CN112598767B - Track behavior analysis method based on space-time big data, terminal equipment and storage medium - Google Patents
Track behavior analysis method based on space-time big data, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a track behavior analysis method based on space-time big data, a terminal device and a storage medium, wherein the method can comprise the following steps: s1, acquiring a space-time big data list of all objects; s2, carrying out average segmentation through each set network and square side length according to the longitude and latitude interval with the maximum track of all objects to obtain a plurality of square geographic space grids; s3, dividing and classifying the object track data according to the date, the hour and the space grids to obtain grid series of the object route track according to the time sequence; s4, calculating the daily active hour score, the daily active network score and the daily grid linear track distance value of each object; s5, calculating the average value of all the object track distance values; s6, calculating the daily behavior track score of each object; s7, comprehensively analyzing the daily behavior track scores of all the objects to obtain the abnormal behavior track objects. By the method, the illegal operation vehicles are identified, analysis is 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 track behavior analysis method based on space-time big data, terminal equipment and a storage medium.
Background
The illegal operation refers to the operation performed without legal operation right, i.e. the operation certificate of the related administrative department is not received according to the regulations and the operation is performed beyond the approval range. For various reasons, there are more and more vehicles for illegal operations. The illegal operation vehicles disturb the normal operation order on one hand and bring a lot of potential safety hazards on the other hand. At present, the illegal operation vehicle is mainly checked manually, the efficiency is low, and the effect is poor.
Disclosure of Invention
The invention aims to provide a track behavior analysis method based on space-time big data, terminal equipment and a storage medium so as to solve the problems. For this purpose, 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 spatio-temporal big data, which may include the steps of:
S1, acquiring a space-time big data list of all objects;
s2, carrying out average segmentation through each set network and square side length according to the longitude and latitude interval with the maximum track of all objects to obtain a plurality of square geographic space grids;
S3, dividing and classifying the object track data according to the date, the hour and the space grids to obtain grid series of the object route track according to the time sequence;
s4, calculating the daily active hour score, the daily active network score and the daily grid linear track distance value of each object;
s5, calculating the average value of all the object track distance values;
S6, calculating the daily behavior track score of each object;
s7, comprehensively analyzing the daily behavior track scores of all the objects to obtain the abnormal behavior track objects.
Further, the grid array in S3 is denoted P＝[P1(xn,ym),P2(xn,ym),P3(xn,ym)…Pk(xn,ym)], where P k(xn,ym) represents the grid coordinates of the kth track.
Further, the calculation formula of the daily active hour score H in S4 is: Where G is the activity constant, G is the daily hour increment factor, and h is the total number of hours with data in the natural hours of the day.
Further, the calculation formula of the daily active network score W in S4 is: Wherein F is the active grid score constant, F is the active space increment coefficient, and n is the active space grid number.
Further, in S4, the calculation formula of the distance value S of the linear trajectory of the grid per day is: where L is the individual grid side length.
Further, the calculation formula of the daily behavior trace score Z of each object in S6 is as followsWherein R is the average value of all the object track distance values.
According to another aspect of the 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, 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 analysis scenes of urban social vehicle track behaviors, and by extracting space-time big data information recorded by the passing of the urban vehicle through a clamping port, further by the algorithm in the method, track data of the track are analyzed and calculated to calculate a behavior score, track behaviors of the vehicle are analyzed, and vehicle information suspected of illegal operation is identified. The method and the system for identifying the illegal operation vehicles in urban operation change the traditional manual investigation method of the illegal operation vehicles, analyze the behavior characteristics of the vehicles more intelligently and efficiently, extract the behavior characteristics of the vehicles in a multi-dimensional way, and realize accurate law enforcement.
Drawings
For further illustration of the various embodiments, the invention is provided with the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments and together with the description, serve to explain the principles of the embodiments. With reference to these matters, one of ordinary skill in the art will understand other possible embodiments and advantages of the present invention. The components in the figures are not drawn to scale and like reference numerals are generally used to designate like components.
FIG. 1 is a flow chart of a method of trajectory behavior analysis based on spatio-temporal big data;
FIG. 2 is a schematic illustration of the partitioning of a geospatial grid in accordance with the present invention;
Fig. 3 is a schematic diagram of trace data in the present invention.
Detailed Description
The invention will now be further described with reference to the drawings and detailed description.
As shown in fig. 1, a track behavior analysis method based on spatio-temporal big data may include the following steps:
s1, acquiring a space-time big data list of all objects. For example, a vehicle passing record is acquired from monitoring data of each intersection and each gate.
S2, according to the longitude and latitude interval with the largest track of all the objects, carrying out average segmentation through each set network and square side length L to obtain n square geographic space grids, as shown in fig. 2.
S3, dividing and classifying the object track data according to the date, hour and space grids to obtain grid series of the object route track according to the time sequence. Fig. 3 shows a specific trajectory of an object. The trajectory data is shown in the following table:
| Sequence number | Date of day | Hours of | 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) |
From the above representation, we can get a grid array P of object route trajectories according to time order:
P＝[P1(xn,ym),P2(xn,ym),P3(xn,ym)…Pk(xn,ym)],
Where P k(xn,ym) represents the grid coordinates of the kth track.
S4, calculating the daily active hour score, the daily active network score and the daily grid linear track distance value of each object, wherein the calculation formula is as follows:
a) Daily active hour score
The total number of hours h with data within a natural hour is counted, h < = 24, e.g. for the table example above, h equals 3.
The active hour time division value H is calculated according to the formula:
Where G is an activity constant (G > 0), larger indicates more active, and G is an hourly increment coefficient per day.
B) Daily active grid score
The value of the corresponding constant and coefficient is defined according to the application scene, wherein the value range F >0 of the active grid value constant F and the value range F >1 of the active space increment coefficient F are required for the active grid value every day. The daily active network score W is calculated as:
Where n is the number of active space grids.
C) Daily trajectory grid straight line distance value
Knowing that the side length of a single grid is L, the linear distance value S of the track grid of the object per day can be calculated, and the specific calculation formula is as follows:
When k is less than or equal to 1, s=0;
When k is greater than 1 and is equal to,
Wherein P nx,Pn y represents the x, y coordinates of the grid where the nth track is located, respectively.
S5, calculating an average value R of all the object track distance values;
Where m represents the total number of objects and Si represents the daily trajectory grid straight line distance value of the ith object.
S6, calculating the daily behavior track score of each object. Knowing the average daily behavior track score R of all objects, setting a daily track score constant M according to an actual application scene, and calculating a daily track score Z if a value range M is more than 0 according to the following calculation formula:
s7, comprehensively analyzing the daily behavior track scores of all the objects to obtain the abnormal behavior track objects. Through track score comparison among objects, the behavior of some abnormal tracks can be found.
The method is applied to analysis scenes of urban social vehicle track behaviors, and by extracting space-time big data information recorded by the passing of the urban vehicle through a clamping port, further by the algorithm in the method, track data of the track are analyzed and calculated to calculate a behavior score, track behaviors of the vehicle are analyzed, and vehicle information suspected of illegal operation is identified. The method and the system for identifying the illegal operation vehicles in urban operation change the traditional manual investigation method of the illegal operation vehicles, analyze the behavior characteristics of the vehicles more intelligently and efficiently, extract the behavior characteristics of the vehicles in a multi-dimensional way, and realize accurate law enforcement.
In an embodiment 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, wherein the processor implements steps S1-S7 of the method as described above when executing the computer program.
Further, the terminal device may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the above-described constituent structures of the terminal device are merely examples of the terminal device, and do not constitute limitation of the terminal device, and may include more or fewer components than those described above, or may combine some components, or different components, e.g., the terminal device may further include an input/output device, a network access device, a bus, etc., which is not limited by the embodiment of the present invention.
Further, the Processor may be a central processing unit (Central Processing Unit, CPU), other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the terminal device, and which connects various parts of the entire terminal device using various interfaces and lines.
The memory may be used to store the computer program and/or modules, and the processor performs various functions by running or executing the computer program and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program 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, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps S1-S7 of the method of the embodiment of the invention when being executed by a processor.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present invention may implement all or part of the flow of the method steps S1 to S7 of the above-described embodiments, or may be implemented by instructing the relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and which when executed by a processor, implements the steps of the respective method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
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 details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. The track behavior analysis method based on the space-time big data is characterized by comprising the following steps of:
S1, acquiring a space-time big data list of all objects;
s2, carrying out average segmentation through each set network and square side length according to the longitude and latitude interval with the maximum track of all objects to obtain a plurality of square geographic space grids;
S3, dividing and classifying the object track data according to the date, the hour and the space grids to obtain grid series of the object route track according to the time sequence;
s4, calculating the daily active hour score, the daily active network score and the daily grid linear track distance value of each object;
the calculation formula of the daily active hour score H is as follows: 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 each day; the calculation formula of the daily active network score W is as follows: /(I) Wherein F is an active grid score constant, F is an active space increment coefficient, and n is an active space grid number; s5, calculating the average value of all the object track distance values;
S6, calculating the daily behavior track score of each object;
Wherein, the calculation formula of the daily behavior track score Z of each object is as follows Wherein R is the average value of all object track distance values, M is the daily track score constant, and S is the daily grid linear track distance value;
s7, comprehensively analyzing the daily behavior track scores of all the objects to obtain the abnormal behavior track objects.
2. The method of claim 1, wherein the grid array in S3 is denoted P＝[P1(xn,ym),P2(xn,ym),P3(xn,ym)…Pk(xn,ym)], where P k(xn,ym) represents the grid coordinates of the kth track.
3. The method of claim 2, wherein the calculation formula of the daily grid linear track distance value S in S4 is: where L is the individual grid side length.
4. 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 1-3 when the computer program is executed.
5. 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 according to any of claims 1-3.
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