CN112598767A - Trajectory behavior analysis method based on space-time big data, terminal device and storage medium - Google Patents
Trajectory behavior analysis method based on space-time big data, terminal device and storage medium Download PDFInfo
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- 238000004458 analytical method Methods 0.000 title claims abstract description 14
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- 230000011218 segmentation Effects 0.000 claims abstract description 4
- 230000006399 behavior Effects 0.000 claims description 25
- 238000004590 computer program Methods 0.000 claims description 17
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Abstract
The invention relates to a trajectory behavior analysis method based on space-time big data, a terminal device and a storage medium, wherein the method comprises the following steps: s1, acquiring a space-time big data list of all objects; s2, according to the latitude and longitude intervals with the maximum tracks of all the objects, carrying out average segmentation through each set network and the side length of the square to obtain a plurality of square geographic space grids; s3, segmenting and classifying the object track data according to date, hour and space grids to obtain a grid array of the object route track according to a time sequence; s4, calculating the value of the active hour per day, the value of the active network per day and the value of the linear track distance of the grid per day of each object; s5, calculating the average value of the track distance values of all the objects; s6, calculating the daily behavior track score of each object; and S7, comprehensively analyzing the daily behavior track scores of all the objects to obtain an abnormal behavior track object. The illegal operating vehicle is identified through the method, the analysis is more intelligent and efficient, and accurate law enforcement is realized.
Description
Technical Field
The invention relates to the field of big data analysis, in particular to a trajectory behavior analysis method based on space-time big data, terminal equipment and a storage medium.
Background
The illegal operation means that operation is carried out without obtaining operation right by law, that is, operation certificates which are verified and issued by relevant departments of charge are not obtained according to regulations and operation is carried out beyond a verification range. For various reasons, vehicles are increasingly operated illegally. On one hand, the illegal operation vehicle disturbs the normal operation order, and on the other hand, the illegal operation vehicle brings a lot of potential safety hazards. At present, illegal operation vehicles are mainly checked manually, so that the efficiency is low and the effect is poor.
Disclosure of Invention
The invention aims to provide a trajectory behavior analysis method based on space-time big data, a terminal device and a storage medium to solve the problems. Therefore, the invention adopts the following specific technical scheme:
according to an aspect of the present invention, there is provided a trajectory behavior analysis method based on spatiotemporal big data, which may include the steps of:
s1, acquiring a space-time big data list of all objects;
s2, according to the latitude and longitude intervals with the maximum tracks of all the objects, carrying out average segmentation through each set network and the side length of the square to obtain a plurality of square geographic space grids;
s3, segmenting and classifying the object track data according to date, hour and space grids to obtain a grid array of the object route track according to a time sequence;
s4, calculating the value of the active hour per day, the value of the active network per day and the value of the linear track distance of the grid per day of each object;
s5, calculating the average value of the track distance values of all the objects;
s6, calculating the daily behavior track score of each object;
and S7, comprehensively analyzing the daily behavior track scores of all the objects to obtain an abnormal behavior track object.
Further, the grid sequence in S3 is represented by P ═ P1(xn,ym),P2(xn,ym),P3(xn,ym)…Pk(xn,ym)]In which P isk(xn,ym) The grid coordinates representing the kth trajectory.
Further, the calculation formula of the active hour per day score H in S4 is:
wherein G is an activity constant, G is an hour increment coefficient of each day, and h is the total number of hours with data in natural hours of a day.
Further, the calculation formula of the daily active network score W in S4 is:
wherein F is an active grid score constant, F is an active space increment coefficient, and n is an active space grid number.
Further, the calculation formula of the distance value S of the grid straight-line trajectory per day in S4 is:
wherein L is the side length of a single grid.
Further, the daily behavior trajectory score Z of each object in S6 is calculated as
Wherein, R is the average value of all object track distance values.
According to another aspect of the present invention, there is also provided a terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, which when executed by the processor implements the steps of the method as described above.
According to yet another aspect of the invention, there is also a computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method as described above.
By adopting the technical scheme, the invention has the beneficial effects that: the method is applied to the analysis scene of the urban social vehicle track behaviors, and by extracting the space-time big data information recorded by the passing of the urban vehicle through the bayonet and further analyzing the track data of the track through the algorithm in the invention to calculate the behavior score, analyzing the track behaviors of the vehicle and identifying the vehicle information suspected of illegal operation. According to the method, the illegal operating vehicles in urban operation are identified, the traditional manual troubleshooting method for the illegal operating vehicles is changed, analysis is more intelligent and efficient, multi-dimensional extraction is carried out on the vehicle behavior characteristics, and accurate law enforcement is realized.
Drawings
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures. Elements in the figures are not drawn to scale and like reference numerals are generally used to indicate like elements.
FIG. 1 is a flow chart of a trajectory behavior analysis method based on spatiotemporal big data;
FIG. 2 is a schematic diagram of the partitioning of a geospatial grid in accordance with the present invention;
fig. 3 is a schematic diagram of the trajectory data in the present invention.
Detailed Description
The invention will now be further described with reference to the accompanying drawings and detailed description.
As shown in fig. 1, a trajectory behavior analysis method based on spatiotemporal big data may include the following steps:
s1, acquiring a space-time big data list of all objects. For example, vehicle passing records are obtained from monitoring data of each intersection and each gate.
And S2, according to the latitude and longitude intervals with the maximum tracks of all the objects, carrying out average segmentation through each set network and the side length L of the square to obtain geographic space grids of n squares, as shown in figure 2.
And S3, segmenting and classifying the object track data according to date, hour and space grids to obtain a grid array of the object route track according to a time sequence. Fig. 3 shows a specific trajectory of an object. The trajectory data is shown in the following table:
| serial number | Date | Hour(s) | Geospatial grid |
| 1 | 2020-09-16 | 15 | p1(x1,y0) |
| 2 | 2020-09-16 | 15 | P2(x1,y1) |
| 3 | 2020-09-16 | 15 | P3(x1,y2) |
| 4 | 2020-09-16 | 15 | P4(x1,y4) |
| 5 | 2020-09-16 | 16 | P5(x4,y4) |
| 6 | 2020-09-16 | 16 | P6(x6,y5) |
| 7 | 2020-09-16 | 16 | P7(x6,y3) |
| 8 | 2020-09-16 | 16 | P8(x8,y4) |
| 9 | 2020-09-16 | 16 | P9(x10,y5) |
| 10 | 2020-09-16 | 16 | P10(x10,y6) |
| 11 | 2020-09-16 | 17 | P11(x10,y8) |
| 12 | 2020-09-16 | 17 | P12(x11,y8) |
According to the above table example, we can obtain the grid number sequence P of the object route trajectory according to the time sequence:
P＝[P1(xn,ym),P2(xn,ym),P3(xn,ym)…Pk(xn,ym)]，
wherein P isk(xn,ym) The grid coordinates representing the kth trajectory.
S4, calculating the value of the active hour per day, the value of the active network per day and the value of the linear track distance of the grid per day of each object, wherein the calculation formula is as follows:
a) daily activity hour score
The total number of hours h with data in natural hours is counted, h < ═ 24, e.g., h equals 3 for the above table example.
The active hour score H is calculated by the formula:
wherein G is an activity constant (G >0), larger indicates more activity, and G is an hour increment coefficient per day.
b) Daily active grid score
The value of the active grid score per day needs to be defined according to the application scene, wherein the value of the active grid score constant F is larger than the value range F0, and the value of the active space increment coefficient F is larger than the value range F1. The calculation formula of the daily active network score W is as follows:
wherein n is the number of active space grids.
c) Linear distance value of daily trace grid
Knowing that the side length of a single grid is L, the linear distance value S of the grid of the track of the object every day can be calculated, and the specific calculation formula is as follows:
when k is less than or equal to 1, S is 0;
when k is>When the pressure of the mixture is 1, the pressure is lower,
wherein, Pnx，Pny represents the x, y coordinates of the grid on which the nth trace is located, respectively.
S5, calculating an average value R of all object track distance values;
where m denotes the total number of objects and Si denotes the daily trajectory grid linear distance value of the ith object.
And S6, calculating the daily behavior track score of each object. Knowing the average daily behavior track scores R of all objects, setting a daily track score constant M according to an actual application scene, wherein the value range M is larger than 0, and then calculating the daily track score Z according to the following formula:
and S7, comprehensively analyzing the daily behavior track scores of all the objects to obtain an abnormal behavior track object. Some abnormal track behaviors can be found through track score comparison between the objects.
The method is applied to the analysis scene of the urban social vehicle track behaviors, and by extracting the space-time big data information recorded by the passing of the urban vehicle through the bayonet and further analyzing the track data of the track through the algorithm in the invention to calculate the behavior score, analyzing the track behaviors of the vehicle and identifying the vehicle information suspected of illegal operation. According to the method, the illegal operating vehicles in urban operation are identified, the traditional manual troubleshooting method for the illegal operating vehicles is changed, analysis is more intelligent and efficient, multi-dimensional extraction is carried out on the vehicle behavior characteristics, and accurate law enforcement is realized.
In an embodiment of the present invention, there is also provided a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps S1-S7 of the method when executing the computer program.
Further, the terminal device may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor, a memory. It is understood by those skilled in the art that the above-mentioned constituent structure of the terminal device is only an example of the terminal device, and does not constitute a limitation to the terminal device, and may include more or less components than the above, or combine some components, or different components, for example, the terminal device may further include an input/output device, a network access device, a bus, and the like, which is not limited by the embodiment of the present invention.
Further, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device and connects the various parts of the entire terminal device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may perform various functions by operating or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Embodiments of the present invention further provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements steps S1-S7 of the above method according to the embodiments of the present invention.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method steps S1-S7 of the present invention may be implemented by a computer program, which can be stored in a computer-readable storage medium and can be executed by a processor to implement the steps of the above-mentioned method embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A trajectory behavior analysis method based on space-time big data is characterized by comprising the following steps:
s1, acquiring a space-time big data list of all objects;
s2, according to the latitude and longitude intervals with the maximum tracks of all the objects, carrying out average segmentation through each set network and the side length of the square to obtain a plurality of square geographic space grids;
s3, segmenting and classifying the object track data according to date, hour and space grids to obtain a grid array of the object route track according to a time sequence;
s4, calculating the value of the active hour per day, the value of the active network per day and the value of the linear track distance of the grid per day of each object;
s5, calculating the average value of the track distance values of all the objects;
s6, calculating the daily behavior track score of each object;
and S7, comprehensively analyzing the daily behavior track scores of all the objects to obtain an abnormal behavior track object.
2. The method of claim 1, wherein the grid number sequence in S3 is represented as P ═ P1(xn,ym),P2(xn,ym),P3(xn,ym)…Pk(xn,ym)]In which P isk(xn,ym) The grid coordinates representing the kth trajectory.
3. The method of claim 2, wherein the activity hour per day score H in S4 is calculated by:
wherein G is an activity constant, G is an hour increment coefficient of each day, and h is the total number of hours with data in natural hours of a day.
4. The method of claim 3, wherein the active network score per day W in S4 is calculated by:
wherein F is an active grid score constant, F is an active space increment coefficient, and n is an active space grid number.
5. The method of claim 4, wherein the distance value S of the grid linear trajectory per day in S4 is calculated by:
wherein L is the side length of a single grid.
6. The method of claim 5, wherein the daily behavior trajectory score Z of each object in S6 is calculated as
Wherein, R is the average value of the track distance values of all the objects, and M is a daily track score constant.
7. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-6 when executing the computer program.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-6.
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