CN107529664B - A kind of traffic based on WIFI signal is passed unimpeded grade detection system - Google Patents
A kind of traffic based on WIFI signal is passed unimpeded grade detection system Download PDFInfo
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Abstract
It passes unimpeded grade detection system the invention discloses a kind of traffic based on WIFI signal, including multiple sub-networks, each sub-network includes a host and several extension sets, the extension set passes through wireless passive perceptual model, acquisition is by mobile terminal device based on the Wifi agreement broadcast data packet that environment is sent around at random, and it screens the wherein data packet with mobile terminal device id information and is retrieved, host by mobile phone to data carry out unified storage and stamp time tag, and it is uploaded in data server and stores, and it analyzes to obtain traffic cross-sectional flow by data, cross-sectional flow, it polymerize the detected value of average hourage, and determine that the traffic in section is passed unimpeded grade.Invention based on the unique ID of portable mobile terminal, acquire data time stamp and detection device location information macro-traffic infomation detection algorithm, realize to mobile terminal acquisition data data mining and analysis, can be applied to road traffic pass unimpeded grade detection.
Description
Technical field
The invention belongs to the improvement of development of Mobile Internet technology more particularly to macro-traffic information monitoring algorithm.
Background technique
Traffic flow data is the important information source of traffic operation dispatching and command system, can be command scheduling, the magnitude of traffic flow
Control and traffic guidance provide decision-making foundation.There are many existing Traffic flow detecting technologies, can be divided into contact according to mounting means
Formula detection mode and non-contact detection mode.Wherein contact measurement technology includes piezoelectricity, pressure pipe detection and loop coil
Detection.The major defect of this technology is that vehicle causes the service life of detector shorter rolling for road, is detected laying
It when device, needs to suspend traffic, destroy road surface, therefore more difficult, use cost height is gone along with sb. to guard him in installation.Non-contact detection technology master
It to be wave frequency detection and video detection.Wave frequency detection is divided into microwave, ultrasonic wave and three kinds infrared etc..Non-contact detection device can lead to
Bracket installation is crossed, easy to maintain, long service life, major defect is the influence vulnerable to weather and outdoor conditions, and it is suitable that there are environment
The problems such as answering property is not strong, volume of transmitted data is big, Detection accuracy is not high and cost is higher.
With the rapid development of China's highway network, freeway traffic flow detects application demand and increases severely.In highway network
In, traffic flow information is equally important, and by flow information, highway network administrative department can understand the reality in each section in real time
When vehicle fleet size information, intuitive road network vehicle load amount is provided, provides accurate data for the scheduling and integrated planning of road network.
But highway network there is a situation where some special, such as highway power supply is inconvenient, information transmission is difficult, with
And fail to lay all kinds of detectors etc. in advance in process of construction, it can not accomplish concentrated type monitoring and management, need to existing detection
Device is further designed and is improved.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of traffic based on WIFI signal pass unimpeded grade detection
System, the present invention is based on the unique ID of portable mobile terminal, the macro-traffic of acquisition data time stamp and detection device location information
Infomation detection algorithm is realized to the data mining and analysis of mobile terminal acquisition data, can be applied to road traffic and pass unimpeded grade
Detection.
In order to solve the above technical problems, the invention adopts the following technical scheme:
A kind of traffic based on WIFI signal is passed unimpeded grade detection system, disposes detection device system, institute along traffic route
Stating detection device system includes multiple sub-networks, and each sub-network includes a host and several extension sets, and the extension set is (single
Detection device) by wireless passive perceptual model, by mobile terminal device, based on Wifi agreement, environment is sent out around at random for acquisition
The broadcast data packet sent, and screen the wherein data packet with mobile terminal device id information and retrieved, stamp extension set mark
Be uploaded to host after label, host by mobile phone to data carry out unified storage and stamp time tag, and be uploaded to data service
It is stored in device, and analyzes to obtain macroscopical section flow, cross-sectional flow, the detected value for polymerizeing averagely hourage by data, and
Determine that the traffic in section is passed unimpeded grade.
Further, the data analysis the following steps are included:
Step 1: the data D of complete period is acquired by extension set,Wherein, DijIt indicates j-th
No. i-th extension set data of subnet；N indicates the number of subnet, and M indicates the extension set number in j-th of subnet；
Step 2: data being sliced and extract section S and complete period (the data D ' of T- Δ t) to be analyzeds,D in formulaisFor the collected data of extension set that number is i under s subnet；
Step 3: spatial match is carried out with corresponding practical section S to the sub-network of deployment, obtain sub-network host with it is corresponding
The extension set deployment scenario list of the number information in section and the sub-network；
Step 4: the data of each extension set acquisition being ranked up according to mobile terminal device ID number, establish data matrix
{Tower(i,s),t}；
Step 5: the ID data list of foundation being classified by the number for appearing in different data matrix: in the time
In section Δ t, what mobile terminal ID only occurred in the single extension set of sub-network, to the corresponding data of mobile terminal ID individually extract into
The subsequent efficiency analysis of row；It, should in period Δ t, mobile terminal ID occurs in sub-network two or more extension set
The corresponding data of mobile terminal ID are directly as valid data；
Step 6: in period Δ t, mobile terminal ID is only in the single extension set appearance of sub-network, to movement end
ID corresponding data in end, which are individually extracted, carries out subsequent efficiency analysis: 1, within the Δ t time, mobile terminal ID is individually dividing
Repeat in the data matrix TOWER of machine, and above situation occur in multiple mobile terminal ID, mark corresponding road section is congestion shape
Condition, and such ID data is denoted as valid data；2, within the Δ t time, do not find mobile terminal ID in single extension set number
According to repeating in matrix TOWER, then traverse whether the sub-network before and after sub-network corresponding road section S identical ID number occurs,
If do not occurred, using the ID data as noise data processing, if occurred in other sub-networks, identify into significant figure
According to list；
Step 7: repeating step 4~6 until data processing finishes；
Step 8: merger will be carried out by step 5 and the 6 resulting valid data of processing, and according to data matrix TOWER institute
The chronological order of position and appearance in corresponding physical space, and ID data matrix divided into it is two-way, and to each list
Subsequent processing is carried out to data；
Step 9: in T- Δ t to T time section, unidirectional valid data total amount is V, V=D { ID }, and by the following method
Carry out practical macroscopical section flow, cross-sectional flow, the detection and calculating for polymerizeing average hourage:
(1) calculating of practical macroscopic view section flow V (T):
In formula, IDiIt is the collected data of extension set numbered as i, F () is the fitting function of effective ID data, and x is
Extension set sum in subnet, i are extension set number in subnet；
(2) cross-sectional flow v (Ln-Lm) calculating:
In formula, v (Ln-Lm) it is L under the subnetmTo LnThe mean flow rate in section, v ' (n-m) are to pass through volume in j-th of subnet
Number mean flow rate (i.e. mean flow rate between number m and n extension set) being calculated to n extension set for m；K is the volume of ID data
Number, x is the sum of ID data；LnFor the physical location for the extension set that number is n in j-th of subnet；It is k-th of ID number by the son
The extension set moment collected that number is n in net；Δ (k) is k-th of ID number power shared in the valid data for calculating data
Weight values；I is the sum of extension set in the subnet, and n, m, n ', m ' is extension set number, the wherein covering of n ' to m ' in jth work song net
Section contains the overlay segment of n to m；ω (n ', m ') indicates to be that (n ', m ') extension set matrix calculates L by numbern'To Lm′Average speed
Shared weight coefficient matrix when spending；
(3) it polymerize average hourage T (Ln-Lm) calculating:
In formula, T (Ln-Lm) it is L under subnetmTo LnThe polymerization in section is averaged hourage；I is extension set sum under subnet；
The extension set moment collected for being n is numbered in the subnet for k-th of ID number；N, m, n ', m ' are extension set volume in jth work song net
Number；ω (n ', m ') indicates to be that (n ', m ') extension set matrix calculates L by numbern'To Lm′It polymerize shared when average hourage
Weight coefficient matrix；Indicate in the section (n '-m ') hourage for being calculated of extension set matrix divided by
The L that extension set space-number is calculated in productn'To Lm′The extension set space-number in section；
Step 10: setting the saturation volume in section as V0, which is v0, which is D, and is set
Traffic is passed unimpeded grade, is shown below:
By by V (T), v (Ln-Lm), T (Ln-Lm) be compared with above formula, it passes unimpeded to confirm that the section is current
Grade.
Further, in step 9, the allocation plan of the weight Δ (x) and ω (n ', m ') are learnt by self feed back, warp
Multiple iterative approach approximate fits are crossed to obtain, the specific steps are as follows:
Firstly, the initial value of Δ (x) He ω (n ', m ') is set separately, the initial value v of output cross-sectional flow is calculated0；
Then, by by the initial value v of cross-sectional flow0Self feed back study is carried out with bias reference system S, passes through successive ignition
It calculates amendment weighting function value and carries out approximate fits, obtain approximate fits function F (),
Then, with the ratio S/v of the output valve S of bias reference system and cross-sectional flow v be judge feedback learning termination according to
According to, and the range of S/v is set in 0.99≤S/v≤1.01；
Finally, iterative fitting is completed and exports the distribution of Δ (x) He ω (n ', m ') when the value of S/v meets and terminates range
Scheme.
Further, and as effective reference unit.
Further, distance is d between the adjacent extension set, and the signal covering radius of single extension set is r, and d > 2r.
The utility model has the advantages that the present invention provides one kind based on the unique ID of portable mobile terminal, acquisition data time stamp and detection
The macro-traffic infomation detection algorithm of device location information is realized to the data mining and analysis of mobile terminal acquisition data, is
Magnitude of traffic flow detector and detection system based on WIFI signal provide the excavation of the depth based on the type data and the magnitude of traffic flow
Detection algorithm is realized, has been filled up application blank of the type data in terms of Vehicle Detection, has been promoted the development in wisdom traffic field.
Detailed description of the invention
Fig. 1 is the road deployment principle schematic diagram of the traffic flow detection system of the present invention based on WIFI signal；
Fig. 2 is the detection algorithm flow diagram of the vehicular traffic flow of the present invention based on WIFI signal；
Fig. 3 is the detection algorithm flow diagram of the cross-sectional flow of the present invention based on WIFI signal；
Fig. 4 is the detection algorithm flow diagram of the average hourage of the polymerization based on WIFI signal of the present invention；
Fig. 5 is the noise reduction screening process schematic diagram of the present invention based on valid data in WIFI signal detection data；
Fig. 6 is the approximate fits process schematic of the present invention based on weight coefficient in WIFI signal；
The deployment schematic diagram of traffic information detection system in Fig. 7 embodiment of the present invention；
Fig. 8 is that output flow speed value and the ratio of bias reference system change over time comparison diagram in the embodiment of the present invention；
Fig. 9 be section of the embodiment of the present invention on through the invention system algorithm flow and flow rate respectively with bias reference system
True value ratio comparison diagram；
Figure 10 is that the traffic in certain section in the embodiment of the present invention is passed unimpeded level diagram.
Specific embodiment
With reference to the accompanying drawing and with specific embodiment, the present invention is furture elucidated.It should be understood that these embodiments are only used for
It is bright the present invention rather than limit the scope of the invention, after the present invention has been read, those skilled in the art are to of the invention
The modification of various equivalent forms falls within the application range as defined in the appended claims.
As shown in Figure 1, the present invention includes multiple sub-networks, each subnet and practical section S are spatial match mapping relations,
The detection device that each subnet section S is disposed includes several extension sets and at least one host；Extension set is as basic sensor
And based on 802.1b agreement (WIFI agreement) for the mobile device hair in monitoring reception ambient enviroment based on WIFI signal agreement
The broadcast data packet sent, and the broadcast data packet is sent to data processing module, the data processing module is to broadcast data
Packet carries out deleting choosing and reading, obtains the ID number of mobile device, and the data numbered including ID number and extension set module are transmitted to master
Machine；Host is then stamped stored after timestamp after be uploaded to remote server.The present invention is to be received based on extension set sensor
To the data packet comprising ID number carry out detection calculating, obtain the traffic cross-sectional flow of corresponding road section.Specifically:
Detection device deployment way of the present invention: in Fig. 1, Tower (j) indicates j-th of subnet master in disposed road network
Machine, Tower (i, j) indicate i-th of extension set in j-th of subnet.Each sub-network includes a host and several extension sets, is divided
Machine quantity can suitably increase and decrease according to road network condition, sub-network maximum coverage range 2Km, single extension set signal cover maximum half
Diameter 250m, user can adjust single extension set estimation range according to actual road conditions condition, and distance d setting only needs to be greater than two between extension set
Extension set estimation range again equally can carry out flexible modulation according to actual road conditions condition.Sub-network deployment density can be according to reality
Traffic environment regulatory requirement is disposed.
Detection device data acquisition flow: the standby i.e. extension set of single detection passes through wireless passive perceptual model, i.e., using TI's
The detector of CC3XXXX family chip, by based on the acquisition of wifi agreement, by mobile terminal device, environment is sent around at random
WIFI broadcast data packet, and screen the wherein data packet with equipment id information and retrieved.It is uploaded after stamping extension set label
To host, the data being collected into are carried out unified storage and stamp time tag by host, and are uploaded in data server and are carried out
Storage, is waited to be analyzed.Inventive algorithm principle process is specifically described:
Step 1: (partial data of T- Δ t), partial data are expressed as acquisition complete periodWherein DijIndicate No. i-th extension set data of j-th of subnet；
Step 2: data being sliced, and extraction and analysis section S and the period (partial data of T- Δ t)
Step 3: it is carried out space with practical section S by physical spatial location mapping, corresponding disposed sub-network
Match, each sub-network host has the corresponding number information of corresponding road section and extension set deployment scenario list；
Step 4: acquisition data being ranked up according to its ID number, and establish data matrix { Tower (i, S), t }；
Step 5: as shown in figure 3, the ID data list of foundation is divided by the number appeared in different Tower
The ID only occurred in a Tower is individually extracted and is analyzed by class, the ID number that will occur in more than two Tower
According to being classified as one kind；
Step 6: the ID only occurred in a Tower being analyzed, situation is divided into following two: 1, in Δ t
Between in section, repeat the ID in the Tower, and this kind of situation largely occurs, having a great deal of ID, this occurs, then
Indicate that congestion occurs in the section, such ID is valid data；If 2, not finding the ID in the Tower in the Δ t period
Repeat, which is rejected, as shown in Figure 5；
Step 7: repetition step 4,5,6 confirm exhaustive data；
Step 8: under incomplete jam situation, after deleting choosing according to above data, re-establishing the associated data of ID number
Matrix { Tower (i, S), t }；
Step 9: according to the relationship of time t and Tower in data matrix { Tower (i, S), t }, data further being classified
For bi-directional data matrix: DLAnd DR, and it is calculated；
Step 10: link flow V (T), flow velocity v (L are obtained by following calculating formulan-Lm), polymerize average hourage T
(Ln-Lm) detection numerical value:
In formula, F () is the fitting function of effective ID data, and x is extension set sum in subnet, and i is extension set number in subnet；
By the data collected it is found that ID number can't be appeared in completely in each detection extension set, ID number exists
Certain randomness, simultaneously because extension set detector is range covering, therefore the collection position of ID number is not necessarily where extension set
Position, and in the position range of extension set present position ± r (r be extension set covering radius).Therefore, weight matrix ω is introduced
(n ', m '), and weight summation is 1, it, can according to above formula to guarantee that matrix integrality, the data for being recorded as 0 do not distribute weight coefficient
,
By above formula, can be derived from:In formula,For kth
A ID number is by the extension set moment collected that number is n in the subnet；N, m, n ', m ' are extension set number in jth work song net.
The data of detection system of the present invention are that accumulation updates at any time, are arranged before carrying out operation Δ (x) and ω (n ', m ')
Initial value, traffic cross-sectional flow V is calculated, and the present invention is obtained into traffic cross-sectional flow and bias reference system such as coil
The traffic cross-sectional flow S that detector measures carries out self feed back study, calculates amendment weighting function Δ (x) and ω by successive ignition
(n ', m ') realizes approximate fits, obtains approximate fits function F (), when bias reference system and the present invention are calculated traffic and break
When the ratio S/v of surface current speed V obtains terminating range, complete iterative fitting and export the Δ (x) under current state and ω (n ',
m′).The data accuracy that the present invention uses interval sampling mode detection algorithm to obtain, when data offset is more than threshold value or is set
After determining certain period of time, it is iterated the allocation plan that fitting exports revised Δ (x) and ω (n ', m ') again.
Detection obtains link flow V (T), flow velocity v (Ln-Lm), polymerize average hourage T (Ln-Lm) after, carry out traffic
The extension set for the grade that passes unimpeded detects.It mainly relies on to traffic current flow, flow velocity and the prediction of hourage, foundation is passed unimpeded
Grade data matrix { flow；Flow velocity；Hourage }.User can press flow saturation degree, flow velocity mean value, trip according to itself handling characteristics
Row time or three's any combination.Due to detection system deployment of the invention can deployment density it is big, calculated result is relatively accurate,
Data irrelevance is can be controlled within 10%, therefore the grade that traffic can be passed unimpeded further is segmented, in existing 3-4 grades of base
Expanded on plinth, to grasp high-speed transit state, accurately more to prevent in time and to manage.
The grade that passes unimpeded classification: setting certain section theory saturation volume as V, and speed limit S, section mileage is D, and theory is a length of when current
T.Section is classified as 6 grades, this grade classification list can establish are as follows:
| The grade that passes unimpeded (Lv) | Pass through flow v | Pass through average rate s | Pass through duration t |
| Dark green (Lv6) | V < 20%V | S > 80%S | t<(D/(0.8xS)) |
| Light green (Lv5) | 20%V < v < 60%V | S > 60%S | t<(D/(0.6xS)) |
| Pale yellow (Lv4) | 40%V < v < 80%V | 40%S < s < 60%S | (D/(0.6xS))<t<(D/0.4xS) |
| Deep yellow (Lv3) | 15%V < v < 60%V | 15%S < s < 40%S | (D/(0.4xS))<t<(D/0.15xS) |
| Red (Lv2) | 5%V < v < 15%V | 5%S < s < 15%S | (D/(0.15xS))<t<(D/0.05xS) |
| Dark red (Lv1) | V < 5%V | S < 5%S | t>(D/(0.05xV)) |
It is tested below with the supreme sea G42 highway section in Beijing, is handed in the road both sides installation and deployment present invention
Through-flow amount detection systems.If Fig. 7 carries out detector deployment in road, monitoring reception ambient enviroment passes through the mobile device on vehicle
The WIFI signal broadcast data packet of sending.With the 5 minutes detector calculating speeds and flow for chronomere, and and Coil Detector
Speed and flow.
As shown in figure 8, the algorithm through the invention of some in above-mentioned section physically obtains cross-sectional flow and coil
The ratio for the flow velocity that detector measures, change with time figure.From figure it is found that the two ratio is all near 1, it is consistent that height is presented
Property and stability.
Be illustrated in figure 9 the flow and flow rate of system algorithm through the invention respectively with the ratio of the true value of bias reference system
It is worth comparison diagram.The ratio between flow has high consistency as we know from the figure；Ratio between flow velocity can be also consistent substantially,
And it is able to maintain the stability of data, obtained result has high reliability and accuracy.
Claims (4)
- The grade detection system 1. a kind of traffic based on WIFI signal is passed unimpeded disposes detection device system along traffic route, described Detection device system includes multiple sub-networks, and each sub-network includes a host and several extension sets, and the extension set passes through nothing The passive perceptual model of line, acquisition is by mobile terminal device based on the Wifi agreement broadcast data that environment is sent around at random Packet, and screen the wherein data packet with mobile terminal device id information and retrieved, it is uploaded to host after stamping extension set label, The data being collected into are carried out unified storage and stamp time tag by host, and are uploaded in data server and are stored, and pass through Data are analyzed to obtain macroscopical section flow, cross-sectional flow, polymerize the detected value of average hourage, and determine that the traffic in section is smooth Row grade；The analyses of the data the following steps are included:Step 1: the data D of complete period is acquired by extension set,Wherein, DijIndicate j-th of subnet No. i-th extension set data；N indicates the number of subnet, and M indicates the extension set number in j-th of subnet；Step 2: data being sliced and extract section S and complete period (the data D of T- Δ t) to be analyzeds',D in formulaisFor the collected data of extension set that number is i under s subnet；Step 3: spatial match being carried out with corresponding practical section S to the sub-network of deployment, obtains sub-network host and corresponding road section Number information and the sub-network extension set deployment scenario list；Step 4: the data of each extension set acquisition being ranked up according to mobile terminal device ID number, establish data matrix { Tower (i,s),t}；Step 5: the ID data list of foundation being classified by the number for appearing in different data matrix: in period Δ In t, what mobile terminal ID only occurred in the single extension set of sub-network, after individually extracting progress to the corresponding data of mobile terminal ID Continuous efficiency analysis；For in period Δ t, mobile terminal ID occurs in sub-network two or more extension set, the movement The corresponding data of Termination ID are directly as valid data；Step 6: in period Δ t, mobile terminal ID is only in the single extension set appearance of sub-network, to mobile terminal ID Corresponding data, which are individually extracted, carries out subsequent efficiency analysis: 1, within the Δ t time, mobile terminal ID is in single extension set Repeating in data matrix TOWER, and above situation occur in multiple mobile terminal ID, mark corresponding road section is congestion, And such ID data is denoted as valid data；2, within the Δ t time, do not find mobile terminal ID in single extension set data square Repeat in battle array TOWER, then traverses whether the sub-network before and after sub-network corresponding road section S identical ID number occurs, if Do not occur, then using the ID data as noise data processing, if occurred in other sub-networks, identifies and arranged into valid data Table；Step 7: repeating step 4~6 until data processing finishes；Step 8: merger will be carried out by step 5 and the 6 resulting valid data of processing, and according to corresponding to data matrix TOWER The chronological order of position and appearance in physical space, and ID data matrix divided into it is two-way, and to each unidirectional number According to progress subsequent processing；Step 9: in T- Δ t to T time section, unidirectional valid data total amount is V, V=D { ID }, and is carried out by the following method Practical macroscopic view section flow, cross-sectional flow, the detection and calculating for polymerizeing average hourage:(1) calculating of practical macroscopic view section flow V (T):In formula, IDiIt is the collected data of extension set numbered as i, F () is the fitting function of effective ID data, and x is subnet Interior extension set sum, i are extension set number in subnet；(2) cross-sectional flow v (Ln-Lm) calculating:In formula, v (Ln-Lm) it is L under the subnetmTo LnThe mean flow rate in section, v ' (n-m) are m to pass through number in j-th of subnet The mean flow rate (i.e. mean flow rate between number m and n extension set) being calculated to n extension set；K is the number of ID data, and x is The sum of ID data；LnFor the physical location for the extension set that number is n in j-th of subnet；It is compiled in the subnet for k-th of ID number Number be n the extension set moment collected；Δ (k) is k-th of ID number weighted value shared in the valid data for calculating data；i For the sum of extension set in the subnet, n, m, n ', m ' is extension set number in jth work song net, and wherein the overlay segment of n ' to m ' includes The overlay segment of n to m；ω (n ', m ') indicates to be that (n ', m ') extension set matrix calculates L by numbern'To Lm′It should when average speed Weight coefficient matrix shared by group result；(3) it polymerize average hourage T (Ln-Lm) calculating:In formula, T (Ln-Lm) it is L under subnetmTo LnThe polymerization in section is averaged hourage；I is extension set sum under subnet；For kth A ID number is by the extension set moment collected that number is n in the subnet；N, m, n ', m ' are extension set number in jth work song net；ω (n ', m ') indicates to be that (n ', m ') extension set matrix calculates L by numbern'To Lm′When polymerizeing average hourage shared by this group of result Weight coefficient matrix；Step 10: setting the saturation volume in section as V0, which is v0, which is D, and sets traffic Pass unimpeded grade, is shown below:By by V (T), v (Ln-Lm), T (Ln-Lm) be compared with above formula, to confirm the current grade that passes unimpeded in the section.
- 2. passing unimpeded grade detection system in the traffic based on WIFI signal according to claim 1, it is characterised in that: step 9 In, the allocation plan of the weight Δ (x) and ω (n ', m ') are learnt by self feed back, approach approximate fits by successive ignition It obtains, the specific steps are as follows:Firstly, the initial value of Δ (x) He ω (n ', m ') is set separately, the initial value v of output cross-sectional flow is calculated0；Then, by by the initial value v of cross-sectional flow0Self feed back study is carried out with bias reference system S, is calculated by successive ignition It corrects weighting function value and carries out approximate fits, obtain approximate fits function F (),It then, is the foundation for judging feedback learning and terminating with the ratio S/v of the output valve S of bias reference system and cross-sectional flow v, and The range of S/v is set in 0.99≤S/v≤1.01；Finally, iterative fitting is completed and exports the distribution side of Δ (x) He ω (n ', m ') when the value of S/v meets and terminates range Case.
- The grade detection system 3. the traffic according to claim 2 based on WIFI signal is passed unimpeded, it is characterised in that: and conduct Effective reference unit.
- The grade detection system 4. the traffic according to claim 2 based on WIFI signal is passed unimpeded, it is characterised in that: the phase Distance is d between adjacent extension set, and the signal covering radius of single extension set is r, and d > 2r.
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