and pedestrians, when the track data is utilized, the processes of storage, analysis, transmission and display exist, and if the track data is high in density, the storage space occupied by the track data is too large, so that the track data needs to be thinned.
Thinning means that the number of points on the curve is reduced to the greatest extent under the condition that the shape of the vectorized curve is unchanged. However, the current method for thinning the track data is single, so that the processed track data is easy to distort, the accuracy is insufficient, and the utilization efficiency of the track data is low.
Disclosure of Invention
The invention provides a track data processing method and device, which are used for solving the defects that track data processed in the prior art is easy to distort and insufficient in accuracy, so that the utilization efficiency of the track data is low, realizing the reduction of data volume, reducing the space occupied by the track data, ensuring the accuracy of information reflected by the track data and improving the utilization efficiency of the track data.
The invention provides a track data processing method, which comprises the following steps: acquiring track data to be processed; based on a Targelas-Puck algorithm, performing thinning treatment on the track data to be processed, and determining a first track point; screening the track data to be processed based on target attribute features, and determining a second track point, wherein the target attribute features are used for representing target service attributes corresponding to each track point in the track data to be processed; and determining target track data based on the first track point and the second track point.
According to the track data processing method provided by the invention, the track data to be processed is screened based on the target attribute characteristics, and a second track point is determined, which comprises the following steps: and comparing the attribute characteristics of each track point in the track data to be processed with the target attribute characteristics, and taking the track point conforming to the target attribute characteristics as a second track point.
According to the track data processing method provided by the invention, the target service attribute comprises the following steps: the motion attribute is used for representing motion state information of the track point, the region attribute is used for representing position state information of the track point, and the period attribute is used for representing time period information corresponding to the track point.
According to the track data processing method provided by the invention, the motion attribute comprises the following steps: acceleration, deceleration, or dwell properties; or the region attribute includes: intersection attributes or berth attributes; or the period attribute includes: emergency period attributes.
According to the track data processing method provided by the invention, the track data to be processed is subjected to thinning processing based on the Fabry-Perot algorithm, and a first track point is determined, which comprises the following steps: obtaining maximum distance values between straight lines formed by connecting all track points in track data with head and tail track points of the track data; and performing thinning processing on the track data based on the target distance threshold value and the maximum distance value, and determining the first track point.
According to the track data processing method provided by the invention, the track data is subjected to thinning processing based on the target distance threshold value and the maximum distance value, and the first track point is determined, which comprises the following steps: and if the maximum distance value is smaller than the target distance threshold value, all track points between the head track point and the tail track point of the track data are deleted, and the head track point and the tail track point of the track data are used as the first track point.
According to the track data processing method provided by the invention, the track data is subjected to thinning processing based on the target distance threshold value and the maximum distance value, and the first track point is determined, which comprises the following steps: and confirming that the maximum distance value is greater than or equal to the target distance threshold, dividing the track data into two sections of reference track data through track points corresponding to the maximum distance value and the head-tail track points, and respectively performing thinning processing on the two sections of reference track data based on the Target Laplace-Prak algorithm.
The present invention also provides a track data processing apparatus, including: the acquisition module is used for acquiring track data to be processed; the first determining module is used for performing thinning processing on the track data to be processed based on a Target Laplace-Prak algorithm to determine a first track point; the second determining module is used for screening the track data to be processed based on target attribute characteristics, determining second track points, wherein the target attribute characteristics are used for representing target service attributes corresponding to each track point in the track data to be processed; and a third determining module, configured to determine target track data based on the first track point and the second track point.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, the processor implementing the steps of any of the trajectory data processing methods described above when executing the computer program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the trajectory data processing method as described in any one of the above.
According to the track data processing method, the first track point is obtained through thinning processing according to the Target Laplace-Prak algorithm, and the second track point is obtained through screening processing according to the target attribute characteristics, so that the target track data is obtained according to the first track point and the second track point, the data amount can be reduced, the space occupied by the track data is reduced, the accuracy of information reflected by the track data is ensured, and the utilization efficiency of the track data is improved.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a track data processing method according to the present invention;
FIG. 2 is a second flow chart of the track data processing method according to the present invention;
FIG. 3 is a schematic diagram of a trajectory data processing method provided by the present invention;
FIG. 4 is a schematic diagram of a track data processing apparatus according to the present invention;
Fig. 5 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The track data processing method and apparatus of the present invention are described below with reference to fig. 1 to 5.
As shown in fig. 1, the present invention provides a track data processing method, which includes: steps 110-140 are as follows.
In step 110, track data to be processed is obtained.
It can be understood that the track data to be processed can be tracks generated by the motion of moving objects such as vehicles, ships or pedestrians, each track point in the track data can be represented in a longitude and latitude form and can be represented in a coordinate form, the track data to be processed can be acquired by adopting GPS equipment, radar equipment, cameras or other sensors, the track data to be processed is composed of a plurality of continuous track points, the motion tracks of the moving objects are formed by connecting all track points, the motion rules of the moving objects can be reflected, the track data to be processed is processed, and the motion information of the moving objects can be extracted.
And 120, performing thinning treatment on the track data to be processed based on the Douglas-Puck algorithm, and determining a first track point.
The Douglas-pramipexole algorithm (Douglas-Peucker algorithm, also known as the lamer-Douglas-pramipexole algorithm, the iterative adaptive point algorithm, and the split and merge algorithm) is an algorithm that approximately represents a curve as a series of points and reduces the number of points. Its advantages are no translation and rotation variability, and certain sampling result after given curve and threshold.
The track data to be processed is subjected to thinning processing by the Targelas-Puck algorithm, partial characteristic points which have little influence on forming a complete track can be removed, and the maximum characteristic points of the track data are reserved, so that the obtained first track points can simply reflect the characteristics of the track data, the simplicity can be ensured, and the characteristics of the track data can be displayed more completely.
And 130, screening the track data to be processed based on target attribute characteristics, and determining a second track point, wherein the target attribute characteristics are used for representing target service attributes corresponding to each track point in the track data to be processed.
It is understood that each track point in the track data has a corresponding service attribute, such as a time attribute, an area attribute or a motion attribute, and each track point in the track data may be represented in terms of longitude and latitude, where each track point corresponds to longitude and latitude information and a service attribute.
The target attribute features are set, all track points of the track data to be processed are screened, the service attribute of the track data to be processed can be compared with the target attribute features, and the track points which accord with the target attribute features are used as second track points. It is of course also possible to use the track point associated with the target attribute feature as the second track point, not limited to the track point conforming to the target attribute feature.
The target attribute features can be attributes which are widely focused or of higher importance in business of a specific industry, and the track points conforming to the target attribute features are universal in business of the industry and have uniform basic data support. Such as: port data is widely used by various shipping institutions, enterprises and organizations as the basis data for the shipping industry. The target attribute features may be: if the track points enter the port area, the track points conforming to the target attribute features are track points entering the port area, and the track data are screened based on the target attribute features, so that the track points in the port area are all reserved, and the track points in the non-port area are deleted, so that a second track point is obtained.
The mode of selecting the service key points is of great general meaning and value, can be multiplexed by industries, and the obtained second track points are track points which are generally concerned and required by the industries, so that the service analysis requirements can be met while the track data volume is reduced.
And 140, determining target track data based on the first track point and the second track point.
It should be understood that, the target track data is determined according to the first track point and the second track point, for example, the first track point and the second track point may be obtained and combined, and the target track data includes both the first track point and the second track point. Of course, a part of the track points may be selected from the first track point and the second track point, for example, the track points may be selected according to a preset distance value.
As shown in fig. 2, the target track data is combined with the first track point and the second track point, so that the advantages of the two screening methods can be concentrated, the track data quantity is reduced, meanwhile, important business key data are reserved, and the information reflected by the track points is more complete and has higher accuracy.
According to the track data processing method, the first track point is obtained through thinning processing according to the Target Laplace-Prak algorithm, and the second track point is obtained through screening processing according to the target attribute characteristics, so that the target track data is obtained according to the first track point and the second track point, the data amount can be reduced, the space occupied by the track data is reduced, the accuracy of information reflected by the track data is ensured, and the utilization efficiency of the track data is improved.
In some embodiments, the step 130 of screening the trajectory data to be processed based on the target attribute features to determine the second trajectory point includes: and comparing the attribute characteristics of each track point in the track data to be processed with the target attribute characteristics, and taking the track point which accords with the target attribute characteristics as a second track point.
It is understood that each track point in the track data has a corresponding service attribute, such as a time attribute, an area attribute or a motion attribute, and each track point in the track data may be represented in terms of longitude and latitude, where each track point corresponds to longitude and latitude information and a service attribute.
The target attribute features are set, all track points of the track data to be processed are screened, the service attribute of the track data to be processed can be compared with the target attribute features, and the track points which accord with the target attribute features are used as second track points.
Such as: the service area traffic flow data is basic data of the expressway, and the target attribute characteristics can be as follows: and if the track points enter the service area, the track points conforming to the target attribute features are track points entering the service area, and the track data are screened based on the target attribute features, namely the track points in the service area are all reserved, and the track points in the non-service area are deleted, so that a second track point is obtained.
In some embodiments, the target business attributes include: the motion attribute is used for representing motion state information of the track point, the region attribute is used for representing position state information of the track point, or the time period attribute is used for representing time period information corresponding to the track point.
It may be appreciated that the target service attribute may be a motion attribute, an area attribute or a period attribute, and each track point may carry at least one of the motion attribute, the area attribute and the period attribute, so that when the target service attribute is determined to be one of the motion attribute, the area attribute or the period attribute, the track data may be screened, for example, the target service attribute may be determined to be the period attribute, for example, the target service attribute may be track point data corresponding to 8:00-10:00 of 3 months of 2021, and when the track point data is screened, the track points corresponding to the period may be accurately matched, and the track points are used as second track points.
In some embodiments, the motion attributes include: acceleration, deceleration, or dwell properties; or the region attribute includes: intersection attributes or berth attributes; or the period attribute includes: emergency period attributes.
It will be appreciated that the motion attribute may represent a motion behavior characteristic of a moving object, for example, an acceleration point, a deceleration point or a dwell point, and mainly is analyzed from the perspective of the motion point, and may be applied to a scene such as: and (3) analyzing running behaviors such as overspeed or sudden braking of the vehicle, and analyzing loitering events of the ship.
The region attribute may represent a location feature of the moving object, that is, sensitive to the trajectory of a specific region, and may be applied to a scene such as: traffic departments may be more sensitive to the trajectories of vehicles or pedestrians at intersections and berthing behavior researchers may be more sensitive to trajectories of vessels during entry into berthing.
The period attribute may represent a temporal characteristic of the moving object, i.e. sensitive to a specific period of time, applicable scenarios such as: during the time period when an emergency or a major disaster occurs, the trajectory of a person or a vehicle is analyzed.
The motion attribute, the region attribute and the time period attribute can be combined to define the business key track point, for example, the time attribute and the region attribute can be combined to analyze scenes such as people flow at a certain moment in a market.
As shown in fig. 3, in some embodiments, the step 120 of thinning the track data to be processed based on the dawsonite-pock algorithm to determine the first track point includes: steps 121-122 are as follows.
And 121, acquiring a maximum distance value between all track points in the track data and a straight line formed by connecting the head track points and the tail track points of the track data.
It will be appreciated that the initial point and the end point of the track data are connected by a straight line, and the vertical distance between all the track points in the track data and the straight line is calculated, that is, a plurality of distance values are obtained, and the maximum distance value is obtained from the plurality of distance values, that is, the track point corresponding to the maximum distance value is determined.
And 122, performing thinning processing on the track data based on the target distance threshold value and the maximum distance value, and determining a first track point.
The maximum distance value and the target distance threshold value may be compared to perform thinning processing on the track data, for example, if the maximum distance value is smaller than the target distance threshold value, all track points between the head track point and the tail track point of the track data are deleted, and the head track point and the tail track point of the track data are used as the first track point.
Of course, if the maximum distance value is greater than or equal to the target distance threshold, the track data can be divided into two sections of reference track data through the track points corresponding to the maximum distance value and the head-tail track points, and the two sections of reference track data are subjected to thinning processing respectively based on the morse-plck algorithm.
As a method for retaining the maximum feature points, the moras-pramipexole algorithm has the basic idea that: virtually connecting a straight line to the first and last points of each curve, solving the distance between all points and the straight line, finding out a maximum distance value dmax, and comparing the maximum distance value dmax with a target distance threshold D; if dmax < D, the middle points on this curve are all truncated; if dmax > =d, reserving the coordinate point corresponding to dmax, dividing the curve into two parts by taking the point as a boundary, and repeatedly using the method for the two parts.
It can be understood that if the maximum distance value is greater than or equal to the target distance threshold, the track point corresponding to the maximum distance value and the initial track point are connected by a straight line, then the track point corresponding to the maximum distance value and the end track point are connected by a straight line, and then the two sections of reference track data are respectively subjected to thinning treatment.
That is, the above process is repeated, iterative operation is performed, the maximum distance value between each section of straight line and the corresponding track point is continuously selected, and the maximum distance value and the corresponding track point are sequentially selected and selected until no track point meets the condition that the maximum distance value is greater than or equal to the target distance threshold value, and finally the track point coordinate meeting the given target distance threshold value is obtained.
The track data processing apparatus provided by the present invention will be described below, and the track data processing apparatus described below and the track data processing method described above may be referred to correspondingly to each other.
As shown in fig. 4, the present invention also provides a track data processing apparatus, including: the acquisition module 410, the first determination module 420, the second determination module 430, and the third determination module 440.
An acquisition module 410, configured to acquire trajectory data to be processed.
The first determining module 420 is configured to perform thinning processing on the track data to be processed based on a dawster-plck algorithm, and determine a first track point.
The second determining module 430 is configured to perform screening processing on the track data to be processed based on the target attribute features, determine a second track point, and the target attribute features are used to characterize the target service attribute corresponding to each track point in the track data to be processed.
The third determining module 440 is configured to determine the target track data based on the first track point and the second track point.
Fig. 5 illustrates a physical schematic diagram of an electronic device, as shown in fig. 5, which may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform a trace data processing method comprising: acquiring track data to be processed; based on a Targelas-Puck algorithm, performing thinning treatment on track data to be processed, and determining a first track point; screening the track data to be processed based on target attribute characteristics, and determining a second track point, wherein the target attribute characteristics are used for representing target service attributes corresponding to each track point in the track data to be processed; the target track data is determined based on the first track point and the second track point.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the track data processing method provided by the above methods, the method comprising: acquiring track data to be processed; based on a Targelas-Puck algorithm, performing thinning treatment on track data to be processed, and determining a first track point; screening the track data to be processed based on target attribute characteristics, and determining a second track point, wherein the target attribute characteristics are used for representing target service attributes corresponding to each track point in the track data to be processed; the target track data is determined based on the first track point and the second track point.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the above-provided trajectory data processing methods, the method comprising: acquiring track data to be processed; based on a Targelas-Puck algorithm, performing thinning treatment on track data to be processed, and determining a first track point; screening the track data to be processed based on target attribute characteristics, and determining a second track point, wherein the target attribute characteristics are used for representing target service attributes corresponding to each track point in the track data to be processed; the target track data is determined based on the first track point and the second track point.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A track data processing method, comprising:
Acquiring track data to be processed;
Based on a Targelas-Puck algorithm, performing thinning treatment on the track data to be processed, and determining a first track point;
screening the track data to be processed based on target attribute features, and determining a second track point, wherein the target attribute features are used for representing target service attributes corresponding to each track point in the track data to be processed;
and determining target track data based on the first track point and the second track point.
2. The track data processing method according to claim 1, wherein the filtering the track data to be processed based on the target attribute features to determine a second track point includes:
and comparing the attribute characteristics of each track point in the track data to be processed with the target attribute characteristics, and taking the track point conforming to the target attribute characteristics as a second track point.
3. The trajectory data processing method of claim 2, wherein the target business attributes include: the motion attribute is used for representing motion state information of the track point, the region attribute is used for representing position state information of the track point, and the period attribute is used for representing time period information corresponding to the track point.
4. A trajectory data processing method as claimed in claim 3, characterized in that said motion attribute comprises: acceleration, deceleration, or dwell properties; or the region attribute includes: intersection attributes or berth attributes; or the period attribute includes: emergency period attributes.
5. The track data processing method according to any one of claims 1 to 4, wherein the performing thinning processing on the track data to be processed based on the daglas-plck algorithm, and determining a first track point, includes:
obtaining maximum distance values between straight lines formed by connecting all track points in track data with head and tail track points of the track data;
and performing thinning processing on the track data based on the target distance threshold value and the maximum distance value, and determining the first track point.
6. The trajectory data processing method of claim 5, wherein the thinning of the trajectory data based on the target distance threshold and the maximum distance value, determining the first trajectory point, comprises:
and if the maximum distance value is smaller than the target distance threshold value, all track points between the head track point and the tail track point of the track data are deleted, and the head track point and the tail track point of the track data are used as the first track point.
7. The trajectory data processing method of claim 5, wherein the thinning of the trajectory data based on the target distance threshold and the maximum distance value, determining the first trajectory point, comprises:
And confirming that the maximum distance value is greater than or equal to the target distance threshold, dividing the track data into two sections of reference track data through track points corresponding to the maximum distance value and the head-tail track points, and respectively performing thinning processing on the two sections of reference track data based on the Target Laplace-Prak algorithm.
8. A track data processing apparatus, comprising:
the acquisition module is used for acquiring track data to be processed;
the first determining module is used for performing thinning processing on the track data to be processed based on a Target Laplace-Prak algorithm to determine a first track point;
The second determining module is used for screening the track data to be processed based on target attribute characteristics, determining second track points, wherein the target attribute characteristics are used for representing target service attributes corresponding to each track point in the track data to be processed;
And a third determining module, configured to determine target track data based on the first track point and the second track point.
9. 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 implements the steps of the trajectory data processing method according to any one of claims 1 to 7 when the program is executed by the processor.
10. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the trajectory data processing method according to any one of claims 1 to 7.
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| CN111654290A (en) * | 2020-04-24 | 2020-09-11 | 北京中交兴路车联网科技有限公司 | Method, device and equipment for compressing vehicle track data and storage medium |
| CN112530158A (en) * | 2020-10-27 | 2021-03-19 | 北方信息控制研究院集团有限公司 | Road network supplementing method based on historical track |
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