CN106448050A - Traffic risk warning method and system of smart phone based on vehicle noise - Google Patents
Traffic risk warning method and system of smart phone based on vehicle noise Download PDFInfo
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- CN106448050A CN106448050A CN201611005062.7A CN201611005062A CN106448050A CN 106448050 A CN106448050 A CN 106448050A CN 201611005062 A CN201611005062 A CN 201611005062A CN 106448050 A CN106448050 A CN 106448050A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
Abstract
The invention relates to a traffic risk warning method and system of smart phones based on vehicle noise, which is characterized in that the system comprises a data acquisition module, a feature extraction module, a central processing unit, a background database, a control module and an early warning module based on the smart phones. A matching relationship and a model are established in the background database by utilizing the acquisition of the training samples. A vehicle noise data group is collected in real time and features of the vehicle noise data group are collected through the smart phones. A result is output by recalling the matching relationship and the model stored in the background database. Analyzing and judging the result to control smart phones to trigger the initial warning and the emergency warning which can promptly remind the commuters of the danger who have hearing loss or are addicted to watching smart phones or listening to music with headset while walking. The traffic risk warning method and system of smart phones based on vehicle noise can also improve the safety of road travelers and reduce the accident rate on the road effectively.
Description
Technical field
The present invention relates to traffic safety and field of acoustics, particularly to a kind of smart mobile phone traffic based on vehicle noise danger
Dangerous method for early warning and system.
Background technology
Nowadays, smart mobile phone also occurs in that potential traffic safety hidden danger while bringing great convenience,
Walking or cycle side earphone in road top listens the phenomenon of mobile phone to be seen everywhere, and sees the mobile phone because wallowing on road or is listened with earphone
Mobile phone and vehicle accident that road vehicle situation led to cannot be noticed often by media report；Additionally, in non-mandrel roller
Crossing or crossing at, some have the special population of dysaudia cannot predict crossing or people in advance because of self disorder
Whether row lateral road blind area has arrival vehicle, and is easily ignored by driver and lead to vehicle accident to occur.For exist these
Traffic safety hidden danger is if it is possible to remind pedestrian to make emergency danger-avoiding to arrival vehicle in advance it is possible to effectively reduce on road
Accident rate, protect special population, improve safe trip rate.
Social intelligence's mobile phone has become as the indispensable communication of society and converter tools now, has spread to people
The every aspect lived, and smart mobile phone itself is already installed with various induction apparatuss and hardware device, using smart mobile phone as
A kind of the instrument giving warning in advance is made to arrival vehicle, for improving safety, to be undoubtedly one kind very feasible and facility can
The method and measure leaning on.
The present invention proposes a kind of smart mobile phone traffic hazard method for early warning based on vehicle noise and system, and vehicle is in road
The noise sending during upper traveling is obvious and is easy to differentiate, and reminds pedestrian to be undoubtedly one kind conveniently using vehicle noise
Mode.
Content of the invention
Based on background above, it is an object of the invention to the row seeing the mobile phone or listening mobile phone with earphone of wallowing on protection road
People and specific group, reduce road accident rate, reach the purpose of the pedestrian that gives warning in advance.
The present invention proposes a kind of smart mobile phone traffic hazard early warning system based on vehicle noise it is characterised in that this intelligence
Mobile phone traffic hazard early warning system can include acquisition module, characteristic extracting module, CPU, background data base, control
Module and warning module, the input of described characteristic extracting module connects acquisition module, and outfan connects CPU,
Described CPU outfan is connected with control module, and mutually communicates with background data base, described control mould
Block is responsible for receiving the control instruction of CPU, and controls the working condition of warning module.
Described smart mobile phone traffic hazard early warning system is as an application developed on intelligent mobile phone platform
Program APP is present in mobile phone main interface.
Described acquisition module, including the mike being placed within smart mobile phone, is responsible for sensing in real time and gathering surrounding
Vehicle noise data, described vehicle noise data includes vehicle noise sound intensity data and voice data；Described feature extraction
Module, is responsible for extracting vehicle noise feature from vehicle noise array of data, and is transmitted to CPU, described
Vehicle noise feature includes vehicle noise sound intensity variation characteristic and audible spectrum feature；
Described CPU, is the decision package of whole system, is communicated with background data base, responsible basis
Vehicle noise feature calls the matching relationship of storage and model in background data base, inputs Current vehicle noise number in model
According to, output result, it is analyzed judging, and send instruction to control module；
Described control module, there is pre-set control program inside, is responsible for receiving the instruction of CPU,
Control the working condition of prior-warning device；
Described warning module, that is, be placed in the usual hardware response apparatus within smart mobile phone, including mobile phone screen, voice
Equipment and vibratory equipment, are responsible for reminding smart phone user to avoid vehicle in time.
In described background data base, the matching relationship of storage and the method for building up step of model are as follows：
B1. gather training sample：The training sample of collection is stored in background data base, and training sample includes vehicle noise
Data, type of vehicle, speed, vehicle and pedestrian's spacing and vehicle and pedestrian's relative distance relation；Described type of vehicle bag
Include large car, in-between car and compact car three types；Described vehicle and pedestrian's relative distance relation include vehicle approaching row
People and sail out of two kinds of relative distance relations of pedestrian；
B2. extract vehicle noise feature：Respectively in vehicle approaching pedestrian with the case of sailing out of two kinds of pedestrian, analyze and extract
Go out common vehicle noise sound intensity variation characteristic when three kinds of type of vehicle are travelled with different speeds, in the case of approaching pedestrian, point
Analyse and extract common vehicle noise audible spectrum feature under different speeds of operation corresponding from three types vehicle respectively；
B3. set up matching relationship：Described matching relationship includes two kinds of matching relationships, and that is, the first is the vehicle noise sound intensity
Matching relationship between variation characteristic and vehicle and pedestrian's relative distance relation, for according to vehicle noise sound intensity variation characteristic Lai
Judge that vehicle is approaching pedestrian or sails out of pedestrian, second be between vehicle noise audible spectrum feature and type of vehicle
Join relation, for type of vehicle is gone out according to vehicle noise audible spectrum characteristic matching；
B4. set up model：Do not consider that vehicle sails out of the situation of pedestrian, set up and three only in the case of vehicle approaching pedestrian
Plant the artificial nerve network model of type of vehicle vehicle noise data corresponding respectively and speed, vehicle and pedestrian's spacing, and
A kind of type of vehicle corresponds to a kind of configuration processor of artificial nerve network model；Described artificial nerve network model is made an uproar to vehicle
Sound data and speed, vehicle and pedestrian's spacing carry out concurrent collaborative process, mutual between internal node in network by adjusting
Annexation, finds the corresponding relation between vehicle noise data and speed, vehicle and pedestrian's spacing；Described ANN
Network model adopts the BP neural network model of simple-type, and its input layer is vehicle noise data, and output layer is speed, vehicle and row
The human world away from.
The storage form of the training sample of described collection is：Set up two storages according to vehicle and pedestrian's relative distance relation
Deposit file, then set up the subfile of three type of vehicle respectively in the store files with pedestrian's relative distance relation for the vehicle,
By above-mentioned speed sample value, vehicle, with pedestrian's spacing sample value and the corresponding vehicle noise data that collects is with classification matrix
Form is stored in subfile.
Matching relationship between described vehicle noise sound intensity variation characteristic and vehicle and pedestrian's relative position relation is：Car
The feature in reduction trend for the noise sound intensity change is sailed out of pedestrian with vehicle and is matched, the feature in the trend of rising and vehicle approaching
Pedestrian matches.
A kind of smart mobile phone traffic hazard method for early warning based on vehicle noise, the method step is as follows：
S1. utilize the vehicle noise data in smart mobile phone mike Real-time Collection surrounding, constitute vehicle noise number
According to array, described vehicle noise array of data includes vehicle noise sound intensity array of data and voice data array；
S2. the vehicle noise array of data collecting is carried out with vehicle noise feature extraction, described vehicle noise feature
Including vehicle noise sound intensity variation characteristic and audible spectrum feature；
S3. according to vehicle noise sound intensity variation characteristic, call the vehicle noise sound intensity change of storage in background data base special
Vehicle of seeking peace carries out mating obtaining a result with the matching relationship of pedestrian's relative distance relation；
If S4. matching result is that vehicle is in approaching pedestrian's state, trigger the first early warning of smart mobile phone immediately, no
Then, smart mobile phone is still in Real-time Collection state；
S5., under the premise of smart mobile phone has triggered first early warning, rear number of units is called according to vehicle noise audible spectrum feature
According to the vehicle noise audible spectrum feature of storage in storehouse and the matching relationship of type of vehicle, match Current vehicle type, thus
Call the artificial nerve network model corresponding to this type of vehicle；
S6. extract last data component from vehicle noise sound intensity array of data and voice data array respectively, that is,
Current vehicle noise data, is input in artificial nerve network model, output current vehicle speed, vehicle and pedestrian's spacing；
S7. distance threshold is determined by current vehicle speed, described distance threshold refer to vehicle braking distance, driver reaction away from
From and system response with a distance from sum；
S8. compare the size of Current vehicle and pedestrian's spacing and distance threshold, if Current vehicle and pedestrian are smaller than
In distance threshold, then trigger the urgent early warning of smart mobile phone immediately, otherwise, still in the Real-time Collection state of vehicle noise data.
Described vehicle noise feature extracting method includes as follows：
S21. the vehicle noise data in smart mobile phone mike timely collection surrounding, constitutes vehicle noise number
According to array, described vehicle noise array of data includes vehicle noise sound intensity array of data and voice data array；
S22. discrete sound intensity data value in sound intensity array of data is done continuous treatment, draws function curve variation diagram,
Extract plots changes feature, as sound intensity variation characteristic；
S23. vehicle noise voice data array is done Fourier transformation and obtain noise spectrum, then extract noise audio frequency frequency
The frequency of spectrum, amplitude and phase property, as audible spectrum feature.
Described first early warning is after smart mobile phone detects vehicle noise, and vibration and voice to cellphone subscriber carry
Wake up, this first early warning does not carry out any interference and controls to smart mobile phone other application programs.
Described urgent early warning is after vehicle enters threshold value with pedestrian's spacing, and system control smart mobile phone carries out vibration and carries
Wake up, interrupt mobile phone current task, automatic switchover mobile phone screen and to insertion voice message mandatory in earphone jack, work as danger
After elimination, then respond immediately to original working condition.
Based on above technical scheme, the present invention using the smart mobile phone of vehicle noise give warning in advance method and system possess with
Lower advantage：
The present invention does not need to install other hardware devices on smart mobile phone or elsewhere, and cost is few and is easy to take
Band.
By gathering, training sample data sets up two kinds of matching relationships to the present invention and the method for BP neural network model makes car
Noise is changed into the alerting signal of smart mobile phone, plays mobile phone personnel for wallowing in or special population gives warning in advance, more have
Sharp protects out administrative staff, reduces accident rate.
The inventive method and system are popularizations of the application and development to smart mobile phone, before having broader development
Scape.
Brief description
Fig. 1 is the smart mobile phone traffic hazard early warning system schematic diagram based on vehicle noise of the present invention；
Fig. 2 is the flow chart of the matching relationship and method for establishing model storing in the background data base of the present invention；
Fig. 3 is the job network schematic diagram of the BP neural network model employed in the present invention；
Fig. 4 is the flow chart of the smart mobile phone traffic hazard method for early warning based on vehicle noise of the present invention；
Fig. 5 is the flow chart of the vehicle noise feature extracting method of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings the present invention is described in further detail.
As shown in figure 1, being the schematic diagram based on the smart mobile phone traffic hazard early warning system of vehicle noise for the present invention：
A kind of smart mobile phone traffic hazard early warning system based on vehicle noise of the present invention, including acquisition module (10),
Characteristic extracting module (20), CPU (30), background data base (40), control module (50) and warning module (60),
The input of described characteristic extracting module (20) connects acquisition module (10), and outfan connects CPU (30), institute
CPU (30) outfan stated is connected with control module (50), and mutually communicates with background data base (40), institute
The control module (50) stated is responsible for receiving the control instruction of CPU (30), and controls the working condition of warning module.
The a kind of of the present invention can be developed as one based on the smart mobile phone traffic hazard early warning system of vehicle noise
Application program be present in intelligent mobile phone platform, with conventional hardware device cooperating in smart mobile phone, user is reached
The effect of danger early warning, its internal module including is as follows：
Acquisition module (10), is responsible for sensing in real time and gathering surrounding vehicles noise data, including vehicle noise sound intensity data
And voice data, the wherein frequency of collection can be arranged it is also possible to voluntarily be arranged by user with default system, gathers m every time respectively
Individual vehicle noise sound intensity data and voice data, constitute vehicle noise array of data, and wherein vehicle noise array of data includes car
Noise sound intensity array of data and voice data array；
Characteristic extracting module (20), is responsible for extracting vehicle noise feature, described vehicle from vehicle noise array of data
Feature of noise includes noise sound intensity variation characteristic and audible spectrum feature, and is transmitted to CPU (30)；
CPU (30), is the decision package of whole system, is communicated with background data base (40), responsible root
Matching relationship and model according to storage in vehicle noise feature and vehicle noise data call background data base (40) are obtained a result
It is analyzed judging, and instruction is sent to control module (50) according to judged result.Wherein, spy is changed according to the vehicle noise sound intensity
Levy the middle vehicle storing with background data base (40) to match with the relative distance relation of pedestrian, judge that vehicle is approaching or sails
From pedestrian, if the nearly pedestrian of vehicle, match vehicle using vehicle noise audible spectrum feature in background data base (40)
Type, and then call this type of vehicle corresponding artificial nerve network model that Current vehicle noise data is processed, output
Current vehicle speed and vehicle and pedestrian's spacing, and then show that threshold distance and vehicle and pedestrian's spacing carry out danger judgement；
Control module (50), there is pre-set control program inside, is responsible for receiving the finger of CPU (30)
Order, controls the working condition of prior-warning device；
Source of early warning (60), that is, provisioned on smart mobile phone usual hardware equipment, be responsible for reminding smart phone user and
When avoid vehicle, including mobile phone vibratory equipment, speech ciphering equipment, when source of early warning (60) receives the instruction of control module (50)
When, vibration signal and the voice messaging prompting cellphone subscriber prerecording are produced for first early warning, for urgent early warning simultaneously
Switch mobile phone screen with displaying information on screen, vibration signal and in earphone jack mandatory insertion voice prompting message remind handss
Machine user's Emergency avoidance.
As shown in Fig. 2 the method for building up step of the matching relationship storing in above-mentioned background data base (40) and model is such as
Under：
B1. gather training sample：Training sample to be gathered include vehicle noise data, type of vehicle, speed, vehicle with
The spacing of pedestrian and both relative position relations, are then stored in data base to training sample according to certain storage mode
In, wherein said vehicle noise data includes vehicle noise sound intensity data and voice data；
Above-mentioned vehicle noise training sample include p speed, g vehicle and pedestrian's spacing, three kinds of type of vehicle and
Common 6pg under two kinds of vehicles and pedestrian position relation is to vehicle noise training sample data；
Above-mentioned storage mode is：Relative distance relation according to vehicle and pedestrian sets up two store files, Ran Houfen
In the store files with the relative distance relation of pedestrian for the vehicle, do not set up the subfile of three type of vehicle, by above-mentioned speed sample
This value, spacing sample value and the corresponding vehicle noise data storage that collects in subfile, wherein in three kinds of type of vehicle
Subfile in speed sample value, vehicle with pedestrian's spacing sample value and the corresponding vehicle noise data that collects is with square of classifying
The form of battle array is stored, and matrix content see table shown：Wherein Vi(i=1,2 ..., p) be p speed training sample value, Sj
(j=1,2 ..., g) be vehicle and pedestrian's spacing training sample value, FijIt is vehicle with vehicle velocity ViIn distance values SjWhen vehicle make an uproar
Speech intensity values, QijIt is vehicle with vehicle velocity ViIn distance values SjWhen vehicle noise audio value；
Above-mentioned type of vehicle includes compact car, in-between car and large car, and wherein compact car includes car, buggy
And the station wagon of less than 7 etc.；In-between car includes medium truck, 7 to 40 middle bus etc.；Large car include trailer,
Engineering truck, the motor buses of more than 40, truck and container car etc.；
Above-mentioned vehicle is to enter according to the distance change trend with pedestrian with the principle of classification of the relative distance relation of pedestrian
Row classification, is divided into approaching pedestrian and sails out of two kinds of situations of pedestrian, gather the row of each type vehicle respectively in both cases
Sail speed, vehicle and pedestrian's spacing and vehicle noise data；
Above-mentioned vehicle and pedestrian's spacing are in the maximum magnitude D that smart mobile phone is able to detect that vehicle noise, should
G distance values obtained by the uniform decile of maximum magnitude；
Above-mentioned speed sample value is uniformly to choose p based on experience value in car speed interval (0,100km/h) respectively
Individual speed is as training sample；
B2. extract vehicle noise feature：First respectively in approaching with the case of sailing out of two kinds of pedestrian, analyze and extract three
Plant common vehicle noise sound intensity variation characteristic when type of vehicle is travelled with different speeds；In the case of approaching pedestrian, analysis is simultaneously
Extract common vehicle noise audible spectrum feature under different speeds corresponding from the vehicle of three types respectively；
B3. set up matching relationship：Described matching relationship includes two kinds, and the first is that vehicle is closed with the relative distance of pedestrian
Matching relationship between system and vehicle noise sound intensity variation characteristic, for judging that vehicle just still sails out of pedestrian in approaching pedestrian；
Matching relationship between the relative distance relation of described vehicle and pedestrian and vehicle noise sound intensity variation characteristic is：Vehicle noise sound
The feature in reduction trend for the strong change is sailed out of pedestrian with vehicle and is matched, the feature in rising trend and vehicle approaching pedestrian's phase
Join；Second is the matching relationship between vehicle noise audible spectrum feature and type of vehicle, large car, in-between car and compact car
Match with three kinds of vehicle noise audible spectrum features respectively, for according to vehicle noise audible spectrum feature identification vehicle class
Type；
B4. set up model：Do not consider that vehicle sails out of the situation of pedestrian, only set up every kind of in the case of vehicle approaching pedestrian
Vehicle noise data corresponding to type of vehicle and the artificial nerve network model of speed, vehicle and pedestrian's spacing；Wherein, described
Artificial nerve network model in vehicle noise data and speed, vehicle and pedestrian's spacing are carried out at distributed parallel information
Reason, by adjust the interconnected relationship between internal node find vehicle noise data and speed, vehicle and pedestrian's spacing it
Between corresponding relation.
Above-mentioned artificial nerve network model is classified according to type of vehicle, calls during artificial nerve network model first
Type of vehicle is gone out according to vehicle noise audible spectrum feature identification, defeated in the corresponding artificial nerve network model of this type of vehicle
Enter Current vehicle noise data, be trained by the number of plies changing hidden layer, obtain current vehicle speed, vehicle and row in output layer
The human world away from.
Above-mentioned artificial neural network adopts the BP neural network of simple-type, as shown in figure 3, this network model's establishment step
As follows：
Wherein, xiExpression vehicle noise data, i=1,2, x1、x2Represent vehicle noise sound intensity data value and audio frequency respectively
Data value；ωjiRepresent that hidden layer implies the weighted value between i-th input node of input layer for the node for j-th, j=1,
2,...,h；γ (x) represents the excitation function of hidden layer；θjRepresent that hidden layer implies the threshold value of node for j-th；WkjRepresent output
The weighted value that k-th output node of layer implies between node for j-th to hidden layer；η (x) represents the excitation function of output layer；bk
The threshold value of expression k-th output node of output layer, k=1,2.
Communication process in BP neural network model for the signal is as follows：
Hidden layer implies the input value of node for j-th
Hidden layer implies the output valve of node for j-th
The input value of k-th output node of output layer
Output valve y of k-th output node of output layerk, i.e. y1And y2Represent speed and vehicle and pedestrian's spacing respectively
Value：
As shown in figure 4, a kind of smart mobile phone traffic hazard method for early warning based on vehicle noise, the method step is as follows：
S1. utilize the vehicle noise data in smart mobile phone mike timely collection surrounding, constitute vehicle and make an uproar
Sound array of data, described vehicle noise array of data includes vehicle noise sound intensity array of data (F1,F2,...,Fm) and audio frequency
Array of data (Q1,Q2,...,Qm)；
S2. the vehicle noise array of data collecting is carried out with vehicle noise feature extraction, described vehicle noise feature
Including vehicle noise sound intensity variation characteristic and audible spectrum feature；
S3. according to vehicle noise sound intensity variation characteristic, the vehicle noise sound intensity of storage in background data base (40) is called to become
Change feature and vehicle carries out mating obtaining a result with the matching relationship of pedestrian's relative distance relation；
If S4. matching result is that vehicle is in approaching pedestrian's state, trigger the first early warning of smart mobile phone immediately, no
Then, smart mobile phone is still in Real-time Collection state；
S5., under the premise of smart mobile phone has triggered first early warning, background data base (40) is called according to audible spectrum feature
The audible spectrum feature of middle storage and the matching relationship of type of vehicle match Current vehicle type, thus calling this type of vehicle
Corresponding artificial nerve network model；
S6. extract last component from vehicle noise sound intensity array of data and voice data array respectively, that is, currently
Vehicle noise data, be input in artificial nerve network model, output current vehicle speed V and vehicle and pedestrian's interval S；
S7. determine that distance threshold L, described distance threshold L refer to vehicle braking distance L by current vehicle speed V1, driver
Reaction distance L2And system response is apart from L3Sum, the wherein relation between speed and vehicle braking distance areFormula
Middle a value in the empirical value of large, medium and small type vehicle, this empirical value as system default value it is also possible to by user according to oneself
Wish voluntarily arrange；
S8. compare the size of distance threshold L and Current vehicle and pedestrian's interval S, if Current vehicle is less than with pedestrian's interval S
Equal to distance threshold L, then trigger the urgent early warning of smart mobile phone immediately, otherwise, still in the Real-time Collection shape of vehicle noise data
State.
Further, described vehicle noise feature includes vehicle noise sound intensity variation characteristic and audible spectrum feature, such as
Shown in Fig. 5, feature extracting method includes as follows：
S21. smart mobile phone mike real-time inductive pick-up vehicle noise sound intensity array of data and voice data array；
S22. discrete sound intensity data value in sound intensity array of data is done continuous treatment, draws function curve variation diagram,
Extract plots changes feature, as sound intensity variation characteristic；
S23. vehicle noise voice data array is done Fourier transformation, obtain noise spectrum, then extract noise audio frequency
The frequency of frequency spectrum, amplitude and phase property, as audible spectrum feature.
Above-mentioned first early warning is the vibration to cellphone subscriber and voice after smart mobile phone detects vehicle and travels noise
Remind, this first early warning does not carry out any interference and controls to smart mobile phone other application programs.
Above-mentioned urgent early warning is after vehicle enters threshold value with pedestrian's spacing, and system control smart mobile phone carries out vibration and carries
Wake up, interrupt mobile phone task, automatic switchover mobile phone screen and insert mandatory voice message in earphone jack, eliminate when dangerous
Afterwards, then respond immediately to original working condition.
The smart mobile phone traffic hazard early warning system based on vehicle noise of the present invention can be used as in smart mobile phone one
Item software application, you can provide free download installation procedure with the smart phone user for each platform, user downloads and pacifies
After filling corresponding program, danger early warning can be carried out using smart mobile phone, improve the safety coefficient of trip.
Above-mentioned smart mobile phone traffic hazard early warning system, after smart mobile phone start, is just constantly in opening, beats
After opening this system, the one-to-one virtual key with function selecting is provided with system interface, each function selecting is divided into
Surely there are multiple rational optionies, can be arranged it is also possible to voluntarily arrange for user with default system.
Using these virtual keys, user can select the frequency of smart mobile phone collection vehicle noise data, and every time
Constitute component number m of vehicle noise array of data；The option having been set according to system, user can be background data base
(40) calculate distance threshold in and desired braking acceleration a is set, or directly desired threshold is set in early warning system
It is worth the braking acceleration a option value arranging in x, system in the braking acceleration empirical value of generally every kind of public vehicles,
The option of threshold distance x is the value taking in the safe distance with pedestrian for the vehicle, if user arranges desired braking and adds simultaneously
Speed a and threshold distance x, then the threshold distance x of system automatic default user setting is effective setting.
Meanwhile, by function selecting virtual key, user to arrange desired early warning alert interval and can remind form,
Prompting form option includes vibrating alert, voice reminder, screens switch prompting, can select one or more, but must be at least
Select a kind of it is also possible to keep whole selected states of system default.
Based on above technical scheme, the present invention utilizes the smart mobile phone traffic hazard method for early warning of vehicle noise and system to have
Standby following advantage：
The present invention does not need to install other hardware devices on smart mobile phone or elsewhere, and cost is few and is easy to take
Band, takes the method setting up matching relationship and BP neural network model so that vehicle noise is changed into by gathering training sample data
The alerting signal of smart mobile phone, plays mobile phone or listens the pedestrian of mobile phone or special population to provide danger on road with earphone for wallowing in
Danger gives warning in advance, and more favourable protects out administrative staff, reduces accident rate；Additionally, the inventive method and system are right
One popularization of the application and development of smart mobile phone, has broader development prospect.
It should be noted last that, above technical scheme has been described in detail to the present invention, those skilled in the art couple
This programme is modified or equivalent, and without departure from the spirit and scope of the technical program, it all should be covered in the present invention
Right in the middle of.
Claims (10)
1. a kind of smart mobile phone traffic hazard early warning system based on vehicle noise is it is characterised in that this smart mobile phone traffic is endangered
Dangerous early warning system includes data acquisition module, characteristic extracting module, CPU, background data base, control module and pre-
Alert module；The input of described characteristic extracting module connects acquisition module, and outfan connects CPU；In described
Central Processing Unit outfan is connected with control module, and mutually communicates with background data base；Described control module is responsible for connecing
Receive the control instruction of CPU, and control the working condition of warning module.
2. a kind of smart mobile phone traffic hazard early warning system based on vehicle noise according to claim 1, its feature exists
In described smart mobile phone traffic hazard early warning system is as an application program developed on intelligent mobile phone platform
APP is present in mobile phone main interface.
3. a kind of smart mobile phone traffic hazard early warning system based on vehicle noise according to claim 1, its feature exists
In described data acquisition module, including the mike being placed within smart mobile phone, the vehicle noise around responsible Real-time Collection
Data, described vehicle noise data includes vehicle noise sound intensity data and voice data；Described characteristic extracting module, is responsible for
Extract vehicle noise feature from vehicle noise array of data, and be transmitted to CPU, described vehicle noise
Feature includes vehicle noise sound intensity variation characteristic and audible spectrum feature；
Described CPU, is the decision package of whole system, is communicated with background data base, is responsible for according to vehicle
Feature of noise calls the matching relationship of storage and model in background data base, inputs Current vehicle noise data in model, defeated
Go out result, be analyzed judging, and send instruction to control module；
Described control module, there is pre-set control program inside, is responsible for receiving the instruction of CPU, controls
The working condition of prior-warning device；
Described warning module, that is, be placed in the usual hardware response apparatus within smart mobile phone, including mobile phone screen, speech ciphering equipment
And vibratory equipment, it is responsible for reminding smart phone user to avoid vehicle in time.
4. a kind of smart mobile phone traffic hazard early warning system based on vehicle noise according to claim 3, its feature exists
In in described background data base, the matching relationship of storage and the method for building up step of model are as follows：
B1. gather training sample：The training sample of collection is stored in background data base, and training sample includes vehicle noise number
According to, type of vehicle, speed, vehicle and pedestrian's spacing and vehicle and pedestrian's relative distance relation, described type of vehicle includes
Large car, in-between car and compact car three types；Described vehicle and pedestrian's relative distance relation include vehicle approaching pedestrian
With sail out of two kinds of relative distance relations of pedestrian；
B2. extract vehicle noise feature：Respectively in vehicle approaching pedestrian with the case of sailing out of two kinds of pedestrian, analyze and extract three
Plant common vehicle noise sound intensity variation characteristic when type of vehicle is travelled with different speeds；In the case of approaching pedestrian, analysis is simultaneously
Extract common vehicle noise audible spectrum feature under different speeds of operation corresponding from three types vehicle respectively；
B3. set up matching relationship：Described matching relationship includes two kinds of matching relationships, and that is, the first is the change of the vehicle noise sound intensity
Matching relationship between feature and vehicle and pedestrian's relative distance relation, for judging according to vehicle noise sound intensity variation characteristic
Vehicle is approaching pedestrian or sails out of pedestrian, and second is to mate pass between vehicle noise audible spectrum feature and type of vehicle
System, for going out type of vehicle according to vehicle noise audible spectrum characteristic matching；
B4. set up model：Do not consider that vehicle sails out of the situation of pedestrian, set up and three kinds of cars only in the case of vehicle approaching pedestrian
Type vehicle noise data corresponding respectively and the artificial nerve network model of speed, vehicle and pedestrian's spacing, and a kind of
Type of vehicle corresponds to a kind of configuration processor of artificial nerve network model；Described artificial nerve network model is to vehicle noise number
Carry out concurrent collaborative process according to speed, vehicle and pedestrian's spacing, by adjusting being connected with each other between internal node in network
Relation, finds the corresponding relation between vehicle noise data and speed, vehicle and pedestrian's spacing；Described artificial neural network mould
Type adopts the BP neural network model of simple-type, and its input layer is vehicle noise data, and output layer is between speed, vehicle and pedestrian
Away from.
5. a kind of smart mobile phone traffic hazard early warning system based on vehicle noise according to claim 4, its feature exists
In the storage form of the described training sample of collection is：Set up two storage literary compositions according to vehicle and pedestrian's relative distance relation
Part, then sets up the subfile of three type of vehicle respectively in the store files with pedestrian's relative distance relation for the vehicle, will be upper
State speed sample value, vehicle with pedestrian's spacing sample value and the corresponding vehicle noise data that collects is in the form of classification matrix
It is stored in subfile.
6. a kind of smart mobile phone traffic hazard early warning system based on vehicle noise according to claim 4, its feature exists
In the matching relationship between described vehicle noise sound intensity variation characteristic and vehicle and pedestrian's relative distance relation is：Vehicle is made an uproar
The feature in reduction trend for the strong change of speech is sailed out of pedestrian with vehicle and is matched, feature and the vehicle approaching pedestrian in the trend of rising
Match.
7. a kind of smart mobile phone traffic hazard method for early warning based on vehicle noise, the method step is as follows：
S1. utilize the vehicle noise data in smart mobile phone mike Real-time Collection surrounding, constitute vehicle noise data number
Group, described vehicle noise array of data includes vehicle noise sound intensity array of data and voice data array；
S2. the vehicle noise array of data collecting is carried out with vehicle noise feature extraction, described vehicle noise feature includes
Vehicle noise sound intensity variation characteristic and audible spectrum feature；
S3. according to vehicle noise sound intensity variation characteristic, call in background data base storage vehicle noise sound intensity variation characteristic and
Vehicle carries out mating obtaining a result with the matching relationship of pedestrian's relative distance relation；
If S4. matching result is that vehicle is in approaching pedestrian's state, trigger the first early warning of smart mobile phone, otherwise, intelligence immediately
Energy mobile phone is still in Real-time Collection state；
S5., under the premise of smart mobile phone has triggered first early warning, background data base is called according to vehicle noise audible spectrum feature
The vehicle noise audible spectrum feature of middle storage and the matching relationship of type of vehicle, match Current vehicle type, thus calling
Artificial nerve network model corresponding to this type of vehicle；
S6. extract last data component from vehicle noise sound intensity array of data and voice data array respectively, that is, currently
Vehicle noise data, be input in artificial nerve network model, output current vehicle speed, vehicle and pedestrian's spacing；
S7. distance threshold is determined by current vehicle speed, described distance threshold refer to vehicle braking distance, driver reaction distance with
And system response is apart from sum；
S8. compare Current vehicle and pedestrian's spacing and the size of distance threshold, if Current vehicle and pedestrian be smaller than being equal to away from
From threshold value, then trigger the urgent early warning of smart mobile phone immediately, otherwise, still in the Real-time Collection state of vehicle noise data.
8. a kind of smart mobile phone traffic hazard method for early warning based on vehicle noise according to claim 7, its feature exists
In vehicle noise feature extracting method includes as follows：
S21. the vehicle noise data in smart mobile phone mike timely collection surrounding, constitutes vehicle noise data number
Group, described vehicle noise array of data includes vehicle noise sound intensity array of data and voice data array；
S22. discrete sound intensity data value in sound intensity array of data is done continuous treatment, draw function curve variation diagram, extract
Plots changes feature, as sound intensity variation characteristic；
S23. vehicle noise voice data array is done Fourier transformation and obtain noise spectrum, then extract noise audible spectrum
Frequency, amplitude and phase property, as audible spectrum feature.
9. a kind of smart mobile phone traffic hazard method for early warning based on vehicle noise according to claim 7, its feature exists
In described first early warning is after smart mobile phone detects vehicle noise, the vibration to cellphone subscriber and voice reminder, and this is first
Secondary early warning does not carry out any interference and controls to smart mobile phone other application programs.
10. a kind of smart mobile phone traffic hazard method for early warning based on vehicle noise according to claim 7, its feature exists
After vehicle enters threshold value with pedestrian's spacing in, described urgent early warning, system control smart mobile phone carry out vibrating alert, in
Cut off the hands machine current task, automatic switchover mobile phone screen and to insertion voice message mandatory in earphone jack, eliminate when dangerous
Afterwards, then respond immediately to original working condition.
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