CN111582319A - Gray estimation method for satellite communication resource utilization rate based on hierarchy - Google Patents

Gray estimation method for satellite communication resource utilization rate based on hierarchy Download PDF

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CN111582319A
CN111582319A CN202010301806.XA CN202010301806A CN111582319A CN 111582319 A CN111582319 A CN 111582319A CN 202010301806 A CN202010301806 A CN 202010301806A CN 111582319 A CN111582319 A CN 111582319A
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何元智
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Abstract

The invention discloses a grey estimation method for satellite communication resource utilization rate based on hierarchy, which can solve the problems of multi-dimension, hierarchy, inconsistent contribution amount and the like of evaluation indexes related to multi-system satellite communication resource utilization rate estimation. The method overcomes the subjectivity of the traditional analytic hierarchy process and the weight randomness of the gray clustering analytic method, and effectively improves the rationality and the real effectiveness of the evaluation.

Description

Gray estimation method for satellite communication resource utilization rate based on hierarchy
Technical Field
The invention relates to the technical field of satellite communication, in particular to a method for estimating the utilization rate of satellite communication resources.
Background
The limited resource capacity of the communication satellite is oriented, and how to efficiently utilize the satellite communication resources to meet the diversified service requirements becomes a key factor influencing the performance of the satellite communication network. When resource utilization rate of a plurality of satellite communication systems is estimated, because people have different attention points and attention angles to satellite communication application services and the influence of satellite communication carriers of different transmission systems on the resource utilization rate is different, evaluation indexes of the satellite communication resource utilization rate have the characteristics of multiple dimensions, multiple layers, inconsistent contribution values to comprehensive evaluation and the like. Traditional evaluation methods such as an analytic hierarchy process and a gray clustering analysis method have the problems of heavy subjective color, strong randomness of weight and the like, and are not suitable for estimating the utilization rate of multi-system satellite communication resources.
Disclosure of Invention
The invention provides a composite estimation method combining an analytic hierarchy process and a gray constant weight clustering analysis process aiming at the characteristics of multidimensional, multilevel and inconsistent contribution quantity of satellite communication resource utilization rate evaluation, overcomes the subjectivity of the traditional analytic hierarchy process and the weight randomness of the gray clustering analysis process, and effectively improves the rationality and the real effectiveness of evaluation.
The method comprises the following specific steps:
a gray clustering estimation method for multi-system satellite communication resource utilization rate based on hierarchy comprises the following steps:
s1, when estimating the satellite communication resource utilization rate of L systems, firstly establishing a layered index system of an estimation target by using an analytic hierarchy process, and establishing the satellite communication resource utilization rate index system by using an independent multi-level hierarchical structure;
s2, determining the first layer of the tree diagram of the index system, namely the lowest layer index, as a clustering index, and distinguishing the clustering index into two types, one type is a quantitative index, and the other type is a gray scale index;
s3, setting a normalization initial value operator according to the sample value range of the quantitative indexes in the clustering indexes, and carrying out normalization processing on the quantitative indexes in the clustering indexes by using the initial value operator to unify the quantitative indexes into dimensionless data in the same value range [0,1 ];
s4, whitening the gray quantity in each clustering index according to the whitening weight function of the gray quantity to obtain a whitening value, wherein the value range of the whitening value is determined as [0,1 ];
s5, recording sample values of M indexes of L systems to be evaluated, and recording the sample value of the index j of the i system as xij(i is more than or equal to 1 and less than or equal to L, and j is more than or equal to 1 and less than or equal to M), thereby obtaining a sample vector X of the index jj＝{xij,1≤i≤L}；
S6, using the index j as a sample vector XjSample value x inijRe-ordering by size to obtain a new sample vector X for index jj’＝{xij’,1≤i≤L}；
S7, setting N gray classes, wherein the gray class 1 is the best, namely the resource utilization rate is the highest, the gray class is 2 times, namely the resource utilization rate is the lowest, and the like, and the gray class N is the worst, namely the resource utilization rate is the lowest;
s8, calculating the threshold phi of the jth index to the gray class kjk：
Figure BDA0002454273520000021
S9, marking Ash class 1 as
Figure BDA0002454273520000022
Ash class k as
Figure BDA0002454273520000023
Ash class N is noted
Figure BDA0002454273520000024
S10, determining a whitening weight function f of the gray amountjk(xij)；
S11, calculating the comprehensive weight of each clustering index about the evaluation target by using an analytic hierarchy process, and taking the comprehensive weight as the weight η of each clustering indexj(j＝1，2，…，M)；
S12, determining the gray clustering coefficient of the ith system relative to the kth gray class:
Figure BDA0002454273520000025
s13, if
Figure BDA0002454273520000026
The system i belongs to the k' grey class, and so on, the belonging grey classes of the L systems can be calculated, thereby completing the estimation of the resource utilization rate of the L systems.
The invention has the advantages that:
(1) the invention utilizes the analytic hierarchy process to establish a multilayer index system of the utilization rate of satellite communication resources, can better reflect the cognition and thinking process of people to the evaluation target, and embodies the hierarchy of the content contained in the evaluation target;
(2) the comprehensive weight of each clustering index on the evaluation target is calculated by using the analytic hierarchy process and is used as the fixed weight value of each clustering index in the gray fixed weight clustering, so that the analytic hierarchy process and the gray fixed weight clustering analysis are organically combined;
(3) the method has the advantages that the weight of each clustering index in gray weighted clustering analysis is usually given according to an expert evaluation mode, the method has great randomness, particularly for a multi-level index system, due to the non-intuition caused by the multi-layer structure, the randomness is more obvious, the method utilizes an analytic hierarchy process to calculate the weight of each clustering index, the comprehensive weight of each clustering index (namely the lowest index of the multi-level index system) on an evaluation target is utilized to reflect the contribution of each index on the survivability, the hierarchy of the evaluation target index system can be effectively embodied, the real contribution of each index on the evaluation target is objectively and accurately reflected, and the effectiveness of gray weighted clustering analysis is improved.
The invention organically combines the analytic hierarchy process and the gray weight-fixed clustering analysis method, fully utilizes the multidimensional hierarchy of the analytic hierarchy process and the comprehensiveness of gray clustering, can effectively overcome the defect of a single evaluation method, and effectively improves the rationality and the real effectiveness of the evaluation method.
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Fig. 1 is a schematic diagram of a hierarchical index system for satellite communication resource utilization in the present invention.
Detailed Description
The first embodiment is as follows:
as shown in fig. 1, a gray clustering estimation method for multi-system satellite communication resource utilization based on hierarchy mainly includes the following steps:
s1, when estimating the satellite communication resource utilization rate of L systems, firstly establishing a layered index system of an estimation target by using an analytic hierarchy process, and establishing the satellite communication resource utilization rate index system by using an independent multi-level hierarchical structure;
s2, determining the first layer of the tree diagram of the index system, namely the lowest layer index, as a clustering index, and distinguishing the clustering index into two types, one type is a quantitative index, and the other type is a gray scale index;
s3, setting a normalization initial value operator according to the sample value range of the quantitative indexes in the clustering indexes, and carrying out normalization processing on the quantitative indexes in the clustering indexes by using the initial value operator to unify the quantitative indexes into dimensionless data in the same value range [0,1 ];
s4, whitening the gray quantity in each clustering index according to the whitening weight function of the gray quantity to obtain a whitening value, wherein the value range of the whitening value is determined as [0,1 ];
s5, recording sample values of M indexes of L systems to be evaluated, and recording the sample value of the index j of the i system as xij(i is more than or equal to 1 and less than or equal to L, and j is more than or equal to 1 and less than or equal to M), thereby obtaining a sample vector X of the index jj＝{xij,1≤i≤L}；
S6, using the index j as a sample vector XjSample value x inijRe-ordering by size to obtain a new sample vector X for index jj’＝{xij’,1≤i≤L}；
S7, setting N gray classes, wherein the gray class 1 is the best, namely the resource utilization rate is the highest, the gray class is 2 times, namely the resource utilization rate is the lowest, and the like, and the gray class N is the worst, namely the resource utilization rate is the lowest;
s8, calculating the threshold phi of the jth index to the gray class kjk：
Figure BDA0002454273520000041
S9, marking Ash class 1 as
Figure BDA0002454273520000042
Ash class k as
Figure BDA0002454273520000043
Ash class N is noted
Figure BDA0002454273520000044
S10, determining a whitening weight function f of the gray amountjk(xij) (ii) a The method specifically comprises the following steps:
step S101, setting fjkA whitening weight function for the jth index with respect to the k gray class;
s102, if the ash class
Figure BDA0002454273520000045
Then
Figure BDA0002454273520000046
Step S103, if the ash class
Figure BDA0002454273520000047
Then
Figure BDA0002454273520000048
S104, if the ash class
Figure BDA0002454273520000049
Then
Figure BDA00024542735200000410
S11, calculating the comprehensive weight of each clustering index about the evaluation target by using an analytic hierarchy process, and taking the comprehensive weight as the weight η of each clustering indexj(j＝1，2，…，M)；
The method comprises the following steps:
firstly, setting a first layer as a top layer target H, and establishing a pairwise judgment matrix between indexes of a second layer:
Figure BDA0002454273520000051
wherein A is1、A2、A3Is a second layer index, symbol
Figure BDA0002454273520000052
All indexes in the representation matrix are lower indexes of the top level target H, Ai→AjIndicates judgment of the A thiRelative to index AjThe degree of importance of the index is represented by aijAnd a is aij×ajiThus, the decision matrix a can be determined as:
Figure BDA0002454273520000053
calculating a feature vector of the judgment matrix A: lambda [ alpha ]A＝(λA1,λA2,λA3)T，λA1、λA2、λA3Are respectively an index A1、A2、A3The composite weight of (a);
next, for the second layer index A1、A2、A3Respectively establishing pairwise judgment matrixes corresponding to the third-layer indexes:
Figure BDA0002454273520000054
Figure BDA0002454273520000055
Figure BDA0002454273520000056
wherein, B1～B10Is an index of the third layer, A1、A2、A3The corresponding lower layer indexes are respectively B1～B3、B4～B7、B8～B10，Bi→BjIndicates judgment of the B thiRelative to the index BjThe degree of importance of the index is bij，bij×bji1, from which it can be determinedThe third layer is a decision matrix:
Figure BDA0002454273520000061
solving the characteristic vectors of each judgment matrix as follows: lambda [ alpha ]B 1＝(λB1,λB2,λB3)T，λB 2＝(λB4,λB5,λB6,λB7)T，λB 3＝(λB8,λB9,λB10)T(ii) a The comprehensive weight method for calculating the indexes of the third layer comprises the following steps:
Figure BDA0002454273520000062
wherein j is the serial number of the index of the third layer, i is the index of the third layer BjThe serial number of the second layer index;
finally, calculating the comprehensive weight of each layer of indexes by analogy;
s12, determining the gray clustering coefficient of the ith system relative to the kth gray class:
Figure BDA0002454273520000063
s13, if
Figure BDA0002454273520000064
The system i belongs to the k' grey class, and so on, the belonging grey classes of the L systems can be calculated, thereby completing the estimation of the resource utilization rate of the L systems.
Example two:
the present invention will be described in further detail with reference to examples, but the present invention is not limited to the examples.
Assuming that resource utilization of the 3 satellite communication systems Sat1, Sat2 and Sat3 needs to be estimated, the specific steps are as follows:
step S1: suppose that the system resource utilization index system has 3 layers, the top index is the resource utilization, the second layer contains 2 indexes A1、A2The third layer is the bottom layer, andcomprises 5 indexes B1～B5In which B is1～B3Is A1Corresponding lower index, B4、B5Is A2And obtaining a satellite communication resource utilization rate index system according to the corresponding lower layer index.
Step S2: 5 indexes B at the bottommost layer in the index system1～B5Determined as a clustering index, where B4The index is a quantitative index, and the other indexes are grey scale indexes, and the obtained resource utilization rate clustering index parameters of the 3 satellite communication systems are shown in table 1.
TABLE 13 resource utilization clustering index parameters for satellite communication systems
Sat1 Sat2 Sat3
B1 Is higher than In Is low in
B2 In Height of Is higher than
B3 Is low in In In
B4 31 34 29
B5 Is higher than Height of In
Step S3: quantitative indices for 3 systems in Table 1 (B)4) In [0,1]]Normalization processing is carried out in the range, and the result is as follows: sat1 is represented by 0.25, Sat1 is represented by 1, and Sat3 is represented by 0.5.
Step S4: whitening is carried out in the range of [0,1] on the clustering indexes of 3 systems in the table 1, and the whitening result of the ash amount index is as follows: high is indicated as 1, high is indicated as 0.75, medium is indicated as 0.5 and low is indicated as 0.25.
Step S5: the sample values of the 5-term clustering index of the 3 systems are shown in table 2.
Sample value of 5-item clustering index of 23 systems in table
xij i＝1 i＝2 i＝3
j＝1 0.75 0.5 0.25
j＝2 0.5 1 0.75
j＝3 0.25 0.5 0.5
j＝4 0.75 1 0.25
j＝5 0.75 1 0.5
Step S6: obtaining a new sample vector
Figure BDA0002454273520000071
Step S7: since there are 3 systems in total, the number of gray is preferably 3, more preferably 1, and less preferably 2, and most preferably 3.
Step S8: calculating the threshold value of each index:
Figure BDA0002454273520000081
step S9: expressions for 3 gray classes are obtained.
Step S10: and solving to obtain whitening weight function values of all indexes of 3 systems, as shown in tables 3-5.
Table 3 whitening weight function value of each index of the satellite communication system 1
fjk(x1j) k＝1 k＝2 k＝3
j＝1 1 0.5 0
j＝2 0.5 0.667 1
j＝3 0.5 0.595 1
j＝4 0.75 0.881 0
j＝5 0.75 1 0.5
Table 4 whitening weight function values for each of the indicators of the satellite communication system 2
fjk(x2j) k＝1 k＝2 k＝3
j＝1 0.667 1 0
j＝2 1 0.667 0
j＝3 1 0.81 0
j＝4 1 0.507 0
j＝5 1 0.667 0
TABLE 5 whitening weight function values for each of the indicators of the satellite communication system 3
fjk(x3j) k＝1 k＝2 k＝3
j＝1 0.333 0.5 1
j＝2 0.75 1 0.5
j＝3 1 0.81 0
j＝4 0.25 0.373 1
j＝5 0.5 0.667 1
Step S11: calculating the comprehensive weight of each clustering index about an evaluation target by using an analytic hierarchy process, and firstly establishing a pairwise judgment matrix of the second-layer index:
Figure BDA0002454273520000082
judging the eigenvector of the matrix A as follows: lambda [ alpha ]A＝(0.333,0.667)T；
Then, constructing a pairwise judgment matrix of the third-layer indexes:
Figure BDA0002454273520000091
judging the characteristic vectors of the matrix as follows: lambda [ alpha ]B 1＝(0.333,0.333,0.333)T，λB 2＝(0.667,0.333)T；
Finally, obtaining the comprehensive weight of the third layer of indexes: wB＝(0.111,0.111,0.111,0.445,0.111)TI.e. the weight η of the cluster index.
Step S12: calculating a clustering coefficient according to the whitening weight function and the weight of the clustering index:
Figure BDA0002454273520000092
step S13:
Figure BDA0002454273520000093
the result shows that Sat1 belongs to ash class 2, Sat2 belongs to ash class 1, and Sat3 belongs to ash class 3, so that the utilization rate of Sat2 resources is the highest, Sat1 times is the lowest, and Sat3 is the lowest.
Because the clustering indexes have different meanings, some indexes are better if the indexes are larger, some indexes are better if the indexes are smaller, and the indexes are not the smallest or the best, the method preprocesses a sample set of the clustering indexes, on one hand, the gray scale is whitened to a whitened value in the same value range, on the other hand, the initial value operator is utilized to convert the sample value of each quantitative clustering index into dimensionless data in the same value range, and therefore the rationality of the calculation method is effectively enhanced.

Claims (3)

1. A grey estimation method for satellite communication resource utilization rate based on hierarchy is characterized by comprising the following steps:
s1, when estimating the satellite communication resource utilization rate of L systems, firstly establishing a layered index system of an estimation target by using an analytic hierarchy process, and establishing the satellite communication resource utilization rate index system by using an independent multi-level hierarchical structure;
s2, determining the first layer of the tree diagram of the index system, namely the lowest layer index, as a clustering index, and distinguishing the clustering index into two types, one type is a quantitative index, and the other type is a gray scale index;
s3, setting a normalization initial value operator according to the sample value range of the quantitative indexes in the clustering indexes, and carrying out normalization processing on the quantitative indexes in the clustering indexes by using the initial value operator to unify the quantitative indexes into dimensionless data in the same value range [0,1 ];
s4, whitening the gray quantity in each clustering index according to the whitening weight function of the gray quantity to obtain a whitening value, wherein the value range of the whitening value is determined as [0,1 ];
s5, recording sample values of M indexes of L systems to be evaluated, and recording the sample value of the index j of the i system as xij(i is more than or equal to 1 and less than or equal to L, and j is more than or equal to 1 and less than or equal to M), thereby obtaining a sample vector X of the index jj＝{xij,1≤i≤L}；
S6, using the index j as a sample vector XjSample value x inijRe-ordering by size to obtain a new sample vector X for index jj’＝{xij’,1≤i≤L}；
S7, setting N gray classes, wherein the gray class 1 is the best, namely the resource utilization rate is the highest, the gray class is 2 times, namely the resource utilization rate is the lowest, and the like, and the gray class N is the worst, namely the resource utilization rate is the lowest;
s8, calculating the threshold phi of the jth index to the gray class kjk：
Figure FDA0002454273510000011
S9, marking Ash class 1 as
Figure FDA0002454273510000012
Ash class k as
Figure FDA0002454273510000013
Ash class N is noted
Figure FDA0002454273510000014
S10, determining a whitening weight function f of the gray amountjk(xij)；
S11, calculating the comprehensive weight of each clustering index about the evaluation target by using an analytic hierarchy process, and taking the comprehensive weight as the weight η of each clustering indexj(j＝1，2，…，M)；
S12, determining the gray clustering coefficient of the ith system relative to the kth gray class:
Figure FDA0002454273510000021
s13, if
Figure FDA0002454273510000022
The system i belongs to the k' grey class, and so on, the belonging grey classes of the L systems can be calculated, thereby completing the estimation of the resource utilization rate of the L systems.
2. The method of estimating utilization of satellite communication resources of claim 1, wherein said step S10 of determining whitening weight function of gray level comprises the steps of:
setting fjkFor the whitening weight function of the jth index with respect to the k gray class,
if ash class
Figure FDA0002454273510000023
Then
Figure FDA0002454273510000024
If ash class
Figure FDA0002454273510000025
Then
Figure FDA0002454273510000026
If ash class
Figure FDA0002454273510000027
Then
Figure FDA0002454273510000028
Wherein w1、w2、w3Is a threshold value of the gray class.
3. The method for estimating utilization of satellite communication resources of claim 1, wherein said method for calculating the comprehensive weight of each cluster index with respect to the utilization of satellite communication resources by using an analytic hierarchy process in step S11 comprises the following steps:
firstly, setting a first layer as a top layer target H, and establishing a pairwise judgment matrix between indexes of a second layer:
Figure FDA0002454273510000029
wherein A is1、A2、A3Is a second layer index, symbol
Figure FDA0002454273510000031
All indexes in the representation matrix are lower indexes of the top level target H, Ai→AjIndicates judgment of the A thiRelative to index AjThe degree of importance of the index is represented by aijAnd a is aij×ajiThus, the decision matrix a can be determined as:
Figure FDA0002454273510000032
calculating a feature vector of the judgment matrix A: lambda [ alpha ]A＝(λA1,λA2,λA3)T，λA1、λA2、λA3Are respectively an index A1、A2、A3The composite weight of (a);
next, for the second layer index A1、A2、A3Respectively establishing pairwise judgment matrixes corresponding to the third-layer indexes:
Figure FDA0002454273510000033
Figure FDA0002454273510000034
Figure FDA0002454273510000035
wherein, B1～B10Is an index of the third layer, A1、A2、A3The corresponding lower layer indexes are respectively B1～B3、B4～B7、B8～B10，Bi→BjIndicates judgment of the B thiRelative to the index BjThe degree of importance of the index is bij，bij×bji1, the decision matrices of the third layer can thus be determined:
Figure FDA0002454273510000041
solving the characteristic vectors of each judgment matrix as follows: lambda [ alpha ]B 1＝(λB1,λB2,λB3)T，λB 2＝(λB4,λB5,λB6,λB7)T，λB 3＝(λB8,λB9,λB10)T(ii) a The comprehensive weight method for calculating the indexes of the third layer comprises the following steps:
Figure FDA0002454273510000042
wherein j is the serial number of the index of the third layer, i is the index of the third layer BjThe serial number of the second layer index;
and finally, calculating the comprehensive weight of each layer of index by analogy.
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