CN103674048A - Dynamic intelligent navigation method based on 3G (3-Generation) network - Google Patents

Abstract

The invention relates to a dynamic intelligent navigation method based on a 3G (3-Generation) network. The dynamic intelligent navigation method is characterized by comprising the following steps: receiving 3G navigation information from a 3G navigation module and building a multilevel hierarchical structure model by a navigation route planning and processing module; determining and describing a scale; creating judgment matrixes; calculating a feature vector, a feature value and a consistency check index of each judgment matrix and performing consistency check; calculating comprehensive weight and determining an optimal route according to a maximum weight rule. According to the dynamic intelligent navigation method, all routes are analyzed by a hierarchical analysis method according to the navigation information of the circulation quantity, the distance and the traffic light number of the 3G navigation module to select out the optimal route; by more accurately selecting out the time-saving and non-congested route, the convenience is brought to a driver and the shortcoming of incapability of accurately sensing the road conditions in real time in the prior art is overcome.

Description

Dynamic intelligent navigation method based on 3G network

Technical field

The invention belongs to vehicle mounted guidance technical field, especially a kind of dynamic intelligent navigation method based on 3G network.

Background technology

Along with the extensive covering in China of the 3G network of Chinese three macroreticular operators, people can experience the variation that high speed internet brings to our life more and more easily.Take CHINAUNICOM as example, and its 3G network of having realized bandwidth 7.2M in China covers, and the 3G network of having realized bandwidth 21M in area, most cities covers, and the 3G network of having realized bandwidth 42M in area, Pearl River Delta covers.But auto navigation still rests on and relies on the software that is cured in navigating instrument and the aspect of traffic radio broadcasting to go perception road conditions, the information obtaining is like this accurate not, can not utilize algorithm accurately to judge optimal route.At present, onboard navigation system is mainly to rely on navigating instrument or the satnav of storing map to realize navigation feature, for example, CN201120176429.8 discloses a kind of automatic navigator, this navigating instrument is to utilize external memory portable hard drive to use after map is stored, can not accurately perception road conditions; CN201210072538.4 discloses a kind of automatic navigator based on Beidou satellite navigation, though such device can utilize satnav, but cannot uses algorithm intelligent recommendation optimal route; CN201110307835.8 discloses a kind of Dialing Method of supporting the vehicle mounted guidance instrument apparatus of multimode 3G network, and this patent is disclosed is a kind of Dialing Method, cannot real-time navigation, determine best route; In addition, current navigational system, in selection schemer process, all adopts the method in advance planning path and navigates according to this path planning, can not and realize navigation feature according to real-time road automatic path planning.

Summary of the invention

The object of the invention is to overcome the deficiencies in the prior art, a kind of dynamic intelligent navigation method based on 3G network is provided, solve the problem that existing onboard navigation system can not be navigated according to real-time road condition information automatic path planning.

The present invention solves existing technical matters and takes following technical scheme to realize:

A dynamic intelligent navigation method based on 3G network, comprises the following steps:

Step 1: the 3G navigation information that navigation path planning processing module reception 3G navigation module is sent the hierarchy Model of setting up Multilevel Hierarchical;

Step 2: determine scale and scale is described;

Step 3: development of judgment matrix;

Step 4: calculate proper vector, eigenwert and the consistency check index of each judgment matrix and carry out consistency desired result;

Step 5: calculate comprehensive weight and according to weight limit principle, determine optimal route.

And the 3G navigation information of described step 1 comprises circulation B1, apart from length B2, red street lamp number B3.

And, the hierarchy Model of described Multilevel Hierarchical comprises destination layer, judgement layer and solution layer, this destination layer is for selecting optimal route A, this judgement layer is for affecting following three factor: circulation B1 of optimal route, apart from length B2, red street lamp number B3, wdp layer is 3G navigation module the detected route of energy.

And the scale of described step 2 comprises no less important, important a little, important, strong important and definitely important, its scale value represents with digital 1,3,5,7,9 respectively, and by 2,4,6,8 intermediate values that represent two adjacent factor of judgment.

And described step 3 judgment matrix adopts Hierarchy Analysis Method to build, each key element of same level be take to the key element of upper level and compare between two as criterion, according to opinion scale, determine its relative Link Importance, comprise following judgment matrix:

(1) with respect to the judgment matrix A-B of system general objective, for judging each factor relative importance comparison of layer;

(2) with respect to the judgment matrix B1-C of circulation, for the relative importance comparison of each scheme;

(3) with respect to the judgment matrix B2-C apart from length, for the relative importance comparison of each scheme;

(4) with respect to the judgment matrix B3-C of traffic lights number, for the relative importance comparison of each scheme.

And the method that described step 4 is calculated the proper vector of each judgment matrix A is:

1. judgment matrix A is pressed to row normalization, column element sum is 1:b _ij=a _ij/ Σ a _ij; Wherein, a _ijthe ratio that represents key element i and key element j relative Link Importance, ratio is larger, and the importance degree of i is just higher;

2. normalized matrix is sued for peace by row: c _ij=Σ b _ij(i=1,2,3 ... n);

3. by c _inormalization: obtain characteristic vector W=(w ₁, w ₂..., w _n), w _i=c _i/ Σ c _i.

And the formula that described step 4 is calculated eigenvalue of maximum λ max is:

${\mathrm{\λ}}_{\max }=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\frac{{\left(\mathrm{Aw}\right)}_i}{w_i}$

Wherein, W is proper vector, the line number that n is judgment matrix, (Aw) _ithe i of representing matrix A capable with the W acquired results that multiplies each other.

And the formula that described step 4 is calculated coincident indicator CI is:

$\mathrm{CI}=\frac{{\mathrm{\λ}}_{\max }-n}{n-1}$

CR=CI/RI, wherein, RI is mean random coincident indicator, the line number that n is judgment matrix.

And the method for described step 4 consistency desired result is: according to analytic hierarchy process principle, utilize the theoretical eigenvalue of maximum λ max of judgment matrix and the difference check consistency of n.

Advantage of the present invention and good effect are:

1, the present invention is received 3G network signal and the residing position of vehicle is positioned and navigated by 3G navigation module, make navigation information promptly and accurately, perception road conditions real-time and accurately are also upgraded map, realize intelligent recommendation optimal route function, solved the problem of utilizing the information delay that memory device, stores map causes.

2, the present invention is according to the circulation of 3G navigation module, apart from navigation informations such as length, traffic lights numbers and adopt analytical hierarchy process to analyze each route, select optimal route, by selecting more exactly, not only save time, but also the route that do not block up, for driver brings convenience, solved the prior art shortcoming of accurate perception road conditions in real time.

Accompanying drawing explanation

Fig. 1 is the circuit block diagram of vehicle-bone 3 G intelligent navigation device;

Fig. 2 is processing flow chart of the present invention;

Fig. 3 is the hierarchy Model schematic diagram of Multilevel Hierarchical.

Embodiment

Below in conjunction with accompanying drawing, the embodiment of the present invention is further described.

A kind of dynamic intelligent navigation method based on 3G network, to realize in vehicle-bone 3 G intelligent navigation device as shown in Figure 1, this vehicle-bone 3 G intelligent navigation device comprises 3G navigation module, navigation path planning processing module, voice cue module and power module, the 5V DC voltage of power module output is connected with 3G navigation module, navigation path planning processing module, voice cue module for its power supply, and navigation path planning processing module is connected with voice cue module with 3G navigation module.Described 3G navigation module is comprised of 3G signal receiving module and navigation module, can process and realize bus location navigation feature the 3G network signal receiving, that is: according to the destination of user's input, calculate navigation information, comprise the vehicle-mounted circulation of every circuit, apart from distance and the traffic lights number of destination, 3G navigation module sends to navigation path planning processing module by the navigation information calculating, and the airmanship that 3G navigation module uses is provided by software cloud service business (as " high moral map " application software).This navigation path planning processing module is connected and composed by single-chip microcomputer and peripheral circuit thereof, its dynamic intelligent navigation software by single-chip microcomputer inside calculates optimal path and exports to voice cue module, and this voice cue module is to carry out Voice Navigation according to guidance path and navigation object current location.

A kind for the treatment of scheme of the dynamic intelligent navigation method based on 3G network, as shown in Figure 2, after initialization, judge whether circulation, apart from the relevant information of length, traffic lights number, if nothing, continue to return obtaining information, if have, set up the hierarchy Model of Multilevel Hierarchical, according to scale development of judgment matrix A, adopt summation method to calculate eigenwert, proper vector and the consistency check index of each judgment matrix, finally determine optimal route.Specifically comprise the following steps:

Step 1: the 3G navigation information that navigation path planning processing module reception 3G navigation module is sent the hierarchy Model of setting up Multilevel Hierarchical.

Navigation path planning processing module, after receiving the circulation of 3G navigation module, navigation information apart from length, traffic lights number, is set up the hierarchy Model of Multilevel Hierarchical, and this hierarchy Model comprises: destination layer, judgement layer and solution layer.As shown in Figure 2, destination layer is " selecting optimal route A "; Judgement layer is for affecting three factor: circulation B1 of optimal route, apart from length B2, red street lamp number B3; Solution layer is 3G navigation module the detected route of energy: route C1, route C2 ...Wherein, circulation B1, apart from the concrete data of length B2, red street lamp number B3, by 3G navigation module, obtain and be transferred to navigation path planning processing module.

Step 2: determine scale and scale is described.

Scale be when any two factors of same level carry out important ratio compared with time, the ratio of their importance is judged, quantize.This method is carried out comparison various factors with five attributes: no less important, important a little, important, strong important and definitely important.Scale value represents by numeral 1,3,5,7,9 respectively, refers to table one.

Table one

Scale	Definition (relatively factor i and j)
		1	Factor i and j no less important
3	Factor i is more important a little than j
		5	Factor i is more important than j
7	Factor i is strong more important than j
		9	Factor i is absolute more important than j
2、4、6、8	The intermediate value of two adjacent factor of judgment

Step 3: development of judgment matrix A

Judgment matrix is the essential information of analytical hierarchy process, is also the important evidence of carrying out weight calculation.Each key element of same level be take to the key element of upper level and compares between two as criterion, according to opinion scale, determine its relative Link Importance, accordingly development of judgment matrix:

(1) judgment matrix A-B, with respect to system general objective, each factor relative importance comparison of judgement layer.

(2) judgment matrix B1-C, with respect to circulation, the relative importance comparison of each scheme.

(3) judgment matrix B2-C, with respect to apart from length, the relative importance comparison of each scheme.

(4) judgment matrix B3-C, with respect to traffic lights number, the relative importance comparison of each scheme.

Step 4: adopt summation method to calculate proper vector, eigenwert and the consistency check index of each judgment matrix A.

(1) calculate the proper vector of each judgment matrix A

1. judgment matrix A is pressed to row normalization, column element sum is 1:b _ij=a _ij/ Σ a _ij; Wherein, a _ijthe ratio that represents key element i and key element j relative Link Importance, ratio is larger, and the importance degree of i is just higher;

2. normalized matrix is sued for peace by row: c _ij=Σ b _ij(i=1,2,3 ... n);

3. by ci normalization: obtain characteristic vector W=(w ₁, w ₂..., w _n), w _i=c _i/ Σ c _i.

(2) eigenvalue of maximum corresponding to calculated characteristics vector W:

${\mathrm{\λ}}_{\max }=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\frac{{\left(\mathrm{Aw}\right)}_i}{w_i}$

Wherein, W is proper vector, the line number that n is judgment matrix, (Aw) _ithe i of representing matrix A capable with the W acquired results that multiplies each other.

(3) calculate coincident indicator and carry out consistency check

Be calculated as follows coincident indicator:

$\mathrm{CI}=\frac{{\mathrm{\λ}}_{\max }-n}{n-1}$

Calculate CI, CR=CI/RI, wherein RI is mean random coincident indicator, each rank mean random coincident indicator can be passed through question blank two (mean random coincident indicator table) and obtain.

Table two, mean random coincident indicator

Exponent number	3	4	5	6	7	8	9	10	11	12	13	14
													RI	0.58	0.89	1.12	1.26	1.36	1.41	1.46	1.49	1.52	1.54	1.56	1.58

Then according to stratification principle, utilize the theoretical eigenvalue of maximum λ max of judgment matrix A and the difference check consistency of n.While it is generally acknowledged CI<0.1, CR<0.1, the consistance of judgment matrix A can be accepted, otherwise again compares between two.

Step 5: calculate comprehensive importance degree (weight), according to weight limit principle, determine optimal route.

With an instantiation, the present invention will be described below:

The 3G navigation module of the present embodiment detects following three routes:

the treatment step of footpath planning processing module is:

The hierarchy Model of the first step, foundation Multilevel Hierarchical as shown in Figure 3.

Second step, determine scale and scale is described, as shown in Table 1.

The 3rd step: development of judgment matrix A, this judgment matrix A comprises judgment matrix A-B, judgment matrix B1-C, judgment matrix B2-C and judgment matrix B3-C.

(1) judgment matrix A-B:

A	B1	B2	B3
				B1	1	1/3	2
B2	3	1	5
				B3	1/2	1/5	1

(2) judgment matrix B1-C:

B1	C1	C2	C3
				C1	1	1/3	1/5
C2	3	1	1/3
				C3	5	3	1

(3) judgment matrix B2-C:

B2	C1	C2	C3
				C1	1	2	7
C2	1/2	1	5
				C3	1/7	1/5	1

(4) judgment matrix B3-C:

B3	C1	C2	C3
				C1	1	3	1/7
C2	1/3	1	1/9
				C3	7	9	1

The 4th step: adopt summation method to calculate proper vector, eigenwert and the consistency check index of each judgment matrix.

(1) calculate proper vector, eigenwert and the consistency check index of judgment matrix A-B

1. press row normalization and normalized matrix sued for peace by row:

A	B1	B2	B3	By row summation
					B1	2/9	5/23	2/8	0.69
B2	6/9	15/23	5/8	1.94
					B3	1/9	3/23	1/8	0.367

By c _inormalization: obtain characteristic vector W=(0.230 0.648 0.122)

2. calculate the eigenwert of judgment matrix A-B

$\mathrm{AW}=\left[\begin{array}{ccc}1& 1/3& 2\\ 3& 1& 5\\ 1/2& 1/5& 1\end{array}\right]{\left[\begin{array}{ccc}0.230& 0.648& 0.122\end{array}\right]}^T$

${\mathrm{AW}}_1=1\&times;0.230+\frac{1}{3}\&times;0.648+2\&times;0.122=0.69$

Can obtain AW similarly ₂=1.948, AW ₃=0.3666.

According to eigenvalue of maximum corresponding to following formula calculated characteristics vector W:

${\mathrm{\&lambda;}}_{\max }=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\frac{{\left(\mathrm{AW}\right)}_i}{n{W}_i}=\frac{0.69}{3\&times;0.230}+\frac{1.948}{3\&times;0.648}+\frac{0.3666}{3\&times;0.122}=3.004$

3. calculate coincident indicator and carry out consistency check

Be calculated as follows coincident indicator:

$\begin{array}{cc}\mathrm{CI}=\frac{{\mathrm{\&lambda;}}_{\max }-n}{n-1}=\frac{3.004-3}{3-1}=0.002& \mathrm{CR}=\frac{\mathrm{CI}}{\mathrm{RI}}=\frac{0.002}{0.58}=0.003\end{array}$

Can find out, CI<0.1, CR<0.1, can accept.

(2) 1. proper vector, eigenwert and the consistency check index of calculating judgment matrix B1-C calculate the proper vector of judgment matrix B1-C

Press row normalization and normalized matrix sued for peace by row:

B1	C1	C2	C3	By row summation
					C1	1/9	1/13	3/23	0.318
C2	3/9	3/13	5/23	0.781
					C3	5/9	9/13	15/23	1.9

By c _inormalization: obtain characteristic vector W=(0.106 0.260 0.634)

2. calculate the eigenwert of judgment matrix B1-C

$\mathrm{AW}=\left[\begin{array}{ccc}1& 1/3& 1/5\\ 3& 1& 1/3\\ 5& 3& 1\end{array}\right]{\left[\begin{array}{ccc}0.106& 0.260& 0.634\end{array}\right]}^T$

AW ₁=0.106+1/3*0.26+1/5*0.634=0.32

Can obtain AW similarly ₂=0.79, AW ₃=1.944

According to eigenvalue of maximum corresponding to following formula calculated characteristics vector W:

${\mathrm{\&lambda;}}_{\max }=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\frac{{\left(\mathrm{AW}\right)}_i}{n{W}_i}=\frac{0.32}{3\&times;0.106}+\frac{0.79}{3\&times;0.26}+\frac{1.944}{3\&times;0.634}=3.04$

3. calculate coincident indicator and carry out consistency check

Be calculated as follows coincident indicator:

$\begin{array}{cc}\mathrm{CI}=\frac{{\mathrm{\&lambda;}}_{\max }-n}{n-1}=\frac{3.04-3}{3-1}=0.02& \mathrm{CR}=\frac{\mathrm{CI}}{\mathrm{RI}}=\frac{0.02}{0.58}=0.034\end{array}$

Can find out, CI<0.1, CR<0.1, can accept.

(3) 1. proper vector, eigenwert and the consistency check index of calculating judgment matrix B2-C press row normalization and normalized matrix are sued for peace by row:

B2	C1	C2	C3	By row summation
					C1	14/23	10/16	7/13	1.772
C2	7/23	5/16	5/13	1.001
					C3	2/23	1/16	1/13	0.226

By c _inormalization: obtain characteristic vector W=(0.592 0.333 0.075)

2. calculate the eigenwert of judgment matrix B2-C

$\mathrm{AW}=\left[\begin{array}{ccc}1& 2& 7\\ \frac{1}{2}& 1& 5\\ \frac{1}{7}& \frac{1}{5}& 1\end{array}\right]{\left[\begin{array}{ccc}0.592& 0.333& 0.075\end{array}\right]}^T$

AW ₁=0.592+2*0.333+7*0.075=1.783

Can obtain AW similarly ₂=1.004AW ₃=0.226.

According to eigenvalue of maximum corresponding to following formula calculated characteristics vector W

${\mathrm{\&lambda;}}_{\max }=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\frac{{\left(\mathrm{AW}\right)}_i}{n{W}_i}=\frac{1.783}{3\&times;0.592}+\frac{1.004}{3\&times;0.333}+\frac{0.226}{3\&times;0.075}=3.013$

3. calculate coincident indicator and carry out consistency check

Be calculated as follows coincident indicator:

$\begin{array}{cc}\mathrm{CI}=\frac{{\mathrm{\&lambda;}}_{\max }-n}{n-1}=\frac{3.013-3}{3-1}=0.0065& \mathrm{CR}=\frac{\mathrm{CI}}{\mathrm{RI}}=\frac{0.065}{0.58}=0.011\end{array}$

Can find out, CI<0.1, CR<0.1, can accept.

(4) calculate proper vector, eigenwert and the consistency check index of judgment matrix B3-C

1. calculate the proper vector of judgment matrix B3-C

Press row normalization and normalized matrix sued for peace by row:

B3	C1	C2	C3	By row summation
					C1	3/25	3/13	9/79	0.465
C2	1/25	1/13	7/79	0.206
					C3	21/25	9/13	63/79	2.330

By c _inormalization: obtain characteristic vector W=(0.155 0.068 0.777)

2. calculate the eigenwert of judgment matrix B3-C

$\mathrm{AW}=\left[\begin{array}{ccc}1& 3& \frac{1}{7}\\ \frac{1}{3}& 1& \frac{1}{9}\\ 7& 9& 1\end{array}\right]{\left[\begin{array}{ccc}0.155& 0.068& 0.777\end{array}\right]}^T$

AW ₁=0.155+3*0.068+1/7*0.777=0.47

Can obtain AW similarly ₂=0.206AW ₃=2.474

According to eigenvalue of maximum corresponding to following formula calculated characteristics vector W

${\mathrm{\&lambda;}}_{\max }=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\frac{{\left(\mathrm{AW}\right)}_i}{n{W}_i}=\frac{0.47}{3\&times;0.155}+\frac{0.206}{3\&times;0.08}+\frac{2.474}{3\&times;0.777}=3.08$

3. calculate coincident indicator and carry out consistency check

$\begin{array}{cc}\mathrm{CI}=\frac{{\mathrm{\&lambda;}}_{\max }-n}{n-1}=\frac{3.08-3}{3-1}=0.04& \mathrm{CR}=\frac{\mathrm{CI}}{\mathrm{RI}}=\frac{0.04}{0.58}=0.069\end{array}$

Can find out, CI<0.1, CR<0.1, can accept.

The 5th step: calculate comprehensive importance degree (weight), according to weight limit principle, determine optimal route

The importance degree of route C1 (weight)=0.230*0.106+0.648*0.592+0.122*0.155=0.427

The importance degree of route C2 (weight)=0.230*0.260+0.648*0.333+0.122*0.068=0.284

The importance degree of route C3 (weight)=0.230*0.634+0.648*0.075+0.122*0.777=0.289

According to the size of each schemes synthesis importance degree, can sort to scheme, decision-making.Level is total to sort as following table:

By upper table, can find out, being kind of a selection circuit route C1 is best route, and navigates by this path.

It is emphasized that; embodiment of the present invention is illustrative; rather than determinate; therefore the present invention includes and be not limited to the embodiment described in embodiment; every other embodiments that drawn by those skilled in the art's technical scheme according to the present invention, belong to the scope of protection of the invention equally.

Claims (9)

1. the dynamic intelligent navigation method based on 3G network, is characterized in that:.

Step 1: the 3G navigation information that navigation path planning processing module reception 3G navigation module is sent the hierarchy Model of setting up Multilevel Hierarchical;

Step 2: determine scale and scale is described;

Step 3: development of judgment matrix;

Step 4: calculate proper vector, eigenwert and the consistency check index of each judgment matrix and carry out consistency desired result;

Step 5: calculate comprehensive weight and according to weight limit principle, determine optimal route.

2. the dynamic intelligent navigation method based on 3G network according to claim 1, is characterized in that: the 3G navigation information of described step 1 comprises circulation B1, apart from length B2, red street lamp number B3.

3. the dynamic intelligent navigation method based on 3G network according to claim 2, it is characterized in that: the hierarchy Model of described Multilevel Hierarchical comprises destination layer, judgement layer and solution layer, this destination layer is for selecting optimal route A, this judgement layer is for affecting following three factor: circulation B1 of optimal route, apart from length B2, red street lamp number B3, wdp layer is 3G navigation module the detected route of energy.

4. the dynamic intelligent navigation method based on 3G network according to claim 1, it is characterized in that: the scale of described step 2 comprises no less important, important a little, important, strong important and definitely important, its scale value 1,3,5,7,9 represents by numeral respectively, and by 2,4,6,8 intermediate values that represent two adjacent factor of judgment.

5. the dynamic intelligent navigation method based on 3G network according to claim 1, it is characterized in that: described step 3 judgment matrix adopts Hierarchy Analysis Method to build, each key element of same level be take to the key element of upper level to be compared between two as criterion, according to opinion scale, determine its relative Link Importance, comprise following judgment matrix:

(1) with respect to the judgment matrix A-B of system general objective, for judging each factor relative importance comparison of layer;

(2) with respect to the judgment matrix B1-C of circulation, for the relative importance comparison of each scheme;

(3) with respect to the judgment matrix B2-C apart from length, for the relative importance comparison of each scheme;

(4) with respect to the judgment matrix B3-C of traffic lights number, for the relative importance comparison of each scheme.

6. the dynamic intelligent navigation method based on 3G network according to claim 1, is characterized in that: the method that described step 4 is calculated the proper vector of each judgment matrix A is:

1. judgment matrix A is pressed to row normalization, column element sum is 1:b _ij=a _ij/ Σ a _ij; Wherein, a _ijthe ratio that represents key element i and key element j relative Link Importance, ratio is larger, and the importance degree of i is just higher;

2. normalized matrix is sued for peace by row: c _ij=Σ b _ij(i=1,2,3 ... n);

3. by c _inormalization: obtain characteristic vector W=(w ₁, w ₂..., w _n), w _i=c _i/ Σ c _i.

7. the dynamic intelligent navigation method based on 3G network according to claim 1, is characterized in that: the formula that described step 4 is calculated eigenvalue of maximum λ max is:

${\mathrm{\&lambda;}}_{\max }=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\frac{{\left(\mathrm{Aw}\right)}_i}{w_i}$

Wherein, W is proper vector, the line number that n is judgment matrix, (Aw) _ithe i of representing matrix A capable with the W acquired results that multiplies each other.

8. the dynamic intelligent navigation method based on 3G network according to claim 1, is characterized in that: the formula that described step 4 is calculated coincident indicator CI is:

$\mathrm{CI}=\frac{{\mathrm{\&lambda;}}_{\max }-n}{n-1}$

CR=CI/RI, wherein, RI is mean random coincident indicator, the line number that n is judgment matrix.

9. the dynamic intelligent navigation method based on 3G network according to claim 1, it is characterized in that: the method for described step 4 consistency desired result is: according to analytic hierarchy process principle, utilize the theoretical eigenvalue of maximum λ max of judgment matrix and the difference check consistency of n.

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