CN106021033A - Cleaning method and cleaning system based on distorted GPS data - Google Patents
Cleaning method and cleaning system based on distorted GPS data Download PDFInfo
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- CN106021033A CN106021033A CN201610384453.8A CN201610384453A CN106021033A CN 106021033 A CN106021033 A CN 106021033A CN 201610384453 A CN201610384453 A CN 201610384453A CN 106021033 A CN106021033 A CN 106021033A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/14—Error detection or correction of the data by redundancy in operation
- G06F11/1402—Saving, restoring, recovering or retrying
- G06F11/1446—Point-in-time backing up or restoration of persistent data
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- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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Abstract
The invention provides a cleaning method based on distorted GPS data. The method comprises the steps that original GPS data is analyzed according to a time and transmission rule; abnormal data is determined and cleaned according to analyzed direction angle and speed data; abnormal speeds and direction angles in a stroke are judged, processed and corrected; corrected GPS data of all time points is acquired according to processed and corrected speed data, direction angle data and original GPS data. According to the method, abnormal values in vehicle GPS data which is collected by Internet of vehicles terminal or mobile terminal equipment in real time and even the data loss condition can be cleaned, and the collected GPS data is recovered to the original GPS data at low cost.
Description
Technical field
The invention belongs to gps data process field, be specifically related to a kind of cleaning method based on distortion gps data and clean system
System.
Background technology
GPS has had the widest application at numerous areas, in car networking industry, and can be real by terminal or mobile terminal equipment
Time collection vehicle GPS track data, be then passed through compression and be transferred to server and decompress and store.During these, often
There will be the abnormal situation about even losing of gps data, a kind of cleaning algorithm for distortion gps data can saved greatly
Recover original gps data in the case of amount cost, car networking industry is had great significance.
Summary of the invention
In order to solve the problems referred to above, the present invention provides a kind of cleaning method based on distortion gps data, described method according to
GPS initial data is resolved by time and transmission rule, and carries out abnormal number according to the deflection parsed and speed data
According to determining and cleaning, then abnormal speed in stroke and deflection judged and processes correction, revising finally according to process
Speed data, deflection data and GPS initial data obtain the GPS of each time point and revise data；
Further, described method includes:
S1: according to time and data packet compressing transmission rule Preliminary Analysis GPS initial data, it is thus achieved that the deflection of each time point
And speed data；
S2: judge the starting point of abnormal data and end point according to the deflection resolved in S1 and speed data and clean
Abnormal data；
S3: repeat S2, and the deflection data after cleaning and speed were resolved according to the time and splices；
S4: data are cut by the burnout time stopped working in data according to the duration of ignition in stroke firing data and stroke
Point, velocity anomaly value in stroke is judged and processes；
S5: the GPS obtaining each time point according to the speed data processed, deflection data and GPS initial data revises
Data；
Further, described S1 is to time point each in GPS initial data and the speed details data of time point before thereof
Summation, obtains the speed data of each time point；
The deflection data of time point each in GPS initial data are calculated by described S1, and computational methods include:
11) first deflection of GPS initial data is defined as the deflection of first time point；
12) if occurring in the middle of GPS initial data, deflection obtains unsuccessfully, then first deflection reacquired does not makes
With incremental representation, it is stipulated that be the deflection of first time point；
13) if occurring, for sky, speed is not 0 to deflection data, then the deflection used a second replaces first time point
Deflection；
14) if 11), 12), 13) condition is all unsatisfactory for, then the deflection of first time point is a upper time point deflection and this
Time point deflection data sum；
Further, described S2 includes that abnormal data starting point judges, abnormal data end point judges and data cleansing；
Abnormal data starting point judges, described abnormal data starting point determination methods includes:
211) speed is from pre-set velocity threshold value VsMore than sport 0；
212), in the case of speed is not zero, deflection suddenlys change to 0 from interval θ～360 ° of-θ；
Abnormal data end point judges, described abnormal data end point determination methods includes:
221) speed is suddenlyd change to pre-set velocity threshold value V from 0sAbove；
222), in the case of speed is not 0, deflection is from 0 sudden change to interval θ～360 ° of-θ；
Data cleansing, described Data Cleaning Method is that deflection and the speed data of abnormal data starting point are all modified to
0；The angle details data of abnormal data end point and speed details data correction are now real angle relative with speed upper one
The angle of individual effective time point and the variable quantity of speed data.
Further, described S3 repeats S2, by the speed after cleaning and deflection data, again resolves gps data right
After temporally by gps data so the deflection of time point and speed data parse, if same time point is before
Gps data occurs, then with the data cover in gps data bag afterwards；
Further, described S4 according to stop working the duration of ignition in stroke firing data and stroke in data flame-out time
Between be some run-length datas by data cutting, every section of stroke is the most sequentially chosen the speed data of 6 time points
{v1, v2, v3, v4, v5, v6As dependent variable, carry out cubic polynomial recurrence with 1～6 for independent variable:
vk=β0+β1k+β2k2+β3k3
K=1,2,3,4,5,6
Then the prediction rate predictions of the 7th second and 99% confidence interval thereof, the rate predictions of the 7th second is:
If the speed observation of the 7th second and predictive value differ by more than a certain specific threshold, and observation is not in prediction
Within 99% confidence interval, then judge that observation is abnormal, replace observation data with predictive value, elapse the most forward to lower a period of time
Between point, repeat above-mentioned exceptional value judge and process operation；
Further, described S5 utilizes each time point angle and speed data that above-mentioned parsing and Data Cleaning Method obtain,
Starting GPS longitude when gathering and GPS latitude data eventually in conjunction with each GPS initial data, recursion obtains the GPS warp of each time point
Degree and GPS latitude, it is thus achieved that GPS revises data；
Further, a kind of purging system based on distortion gps data, described system includes inputting data module, data
Processing module and output data module, described input data module connects output data module by data processing module；
Input data module, described input data module is used for inputting GPS initial data；
Data processing module, described data processing module is used for analyzing GPS initial data, judging abnormal data and cleaning
With correction gps data；
Output data module, described output data module is used for exporting GPS and revises data；
Further, described data processing module includes data analysis unit, abnormal data judging unit, data cleansing list
Unit and data concatenation unit, described data processing module includes data analysis unit, abnormal data judging unit, data cleansing list
Unit and data concatenation unit are sequentially connected with；
Further, described data cleansing unit includes that deflection data correction subelement and speed data process son list
Unit, described deflection data correction subelement and speed data process the equal one end of subelement and connect abnormal data judging unit, separately
One end connects data concatenation unit；
The present invention can to the exceptional value in the vehicle GPS data of car networked terminals or mobile terminal equipment Real-time Collection even
Event of data loss is carried out, and recovers the gps data collected to raw GPS data with relatively low cost.
Accompanying drawing explanation
Fig. 1 is the module map of system of the present invention.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, right
The present invention is explained in further detail.Should be appreciated that specific embodiment described herein is used only for explaining the present invention, and
It is not used in the restriction present invention.On the contrary, the present invention contain any be defined by the claims do in the spirit and scope of the present invention
Replacement, amendment, equivalent method and scheme.Further, in order to make the public that the present invention to be had a better understanding, below to this
During the details of invention describes, detailed describe some specific detail sections.Do not have these thin for a person skilled in the art
The description of joint part can also understand the present invention completely.
The invention will be further described with specific embodiment below in conjunction with the accompanying drawings, but not as a limitation of the invention.
Below for the most preferred embodiment of enumerating of the present invention:
The present invention provides a kind of cleaning method based on distortion gps data, and described method is according to time and transmission rule pair
GPS initial data resolves, and carries out abnormal data according to the deflection parsed and speed data and determine and clean, more right
In stroke, abnormal speed and deflection carry out judging and process correction, finally according to processing corrected speed data, deflection
Data and GPS initial data obtain the GPS of each time point and revise data, and described method specifically includes:
S1: according to time and data packet compressing transmission rule Preliminary Analysis GPS initial data, it is thus achieved that the deflection of each time point
And speed data；
S2: judge the starting point of abnormal data and end point according to the deflection resolved in S1 and speed data and clean
Abnormal data；
S3: repeat S2, and the deflection data after cleaning and speed were resolved according to the time and splices；
S4: data are cut by the burnout time stopped working in data according to the duration of ignition in stroke firing data and stroke
Point, velocity anomaly value in stroke is judged and processes；
S5: the GPS obtaining each time point according to the speed data processed, deflection data and GPS initial data revises
Data.
Described S1 to time point each in GPS initial data and before time point speed details data summation, obtain
The speed data of each time point；
The deflection data of time point each in GPS initial data are calculated by described S1, and computational methods include:
11) first deflection of GPS initial data is defined as the deflection of first time point；
12) if occurring in the middle of GPS initial data, deflection obtains unsuccessfully, then first deflection reacquired does not makes
With incremental representation, it is stipulated that be the deflection of first time point；
13) if occurring, for sky, speed is not 0 to deflection data, then the deflection used a second replaces first time point
Deflection；
14) if 11), 12), 13) condition is all unsatisfactory for, then the deflection of first time point is a upper time point deflection and this
Time point deflection data sum.
Described S2 includes that abnormal data starting point judges, abnormal data end point judges and data cleansing；
Abnormal data starting point judges, described abnormal data starting point determination methods includes:
211) speed is from pre-set velocity threshold value VsMore than sport 0；
212), in the case of speed is not zero, deflection suddenlys change to 0 from interval θ～360 ° of-θ；
Abnormal data end point judges, described abnormal data end point determination methods includes:
221) speed is suddenlyd change to pre-set velocity threshold value V from 0sAbove；
222), in the case of speed is not 0, deflection is from 0 sudden change to interval θ～360 ° of-θ；
Data cleansing, described Data Cleaning Method is that deflection and the speed data of abnormal data starting point are all modified to
0；The angle details data of abnormal data end point and speed details data correction are now real angle relative with speed upper one
The angle of individual effective time point and the variable quantity of speed data.
Described S3 repeats S2, by the speed after cleaning and deflection data, again resolves gps data and the most temporally will
So the deflection of time point and speed data parse in gps data, if same time point in gps data before
Through occurring, then with the data cover in gps data afterwards.
Described S4 according to the burnout time in data of stopping working the duration of ignition in stroke firing data and stroke by data
Cutting is some run-length datas, and every section of stroke is the most sequentially chosen the speed data { v of 6 time points1, v2, v3,
v4, v5, v6As dependent variable, carry out cubic polynomial recurrence with 1～6 for independent variable:
vk=β0+β1k+β2k2+β3k3
K=1,2,3,4,5,6
Then the prediction rate predictions of the 7th second and 99% confidence interval thereof, the rate predictions of the 7th second is:
If the speed observation of the 7th second and predictive value differ by more than a certain specific threshold, and observation is not in prediction
Within 99% confidence interval, then judge that observation is abnormal, replace observation data with predictive value, elapse the most forward to lower a period of time
Between point, repeat above-mentioned exceptional value judge and process operation.
Described S5 utilizes each time point angle and speed data that above-mentioned parsing and Data Cleaning Method obtain, in conjunction with each
GPS initial data starts GPS longitude when gathering and GPS latitude data eventually, and recursion obtains GPS longitude and the GPS of each time point
Latitude, it is thus achieved that GPS revises data.
A kind of purging system based on distortion gps data, described system include input data module, data processing module and
Output data module, described input data module connects output data module by data processing module；
Input data module, described input data module is used for inputting GPS initial data；
Data processing module, described data processing module is used for analyzing GPS initial data, judging abnormal data and cleaning
With correction gps data；
Output data module, described output data module is used for exporting GPS and revises data.
Described data processing module includes data analysis unit, abnormal data judging unit, data cleansing unit and data
Concatenation unit, described data processing module includes data analysis unit, abnormal data judging unit, data cleansing unit and data
Concatenation unit is sequentially connected with.
Described data cleansing unit includes that deflection data correction subelement and speed data process subelement, described direction
Angular data correction subelement and speed data process the equal one end of subelement and connect abnormal data judging unit, and the other end connects data
Concatenation unit.
Present invention situation when actually used is as follows:
The input data related in the present invention be certain customer data bag data (include several continuous print packets, each
The some data of packet, including data acquisition time, GPS longitude, GPS latitude, GPS velocity, GPS deflection, speed details,
The fields such as deflection details), stroke firing data, stroke stops working data；
Output data are the gps data through over cleaning.
One, packet is temporally resolved
(1), its original orientation angular velocity data is resolved
According to time and the deflection of each time point of data packet compressing transmission rule Preliminary Analysis and speed data.To packet
The speed details data summation of interior each time point and before time point, obtains the GPS velocity data of each time point；
The deflection data of each time point are calculated from deflection details data by following rule:
1, in packet, the deflection of first time point must be first deflection details data of packet itself；
If occurring in the middle of 2 packets, deflection obtains unsuccessfully, and first deflection reacquired does not uses increment list
Show, be also deflection details data itself；
If 3 occur deflection details data be empty and speed be not 0 situation, the deflection used a second replaces；
4, in the case of other, deflection is a upper time point deflection and this time point deflection details data sum.
(2), deflection, speed invalid data determine and clean
The deflection that goes out in conjunction with Preliminary Analysis, speed data, it is judged that abnormal data.When abnormal data is the most all with one
The form of point or continuous multiple time point occurs, therefore has only to judge that abnormal data starting point and end point can be judged
The position of abnormal data, and then be modified.
Abnormal data starting point judges:
(1) speed is from pre-set velocity threshold value VsMore than sport 0；
(2), in the case of speed is not zero, deflection suddenlys change to 0 from interval θ～360 ° of-θ.
Above-mentioned two situations one of which occurs i.e. judging that this time point is abnormal data starting point, and cleaning method is abnormal
Deflection details and the speed details data of data starting point are all modified to 0.
Abnormal data end point judges:
(1) speed is suddenlyd change to pre-set velocity threshold value V from 0sAbove；
(2), in the case of speed is not 0, deflection is from 0 sudden change to interval θ～360 ° of-θ.
Above-mentioned two situations one of which occurs i.e. judging that this time point is abnormal data end point, and cleaning method is abnormal
The deflection details data of ED point and speed details data correction are now relative with speed upper of true directions angle
The deflection of effective time point and the variable quantity of speed data.
(3) data parsing and splicing after, cleaning
Repeat step (), by the speed details after cleaning and deflection details data, again resolve packet.Then
Temporally by packet so the deflection of time point and speed data parse, if same time point is in data before
Bag occurs, then with the data cover in packet afterwards.
Two, in stroke, velocity anomaly value judges and processes
According to the burnout time stopped working in data the duration of ignition in stroke firing data and stroke by data cutting it is
Some run-length datas.
Every section of stroke is the most sequentially chosen the speed data { v of 6 time points1, v2, v3, v4, v5, v6Conduct
Dependent variable, carries out cubic polynomial recurrence with 1～6 for independent variable:
vk=β0+β1k+β2k2+β3k3
K=1,2,3,4,5,6
Then the prediction rate predictions of the 7th second and 99% confidence interval thereof, the rate predictions of the 7th second is:
If the speed observation of the 7th second and predictive value differ by more than a certain specific threshold, and observation is not in prediction
Within 99% confidence interval, then judge that observation is abnormal, replace observation data with predictive value.
Elapse the most forward to future time point, repeat above-mentioned exceptional value and judge and process operation.
Three, each time point GPS longitude and latitude in stroke is calculated
Utilize each time point deflection and speed data that above-mentioned parsing and data cleansing mode obtain, in conjunction with each packet
Starting GPS longitude when gathering and GPS latitude data, recursion obtains GPS longitude and the GPS latitude of each time point.
Embodiment described above, the simply one of the present invention more preferably detailed description of the invention, those skilled in the art
The usual variations and alternatives that member is carried out in the range of technical solution of the present invention all should comprise within the scope of the present invention.
Claims (10)
1. a cleaning method based on distortion gps data, it is characterised in that described method is according to time and transmission rule pair
GPS initial data resolves, and carries out abnormal data according to the deflection parsed and speed data and determine and clean, more right
In stroke, abnormal speed and deflection carry out judging and process correction, finally according to processing corrected speed data, deflection
Data and GPS initial data obtain the GPS of each time point and revise data.
Method the most according to claim 1, it is characterised in that described method includes:
S1: according to time and data packet compressing transmission rule Preliminary Analysis GPS initial data, it is thus achieved that the deflection of each time point and speed
Degrees of data；
S2: judge the starting point of abnormal data and end point according to the deflection resolved in S1 and speed data and clean exception
Data；
S3: repeat S2, and the deflection data after cleaning and speed were resolved according to the time and splices；
S4: according to the burnout time stopped working in data the duration of ignition in stroke firing data and stroke by data cutting, right
In stroke, velocity anomaly value judges and processes；
S5: the GPS obtaining each time point according to the speed data processed, deflection data and GPS initial data revises data.
Method the most according to claim 2, it is characterised in that described S1 to time point each in GPS initial data and
The speed details data summation of time point, obtains the speed data of each time point before；
The deflection data of time point each in GPS initial data are calculated by described S1, and computational methods include:
11) first deflection of GPS initial data is defined as the deflection of first time point；
12) if occurring in the middle of GPS initial data, deflection obtains unsuccessfully, then first deflection reacquired does not uses increasing
Amount represents, it is stipulated that be the deflection of first time point；
13) if occurring, for sky, speed is not 0 to deflection data, then the deflection used a second replaces the direction of first time point
Angle；
14) if 11), 12), 13) condition is all unsatisfactory for, then the deflection of first time point is a upper time point deflection and this time point
Deflection data sum.
Method the most according to claim 3, it is characterised in that described S2 includes that abnormal data starting point judges, extremely
ED point judges and data cleansing；
Abnormal data starting point judges, described abnormal data starting point determination methods includes:
211) speed is from pre-set velocity threshold value VsMore than sport 0；
212), in the case of speed is not zero, deflection suddenlys change to 0 from interval θ～360 ° of-θ；
Abnormal data end point judges, described abnormal data end point determination methods includes:
221) speed is suddenlyd change to pre-set velocity threshold value V from 0sAbove；
222), in the case of speed is not 0, deflection is from 0 sudden change to interval θ～360 ° of-θ；
Data cleansing, described Data Cleaning Method is that deflection and the speed data of abnormal data starting point are all modified to 0；Different
The angle details data of regular data end point have for now relative with speed upper one of real angle with speed details data correction
The angle of effect time point and the variable quantity of speed data.
Method the most according to claim 4, it is characterised in that described S3 repeats S2, by the speed after cleaning and direction
Angular data, again resolve gps data the most temporally by gps data so the deflection of time point and speed data parse
Come, if same time point has occurred in gps data before, then with the data cover in gps data bag afterwards.
Method the most according to claim 3, it is characterised in that described S4 according to the duration of ignition in stroke firing data with
And data cutting is some run-length datas by the burnout time that stroke stops working in data, every section of stroke is the most sequentially selected
Take the speed data { v of 6 time points1, v2, v3, v4, v5, v6As dependent variable, carry out cubic polynomial with 1～6 for independent variable
Return:
vk=β0+β1k+β2k2+β3k3
K=1,2,3,4,5,6
Then the prediction rate predictions of the 7th second and 99% confidence interval thereof, the rate predictions of the 7th second is:
If the speed observation of the 7th second and predictive value differ by more than a certain specific threshold, and observation is not put the 99% of prediction
Within letter interval, then judge that observation is abnormal, replace observation data with predictive value, elapse the most forward to future time point,
Repeat above-mentioned exceptional value judge and process operation.
Method the most according to claim 6, it is characterised in that described S5 utilizes above-mentioned parsing and Data Cleaning Method to obtain
Each time point angle and speed data, eventually start GPS longitude when gathering and GPS latitude data in conjunction with each GPS initial data,
Recursion obtains GPS longitude and the GPS latitude of each time point, it is thus achieved that GPS revises data.
8. a purging system based on distortion gps data, based on the cleaning method one of the claims 1-7 Suo Shu, its
Being characterised by, described system includes inputting data module, data processing module and output data module, described input data module
Output data module is connected by data processing module；
Input data module, described input data module is used for inputting GPS initial data；
Data processing module, described data processing module is used for analyzing GPS initial data, judging abnormal data and cleaning and repair
Positive gps data；
Output data module, described output data module is used for exporting GPS and revises data.
System the most according to claim 8, it is characterised in that described data processing module includes data analysis unit, different
Regular data judging unit, data cleansing unit and data concatenation unit, described data processing module includes data analysis unit, different
Regular data judging unit, data cleansing unit and data concatenation unit are sequentially connected with.
System the most according to claim 9, it is characterised in that described data cleansing unit includes deflection data correction
Subelement and speed data process subelement, described deflection data correction subelement and speed data and process the equal one end of subelement
Connecting abnormal data judging unit, the other end connects data concatenation unit.
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| CN112287178A (en) * | 2020-10-30 | 2021-01-29 | 腾讯科技（深圳）有限公司 | Data cleaning method and device, electronic equipment and storage medium |
| CN112287178B (en) * | 2020-10-30 | 2023-11-17 | 腾讯科技（深圳）有限公司 | Data cleaning method and device, electronic equipment and storage medium |
| CN113068130A (en) * | 2021-03-29 | 2021-07-02 | 宁波趣行智能科技有限公司 | Traffic track data receiving and processing method of intelligent vehicle-mounted equipment |
| CN113068130B (en) * | 2021-03-29 | 2022-09-27 | 宁波趣行智能科技有限公司 | Traffic track data receiving and processing method of intelligent vehicle-mounted equipment |
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