CN115017252B - Intelligent driving track playback system of mobile phone digital car key - Google Patents

Intelligent driving track playback system of mobile phone digital car key Download PDF

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CN115017252B
CN115017252B CN202210944707.2A CN202210944707A CN115017252B CN 115017252 B CN115017252 B CN 115017252B CN 202210944707 A CN202210944707 A CN 202210944707A CN 115017252 B CN115017252 B CN 115017252B
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abnormal
vehicle
driving
data
track
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CN115017252A (en
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薛卫平
白英奇
黄海峰
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Hangsheng Vehicle Cloud Tech Co ltd
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Hangsheng Vehicle Cloud Tech Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0841—Registering performance data
    • G07C5/085—Registering performance data using electronic data carriers
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/40—Conversion to or from variable length codes, e.g. Shannon-Fano code, Huffman code, Morse code
    • H03M7/4093—Variable length to variable length coding

Abstract

The invention relates to the technical field of data transmission, in particular to a driving track intelligent playback system of a mobile phone digital vehicle key, which comprises: the device comprises a data acquisition module, a track division module, a first parameter acquisition module, a second parameter acquisition module, a compression storage module and a playback module. The intelligent playback system for the vehicle track of the mobile phone digital vehicle key, provided by the invention, utilizes a positioning technology and a data acquisition technology to obtain position information and driving state information in the driving process, and compresses and stores the collected information based on priority through a data compression technology to realize the playback of the vehicle track with small storage data and high retrieval efficiency.

Description

Intelligent driving track playback system of mobile phone digital car key
Technical Field
The invention relates to the technical field of data transmission, in particular to an intelligent driving track playback system of a mobile phone digital vehicle key.
Background
A mobile phone digital car key is an innovative technology under the intelligent revolution of cars. The intelligent automobile unlocking system is concerned by more and more automobile enterprises, and can enable an automobile owner to unlock the automobile through a smart phone and perform related operations on the automobile, for example, a driving track is played back through a digital automobile key of the smart phone. The recording and playback of vehicle tracks are one of the extremely important functions in the driving process of automobiles, and the existing driving track system is used for recording and storing data based on a navigation map and then drawing and playing back the map. However, the driving track playback generally needs to be reserved for half a year or more, the storage method needs a large amount of storage space, relatively speaking, resources are wasted, the storage sequence is stored according to a time sequence, and a great amount of calculation power is needed to search and locate data on the premise of a huge amount of data.
At present, a mobile phone digital car key is mobile phone software which is installed on a mobile phone and used for unlocking a car and recording car running information, and the mobile phone software integrates a car unlocking function and a car navigation record playback function. The mobile phone and the vehicle are usually connected through communication such as NFC and Bluetooth, the vehicle can be unlocked through the mobile phone digital vehicle key, and the vehicle running information can be recorded under the condition that GPS signals are normal.
In the process of vehicle driving, position information and driving state information need to be recorded, recorded data are compressed and stored, the stored compressed data are decompressed at the later stage according to needs, and the decompressed recorded data are played back on a map of a mobile phone end to obtain a driving track of a vehicle; the vehicle can record the driving track of the vehicle and the position information and the vehicle state information of each moment on the track in the mobile phone digital vehicle key at a place where the GPS signal is normal, and can accurately play back the driving track of the vehicle; however, the vehicle cannot be accurately positioned at a place with poor GPS signals, and the position information and the vehicle state information of the vehicle cannot be recorded in the mobile phone digital vehicle key, which is considered as an abnormal driving road section, and it is difficult to obtain an accurate driving track in real time. Therefore, in order to ensure the integrity of the running data compression of the abnormal running road section and ensure that the deviation of the subsequent abnormal running track playback is small, the invention provides the intelligent running track playback system of the mobile phone digital car key.
Disclosure of Invention
In order to solve the above technical problem, an object of the present invention is to provide an intelligent playback system for a vehicle track of a mobile phone digital vehicle key, comprising:
the data acquisition module is used for acquiring the driving data of abnormal driving of the vehicle; acquiring initial coordinates of the starting time and ending coordinates of the abnormal running of the vehicle through a mobile phone digital vehicle key; the driving data comprises position data and vehicle state information which are acquired when the vehicle runs abnormally;
the track division module is used for acquiring the abnormal driving track of the vehicle according to the driving data and fitting to acquire a driving track function; obtaining predicted position points of a plurality of turns when the vehicle runs abnormally by performing high-order derivation on the driving track function;
acquiring the overall united difference of abnormal vehicle running according to the coordinates of the plurality of predicted position points; screening out a plurality of accurate position points from the plurality of predicted position points according to the coordinates of the plurality of predicted position points and the overall combined difference of abnormal vehicle running;
dividing the abnormal driving track into a plurality of abnormal intervals according to a plurality of accurate position points;
the first parameter acquisition module is used for acquiring the distribution weight of each abnormal interval according to the coordinates of the initial accurate position point and the final accurate position point of each abnormal interval and the number of the predicted position points in each abnormal interval;
acquiring the weight value of each abnormal interval according to the distribution weight value of each abnormal interval and the maximum value and the minimum value in the distribution weight values of all the abnormal intervals;
the second parameter acquisition module is used for acquiring a simulation termination coordinate of the abnormal running termination moment of the vehicle according to the running data; acquiring the integral error degree of the abnormal driving track according to the initial coordinate, the termination coordinate and the simulated termination coordinate of the abnormal driving of the vehicle; acquiring an error value of each abnormal interval according to the weight value of each abnormal interval and the integral error degree of the abnormal driving track;
the compression storage module is used for performing Huffman coding on the driving data in each abnormal interval and performing lossless compression to obtain first compression data according to the probability product of the error value of each abnormal interval and the driving data in the corresponding abnormal interval as a weight; the mobile phone digital car key is used for acquiring driving data of a vehicle during normal driving in each driving process of the vehicle, performing lossy compression on the driving data of the vehicle during normal driving to acquire second compressed data, and storing the first compressed data and the second compressed data in the mobile phone digital car key;
and the playback module is used for decompressing the stored second compressed data and then directly playing back the decompressed second compressed data, and the first compressed data is corrected and then a vehicle driving track is reconstructed and played back.
In one embodiment, in the trajectory division module, the driving trajectory function is fit according to position data acquired by a vehicle in an abnormal driving process; the position data comprises longitude and latitude coordinates of the vehicle at each moment in the abnormal driving process; the independent variable of the driving track function is longitude, and the dependent variable is latitude.
In one embodiment, the predicted location points of the plurality of turns are obtained by:
obtaining a second derivative function by carrying out second order derivation on the traveling track function; meanwhile, carrying out third-order derivation on the traveling track function to obtain a third-order derivative function;
and taking a plurality of coordinate points corresponding to the driving track function, which enable the second derivative function to be equal to zero and enable the third derivative function not to be equal to zero, as predicted position points of a plurality of turns.
In an embodiment, in the trajectory segmentation module, the plurality of accurate position points are obtained according to the following steps:
according to the coordinate of the first predicted position point in the plurality of predicted position points and the second predicted position point
Figure DEST_PATH_IMAGE001
Calculating the coordinates of the predicted position points to obtain the first predicted position point to the second predicted position point
Figure 294354DEST_PATH_IMAGE001
Interval difference of the predicted position points; when the interval difference is equal to the overall combined difference of abnormal vehicle running, judging whether the interval difference is equal to the overall combined difference of abnormal vehicle running
Figure 477073DEST_PATH_IMAGE001
The predicted position points are accurate position points;
then by the first
Figure 850286DEST_PATH_IMAGE002
And sequentially carrying out the calculation on the coordinates of the predicted position points and the coordinates of the subsequent predicted position points, and sequentially carrying out analogy to obtain a plurality of accurate position points.
In one embodiment, the calculation formula of the overall combined difference of the abnormal driving of the vehicle is as follows:
Figure DEST_PATH_IMAGE003
in the formula (I), the compound is shown in the specification,
Figure 714337DEST_PATH_IMAGE004
the overall united difference of the abnormal running of the vehicle is represented;
Figure DEST_PATH_IMAGE005
is shown as
Figure DEST_PATH_IMAGE007
Coordinates of the predicted location points;
Figure 918922DEST_PATH_IMAGE008
，
Figure DEST_PATH_IMAGE009
representing the total number of predicted location points.
In an embodiment, in the first parameter obtaining module, a calculation formula of a distribution weight of each abnormal interval is as follows:
Figure DEST_PATH_IMAGE011
in the formula (I), the compound is shown in the specification,
Figure 803701DEST_PATH_IMAGE012
is shown as
Figure DEST_PATH_IMAGE013
The distribution weight of each abnormal interval;
Figure 805155DEST_PATH_IMAGE014
is shown as
Figure 343670DEST_PATH_IMAGE013
Coordinates of the initial accurate position points of the abnormal intervals;
Figure DEST_PATH_IMAGE015
is shown as
Figure 746970DEST_PATH_IMAGE013
Coordinates of the ending accurate position point of each abnormal interval;
Figure 333809DEST_PATH_IMAGE016
is a first
Figure 291401DEST_PATH_IMAGE013
The number of predicted location points within an interval;
Figure DEST_PATH_IMAGE017
is a first
Figure 887467DEST_PATH_IMAGE013
The number of predicted position points and accurate position points are included in the individual interval.
In an embodiment, the data acquisition module further comprises driving data for acquiring abnormal driving of the vehicle in a plurality of different time periods during each driving process of the vehicle; dividing the abnormal running track corresponding to each time period into a plurality of abnormal intervals according to a track dividing module;
acquiring a weight value of each abnormal interval corresponding to each time period through a first parameter acquisition module;
acquiring the integral error degree of the abnormal running track corresponding to each time period and the error value of each corresponding abnormal interval through a second parameter acquisition module;
performing Huffman coding on the running data in each abnormal interval corresponding to each time period by using a compression storage module to perform lossless compression to obtain first compressed data, wherein the product of the error value of each abnormal interval corresponding to each time period and the probability of the running data in the corresponding abnormal interval is used as a weight; and storing the first compressed data in the mobile phone digital car key.
In one embodiment, in the storage process, the running data is stored hierarchically according to the overall error of the abnormal running track corresponding to each time segment in each running process of the vehicle and the weight value of each abnormal interval corresponding to each time segment.
In one embodiment, the hierarchical storage is performed according to the following steps:
establishing first priority storage layers, establishing a second priority storage layer for each first priority storage layer, and establishing a third priority storage layer for each second priority storage layer;
sequentially storing the running data of the abnormal running tracks of different time periods of each running in a first priority storage layer according to the sum of the integral error degrees of the abnormal running tracks of the different time periods of each running of the vehicle;
sequentially storing the running data of the abnormal running track of each time period in a second priority storage layer in the first priority storage layer according to the sum of the error values of each abnormal interval in the abnormal running track of each time period;
and sequentially storing the running data of each abnormal section in a third priority storage layer in the second priority storage layer according to the error value of each abnormal section in the abnormal running track.
In one embodiment, in the playback module, when the driving track of the vehicle is played back in the mobile phone digital vehicle key, the first compressed data and the second compressed data in the driving process are retrieved from the compressed storage module, the first compressed data and the second compressed data are sequenced based on the time sequence, the driving data in normal driving is directly decompressed and played back, and after the driving data in abnormal driving is corrected, the driving track of the vehicle is reconstructed and played back.
The invention has at least the following beneficial effects:
the invention provides a driving track intelligent playback system of a mobile phone digital vehicle key, which obtains driving data of abnormal driving of a vehicle through a data acquisition module, analyzes the driving data of the abnormal driving of the vehicle through the integral error analysis of the abnormal driving of the vehicle, reflects the integral error of each abnormal driving track to the maximum extent, divides each abnormal driving track into abnormal sections through inflection points, analyzes the abnormal sections, carries out error distribution according to the analysis result, judges the running road length and the accumulated condition of the error of each abnormal section, thereby obtaining the abnormal degree of each abnormal section, further carries out lossless compression on the driving data of each abnormal section in the abnormal driving track by utilizing a Manchester coding algorithm according to the abnormal degree of the abnormal sections, the larger the abnormal degree is, the shorter the coding is, the higher the safety is, the integrity of the driving data in the whole abnormal driving track can be ensured, and the situation that the abnormal driving data originally cause more abnormality of the data in the compression process is avoided; the data with small memory space is realized, and the occupation of the memory space is reduced; in addition, the invention also carries out priority level layer storage on the driving data according to the abnormal degree, improves the subsequent retrieval efficiency, and can quickly retrieve the driving data of the abnormal driving track, thereby realizing the playback of the driving track with small storage data and high retrieval efficiency.
The invention mainly utilizes a gyroscope positioning technology and a data acquisition technology to obtain the position information of a vehicle in the driving process and the state information of the vehicle in the driving process, carries out priority compression on the collected driving data through a data compression technology, stores the driving data of an abnormal driving track in a mobile phone digital vehicle key under the condition that a vehicle GPS signal is normal, and records the driving track of the vehicle and the position information and the vehicle state information of each moment on the track in the mobile phone digital vehicle key through lossy compression when the vehicle is in a place where the GPS signal is normal, thereby realizing small-storage-quantity data.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of 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 other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a block diagram of a structure of a system for intelligently playing back a vehicle track of a mobile phone digital vehicle key according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined purpose, the following describes the following detailed description of the track intelligent playback system of the mobile phone digital car key according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, the different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The mobile phone digital car key is mobile phone software which is installed on a mobile phone and used for unlocking a car and recording the running information of the car, and the mobile phone software integrates the car unlocking function and the car navigation record playback function. The mobile phone and the vehicle are usually connected through communication such as NFC and Bluetooth, the vehicle can be unlocked through the mobile phone digital vehicle key, and the vehicle running information can be recorded under the condition that GPS signals are normal.
The invention aims at the scene that in the running process of a vehicle, under the condition that a GPS signal of the vehicle is normal, the position information and running state information of the vehicle can be recorded in a mobile phone digital vehicle key in real time, then the subsequent running track playback is carried out on a map of the mobile phone digital vehicle key according to the recorded data, but the vehicle cannot be accurately positioned in a place with a weaker GPS signal, the running data of the vehicle cannot be recorded in the mobile phone digital vehicle key in real time, the coordinate position can be calculated only through a gyroscope and a speed sensor arranged on the vehicle, but larger abnormity or error exists with the running data recorded when the GPS signal is normal, so the abnormal degree in the abnormal running state in the running data is calculated, then data compression is carried out according to the abnormal degree, the running data is stored in the mobile phone digital vehicle key in a priority level mode according to the abnormal degree, the integrity of the running data is ensured, and meanwhile, the running track playback with small storage amount data and high searching efficiency is realized.
The invention provides a traffic track intelligent playback system of a mobile phone digital car key, which mainly utilizes a gyroscope positioning technology and a data acquisition technology to obtain position information of a vehicle in a driving process and state information of the vehicle in the driving process, carries out priority compression on the collected driving data through a data compression technology, stores the driving data of an abnormal traffic track in the mobile phone digital car key under the condition that a vehicle GPS signal is normal, and records the traffic track of the vehicle and the position information and the vehicle state information at each moment on the track in the mobile phone digital car key through lossy compression when the vehicle is in a place where the GPS signal is normal, thereby realizing small-memory data.
The following describes a specific scheme of the intelligent playback system of the vehicle track of the mobile phone digital vehicle key provided by the invention in detail with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a structure of a system for intelligently playing back a vehicle track of a mobile phone digital vehicle key according to an embodiment of the present invention is shown, where the system for intelligently playing back a vehicle track of a mobile phone digital vehicle key includes: the device comprises a data acquisition module, a track division module, a first parameter acquisition module, a second parameter acquisition module, a compression storage module and a playback module;
the data acquisition module is used for acquiring the driving data of abnormal driving of the vehicle; acquiring initial coordinates of the starting time and ending coordinates of the abnormal running of the vehicle; the driving data comprises position data and vehicle state information which are acquired when the vehicle runs abnormally;
in this embodiment, the data acquisition module is configured to use longitude and latitude coordinates acquired by a gyroscope when a vehicle runs as position data, and acquire vehicle state information including driving speed and vehicle direction information during driving; the position data of the vehicle in the normal running process is directly collected by using a positioning technology in a mobile phone digital vehicle key, such as a GPS, a Beidou and the like; the driving data of the abnormal driving of the vehicle mainly depends on a gyroscope, a speed sensor and the like mounted on the vehicle. Note that the start time of the abnormal driving state is recorded as
Figure 879694DEST_PATH_IMAGE018
，
Figure 637434DEST_PATH_IMAGE018
The longitude and latitude coordinates of the time are initial coordinates
Figure DEST_PATH_IMAGE019
The abnormal driving state ending time is
Figure 941377DEST_PATH_IMAGE020
，
Figure 482080DEST_PATH_IMAGE020
Longitude and latitude coordinates of the moment are termination coordinates
Figure DEST_PATH_IMAGE021
. It should be noted that the position information and the vehicle state information of the starting time and the ending time of each section of abnormal driving state can be obtained by a mobile phone digital vehicle key; the starting time of the abnormal state is the last time of losing the GPS signalEngraving; the abnormal state termination time is the first time when the GPS signal is re-received.
The track division module is used for acquiring the abnormal driving track of the vehicle according to the driving data and fitting to acquire a driving track function; obtaining a plurality of predicted turning position points when the vehicle runs abnormally by performing high-order derivation on the running track function;
acquiring the overall united difference of abnormal vehicle running according to the coordinates of the plurality of predicted position points; screening out a plurality of accurate position points from the plurality of predicted position points according to the coordinates of the plurality of predicted position points and the overall combined difference of abnormal vehicle running;
dividing the abnormal running track into a plurality of abnormal intervals according to a plurality of accurate position points;
in the track division module, a driving track function is formed by fitting according to position data acquired in the abnormal driving process of the vehicle; the position data comprises longitude and latitude coordinates of the vehicle at each moment in the abnormal driving process; the independent variable of the driving track function is longitude, and the dependent variable is latitude.
It should be noted that, in order to ensure the integrity of the driving data in the abnormal driving track in the compression process, the abnormal driving track is subjected to mark point partition selection, then the section characteristics are analyzed, error distribution is performed according to the analysis result, and the driving data is compressed according to the priority of the error distribution.
In the embodiment, the longitude and latitude information in the driving data is used for simulating the abnormal driving track, the abnormal driving track is obtained by using the coordinates of a starting point and an ending point of the vehicle in the abnormal driving process as fixed points and then performing triangular reconstruction by using the simulated coordinates calculated by using the gyroscope and the speed sensor data in the driving process as variable points.
The predicted position points of the plurality of turns are obtained according to the following steps:
obtaining a second derivative function by carrying out second order derivation on the traveling track function; meanwhile, carrying out third-order derivation on the traveling track function to obtain a third-order derivative function; and taking a plurality of coordinate points corresponding to the driving track function, which enable the second derivative function to be equal to zero and enable the third derivative function not to be equal to zero, as predicted position points of a plurality of turns.
In the embodiment, the function of longitude and latitude is simulated and generated by using the position data of the driving data of the vehicle in the abnormal driving process, namely the driving track function
Figure 922288DEST_PATH_IMAGE022
To utilize
Figure 726296DEST_PATH_IMAGE022
Obtaining the predicted position points of a plurality of turns, wherein the specific process is as follows:
first, the function of the driving track
Figure 720797DEST_PATH_IMAGE022
The high-order derivation is carried out, and the purpose of the high-order derivation is to carry out the function of the traveling track
Figure 393087DEST_PATH_IMAGE022
And judging the inflection point, wherein the inflection point in the driving track function can preliminarily consider the position of the vehicle with the large probability turning in the driving track. Second, a second derivative function is obtained
Figure DEST_PATH_IMAGE023
And third derivative function
Figure 687802DEST_PATH_IMAGE024
Then all such second derivative functions are selected
Figure DEST_PATH_IMAGE025
And is
Figure 193869DEST_PATH_IMAGE026
Corresponding to
Figure 737983DEST_PATH_IMAGE009
The point is used as a predicted position point
Figure DEST_PATH_IMAGE027
(ii) a Wherein the content of the first and second substances,
Figure 417226DEST_PATH_IMAGE027
is shown as
Figure 35289DEST_PATH_IMAGE028
Coordinates of the predicted location points; the concrete practical meaning of the predicted position point is the position point of the vehicle with high probability of turning in the driving track.
The calculation formula of the overall united difference of the abnormal running of the vehicle is as follows:
Figure 446679DEST_PATH_IMAGE003
in the formula (I), the compound is shown in the specification,
Figure 212510DEST_PATH_IMAGE004
representing the overall united difference of the abnormal running of the vehicle;
Figure 101969DEST_PATH_IMAGE005
is shown as
Figure DEST_PATH_IMAGE029
Coordinates of the predicted location points;
Figure 433593DEST_PATH_IMAGE008
，
Figure 750305DEST_PATH_IMAGE009
representing the total number of predicted location points.
Figure 269011DEST_PATH_IMAGE004
Representing the overall united difference of the abnormal running of the vehicle; specifically, through variance calculation, the overall united difference of abnormal vehicle running is represented through the difference value between a single predicted position point and all the predicted position points of the whole.
In the track division module, a plurality of accurate position points are obtained according to the following steps:
according to the coordinate of the first predicted position point in the plurality of predicted position points and the second predicted position point
Figure 758898DEST_PATH_IMAGE001
Calculating the coordinates of the predicted position points to obtain the first predicted position point to the second predicted position point
Figure 289236DEST_PATH_IMAGE001
The interval difference of each predicted position point; when the interval difference is equal to the integral combination difference of the abnormal running of the vehicle, the interval difference is compared with the interval difference
Figure 901483DEST_PATH_IMAGE001
The predicted position points are accurate position points;
then by the first
Figure 782852DEST_PATH_IMAGE002
And sequentially carrying out the calculation on the coordinates of the predicted position points and the coordinates of the subsequent predicted position points, and sequentially carrying out analogy to obtain a plurality of accurate position points.
In this embodiment, a plurality of abnormal sections of the abnormal driving trajectory are obtained, and section end point values are used as accurate position points, so that the abnormal sections are combined with the integral united differences
Figure 404326DEST_PATH_IMAGE004
The termination conditions are specifically as follows:
firstly, using the first and second predicted position points as basis, making the first and second predicted position points as interval calculation interval difference, using the calculation mode of said interval calculation interval difference and overall combined difference calculation formula, then judging that its interval difference is identical to overall combined difference, if it is not identical, adding next predicted position point to make recalculation until reaching the first predicted position point
Figure 851488DEST_PATH_IMAGE028
A predicted position point
Figure 510002DEST_PATH_IMAGE027
When the interval difference is equal to the overall combined difference, the first one is selected
Figure 737721DEST_PATH_IMAGE028
The predicted position points are used as accurate position points; then by the first
Figure 772673DEST_PATH_IMAGE030
A candidate mark point
Figure DEST_PATH_IMAGE031
The calculation is restarted, all the accurate position points can be screened, and the accurate position points are obtained according to the method
Figure 933396DEST_PATH_IMAGE032
The marking points divide the abnormal driving track into a plurality of abnormal intervals according to a plurality of accurate position points; the overall data between every two adjacent accurate position points is an abnormal interval of the driving track, and the total data is
Figure DEST_PATH_IMAGE033
And (5) an abnormal driving track interval. For understanding, similar to iterative computation in obtaining accurate location points, e.g. the computation will be performed
Figure 90708DEST_PATH_IMAGE004
From 1 to 1
Figure 743406DEST_PATH_IMAGE009
Become to calculate
Figure 582049DEST_PATH_IMAGE034
=1 to
Figure 800541DEST_PATH_IMAGE009
=2
Figure 66437DEST_PATH_IMAGE004
Same termination, different continuation of calculation
Figure 940852DEST_PATH_IMAGE034
=1 to
Figure 707820DEST_PATH_IMAGE009
=3
Figure 921764DEST_PATH_IMAGE004
The same is terminated, the different continues to calculate
Figure 155299DEST_PATH_IMAGE034
=1 to
Figure 844906DEST_PATH_IMAGE009
=4
Figure 759773DEST_PATH_IMAGE004
Sequentially calculate to
Figure 218436DEST_PATH_IMAGE028
A predicted position point
Figure 622872DEST_PATH_IMAGE027
When the interval difference is equal to the overall combined difference, the first one is selected
Figure 409563DEST_PATH_IMAGE028
The predicted position points are used as accurate position points.
In the present embodiment, in order to clarify the error of each abnormal section in the abnormal travel locus, the overall error distribution is performed for each abnormal section to the second order
Figure 252754DEST_PATH_IMAGE013
An interval as an example
Figure DEST_PATH_IMAGE035
The method comprises the following steps:
the first parameter acquisition module is used for acquiring the distribution weight of each abnormal interval according to the coordinates of the initial accurate position point and the coordinates of the final accurate position point of each abnormal interval and the number of the predicted position points in each abnormal interval;
acquiring a weight value of each abnormal interval according to the distribution weight value of each abnormal interval and the maximum value and the minimum value in the distribution weight values of all the abnormal intervals;
in the first parameter obtaining module, a calculation formula of the distribution weight of each abnormal interval is as follows:
Figure DEST_PATH_IMAGE037
in the formula (I), the compound is shown in the specification,
Figure 300344DEST_PATH_IMAGE012
is shown as
Figure 875682DEST_PATH_IMAGE013
The distribution weight of each abnormal interval;
Figure 415248DEST_PATH_IMAGE014
is shown as
Figure 62130DEST_PATH_IMAGE013
Coordinates of initial accurate position points of the abnormal intervals;
Figure 105172DEST_PATH_IMAGE015
denotes the first
Figure 851411DEST_PATH_IMAGE013
Coordinates of the ending accurate position point of each abnormal interval;
Figure 737328DEST_PATH_IMAGE016
is a first
Figure 797688DEST_PATH_IMAGE013
The number of predicted position points within each anomaly interval;
Figure 85449DEST_PATH_IMAGE017
is as follows
Figure 2590DEST_PATH_IMAGE013
The total number of predicted location points and accurate location points are included within each anomaly interval.
If it is calculated
Figure 516748DEST_PATH_IMAGE038
The larger the position change frequency, the larger the trend of the overall position change frequency in the interval, and the error accumulation is easier to occur under the condition of the larger position change frequency; then, the number of the predicted position points is used for calculating the ratio of the number of the whole track points in the abnormal interval
Figure DEST_PATH_IMAGE039
The predicted position point is the inflection point position of a driving track function in the driving process, the inflection point position is a vehicle direction change point in a simulation track in the driving process, and errors are easy to occur in the direction change in the driving process in the basic theory; finally, the number of the integral track points of the whole interval is used for amplification
Figure 708695DEST_PATH_IMAGE040
The more the number of the whole driving track points in the whole interval is, the longer the actual driving distance in the interval is, and the longer the distance is, the more errors are easily accumulated. The whole track point comprises a predicted position point and an accurate position point in the abnormal interval.
In this embodiment, the above calculation is performed for each abnormal interval, and the distribution weight value sequence of each interval can be obtained
Figure DEST_PATH_IMAGE041
：
Figure DEST_PATH_IMAGE043
Then, the weight value sequence is distributed to calculate the weight value of each abnormal interval, so as to
Figure 319805DEST_PATH_IMAGE013
An abnormalitySection as an example, the weight value of the abnormal section
Figure 673426DEST_PATH_IMAGE044
The calculation is as follows:
Figure 533934DEST_PATH_IMAGE046
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE047
distributing the minimum weight in the weight sequence;
Figure 529572DEST_PATH_IMAGE048
distributing the weight value to the maximum distributing weight value in the distributing weight value sequence;
Figure 198451DEST_PATH_IMAGE012
is shown as
Figure 660656DEST_PATH_IMAGE013
The distribution weight of each abnormal interval;
Figure 8461DEST_PATH_IMAGE044
is shown as
Figure 11052DEST_PATH_IMAGE013
The weight value of each abnormal interval.
And calculating all the distribution weight values in the above mode to obtain the weight value of each abnormal interval.
In this embodiment, in order to assign an error value to each abnormal interval, the overall error degree of the entire abnormal trajectory is obtained first, and the error value is assigned by the weight value of each abnormal interval, which is specifically as follows:
the second parameter acquisition module is used for acquiring a simulation termination coordinate of the abnormal running termination moment of the vehicle according to the running data; acquiring the integral error degree of the abnormal driving track according to the initial coordinate, the termination coordinate and the simulated termination coordinate of the abnormal driving of the vehicle; acquiring an error value of each abnormal interval according to the weight value of each abnormal interval and the integral error degree of the abnormal driving track;
in the embodiment, in order to obtain the overall error when the vehicle runs abnormally, the overall error degree of the abnormal driving track is obtained according to the following steps:
acquiring initial coordinates and ending coordinates of abnormal running of the vehicle; in the present embodiment, the starting time of recording the abnormal driving state is
Figure 3279DEST_PATH_IMAGE018
，
Figure 772738DEST_PATH_IMAGE018
The longitude and latitude coordinates of the time are initial coordinates
Figure 217626DEST_PATH_IMAGE019
The abnormal driving state ending time is
Figure 23908DEST_PATH_IMAGE020
，
Figure 260854DEST_PATH_IMAGE020
Longitude and latitude coordinates of time are termination coordinates
Figure 799283DEST_PATH_IMAGE021
. The starting time of the abnormal state is the last time of losing the GPS signal; the abnormal state termination moment is the first moment when the GPS signal is received again;
then, the gyroscope data and the speed data collected by the data collection module in the abnormal driving state are subjected to simulation calculation of the driving track data by utilizing the prior art to obtain the simulated abnormal driving track, and the last longitude and latitude coordinate in the calculated abnormal driving track is recorded as a simulation termination coordinate
Figure DEST_PATH_IMAGE049
；
Acquiring the integral error degree of the abnormal driving track according to the initial coordinate, the termination coordinate and the simulated termination coordinate of the abnormal driving of the vehicle; the specific calculation formula is as follows:
Figure DEST_PATH_IMAGE051
in the formula (I), the compound is shown in the specification,
Figure 856100DEST_PATH_IMAGE052
representing the integral error degree of the abnormal driving track;
Figure 528390DEST_PATH_IMAGE049
a simulation end coordinate indicating an end time of abnormal travel of the vehicle;
Figure 823105DEST_PATH_IMAGE019
initial coordinates representing abnormal travel of the vehicle;
Figure 63594DEST_PATH_IMAGE021
the end coordinates of abnormal travel of the vehicle. In this embodiment, the difference between the simulation end coordinate and the initial coordinate of the abnormal driving path of the simulated vehicle in the abnormal state is compared with the difference between the end coordinate and the initial coordinate, and the error analysis is performed on the start-stop coordinate of the whole abnormal driving, where the initial coordinate and the end coordinate are actual values, and the simulation end coordinate is a simulation calculated value, and the whole error of the simulated abnormal driving path can be reflected to the maximum extent by performing the error calculation on the whole abnormal driving interval with the simulation calculated value and the actual value. It should be noted that, in this embodiment, the overall error of the abnormal trajectory is mainly obtained according to the actual running condition of the vehicle, and the starting point and the ending point of each abnormal trajectory are not the same coordinate point.
In order to reflect the error of the driving data of each abnormal section in the abnormal driving track most truly, the overall error of the abnormal driving track is distributed to each abnormal section in the abnormal driving track, which is specifically as follows:
finally, error distribution is carried out according to the weight value of each abnormal interval to
Figure 873287DEST_PATH_IMAGE013
For example, the error value after distribution is
Figure DEST_PATH_IMAGE053
The calculation method is as follows:
Figure DEST_PATH_IMAGE055
in the formula (I), the compound is shown in the specification,
Figure 818109DEST_PATH_IMAGE053
is shown as
Figure 373855DEST_PATH_IMAGE013
Error values of the individual abnormal intervals;
Figure 909879DEST_PATH_IMAGE044
denotes the first
Figure 613393DEST_PATH_IMAGE013
A weight value of each abnormal interval;
Figure 237272DEST_PATH_IMAGE052
integral error degree for representing abnormal driving track
Thus, the distributed error value of each abnormal section in all the abnormal driving tracks is obtained
Figure 303317DEST_PATH_IMAGE056
Error value
Figure 416767DEST_PATH_IMAGE056
That is, the degree of abnormality of the travel data in the abnormal section.
The compression storage module is used for performing Huffman coding on the driving data in each abnormal interval and performing lossless compression to obtain first compression data according to the probability product of the error value of each abnormal interval and the driving data in the corresponding abnormal interval as a weight; and stores the first compressed data.
In this embodiment, the above is to calculate the abnormal degree of the driving data in different abnormal intervals in a section of abnormal driving state of the vehicle in a driving process, and then perform lossless compression on the driving data of the abnormal trajectory by using the huffman coding algorithm according to the abnormal degree, and the specific method is as follows:
by the first
Figure 545260DEST_PATH_IMAGE013
Taking an abnormal interval as an example, the conventional Huffman coding method is based on the first one
Figure 35147DEST_PATH_IMAGE058
The probability of each data in each abnormal interval appearing in all abnormal intervals is used as a weight value to code, and the data appearance probability cannot reflect the abnormal degree of the abnormal data in the embodiment, according to the second step
Figure 690119DEST_PATH_IMAGE058
Error value of each abnormal interval and corresponding first
Figure 443311DEST_PATH_IMAGE058
Taking the probability product of the occurrence of the driving data in the abnormal interval as a weight, and comparing the weight with the weight
Figure 121417DEST_PATH_IMAGE058
Carrying out Huffman coding on the driving data in the abnormal intervals and carrying out lossless compression to obtain first compressed data; and stores the first compressed data.
In this embodiment, the data acquisition module further includes driving data for acquiring abnormal driving of the vehicle in a plurality of different time periods during each driving of the vehicle;
dividing the abnormal running track corresponding to each time period into a plurality of abnormal intervals according to a track dividing module;
acquiring a weight value of each abnormal interval corresponding to each time period through a first parameter acquisition module;
acquiring the integral error degree of the abnormal running track corresponding to each time period and the error value of each corresponding abnormal interval through a second parameter acquisition module;
the error value of each abnormal interval corresponding to each time period and the probability product of the running data in the corresponding abnormal interval are used as weight values through a compression storage module, and the running data in each abnormal interval corresponding to each time period are subjected to Huffman coding and lossless compression to obtain first compression data; and stores the first compressed data.
The compression storage module is used for acquiring running data of the vehicle when the vehicle normally runs in each running process of the vehicle, performing lossy compression on the running data of the vehicle when the vehicle normally runs to acquire second compressed data, and storing the second compressed data. The lossy compression is compression by predictive coding or transform coding.
In the storage process, the driving data is stored in a layered mode according to the integral combined difference of the abnormal driving tracks corresponding to each time period in each driving process of the vehicle and the weight value of each abnormal interval corresponding to each time period.
In the embodiment, when the compressed data is stored, for the driving data when the vehicle normally drives, real-time lossy compression is performed to obtain second compressed data, and the second compressed data is separately stored based on time sequence; in addition, in the embodiment, two storage partitions are provided to respectively store the running data when the vehicle normally runs and the running data when the vehicle abnormally runs, and the specific steps of hierarchically storing the first compressed data corresponding to the running data when the vehicle abnormally runs are as follows:
s1, carrying out priority calculation on overall data in multiple driving processes by using the overall error degree, specifically to the overall error degree under all abnormal driving states in each driving book
Figure 211733DEST_PATH_IMAGE052
Summing, sorting all the summed values in ascending order, and obtaining the overall error degree
Figure 658895DEST_PATH_IMAGE052
The single driving process corresponding to the minimum value of the summation value shows that the total amount of the driving data is less, so the storage priority is lowest; degree of integral error
Figure 317409DEST_PATH_IMAGE052
The single driving process corresponding to the maximum value of the summation value shows that the total amount of the driving data is large, and all the storage priorities are highest.
S2, calculating the storage priority of the running data based on the abnormal degree of all the running data in each running process, specifically calculating the integral error degree corresponding to the running data generated in all the abnormal running states
Figure 482812DEST_PATH_IMAGE052
Sorting in ascending order and the overall error degree
Figure 642397DEST_PATH_IMAGE052
The storage priority of the driving data generated in the maximum abnormal driving state is highest; degree of integral error
Figure 881749DEST_PATH_IMAGE052
The running data generated in the minimum abnormal running state is stored with the lowest priority.
S3, calculating a running data storage stage generated in each abnormal running state, specifically calculating the abnormal degree of the running data corresponding to a single abnormal interval
Figure 773481DEST_PATH_IMAGE053
Sorting in ascending order, degree of abnormality of the running data in the abnormal section
Figure 488497DEST_PATH_IMAGE053
The storage priority of the running data corresponding to the largest abnormal section is highest, and the abnormal degree of the running data of the abnormal section
Figure 858298DEST_PATH_IMAGE053
The storage priority of the travel data corresponding to the smallest abnormal section is lowest.
S4, performing priority-based layered storage on all the driving data, specifically, establishing a first priority storage layer, establishing a second priority storage layer in each layer of the first priority storage layer, establishing a third priority storage layer in each layer of the second priority storage layer, storing single driving data with the highest priority in the S1 in the highest layer of the first priority storage layer, and then performing layered storage according to the descending order of the priority until the single driving data with the lowest priority are stored in the lowest layer; the highest layer in the second priority layer stores the driving data generated in the abnormal driving state with the highest priority in the S2, and then the driving data generated in the abnormal driving state with the lowest priority is stored in a layered manner according to the descending order of the priority until the lowest layer stores the driving data generated in the abnormal driving state with the lowest priority; and the highest layer in the third priority storage layer stores the driving data corresponding to the priority with the highest priority in the S3, and then the driving data are stored in a layered mode according to the descending order of the priorities until the driving data with the lowest priority are stored in the lowest layer.
And the playback module is used for reconstructing the track of each driving of the vehicle after decompressing the stored compressed data and transmitting the track to the display end for playback. In this embodiment, in order to play back a certain trajectory of a vehicle, first compressed data and second compressed data corresponding to a driving process of the vehicle are retrieved from the compressed storage module, the first compressed data and the second compressed data are sorted based on time sequence, driving data during normal driving is directly decompressed and played back, and driving data during abnormal driving is corrected by combining with historical data, and then the reconstructed trajectory is played back. In the present embodiment, a large data correction method or a leveling correction method is used for the travel data correction.
In summary, the invention provides a system for intelligently replaying a driving track of a mobile phone digital vehicle key, which obtains driving data of abnormal driving of a vehicle through a data acquisition module, analyzes the whole error of the abnormal driving of the vehicle for the driving data of the abnormal driving of the vehicle, reflects the whole error of each abnormal driving track to the maximum extent, divides each abnormal driving track into abnormal intervals through inflection points, analyzes the abnormal intervals, distributes errors according to the analysis result, judges the running path length and the accumulated condition of the errors of each abnormal interval, so as to obtain the abnormal degree of each abnormal interval, and performs lossless compression on the driving data of each abnormal interval in the abnormal driving tracks by using a Manhough coding algorithm according to the abnormal degree of the abnormal intervals, wherein the larger the abnormal degree is, the shorter the coding is, the higher the safety is, the integrity of the driving data in the whole abnormal driving tracks can be ensured, and the situation that the abnormal driving data is originally in the compression process is avoided, so that the data is more abnormal; the data with small memory space is realized, and the occupation of the memory space is reduced; in addition, the invention also carries out priority level layer storage on the driving data according to the abnormal degree, improves the subsequent retrieval efficiency, and can quickly retrieve the driving data of the abnormal driving track, thereby realizing the playback of the driving track with small storage data and high retrieval efficiency.
It should be noted that: the sequence of the above embodiments of the present invention is only for description, and does not represent the advantages or disadvantages of the embodiments. And specific embodiments thereof have been described above. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit of the present invention are intended to be included therein.

Claims (8)

1. The utility model provides a driving track intelligence playback system of cell-phone digital car key which characterized in that includes:
the data acquisition module is used for acquiring the driving data of abnormal driving of the vehicle; acquiring initial coordinates of the starting time and ending coordinates of abnormal vehicle running through a mobile phone digital vehicle key; the driving data comprises position data and vehicle state information which are acquired when the vehicle runs abnormally;
the track division module is used for acquiring the abnormal traffic track of the vehicle according to the running data and fitting to acquire a traffic track function; obtaining a plurality of predicted turning position points when the vehicle runs abnormally by performing high-order derivation on the running track function;
acquiring the overall united difference of abnormal vehicle running according to the coordinates of the plurality of predicted position points; screening a plurality of accurate position points from the plurality of predicted position points according to the coordinates of the plurality of predicted position points and the overall combined difference of abnormal vehicle running;
dividing the abnormal driving track into a plurality of abnormal intervals according to a plurality of accurate position points;
the calculation formula of the overall united difference of the abnormal running of the vehicle is as follows:
Figure 14771DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 904230DEST_PATH_IMAGE002
the overall united difference of the abnormal running of the vehicle is represented;
Figure 471740DEST_PATH_IMAGE003
denotes the first
Figure 54031DEST_PATH_IMAGE004
Coordinates of the predicted location points;
Figure 572737DEST_PATH_IMAGE005
，
Figure 265886DEST_PATH_IMAGE006
represents the total number of predicted location points;
the first parameter acquisition module is used for acquiring the distribution weight of each abnormal interval according to the coordinates of the initial accurate position point and the final accurate position point of each abnormal interval and the number of the predicted position points in each abnormal interval;
acquiring the weight value of each abnormal interval according to the distribution weight value of each abnormal interval and the maximum value and the minimum value in the distribution weight values of all the abnormal intervals;
the calculation formula of the distribution weight of each abnormal interval is as follows:
Figure 920859DEST_PATH_IMAGE007
in the formula (I), the compound is shown in the specification,
Figure 674051DEST_PATH_IMAGE008
is shown as
Figure 680053DEST_PATH_IMAGE009
The distribution weight of each abnormal interval;
Figure 176893DEST_PATH_IMAGE010
denotes the first
Figure 653749DEST_PATH_IMAGE009
Coordinates of initial accurate position points of the abnormal intervals;
Figure 436897DEST_PATH_IMAGE011
is shown as
Figure 805562DEST_PATH_IMAGE009
Coordinates of the ending accurate position point of each abnormal interval;
Figure 965147DEST_PATH_IMAGE012
is a first
Figure 735657DEST_PATH_IMAGE009
The number of predicted location points within an interval;
Figure 689707DEST_PATH_IMAGE013
is as follows
Figure 545667DEST_PATH_IMAGE009
The number of the predicted position points and the accurate position points are included in each interval;
the second parameter acquisition module is used for acquiring a simulation termination coordinate of the abnormal running termination moment of the vehicle according to the running data; acquiring the integral error degree of the abnormal driving track according to the initial coordinate, the termination coordinate and the simulated termination coordinate of the abnormal driving of the vehicle; acquiring an error value of each abnormal interval according to the weight value of each abnormal interval and the integral error degree of the abnormal driving track;
the calculation formula of the overall error degree of the abnormal driving track is as follows:
Figure 10409DEST_PATH_IMAGE014
in the formula (I), the compound is shown in the specification,
Figure 369846DEST_PATH_IMAGE015
representing the integral error degree of the abnormal driving track;
Figure 760376DEST_PATH_IMAGE016
a simulation end coordinate indicating an end time of abnormal travel of the vehicle;
Figure 103633DEST_PATH_IMAGE017
initial coordinates representing abnormal travel of the vehicle;
Figure 605021DEST_PATH_IMAGE018
a termination coordinate of abnormal travel of the vehicle;
the compression storage module is used for performing Huffman coding on the driving data in each abnormal interval and performing lossless compression to obtain first compression data according to the probability product of the error value of each abnormal interval and the driving data in the corresponding abnormal interval as a weight; the mobile phone digital car key is used for acquiring running data of a vehicle when the vehicle normally runs in each running process of the vehicle, performing lossy compression on the running data of the vehicle when the vehicle normally runs to acquire second compressed data, and storing the first compressed data and the second compressed data in the mobile phone digital car key;
and the playback module is used for decompressing the stored second compressed data and then directly playing back the second compressed data, and the first compressed data is corrected and then the vehicle driving track is reconstructed for playing back the first compressed data.
2. The system for intelligently replaying the vehicle track of the mobile phone digital vehicle key according to claim 1, wherein in the track division module, the vehicle track function is fit according to position data collected by a vehicle in an abnormal driving process; the position data comprises longitude and latitude coordinates of the vehicle at each moment in the abnormal driving process; the independent variable of the driving track function is longitude, and the dependent variable is latitude.
3. The system for intelligently replaying the driving track of a mobile phone digital car key according to claim 2, wherein the predicted position points of the plurality of turns are obtained according to the following steps:
obtaining a second derivative function by carrying out second order derivation on the traveling track function; meanwhile, third-order derivation is carried out on the traveling track function to obtain a third-order derivative function;
and taking a plurality of coordinate points corresponding to the driving track function, which enable the second derivative function to be equal to zero and enable the third derivative function not to be equal to zero, as predicted position points of a plurality of turns.
4. The system for intelligently replaying the driving track of the mobile phone digital car key according to claim 1, wherein in the track division module, the plurality of accurate position points are obtained according to the following steps:
according to the coordinate of the first predicted position point in the plurality of predicted position points and the second predicted position point
Figure 350124DEST_PATH_IMAGE019
Calculating the coordinates of the predicted position points to obtain the first predicted position point to the second predicted position point
Figure 911555DEST_PATH_IMAGE019
Interval difference of the predicted position points; when the interval difference is equal to the integral combination difference of the abnormal running of the vehicle, the interval difference is compared with the interval difference
Figure 476528DEST_PATH_IMAGE019
The predicted position points are accurate position points;
then by the first
Figure 303581DEST_PATH_IMAGE020
The coordinates of the predicted position points and the coordinates of the subsequent predicted position points are calculated in sequence, and a plurality of accurate position points are obtained by analogy in sequence.
5. The system for intelligently replaying the driving track of the mobile phone digital car key according to claim 1, wherein the data acquisition module further comprises driving data for acquiring abnormal driving of the vehicle in a plurality of different time periods during each driving process of the vehicle; dividing the abnormal running track corresponding to each time period into a plurality of abnormal intervals according to a track dividing module;
acquiring a weight value of each abnormal interval corresponding to each time period through a first parameter acquisition module;
acquiring the integral error degree of the abnormal running track corresponding to each time period and the error value of each corresponding abnormal interval through a second parameter acquisition module;
performing Huffman coding on the running data in each abnormal interval corresponding to each time period by using a compression storage module to perform lossless compression to obtain first compressed data, wherein the product of the error value of each abnormal interval corresponding to each time period and the probability of the running data in the corresponding abnormal interval is used as a weight; and storing the first compressed data in the mobile phone digital car key.
6. The system for intelligently replaying the driving track of the mobile phone digital car key according to claim 5, wherein in the storage process, the driving data is stored hierarchically according to the overall error degree of the abnormal driving track corresponding to each time period in each driving process of the car and the weight value of each abnormal interval corresponding to each time period.
7. The system for intelligently replaying the driving track of the mobile phone digital car key according to claim 6, wherein the hierarchical storage is performed according to the following steps:
establishing first priority storage layers, establishing a second priority storage layer for each first priority storage layer, and establishing a third priority storage layer for each second priority storage layer;
sequentially storing the running data of the abnormal running tracks of different time periods of each running in a first priority storage layer according to the sum of the overall error degrees of the abnormal running tracks of the different time periods in the running process of the vehicle;
sequentially storing the running data of the abnormal running track of each time period in a second priority storage layer in the first priority storage layer according to the sum of the error values of each abnormal interval in the abnormal running track of each time period;
and sequentially storing the running data of each abnormal section in a third priority storage layer in the second priority storage layer according to the error value of each abnormal section in the abnormal running track.
8. The system of claim 1, wherein in the playback module, when the vehicle trajectory is played back in the mobile phone digital vehicle key, the first compressed data and the second compressed data of the driving process are retrieved from the compressed storage module, the first compressed data and the second compressed data are sorted based on the time sequence, the driving data during normal driving is directly decompressed and played back, and the driving data during abnormal driving is corrected and the vehicle trajectory is reconstructed for playback.
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