CN105160883A - Energy-saving driving behavior analysis method based on big data - Google Patents

Energy-saving driving behavior analysis method based on big data Download PDF

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CN105160883A
CN105160883A CN201510683076.3A CN201510683076A CN105160883A CN 105160883 A CN105160883 A CN 105160883A CN 201510683076 A CN201510683076 A CN 201510683076A CN 105160883 A CN105160883 A CN 105160883A
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vehicle
energy
oil consumption
user
behavior
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CN105160883B (en
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刘琳
华新泽
冯辉宗
李锐
李永福
岑明
韩利夫
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Chongqing University of Post and Telecommunications
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Chongqing University of Post and Telecommunications
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Abstract

The invention relates to an energy-saving driving behavior analysis method based on big data and belongs to the technical field of intelligent transportation and automobile energy conservation. The method comprises that a vehicle-mounted terminal and a road side system collects vehicle correlative information, environment information and traffic information in a vehicle driving process and send the collected information to an Internet of Vehicles information service background via the vehicle-mounted terminal; the information service background performs energy-saving driving analysis by means of a same-ID vehicle data vertical analysis model, a same-kind vehicle data horizontal analysis model, and a same-characteristic vehicle data contrastive analysis model, and finally feeds an energy-saving driving suggestion back to a user in a vehicle-mounted terminal voice broadcast and screen display manner in real time or after a journey finishes. The method may provide economic and reliable driving suggestions for a driver in a driving process and provide a theoretical support for vehicle design and improvement for vehicle enterprises so as to achieve an energy-saving and emission reduction effect.

Description

A kind of energy-saving driving behavior analysis method based on large data
Technical field
The invention belongs to intelligent transportation and automobile energy-saving technology field, relate to a kind of energy-saving driving behavior analysis method based on large data.
Background technology
At present, research is driven for vehicle energy saving both domestic and external, main still to carry out monitoring for some parameter of single unit vehicle, as vehicle oil consumption, technology status and driving behavior analysis etc., these analytical approachs can realize energy-conservation object to a certain extent, but along with rise and the development of car networking, increasing vehicle can conveniently access network, in addition, due to the development of field of traffic, the road side system of traffic can be reflected in real time, navigational system has also covered comprehensively, this means that the analysis of energy-saving driving behavior is not only confined to single unit vehicle, but can based on whole large data platform.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of energy-saving driving behavior analysis method based on large data, the method can provide economic, reliable driving to advise in the process of moving for driver and provide theories integration for car enterprise to the design of vehicle and improvement, thus reaches the effect of energy-saving and emission-reduction.
For achieving the above object, the invention provides following technical scheme:
A kind of energy-saving driving behavior analysis method based on large data, the method is in vehicle traveling process, collect vehicle-related information, environmental information and transport information by car-mounted terminal, satellite and road side system, and the information collected is sent to car networked information service background through car-mounted terminal; Information service backstage is by same ID vehicle data vertical analysis model, same types of vehicles data horizontal analysis model and carry out energy-saving driving analysis with feature vehicle data comparative analysis model, and energy-saving driving suggestion feeds back to user by the mode of car-mounted terminal voice broadcast and screen display in real time or at the end of LAP the most at last.
Further, described same ID vehicle data vertical analysis model is differentiated by car-mounted terminal and records the urgency acceleration of driver, anxious deceleration, zig zag behavior, same ID vehicle real time data and historical data are analyzed, correct the driving bad habit of driver and not energy-conservation behavior in time;
Concrete analysis process comprises:
1) driving behavior of vehicle operator differentiated and quantize, first five classes being divided into the not power-save operation behavior of driver: anxious acceleration, anxious deceleration, zig zag, anxious acceleration+zig zag and the+zig zag that suddenly slows down; Again classification and quantification are carried out to five class behaviors;
2) Real-time Feedback is carried out to data: after quantification is weighted to driver behavior, obtain the weights mark of often kind of behavior different stage, in driver's driving process, when a certain behavior of driver reaches respective degrees, car-mounted terminal can send to driver and warns and show this oil consumption moment;
3) number of times of car-mounted terminal statistics above-mentioned behavior of user in certain effective driving time, obtain corresponding energy-conservation behavior to divide, and compare with historical scores, understand oneself above-mentioned behavior during this period of time whether to reduce, for the calculating that the energy-conservation behavior of user divides, employing be each class behavior weights add up.
Further, described classification is carried out to five class behaviors and quantification comprises: classification is carried out to three class basic acts and additional weights, for intersecting the generation of behavior, employing be the method that weights are multiplied:
When acceleration is at am/s 2to bm/s 2belong to slight anxious acceleration in scope, grade is L, and weights are 1; At bm/s 2to cm/s 2belong to the anxious acceleration of moderate in scope, grade is M, and weights are 2; When acceleration is greater than cm/s 2time belong to that severe is anxious to be accelerated, grade is F, and weights are 3, wherein (a<b<c); In like manner anxious deceleration and zig zag also can carry out weights distribution with reference to related data;
Further, described same types of vehicles data horizontal analysis model by collecting vehicle average fuel consumption over a period, instantaneous fuel consumption information, the oil consumption difference situation between the same types of vehicles that the vehicle Actual Burnoff amount of the every type of methods analyst of utilization variance inspection and the difference of theoretical fuel consumption and comparative analysis different vendor produce.
Described analysis vehicle Actual Burnoff amount and theoretical fuel consumption comprise: by the fuel consumption data of collection vehicle multiple time period, try to achieve the practical oil consumption of average fuel consumption as vehicle of vehicle, and carry out difference analysis with the theoretical oil consumption of the type vehicle, make car look forward to understanding this vehicle vehicle after putting on market, whether there is the situation that practical oil consumption significantly exceeds theoretical oil consumption, for car enterprise vehicle oil consumption Theoretical Design afterwards provides theoretical and data supporting, specifically comprise the following steps:
1) oil consumption of the multiple time periods recorded in collection vehicle driving process, obtains one group of data and is designated as (x 1, x 2, x 3... x n), then the practical oil consumption of this vehicle is designated as
2) difference test:
1. due to N (μ, σ that the average fuel consumption data of same types of vehicles are Normal Distribution in theory 2), wherein μ is expectation value, is the theoretical fuel consumption values that car is looked forward to calculating based on large data, σ 2be designated as variance;
2. null hypothesis H is 0: μ=μ 0
3. selection check statistic: U = X &OverBar; - &mu; &sigma; / n
If 4. for level of significance, illustrate this vehicle practical oil consumption level and theoretical oil consumption variant, and to differ greatly;
3) by difference test, car enterprise can receive two useful informations: whether the practical oil consumption between the vehicle of same type has larger difference; Whether the vehicle that practical oil consumption exceeds theoretical oil consumption is more;
Further, the oil consumption difference situation between the same types of vehicles produced of described comparative analysis different vendor specifically comprises:
1) data acquisition and classification: do to classify to the data gathered by the distance travelled of vehicle;
2) otherness of vehicle oil consumption under different distance travelled that different manufacturers is produced is compared;
Further, described same feature vehicle data comparative analysis model is that the user by having same characteristic features to some classifies, arbitrary member in each class compares with the oil consumption of optimum drive person, in the Power Saving Class points-scoring system of correspondence, to mark rank to user, finally provide energy-saving driving to advise.
Further, the comparative analysis of described same feature vehicle data specifically comprises:
1) users classification of same characteristic features: the classification criterion of the user of same characteristic features comprises average speed per hour, time period, weather and traffic events, route distance, speed per hour 60km/h driving time ratio, parking rate, the time scale of parking, vehicle feature, adopt PCA algorithm dimensionality reduction, then use k-means algorithm to classify;
2) divide and score rank analysis with feature user Power Saving Class: after users classification completes, the energy-saving driving behavior of same class users is analyzed, use fuzzy control theory, by estimating Power Saving Class to the monitoring of the acceleration in vehicle driving process and acceleration change amount; Energy-saving driving behavior in user's each run is given a mark, and the rank of user is fed back to car-mounted terminal;
3) optimum oil consumption compares and energy-saving driving suggestion feedback: it is the oil consumption of the LAP of user and the user's optimum oil consumption with condition down train are compared that described optimum oil consumption compares, the determination of calculated fuel consumption amount is based on the oil consumption basis of users all in this stroke, adopt be remove front 10% and rear 10% user, get the oil consumption mean value of user of centre 80%; Described energy-saving driving suggestion is a kind of strategy that effectively can improve the behavior of user's energy-saving driving drawn according to the Correlative data analysis such as points-scoring system and vehicle acceleration.
Further, the described strategy that effectively can improve the behavior of user's energy-saving driving comprises: be first compared by the oil consumption of the optimum drive person with identical conditions down train, show that whether user's oil consumption is normal, if oil consumption is normal, then inform that user keeps and gives certain virtual reward, if user's oil consumption is abnormal, be then averaged acceleration and acceleration change component analysis further; Rational for average acceleration, user needs to adjust the dynamics of stepping on the gas in driving procedure, to control the variable quantity of acceleration; , acceleration change amount unreasonable for average acceleration is rational, and user needs to reduce throttle in the process of moving; For average acceleration and acceleration change amount all irrational, then user should reduce the dynamics that throttle reduces to step on the gas again.
Beneficial effect of the present invention is: method of the present invention can analyze the energy-saving driving behavior over a period of same vehicle, the differentiation of as anxious in driver three (anxious acceleration, anxious deceleration, zig zag) behavior, can correct the driving bad habit of driver and not energy-conservation behavior in time; Analyze the information such as same type vehicle average fuel consumption over a period, for car enterprise vehicle oil consumption Theoretical Design afterwards provides theoretical and data supporting; And compare the user of same condition down train, and the energy-saving driving behavior that the data that combination is compared are driver is graded, and provides energy-saving driving to advise.
Accompanying drawing explanation
In order to make object of the present invention, technical scheme and beneficial effect clearly, the invention provides following accompanying drawing and being described:
Fig. 1 is the analytic system structural drawing of the energy-saving driving behavior based on large data;
Fig. 2 is same ID vehicle data vertical analysis process flow diagram;
Fig. 3 is same types of vehicles data horizontal analysis process flow diagram;
Fig. 4 is energy-saving driving suggestion feedback mechanism process flow diagram;
Fig. 5 is same feature vehicle data comparative analysis process flow diagram.
Embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.
The object of this invention is to provide a kind of analytical approach of the energy-saving driving behavior based on large data, the energy-saving driving behavior over a period of same vehicle can be analyzed, the differentiation of as anxious in driver three (anxious acceleration, anxious deceleration, zig zag) behavior, can correct the driving bad habit of driver and not energy-conservation behavior in time; Analyze the information such as same type vehicle average fuel consumption over a period, for car enterprise vehicle oil consumption Theoretical Design afterwards provides theoretical and data supporting; And compare the user of same condition down train, and the energy-saving driving behavior that the data that combination is compared are driver is graded, and provides energy-saving driving to advise.
Fig. 1 is the analytic system structural drawing of the energy-saving driving behavior based on large data, in vehicle traveling process, pass through car-mounted terminal, satellite and road side system collect vehicle-related information, environmental information and transport information etc., car networked information service background is sent to through car-mounted terminal, energy-saving driving analysis is carried out by corresponding model in backstage, as the vertical analysis of ID vehicle data, same types of vehicles data horizontal analysis and with feature vehicle data comparative analysis etc., energy-saving driving suggestion feeds back to user by the mode of car-mounted terminal voice broadcast and screen display in real time or at the end of LAP the most at last.
Based on this, the present invention sets up three kinds of analytical models: with ID vehicle data vertical analysis model, same types of vehicles data horizontal analysis model and with feature vehicle data comparative analysis model.
With ID vehicle data vertical analysis model:
The main operand of vehicle driver in the process of moving has throttle, brake, clutch coupling and gear, some drivers due to experience lack or driving habits bad, take to operate improperly in inappropriate road conditions, this not only can cause loss to vehicle itself, and vehicle fuel consumption also can be caused to increase.Based on this, the present invention is differentiated by car-mounted terminal and records driver's three and walks rapidly as (anxious to accelerate, suddenly to slow down, zig zag), analyzes, correct the driving bad habit of driver and not energy-conservation behavior in time to same ID vehicle data.Fig. 2 is same ID vehicle data vertical analysis process flow diagram.
1) to differentiation and the quantification of driving behavior:
Driver is as the principal factors in people-Che-Lu, and due to the non-linear and time variation of itself, the behavioral trait in driving procedure is perceptual changeable often, is difficult to quantificational description accurately.Based on this, what the present invention adopted is dynamic weighting comprehensive quantification model, first classification has been done to the not power-save operation behavior of driver, mainly be divided three classes: anxious acceleration, anxious deceleration and zig zag, this three class is driver's modal behavior in the process of moving, and is also the principal element causing vehicle oil consumption large.In driver's actual mechanical process, this few class behavior is with occurring sometimes.First the present invention has done classification to this three classes basic act of driver and additional weights.As follows:
Table 1: driver's basic driving behavior grade quantizing table
When acceleration is at am/s 2to bm/s 2belong to slight anxious acceleration in scope, grade is L, and weights are 1; At bm/s 2to cm/s 2belong to the anxious acceleration of moderate in scope, grade is M, and weights are 2; When acceleration is greater than cm/s 2time belong to that severe is anxious to be accelerated, grade is F, and weights are 3; In like manner anxious deceleration and zig zag also can carry out weights distribution with reference to related data.
For the generation of intersection behavior, what the present invention adopted is the method that weights are multiplied, and intersection behavior mainly contains two classes: anxious acceleration+zig zag, and suddenly slow down+zig zag.
Table 2: driver intersects driving behavior grade quantizing table
2) Real-time Feedback:
After quantification is weighted to driver behavior, obtain the weights mark of often kind of behavior different stage, in driver's driving process, when a certain behavior of driver reaches respective degrees, car-mounted terminal can send to driver and warns and show this oil consumption moment, such as vehicle accelerates suddenly when turning, and acceleration and turning speed are when all exceeding maximum safe level, belong to the anxious acceleration+severe zig zag of severe, car-mounted terminal can automatically accelerate too fast with voice informing user turn inside diameter and the oil consumption of this moment be shown.
3) history compares:
Conveniently user is to the understanding of oneself a period of time energy-saving behavior, car-mounted terminal can add up the number of times of these behaviors of user in certain effective driving time, obtain corresponding energy-conservation behavior to divide, and compare with historical scores, understand oneself " three is anxious " behavior during this period of time whether to reduce, for the calculating that the energy-conservation behavior of user divides, the present invention adopt be each class behavior weights add up.Reflect in addition user's energy-saving efficiency the most direct data be exactly fuel consumption number, by the fuel consumption in the certain effectively driving time of recording user, and compare with historical data, understand oneself oil consumption situation and whether make moderate progress.
Same types of vehicles data horizontal analysis model:
The horizontal analysis of same types of vehicles data is by collecting the information such as vehicle average fuel consumption over a period, instantaneous oil consumption, the vehicle Actual Burnoff amount of the every type of methods analyst of utilization variance inspection and the difference of theoretical fuel consumption and the oil consumption difference situation contrasted between same types of vehicles that different vendor produces, Fig. 3 is same types of vehicles data horizontal analysis process flow diagram.
1) look forward to gross data with car to compare:
Car enterprise is by the fuel consumption data of collection vehicle multiple time period, try to achieve the practical oil consumption of average fuel consumption as vehicle of vehicle, and carry out difference analysis with the theoretical oil consumption of the type vehicle, make car look forward to understanding this vehicle vehicle after putting on market, whether there is the situation that practical oil consumption significantly exceeds theoretical oil consumption, for car enterprise vehicle oil consumption Theoretical Design afterwards provides theoretical and data supporting.In addition for the user of some fuel consumption exceptions, Che Qihui notifies that this user takes adequate measures.Make a concrete analysis of as follows:
(1) oil consumption of the multiple time periods recorded in collection vehicle driving process, obtains one group of data and is designated as (x 1, x 2, x 3... x n), then the practical oil consumption of this vehicle is designated as
(2) difference test:
1. due to N (μ, σ that the average fuel consumption data of same types of vehicles are Normal Distribution in theory 2), wherein μ is expectation value, is the theoretical fuel consumption values that car is looked forward to calculating based on large data, σ 2be designated as variance.
2. null hypothesis H is 0: μ=μ 0
3. selection check statistic: U = X &OverBar; - &mu; &sigma; / n
If 4. for level of significance.Illustrate this vehicle practical oil consumption level and theoretical oil consumption variant, and to differ greatly.
(3) by difference test, car enterprise can receive two useful informations:
Whether the practical oil consumption between the vehicle of 1. same type has larger difference;
2. whether practical oil consumption to exceed the vehicle of theoretical oil consumption more.
This can help car to look forward to doing further improvement to this type of vehicle, reduces this otherness.In addition, the conveniently improvement of user self, this species diversity can feed back to car-mounted terminal with the form of rank, and whether final user, according to actual conditions, selects to overhaul.
2) compare with the vehicle of other producers:
Along with the rise of car networking, each producer is inevitable to the shared of vehicle driving data, and for the price that different manufacturers is produced, weight, power and the identical vehicle of other each factors, the large young pathbreaker of fuel consumption is the key that car is looked forward to getting the upper hand of in the market.Based on this, Main Analysis of the present invention be the average fuel consumption of vehicle that same type different manufacturers is produced, instantaneous oil consumption, make a concrete analysis of as follows:
(1) data acquisition and classification: due to the different oil consumption situation differences to some extent that can cause vehicle of vehicle usage degree, therefore need to do to classify to the data gathered by the distance travelled of vehicle, if the vehicle travelling 10000-20000 kilometers is a usage degree rank, obtain the average fuel consumption level of same type vehicle at different usage degree of each manufacturer production.
(2) analytical model:
The same vehicle of different manufacturers more mainly compares the otherness of same types of vehicles oil consumption level under different usage degree that different manufacturers is produced.Be described for producer one and producer two, following table is first type of vehicle average fuel consumption value under different usage degree of producer one and producer two production collected.
Table 3: the average fuel consumption of the vehicle of the different usage degrees of each manufacturer production
Wherein X 1nrepresent that a certain type usage degree of producer one production is the fuel consumption per hundred kilometers average level of all vehicles of n.Also by drawing broken line graph, analysis result is fed back to more intuitively car enterprise, facilitate car to look forward to understanding the gap of vehicle under different usage degree and between the vehicle of other manufacturer production of oneself, for car, enterprise provides data explanation in market orientation from now on and marketing strategy formulation.
With feature vehicle data comparative analysis model:
When driver drives a vehicle under the same conditions, running environment residing for vehicle is similar, its oil consumption situation also ought to be more or less the same, if the driver drives vehicle fuel consumption that some driver compares other is excessive, this is probably because misoperation caused, by classifying to the user of some information to same characteristic features based on this with the comparative analysis of feature vehicle data, arbitrary member in each class compares with the oil consumption of optimum drive person, in the Power Saving Class points-scoring system of correspondence, to mark rank to user, energy-saving driving is finally provided to advise, Fig. 4 is energy-saving driving suggestion feedback mechanism process flow diagram, Fig. 5 is same feature vehicle data comparative analysis process flow diagram.
1) users classification of same characteristic features:
The classification criterion of same characteristic features user comprises the time scale, vehicle feature etc. of average speed per hour, time period, weather and traffic events, route distance, speed per hour 60km/h driving time ratio, parking rate, parking.Between considering between these indexs separately, there is correlativity and dimension is too high, be difficult to adopt traditional clustering method to classify, therefore first adopt PCA principal component analysis (PCA) to carry out dimensionality reduction herein, then by three-dimensional k-means clustering procedure, user is classified.
(1) PCA dimensionality reduction:
Principal component analysis (PCA) (PCA) is exactly that multiple variable is turned to a few overall target.Suppose there be n sample, each sample has m variable description, and the data of each variable are made normalized, constitutes n × m rank data matrix:
X = x 11 x 12 &Lambda; x 1 m x 21 x 22 &Lambda; x 2 m M M M x n 1 x n 2 &Lambda; x n m
Step 1 calculates correlation matrix:
R = r 11 r 12 &Lambda; r 1 m r 21 r 22 &Lambda; r 2 m M M M r m 1 r m 2 &Lambda; r m m
In formula, r ij(i, j=1,2,3...m) represents primal variable x iwith x jrelated coefficient, its computing formula is:
r i j = &Sigma; k = 1 m ( x k i - x &OverBar; i ) ( x k j - x &OverBar; j ) &Sigma; k = 1 m ( x k i - x &OverBar; i ) 2 &Sigma; k = 1 m ( x k j - x &OverBar; j ) 2
Only need to calculate triangle element on it, r ijand r jiequal.
Step 2 calculates eigenwert and proper vector
Separate secular equation | R-λ E|=0, obtains eigenvalue λ i(i=1,2...m), and make its order arrangement by size, i.e. λ 1>=λ 2>=... λ m>=0; Then obtain respectively corresponding to λ iproper vector q i(i=1,2...m).
Step 3 calculates principal component contributor rate and contribution rate of accumulative total
Major component Z icontribution rate: &lambda; i &Sigma; k = 1 m &lambda; k ( i = 1 , 2 ... m ) , Contribution rate of accumulative total: &Sigma; k = 1 p &lambda; k &Sigma; k = 1 m &lambda; k ( i = 1 , 2 ... m ) , Get three eigenvalue λ that contribution rate is maximum 1, λ 2, λ 3corresponding major component.
Step 4 calculates major component load P ( z k , x i ) = &lambda; k q k i ( i = 1 , 2 , ... m , k = 1 , 2 , 3 )
Principal component scores can be calculated further thus:
Z = z 11 z 12 z 13 M M M z n 1 z n 2 z n 3
(2) based on three-dimensional K-means cluster:
K-means algorithm is a kind of indirect clustering method based on similarity measurement between sample, belongs to unsupervised learning method.This algorithm take k as parameter, and n object is divided into k bunch, in making bunch, have higher similarity, and bunch between similarity lower.The calculating of similarity is carried out according to the mean value of object in bunch (be counted as bunch center of gravity).This algorithm is Stochastic choice k object first, and each object represents the barycenter of a cluster.For remaining each object, according to the distance between this object and each cluster barycenter, it is assigned in cluster the most similar with it.Then, the new barycenter of each cluster is calculated.Repeat said process, until criterion function convergence.
After PCA dimensionality reduction, judge that the data of user characteristics reduce to three-dimensional by multidimensional, each like this sample point has a unique coordinate corresponding with it in space, and concrete step is as follows:
1, after LAP terminates, obtain three major components after each index of the user collected is calculated by PCA, the feature of this n sample can be described by these three major component indexs, as follows:
Z = z 11 z 12 z 13 M M M z n 1 z n 2 z n 3
A random selecting k sample is z as initial clustering center of mass point (clustercentroids) 1, z 2... z k∈ R 3.
2, the classification for user characteristics is based upon in this k initial clustering center of mass point, for each sample point, calculates the Euclidean distance of itself and initial sample point, judge the class that it should belong to:
x ( i ) = arg min j | | x ( i ) - z j | | 2
For each class j, recalculate such barycenter:
z j = &Sigma; i = 1 m 1 { c ( i ) = j } x ( i ) &Sigma; i = 1 m 1 { c ( i ) = j }
K is our cluster numbers given in advance, c (i)that class that representative sample i and k class middle distance is nearest, c (i)value be 1 in k.Barycenter z jrepresent us to the conjecture of center of a sample's point belonging to same class.
By this algorithm, the driving characteristics of user is divided into k class, makes the difference distinguished again during the similarity Datong District between each class user between all types of user.
2) divide and score rank analysis with feature user Power Saving Class:
Power Saving Class points-scoring system is after users classification completes, the energy-saving driving behavior of same class users is analyzed, the present invention uses fuzzy control theory, by estimating Power Saving Class to the monitoring of the acceleration in vehicle driving process and acceleration change amount.Analytical model is as follows:
The acceleration a of vehicle and acceleration change amount Δ a judges automobile energy-saving whether Important Parameters, and wherein the fuzzy set of a is S (Small), M (Medium) and L (large); The fuzzy set of Δ a is N (Negative), M (Medium) and P (positive), grade classification has VW (VeryWasting), W (Wasting) M (Medium), E (Economic) and VE (VeryEconomic).Power Saving Class fuzzy matrix is as follows:
Table 4: Power Saving Class fuzzy matrix
Wherein acceleration a and acceleration change amount Δ a be all pick up the car a LAP terminate after mean value, and compare divided rank with other vehicles under identical conditions, k-means algorithm can be used, here do not repeat.
In order to the more intuitive degree of power conservation reflecting its current driving behavior to user, what the present invention adopted is give a mark on the basis of grade classification, and scoring criterion is: VW=2 divides, and W=4 divides, and M=6 divides, and E=8 divides, and VE=10 divides.Wherein best result is 10 points.
Table 5: Power Saving Class score value divides table
Grade VW W M E VE
Score 2 4 6 8 10
By giving a mark to the energy-saving driving behavior in user's each run, and the rank of user being fed back to car-mounted terminal, can well user be encouraged, allow more user participation of being interested in come in.
3) optimum oil consumption compares and energy-saving driving suggestion feedback:
Optimum oil consumption compares:
It is the oil consumption of the LAP of user and the user's optimum oil consumption with condition down train are compared that optimum oil consumption compares, the determination of calculated fuel consumption amount is based on the oil consumption basis of users all in this stroke, consider there are some special statuss, what the present invention adopted is remove front 10% and rear 10% user, get the oil consumption mean value of user of centre 80%.Running environment residing for vehicle is similar, its oil consumption situation also ought to be more or less the same, if the driver drives vehicle fuel consumption that some driver compares other is excessive, this is probably because misoperation caused, by compared with optimum oil consumption, impel user to the timely concern of oneself energy-saving driving behavior.
Energy-saving driving suggestion feedback:
Energy-saving driving suggestion is a kind of strategy that effectively can improve the behavior of user's energy-saving driving drawn according to Correlative data analysis such as the driving behaviors of points-scoring system and driver.
First this mechanism be compared by the oil consumption of the optimum drive person with same driving characteristics down train, show that whether user's oil consumption is normal, if oil consumption is normal, then inform that user keeps and gives certain virtual reward, if user's oil consumption is abnormal, then further driver behavior is analyzed; By the driving behavior of driver in LAP and the energy-conservation driving behavior of standard are contrasted, analyze which behavior of driver and may there is not energy factor, then propose corresponding proposed projects according to these behaviors, user can select accept suggestion and do not accept suggestion voluntarily.
What finally illustrate is, above preferred embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although by above preferred embodiment to invention has been detailed description, but those skilled in the art are to be understood that, various change can be made to it in the form and details, and not depart from claims of the present invention limited range.

Claims (9)

1. the energy-saving driving behavior analysis method based on large data, it is characterized in that: the method is in vehicle traveling process, collect vehicle-related information, environmental information and transport information by car-mounted terminal and road side system, and the information collected is sent to car networked information service background through car-mounted terminal; Information service backstage is by same ID vehicle data vertical analysis model, same types of vehicles data horizontal analysis model and carry out energy-saving driving analysis with feature vehicle data comparative analysis model, and energy-saving driving suggestion feeds back to user by the mode of car-mounted terminal voice broadcast and screen display in real time or at the end of LAP the most at last.
2. the energy-saving driving behavior analysis method based on large data according to claim 1, it is characterized in that: described same ID vehicle data vertical analysis model is differentiated by car-mounted terminal and records the urgency acceleration of driver, anxious deceleration, zig zag behavior, same ID vehicle real time data and historical data are analyzed, correct the driving bad habit of driver and not energy-conservation behavior in time;
Concrete analysis process comprises:
1) driving behavior of vehicle operator differentiated and quantize, first five classes being divided into the not power-save operation behavior of driver: anxious acceleration, anxious deceleration, zig zag, anxious acceleration+zig zag and the+zig zag that suddenly slows down; Again classification and quantification are carried out to five class behaviors;
2) Real-time Feedback is carried out to data: after quantification is weighted to driver behavior, obtain the weights mark of often kind of behavior different stage, in driver's driving process, when a certain behavior of driver reaches respective degrees, car-mounted terminal can send to driver and warns and show this oil consumption moment;
3) number of times of car-mounted terminal statistics above-mentioned behavior of user in certain effective driving time, obtain corresponding energy-conservation behavior to divide, and compare with historical scores, understand oneself above-mentioned behavior during this period of time whether to reduce, for the calculating that the energy-conservation behavior of user divides, employing be each class behavior weights add up.
3. the energy-saving driving behavior analysis method based on large data according to claim 2, it is characterized in that: described classification is carried out to five class behaviors and quantification comprises: classification is carried out to three class basic acts and additional weights, for intersecting the generation of behavior, employing be the method that weights are multiplied:
When acceleration is at am/s 2to bm/s 2belong to slight anxious acceleration in scope, grade is L, and weights are 1; At bm/s 2to cm/s 2belong to the anxious acceleration of moderate in scope, grade is M, and weights are 2; When acceleration is greater than cm/s 2time belong to that severe is anxious to be accelerated, grade is F, and weights are 3, wherein (a<b<c); In like manner anxious deceleration and zig zag also can carry out weights distribution with reference to related data.
4. the energy-saving driving behavior analysis method based on large data according to claim 1, it is characterized in that: described same types of vehicles data horizontal analysis model by collecting vehicle average fuel consumption over a period, instantaneous fuel consumption information, the oil consumption difference situation between the same types of vehicles that the vehicle Actual Burnoff amount of the every type of methods analyst of utilization variance inspection and the difference of theoretical fuel consumption and comparative analysis different vendor produce.
5. the energy-saving driving behavior analysis method based on large data according to claim 4, it is characterized in that: described analysis vehicle Actual Burnoff amount and theoretical fuel consumption comprise: by the fuel consumption data of collection vehicle multiple time period, try to achieve the practical oil consumption of average fuel consumption as vehicle of vehicle, and carry out difference analysis with the theoretical oil consumption of the type vehicle, make car look forward to understanding this vehicle vehicle after putting on market, whether there is the situation that practical oil consumption significantly exceeds theoretical oil consumption, for car enterprise vehicle oil consumption Theoretical Design afterwards provides theoretical and data supporting, specifically comprise the following steps:
1) oil consumption of the multiple time periods recorded in collection vehicle driving process, obtains one group of data and is designated as (x 1, x 2, x 3... x n), then the practical oil consumption of this vehicle is designated as
2) difference test:
1. due to N (μ, σ that the average fuel consumption data of same types of vehicles are Normal Distribution in theory 2), wherein μ is expectation value, is the theoretical fuel consumption values that car is looked forward to calculating based on large data, σ 2be designated as variance;
2. null hypothesis H is 0: μ=μ 0
3. selection check statistic:
If 4. for level of significance, illustrate this vehicle practical oil consumption level and theoretical oil consumption variant, and to differ greatly;
3) by difference test, car enterprise can receive two useful informations: whether the practical oil consumption between the vehicle of same type has larger difference; Whether the vehicle that practical oil consumption exceeds theoretical oil consumption is more.
6. the energy-saving driving behavior analysis method based on large data according to claim 4, is characterized in that: the oil consumption difference situation between the same types of vehicles that described comparative analysis different vendor produces specifically comprises:
1) data acquisition and classification: do to classify to the data gathered by the distance travelled of vehicle; 2) otherness of vehicle oil consumption under different distance travelled that different manufacturers is produced is compared.
7. the energy-saving driving behavior analysis method based on large data according to claim 1, it is characterized in that: described same feature vehicle data comparative analysis model is that the user by having same characteristic features to some classifies, arbitrary member in each class compares with the oil consumption of optimum drive person, in the Power Saving Class points-scoring system of correspondence, to mark rank to user, finally provide energy-saving driving to advise.
8. the energy-saving driving behavior analysis method based on large data according to claim 7, is characterized in that: the comparative analysis of described same feature vehicle data specifically comprises:
1) users classification of same characteristic features: the classification criterion of the user of same characteristic features comprises average speed per hour, time period, weather and traffic events, route distance, speed per hour 60km/h driving time ratio, parking rate, the time scale of parking, vehicle feature, adopt PCA algorithm dimensionality reduction, then use k-means algorithm to process;
2) divide and score rank analysis with feature user Power Saving Class: after users classification completes, the energy-saving driving behavior of same class users is analyzed, use fuzzy control theory, by estimating Power Saving Class to the monitoring of the acceleration in vehicle driving process and acceleration change amount; Energy-saving driving behavior in user's each run is given a mark, and the rank of user is fed back to car-mounted terminal;
3) optimum oil consumption compares and energy-saving driving suggestion feedback: it is the oil consumption of the LAP of user and the user's optimum oil consumption with condition down train are compared that described optimum oil consumption compares, the determination of calculated fuel consumption amount is based on the oil consumption basis of users all in this stroke, adopt be remove front 10% and rear 10% user, get the oil consumption mean value of user of centre 80%; Described energy-saving driving suggestion is a kind of strategy that effectively can improve the behavior of user's energy-saving driving drawn according to the Correlative data analysis such as points-scoring system and vehicle acceleration.
9. the energy-saving driving behavior analysis method based on large data according to claim 8, it is characterized in that: the described strategy that effectively can improve the behavior of user's energy-saving driving comprises: be first compared by the oil consumption of the optimum drive person with identical conditions down train, show that whether user's oil consumption is normal, if oil consumption is normal, then inform that user keeps and gives certain virtual reward, if user's oil consumption is abnormal, be then averaged acceleration and acceleration change component analysis further; Rational for average acceleration, user needs to adjust the dynamics of stepping on the gas in driving procedure, to control the variable quantity of acceleration; , acceleration change amount unreasonable for average acceleration is rational, and user needs to reduce throttle in the process of moving; For average acceleration and acceleration change amount all irrational, then user should reduce the dynamics that throttle reduces to step on the gas again.
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