CN115909545A - Vehicle track data compression method and device based on fuzzy coding - Google Patents

Vehicle track data compression method and device based on fuzzy coding Download PDF

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CN115909545A
CN115909545A CN202211688141.8A CN202211688141A CN115909545A CN 115909545 A CN115909545 A CN 115909545A CN 202211688141 A CN202211688141 A CN 202211688141A CN 115909545 A CN115909545 A CN 115909545A
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fuzzy
track data
character
coding
data
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李英龙
许馨宸
刘帆
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Zhejiang University of Technology ZJUT
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Abstract

The invention discloses a vehicle track data compression method based on fuzzy coding, which comprises the following steps: (1) And acquiring track data in the edge Internet of vehicles environment, determining a membership function, and fuzzifying the track data. (2) Fuzzy division and fuzzy character definition, and converting the track data into fuzzy characters. (3) And based on the track data represented by the fuzzy characters, compressing by adopting Huffman coding. The method of the invention carries out fuzzy coding on the track data and has a light privacy protection function. And the track data expression based on fuzzy characters is adopted, so that the size of the track data can be effectively reduced, the communication traffic of the edge Internet of vehicles data is remarkably reduced, and the bandwidth consumption is reduced. The invention further compresses the track data by adopting Huffman coding, thereby greatly improving the compression effect.

Description

Vehicle track data compression method and device based on fuzzy coding
Technical Field
The invention relates to the technical field of track compression and fuzzy theory, in particular to a vehicle track data compression method and device based on fuzzy coding.
Background
The vehicle trajectory data contains rich information, and mining and analyzing the data is helpful for users or city planners to make better decisions, so that the vehicle trajectory data has further analysis and wide application value. For example, the trajectory data can predict the trajectory of the vehicle, the urban traffic and the demand of passengers, identify dangerous driving behaviors of users, optimize and design traffic routes, and the like. The track of the vehicle can be collected, transmitted and stored to the cloud data center in real time through the vehicle-mounted GPS equipment. However, a huge amount of data is generated along with the accumulation of time and space, and bandwidth resources and space storage resources are consumed seriously. The track data are compressed and then transmitted to the cloud, and the problems can be effectively solved. Under the car networking environment, make full use of edge device's computational capability, can be quick compress the orbit data, both saved the communication bandwidth in the orbit data transmission, satisfied the real-time nature demand of some applications simultaneously.
On the other hand, with the wide application of the internet of vehicles, the security and privacy problems of the vehicle information are more and more emphasized by people. If the data are directly transmitted in the car networking environment, an attacker can easily acquire the information of the user by forging a base station, masquerading a legal terminal and other attack means, so that the privacy of the vehicle user is revealed, and even the personal safety of the vehicle user is threatened. One technique currently employed to protect user security and privacy is a security authentication technique, which implements identity authentication through digital signature and encryption. However, this approach not only increases the communication burden of the vehicle networking environment, which is inherently resource-limited, but also exposes the privacy of the vehicle user when decrypted by an attacker.
In order to solve the above problems, in an edge internet-of-vehicles environment, how to design a reasonable trajectory data structure to represent vehicle trajectory data and create a trajectory compression model on the basis of the reasonable trajectory data structure is a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a vehicle track data compression method and device based on fuzzy coding, aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: in a first aspect, the present invention provides a track data compression method based on fuzzy coding, which comprises the following steps:
(1) Track data fuzzification: acquiring vehicle track data, acquiring a vehicle displacement increment sequence, and determining parameters of a membership function according to statistical data of the vehicle displacement increment sequence so as to define the membership function;
(2) Fuzzy partition and fuzzy character definition: dividing a plurality of domains according to the intersection region of the membership function, respectively representing the displacement increment value of the domain with fuzzy characters, and converting the trajectory data into fuzzy character representation;
(3) Fuzzy character-based compression: and counting the frequency of each fuzzy character in all fuzzy character strings, and compressing the track data represented based on the fuzzy characters by adopting Huffman coding.
Further, in the step (1), the detailed step of the vehicle trajectory data fuzzification is as follows:
(1-1) acquisition of trajectory data: firstly, a vehicle end acquires a section of track position sequence represented by (x, y, t) in real time through vehicle-mounted GPS equipment; then, respectively calculating displacement and time interval in the x direction (longitude) and the y direction (latitude) to obtain two displacement sequences and a time sequence; finally, extracting the signs of the numerical values in the displacement sequence to obtain a displacement increment sequence and a sign sequence in the x direction and a displacement increment sequence and a sign sequence in the y direction;
(1-2) determination of membership function: defining r fuzzy sets represented by trapezoidal membership degrees, and determining parameters of the membership degree function according to statistical data of the vehicle displacement increment sequence.
Further, in the step (2), the detailed steps of fuzzy partition and fuzzy character definition are as follows:
(2-1) fuzzy partition: r uneven partitions are formed through the intersection points of the determined membership function;
(2-2) fuzzy character definition: describing each partition of the fuzzy partition by fuzzy characters; and defining a displacement increment value of the partition where the fuzzy character description is located, wherein the fuzzy division represented by each fuzzy character enables the membership degree of the corresponding fuzzy set to be larger than the membership degree of other fuzzy sets on the fuzzy division.
Further, in step (3), given the fuzzy character string to be compressed, the detailed steps of fuzzy character-based compression are as follows:
(3-1) counting the occurrence frequency of fuzzy characters: counting the frequency of each fuzzy character in all fuzzy character strings;
(3-2) encoding the fuzzy character according to a Huffman tree: firstly, according to the occurrence frequency of fuzzy characters, establishing a Huffman tree according to the principle that the greater the occurrence frequency is, the shorter the coding is, and then, coding the fuzzy characters according to left 0 and right 1;
(3-3) converting the track data represented by the fuzzy character into a form of 01 strings, wherein in the data representation of a computer, the space occupied by one character is 8 bits, the size after the Huffman coding is 2 bits, and the data size is compressed by 4 times.
In a second aspect, the present invention provides a track data compression apparatus based on fuzzy coding, including a memory and one or more processors, where the memory stores executable codes, and the processors execute the executable codes to implement the steps of the track data compression method based on fuzzy coding.
In a third aspect, the present invention provides a computer-readable storage medium, on which a program is stored, which, when executed by a processor, performs the steps of the method for track data compression based on fuzzy coding. The invention has the following beneficial effects:
(1) The track data is subjected to fuzzy coding, and the light-weight privacy protection function is achieved.
(2) And the track data expression based on fuzzy characters obviously reduces the communication traffic of edge Internet of vehicles data and reduces the bandwidth consumption.
(3) And the track data is further compressed by adopting Huffman coding, so that the compression effect is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart of a track data compression method based on fuzzy coding according to the present invention;
FIG. 2 is a schematic diagram of vehicle trajectory data acquisition in an edge Internet of vehicles environment;
FIG. 3 is a schematic diagram of a case of non-uniformly divided membership function with a fuzzy set number of 5;
FIG. 4 is a schematic diagram of a Huffman tree and a construction case of fuzzy character encoding;
fig. 5 is a structural diagram of a track data compression device based on fuzzy coding according to the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, the invention provides a track data compression method based on fuzzy coding, comprising the following steps:
(1) Track data fuzzification: and determining parameters of the membership function according to the statistical data of the vehicle track data so as to define the membership function. The detailed steps of the track data fuzzification of the vehicle are as follows:
(1-1) acquisition of trajectory data: as shown in fig. 2, firstly, a vehicle end acquires a track position sequence represented by (x, y, t) in real time through an on-board GPS device. Then, spatial displacement and time interval in the x direction (longitude) and the y direction (latitude) are respectively calculated, and space-time data splitting is carried out to obtain two displacement sequences and a time sequence. Finally, extracting the signs of the numerical values in the displacement sequence to obtain the displacement increment sequence in the x direction
Figure BDA0004011847260000031
And the symbol sequence->
Figure BDA0004011847260000032
A shift increment sequence in y-direction>
Figure BDA0004011847260000033
And the symbol sequence->
Figure BDA0004011847260000034
Wherein n is the number of track positions,
Figure BDA0004011847260000035
denotes t 1 Time relative to t 0 At a moment in time, an increment of the displacement in the x-direction>
Figure BDA0004011847260000036
Represents t n Time of day trajectory position relative to t n-1 Displacement increment in the x-direction at a time instant>
Figure BDA0004011847260000037
Represents->
Figure BDA0004011847260000038
The sign of the displacement of (a); />
Figure BDA0004011847260000039
Represents t 1 Time relative to t 0 The incremental displacement in the y-direction at that time,
Figure BDA00040118472600000310
represents t n Time of day trajectory position relative to t n-1 A displacement increment in the y-direction at a moment in time>
Figure BDA00040118472600000311
Represents->
Figure BDA00040118472600000312
The sign of the displacement of (2).
(1-2) determination of membership function: and determining parameters of the membership function according to the statistical data of the vehicle track data so as to define the membership function. Fuzzy sets of r trapezoidal membership representations (as in the case of fig. 3, where r is 5) are defined for displacement increments in the x-direction and displacement increments in the y-direction, respectively. Equations (1) to (3) represent membership functions when r is 5. And determining parameters of the membership function according to the statistical data of the vehicle displacement increment sequence, as shown in a formula (4).
Figure BDA0004011847260000041
Figure BDA0004011847260000042
Figure BDA0004011847260000043
Figure BDA0004011847260000044
Wherein,
Figure BDA0004011847260000045
is a membership function of the 1 st to 5 th fuzzy set>
Figure BDA0004011847260000046
The fuzzy set membership function parameters are determined according to historical data distribution. />
Figure BDA0004011847260000047
And &>
Figure BDA0004011847260000048
The minimum, maximum and average values of the displacement increments of two adjacent positions in the x and y directions are divided. />
(2) Fuzzy partition and fuzzy character definition: and dividing a domain of discourse according to the membership function, expressing the domain of discourse by using fuzzy characters, and converting the track data into the expression of the fuzzy characters. The detailed steps of fuzzy division and fuzzy character definition are as follows:
(2-1) fuzzy partition: when r is 5, the intersection points of the membership functions determined by the formulas (1) to (4)Will form 5 uneven partitions, i.e.
Figure BDA0004011847260000049
Figure BDA00040118472600000410
(2-2) fuzzy character definition: the various partitions of the fuzzy partition are described by fuzzy characters. Defining the partition in which the fuzzy character 'N' description is located
Figure BDA00040118472600000411
In this interval, the fuzzy set FD 1 Membership of the corresponding membership function being greater than other fuzzy sets FD i (i =2,3,4,5). Similarly, the definition of fuzzy characters 'C', 'M', 'F' and 'G' describes a section->
Figure BDA00040118472600000412
And &>
Figure BDA00040118472600000413
The membership degree corresponding to each fuzzy set is larger than the membership degree of other fuzzy sets in the interval among different division areas.
(3) Fuzzy character-based compression: and compressing the track data based on the fuzzy character representation by using Huffman coding. Given a fuzzy character string to be compressed, the detailed steps of fuzzy character-based compression are as follows:
(3-1) counting the occurrence frequency of fuzzy characters: and counting the frequency of each fuzzy character in all fuzzy character strings. As shown in table 1:
TABLE 1 fuzzy character and frequency of occurrence
Fuzzy character Frequency of occurrence
C 0.1
M 0.4
F 0.3
L 0.2
(3-2) encoding the fuzzy character according to a Huffman tree: firstly, according to the occurrence frequency of fuzzy characters, a Huffman tree is created according to the principle that the greater the occurrence frequency is, the shorter the coding is, and then the fuzzy characters are coded according to left 0 and right 1. As shown in fig. 4.
(3-3) converting the trajectory data represented by the fuzzy character into a track data represented by 01 strings: then, according to the huffman coding of fig. 4, the code of the fuzzy character M is 0, the code of the fuzzy character F is 10, the code of the fuzzy character C is 110, and the code of the fuzzy character L is 111, then the huffman coding of the fuzzy character string MFL is 010111. In the data representation of a computer, generally, the space occupied by one character is 8 bits, the space occupied by the fuzzy character string MFL is 24 bits, the size after huffman coding is 6 bits, and the data size is compressed by 4 times. All fuzzy character strings are subjected to Huffman coding to form 01 strings, and the space size of data is remarkably reduced. Corresponding to the embodiment of the track data compression method based on fuzzy coding, the invention also provides an embodiment of a track data compression device based on fuzzy coding.
Referring to fig. 5, an apparatus for track data compression based on fuzzy coding according to an embodiment of the present invention includes a memory and one or more processors, where the memory stores executable codes, and the processors execute the executable codes to implement a track data compression method based on fuzzy coding according to the above embodiment.
The track data compression device based on fuzzy coding of the embodiment of the invention can be applied to any equipment with data processing capability, such as computers and other equipment or devices. The apparatus embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. The software implementation is taken as an example, and as a device in a logical sense, a processor of any device with data processing capability reads corresponding computer program instructions in the nonvolatile memory into the memory for operation. In terms of hardware, as shown in fig. 5, the present invention is a hardware structure diagram of any device with data processing capability where a track data compression apparatus based on fuzzy coding is located, and besides the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 5, in an embodiment, any device with data processing capability where the apparatus is located may also include other hardware according to the actual function of the any device with data processing capability, which is not described again.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the invention. One of ordinary skill in the art can understand and implement it without inventive effort.
An embodiment of the present invention further provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the method for compressing track data based on fuzzy coding in the foregoing embodiments is implemented.
The computer readable storage medium may be an internal storage unit, such as a hard disk or a memory, of any data processing device described in any previous embodiment. The computer readable storage medium may also be any external storage device of a device with data processing capabilities, such as a plug-in hard disk, a Smart Media Card (SMC), an SD Card, a Flash memory Card (Flash Card), etc. provided on the device. Further, the computer readable storage medium may include both internal storage units and external storage devices of any data processing capable device. The computer-readable storage medium is used for storing the computer program and other programs and data required by the arbitrary data processing capable device, and may also be used for temporarily storing data that has been output or is to be output.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.

Claims (6)

1. A track data compression method based on fuzzy coding is characterized by comprising the following steps:
(1) Track data fuzzification: acquiring vehicle track data, acquiring a vehicle displacement increment sequence, and determining parameters of a membership function according to statistical data of the vehicle displacement increment sequence so as to define the membership function;
(2) Fuzzy partition and fuzzy character definition: dividing a plurality of domains according to the intersection region of the membership function, respectively representing the displacement increment value of the domain with fuzzy characters, and converting the trajectory data into fuzzy character representation;
(3) Fuzzy character-based compression: and counting the frequency of each fuzzy character in all fuzzy character strings, and compressing the track data represented based on the fuzzy characters by adopting Huffman coding.
2. The fuzzy-coding-based trajectory data compression method according to claim 1, wherein in step (1), the vehicle trajectory data is blurred as follows:
(1-1) acquisition of trajectory data: firstly, a vehicle end acquires a section of track position sequence represented by (x, y, t) in real time through vehicle-mounted GPS equipment; then, respectively calculating displacement and time interval in the x direction (longitude) and the y direction (latitude) to obtain two displacement sequences and a time sequence; finally, extracting numerical value symbols in the displacement increment sequence to obtain a displacement increment sequence and a symbol sequence in the x direction and a displacement increment sequence and a symbol sequence in the y direction;
(1-2) determination of membership function: defining r fuzzy sets represented by trapezoidal membership degrees, and determining parameters of the membership degree function according to the statistical data of the vehicle displacement increment sequence.
3. The method for track data compression based on fuzzy coding according to claim 2, wherein in step (2), the detailed steps of fuzzy partition and fuzzy character definition are as follows:
(2-1) fuzzy partition: forming r uneven partitions through the intersection points of the determined membership function;
(2-2) fuzzy character definition: describing each partition of the fuzzy partition by a fuzzy character; and defining a displacement increment value of the partition where the fuzzy character description is located, wherein the membership degree of the fuzzy partition represented by each fuzzy character is larger than the membership degree of other fuzzy sets on the fuzzy partition.
4. The fuzzy-coding-based trajectory data compression method of claim 2, wherein in step (3), given the fuzzy character string to be compressed, the detailed steps of fuzzy-character-based compression are as follows:
(3-1) counting the occurrence frequency of fuzzy characters: counting the frequency of each fuzzy character in all fuzzy character strings;
(3-2) encoding the ambiguous character according to a Huffman tree: firstly, according to the occurrence frequency of fuzzy characters, establishing a Huffman tree according to the principle that the greater the occurrence frequency is, the shorter the code is, and then, coding the fuzzy characters according to left 0 and right 1;
(3-3) converting the track data represented by the fuzzy character into a form of 01 strings, wherein in the data representation of a computer, the space occupied by one character is 8 bits, the size after the Huffman coding is 2 bits, and the data size is compressed by 4 times.
5. Track data compression device based on fuzzy coding, comprising a memory and one or more processors, wherein the memory stores executable code, and the processors are configured to implement the steps of a track data compression method based on fuzzy coding according to any one of claims 1 to 4 when executing the executable code.
6. A computer-readable storage medium, on which a program is stored, which, when being executed by a processor, carries out the steps of a method for track data compression based on fuzzy coding as claimed in any one of claims 1 to 4.
CN202211688141.8A 2022-12-22 2022-12-22 Vehicle track data compression method and device based on fuzzy coding Pending CN115909545A (en)

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