WO2022032782A1 - Procédé et système destinés à prédire une zone d'itinérance pour un véhicule - Google Patents

Procédé et système destinés à prédire une zone d'itinérance pour un véhicule Download PDF

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Publication number
WO2022032782A1
WO2022032782A1 PCT/CN2020/114607 CN2020114607W WO2022032782A1 WO 2022032782 A1 WO2022032782 A1 WO 2022032782A1 CN 2020114607 W CN2020114607 W CN 2020114607W WO 2022032782 A1 WO2022032782 A1 WO 2022032782A1
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WIPO (PCT)
Prior art keywords
vehicle
trajectory
data
tested
running
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PCT/CN2020/114607
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English (en)
Chinese (zh)
Inventor
宁磊
张润洲
孙瑞泽
潘晶
李发君
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深圳技术大学
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Publication of WO2022032782A1 publication Critical patent/WO2022032782A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W8/00—Network data management
    • H04W8/02—Processing of mobility data, e.g. registration information at HLR [Home Location Register] or VLR [Visitor Location Register]; Transfer of mobility data, e.g. between HLR, VLR or external networks

Definitions

  • the invention relates to the technical field of vehicle intelligent management, in particular to a vehicle roaming area prediction method and system.
  • the present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the present invention proposes a vehicle roaming area prediction method, which can accurately predict the roaming area of the vehicle.
  • the present invention also provides a vehicle roaming area prediction system with the above-mentioned vehicle roaming area prediction method.
  • the present invention also provides a computer-readable storage medium having the above-mentioned vehicle roaming area prediction method.
  • the method for predicting a vehicle roaming area includes the following steps: S100, obtaining the running trajectory data of the vehicle to be tested through the Internet of Things, dividing the running trajectory data by time, and extracting the running trajectory data of the vehicle to be tested. Similarity characteristics of the moving trajectory of the vehicle; S200, obtain a first position of the vehicle to be tested according to the running trajectory data, and extract the distribution characteristics of the moving distance of the vehicle to be tested based on the first position; S300, according to the The similarity feature of the moving trajectory and the distribution feature of the moving distance are used to obtain the predicted roaming area of the vehicle to be tested.
  • the method for predicting a vehicle roaming area has at least the following beneficial effects: obtaining vehicle trajectory data through the Internet of Things, extracting features, intelligently predicting the vehicle roaming area, and obtaining the daily location of the vehicle, which can be used for mobile network
  • the erection provides a data reference basis.
  • the step S100 includes: S110, dividing the running trajectory data by time based on a preset division rule to obtain several sub-sequence trajectory data; S120, based on the sub-sequence trajectory data the mean value, obtain the fitting value of the sub-sequence trajectory data by the method of straight line fitting; S130 , calculate the average value of the fitting value of the sub-sequence trajectory data to obtain the moving trajectory similarity feature.
  • the preset division rule is configured to: divide the running track data according to a time series length of 2 k , where k is a positive integer, and select a plurality of consecutive k values to separate the The running trajectory data is divided.
  • the step S120 includes: S121, calculating the mean value of the trajectory of the sub-sequence trajectory data, and obtaining the cumulative deviation and the trajectory range according to the mean value of the trajectory; S122, according to the mean value of the trajectory It is worth obtaining the standard deviation of the trajectory of the subsequence trajectory data; S123, according to the ratio of the trajectory range to the standard deviation of the trajectory, take the logarithm of the time series difference value of the subsequence trajectory data, and use the least squares and method to do straight line fitting to obtain the fitted value.
  • the step S200 includes: S210, traversing the trajectory points in the running trajectory data, and separately calculating the coordinates of each of the trajectory points and the other trajectory points in the running trajectory data scalar distance between the coordinates of The vector distance of the first position is stored in a vector distance set; S230, based on a cumulative distribution function, the moving distance distribution feature is extracted from the vector distance set.
  • the method before the step S210, further includes: clearing the trajectory points in the running trajectory data according to a preset rule, and clearing the repeated trajectory points within a preset time threshold.
  • the step S300 includes: S310, acquiring the running trajectory data of the vehicle to be tested in real time; S320, updating the predicted roaming trajectory of the vehicle to be tested according to the similarity feature of the moving trajectory ; S330, obtain the predicted roaming area of the vehicle to be tested according to the distribution characteristics of the moving distance.
  • the vehicle roaming area prediction system includes: a data acquisition module for connecting with a vehicle to be tested through the Internet of Things to obtain the running track data of the vehicle to be tested; a similarity feature extraction module, is used to divide the running track data by time, and extract the similarity feature of the moving track of the vehicle to be tested; a distance feature extraction module is used to obtain the first position of the vehicle to be tested according to the running track data , based on the first position to extract the moving distance distribution feature of the vehicle to be tested; the predicting roaming area module is used to obtain the Predict the roaming area.
  • the vehicle roaming area prediction system has at least the following beneficial effects: obtaining vehicle trajectory data through the Internet of Things, extracting features, intelligently predicting the vehicle roaming area, and obtaining the daily area location of the vehicle, which can be used for mobile networks.
  • the erection provides data basis.
  • the similarity feature extraction module further includes: a subsequence dividing module, configured to divide the running trajectory data by time based on a preset dividing rule to obtain several subsequence trajectory data;
  • the sequence fitting module is used to obtain the fitting value of the subsequence trajectory data by means of straight line fitting based on the mean value of the subsequence trajectory data;
  • the similarity feature output module is used to calculate the subsequence trajectory The average value of the fitting values of the data is used to obtain the similarity feature of the moving trajectory.
  • a computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, implements the method of the embodiment of the first aspect of the present invention.
  • the computer storage medium has at least the following beneficial effects: obtaining vehicle trajectory data through the Internet of Things, extracting features, intelligently predicting the roaming area of the vehicle, and obtaining the daily location of the vehicle, which can be used for the erection of mobile networks. Provide reference data.
  • FIG. 1 is a schematic flowchart of a method according to an embodiment of the present invention.
  • FIG. 2 is a schematic diagram of data interaction in a method according to an embodiment of the present invention.
  • FIG. 3 is a schematic block diagram of modules of a system according to an embodiment of the present invention.
  • a data collection module 100 a similarity feature extraction module 200, a distance feature extraction module 300, and a predicted roaming area module 400;
  • Subsequence dividing module 210 Subsequence fitting module 220 , similarity feature outputting module 230 .
  • the meaning of several is one or more, the meaning of multiple is two or more, greater than, less than, exceeding, etc. are understood as not including this number, above, below, within, etc. are understood as including this number . If it is described that the first and the second are only for the purpose of distinguishing technical features, it cannot be understood as indicating or implying relative importance, or indicating the number of the indicated technical features or the order of the indicated technical features. relation.
  • the method of the embodiment of the present invention includes: S100, connecting with the vehicle to be tested through the Internet of Things, obtaining the running trajectory data of the vehicle to be tested, dividing the running trajectory data by time based on certain rules, and extracting the running trajectory data to be tested.
  • the similarity feature of the moving trajectory of the vehicle S200, the first position of the vehicle to be tested is obtained according to the running trajectory data, and the distribution feature of the moving distance of the vehicle to be tested is extracted based on the first position; S300, according to the similarity feature of the moving trajectory and the movement
  • the distance distribution characteristics are used to obtain the predicted roaming area of the vehicle to be tested.
  • the steps of the method for extracting similarity features of moving tracks are as follows. First, obtain the historical trajectory data set of the vehicle to be tested in the Internet of Things.
  • the information of a single trajectory point in the trajectory data set usually includes the coordinates of the trajectory point, elapsed time and other information; according to the time series length 2 k , the historical trajectory data set is divided into several subsections
  • the sequence data set L ⁇ L 1 , L 2 , . . . , L n ⁇ . Select multiple consecutive k values to divide the historical trajectory dataset. Assuming that the trajectory data of the vehicle to be tested is collected at a preset time interval frequency (for example: 1 minute), a series of trajectory points N 1 , N 2 , . . .
  • N m are obtained; it can be divided into (N 1 , N 2 ) for the first time , (N 3 , N 4 ), (N 5 , N 6 ), etc. and so on; and then divided into (N 1 , N 2 , N 3 , N 4 ), (N 5 , N 6 , N 7 , N 8 ) ) and so on and so on; for each subsequent division, the number of trajectory points in a single group is twice the previous one.
  • each group of trajectory points may partially overlap; it is not necessary to start grouping calculation from two points, and the frequency of collecting trajectory data is fixed.
  • the calculation process of the fitting value of a single set of trajectory data is as follows, including:
  • Step 1 First obtain the time series difference of the grouped trajectory data during this division
  • Step 2 Calculate the mean value of the trajectory of this group of trajectory data (that is, the latitude and longitude coordinate position) according to formula 1:
  • r is the number of trajectory points in the i-th group of trajectory data
  • x ij is the latitude and longitude coordinate position of the j-th trajectory point in the i-th group of trajectory data.
  • Step 3 Take the logarithm of the time series difference corresponding to R i /S i and this group of trajectory data, and use the method of least square sum to do straight line fitting to obtain the fitted value of this group of trajectory data.
  • the moving distance distribution feature extraction process in the embodiment of the present invention is as follows, including:
  • Step 1 Obtain the historical trajectory data set, clean the trajectory data, and clean up the repeated trajectory points generated by the preset time threshold not moving (for example: greater than or equal to 5 minutes, the vehicle has not moved, the trajectory points have not changed, the trajectory collected at this time The data are all repeated values);
  • Step 2 Traverse the trajectory points in the remaining trajectory data sets, and for any trajectory P, find the sum of the scalar distances between P and all other trajectory points in the trajectory data, and mark the trajectory point with the smallest scalar distance as the first. Location;
  • Step 3 Calculate the vector distance between each trajectory point in the trajectory data set and the first position in chronological order, and add the vector distance and the relevant information of the trajectory point (such as the coordinate position of the trajectory point and the time of the trajectory point) into set V;
  • Step 4 Extract the moving distance distribution feature based on the set V through the cumulative distribution function.
  • the similarity characteristics of the moving trajectory and the distribution characteristics of the moving distance can be obtained respectively.
  • the similarity feature extraction of the moving trajectory can obtain the self-similar features of the moving trajectory of the vehicle itself; the moving distance distribution feature extraction can further extract the roaming characteristics of the vehicle based on the self-similar features of the vehicle, that is, the daily vehicle moving trajectory is most of the time. Concentrate on a certain area, not randomly distributed on the map.
  • the current trajectory data can be uploaded in real time through the Internet of Things, and the predicted roaming trajectory of the vehicle to be tested can be updated in real time according to the similarity between the current trajectory data and the moving trajectory.
  • the predicted roaming area of the vehicle to be tested that is, the predicted destination of the vehicle.
  • the system of the embodiment of the present invention includes: a data collection module 100 , a similarity feature extraction module 200 , a distance feature extraction module 300 , and a roaming area prediction module 400 .
  • the data acquisition module is used to connect with the vehicle to be tested through the Internet of Things to obtain the running trajectory data of the vehicle to be tested; the similarity feature extraction module is used to divide the running trajectory data by time and extract the similarity of the moving trajectory of the vehicle to be tested.
  • the distance feature extraction module is used to obtain the first position of the vehicle to be tested according to the running trajectory data, and based on the first position to extract the distribution characteristics of the moving distance of the vehicle to be tested;
  • the prediction roaming area module is used according to the similarity feature of the moving trajectory As well as the distribution characteristics of the moving distance, the predicted roaming area of the vehicle to be tested is obtained.
  • the predicting roaming area module 400 in the embodiment of the present invention further includes: a subsequence division module, configured to divide the running trajectory data by time based on a preset division rule, to obtain several subsequence trajectory data; a subsequence fitting module, used for Based on the mean value of the subsequence trajectory data, the fitting value of the subsequence trajectory data is obtained by the method of straight line fitting; the similarity feature output module is used to calculate the average value of the fitting value of the subsequence trajectory data to obtain the similarity of the moving trajectories. sexual characteristics.
  • blocks in the block diagrams and flowchart illustrations support combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, can be implemented by special purpose hardware computer systems, or combinations of special purpose hardware and computer instructions, that perform the specified functions, elements, or steps.
  • Program modules, applications, and the like described herein may include one or more software components, including, for example, software objects, methods, data structures, and the like. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functions described herein (eg, one or more operations of the exemplary methods described herein) be executed.
  • Software components can be coded in any of a variety of programming languages.
  • An exemplary programming language may be a low-level programming language, such as assembly language associated with a particular hardware architecture and/or operating system platform.
  • Software components that include assembly language instructions may need to be converted into executable machine code by an assembler prior to execution by a hardware architecture and/or platform.
  • Another exemplary programming language may be a higher level programming language that is portable across multiple architectures.
  • Software components including higher level programming languages may need to be converted into an intermediate representation by an interpreter or compiler before execution.
  • Other examples of programming languages include, but are not limited to, macro languages, shell or command languages, job control languages, scripting languages, database query or search languages, or report writing languages.
  • a software component containing instructions from one of the above-described programming language examples can be directly executed by an operating system or other software component without first being converted to another form.
  • Software components may be stored as files or other data storage constructs. Software components with similar types or related functions may be stored together, for example, in a particular directory, folder, or library. Software components may be static (eg, preset or fixed) or dynamic (eg, created or modified at execution time).

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Databases & Information Systems (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé et un système destinés à prédire une zone d'itinérance pour un véhicule. Les étapes du procédé consistent : à acquérir, par le biais d'un internet des objets, des données de trajet de déplacement d'un véhicule soumis à la prédiction, à diviser des données de trajet de déplacement en groupes en fonction du temps, et à extraire un attribut de similitude de trajet de déplacement du véhicule (S100) ; à obtenir, en fonction des données de trajet de déplacement, une première position du véhicule, et à extraire, sur la base de la première position, un attribut de répartition de distances de déplacement du véhicule (S200) ; et à obtenir, en fonction de l'attribut de similitude de trajet de déplacement et de l'attribut de répartition de distances de déplacement, une zone d'itinérance prédite pour le véhicule (S300). Des données de trajet d'un véhicule sont acquises par le biais d'un internet des objets, et des attributs associés sont alors extraits, afin de prédire intelligemment une zone d'itinérance pour le véhicule et d'acquérir les positions de zones dans lesquelles le véhicule est situé sur une base quotidienne, produisant ainsi des données de référence destinées à établir un réseau mobile.
PCT/CN2020/114607 2020-08-12 2020-09-10 Procédé et système destinés à prédire une zone d'itinérance pour un véhicule WO2022032782A1 (fr)

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