CN108881824A - A kind of quick identification management method and device of video surveillance point service class - Google Patents
A kind of quick identification management method and device of video surveillance point service class Download PDFInfo
- Publication number
- CN108881824A CN108881824A CN201810597731.7A CN201810597731A CN108881824A CN 108881824 A CN108881824 A CN 108881824A CN 201810597731 A CN201810597731 A CN 201810597731A CN 108881824 A CN108881824 A CN 108881824A
- Authority
- CN
- China
- Prior art keywords
- video surveillance
- matrix
- several
- service class
- surveillance point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
- H04L67/61—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources taking into account QoS or priority requirements
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Multimedia (AREA)
- Closed-Circuit Television Systems (AREA)
Abstract
Quick identification management method and device, this method the invention discloses a kind of video surveillance point service class include：The corresponding total rank order filtering value of several video surveillance points is calculated by the normalized vector of influence degree matrix and several factors comparison matrix between several factors；According to total rank order filtering value, several video surveillance points are ranked up, and service class division is carried out to several video surveillance points after sequence.The device includes storage medium for storing program and the processor that the quick identification management method is executed for loading procedure.By using the present invention, service class belonging to video surveillance point can be quick and precisely distinguished in the video surveillance point of magnanimity, crucial decision information is provided for service provider, guarantees the service that service provider is capable of providing more specific aim, is more in line with user demand.The method of the present invention and device can be widely applied in IT service management field.
Description
Technical field
The present invention relates in IT service management technology more particularly to smart city video monitoring system operation maintenance management
Service level managment technology, the quick identification management method and device of specially a kind of video surveillance point service class.
Background technique
It is indispensable one of the important component in smart city for video monitoring system.An especially line city
The video surveillance point in city, for quantity often in terms of millions of, the scale of video monitoring system is increasing, while user is to operation
Safeguard that the requirement of timeliness is also higher and higher, the difficulty that guarantee system operates normally also increasingly increases, therefore many units introduce
The management philosophy of ITIL (IT infrastructure library) establishes video monitoring operation maintenance management system, and using information-based
Means improve maintenance efficiency.However, the service level managment as one of ITIL core concept, runs in video monitoring and ties up
In shield practice, is not implemented and applied well, clothes should be had by then causing important video surveillance point that could not obtain in this way
The maintenance for rank of being engaged in, affects the use of video monitoring system, to reduce to the effective of video monitoring system operation and maintenance
Property and user is reduced to the satisfaction of operating maintenance service.
Although domestic and foreign scholars have done a large amount of research on theory and practice to IT service management at present, its heat studied
Point and emphasis be mainly how the process of standardized operation maintenance, and realized using information-based means, and for video prison
The theory of video surveillance point service class is identified and defined in control system based on the actual use situation of user and practices method
Do not have substantially then so that the operation and maintenance effect of video monitoring system fail be improved significantly.Therefore, a kind of video is designed
Quick identification and the Managed Solution of monitoring point service class have great research significance and practical application value.
Summary of the invention
In order to solve the above-mentioned technical problem, the object of the present invention is to provide a kind of quick knowledges of video surveillance point service class
It don't bother about reason method and device.
First technical solution of the present invention is：A kind of quick identification manager of video surveillance point service class
Method includes the following steps：
Obtain the influence degree matrix between several factors, wherein the factor is to influence video surveillance point seeervice level
Other factor；
According to several factors, the comparison matrix of several factors corresponding to several video surveillance points is constructed；
The first normalized vector corresponding to the influence degree matrix between several factors is calculated, and calculates several
Factor compares corresponding second normalized vector of matrix；
According to the first normalized vector and the second normalized vector, it is corresponding total to calculate several video surveillance points
Rank order filtering value；
According to the corresponding total rank order filtering value of several video surveillance points, several video surveillance points are arranged
Sequence；
Service class division is carried out to several video surveillance points after sequence.
Further, the influence degree matrix between several described factors is constructed by the first construction step obtains
Matrix；First construction step includes：
According to the mapping relations between preset scale and influence degree, the influence degree between any two factor is found out
Reduced value；
According to the influence degree reduced value found out, building obtains the influence degree matrix between several factors.
Further, the factor includes the weight of video surveillance point and the operation data of video surveillance point；It is described several
A factor comparison matrix includes weight comparison matrix and the behaviour of at least one video surveillance point of at least one video surveillance point
Make data comparison matrix.
Further, the weight comparison matrix of the video surveillance point is to construct the square obtained by the second construction step
Battle array；Second construction step includes：
According to the mapping relations between preset scale and video surveillance point significance level, any two video monitoring is found out
Importance value between the weight of point；
According to the importance value found out, building obtains the weight comparison matrix of video surveillance point.
Further, the operation data comparison matrix of the video surveillance point is constructed by third construction step obtains
Matrix；The third construction step includes：
Obtain the operation data of several video surveillance points；
After carrying out conversion processing to the operation data of several video surveillance points, so that building obtains several video monitorings
The operation data initial matrix of point；
Video surveillance point is obtained after being standardized to operation data initial matrix according to preset range scale
Operation data compare matrix.
Further, the operation data of the video surveillance point include the calling frequency of video surveillance point, patrol control duration and/or
Video recording copy number.
Further, the first normalized vector corresponding to the influence degree matrix calculated between several factors, with
And the first checking procedure is equipped with before the step for calculating several factors comparison matrix corresponding second normalized vector；
First checking procedure includes：
To between several factors influence degree matrix and several factors comparison matrix carry out consistency check.
Further, described according to the corresponding total rank order filtering value of several video surveillance points, to several videos
The second checking procedure is equipped with before the step for monitoring point is ranked up；Second checking procedure includes：
Total rank order filtering value corresponding to several video surveillance points carries out consistency check.
Further, the step for several video surveillance points after described pair of sequence carry out service class division, it is specific
Including：
Service class division is carried out to several video surveillance points after sequence using the calculation of arithmetic progression；
Wherein, the number for the video surveillance point that the service class of highest priority includes is minimum；One service class is corresponding
At least one O&M strategy.
Second technical solution of the present invention is：A kind of quick identification management dress of video surveillance point service class
It sets, including：
At least one processor；
At least one processor, for storing at least one program；
When at least one described program is executed by least one described processor, so that at least one described processor is realized
A kind of quick identification management method of the video surveillance point service class.
The method of the present invention and the beneficial effect of device are：The present invention can pass through several after determining the required factor
The normalized vector of influence degree matrix and several factors comparison matrix between a factor, to calculate several videos
The corresponding total rank order filtering value in monitoring point, then, according to the corresponding total sequence power of several video surveillance points to
Magnitude is ranked up several video surveillance points, and carries out service class division to several video surveillance points after sequence,
As it can be seen that the present invention can fast and effeciently distinguish different service class to the video surveillance point of magnanimity, be in conjunction with actual needs
Service provider provides crucial decision information, thus guarantee service provider be capable of providing more specific aim, be more in line with use
The service of family demand, specific economic results in society include：1, existing O&M efficiency of service, accuracy can greatly be improved
And specific aim, a large amount of cost of human resources is saved, while improving the service management level of industry, preferably meets user's need
It asks；2, it can promote the use of on a large scale in other traffic facility management, be conducive to the Fast-Maintenance of these facilities and meet user
Demand, compatible applicability is high.
Detailed description of the invention
Fig. 1 is a kind of step flow chart of the quick identification management method of video surveillance point service class of the present invention；
Fig. 2 is the first specific embodiment service class schematic diagram of several video surveillance points；
Fig. 3 is the second specific embodiment service class schematic diagram of several video surveillance points；
Fig. 4 is a kind of structural schematic diagram of the quick identification managing device of video surveillance point service class of the present invention.
Specific embodiment
The present invention is described in further detail in the following with reference to the drawings and specific embodiments.In for the examples below
Number of steps is arranged only for the purposes of illustrating explanation, does not do any restriction to the sequence between step, each in embodiment
The execution sequence of step can be adaptively adjusted according to the understanding of those skilled in the art.
Embodiment 1
The embodiment of the invention provides a kind of quick identification management methods of video surveillance point service class, such as Fig. 1 and Fig. 2
It is shown, include the following steps：
S101, influence degree matrix between several factors is obtained, wherein the factor is to influence video surveillance point clothes
The factor for rank of being engaged in, as influence factor.
Specifically, the step S101 is preferably included：
S1011, it determines to influence the factor of video surveillance point service class；
In the present embodiment, according to actual monitor and control facility regulatory requirement, it is preferable to determine be used as to influence using following factor
The factor of video surveillance point service class：The weight A1 of factor 1, video surveillance point；Factor 2, the calling frequency of video surveillance point
A2；Control duration A3 is patrolled after factor 3, video surveillance point are called；The video recording of factor 4, video surveillance point copies number A4；Its
In, 2~factor of factor 4 belongs to the operation data of video surveillance point；
S1012, influence degree matrix between several factors is constructed；In the present embodiment, specifically construct it is above-mentioned because
Influence degree matrix between this 4 factors of plain 1~factor 4；
Specifically, it is necessary first to influence degree of clearly each factor to service class：In the present embodiment, using comparing square
The mode of battle array indicates influence degree, forms a comparison matrix A=(aij)p×p(p is the number of factor, in the present embodiment,
P is 4, i.e. A=(aij)4×4), A is the influence degree matrix between several described factors；Wherein, a in matrixijIt indicates
For i-th of influence factor AiRelative to j-th of influence factor AjComparison result；The comparison result preferably uses the scale of 1-5
It indicates, the specific meaning of each scale data can be as shown in table 1 below：
Table 1
| Scale | Meaning |
| 1 | I-th of influence factor is identical as the influence of j-th of factor |
| 2 | I-th of influence factor is slightly stronger than the influence of j-th of factor |
| 3 | I-th of influence factor is stronger than the influence of j-th of factor |
| 4 | I-th of influence factor is stronger than the influence of j-th of factor |
| 5 | I-th of influence factor is obviously stronger than the influence of j-th of factor |
Wherein, when the comparison result, i.e. i≤j of matrix A leading diagonal or more, the comparison result in matrix A is directly utilized
Above-mentioned table 1 is determined；And matrix A leading diagonal comparison result below, i.e. i>When j,And it is all
aijIt is all larger than 0, herein, i=1,2,3,4, j=1,2,3,4；As it can be seen that for the influence degree square between several described factors
Battle array, specific construction step (i.e. the first construction step) comprise preferably：
According to the mapping relations (i.e. above-mentioned mapping table 1) between preset scale and influence degree, any two are found out
Influence degree reduced value between a factor；Wherein, the influence degree is specially the influence to video surveillance point service class
Degree；
According to the influence degree reduced value found out, building obtains the influence degree matrix between several factors.
It preferably for the scale of above-mentioned 1-5, in the light of actual conditions can also further be refined, for example, can also set
Setting scale is respectively 1.2,1.3,1.4,1.5 etc. and their corresponding influence degree situations.
S102, according to several factors, construct several factors corresponding to several video surveillance points comparison matrix.
In the present embodiment, the classification number of the factor is 4, therefore, then constructs 4 factor comparison matrixes.Specifically
Construction step is as follows.
1., factor 1：The weight A1 of video surveillance point；The weight for constructing video surveillance point compares matrix.
In the present embodiment, the importance (i.e. weight) of each video surveillance point is indicated by the way of comparator matrix；
If the total number of traffic video monitoring point is n, then the weight comparison matrix of video surveillance point can be formed：B1=(b1ij)n×n；
Wherein, b1ijIndicate the significance level comparing result between i-th of video surveillance point and j-th of video surveillance point, such as
I-th of video surveillance point is higher than the significance level of j-th of video surveillance point, slightly higher, obviously strong etc. or significance level is identical,
Equally, in the present embodiment, it is indicated using the scale of 1-5；Specifically, if b1ij>When 1, i-th of video surveillance point ratio is indicated
J-th of video surveillance point is important, if b1ijWhen=1, indicate that i-th of video surveillance point and j-th of video surveillance point are of equal importance,
If b1ij<When 1, indicates that than j-th video surveillance point of i-th of video surveillance point is inessential, indicated with the inverse of 1-5, and it is above-mentioned
The building rule of matrix A is similar.As it can be seen that comparing matrix, construction step (the i.e. second building step for the weight of video surveillance point
Suddenly it preferably includes)：
According to the mapping relations between preset scale and video surveillance point significance level, any two video monitoring is found out
Importance value between the weight of point；
According to the importance value found out, building obtains the weight comparison matrix of video surveillance point.
2., factor 2~4：Control duration A3, view are patrolled after calling frequency A2, the video surveillance point of video surveillance point are called
The video recording of frequency monitoring point copies number A4；It constructs the calling frequency comparison matrix B 2 of video surveillance point, patrol control duration comparison square
Battle array B3, video recording copy number compare matrix B 4, that is to say, that construct the operation data comparison matrix of video surveillance point.
Specifically, generally in video monitoring system, record has each user to record the calling of video monitoring, including adjusts
Monitoring point patrols and controls duration etc.；In addition, system also will record the relative recording of copy video monitoring.
Based on above-mentioned record case, then it can form the calling frequency of video surveillance point, patrol control duration and video recording copy
The comparison matrix B 2=(b2 of numberij)n×n, B3=(b3ij)n×n, B4=(b4ij)n×n.Due to operation data classification number simultaneously
It is not only 3, can also is 2,4,6 etc., therefore, matrix, expression is compared for the operation data of video surveillance point
Formula can be：Bq=(bqij)n×n, Bq be expressed as operation data corresponding to q-th of operation data type comparison matrix.
A. for matrix B 2, specific construction step is as follows：
Firstly, obtaining the calling frequency of n video surveillance point；
Then, after carrying out conversion processing to the calling frequency of n video surveillance point, so that building obtains n video monitoring
The calling frequency initial matrix of point；
Specifically, in measurement period, the calling frequency of the n video surveillance point acquired is respectively { x '1,x′2,…
x′i…x′n, x 'nFor the calling frequency of n-th of video surveillance point, and calculation formula used by the conversion is handled is：
As it can be seen that the element called in frequency initial matrix B2'Wherein, i=1,2,3 ..., n, j=
1,2,3,……,n；
Then, according to preset range scale, after being standardized to operation data initial matrix, video prison is obtained
The operation data of control point compares matrix；
Specifically, according to the scale of 1-5 (i.e. preset range scale is 1-5), matrix B 2' is standardized
Afterwards, the calling frequency comparison matrix B 2 of video surveillance point is obtained；Specifically, calculation formula packet used by the standardization
It has included：
The element of leading diagonal for matrix B 2 or more, numerical computational formulas are：
Wherein, min { b2ij' to be expressed as in B2' numerical value be the smallest element numerical value, max { b2ij' it is expressed as number in B2'
Value is maximum element numerical value；Be out to out value in addition, for " 5 " in above-mentioned formula, if out to out value be 8 (or
Other numerical value) when, " 5 " in above-mentioned formula then accordingly modify；
Element below for the leading diagonal of matrix B 2, numerical computational formulas are：
According to this, it can establish to obtain B2=(b2ij)n×nMatrix, wherein b2ijNumerical value is bigger to represent i-th of video monitoring
The calling frequency of point is more than the calling frequency of j-th of video surveillance point.
B. for matrix B 3, specific construction step is as follows：
Firstly, n video surveillance point of acquisition patrols control duration；
Then, patrolling after control duration carries out conversion processing to n video surveillance point, so that building obtains n video monitoring
Point patrols control duration initial matrix；
Specifically, in measurement period, the control duration of patrolling of the n video surveillance point acquired is respectively { y '1,y′2,…
y′i…y′n, y ' is that n-th video surveillance point patrols control duration, and the used calculation formula of conversion processing is：
As it can be seen that the element patrolled in control duration initial matrix B3'Wherein, i=1,2,3 ..., n, j=
1,2,3,……,n；
Then, according to preset range scale, after being standardized to operation data initial matrix, video prison is obtained
The operation data of control point compares matrix；
Specifically, according to the scale of 1-5 (i.e. preset range scale is 1-5), matrix B 3' is standardized
Afterwards, obtain video surveillance point patrols control duration comparison matrix B 3；Specifically, calculation formula packet used by the standardization
It has included：
The element of leading diagonal for matrix B 3 or more, numerical computational formulas are：
Wherein, min { b3ij' to be expressed as in B3' numerical value be the smallest element numerical value, max { b3ij' it is expressed as number in B3'
Value is maximum element numerical value；Be out to out value in addition, for " 5 " in above-mentioned formula, if out to out value be 8 (or
Other numerical value) when, " 5 " in above-mentioned formula then accordingly modify；
Element below for the leading diagonal of matrix B 3, numerical computational formulas are：
According to this, it can establish to obtain B3=(b3ij)n×nMatrix, wherein b3ijNumerical value is bigger to represent i-th of video monitoring
Point patrol control duration than j-th video surveillance point to patrol control duration longer.
C. for matrix B 4, specific construction step is as follows：
Firstly, the number that the video recording copy number of n video surveillance point of acquisition, i.e. video surveillance point are copied video recording；
Then, after carrying out conversion processing to the video recording copy number of n video surveillance point, so that building obtains n video
The video recording of monitoring point copies number initial matrix；
Specifically, in measurement period, the video recording copy number of the n video surveillance point acquired is respectively { z '1,
z′2,…z′i…z′n, z 'nNumber is copied for the video recording of n-th of video surveillance point, and is calculated used by conversion processing
Formula is：
As it can be seen that the element in the video recording copy number initial matrix B4'Wherein, i=1,2,3 ...,
N, j=1,2,3 ..., n；
Then, according to preset range scale, after being standardized to operation data initial matrix, video prison is obtained
The operation data of control point compares matrix；
Specifically, according to the scale of 1-5 (i.e. preset range scale is 1-5), matrix B 4' is standardized
Afterwards, the video recording copy number comparison matrix B 4 of video surveillance point is obtained；Specifically, it is calculated used by the standardization public
Formula includes：
The element of leading diagonal for matrix B 4 or more, numerical computational formulas are：
Wherein, min { b4ij' to be expressed as in B4' numerical value be the smallest element numerical value, max { b4ij' it is expressed as number in B4'
Value is maximum element numerical value；Be out to out value in addition, for " 5 " in above-mentioned formula, if out to out value be 8 (or
Other numerical value) when, " 5 " in above-mentioned formula then accordingly modify；
Element below for the leading diagonal of matrix B 4, numerical computational formulas are：
According to this, it can establish to obtain B4=(b4ij)n×nMatrix, wherein b4ijNumerical value is bigger to represent i-th of video monitoring
Video recording of the video recording copy number than j-th of video surveillance point of point copies often.
In conclusion the operation data comparison matrix of the video surveillance point is to construct to obtain by third construction step
Matrix；The third construction step includes：
S1021, the operation data for obtaining several video surveillance points；
S1022, after carrying out conversion processing to the operation data of several video surveillance points, several views are obtained to construct
The operation data initial matrix of frequency monitoring point；
Preferably, calculation formula used by the conversion processing is：
Wherein, the i=1,2,3 ..., n, j=1,2,3 ..., n；N is the total number of video surveillance point；bqij' be
The data value that the i-th row jth arranges in operation data initial matrix corresponding to q-th of operation data type；m'iFor i-th of video
The operation data of monitoring point, m'jFor the operation data of j-th of video surveillance point；
S1023, video is obtained after being standardized to operation data initial matrix according to preset range scale
The operation data of monitoring point compares matrix B q；
Specifically, the step S1023 is preferably：According to the scale of 1-h, operation data initial matrix is standardized
After processing, the operation data comparison matrix of video surveillance point is obtained；Wherein, calculation formula packet used by the standardization
It has included：
The element of leading diagonal for matrix B q or more, numerical computational formulas are：
Wherein, bqijIt is expressed as the i-th row jth column in the comparison matrix of operation data corresponding to q-th of operation data type
Data value, min { bqij' be expressed as operation data initial matrix corresponding to q-th of operation data type of Bq'() in numerical value be
The smallest element numerical value, max { bqij' to be expressed as in Bq' numerical value be maximum element numerical value, h is out to out value；
Element below for the leading diagonal of matrix B q, numerical computational formulas are：
Successively, it can establish to obtain Bq=(bqij)n×nMatrix.
S103, the first checking procedure：To the influence degree matrix A and several factors comparison square between several factors
Battle array B1~B4 carries out consistency check.
Specifically, it in order to guarantee the consistency of comparing result in above-mentioned 5 matrixes, then needs to carry out 5 matrixes consistent
Property examine, if matrix does not pass through consistency check, need to adjust matrix and carry out consistency check again, until upchecking
Until；
For the step 103, preferably include：
Firstly, building consistency check index：
In formula, n1 is the sum of the diagonal entry number of matrix, as the sum of the characteristic root of matrix；λ is the maximum of matrix
Characteristic value.
Secondly, building is consistent to examine ratio：
In formula, RI is random index, can be inquired by matrix theory and obtain different matrix diagonals line elements
The sum of random index corresponding to n1, as shown in the following Table 2：
Table 2
| n1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| RI | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 |
Then, the influence degree matrix between several factors is calculated separately out using above-mentioned consistent inspection ratio formula
And the consistent inspection ratio of several factors comparison matrix；
As calculated consistent inspection ratio CR<When 0.1, then it is assumed that the inconsistent degree of corresponding matrix is in permissible range
Within, at this point, then can be using its normalization characteristic vector as weight vector, conversely, then needing to reconfigure comparison matrix；
Specifically, consistency check is carried out respectively to five comparator matrixs of A, B1, B2, B3, B4 respectively, if A, B1 do not pass through
When, then A, B1 are readjusted until passing through；It is to need to examine there are problem on calculating if B2, B3, B4 are obstructed out-of-date
Correction is looked into until passing through.
S104, the first normalized vector corresponding to influence degree matrix between several factors is calculated, and calculated
Several factors compare corresponding second normalized vector of matrix.
Specifically, when between several factors influence degree matrix and several factors comparison matrix pass through one
After cause property is examined, feature vector { ω corresponding to the maximum eigenvalue λ of matrix can be obtained simultaneously1,ω2…ωi…ωn, benefit
This feature vector can be normalized with following formula：
After normalized, available new normalized vector { w1,w2…wi…wn}。
Here, calculating separately normalized vector value corresponding to this five comparison matrixes of A, B1, B2, B3, B4, respectively：
{wa1,wa2,wa3,wa4}、{w11,w12…w1n}、{w21,w22…w2n}、{w31,w32…w3n}、{w41,w42…w4n}。
As it can be seen that specifically preferably including following steps for the step S104：
S1041, to acquire corresponding to the maximum eigenvalue of influence degree matrix between several factors first special
Levy vector matrix；
Acquire second feature vector matrix corresponding to the maximum eigenvalue of several factors comparison matrix；
S1042, first eigenvector matrix and second feature vector matrix are normalized respectively, to obtain
First normalized vector matrix and the second normalized vector matrix；Wherein, calculation formula used by the normalized is such as
Shown in lower：
In formula, ωiIt is expressed as ith feature vector value in eigenvectors matrix,It is expressed as in eigenvectors matrix
The sum of all feature vector values；wiIt is expressed as to ωiRear obtained numerical value is normalized；Features described herein to
Moment matrix includes the first, second eigenvectors matrix.
S105, according to the first normalized vector and the second normalized vector, it is right respectively to calculate several video surveillance points
The total rank order filtering value answered.
Specifically, the corresponding total rank order filtering value of the video surveillance point, calculation formula are as follows：
In formula, bjIt is expressed as the corresponding total rank order filtering value of j-th of video surveillance point；P is expressed as the type of factor
It counts, in the present embodiment, p 4；waiIt is expressed as i-th of element in normalized vector matrix corresponding to matrix A；wijIt is expressed as
J-th of element in normalized vector matrix corresponding to i factor comparison matrix.
S106, total rank order filtering value corresponding to several video surveillance points carry out consistency check.
Specifically, it after calculating the corresponding total rank order filtering value of each video surveillance point, needs to regard several
The corresponding total rank order filtering value in frequency monitoring point carries out consistency check；Total sequence power corresponding for several video surveillance points
Vector value, its consistency index CI' formula are as follows：
In formula, CIiConsistency check index corresponding to matrix is compared for i-th of factor；In the present embodiment, 4 p,
So CI1It is expressed as consistency check index corresponding to the 1st factor comparison matrix B 1；CI2It is expressed as the 2nd factor comparison
Consistency check index corresponding to matrix B 2；CI3Consistency check corresponding to the 3rd factor comparison matrix B 3 is expressed as to refer to
Mark；CI4It is expressed as consistency check index corresponding to the 4th factor comparison matrix；
Total rank order filtering value corresponding for several video surveillance points, its consistency ratio are：
In formula, RI' is random index, identical as above-mentioned RI；
When using above-mentioned consistency ratio formula come consistency ratio CR' corresponding to calculated total rank order filtering value
<When 0.1, then it represents that decision can be carried out according to result represented by total rank order filtering value, conversely, then needing to rethink structure
Build factor comparison matrix.
The corresponding total rank order filtering value of finally obtained several video surveillance points is { b1,b2…bn, bjRepresent jth
The corresponding total rank order filtering value of a video surveillance point, and what this vector value indicated is then Priority Service coefficient, bjIt is worth bigger, generation
The service priority level of the table video surveillance point is higher.
S107, according to the corresponding total rank order filtering value of several video surveillance points, to several video surveillance points
It is ranked up.
Specifically, according to the corresponding total rank order filtering value of several video surveillance points, according to weight vector value by big
To small sequence, several video surveillance points are ranked up.
S108, service class division is carried out to several video surveillance points after sequence.
Specifically, as shown in figure 3, to n video surveillance point using the mode for dividing k layer-management, wherein first layer is optimal
First rank, i.e. first layer are highest priority, are so analogized, and kth layer is then minimum one layer of priority, i.e., kth layer is excellent
First grade is minimum；Situation is put into according to actual manpower, financial resources, if being in first layer and carrying out the video surveillance point of override processing
Quantity be C1And the difference between the quantity of next layer of video surveillance point and the quantity of upper one layer of video surveillance point is solid
Fixed, i.e. the quantity of each layer of video surveillance point forms arithmetic progression in k layers；And in the present embodiment, the arithmetic progression
Tolerance be：
So, the quantity of i-th layer of video surveillance point is：
Ci=C1+(i-1)×d
In formula, i=1,2,3 ..., k；One layer of one service class of correspondence；
As it can be seen that the step S108 is preferably included：
S1081, service class is carried out to several video surveillance points after sequence using the calculation of arithmetic progression
It divides；
Specifically, the calculation formula of the arithmetic progression is：
Ci=C1+(i-1)×d
Wherein, the number for the video surveillance point that the service class of highest priority includes is minimum；One service class is corresponding
At least one O&M strategy；
And by quantity of the above-mentioned arithmetic progression formula to set each layer of video surveillance point after, according to arrange it is suitable
The video surveillance point of corresponding number is successively divided in corresponding level by sequence from front to back, i.e., in corresponding service class, with
It realizes that the service class of n video surveillance point divides, reaches the quick identification of service class locating for video surveillance point；
Moreover, determining service management level (i.e. service class) belonging to each video surveillance point in this measurement period
It afterwards, can be according to Support strategy and service plan corresponding to the service class, come to the video for belonging to the service class
Monitoring point carries out corresponding O&M operation；Wherein, the Support strategy of different service class and corresponding service plan are
It determines according to the actual situation.
In daily service management work, the regular operation above process is needed to identify each monitoring point to redefine
The service class situation of position, and when high level monitoring point breaks down, then it should pay the utmost attention to repair the monitoring point, with
Meet actual work requirements.
Embodiment 2
The embodiment of the invention also provides a kind of quick identification management method of video surveillance point service class, including it is following
Step：
S201, influence degree matrix between several factors is obtained, wherein the factor is to influence video surveillance point clothes
The factor for rank of being engaged in, as influence factor.
Specifically, the step S201 is preferably included：
S2011, it determines to influence the factor of video surveillance point service class；
In the present embodiment, according to actual traffic facility management demand, 3 factors that use are determined, respectively video is supervised
It controls and patrols control duration A3 after the weight A1 of point, the calling frequency A2 of video surveillance point, video surveillance point are called；
S2012, influence degree matrix between several factors is constructed；
Specifically, three factors are compared using 1~5 scale, as shown in table 3 below.
Table 3
| Comparison | Point weight A1 | Call frequency A2 | Patrol control duration A3 |
| Point weight A1 | 1 | 3/2 | 2 |
| Call frequency A2 | 2/3 | 1 | 2 |
| Patrol control duration A3 | 1/2 | 1/2 | 1 |
It can be obtained according to above-mentioned table 3, the influence degree matrix A between this 3 factors is specific as follows：
S202, according to several factors, construct several factors corresponding to several video surveillance points comparison matrix.
Specifically, in the present embodiment, there are 6 video surveillance points, respectively b1、b2、b3、b4、b5、b6, according to each prison
The location of control point importance, i.e. weight construct the weight comparison matrix B of video surveillance point1, B1As follows：
And for factor 2 and 3, i.e. A2 and A3, construct their comparison matrix B2And B3, then specific as follows shown：
Firstly, the operation note obtained from video monitoring system is as shown in table 4 below：
4 video monitoring system operation note (part) of table
Can count to obtain by upper table, in measurement period, the calling frequency of this 6 video surveillance points be respectively 32,
10,18,29,5,22 }, it thus can get comparison matrix B2, specific as follows：
Equally, it can count to obtain by upper table, in measurement period, this 6 video surveillance points patrol control total duration difference
For { 2291,430,3395,7237,1119,4668 }, comparison matrix B thus can get3, as follows：
So far, four comparisons matrix As, B are constituted1、B2、B3。
S203, the first checking procedure：To the influence degree matrix A and several factors comparison square between several factors
Battle array B1~B3Carry out consistency check.
Specifically, by calculating, maximum eigenvalue λ=3.0183, the n1=3 of A matrix, then：
And 2 obtain corresponding RI=0.58 by tabling look-up, then：
Due to CRA<0.1, therefore matrix A passes through consistency check；
Similarly, to B1、B2、B3Matrix carries out consistency check, as a result as shown in table 5 below：
Table 5
| B1 | B2 | B3 | |
| λ | 6.427 | 6.0098 | 6.1999 |
| n1 | 6 | 6 | 6 |
| CI | 0.0854 | 0.002 | 0.04 |
| RI | 1.24 | 1.24 | 1.24 |
| CR | 0.0689 | 0.0016 | 0.0322 |
| Consistency check result | Pass through | Pass through | Pass through |
As it can be seen that B1、B2、B3Examined by consistency.
S204, the first normalized vector corresponding to influence degree matrix between several factors is calculated, and calculated
Several factors compare corresponding second normalized vector of matrix.
Specifically, calculating matrix A, B1、B2、B3Normalized vector, specially：
The corresponding vector of the maximum eigenvalue of A matrix is { 0.751,0.5731,0.328 }, and normalized vector is wA=
{0.4546,0.3469,0.1985}；
B1The corresponding vector of the maximum eigenvalue of matrix be 0.6648,0.633,0.1346,0.2193,0.2575,
0.1577 }, and normalized vector be wB1={ 0.3216,0.3063,0.0651,0.1061,0.1246,0.0763 }；
B2The corresponding vector of the maximum eigenvalue of matrix be 0.535,0.1948,0.3475,0.5630,0.1186,
0.4733 }, and normalized vector be wB2={ 0.2397,0.0873,0.1557,0.2522,0.0531,0.2120 }；
B3The corresponding vector of the maximum eigenvalue of matrix be 0.1630,0.0951,0.3038,0.5705,0.2464,
0.6970 }, and normalized vector be wB3={ 0.0785,0.0458,0.1464,0.2748,0.1187,0.3358 }.
S205, according to the first normalized vector and the second normalized vector, it is right respectively to calculate several video surveillance points
The total rank order filtering value answered.
Specifically, in conjunction with the normalized vector of aforementioned four matrix, total rank order filtering of 6 video surveillance points is calculated,
Shown in table 6 specific as follows：
Table 6
It can obtain, total rank order filterings of 6 video surveillance points 0.2449,0.1786,0.1127,0.1903,0.0986,
0.1749}。
S206, total rank order filtering value corresponding to several video surveillance points carry out consistency check.
Specifically, total rank order filtering of 6 video surveillance points based on above-mentioned acquisition, is calculated its CI'=
0.0474, CR'=0.0383<0.1, therefore total rank order filtering, by consistency check, this always sorts feasible.
S207, according to the corresponding total rank order filtering value of several video surveillance points, to several video surveillance points
It is ranked up.
Specifically, total rank order filtering based on above-mentioned 6 video surveillance points, service class from high to low be respectively b1,
b4、b2、b6、b3、b5。
S208, service class division is carried out to several video surveillance points after sequence.
Specifically, in the present embodiment, if 63 layer-managements of video surveillance point point, wherein the video surveillance point of first layer
Quantity be 1, and the tolerance being calculated is 1, then the quantity of the video surveillance point of the second layer is 2, the video of third layer
The quantity of monitoring point is 3.Thus, monitoring point b1 belongs to the highest first layer of priority level, b4, b2 belong to sub-priority
Other second layer, b6, b3, b5 belong to the minimum third layer of priority, the tactful kimonos further managed according to practical situation
Business scheme.
It can be obtained based on above content, advantage for present invention includes：
1, the service level managment in IT is introduced into traffic facility management, is conducive to the maintenance pipe for instructing means of transportation
Reason plan, avoids the blindness of traffic maintenance and without planned maintenance；
2, the operation notes such as number recorded a video by the called number of statistics video surveillance point, the duration for patrolling control, copy,
And the importance of video surveillance point is combined, it is determined in each period locating for monitoring point so as to dynamic, quantification measurement
Service class changes tradition and qualitatively judges monitoring point seeervice level method for distinguishing, more meets existing user's actual need, and
It is high with accuracy, real-time, flexibility；
3, the facilities management point of different service levels can be defined according to the actual demand of traffic O&M unit, so that
Means of transportation operation management more rationally, actual effect；
4, existing O&M efficiency of service can be greatly improved, saves a large amount of cost of human resources, while improving industry
Service management level, preferably meet user demand；
5, other traffic facility management can be promoted the use of on a large scale, be conducive to the Fast-Maintenance of these facilities and meet use
It is high to be applicable in compatibility for the demand at family.
Embodiment 3
The embodiment of the invention provides a kind of quick identification managing devices of video surveillance point service class, as shown in figure 4,
Including：
At least one processor 301；
At least one processor 302, for storing at least one program；
When at least one described program is executed by least one described processor 301, so that at least one described processor
301 realize a kind of quick identification management method of video surveillance point service class.
Suitable for present apparatus embodiment, present apparatus embodiment is implemented content in above method embodiment
Function is identical as above method embodiment, and the beneficial effect reached and above method embodiment beneficial effect achieved
It is identical.
It is to be illustrated to preferable implementation of the invention, but the invention is not limited to the implementation above
Example, those skilled in the art can also make various equivalent variations on the premise of without prejudice to spirit of the invention or replace
It changes, these equivalent deformations or replacement are all included in the scope defined by the claims of the present application.
Claims (10)
1. a kind of quick identification management method of video surveillance point service class, which is characterized in that include the following steps：
Obtain the influence degree matrix between several factors, wherein the factor is to influence video surveillance point service class
Factor；
According to several factors, the comparison matrix of several factors corresponding to several video surveillance points is constructed；
The first normalized vector corresponding to the influence degree matrix between several factors is calculated, and calculates several factors
Compare corresponding second normalized vector of matrix；
According to the first normalized vector and the second normalized vector, the corresponding total sequence of several video surveillance points is calculated
Weight vector value；
According to the corresponding total rank order filtering value of several video surveillance points, several video surveillance points are ranked up；
Service class division is carried out to several video surveillance points after sequence.
2. the quick identification management method of a kind of video surveillance point service class according to claim 1, which is characterized in that institute
Stating the influence degree matrix between several factors is to construct the matrix obtained by the first construction step；First building
Step includes：
According to the mapping relations between preset scale and influence degree, the influence degree comparison between any two factor is found out
Value；
According to the influence degree reduced value found out, building obtains the influence degree matrix between several factors.
3. a kind of quick identification management method of video surveillance point service class according to claim 1 or claim 2, feature exist
In the factor includes the weight of video surveillance point and the operation data of video surveillance point；Several described factors compare square
Battle array includes that the weight comparison matrix of at least one video surveillance point and the operation data of at least one video surveillance point compare square
Battle array.
4. the quick identification management method of a kind of video surveillance point service class according to claim 3, which is characterized in that institute
The weight comparison matrix for stating video surveillance point is to construct the matrix obtained by the second construction step；Second construction step
Including：
According to the mapping relations between preset scale and video surveillance point significance level, any two video surveillance point is found out
Importance value between weight；
According to the importance value found out, building obtains the weight comparison matrix of video surveillance point.
5. the quick identification management method of a kind of video surveillance point service class according to claim 3, which is characterized in that institute
The operation data comparison matrix for stating video surveillance point is to construct the matrix obtained by third construction step；The third building
Step includes：
Obtain the operation data of several video surveillance points；
After carrying out conversion processing to the operation data of several video surveillance points, so that building obtains several video surveillance points
Operation data initial matrix；
According to preset range scale, after being standardized to operation data initial matrix, the behaviour of video surveillance point is obtained
Make data comparison matrix.
6. the quick identification management method of a kind of video surveillance point service class according to claim 3, which is characterized in that institute
The operation data for stating video surveillance point includes the calling frequency of video surveillance point, patrols control duration and/or video recording copy number.
7. a kind of quick identification management method of video surveillance point service class according to claim 1 or claim 2, feature exist
In, the first normalized vector corresponding to the influence degree matrix calculated between several factors, and calculate several
The first checking procedure is equipped with before the step for factor comparison matrix corresponding second normalized vector；Described first examines
Step includes：
To between several factors influence degree matrix and several factors comparison matrix carry out consistency check.
8. a kind of quick identification management method of video surveillance point service class according to claim 1 or claim 2, feature exist
In, it is described according to the corresponding total rank order filtering value of several video surveillance points, several video surveillance points are arranged
The second checking procedure is equipped with before the step for sequence；Second checking procedure includes：
Total rank order filtering value corresponding to several video surveillance points carries out consistency check.
9. a kind of quick identification management method of video surveillance point service class according to claim 1 or claim 2, feature exist
In, described pair sequence after several video surveillance points carry out service class division the step for, specifically include：
Service class division is carried out to several video surveillance points after sequence using the calculation of arithmetic progression；
Wherein, the number for the video surveillance point that the service class of highest priority includes is minimum；One service class is corresponding at least
One O&M strategy.
10. a kind of quick identification managing device of video surveillance point service class, which is characterized in that including：
At least one processor；
At least one processor, for storing at least one program；
When at least one described program is executed by least one described processor, so that at least one described processor is realized as weighed
Benefit requires a kind of any one of 1-9 quick identification management method of video surveillance point service class.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810597731.7A CN108881824B (en) | 2018-06-11 | 2018-06-11 | Method and device for rapidly identifying and managing service level of video monitoring point |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810597731.7A CN108881824B (en) | 2018-06-11 | 2018-06-11 | Method and device for rapidly identifying and managing service level of video monitoring point |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108881824A true CN108881824A (en) | 2018-11-23 |
| CN108881824B CN108881824B (en) | 2021-02-26 |
Family
ID=64338662
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810597731.7A Active CN108881824B (en) | 2018-06-11 | 2018-06-11 | Method and device for rapidly identifying and managing service level of video monitoring point |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108881824B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080301081A1 (en) * | 2007-05-31 | 2008-12-04 | Symantec Corporation | Method and apparatus for generating configuration rules for computing entities within a computing environment using association rule mining |
| CN102065283A (en) * | 2010-12-23 | 2011-05-18 | 杭州华三通信技术有限公司 | Storage management method and device of video monitoring data |
| CN103914482A (en) * | 2013-01-07 | 2014-07-09 | 上海宝信软件股份有限公司 | CMDB (Configuration Management Date Base) based centralized monitoring event influence determination method |
| CN107609754A (en) * | 2017-08-25 | 2018-01-19 | 国网辽宁省电力有限公司 | A kind of grid information system performance monitoring system and method |
-
2018
- 2018-06-11 CN CN201810597731.7A patent/CN108881824B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080301081A1 (en) * | 2007-05-31 | 2008-12-04 | Symantec Corporation | Method and apparatus for generating configuration rules for computing entities within a computing environment using association rule mining |
| CN102065283A (en) * | 2010-12-23 | 2011-05-18 | 杭州华三通信技术有限公司 | Storage management method and device of video monitoring data |
| CN103914482A (en) * | 2013-01-07 | 2014-07-09 | 上海宝信软件股份有限公司 | CMDB (Configuration Management Date Base) based centralized monitoring event influence determination method |
| CN107609754A (en) * | 2017-08-25 | 2018-01-19 | 国网辽宁省电力有限公司 | A kind of grid information system performance monitoring system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108881824B (en) | 2021-02-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103412875B (en) | CDN strategy adjusting method based on AHP decision model | |
| Gong et al. | The optimal group consensus models for 2-tuple linguistic preference relations | |
| CN104809658B (en) | A kind of rapid analysis method of low-voltage distribution network taiwan area line loss | |
| CN109740863A (en) | Integrated evaluating method based on big plant-grid connection system | |
| CN108446712B (en) | ODN network intelligent planning method, device and system | |
| CN103914482B (en) | Centralized Monitoring event influence property based on CMDB determines method | |
| CN110932901A (en) | Alarm level adjusting method and system | |
| CN113379238A (en) | Risk assessment method and device and electronic equipment | |
| CN110428191B (en) | Method for identifying fragile nodes of power distribution network | |
| CN114254800A (en) | Method, terminal and storage medium for power load prediction | |
| CN108881824A (en) | A kind of quick identification management method and device of video surveillance point service class | |
| CN116468967B (en) | Sample image screening method and device, electronic equipment and storage medium | |
| CN111652459A (en) | Power grid evaluation method considering new energy consumption multi-index connotation | |
| Lijuan et al. | A network security evaluation method based on FUZZY and RST | |
| Deng et al. | Conflicting evidence combination method based on evidence distance and belief entropy | |
| CN115689201A (en) | Multi-criterion intelligent decision optimization method and system for enterprise resource supply and demand allocation | |
| CN114880406A (en) | Data management method and device | |
| CN115907719A (en) | Hierarchical operation and maintenance management method and device for charging station | |
| CN114997677A (en) | Low-voltage problem analysis method, system, computer equipment and storage medium | |
| CN111062604B (en) | Meteorological disaster-based power grid business risk assessment method, device and equipment | |
| CN110727912B (en) | Method for selecting differential transformation scheme of power secondary equipment | |
| CN109447490B (en) | User address-based abnormal change relation discrimination method | |
| CN114139847A (en) | Importance evaluation method, device and equipment for intelligent power distribution network construction target | |
| CN113919800A (en) | Work order processing method, device and equipment | |
| CN114022045A (en) | Regional economic development level determining method and device and terminal equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 510030 floors 1-6 on the west side of No. 10 Guangwei Road, Yuexiu District, Guangzhou City, Guangdong Province Patentee after: Guangzhou transportation planning and Research Institute Co.,Ltd. Address before: 510030 10th floor, 80 Jixiang Road, Yuexiu District, Guangzhou City, Guangdong Province Patentee before: GUANGZHOU TRANSPORT PLANNING Research Institute |
|
| CP03 | Change of name, title or address |