CN105539450A - Automatic identification method and device of driving stroke - Google Patents
Automatic identification method and device of driving stroke Download PDFInfo
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- CN105539450A CN105539450A CN201510873863.4A CN201510873863A CN105539450A CN 105539450 A CN105539450 A CN 105539450A CN 201510873863 A CN201510873863 A CN 201510873863A CN 105539450 A CN105539450 A CN 105539450A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
Abstract
The invention provides an automatic identification method and device of a driving stroke. The automatic identification method comprises the following steps of collecting first data values of mobile terminal motion state data monitored by a motion state sensor; identifying whether a mobile terminal user is in a driving state or not according to the first data values, and if yes, determining the moment when the first data values are generated as a start moment of the driving stroke; from the start moment of the driving stroke, persistently collecting second data values of the mobile terminal motion state data monitored by the motion state sensor and data values of GPS sensor data; and determining an end moment of the driving stroke according to the second data values and the data values of the GPS sensor data. Compared with an automatic identification method of the driving stroke based on GPS sensors in the prior art, the automatic identification method provided by the invention has the advantages that the electric consumption is reduced, and prolonging of the battery life of a mobile terminal is facilitated.
Description
Technical field
The present invention relates to field of road traffic safety, particularly relate to a kind of automatic identifying method and the device of driving stroke.
Background technology
Chaufeur is as the manipulator of vehicle, and its driving behavior determines the safety of road traffic system to a great extent.Along with the lifting of driver behavior attention rate, the correlative studys such as similar driver behavioural analysis, driver fatigue analysis, the assessment of driver style, driver proficiency assessment deepen continuously and perfect.Meanwhile, the Related products such as driving behavior monitoring, assessment, management also continue to bring out.
Along with the development of mobile terminal technology, each data sensor utilizing it integrated can gather the data relevant to driving behavior, therefore, using mobile terminal as driving behavior data trap, for chaufeur provides driving behavior analysis service, it is product form comparatively popular at present.
Driving information statistics is stroke with the minimum particle size of driving behavior analysis, and stroke is that mobile terminal user is driven to the time period of getting off and terminating to drive from getting on the bus.Drive the automatic identification of stroke automatically to detect get on the bus moment of starting to drive and mobile terminal user of mobile terminal user and get off the moment stopping driving, do not need the manually opened stroke of mobile terminal user and terminate stroke.
The automatic identifying method of existing driving stroke generally adopts the gps data based on the GPS acquisition module collection in mobile terminal to carry out identification judgement.As long as this automatic identifying method requires that mobile terminal is in open state, just require the continuous image data of GPS acquisition module.But, because the power consumption of GPS acquisition module image data is large, so the electricity causing the automatic identifying method of the driving stroke based on gps data of the prior art to consume is comparatively large, and this method can shorten the battery life of mobile terminal.
Summary of the invention
In view of this, the invention provides a kind of automatic identifying method and the device of driving stroke, to reduce electric quantity consumption amount, and extend the battery life of mobile terminal.
In order to solve the problems of the technologies described above, present invention employs following technical scheme:
Drive an automatic identifying method for stroke, comprising:
Gather the first data value of the motion state of mobile terminal data of state of kinematic motion Sensor monitoring;
Whether be in motoring condition according to described first data value identification mobile terminal user, if so, determine that producing moment corresponding to described first data value is the initial time driving stroke;
Begin from the initial time of described driving stroke, the second data value of the motion state of mobile terminal data of continuous collecting state of kinematic motion Sensor monitoring and the data value of GPS sensing data;
Data value according to described second data value and GPS sensing data determines the end time of driving stroke.
Drive an automatic identification equipment for stroke, comprising:
First collecting unit, for gathering the first data value of the motion state of mobile terminal data of state of kinematic motion Sensor monitoring;
Whether diagnosis unit, for being in motoring condition according to described first data value identification mobile terminal user;
First determining unit, for when the recognition result of described diagnosis unit is for being, determines that producing moment corresponding to described first data value is the initial time driving stroke;
Second collecting unit, begins for the initial time from described driving stroke, the second data value of the motion state of mobile terminal data of continuous collecting state of kinematic motion Sensor monitoring and the data value of GPS sensing data;
Second determining unit, the data value according to described second data value and GPS sensing data determines the end time of driving stroke.
Compared to prior art, the present invention has following beneficial effect:
As seen through the above technical solutions, the automatic identifying method of driving stroke provided by the invention and device utilize the motion state of mobile terminal data of state of kinematic motion Sensor monitoring to determine to drive the initial time of stroke, so, before determining that driving stroke starts, only need start state of kinematic motion working sensor to monitor motion state of mobile terminal data, without the need to starting GPS sensor.Due to the electricity consumed when the electricity consumed during state of kinematic motion working sensor is less than GPS working sensor, so, compared to the automatic identifying method of the driving stroke based on GPS sensor of the prior art, method provided by the invention reduces consumption of current, is conducive to the battery life extending mobile terminal.
Accompanying drawing explanation
In order to be expressly understood the specific embodiment of the present invention, the accompanying drawing used is made a brief description below when describing the specific embodiment of the present invention.Apparently, these accompanying drawings are only section Example of the present invention.
Fig. 1 is the automatic identifying method schematic flow sheet of the driving stroke that the embodiment of the present invention provides;
Fig. 2 is the method flow schematic diagram choosing default temporal signatures that the embodiment of the present invention provides;
The eigenwert distribution histogram that Fig. 3 is the standard deviation that provides of the embodiment of the present invention under static, walking and motoring condition;
The eigenwert that Fig. 4 is the standard deviation that provides of the embodiment of the present invention under static, walking and motoring condition intersects schematic diagram;
Fig. 5 is the method flow schematic diagram of the mobile terminal user state that the first data value utilizing FUZZY PROBABILITY ANALYSIS to gather that the embodiment of the present invention provides is corresponding;
Fig. 6 is the method flow schematic diagram of the end time of the determination driving stroke that the embodiment of the present invention provides;
Fig. 7 is remaining static or the method flow schematic diagram of ambulatory status according to the second data value determination mobile terminal user of providing of the embodiment of the present invention;
Fig. 8 is the data frequency curve synoptic diagram of quiescence and the outer quiescence of car in embodiment of the present invention car;
Fig. 9 is the method flow schematic diagram that judgement mobile terminal user that the embodiment of the present invention provides is positioned at outside Che Nei or car;
Figure 10 is the automatic identification equipment structural representation of the driving stroke that the embodiment of the present invention provides;
Figure 11 is the concrete structure schematic diagram of the diagnosis unit that the embodiment of the present invention provides;
Figure 12 is the concrete structure schematic diagram of the second determining unit that the embodiment of the present invention provides;
Figure 13 is the concrete structure schematic diagram of the second judgment sub-unit that the embodiment of the present invention provides.
Detailed description of the invention
For make goal of the invention of the present invention, technological means and technique effect clearly, complete, below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in detail.
It should be noted that, the embodiment of the present invention for state of kinematic motion sensor for acceleration pick-up is described.This acceleration pick-up is on mobile terminals integrated, thus can monitor the acceleration of motion of mobile terminal.In order to realize the automatic identification to driving stroke, the embodiment of the present invention requires when mobile terminal user is positioned at car, and mobile terminal needs to be placed in car, when mobile terminal user walking, needs mobile terminal user to carry with mobile terminal.
In addition, the mobile terminal described in the embodiment of the present invention can be the terminal such as mobile phone, iPad.
Fig. 1 is the automatic identifying method schematic flow sheet of the driving stroke that the embodiment of the present invention provides.As shown in Figure 1, the method comprises the following steps:
First data value of the mobile terminal acceleration information of S11, the monitoring of collection acceleration pick-up:
It should be noted that, the mobile terminal acceleration information that the embodiment of the present invention utilizes acceleration pick-up to monitor determines the initial time driving stroke.As long as this just requires that mobile terminal is in open state, acceleration pick-up just constantly monitors the data value of mobile terminal acceleration information.
S12, whether be in motoring condition according to the first data value identification mobile terminal user, if so, perform step S13, if not, return and perform step S11:
S13, determine that producing moment corresponding to described first data value is the initial time driving stroke.
S14, to begin from the initial time of described driving stroke, second data value of mobile terminal acceleration information of continuous collecting acceleration pick-up monitoring and the data value of GPS sensing data:
After the initial time driving stroke starts, start integrated GPS sensor on mobile terminals, make the gps data of this GPS Sensor monitoring mobile terminal.And after the initial time driving stroke starts, acceleration pick-up continues the acceleration information of monitoring mobile terminal.
Determine the end time of driving stroke in order to follow-up, need in time driving the initial time of stroke, second data value of mobile terminal acceleration information of continuous collecting acceleration pick-up monitoring and the data value of GPS sensing data.
S15, determine the end time of driving stroke according to the data value of described second data value and GPS sensing data.
The detailed description of the invention of the automatic identifying method of the driving stroke provided for the embodiment of the present invention above.In this embodiment, the acceleration information monitored according to acceleration pick-up determines the initial time driving stroke, so, before driving stroke does not start, without the need to starting GPS working sensor, only after driving stroke starts, just can start GPS sensor, therefore, as long as this method avoid mobile terminal-opening in prior art, GPS sensor just monitors the problem of the gps data of mobile terminal.And due to the electricity consumed when the electricity consumed when acceleration pick-up works is less than GPS working sensor, therefore, method provided by the invention reduces consumption of current, be conducive to the battery life extending mobile terminal.
In addition, in embodiments of the present invention, the gps data that GPS sensor detects may be used for recording wheelpath, simultaneously for driving the automatic identification of stroke.
Further, in the automatic identifying of driving stroke, mobile terminal may artificially be moved, so, the acceleration/accel causing acceleration pick-up to be monitored is not the acceleration/accel of mobile terminal user self, and then causes only to identify according to acceleration information the initial time driving stroke exactly.But, in embodiments of the present invention, drive after stroke starts when identifying, start GPS Sensor monitoring gps data, then the mode combined according to acceleration information and GPS sensing data determines the end time of driving stroke, because GPS sensing data is not by the impact of the artificial movement of mobile terminal, therefore, the inaccurate identification of the driving stroke that the method can avoid the artificial movement of mobile terminal to cause.Only determine the method for driving stroke by single data compared to prior art, the end time of the driving stroke that the embodiment of the present invention is determined is more accurate.
Introduce the detailed description of the invention of step S12 below.
It should be noted that, acceleration information can comprise multiple temporal signatures, such as, can comprise: aviation value, standard deviation, maxim, minimum value, first quartile, the 3rd quartile, kurtosis, the kurtosis of acceleration information, be less than first quartile according to all data sum of squares, be greater than the 3rd quartile according to all data sum of squares etc.Therefore, can identify whether mobile terminal user is in motoring condition by the temporal signatures of the first data value in the step S12 of above-described embodiment.Alternatively, the default temporal signatures of the first data value can be utilized to identify whether mobile terminal user is in motoring condition.The embodiment utilizing this default temporal signatures identification mobile terminal user whether to be in motoring condition only can realize the identification to mobile terminal user state with the default temporal signatures of an index and acceleration information, compared to using complicated sorting algorithm, greatly reduce the complexity of calculating, improve computational efficiency.
In embodiments of the present invention, described default temporal signatures is the minimum temporal signatures of the eigenwert transposition section accounting of temporal signatures.Wherein, the eigenwert transposition section of a temporal signatures be that the eigenwert of this temporal signatures remains static mobile terminal user, ambulatory status and motoring condition time the union of overlapping interval between two of distributed area.The eigenwert transposition section accounting of a temporal signatures for described between two the union of overlapping interval and the eigenwert of this temporal signatures remain static mobile terminal user, ambulatory status and motoring condition time distributed area the ratio of union.
When the default temporal signatures of employing first data value identifies whether mobile terminal user is in motoring condition, need the default temporal signatures obtaining acceleration information in advance.As a specific embodiment of the present invention, the embodiment of the present invention can choose default temporal signatures by the method for minimum transposition section accounting.From multiple temporal signatures of acceleration information, choose the method flow of default temporal signatures as shown in Figure 2, it comprises the following steps:
S21, in certain hour window, temporal signatures extraction is carried out to the acceleration information of static, walking, driving condition:
In embodiments of the present invention, the temporal signatures of acceleration information can comprise acceleration information aviation value, standard deviation, maxim, minimum value, first quartile, the 3rd quartile, kurtosis, kurtosis, be less than first quartile according to all data sum of squares, be greater than the 3rd quartile according to all data sum of squares etc.
Integrated acceleration information on mobile terminals comprises transverse acceleration x, forward acceleration y and longitudinal acceleration z, and the acceleration information collected represents with a, wherein,
S22, calculate the eigenwert distributed area of eigenwert under quiescence, ambulatory status and motoring condition of each temporal signatures respectively:
In embodiments of the present invention, respectively calculate above-mentioned steps extract aviation value, standard deviation, maxim, minimum value, first quartile, the 3rd quartile, kurtosis, kurtosis, be less than first quartile according to all data sum of squares, be greater than the eigenwert of the 3rd quartile according to all data sum of squares eigenwert distributed area A, B and C under quiescence, motoring condition and ambulatory status.
The eigenwert how calculating each temporal signatures eigenwert distributed area A, B, C under quiescence, motoring condition and ambulatory status are described for standard deviation below.
According to the standard deviation histogram of the eigenwert distribution core standard deviation of standard deviation under quiescence, ambulatory status and motoring condition under static, walking and motoring condition, as shown in Figure 3.According to the standard deviation histogram under three states drawn out, standard deviation eigenwert distributed area A, B and C under quiescence, motoring condition and ambulatory status can be obtained.
As can be seen from Figure 3, standard deviation is [0.03,0.08] at the distributed area A of quiescence, and standard deviation is [0.05,0.7] at the distributed area B of motoring condition, and standard deviation is [1,5.3] at the distributed area C of ambulatory status
S23, calculate the union of eigenwert at the transposition section between two of the eigenwert distributed area of quiescence, ambulatory status and motoring condition of each temporal signatures:
Still illustrate for above-mentioned standard deviation.Standard deviation in the computing formula of the transposition section of the eigenwert distributed area A of quiescence and the eigenwert distributed area B of motoring condition is:
A∩B＝[0.05,0.08]；
Standard deviation in the computing formula of the transposition section of the eigenwert distributed area A of quiescence and the eigenwert distributed area C of ambulatory status is:
A∩C＝Φ，
Standard deviation in the computing formula of the transposition section of the eigenwert distributed area B of motoring condition and the eigenwert distributed area C of ambulatory status is:
B∩C＝Φ，
Then the computing formula of the union D of the transposition section between two of distributed area A, B, C is:
D＝(A∩B)∪(A∩C)∪(B∩C)＝[0.05,0.08]
S24, the union calculating the transposition section between two of each temporal signatures account for the accounting of the eigenwert distributed area union of quiescence, ambulatory status and motoring condition:
Still illustrate for standard deviation.The computing formula that the union D of the transposition section between two of standard deviation accounts for the accounting q of the union of distributed area A, B, C is as follows:
q＝D/(A∪B∪C)＝[(A∩B)∪(A∩C)∪(B∩C)]/(A∪B∪C)
＝(0.08-0.05)/[(0.08-0.03)+(0.7-0.08)+(5.3-1)]
Adopt the method identical with the transposition section accounting calculating standard deviation, calculate the transposition section accounting q of other each temporal signatures of acceleration information.
S25, compare the accounting size of the union of the transposition section between two of each temporal signatures, temporal signatures minimum for transposition section accounting be defined as default temporal signatures:
Relatively the size of the accounting q of the union of the transposition section between two of each temporal signatures of acceleration information, is defined as default temporal signatures by temporal signatures minimum for q.
The default temporal signatures that can be will speed up degrees of data by the method shown in above Fig. 2 is chosen out.It should be noted that, the above-mentioned process choosing the default temporal signatures of acceleration information should be carried out before the state identifying mobile terminal user.
It should be noted that, the transposition section between the eigenwert distributed area of different conditions is less, utilizes the eigenwert of temporal signatures more more adequately can determine the state of mobile terminal user.Therefore, the eigenwert distributed area of the default temporal signatures ideally selected under quiescence, motoring condition and ambulatory status does not intersect each other, i.e. q=0.
For acceleration information, because the eigenwert of quiescence and ambulatory status differs greatly, the eigenwert under two states does not have overlapping the intersection.But, during due to motoring condition, likely there will be the moment of traffic congestion or wait traffic lights, thus there will be quiescence and the eigenwert similar with ambulatory status when motoring condition.So the eigenwert distributed area of motoring condition generally all can have with the eigenwert distributed area of quiescence and ambulatory status and intersects, as shown in Figure 4.Wherein, α
1it is the lower limit of motoring condition feature distributed area; α
2it is the upper limit of quiescence feature distributed area; α
3it is the lower limit of ambulatory status feature distributed area; α
4it is the upper limit of motoring condition feature distributed area.
In this case, eigenwert only by presetting temporal signatures is difficult to determine the state residing for mobile terminal user exactly, therefore, as a specific embodiment of the present invention, the mobile terminal user state that first data value that the methods analyst of fuzzy probability can be utilized to gather is corresponding.Detailed description of the invention specifically shown in Figure 5.
When the default temporal signatures by acceleration information identifies whether mobile terminal user is in motoring condition, as shown in Figure 5, it comprises the specific implementation process of step S12:
S121, extract the eigenwert of the default temporal signatures of the first data value:
The default temporal signatures of the method determination acceleration information according to Fig. 2, extracts the eigenwert of the default temporal signatures of the first data value of acceleration information.The default time domain of the first data value of the acceleration information that setting is extracted is disliked whole eigenwert and is represented with x.
Distributed area lower limit when the eigenwert of the default temporal signatures of described first data value that S122, basis are extracted, default the distributed area bound of eigenwert when motoring condition of temporal signatures, the distributed area upper limit of quiescence and ambulatory status, calculates the probability P that mobile terminal user remains static respectively
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walk:
Distributed area lower limit when distributed area bound when can determine the eigenwert of this default temporal signatures in quiescence according to distributed area C when distributed area B during distributed area A, ambulatory status when quiescence of the eigenwert of default temporal signatures and motoring condition when the distributed area upper limit, motoring condition and ambulatory status.
This step is according to the eigenwert x of the default temporal signatures of the first data value extracted, preset the distributed area lower limit of eigenwert when the distributed area upper limit of the distributed area bound that motoring condition is, quiescence and ambulatory status of temporal signatures, calculate the probability P that mobile terminal user remains static respectively
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walk:
Wherein, the probability P of quiescence
idlecomputing formula as shown in formula (1):
The probability P of motoring condition
drivecomputing formula as shown in formula (2):
The probability P of ambulatory status
walkcomputing formula as shown in formula (3):
In formula (1), (2) and (3),
α
1it is the lower limit of motoring condition feature distributed area;
α
2it is the upper limit of quiescence feature distributed area;
α
3it is the lower limit of ambulatory status feature distributed area;
α
4it is the upper limit of motoring condition feature distributed area.
S123, compare the probability P of quiescence
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walksize each other, to determine whether mobile terminal user is in motoring condition;
As the specific embodiment of the present invention, work as P
idle> P
drivetime, mobile terminal user remain static;
Work as P
idle< P
drivetime, mobile terminal user is in motoring condition;
Work as P
walk< P
drivetime, mobile terminal user is in motoring condition;
Work as P
walk> P
drivetime, mobile terminal user is in ambulatory status.
Can realize whether being in motoring condition according to the first data value identification mobile terminal user by above method, when mobile terminal user is in motoring condition, perform step S13.
As a specific embodiment of the present invention, as shown in Figure 6, it comprises the following steps the detailed description of the invention of step S15:
S151, judge whether the data value variable quantity of GPS sensing data is greater than predetermined threshold value, if so, perform step S152, if not, perform step S153:
Due to from driving the initial time of stroke, start GPS sensor, and the after this data value of GPS sensing data that arrives of continuous collecting GPS Sensor monitoring.The data value variable quantity of the GPS sensing data in section during this period of time can be obtained according to the data value of the GPS sensing data of current time and the data value of GPS sensing data of initial time of driving stroke.Judge whether the data value variable quantity of this GPS sensing data is greater than predetermined threshold value, if so, perform step S152, if not, perform step S153.
S152, determine that mobile terminal user is in motoring condition, return and perform step S14:
When mobile terminal user is in motoring condition, return and perform step S14: second data value of mobile terminal acceleration information of continuous collecting acceleration pick-up monitoring and the data value of GPS sensing data, and order performs step S15: the data value according to described second data value and GPS sensing data determines the end time of driving stroke.
S153, to remain static or ambulatory status according to the second data value identification mobile terminal user, when mobile terminal user remains static, perform step S154, when mobile terminal user is in ambulatory status, perform step S155:
When mobile terminal user is not in motoring condition, mobile terminal user or remain static, or be in ambulatory status.Therefore, when mobile terminal user is not in motoring condition, remain static or ambulatory status according to the second data value identification mobile terminal user.When mobile terminal user remains static, perform step S154, when mobile terminal user is in ambulatory status, perform step S155.
It should be noted that, this step remain static according to the second data value identification mobile terminal user or the method for ambulatory status with whether be in the method for motoring condition according to the first data value identification mobile terminal user similar, it is concrete as shown in Figure 7, and it comprises the following steps:
S1531, extract the eigenwert of the default temporal signatures of the second data value.
S1532, according to the eigenwert of the default temporal signatures of the second data value extracted, preset the distributed area bound of eigenwert when motoring condition of temporal signatures, the distributed area upper limit of quiescence and ambulatory status time distributed area lower limit, calculate the probability P that mobile terminal user remains static respectively
idle, motoring condition P
driveprobability and ambulatory status P
walkprobability:
According to above-mentioned formula (1), (2) and (3), calculate the probability P that mobile terminal user remains static respectively
idle, motoring condition P
driveprobability and ambulatory status P
walkprobability.
S1533, compare the probability P of quiescence
idle, motoring condition P
driveprobability and ambulatory status P
walkprobability size each other, to determine that mobile terminal user remains static or ambulatory status;
Work as P
idle> P
drivetime, mobile terminal user remain static;
Work as P
walk> P
drivetime, mobile terminal user is in ambulatory status.
S154, judge whether mobile terminal user is positioned at car according to the second data value, if so, return and perform step S14, if not, perform step S155.
Because quiescence is divided into outside static in car and car static, they have similar temporal signatures, so, utilize temporal signatures to be difficult to distinguish quiescence and the outer quiescence of car in car.But cause data-at-rest and the outer data-at-rest of car in car to there is bigger difference at frequency domain characteristic due to the vibration of vehicle, therefore, the frequency domain character of data can be utilized to judge, and mobile terminal user is positioned at outside Che Nei or car.
Being the outer quiescence of quiescence or car in car to distinguish quiescence, needing first to understand data frequency domain characteristic during the outer quiescence of quiescence and car in car.
In embodiments of the present invention, respectively Fast Fourier Transform (FFT) is carried out to the quiescence data of mobile terminal user outside Che Nei and car, obtain data frequency domain characteristic curve synoptic diagram, as shown in Figure 8.As shown in Figure 8, in car, quiescence data and the outer quiescence data of car are when the frequency band of 2-10Hz, and signal amplitude exists larger difference.Therefore, the frequency-domain index in this frequency band range is utilized can to distinguish quiescence and the outer quiescence of car in car.
Based on this, this step is by carrying out frequency domain character analysis to the second data value, and judge that mobile terminal user is positioned at detailed description of the invention outside Che Nei or car as shown in Figure 9, it comprises the following steps:
S1541, obtain the frequency-domain index of the second data value; Described frequency-domain index is the sum of squares of data in 2-10Hz frequency band.
Second difference of the first difference of frequency-domain index and the frequency-domain index of described second data value and the outer frequency-domain index of car in S1542, the frequency-domain index calculating described second data value and car.
S1543, to judge that according to described first difference and described second difference the frequency-domain index of described second data value is closer to frequency-domain index in car or the outer frequency-domain index of car, when the frequency-domain index of described second data value is closer in car during frequency-domain index, perform step S1544, determine that mobile terminal user is in car, when the frequency-domain index of described second data value frequency-domain index outer closer to car, perform step S1545.
S1544, determine that mobile terminal user is in car.
S1545, determine that mobile terminal user is in outside car.
S155, determine that the moment of generation second data value is drive the end time of stroke.
After determining the end time of driving stroke, the whole process of driving stroke can be determined.
It should be noted that, the determination provided in the embodiment of the present invention is driven in the detailed description of the invention of the end time of stroke, quiescence is divided into quiescence and the outer quiescence of car in car, by the analysis to acceleration information frequency domain characteristic, thus identify mobile terminal user and to be in car quiescence or the outer quiescence of car, the method effectively prevent static in the long-time car that causes due to traffic congestion, traffic signal lamp and is mistaken as the situation that stroke terminates.In addition, determine in the detailed description of the invention of the end time of driving stroke provided by the invention, when detecting that the data value variable quantity of GPS sensing data is less than predetermined threshold value, start the identification of quiescence and ambulatory status immediately, therefore, the method accurately and in real time can realize the identification in stroke end moment.
The detailed description of the invention of the automatic identifying method of the driving stroke provided for the embodiment of the present invention above.In this embodiment, illustrate for acceleration information.In fact, in embodiments of the present invention, acceleration information is only the example of motion state of mobile terminal data, and as the expansion of the embodiment of the present invention, motion state of mobile terminal data can also be angular acceleration data.When motion state of mobile terminal data are angular acceleration data, the state of kinematic motion sensor for detection angle acceleration information can be gyroscope.In addition, the motion state of mobile terminal data described in the embodiment of the present invention are not limited to acceleration information and angular acceleration data, can also be other state of kinematic motion data.In embodiments of the present invention, the sensor being used for monitoring motion state of mobile terminal data can be referred to as state of kinematic motion sensor.
Based on the automatic identifying method of the driving stroke that above-described embodiment provides, the embodiment of the present invention additionally provides the automatic identification equipment driving stroke.Specifically see Figure 10.Figure 10 is the automatic identification equipment structural representation of the driving stroke that the embodiment of the present invention provides, and as shown in Figure 10, this device comprises with lower unit:
First collecting unit 101, for gathering the first data value of the motion state of mobile terminal data of state of kinematic motion Sensor monitoring;
Whether diagnosis unit 102, for being in motoring condition according to described first data value identification mobile terminal user;
First determining unit 103, for when the recognition result of described diagnosis unit is for being, determines that producing moment corresponding to described first data value is the initial time driving stroke;
Second collecting unit 104, begins for the initial time from described driving stroke, the second data value of the motion state of mobile terminal data of continuous collecting state of kinematic motion Sensor monitoring and the data value of GPS sensing data;
Second determining unit 105, the data value according to described second data value and GPS sensing data determines the end time of driving stroke.
The automatic identification equipment of the driving stroke provided by the embodiment of the present invention, as long as can avoid mobile terminal-opening in prior art, GPS sensor just monitors the problem of the gps data of mobile terminal.And, due to the electricity consumed when the electricity consumed when acceleration pick-up works is less than GPS working sensor, therefore, reduce consumption of current by device provided by the invention, be conducive to the battery life extending mobile terminal.
In addition, the automatic identification equipment of the driving stroke that the embodiment of the present invention provides determines the end time of driving stroke according to the mode that acceleration information and GPS sensing data combine, because GPS sensing data is not by the impact of the artificial movement of mobile terminal, therefore, the inaccurate identification of this device driving stroke that the artificial movement of mobile terminal can be avoided to cause.Only determine the method for driving stroke by single data compared to prior art, the end time of the driving stroke that the embodiment of the present invention is determined is more accurate.
As specific embodiments of the invention, in order to reduce computation complexity, improve computational efficiency, as shown in figure 11, described diagnosis unit 102 specifically can comprise
First extracts subelement 1021, for extracting the eigenwert of the default temporal signatures of described first data value;
First recognin unit 1022, whether the eigenwert identification mobile terminal user according to the default temporal signatures of described first data value is in motoring condition;
Wherein, described default temporal signatures is the minimum temporal signatures of the eigenwert transposition section accounting of the temporal signatures of these motion state of mobile terminal data;
Wherein, the eigenwert transposition section of a temporal signatures be that the eigenwert of this temporal signatures remains static mobile terminal user, ambulatory status and motoring condition time the union of overlapping interval between two of distributed area;
The eigenwert transposition section accounting of a temporal signatures for described between two the union of overlapping interval and the eigenwert of this temporal signatures remain static mobile terminal user, ambulatory status and motoring condition time distributed area the ratio of union.
As a specific embodiment of the present invention, in order to identify the state of mobile terminal user exactly, as shown in figure 11, described first recognin unit 1022 can specifically comprise:
First computation subunit 10221, distributed area lower limit during the distributed area upper limit and ambulatory status for the eigenwert according to the default temporal signatures of described first data value extracted, the distributed area bound of eigenwert when motoring condition presetting temporal signatures, quiescence, calculates the probability P that mobile terminal user remains static respectively
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walk;
First compares subelement 10222, for comparing the probability P of quiescence
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walksize each other, to determine whether mobile terminal user is in motoring condition;
Wherein, P is worked as
idle> P
drivetime, mobile terminal user remain static;
Work as P
idle< P
drivetime, mobile terminal user is in motoring condition;
Work as P
walk< P
drivetime, mobile terminal user is in motoring condition;
Work as P
walk> P
drivetime, mobile terminal user is in ambulatory status.
As a specific embodiment of the present invention, described first computation subunit 10221 comprises quiescence probability P
idlecomputation subunit, motoring condition probability P
drivecomputation subunit and ambulatory status probability P
walkcomputation subunit;
Wherein, described quiescence probability P
idlecomputation subunit specifically comprise the subelement calculated according to formula (1);
Described motoring condition probability P
drivecomputation subunit specifically comprise the subelement calculated according to formula (2);
Described ambulatory status probability P
walkcomputation subunit specifically comprise the subelement calculated according to formula (3);
Wherein, the expression formula of formula (1), formula (2) and formula (3), as described in embodiment of the method, is not described in detail at this.
As another specific embodiment of the present invention, in order to determine the end time of driving stroke exactly, as shown in figure 12, described second determining unit 105 can comprise:
First judgment sub-unit 1051, for judging whether the data value variable quantity of GPS sensing data is greater than predetermined threshold value, if, mobile terminal user is in motoring condition, triggers the second data value of motion state of mobile terminal data and the data value of GPS sensing data of described second collecting unit continuous collecting state of kinematic motion Sensor monitoring;
Second recognin unit 1052, for when the judged result of described first judgment sub-unit is no, determine that mobile terminal user is in non-driving state, and remain static or ambulatory status according to the second data value identification mobile terminal user of motion state of mobile terminal data;
Second judgment sub-unit 1053, for when mobile terminal user remains static, judge whether mobile terminal user is positioned at car according to the second data value, if so, the second data value of motion state of mobile terminal data and the data value of GPS sensing data of described second collecting unit continuous collecting state of kinematic motion sensor is triggered;
First determines subelement 1054, and for when the judged result of described second judgment sub-unit is no or when mobile terminal user is in ambulatory status, the moment of determining to produce described second data value is the end time of driving stroke.
As another specific embodiment of the present invention, the algorithm in order to avoid the long-time static initiation caused due to traffic congestion, traffic signal lamp terminates the situation of stroke in advance, and as shown in figure 13, described second judgment sub-unit 1053 can comprise:
Obtain subelement 10531, for obtaining the frequency-domain index of the second data value; Described frequency-domain index is the sum of squares of data in 2-10Hz frequency band;
Second computation subunit 10532, for calculating the second difference of the first difference of frequency-domain index and the frequency-domain index of described second data value and the outer frequency-domain index of car in the frequency-domain index of described second data value and car;
3rd judgment sub-unit 10533, for judging that according to described first difference and described second difference the frequency-domain index of described second data value is closer to frequency-domain index in car or the outer frequency-domain index of car, when the frequency-domain index of described second data value is closer in car during frequency-domain index, determine that mobile terminal user is in car, when the frequency-domain index of described second data value frequency-domain index outer closer to car, determine that mobile terminal user is in outside car.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (14)
1. drive an automatic identifying method for stroke, it is characterized in that, comprising:
Gather the first data value of the motion state of mobile terminal data of state of kinematic motion Sensor monitoring;
Whether be in motoring condition according to described first data value identification mobile terminal user, if so, determine that producing moment corresponding to described first data value is the initial time driving stroke;
Begin from the initial time of described driving stroke, the second data value of the motion state of mobile terminal data of continuous collecting state of kinematic motion Sensor monitoring and the data value of GPS sensing data;
Data value according to described second data value and GPS sensing data determines the end time of driving stroke.
2. method according to claim 1, is characterized in that, describedly whether is in motoring condition according to described first data value identification mobile terminal user, specifically comprises:
Extract the eigenwert of the default temporal signatures of described first data value;
Whether the eigenwert identification mobile terminal user according to the default temporal signatures of described first data value is in motoring condition;
Wherein, described default temporal signatures is the minimum temporal signatures of the eigenwert transposition section accounting of the temporal signatures of these motion state of mobile terminal data;
Wherein, the eigenwert transposition section of a temporal signatures be that the eigenwert of this temporal signatures remains static mobile terminal user, ambulatory status and motoring condition time the union of overlapping interval between two of distributed area;
The eigenwert transposition section accounting of a temporal signatures for described between two the union of overlapping interval and the eigenwert of this temporal signatures remain static mobile terminal user, ambulatory status and motoring condition time distributed area the ratio of union.
3. method according to claim 2, is characterized in that, whether the eigenwert identification mobile terminal user of the described default temporal signatures according to described first data value is in motoring condition, specifically comprises:
Distributed area lower limit during the distributed area upper limit and ambulatory status according to the eigenwert of the default temporal signatures of described first data value extracted, the distributed area bound of eigenwert when motoring condition presetting temporal signatures, quiescence, calculates the probability P that mobile terminal user remains static respectively
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walk;
The relatively probability P of quiescence
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walksize each other, to determine whether mobile terminal user is in motoring condition;
Wherein, P is worked as
idle> P
drivetime, mobile terminal user remain static;
Work as P
idle< P
drivetime, mobile terminal user is in motoring condition;
Work as P
walk< P
drivetime, mobile terminal user is in motoring condition;
Work as P
walk> P
drivetime, mobile terminal user is in ambulatory status.
4. method according to claim 3, it is characterized in that, distributed area lower limit when the eigenwert of the default temporal signatures of described first data value that described basis is extracted, default the distributed area bound of eigenwert when motoring condition of temporal signatures, the distributed area upper limit of quiescence and ambulatory status, calculates the probability P that mobile terminal user remains static respectively
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walk, specifically comprise:
The probability P of quiescence
idlecomputing formula as shown in formula (1):
The probability P of motoring condition
drivecomputing formula as shown in formula (2):
The probability P of ambulatory status
walkcomputing formula as shown in formula (3):
In formula (1), (2) and (3),
α
1it is the lower limit of motoring condition feature distributed area;
α
2it is the upper limit of quiescence feature distributed area;
α
3it is the lower limit of ambulatory status feature distributed area;
α
4it is the upper limit of motoring condition feature distributed area.
5. the method according to any one of claim 1-4, is characterized in that, the end time of the described data value determination stroke according to described second data value and GPS sensing data, specifically comprises:
Judge whether the data value variable quantity of GPS sensing data is greater than predetermined threshold value, if, mobile terminal user is in motoring condition, returns the second data value of motion state of mobile terminal data and the data value of GPS sensing data that perform described continuous collecting state of kinematic motion Sensor monitoring; If not, mobile terminal user is in non-driving state, performs and remains static or ambulatory status according to the second data value identification mobile terminal user of motion state of mobile terminal data;
When mobile terminal user remains static, judge whether mobile terminal user is positioned at car according to the second data value, if so, the second data value of motion state of mobile terminal data performing described continuous collecting state of kinematic motion sensor and the data value of GPS sensing data is returned; If not, the moment of determining to produce described second data value is the end time of driving stroke;
When mobile terminal user is in ambulatory status, the moment of determining to produce described second data value is the end time of driving stroke.
6. method according to claim 5, is characterized in that, describedly judges whether mobile terminal user is positioned at car, specifically comprises according to the second data value:
Obtain the frequency-domain index of the second data value; Described frequency-domain index is the sum of squares of data in 2-10Hz frequency band;
Calculate the second difference of the first difference of frequency-domain index and the frequency-domain index of described second data value and the outer frequency-domain index of car in the frequency-domain index of described second data value and car;
Judge that the frequency-domain index of described second data value is closer to frequency-domain index in car or the outer frequency-domain index of car according to described first difference and described second difference, when the frequency-domain index of described second data value is closer in car during frequency-domain index, determine that mobile terminal user is in car, when the frequency-domain index of described second data value frequency-domain index outer closer to car, determine that mobile terminal user is in outside car.
7. method according to claim 5, is characterized in that, described the second data value identification mobile terminal user according to motion state of mobile terminal data remains static or ambulatory status, specifically comprises:
Extract the eigenwert of the default temporal signatures of the second data value;
According to the eigenwert of the default temporal signatures of the second data value extracted, preset the distributed area bound of eigenwert when motoring condition of temporal signatures, the distributed area upper limit of quiescence and ambulatory status time distributed area lower limit, calculate the probability P that mobile terminal user remains static respectively
idle, motoring condition P
driveprobability and ambulatory status P
walkprobability;
The relatively probability P of quiescence
idle, motoring condition P
driveprobability and ambulatory status P
walkprobability size each other, to determine that mobile terminal user remains static or ambulatory status;
Work as P
idle> P
drivetime, mobile terminal user remain static;
Work as P
walk> P
drivetime, mobile terminal user is in ambulatory status.
8. the method according to any one of claim 1-4, is characterized in that, described motion state of mobile terminal data are acceleration information.
9. drive an automatic identification equipment for stroke, it is characterized in that, comprising:
First collecting unit, for gathering the first data value of the motion state of mobile terminal data of state of kinematic motion Sensor monitoring;
Whether diagnosis unit, for being in motoring condition according to described first data value identification mobile terminal user;
First determining unit, for when the recognition result of described diagnosis unit is for being, determines that producing moment corresponding to described first data value is the initial time driving stroke;
Second collecting unit, begins for the initial time from described driving stroke, the second data value of the motion state of mobile terminal data of continuous collecting state of kinematic motion Sensor monitoring and the data value of GPS sensing data;
Second determining unit, the data value according to described second data value and GPS sensing data determines the end time of driving stroke.
10. device according to claim 9, is characterized in that, described diagnosis unit comprise:
First extracts subelement, for extracting the eigenwert of the default temporal signatures of described first data value;
First recognin unit, whether the eigenwert identification mobile terminal user according to the default temporal signatures of described first data value is in motoring condition;
Wherein, described default temporal signatures is the minimum temporal signatures of the eigenwert transposition section accounting of the temporal signatures of these motion state of mobile terminal data;
Wherein, the eigenwert transposition section of a temporal signatures be that the eigenwert of this temporal signatures remains static mobile terminal user, ambulatory status and motoring condition time the union of overlapping interval between two of distributed area;
The eigenwert transposition section accounting of a temporal signatures for described between two the union of overlapping interval and the eigenwert of this temporal signatures remain static mobile terminal user, ambulatory status and motoring condition time distributed area the ratio of union.
11. devices according to claim 10, is characterized in that, described first recognin unit comprises:
First computation subunit, distributed area lower limit during the distributed area upper limit and ambulatory status for the eigenwert according to the default temporal signatures of described first data value extracted, the distributed area bound of eigenwert when motoring condition presetting temporal signatures, quiescence, calculates the probability P that mobile terminal user remains static respectively
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walk;
First compares subelement, for comparing the probability P of quiescence
idle, motoring condition probability P
drivewith the probability P of ambulatory status
walksize each other, to determine whether mobile terminal user is in motoring condition;
Wherein, P is worked as
idle> P
drivetime, mobile terminal user remain static;
Work as P
idle< P
drivetime, mobile terminal user is in motoring condition;
Work as P
walk< P
drivetime, mobile terminal user is in motoring condition;
Work as P
walk> P
drivetime, mobile terminal user is in ambulatory status.
12. devices according to claim 11, is characterized in that, described first computation subunit comprises quiescence probability P
idlecomputation subunit, motoring condition probability P
drivecomputation subunit and ambulatory status probability P
walkcomputation subunit;
Wherein, described quiescence probability P
idlecomputation subunit specifically comprise the subelement calculated according to formula (1);
Described motoring condition probability P
drivecomputation subunit specifically comprise the subelement calculated according to formula (2);
Described ambulatory status probability P
walkcomputation subunit specifically comprise the subelement calculated according to formula (3);
Wherein, formula (1) is as follows:
Formula (2) is as follows:
Formula (3) is as follows:
In formula (1), (2) and (3),
α
1it is the lower limit of motoring condition feature distributed area;
α
2it is the upper limit of quiescence feature distributed area;
α
3it is the lower limit of ambulatory status feature distributed area;
α
4it is the upper limit of motoring condition feature distributed area.
13. devices according to any one of claim 9-12, it is characterized in that, described second determining unit comprises:
First judgment sub-unit, for judging whether the data value variable quantity of GPS sensing data is greater than predetermined threshold value, if, mobile terminal user is in motoring condition, triggers the second data value of motion state of mobile terminal data and the data value of GPS sensing data of described second collecting unit continuous collecting state of kinematic motion Sensor monitoring;
Second recognin unit, for when the judged result of described first judgment sub-unit is no, determine that mobile terminal user is in non-driving state, and remain static or ambulatory status according to the second data value identification mobile terminal user of motion state of mobile terminal data;
Second judgment sub-unit, for when mobile terminal user remains static, judge whether mobile terminal user is positioned at car according to the second data value, if so, the second data value of motion state of mobile terminal data and the data value of GPS sensing data of described second collecting unit continuous collecting state of kinematic motion sensor is triggered;
First determines subelement, and for when the judged result of described second judgment sub-unit is no or when mobile terminal user is in ambulatory status, the moment of determining to produce described second data value is the end time of driving stroke.
14. devices according to claim 13, is characterized in that, described second judgment sub-unit comprises:
Obtain subelement, for obtaining the frequency-domain index of the second data value; Described frequency-domain index is the sum of squares of data in 2-10Hz frequency band;
Second computation subunit, for calculating the second difference of the first difference of frequency-domain index and the frequency-domain index of described second data value and the outer frequency-domain index of car in the frequency-domain index of described second data value and car;
3rd judgment sub-unit, for judging that according to described first difference and described second difference the frequency-domain index of described second data value is closer to frequency-domain index in car or the outer frequency-domain index of car, when the frequency-domain index of described second data value is closer in car during frequency-domain index, determine that mobile terminal user is in car, when the frequency-domain index of described second data value frequency-domain index outer closer to car, determine that mobile terminal user is in outside car.
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