CN109044379A - A kind of fatigue driving judgment method, system, equipment and computer storage medium - Google Patents
A kind of fatigue driving judgment method, system, equipment and computer storage medium Download PDFInfo
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- CN109044379A CN109044379A CN201810672679.7A CN201810672679A CN109044379A CN 109044379 A CN109044379 A CN 109044379A CN 201810672679 A CN201810672679 A CN 201810672679A CN 109044379 A CN109044379 A CN 109044379A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
Abstract
This application discloses a kind of fatigue driving judgment method, system, equipment and computer storage mediums, wherein this method comprises: obtaining the real-time sign information of driver；Determine the corresponding fatigue strength parameter of real-time sign information；Calculate the vector coefficient of variation of fatigue strength parameter during each unit, the vector coefficient of variation is the ratio of the average value of the standard deviation and fatigue strength parameter of fatigue strength parameter during unit；Determine the vector coefficient of variation with the variation relation during unit, with based on variation relation judge driver whether fatigue driving.Involved algorithm is only the ratio of standard deviation, average value and standard deviation and average value in a kind of fatigue driving judgment method disclosed in the present application, system, equipment and computer readable storage medium, it is conventional simple algorithm, the prior art is compared, the application handle to data that algorithm used is relatively simple, can improve the detection efficiency that detection is driven to driver fatigue to a certain extent.
Description
Technical field
This application involves technical field of information processing, more specifically to a kind of fatigue driving judgment method, system,
Equipment and computer storage medium.
Background technique
In the driving process of automobile, if driver is in driving condition for a long time, fatigue driving will be formed, this meeting
Seriously endanger the safety of driver.
In order to avoid driver tired driving, a kind of existing method for detecting fatigue driving be based on voice personal characteristics and
The driving fatigue detection method of model adaptation, firstly, extracting the linear character and nonlinear characteristic of driver's speech samples；Its
It is secondary, driver's identity is differentiated using the Speaker Identification algorithm based on VQ (voice quality, volume)；Then, according to driving
The individual fatigue characteristic difference of people, using Relief (Relevant Features, feature selecting algorithm) algorithm filters out can
The phonetic feature for sufficiently reflecting its tired information constructs tired personal feature vector；Finally, using SVM (Support Vector
Machine, support vector machines) sorting algorithm establishes the adaptive fatigue detecting model for driving individual human, and carries out sample to model
This training and driving fatigue detection.
However, the existing driving fatigue detection method based on voice personal characteristics and model adaptation, complex steps, consumption
Duration, so that lower to the detection efficiency of the fatigue detecting of driver.
In conclusion how to improve to driver fatigue drive detection detection efficiency be current those skilled in the art urgently
Problem to be solved.
Summary of the invention
The purpose of the application is to provide a kind of fatigue driving judgment method, can solve how to improve to a certain extent pair
Driver fatigue drives the technical issues of detection efficiency of detection.Present invention also provides a kind of fatigue drivings to judge system, sets
Standby and computer readable storage medium.
To achieve the goals above, the application provides the following technical solutions:
A kind of fatigue driving judgment method, comprising:
Obtain the real-time sign information of driver；
Determine the corresponding fatigue strength parameter of the real-time sign information；
The vector coefficient of variation of fatigue strength parameter during each unit is calculated, the vector coefficient of variation is institute
State the ratio of the average value of the standard deviation and fatigue strength parameter of the fatigue strength parameter during unit；
The vector coefficient of variation is determined with the variation relation during the unit, to drive based on variation relation judgement
Sail people whether fatigue driving.
Preferably, the corresponding fatigue strength parameter of the determination real-time sign information, comprising:
Calculate the product of each sign data proportionality coefficient corresponding with the sign data in the real-time sign information；
Determine all corresponding products of the sign data and be the fatigue strength parameter.
Preferably, the vector coefficient of variation for calculating the fatigue strength parameter during each unit, comprising:
The corresponding fatigue strength matrix of the fatigue strength parameter is generated, the parameter of the fatigue strength matrix includes each described tired
Labor degree parameter and the fatigue strength parameter are periodically carved really；
Vector variation lines of fatigue strength parameter during each unit are calculated according to the fatigue strength matrix
Number.
Preferably, the real-time sign information for obtaining driver, comprising:
Obtain the real-time sign information for the intelligent wearable device acquisition that driver wears.
Preferably, after the real-time sign information for obtaining driver, the determination real-time sign information is corresponding
Fatigue strength parameter before, further includes:
Judge that the real-time sign information obtained whether in corresponding variation range, drives if it is not, then issuing prompt
People judges the prompt information whether intelligent wearable device works normally, and reacquires driver's after preset time period
Real-time sign information.
Preferably, after the determination vector coefficient of variation is with the variation relation during the unit, further includes:
Judge whether the vector coefficient of variation during presently described unit is more than or equal to the fatigue driving vector coefficient of variation, if
It is then to issue prompt information；If it is not, the vector coefficient of variation during then estimating next unit according to the variation relation
Whether the fatigue driving vector coefficient of variation is more than or equal to, if so, issuing prompt information；
Wherein, the fatigue driving vector coefficient of variation is to be driven according to the expression that the history sign information of driver determines
The minimum value of the vector coefficient of variation of people's fatigue driving.
Preferably, the real-time sign information includes real-time heart rate, real-time body temperature, heart real time, real-time blood pressure.
A kind of fatigue driving judges system, comprising:
Module is obtained, for obtaining the real-time sign information of driver；
First determining module, for determining the corresponding fatigue strength parameter of the real-time sign information；
First computing module, for calculating the vector coefficient of variation of fatigue strength parameter during each unit, institute
Stating the vector coefficient of variation is the standard deviation of the fatigue strength parameter and the average value of the fatigue strength parameter during the unit
Ratio；
Second determining module, for determining the vector coefficient of variation with the variation relation during the unit, to be based on
The variation relation judge driver whether fatigue driving.
Preferably, first determining module includes:
First computing unit, it is corresponding with the sign data for calculating each sign data in the real-time sign information
Proportionality coefficient product；
First determination unit, for determine all corresponding products of the sign data and for the fatigue strength join
Number.
Preferably, first computing module includes:
First generation unit, for generating the corresponding fatigue strength matrix of the fatigue strength parameter, the fatigue strength matrix
Parameter includes that each fatigue strength parameter and the fatigue strength parameter are periodically carved really；
Second computing unit, for calculating the fatigue strength parameter in each unit phase according to the fatigue strength matrix
The interior vector coefficient of variation.
Preferably, the acquisition module includes:
Acquiring unit, the real-time sign information of the intelligent wearable device acquisition for obtaining driver's wearing.
Preferably, further includes:
First judgment module, for being obtained described in the intelligent wearable device acquisition that driver wears in the acquiring unit
After real-time sign information, before first determining module determines the corresponding fatigue strength parameter of the real-time sign information, sentence
Whether the disconnected real-time sign information obtained is in corresponding variation range, if it is not, then issuing described in prompt driver's judgement
The prompt information whether intelligent wearable device works normally, and prompt the acquiring unit reacquisition to drive after preset time period
Sail the real-time sign information of the intelligent wearable device acquisition of people's wearing.
Preferably, further includes:
Second judgment module, for determining the vector coefficient of variation with during the unit in second determining module
Variation relation after, judge the vector coefficient of variation during presently described unit whether be more than or equal to fatigue driving vector variation
Coefficient, if so, issuing prompt information；If it is not, the vector during then estimating next unit according to the variation relation
Whether the coefficient of variation is more than or equal to the fatigue driving vector coefficient of variation, if so, issuing prompt information；
Wherein, the fatigue driving vector coefficient of variation is to be driven according to the expression that the history sign information of driver determines
The minimum value of the vector coefficient of variation of people's fatigue driving.
Preferably, the real-time sign information includes real-time heart rate, real-time body temperature, heart real time, real-time blood pressure.
A kind of fatigue driving judges equipment, comprising:
Memory, for storing computer program；
Processor realizes the step of as above any fatigue driving judgment method when for executing the computer program
Suddenly.
A kind of computer readable storage medium is stored with computer program in the computer readable storage medium, described
The step of as above any described fatigue driving judgment method is realized when computer program is executed by processor.
A kind of fatigue driving judgment method provided by the present application, obtains the real-time sign information of driver；Determine real-time volume
Reference ceases corresponding fatigue strength parameter；Calculate the vector coefficient of variation of fatigue strength parameter during each unit, vector variation
Coefficient be unit during fatigue strength parameter standard deviation and fatigue strength parameter average value ratio；Determine the vector coefficient of variation
With the variation relation during unit, with based on variation relation judge driver whether fatigue driving.One kind provided by the present application is tired
Please the ratio that algorithm involved in judgment method is only standard deviation, average value and standard deviation and average value is sailed,
It is conventional simple algorithm, and extraction linear character in the prior art and nonlinear characteristic, the Speaker Identification based on VQ are calculated
Method, Relief algorithm, SVM algorithm are more complicated algorithms, so compared with prior art, the application to data at
Reason algorithm used is relatively simple, can improve the detection efficiency that detection is driven to driver fatigue to a certain extent.This Shen
A kind of fatigue driving detecting system, equipment and the computer readable storage medium that please be provided also solve accordingly to a certain extent
Technical problem.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of application for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of first pass figure of fatigue driving method provided by the embodiments of the present application；
Fig. 2 is a kind of second flow chart of fatigue driving method provided by the embodiments of the present application；
Fig. 3 is a kind of third flow chart of fatigue driving method provided by the embodiments of the present application；
Fig. 4 is a kind of 4th flow chart of fatigue driving method provided by the embodiments of the present application；
Fig. 5 is the structural schematic diagram that a kind of fatigue driving provided by the embodiments of the present application judges system；
Fig. 6 is the structural schematic diagram that a kind of fatigue driving provided by the embodiments of the present application judges equipment；
Fig. 7 is another structural schematic diagram that a kind of fatigue driving provided by the embodiments of the present application judges equipment.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.It is based on
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall in the protection scope of this application.
The movement executing subject of each step can be in a kind of fatigue driving judgment method provided by the embodiments of the present application
A kind of fatigue driving provided by the embodiments of the present application judges system, and the system can be built in computer, server etc., institute
It can be built-in with the movement executing subject of each step in a kind of fatigue driving judgment method provided by the embodiments of the present application
Computer, server of the system etc..For convenience, here by a kind of fatigue driving judgement provided by the embodiments of the present application
The movement executing subject of each step is set as a kind of fatigue driving provided by the embodiments of the present application and judges system in method, referred to as
Judgement system.
A kind of existing method for detecting fatigue driving is examined based on the driving fatigue of voice personal characteristics and model adaptation
Survey method, firstly, extracting the linear character and nonlinear characteristic of driver's speech samples；Secondly, using the speaker based on VQ
Recognizer differentiates driver's identity；Then, it according to the individual fatigue characteristic difference of driver, is filtered out using Relief algorithm
The phonetic feature that can sufficiently reflect its tired information constructs tired personal feature vector；Finally, being built using svm classifier algorithm
The vertical adaptive fatigue detecting model for driving individual human, and sample training and driving fatigue detection are carried out to model.However, existing
Driving fatigue detection method of some based on voice personal characteristics and model adaptation, needs repeatedly to handle data, and
Processing algorithm complexity used, complex steps, time-consuming, so that lower to the detection efficiency of the fatigue detecting of driver.And this
The step of applying for a kind of fatigue driving judgment method provided is simple, and judging efficiency is higher.
Referring to Fig. 1, Fig. 1 is a kind of first pass figure of fatigue driving method provided by the embodiments of the present application.
A kind of fatigue driving judgment method provided by the embodiments of the present application, may include steps of:
Step S101: the real-time sign information of driver is obtained.
In practical application, judge that the real-time sign information for the driver that system obtains may include the real-time heart of driver
Rate, the real-time body temperature of driver, the heart real time of driver, real-time blood pressure of driver etc..In concrete application scene, for side
Just, the real-time sign information of driver is accurately obtained, and avoids the influence to driver as far as possible, it is available to be worn on
The real-time sign information for driving the intelligent wearable device acquisition in human arm, in such cases, when judgement system is built in service
When in device etc., judge that system can receive by the real-time sign information of vehicle intelligent terminal forwarding intelligent wearable device acquisition,
The real-time sign information of driver that i.e. intelligent wearable device acquires itself is sent to vehicle intelligent terminal, vehicle intelligent terminal
Itself received real-time sign information is sent to judgement system again.The application does not limit intelligent wearable device specifically
Determine, including but not limited to such as intelligentized wearable device of wrist-watch, bracelet, glasses, dress ornament.In addition it is also possible to be according to preparatory
The time interval of setting obtains the real-time sign information of driver, and preset time interval can according to actual needs really
It is fixed, such as 1 minute, 5 minutes etc.；It is also possible to obtain the real-time sign information of driver according to different time intervals, for example,
Start in the first time period driven in driver, first time period can for driver keep normal driving state it is lasting when
Between, the real-time sign information of driver is obtained with first time interval, and after driver starts to drive second time period, with the
Two time intervals obtain the real-time sign information of driver, and first time interval is greater than the second time interval, and specific value is equal
It can be determine according to actual needs；It is also possible to the real-time continuous real-time sign information etc. for obtaining driver, the application is herein
It is not specifically limited.
Step S102: the corresponding fatigue strength parameter of real-time sign information is determined.
Judgement system can determine that real-time sign information is corresponding tired after getting the real-time sign information of driver
Labor degree parameter, fatigue strength parameter mentioned here refer to being able to reflect driver whether the parameter of fatigue driving.Due to driving
When people's fatigue driving, every physical function of driver may fatigue driving non-with driver when difference, so here may be used
To determine its corresponding fatigue strength parameter according to real-time sign information.Specifically, when can first acquire the non-fatigue driving of driver
Real-time sign information and its corresponding fatigue strength parameter, real-time sign information when acquisition driver fatigue drives and its is right
The fatigue strength parameter answered, the corresponding relationship of real-time sign information Yu fatigue strength parameter is determined by these two types of parameters, later basis
The corresponding relationship determines the corresponding fatigue strength parameter of real-time sign information.
Step S103: calculating the vector coefficient of variation of fatigue strength parameter during each unit, and the vector coefficient of variation is
The ratio of the average value of the standard deviation of fatigue strength parameter and fatigue strength parameter during unit.
Judgement system can calculate fatigue strength parameter every after the corresponding fatigue strength parameter of the real-time sign information of determination
The vector coefficient of variation during a unit, during unit mentioned here namely unit interval, specific value can be with
Determine according to actual needs, for example it can be the adjacent acquisition interval for obtaining real-time sign information twice, be also possible to obtain
The multiple at interval, such as 3 times, 5 times etc., at this point, the quantity of the real-time sign information during unit can be the multiple, than
Such as, it is divided between acquisition 1 minute, namely obtained primary real-time sign information every one minute, obtain interval during unit 3
Times, namely obtain between be divided into 3 minutes namely unit during real-time sign information quantity be 3, correspondingly, the unit phase
The quantity of interior fatigue strength parameter is just 3；The vector coefficient of variation mentioned here is fatigue strength parameter during unit
The ratio of the average value of standard deviation and fatigue strength parameter is 3 citings with the quantity of the fatigue strength parameter during unit, then unit
During the vector coefficient of variation be just the standard deviation of every 3 fatigue strength parameters Yu the average value of 3 fatigue strength parameters ratio
Value.In concrete application scene, in order to accelerate to calculate the calculating of the vector coefficient of variation of fatigue strength parameter during each unit
Speed can also calculate the vector coefficient of variation by matrix, then fatigue strength parameter is calculated in the application during each unit
The interior vector coefficient of variation, is specifically as follows: generating the corresponding fatigue strength matrix of fatigue strength parameter, the parameter packet of fatigue strength matrix
It includes each fatigue strength parameter and fatigue strength parameter is periodically carved really, fatigue strength parameter mentioned here is periodically carved to obtain really
The acquisition moment of the real-time sign information of the fatigue strength parameter；Fatigue strength parameter is calculated in each unit phase according to fatigue strength matrix
The interior vector coefficient of variation.
Step S104: the vector coefficient of variation is determined with the variation relation during unit, to drive based on variation relation judgement
People whether fatigue driving.
Judgement system can determine after calculating the vector coefficient of variation of the fatigue strength parameter in each unit during
The vector coefficient of variation is with the variation relation during unit, since the vector coefficient of variation is relevant coefficient with fatigue strength parameter, and
Fatigue strength parameter be again be able to reflect driver whether the parameter of fatigue driving, it is possible to according to the vector coefficient of variation with unit
The variation relation of period come judge driver whether fatigue driving, specifically, the vector during may determine that current one becomes
Whether different coefficient is more than or equal to the fatigue driving vector coefficient of variation, if so, judge that fatigue driving occurs in driver, if it is not,
Then judge that fatigue driving does not occur in driver, the fatigue driving vector coefficient of variation mentioned here is going through according to driver
What history sign information determined indicates the minimum value for the vector coefficient of variation that driver fatigue drives.In concrete application scene, in order to
Obtaining driver, the moment is the variation relation of initial time by bus from currently, can first be calculated according to the vector coefficient of variation initial tired
Labor amount OF, then calculate fatigue accumulation amount FC finally calculates driving fatigue cumulant DFC, according to the initiated failure amount being calculated,
Fatigue accumulation amount and driving fatigue cumulant determine that the vector coefficient of variation changes with the variation during unit, in which:
OF=RRVCInitial value-RRVCIdeal value；
FC=RRVCi-RRVCIdeal value, i=0,1,2 ... N；
DFC=OF+FC=RRVCi+RRVCInitial value-2RRVCIdeal value；
Wherein, RR VC indicates the vector coefficient of variation；I indicates the sequence during current unit calculated；RRVCIdeal valueTable
Show the ideal vector coefficient of variation of the driver obtained by history sign information；RRVCInitial valueWhen indicating that driver just starts to drive
The vector coefficient of variation；RRVCiIndicate the vector coefficient of variation during i-th of unit.
A kind of fatigue driving judgment method provided by the present application, obtains the real-time sign information of driver；Determine real-time volume
Reference ceases corresponding fatigue strength parameter；Calculate the vector coefficient of variation of fatigue strength parameter during each unit, vector variation
Coefficient be unit during fatigue strength parameter standard deviation and fatigue strength parameter average value ratio；Determine the vector coefficient of variation
With the variation relation during unit, with based on variation relation judge driver whether fatigue driving.One kind provided by the present application is tired
Please the ratio that algorithm involved in judgment method is only standard deviation, average value and standard deviation and average value is sailed,
It is conventional simple algorithm, and extraction linear character in the prior art and nonlinear characteristic, the Speaker Identification based on VQ are calculated
Method, Relief algorithm, SVM algorithm are more complicated algorithms, so compared with prior art, the application to data at
Reason algorithm used is relatively simple, can improve the detection efficiency that detection is driven to driver fatigue to a certain extent.
Referring to Fig. 2, a kind of fatigue driving judgment method provided by the embodiments of the present application, can specifically include:
Step S201: the real-time sign information of driver is obtained.
Step S202: multiplying for each sign data proportionality coefficient corresponding with sign data in real-time sign information is calculated
Product.
In practical application, each sign data in real-time sign information to judge driver whether the influence of fatigue driving
It is different, it is possible to predefine each sign data to judge driver whether the proportionality coefficient of fatigue driving, then count
Calculate the product of each sign data proportionality coefficient corresponding with sign data.
Step S203: determine the corresponding product of all sign datas and be fatigue strength parameter.
Judgement system each sign data in calculating real-time sign information proportionality coefficient corresponding with sign data
It, can be using the sum of the corresponding product of all sign datas as fatigue strength parameter after product.Using this fatigue strength parameter
Calculation method can make determine fatigue strength parameter process it is simple, and then can improve to a certain extent detection driver
Whether the detection efficiency of fatigue driving.
Step S204: calculating the vector coefficient of variation of fatigue strength parameter during each unit, and the vector coefficient of variation is
The ratio of the average value of the standard deviation of fatigue strength parameter and fatigue strength parameter during unit.
Step S205: the vector coefficient of variation is determined with the variation relation during unit, to drive based on variation relation judgement
People whether fatigue driving.
Associated description about other steps please refers to corresponding contents among the above, and which is not described herein again.
Referring to Fig. 3, a kind of fatigue driving judgment method provided by the embodiments of the present application, is specifically as follows:
Step S301: the real-time sign information for the intelligent wearable device acquisition that driver wears is obtained.
Step S302: the real-time sign information obtained is judged whether in corresponding variation range, if it is not, thening follow the steps
S303, if so, thening follow the steps S304.
Step S303: it issues prompt driver and judges the prompt information whether intelligent wearable device works normally, and pre-
If reacquiring the real-time sign information for the intelligent wearable device acquisition that driver wears after the period.
In practical application, due to external environment influence or the wearing fault between driver and intelligent wearable device
Etc. factors influence, in fact it could happen that the real-time sign information of acquisition deviates considerably from the case where history sign information of driver, this
When, in order to guarantee that the accuracy of the real-time sign information obtained can first be sentenced after getting the real-time sign information of driver
Whether the disconnected real-time sign information obtained is in corresponding variation range, if so, proving that the real-time sign information obtained is quasi-
True, if it is not, then issuing prompt driver judges the prompt information whether intelligent wearable device works normally, and in preset time
The real-time sign information for the intelligent wearable device acquisition that driver wears is reacquired after section, preset time period here is to drive
People judges whether intelligent wearable device works normally, and in intelligent wearable device non-normal working by intelligent wearable device tune
The whole time to work normally.
Step S304: the corresponding fatigue strength parameter of real-time sign information is determined.
Step S305: calculating the vector coefficient of variation of fatigue strength parameter during each unit, and the vector coefficient of variation is
The ratio of the average value of the standard deviation of fatigue strength parameter and fatigue strength parameter during unit.
Step S306: the vector coefficient of variation is determined with the variation relation during unit, to drive based on variation relation judgement
People whether fatigue driving.
The description as described in other steps please refers to the associated description in previous embodiment, and which is not described herein again.
Referring to Fig. 4, a kind of fatigue driving judgment method provided by the embodiments of the present application, is specifically as follows:
Step S401: the real-time sign information of driver is obtained.
Step S402: the corresponding fatigue strength parameter of real-time sign information is determined.
Step S403: calculating the vector coefficient of variation of fatigue strength parameter during each unit, and the vector coefficient of variation is
The ratio of the average value of the standard deviation of fatigue strength parameter and fatigue strength parameter during unit.
Step S404: determine the vector coefficient of variation with the variation relation during unit.
Step S405: whether the vector coefficient of variation during judging current one is more than or equal to fatigue driving vector variation lines
Number, if so, thening follow the steps S407；If it is not, thening follow the steps S406.
Step S406: whether the vector coefficient of variation during estimating next unit according to variation relation is more than or equal to fatigue
The vector coefficient of variation is driven, if so, thening follow the steps S407.
Step S407: prompt information is issued；
Wherein, the fatigue driving vector coefficient of variation is tired according to the determining expression driver of the history sign information of driver
Please the minimum value for the vector coefficient of variation sailed.
In practical application, judge that system after determining the vector coefficient of variation with the variation relation during unit, can first be sentenced
Whether the vector coefficient of variation during disconnected current one is more than or equal to the fatigue driving vector coefficient of variation, if it is not, then according to change
Whether the vector coefficient of variation during change relationship estimates next unit is more than or equal to the fatigue driving vector coefficient of variation, if
It is then to issue prompt information, prompt information mentioned here can be prompt driver i.e. by the prompt information of fatigue driving.Tool
In body application scenarios, judge that the vector coefficient of variation of system during judging unit is less than the fatigue driving vector coefficient of variation
Afterwards, duration needed for reaching the fatigue driving vector coefficient of variation can also be estimated according to variation relation, then accurate prompt drives
There is the time of fatigue driving in people.
The description as described in other steps please refers to the related content in above-described embodiment, and which is not described herein again.
Present invention also provides a kind of fatigue drivings to judge system, drives with a kind of fatigue provided by the embodiments of the present application
Sail the correspondence effect that judgment method has.Referring to Fig. 5, Fig. 5 is a kind of fatigue driving judgement system provided by the embodiments of the present application
The structural schematic diagram of system.
A kind of fatigue driving provided by the embodiments of the present application judges system, may include:
Module 101 is obtained, for obtaining the real-time sign information of driver；
First determining module 102, for determining the corresponding fatigue strength parameter of real-time sign information；
First computing module 103, for calculating the vector coefficient of variation of fatigue strength parameter during each unit, vector
The coefficient of variation be unit during fatigue strength parameter standard deviation and fatigue strength parameter average value ratio；
Second determining module 104, for determining the vector coefficient of variation with the variation relation during unit, to close based on variation
System judge driver whether fatigue driving.
In a kind of fatigue driving judgement system provided by the present application, the first determining module may include:
First computing unit, for calculating each sign data and the corresponding ratio system of sign data in real-time sign information
Several products；
First determination unit, for determine the corresponding product of all sign datas and for fatigue strength parameter.
In a kind of fatigue driving judgement system provided by the present application, the first computing module may include:
First generation unit, for generating the corresponding fatigue strength matrix of fatigue strength parameter, the parameter of fatigue strength matrix includes
Each fatigue strength parameter and fatigue strength parameter are periodically carved really；
Second computing unit becomes for calculating vector of fatigue strength parameter during each unit according to fatigue strength matrix
Different coefficient.
In a kind of fatigue driving judgement system provided by the present application, obtaining module may include:
Acquiring unit, the real-time sign information of the intelligent wearable device acquisition for obtaining driver's wearing.
In a kind of fatigue driving judgement system provided by the present application, can also include:
First judgment module, in the real-time sign for obtaining the intelligent wearable device acquisition that module acquisition driver wears
After information, before the first determining module determines the corresponding fatigue strength parameter of real-time sign information, the real-time sign obtained is judged
Whether information is in corresponding variation range, if it is not, then issuing prompt driver judges whether intelligent wearable device works normally
Prompt information, and after preset time period prompt acquiring unit reacquire driver wear intelligent wearable device acquisition
Real-time sign information.
In a kind of fatigue driving judgement system provided by the present application, can also include:
Second judgment module, for the second determining module determine the vector coefficient of variation with the variation relation during unit it
Afterwards, whether the vector coefficient of variation during judging current one is more than or equal to the fatigue driving vector coefficient of variation, if so, issuing
Prompt information；If it is not, whether the vector coefficient of variation during then estimating next unit according to variation relation is more than or equal to fatigue
The vector coefficient of variation is driven, if so, issuing prompt information；
Wherein, the fatigue driving vector coefficient of variation is tired according to the determining expression driver of the history sign information of driver
Please the minimum value for the vector coefficient of variation sailed.
In a kind of fatigue driving judgement system provided by the present application, real-time sign information may include real-time heart rate, in real time
Body temperature, heart real time, real-time blood pressure.
Present invention also provides a kind of fatigue drivings to judge equipment and computer readable storage medium, all has the application
A kind of correspondence effect that fatigue driving judgment method has that embodiment provides.Referring to Fig. 6, Fig. 6 mentions for the embodiment of the present application
A kind of fatigue driving supplied judges the structural schematic diagram of equipment.
A kind of fatigue driving provided by the embodiments of the present application judges equipment, may include memory 201, processor 202, place
Reason device 202 realizes following steps when executing the computer program stored in memory 201:
Obtain the real-time sign information of driver；
Determine the corresponding fatigue strength parameter of real-time sign information；
The vector coefficient of variation of fatigue strength parameter during each unit is calculated, during the vector coefficient of variation is unit
The ratio of the average value of the standard deviation and fatigue strength parameter of fatigue strength parameter；
The vector coefficient of variation is determined with the variation relation during unit, to judge whether driver is tired based on variation relation
It drives.
A kind of fatigue driving provided by the embodiments of the present application judges in equipment, is stored in memory 201 and calculates loom journey
Specific implementation when processor 202 executes the computer subprogram stored in memory 201: sequence calculates every in real-time sign information
The product of item sign data proportionality coefficient corresponding with sign data；Determine the corresponding product of all sign datas and be tired
Labor degree parameter.
A kind of fatigue driving provided by the embodiments of the present application judges in equipment, is stored in memory 201 and calculates loom journey
Specific implementation when processor 202 executes the computer subprogram stored in memory 201: sequence it is corresponding to generate fatigue strength parameter
Fatigue strength matrix, the parameter of fatigue strength matrix include that each fatigue strength parameter and fatigue strength parameter are periodically carved really；According to tired
Labor degree matrix calculates the vector coefficient of variation of fatigue strength parameter during each unit.
A kind of fatigue driving provided by the embodiments of the present application judges in equipment, is stored in memory 201 and calculates loom journey
Specific implementation when processor 202 executes the computer subprogram stored in memory 201: sequence obtains the intelligence that driver wears
The real-time sign information of wearable device acquisition.
A kind of fatigue driving provided by the embodiments of the present application judges in equipment, is stored in memory 201 and calculates loom journey
Specific implementation when processor 202 executes the computer subprogram stored in memory 201: sequence obtains the intelligence that driver wears
After the real-time sign information of wearable device acquisition, before determining the corresponding fatigue strength parameter of real-time sign information, judge to obtain
Real-time sign information whether in corresponding variation range, if it is not, then issue prompt driver judge that intelligent wearable device is
The prompt information of no normal work, and the reality for the intelligent wearable device acquisition that reacquisition driver wears after preset time period
When sign information.
A kind of fatigue driving provided by the embodiments of the present application judges in equipment, is stored in memory 201 and calculates loom journey
Specific implementation when processor 202 executes the computer subprogram stored in memory 201: sequence determines the vector coefficient of variation with list
After variation relation during position, whether the vector coefficient of variation during judging current one is more than or equal to the change of fatigue driving vector
Different coefficient, if so, issuing prompt information；If it is not, the vector variation lines during then estimating next unit according to variation relation
Whether number is more than or equal to the fatigue driving vector coefficient of variation, if so, issuing prompt information；Wherein, fatigue driving vector makes a variation
Coefficient is the minimum value for the vector coefficient of variation that the expression driver fatigue determined according to the history sign information of driver drives.
A kind of fatigue driving provided by the embodiments of the present application judges in equipment that real-time sign information includes real-time heart rate, reality
Shi Tiwen, heart real time, real-time blood pressure.
Referring to Fig. 7, another kind fatigue driving provided by the embodiments of the present application judges include: in equipment and handle
The input port 203 that device 202 connects is used for transmission the extraneous order inputted to processor 202；What is connect with processor 202 is aobvious
Show unit 204, the processing result for video-stream processor 202 is to the external world；The communication module 205 connecting with processor 202, is used for
Realize that fatigue driving judges equipment and extraneous communication.Display unit 202 can make display for display panel, laser scanning
Deng；Communication mode used by communication module 205 includes but is not limited to that mobile high definition chained technology (HML), general serial are total
Line (USB), is wirelessly connected high-definition media interface (HDMI): adopting wireless fidelity technology (WiFi), Bluetooth Communication Technology, low-power consumption
Bluetooth Communication Technology, the communication technology based on IEEE802.11s.
A kind of computer readable storage medium provided by the embodiments of the present application is stored with meter in computer readable storage medium
Calculation machine program, realizes following steps when computer program is executed by processor:
Obtain the real-time sign information of driver；
Determine the corresponding fatigue strength parameter of real-time sign information；
The vector coefficient of variation of fatigue strength parameter during each unit is calculated, during the vector coefficient of variation is unit
The ratio of the average value of the standard deviation and fatigue strength parameter of fatigue strength parameter；
The vector coefficient of variation is determined with the variation relation during unit, to judge whether driver is tired based on variation relation
It drives.
In a kind of computer readable storage medium provided by the embodiments of the present application, it is stored in computer readable storage medium
Specific implementation when computer subprogram is executed by processor: computer subprogram calculates each sign number in real-time sign information
According to the product of proportionality coefficient corresponding with sign data；Determine the corresponding product of all sign datas and join for fatigue strength
Number.
In a kind of computer readable storage medium provided by the embodiments of the present application, it is stored in computer readable storage medium
Specific implementation when computer subprogram is executed by processor: computer subprogram generates the corresponding fatigue strength square of fatigue strength parameter
Battle array, the parameter of fatigue strength matrix includes that each fatigue strength parameter and fatigue strength parameter are periodically carved really；According to fatigue strength matrix
Calculate the vector coefficient of variation of fatigue strength parameter during each unit.
In a kind of computer readable storage medium provided by the embodiments of the present application, it is stored in computer readable storage medium
Specific implementation when computer subprogram is executed by processor: computer subprogram obtains the intelligent wearable device that driver wears
The real-time sign information of acquisition.
In a kind of computer readable storage medium provided by the embodiments of the present application, it is stored in computer readable storage medium
Specific implementation when computer subprogram is executed by processor: computer subprogram obtains the intelligent wearable device that driver wears
After the real-time sign information of acquisition, before determining the corresponding fatigue strength parameter of real-time sign information, the real-time volume obtained is judged
Whether reference ceases in corresponding variation range, if it is not, then issuing prompt driver judges the whether normal work of intelligent wearable device
The prompt information of work, and the real-time volume reference for the intelligent wearable device acquisition that reacquisition driver wears after preset time period
Breath.
In a kind of computer readable storage medium provided by the embodiments of the present application, it is stored in computer readable storage medium
Specific implementation when computer subprogram is executed by processor: computer subprogram determines the vector coefficient of variation with during unit
After variation relation, whether the vector coefficient of variation during judging current one is more than or equal to the fatigue driving vector coefficient of variation,
If so, issuing prompt information；If it is not, whether the vector coefficient of variation during then estimating next unit according to variation relation is big
In being equal to the fatigue driving vector coefficient of variation, if so, issuing prompt information；Wherein, the fatigue driving vector coefficient of variation is root
According to the minimum value for the vector coefficient of variation for indicating driver fatigue driving that the history sign information of driver determines.
In a kind of computer readable storage medium provided by the embodiments of the present application, real-time sign information include real-time heart rate,
Real-time body temperature, heart real time, real-time blood pressure.
Computer readable storage medium mentioned here include random access memory (RAM), memory, read-only memory (ROM),
Institute is public in electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field
Any other form of storage medium known.
A kind of fatigue driving provided by the embodiments of the present application judges related in system, equipment and computer readable storage medium
Partial explanation refers to the detailed description of corresponding part in a kind of fatigue driving judgment method provided by the embodiments of the present application,
This is repeated no more.In addition, in above-mentioned technical proposal provided by the embodiments of the present application with correspond in the prior art technical solution realize
The consistent part of principle is simultaneously unspecified, in order to avoid excessively repeat.
The foregoing description of the disclosed embodiments makes those skilled in the art can be realized or use the application.To this
A variety of modifications of a little embodiments will be apparent for a person skilled in the art, and the general principles defined herein can
Without departing from the spirit or scope of the application, to realize in other embodiments.Therefore, the application will not be limited
It is formed on the embodiments shown herein, and is to fit to consistent with the principles and novel features disclosed in this article widest
Range.
Claims (10)
1. a kind of fatigue driving judgment method characterized by comprising
Obtain the real-time sign information of driver；
Determine the corresponding fatigue strength parameter of the real-time sign information；
The vector coefficient of variation of fatigue strength parameter during each unit is calculated, the vector coefficient of variation is the list
The ratio of the average value of the standard deviation and fatigue strength parameter of the fatigue strength parameter during position；
The vector coefficient of variation is determined with the variation relation during the unit, to judge driver based on the variation relation
Whether fatigue driving.
2. the method according to claim 1, wherein the corresponding fatigue strength of the determination real-time sign information
Parameter, comprising:
Calculate the product of each sign data proportionality coefficient corresponding with the sign data in the real-time sign information；
Determine all corresponding products of the sign data and be the fatigue strength parameter.
3. the method according to claim 1, wherein described calculate the fatigue strength parameter during each unit
The interior vector coefficient of variation, comprising:
The corresponding fatigue strength matrix of the fatigue strength parameter is generated, the parameter of the fatigue strength matrix includes each fatigue strength
Parameter and the fatigue strength parameter are periodically carved really；
The vector coefficient of variation of fatigue strength parameter during each unit is calculated according to the fatigue strength matrix.
4. method according to any one of claims 1 to 3, which is characterized in that the real-time volume reference for obtaining driver
Breath, comprising:
Obtain the real-time sign information for the intelligent wearable device acquisition that driver wears.
5. according to the method described in claim 4, it is characterized in that, the intelligent wearable device acquisition for obtaining driver and wearing
The real-time sign information after, before the corresponding fatigue strength parameter of the determination real-time sign information, further includes:
Judge that the real-time sign information obtained whether in corresponding variation range, is sentenced if it is not, then issuing prompt driver
The prompt information whether intelligent wearable device of breaking works normally, and reacquire what driver wore after preset time period
The real-time sign information of intelligent wearable device acquisition.
6. according to the method described in claim 4, it is characterized in that, the determination vector coefficient of variation is with the unit phase
Between variation relation after, further includes:
Judge whether the vector coefficient of variation during presently described unit is more than or equal to the fatigue driving vector coefficient of variation, if so,
Then issue prompt information；If it is not, the vector coefficient of variation during then estimating next unit according to the variation relation is
It is no to be more than or equal to the fatigue driving vector coefficient of variation, if so, issuing prompt information；
Wherein, the fatigue driving vector coefficient of variation is tired according to the determining expression driver of the history sign information of driver
Please the minimum value for the vector coefficient of variation sailed.
7. a kind of fatigue driving judges system characterized by comprising
Module is obtained, for obtaining the real-time sign information of driver；
First determining module, for determining the corresponding fatigue strength parameter of the real-time sign information；
First computing module, for calculating the vector coefficient of variation of fatigue strength parameter during each unit, the arrow
The ratio of the average value of the standard deviation and fatigue strength parameter of the fatigue strength parameter during the coefficient of variation is measured as the unit
Value；
Second determining module, for determining the vector coefficient of variation with the variation relation during the unit, based on described
Variation relation judge driver whether fatigue driving.
8. fatigue driving according to claim 7 judges system, which is characterized in that further include:
First judgment module, for obtaining the described real-time of the intelligent wearable device acquisition that driver wears in the acquiring unit
After sign information, before first determining module determines the corresponding fatigue strength parameter of the real-time sign information, judgement is obtained
Whether the real-time sign information taken is in corresponding variation range, if it is not, then issuing prompt driver judges the intelligence
The prompt information whether wearable device works normally, and prompt the acquiring unit to reacquire driver after preset time period
The real-time sign information of the intelligent wearable device acquisition of wearing.
9. a kind of fatigue driving judges equipment characterized by comprising
Memory, for storing computer program；
Processor realizes that fatigue driving as claimed in any one of claims 1 to 6 such as judges when for executing the computer program
The step of method.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer in the computer readable storage medium
Program realizes such as fatigue driving judgement side as claimed in any one of claims 1 to 6 when the computer program is executed by processor
The step of method.
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