CN113705965A - Satellite observation scheme screening method and system based on intuitive language preference relation particles - Google Patents
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Abstract
The embodiment of the invention provides a satellite observation scheme screening method and system based on intuitive language preference relation particles, and belongs to the technical field of satellite observation method decision making. The method comprises the following steps: acquiring an evaluation information matrix of each evaluation system; inserting a plurality of segmentation points into the language item set of the intuitive language preference relationship to obtain information particles of each language item; setting flexibility to determine information particles corresponding to membership degrees and non-membership degrees; optimizing the interception point and the flexibility by adopting a PSO algorithm to obtain optimal information particles; updating each evaluation information matrix according to the optimal information particles of each evaluation information; aggregating all evaluation system evaluation information matrixes to obtain an aggregated fusion evaluation information matrix; obtaining evaluation information of each satellite observation scheme according to an intuitionistic language induced ordered weighted average operator, and obtaining a score function value corresponding to each evaluation information; and selecting the satellite observation scheme with the maximum score function value as the optimal scheme.
Description
Technical Field
The invention relates to the technical field of decision making of satellite observation methods, in particular to a satellite observation scheme screening method and system based on intuitive language preference relation particles.
Background
The remote sensing satellite observation scheme evaluation problem can be regarded as a multi-attribute decision (MCDM) problem, multi-attribute decision methods comprise an analytic hierarchy process, a TOPSIS method and an optimal and worst method, and the optimal scheme can be selected from a plurality of preset observation schemes based on a designed index system by using the multi-attribute decision method. Under the excitation of the MCDM method, students research various evaluation methods to solve the evaluation problem of the observation scheme of the remote sensing satellite, so that the evaluation process is more objective and scientific. However, in some complex evaluation cases, due to the inherent ambiguity and the extrinsic complexity of the evaluation problem, the existing evaluation methods are not sufficient to accurately describe the viewpoint of the evaluation system based on various evaluation angles, so that it is difficult to accurately select the optimal solution from a variety of satellite observation solutions.
Disclosure of Invention
The embodiment of the invention aims to provide a satellite observation scheme screening method and system based on intuitive language preference relation particles, and the method and system can be used for fusing evaluation results of evaluation systems based on different evaluation angles, so that the accuracy of satellite observation scheme selection is improved.
In order to achieve the above object, an embodiment of the present invention provides a satellite observation scheme screening method based on an intuitive language preference relationship particle, including:
acquiring an evaluation information matrix of each evaluation system;
inserting a plurality of segmentation points into a language item set of a preset intuitive language preference relation to obtain information particles of each language item;
setting the flexibility of each evaluation system to determine information particles corresponding to membership degrees and non-membership degrees in each evaluation information matrix;
optimizing the interception point and the flexibility by adopting a PSO algorithm to respectively obtain optimal information particles corresponding to each linguistic item, membership degree and non-membership degree in the evaluation information matrix;
updating each evaluation information matrix according to the optimal information particles corresponding to each language item, membership degree and non-membership degree in each evaluation information;
adopting an intuitionistic language dominant evidence generalized compensation weighted average operator to aggregate the evaluation information matrixes of all the evaluation systems to obtain an aggregated fusion evaluation information matrix;
obtaining evaluation information of each satellite observation scheme according to an intuitionistic language induced ordered weighted average operator, and obtaining a score function value corresponding to each evaluation information;
and selecting the satellite observation scheme with the maximum score function value as an optimal scheme.
Optionally, the obtaining the evaluation information matrix of each evaluation system includes:
the evaluation information matrix is expressed by equation (1),
wherein the content of the first and second substances,
in satellite Observation scheme D for evaluation System kiAnd satellite Observation plan DjIn betweenThe information is evaluated in such a way that,
for evaluating information
Term of Chinese, muij kFor evaluating information
Corresponding degree of membership, vij kFor evaluating information
And corresponding non-membership degrees, wherein n is the total number of satellite observation schemes, and K is the total number of evaluation systems.
Optionally, the setting of the flexibility of each evaluation system to determine information particles corresponding to membership degrees and non-membership degrees in each evaluation information matrix includes:
determining the degree of flexibility according to equation (2),
where K is the total number of evaluation systems, γ is a predetermined level of granularity, γ iskEvaluating the individual flexibility corresponding to the system k;
determining information particles corresponding to each membership degree and each non-membership degree according to a formula (3) and a formula (4),
wherein the content of the first and second substances,
observation scheme D on satellite for evaluation system of kthiAnd satellite Observation plan DjThe left end point of the information particle corresponding to the membership degree of the evaluation information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe right end point of the information particle corresponding to the membership degree of the evaluation information,
observation scheme D on satellite for evaluation system of kthiAnd satellite Observation plan DjThe degree of membership in the evaluation information in between,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe left end point of the information particle corresponding to the non-membership degree in the evaluation information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe right end point of the information particle corresponding to the non-membership degree in the evaluation information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjNon-membership in the evaluation information in between.
Optionally, the optimizing the intercept point and the flexibility by using a PSO algorithm to obtain the optimal information particles corresponding to each linguistic item, membership degree, and non-membership degree in the evaluation information matrix respectively includes:
inputting the interception point and the flexibility into a PSO algorithm to generate current information particles;
randomly selecting 500 combinations of linguistic items, membership degrees and non-membership degrees from the interval where the current information particles are located, calculating the optimization criterion corresponding to the current information particles according to formulas (5) to (9),
M＝κ·M1+(1-κ)·M2， (5)
wherein M is the optimization criterion, K is a preset parameter value, K is the total number of the evaluation system, n is the total number of the satellite observation schemes, g +1 is the number of the language items in the preset language item set,
observation scheme D in satellite for aggregating K evaluation systemsiAnd satellite Observation plan DjTo evaluate the numerical form of the linguistic item of information,
observation scheme D in satellite for aggregating K evaluation systemsiAnd satellite Observation plan DjThe degree of membership of the information is evaluated,
observation scheme D in satellite for aggregating K evaluation systemsiAnd satellite Observation plan DjThe non-membership of the information is evaluated,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjTo evaluate the numerical form of the linguistic item in the information, muij kObservation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe degree of membership in the information is evaluated,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe degree of non-membership in the information is evaluated,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DlTo evaluate the numerical form of the terms in the information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DlThe degree of membership of the evaluation information therebetween,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DlThe degree of non-membership of the evaluation information therebetween,
observation plan D at satellite for k-th evaluation systemlAnd satellite Observation plan DjThe numerical form of the language term of the evaluation information in between,
observation plan D at satellite for k-th evaluation systemlAnd satellite Observation plan DjThe degree of membership of the evaluation information therebetween,
on satellite for the kth evaluation systemObservation scheme DlAnd satellite Observation plan DjNon-membership of the evaluation information therebetween.
Calculating a fitness function corresponding to the current information particles according to a formula (10),
wherein M isiThe optimization criteria corresponding to the combination of the ith linguistic item, the membership degree and the non-membership degree;
judging whether the current iteration number is larger than or equal to a preset iteration number threshold value,
under the condition that the current iteration number is judged to be smaller than a preset iteration number threshold value, updating the particles of the PSO algorithm according to a formula (11) to a formula (13), randomly selecting 500 combinations of linguistic items, membership degrees and non-membership degrees from the interval where the current information particles are located again, calculating the optimization criterion corresponding to the current information particles according to a formula (5) to a formula (9), and executing corresponding steps of the method until the current iteration number is judged to be larger than or equal to the iteration number threshold value,
v(m+1)＝ζ(m)×v(m)+d1a1·(xl-x)+d2a2·(xg-x)， (11)
where v (m +1) is the velocity of the particle after update, ζ (m) is the shrinkage factor, v (m) is the velocity before update, m is the current iteration number, d1And d2As acceleration constant, a1And a2Is [0,1 ]]Two random numbers of the interval, xlFor the current locally optimal solution, xgFor the current global optimal solution, x is the current position of the particle;
ζ(m)＝(totnum-m)×(ζmax-ζmin)/totnum， (12)
wherein totnum is the threshold value of the iteration times, ζmaxIs the maximum value of the coefficient of contraction, ζminIs the minimum value of the shrinkage factor;
x(m+1)＝x(m)+v(m+1)， (13)
wherein x (m +1) is the position of the particle after updating, and x (m) is the position of the particle before updating;
and outputting the optimal interception point and the optimal flexibility when judging that the current iteration times are larger than the iteration time threshold.
Optionally, the updating each evaluation information matrix according to the optimal information particles corresponding to the respective linguistic items, the membership degrees and the non-membership degrees in each evaluation information includes:
updating the evaluation information matrix according to equation (14),
wherein, IrkIn satellite observation scenario D for updated evaluation system kiAnd satellite Observation plan DjThe evaluation information of the time interval between the first and second evaluation,
for updated assessment information
Term of (1), Irij kEvaluation information, mu, generated for a value randomly selected from the optimal information particlesij ′kMembership, v, generated for a randomly selected value in the optimal information particleij ′kAnd generating a non-membership degree for a randomly selected numerical value in the optimal information particles, wherein n is the total number of the satellite observation schemes, and K is the total number of the evaluation systems.
Optionally, the aggregating the evaluation information matrices of all the evaluation systems by using an intuitive language dominant evidence generalized compensation weighted average operator to obtain an aggregated fusion evaluation information matrix includes:
obtaining the fusion assessment information matrix according to formula (15)
Wherein, θ (Ir)ij ′k) Observing scheme D at satellite for updated k-th evaluation systemiAnd satellite Observation plan DlThe numerical form of the language term of the evaluation information in between,
observing scheme D at satellite for updated k-th evaluation systemiAnd satellite Observation plan DlThe degree of membership of the evaluation information therebetween,
observing scheme D at satellite for updated k-th evaluation systemiAnd satellite Observation plan DlThe degree of non-membership of the evaluation information therebetween,
for the updated evaluation information of the tth evaluation system,
for the updated evaluation information of the kth evaluation system, θ (Ir)ij ′t) Satellite observation scenario D for updated tth evaluation systemiAnd satellite Observation plan DlThe numerical form of the language term of the evaluation information in between,
satellite observation scenario D for updated tth evaluation systemiAnd satellite Observation plan DlThe degree of membership of the evaluation information therebetween,
satellite observation scenario D for updated tth evaluation systemiAnd satellite Observation plan DlNon-membership of the evaluation information therebetween.
Optionally, the obtaining the evaluation information of each satellite observation scheme according to an intuitive language-induced ordered weighted average operator, and obtaining a score function value corresponding to each evaluation information includes:
the evaluation information of each satellite observation scenario is calculated according to equation (23),
wherein, QGDDiIs the evaluation information of the ith satellite observation scheme, j is 1,2, and n, σ (j) is the ranking of the evaluation information, and θ (Ir)iσ(j) c) For the ith in the aggregated evaluation informationThe language items in the jth assessment information in the satellite observation scheme are arranged according to a descending principle,
for the membership degrees in the j evaluation information arranged according to the descending principle in the ith satellite observation scheme in the aggregated evaluation information,
the non-membership degree, w, in the j evaluation information arranged according to the descending principle in the ith satellite observation scheme in the aggregated evaluation informationiThe weight corresponding to the ith satellite observation scheme;
a score function of the evaluation information of each satellite observation scenario is calculated according to formula (24),
S(QGDDi)＝θ(QGDDi)(1+μi-vi)， (24)
wherein, θ (QGDD)i) In the form of a number, mu, of terms corresponding to the evaluation information of the ith satellite observation planiMembership degree, v, corresponding to the evaluation information of the ith satellite observation schemeiAnd the non-membership degree corresponding to the evaluation information of the ith satellite observation scheme.
In another aspect, the present invention further provides a satellite observation scenario screening system based on intuitive language preference relationship particles, which includes a processor configured to execute the screening method according to any one of the above-mentioned methods.
In one aspect, the invention also provides a computer readable storage medium having stored thereon instructions for reading by a machine to cause the machine to perform a screening method as described in any one of the above.
Through the technical scheme, the method and the system for screening the satellite observation scheme based on the intuitive language preference relation particles convert and fuse the evaluation information matrixes of each evaluation system by adopting the mode of the intuitive language preference relation particles and combining the PSO algorithm, thereby finally obtaining the evaluation information of each satellite observation scheme in the fused evaluation matrix and finally determining the optimal satellite observation scheme through the calculation of the score function. The method and the system overcome the technical defect that the screening method in the prior art cannot simultaneously consider a plurality of evaluation systems, and improve the screening precision of the satellite observation scheme.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a flow diagram of a method for intuitive language preference relationship particle based satellite observation scenario screening according to one embodiment of the present invention;
FIG. 2 is a flow diagram of a PSO algorithm according to one embodiment of the invention;
fig. 3 is a graph of fitness value as a function of iteration number in a PSO algorithm according to one embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart illustrating a satellite observation scenario screening method based on intuitive language preference relation particles according to an embodiment of the present invention. In this fig. 1, the method may include:
in step S10, an evaluation information matrix of each evaluation system is acquired;
in step S11, inserting a plurality of cut points in a set of language items of a preset intuitive language preference relationship to obtain an information particle of each language item;
in step S12, the flexibility of each evaluation system is set to determine information particles corresponding to membership degrees and non-membership degrees in each evaluation information matrix;
in step S13, a PSO algorithm is used to optimize the intercept points and the flexibility to obtain optimal information particles corresponding to each linguistic item, membership degree, and non-membership degree in the evaluation information matrix, respectively;
in step S14, updating each evaluation information matrix according to the optimal information particles corresponding to each linguistic item, membership degree, and non-membership degree in each evaluation information;
in step S15, aggregating all evaluation system evaluation information matrices by using an intuitive language dominant evidence generalized compensation weighted average operator to obtain an aggregated fusion evaluation information matrix;
in step S16, obtaining evaluation information of each satellite observation scenario according to the intuitive language-induced ordered weighted average operator, and obtaining a score function value corresponding to each evaluation information;
in step S17, the satellite observation scenario with the largest score function value is selected as the optimal scenario.
In this fig. 1, each element of the evaluation information matrix may be a matrix for representing the evaluation result of each evaluation system for every two satellite observation scenarios. Specifically, the evaluation information matrix can be expressed by formula (1),
wherein the content of the first and second substances,
in satellite Observation scheme D for evaluation System kiAnd satellite Observation plan DjThe evaluation information of the time interval between the first and second evaluation,
for evaluating information
Term of Chinese, muij kFor evaluating information
Corresponding degree of membership, vij kFor evaluating information
And corresponding non-membership degrees, wherein n is the total number of satellite observation schemes, and K is the total number of evaluation systems.
The evaluation system may be a device based on different evaluation levels and evaluation angles, or may be an expert based on different fields, and the like. Considering that the evaluation results of different evaluation systems have different degrees of influence on the task target, each evaluation system may have a priority relationship, that is, K evaluation systems may be sorted according to the priority relationship. Each evaluation system may be sorted and represented by a number K, and then based on the priority relationship (from large to small) of each evaluation system, the evaluation system may be represented in the form of 1, …, K, with 1 ≦ K ≦ K.
The intuitive language preference relationship is used for representing the evaluation result of the evaluation system among different satellite observation schemes. A commonly used intuitive language preference relationship mainly includes a plurality of language item sets composed of a plurality of language items. Specifically, the set of terms may be H ═ H0，h1，...，hgIn which h0，h1,...，hgFor the terms in the set of terms, 0.,. g is the number of each term, respectively. However, the terms of the conventional term set are relatively fixed, and the operability is relatively limited when the evaluation information matrixes of different evaluation systems are fused. Therefore, in this embodiment, a plurality of cut points may be inserted into the set of terms through step S11 to obtain information particles of each term. In particular, the set of cut points may be denoted as { c }1,c2，...，cgIn which c is1，c2，...，cgIs the cut-off point in the set. After inserting the cut-off point, the generated information particles can be represented as I1，I2，...，Ig+1And I is1＝[0，c1)，I2＝[c1,c2)…，Ig+1＝[cg，1]。
Although the language term in the evaluation information matrix has already been obtained in equation (1)
Middle corresponding degree of membership muij kAnd non-membership vij k. However, since the set of language items is subdivided in step S11, each language item corresponds to a different information particle. Then with the original degree of membership muij kAnd non-membership vij kThe de-matching of the corresponding information particles clearly deviates from the intended meaning of the originally evaluated information matrix. Therefore, in this embodiment, the membership and non-membership corresponding to the information granule corresponding to each language item need to be determined again in step S12. However, since the cut-off point at this time is still an uncertain value, the membership degree and the non-membership degree cannot be determined naturally. Thus, the individual flexibility of its variation can be determined for the degree of membership and the degree of non-membership. Specifically, the flexibility may be determined according to equation (2),
where K is the total number of evaluation systems, γ is a predetermined level of granularity, γ iskEvaluating the individual flexibility corresponding to the system k; then determining information particles corresponding to each membership degree and each non-membership degree according to a formula (3) and a formula (4),
wherein the content of the first and second substances,
observation scheme D on satellite for evaluation system of kthiAnd satellite Observation plan DjThe left end point of the information particle corresponding to the membership degree of the evaluation information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe right end point of the information particle corresponding to the membership degree of the evaluation information,
observation scheme D on satellite for evaluation system of kthiAnd satellite Observation plan DjThe degree of membership in the evaluation information in between,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe left end point of the information particle corresponding to the non-membership degree in the evaluation information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe right end point of the information particle corresponding to the non-membership degree in the evaluation information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjNon-membership in the evaluation information in between.
Although the information particles of each language item and the membership degree and non-membership degree corresponding to each information particle are respectively determined in step S11 and step S12, the fusion result of each evaluation information matrix will be greatly changed along with the change of the segmentation point and the non-membership degree of the membership degree. Therefore, in this embodiment, in order to obtain the optimal information granule, membership degree, and non-membership degree, the partition point, membership degree, and non-membership degree (flexibility) may be further optimized by using the PSO algorithm through step S13, so as to obtain the optimal information granule, membership degree, and non-membership degree. For the PSO algorithm, although it may be a method known to those skilled in the art. However, in a preferred example of the present invention, the cut-off point, membership and non-membership (degree of flexibility) have an impact on the consistency level of the fused results, which may be the inclusion of steps as shown in fig. 2. In this fig. 2, the method may include:
inputting the cut-off point and the flexibility into the PSO algorithm to generate current information particles in step S20;
in step S21, 500 combinations of linguistic items (information particles), membership degrees and non-membership degrees are randomly selected from the interval where the current information particle is located, and the optimization criteria corresponding to the current information particle is calculated according to the formulas (5) to (9),
M＝κ·M1+(1-κ)·M2， (5)
where M is the optimization criterion, κ is a preset parameter value, which in a preferred example of the present invention may be greater than 0.5, K is the total number of evaluation systems, n is the total number of satellite observation scenarios, g +1 is the number of terms in a preset set of terms,
observation scheme D in satellite for aggregating K evaluation systemsiAnd satellite Observation plan DjTo evaluate the numerical form of the linguistic item of information,
observation scheme D in satellite for aggregating K evaluation systemsiAnd satellite Observation plan DjThe degree of membership of the information is evaluated,
observation scheme D in satellite for aggregating K evaluation systemsiAnd satellite Observation plan DjThe non-membership of the information is evaluated,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjTo evaluate the numerical form of the linguistic item in the information, muij kObservation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe degree of membership in the information is evaluated,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe degree of non-membership in the information is evaluated,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DlTo evaluate the numerical form of the terms in the information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DlThe degree of membership of the evaluation information therebetween,
observation at satellite for kth evaluation systemScheme DiAnd satellite Observation plan DlThe degree of non-membership of the evaluation information therebetween,
observation plan D at satellite for k-th evaluation systemlAnd satellite Observation plan DjThe numerical form of the language term of the evaluation information in between,
observation plan D at satellite for k-th evaluation systemlAnd satellite Observation plan DjThe degree of membership of the evaluation information therebetween,
observation plan D at satellite for k-th evaluation systemlAnd satellite Observation plan DjNon-membership of the evaluation information therebetween.
In step S22, a fitness function corresponding to the current information particle is calculated according to formula (10),
wherein M isiThe optimization criteria corresponding to the combination of the ith linguistic item, the membership degree and the non-membership degree;
in step S23, it is determined whether the current iteration count is greater than or equal to a preset iteration count threshold,
in step S24, when the current iteration number is judged to be less than the predetermined iteration number threshold, the particle of the PSO algorithm is updated according to the formulas (11) to (13), combinations of 500 terms (information particles), membership degrees and non-membership degrees are randomly selected again from the interval where the current information particle is located, the optimization criterion corresponding to the current information particle is calculated according to the formulas (5) to (9), and the corresponding steps of the method are executed until the current iteration number is judged to be greater than or equal to the iteration number threshold,
v(m+1)＝ζ(m)×v(m)+d1a1·(xl-x)+d2a2·(xg-x)， (11)
where v (m +1) is the velocity of the particle after update, ζ (m) is the shrinkage factor, v (m) is the velocity before update, m is the current iteration number, d1And d2As acceleration constant, a1And a2Is [0,1 ]]Two random numbers of the interval, xlFor the current locally optimal solution, xgFor the current global optimal solution, x is the current position of the particle;
ζ(m)＝(totnum-m)×(ζmax-ζmin)/totnum， (12)
wherein totnum is an iteration number threshold value, ζmaxIs the maximum value of the coefficient of contraction, ζminIs the minimum value of the shrinkage factor;
x(m+1)＝x(m)+v(m+1)， (13)
where x (m +1) is the position of the particle after update, and x (m) is the position of the particle before update.
In step S25, in the case where it is determined that the current iteration count is greater than or equal to the iteration count threshold, the optimal cut point and flexibility are output.
In the case where the optimal information particles corresponding to the terms, the degrees of membership, and the degrees of non-membership are determined in step S13, each of the evaluation information matrices is updated through step S14. Specifically, the evaluation information matrix may be updated according to formula (14),
wherein, IrkIn satellite observation scenario D for updated evaluation system kiAnd satellite Observation plan DjThe evaluation information of the time interval between the first and second evaluation,
for updated assessment information
Term of (1), Irij kFor random selection in the optimal information particlesIs generated by a value ofij ′kMembership, v, generated for a randomly selected value in the optimal information particleij ′kAnd generating a non-membership degree for a randomly selected numerical value in the optimal information particles, wherein n is the total number of the satellite observation schemes, and K is the total number of the evaluation systems.
In order to obtain an aggregated evaluation information matrix (i.e., a fusion evaluation information matrix) according to the updated evaluation information matrix, each evaluation information matrix needs to be aggregated. Specifically, step S15 adopts an intuitive language dominant evidence generalized compensation weighted average operator to aggregate all evaluation system evaluation information matrices to obtain an aggregated fusion evaluation information matrix. More specifically, the fusion evaluation information matrix is obtained according to the formula (15)
Wherein, θ (Ir)ij ′k) Observing scheme D at satellite for updated k-th evaluation systemiAnd satellite Observation plan DlThe numerical form of the language term of the evaluation information in between,
observing scheme D at satellite for updated k-th evaluation systemiAnd satellite Observation plan DlThe degree of membership of the evaluation information therebetween,
observing scheme D at satellite for updated k-th evaluation systemiAnd satellite Observation plan DlThe degree of non-membership of the evaluation information therebetween,
for the updated evaluation information of the tth evaluation system,
for the updated evaluation information of the kth evaluation system, θ (Ir)ij ′t) Satellite observation scenario D for updated tth evaluation systemiAnd satellite Observation plan DlThe numerical form of the language term of the evaluation information in between,
for the updated tth evaluation systemUnified satellite observation scheme DiAnd satellite Observation plan DlThe degree of membership of the evaluation information therebetween,
satellite observation scenario D for updated tth evaluation systemiAnd satellite Observation plan DlNon-membership of the evaluation information therebetween.
Since the fused evaluation information matrix obtained in step S15 fuses the evaluation information matrices of all the evaluation systems, the obtained fused evaluation information matrix can truly reflect the evaluation results of all the evaluation systems. Therefore, only the elements in the fusion evaluation information matrix need to be processed, so as to obtain the quality of each satellite observation scheme, that is, step S16: and obtaining the evaluation information of each satellite observation scheme according to the intuition language-induced ordered weighted average operator, and obtaining a score function value corresponding to each evaluation information. Specifically, the step S16 may be to calculate the evaluation information of each satellite observation scenario according to the formula (23),
wherein, QGDDiIs the evaluation information of the ith satellite observation scheme, j is 1,2, and n, σ (j) is the ranking of the evaluation information, and θ (Ir)iσ(j) c) For the language items in the jth evaluation information arranged according to the descending principle in the ith satellite observation scheme in the aggregated evaluation information,
for the membership degrees in the j evaluation information arranged according to the descending principle in the ith satellite observation scheme in the aggregated evaluation information,
the non-membership degree, w, in the j evaluation information arranged according to the descending principle in the ith satellite observation scheme in the aggregated evaluation informationiFor the ith satellite viewMeasuring the weight corresponding to the scheme; then calculating the score function of the evaluation information of each satellite observation scheme according to the formula (24),
S(QGDDi)＝θ(QGDDi)(1+μi-vi)， (24)
wherein, θ (QGDD)i) In the form of a number, mu, of terms corresponding to the evaluation information of the ith satellite observation planiMembership degree, v, corresponding to the evaluation information of the ith satellite observation schemeiAnd the non-membership degree corresponding to the evaluation information of the ith satellite observation scheme.
Finally, the satellite observation scenario with the largest score function value is selected as the optimal scenario through step S17.
In another aspect, the present invention further provides a satellite observation scenario screening system based on intuitive language preference relationship particles, which includes a processor configured to execute the screening method according to any one of the above-mentioned methods.
In one aspect, the invention also provides a computer readable storage medium having stored thereon instructions for reading by a machine to cause the machine to perform a screening method as described in any one of the above.
Through the technical scheme, the method and the system for screening the satellite observation scheme based on the intuitive language preference relation particles convert and fuse the evaluation information matrixes of each evaluation system by adopting the mode of the intuitive language preference relation particles and combining the PSO algorithm, thereby finally obtaining the evaluation information of each satellite observation scheme in the fused evaluation matrix and finally determining the optimal satellite observation scheme through the calculation of the score function. The method and the system overcome the technical defect that the screening method in the prior art cannot simultaneously consider a plurality of evaluation systems, and improve the screening precision of the satellite observation scheme.
To further determine the technical effect of the method provided by the present invention, in an embodiment of the present invention, the method provided by the present invention may be adopted to observe four satellite observation schemes (D ═ { D ═ D)1,D2,D3,D4}) were screened.In this embodiment, the evaluation system may be denoted as 1,2, and 3; and the priority of each evaluation system decreases in turn. For the evaluation criteria of the four satellite observation scenarios, an evaluation system as shown in table 1 may be employed,
TABLE 1
Based on the index system in table 1, the language item set H ═ H can be predefined0Very low, h1Very low, h2Low, h3Medium, h4High, h5Very high, h6Very high. And each evaluation system evaluates each satellite observation scheme through the language item set so as to obtain a corresponding evaluation information matrix. Each of the evaluation information matrices is as follows,
wherein neg (h)i) Representing a language term hiAnd the supplementary qualitative value of, and the neg (h)i) Can pass through the linguistic item hiTo calculate. If i is 4, h4The corresponding information particle is [0.25,0.32 ]]. If we follow the interval [0.25,0.32 ]]The value of the random sampling in (1) is 0.29, then neg (h)i) The corresponding value is 0.71.
In this embodiment, we can use the PSO algorithm to map the linguistic items, the membership degree and the non-membership degree in the intuitive language preference relationship to information particles, and the experimental parameter values needed to be used are as follows:
1. consists of 100 particles in the PSO. Since the run with more particles in the PSO algorithm in this experiment would yield similar results. Therefore, 100 particles were used in this experiment to calculate the optimal fitness function value.
2. The number of iterations is set to 500 because the result of the fitness function does not change further as the number of iterations increases when the number of iterations is greater than 500.
3. The parameter in the particle velocity update formula is d1＝d2＝2。
4. In the optimization criterion, the parameter κ is set to 0.75.
After obtaining each evaluation information matrix, the linguistic items, the membership degree and the non-membership degree in the intuitive language preference relationship are converted into corresponding information particles, and gamma is predefined to be 2. Fig. 3 shows the values of the fitness function based on 500 iterations in the PSO. From this figure 3 we can see that the fitness function value is at most 0.7924. And the value of the obtained vector P is [0.0007,0.0320,0.1320,0.2320,0.3320,0.4320,0.6669,0.7751,4.5580]From there, we find that the information particles corresponding to each language item in the language item set are h0：[0,0.0007),h1：[0.0007,0.0320),h2：[0.0320,0.1320),h3：[0.1320,0.2320),h4：[0.2320,0.3320),h5：[0.3320,0.4320),h6：[0.4320,1]And the personal flexibility of each member of the evaluation group is gamma1＝0.6669,γ2＝0.7751,γ3＝4.5580。
Obtaining the matrix IR through the acquired linguistic item, membership degree and non-membership of the optimal information particles and random sampling1,IR2And IR3，
Obtaining aggregated intuitive language preference relationship IR using an intuitive language dominance evidence generalized compensated weighted average operator (λ 8, p 2)cAs will be shown below, the following,
aggregating IR using ILIWA operatorscAnd obtaining the QGDD of each observation scheme from the evaluation information of each remote sensing satellite observation scheme, and sequencing the observation schemes from large to small based on a score function.
First, the word "most" (q (r) ═ r) is measured according to the fuzzy language using the existing method1/2) Calculating weights in ILIOWA operator to obtain QGDD of each observation schemei(i is 1,2,3,4), the calculation results are as follows,
QGDD1＝<0.2698,(0.5140,0.3478)>,QGDD2＝<0.2539,(0.3165,0.3307)>,
QGDD3＝<0.6981,(0.4894,0.3483)>,QGDD4＝<0.7118,(0.3225,0.2298)>.
then, the observation scheme QGDD is calculated by the consensus of the scoring functioni(i ═ 1,2,3,4), the result calculated is:
S(QGDD1)＝0.3146,S(QGDD2)＝0.2503,
S(QGDD3)＝0.7966,S(QGDD4)＝0.7778.
by comparing S (QGDD)i) (i ═ 1,2,3,4), and the global ranking of the telemetry satellite observation scheme is obtained as D3＞D4＞D1＞D2. Thus, Observation scheme D3Is an optimal observation scheme.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (9)
1. A satellite observation scheme screening method based on intuitive language preference relation particles is characterized by comprising the following steps:
acquiring an evaluation information matrix of each evaluation system;
inserting a plurality of segmentation points into a language item set of a preset intuitive language preference relation to obtain information particles of each language item;
setting the flexibility of each evaluation system to determine information particles corresponding to membership degrees and non-membership degrees in each evaluation information matrix;
optimizing the interception point and the flexibility by adopting a PSO algorithm to respectively obtain optimal information particles corresponding to each linguistic item, membership degree and non-membership degree in the evaluation information matrix;
updating each evaluation information matrix according to the optimal information particles corresponding to each language item, membership degree and non-membership degree in each evaluation information;
adopting an intuitionistic language dominant evidence generalized compensation weighted average operator to aggregate the evaluation information matrixes of all the evaluation systems to obtain an aggregated fusion evaluation information matrix;
obtaining evaluation information of each satellite observation scheme according to an intuitionistic language induced ordered weighted average operator, and obtaining a score function value corresponding to each evaluation information;
and selecting the satellite observation scheme with the maximum score function value as an optimal scheme.
2. The screening method according to claim 1, wherein the obtaining of the evaluation information matrix of each evaluation system includes:
the evaluation information matrix is expressed by equation (1),
wherein the content of the first and second substances,
in satellite Observation scheme D for evaluation System kiAnd satellite Observation plan DjThe evaluation information of the time interval between the first and second evaluation,
for evaluating information
Term of Chinese, muij kFor evaluating information
Corresponding degree of membership, vij kFor evaluating information
And corresponding non-membership degrees, wherein n is the total number of satellite observation schemes, and K is the total number of evaluation systems.
3. The method of claim 1, wherein the setting of the flexibility of each evaluation system to determine the information particles corresponding to the membership degree and the non-membership degree in each evaluation information matrix comprises:
determining the degree of flexibility according to equation (2),
where K is the total number of evaluation systems, γ is a predetermined level of granularity, γ iskEvaluating the individual flexibility corresponding to the system k;
determining information particles corresponding to each membership degree and each non-membership degree according to a formula (3) and a formula (4),
wherein the content of the first and second substances,
observation scheme D on satellite for evaluation system of kthiAnd satellite Observation plan DjThe left end point of the information particle corresponding to the membership degree of the evaluation information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe right end point of the information particle corresponding to the membership degree of the evaluation information,
observation scheme D on satellite for evaluation system of kthiAnd satellite Observation plan DjThe degree of membership in the evaluation information in between,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe left end point of the information particle corresponding to the non-membership degree in the evaluation information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe right end point of the information particle corresponding to the non-membership degree in the evaluation information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjIn betweenNon-membership in the information.
4. The screening method according to claim 1, wherein the optimizing the cut points and the flexibility by using the PSO algorithm to obtain the optimal information particles corresponding to each linguistic item, membership degree and non-membership degree in the evaluation information matrix respectively comprises:
inputting the interception point and the flexibility into a PSO algorithm to generate current information particles;
randomly selecting 500 combinations of linguistic items, membership degrees and non-membership degrees from the interval where the current information particles are located, calculating the optimization criterion corresponding to the current information particles according to formulas (5) to (9),
M＝κ·M1+(1-κ)·M2， (5)
wherein M is the optimization criterion, K is a preset parameter value, K is the total number of the evaluation system, n is the total number of the satellite observation schemes, g +1 is the number of the language items in the preset language item set,
observation scheme D in satellite for aggregating K evaluation systemsiAnd satellite Observation plan DjTo evaluate the numerical form of the linguistic item of information,
observation scheme D in satellite for aggregating K evaluation systemsiAnd satellite Observation plan DjThe degree of membership of the information is evaluated,
observation scheme D in satellite for aggregating K evaluation systemsiAnd satellite Observation plan DjThe non-membership of the information is evaluated,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjTo evaluate the numerical form of the linguistic item in the information, muij kObservation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe degree of membership in the information is evaluated,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DjThe degree of non-membership in the information is evaluated,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DlTo evaluate the numerical form of the terms in the information,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DlThe degree of membership of the evaluation information therebetween,
observation plan D at satellite for k-th evaluation systemiAnd satellite Observation plan DlThe degree of non-membership of the evaluation information therebetween,
observation plan D at satellite for k-th evaluation systemlAnd satellite Observation plan DjThe numerical form of the language term of the evaluation information in between,
observation plan D at satellite for k-th evaluation systemlAnd satellite Observation plan DjThe degree of membership of the evaluation information therebetween,
observation plan D at satellite for k-th evaluation systemlAnd satellite Observation plan DjNon-membership of the evaluation information therebetween;
calculating a fitness function corresponding to the current information particles according to a formula (10),
wherein M isiThe optimization criteria corresponding to the combination of the ith linguistic item, the membership degree and the non-membership degree;
judging whether the current iteration number is larger than or equal to a preset iteration number threshold value,
under the condition that the current iteration number is judged to be smaller than a preset iteration number threshold value, updating the particles of the PSO algorithm according to a formula (11) to a formula (13), randomly selecting 500 combinations of linguistic items, membership degrees and non-membership degrees from the interval where the current information particles are located again, calculating the optimization criterion corresponding to the current information particles according to a formula (5) to a formula (9), and executing corresponding steps of the method until the current iteration number is judged to be larger than or equal to the iteration number threshold value,
v(m+1)＝ζ(m)×v(m)+d1a1·(xl-x)+d2a2·(xg-x)， (11)
where v (m +1) is the velocity of the particle after update, ζ (m) is the shrinkage factor, v (m) is the velocity before update, m is the current iteration number, d1And d2As acceleration constant, a1And a2Is [0,1 ]]Two random numbers of the interval, xlFor the current locally optimal solution, xgFor the current global optimal solution, x is the current position of the particle;
ζ(m)＝(totnum-m)×(ζmax-ζmin)/totnum， (12)
wherein totnum is the threshold value of the iteration times, ζmaxIs the maximum value of the coefficient of contraction, ζminIs the minimum value of the shrinkage factor;
x(m+1)＝x(m)+v(m+1)， (13)
wherein x (m +1) is the position of the particle after updating, and x (m) is the position of the particle before updating;
and outputting the optimal interception point and the optimal flexibility when judging that the current iteration times are larger than the iteration time threshold.
5. The screening method of claim 1, wherein updating each of the evaluation information matrices according to the optimal information particles corresponding to the terms, membership degrees, and non-membership degrees in each of the evaluation information matrices comprises:
updating the evaluation information matrix according to equation (14),
wherein, IrkIn satellite observation scenario D for updated evaluation system kiAnd satellite Observation plan DjThe evaluation information of the time interval between the first and second evaluation,
for updated assessment information
Term of (1), Irij kEvaluation information, mu, generated for a value randomly selected from the optimal information particlesij′kMembership, v, generated for a randomly selected value in the optimal information particleij′kAnd generating a non-membership degree for a randomly selected numerical value in the optimal information particles, wherein n is the total number of the satellite observation schemes, and K is the total number of the evaluation systems.
6. The screening method according to claim 1, wherein the aggregating all evaluation system evaluation information matrices by using an intuitive language dominant evidence generalized compensation weighted average operator to obtain an aggregated fused evaluation information matrix comprises:
obtaining the fusion assessment information matrix according to formula (15)
Wherein, θ (Ir)ij′k) Observing scheme D at satellite for updated k-th evaluation systemiAnd satellite Observation plan DlThe numerical form of the language term of the evaluation information in between,
observing scheme D at satellite for updated k-th evaluation systemiAnd satellite Observation plan DlThe degree of membership of the evaluation information therebetween,
observing scheme D at satellite for updated k-th evaluation systemiAnd satellite Observation plan DlThe degree of non-membership of the evaluation information therebetween,
for the updated evaluation information of the tth evaluation system,
for the updated evaluation information of the kth evaluation system, θ (Ir)ij′t) Satellite observation scenario D for updated tth evaluation systemiAnd satellite Observation plan DlThe numerical form of the language term of the evaluation information in between,
satellite observation scenario D for updated tth evaluation systemiAnd satellite Observation plan DlThe degree of membership of the evaluation information therebetween,
satellite observation scenario D for updated tth evaluation systemiAnd satellite Observation plan DlNon-membership of the evaluation information therebetween.
7. The screening method of claim 1, wherein obtaining the evaluation information for each satellite observation scenario according to an intuitive language-induced ordered weighted average operator, and obtaining the score function value corresponding to each evaluation information comprises:
the evaluation information of each satellite observation scenario is calculated according to equation (23),
wherein, QGDDiIs the evaluation information of the ith satellite observation scheme, j is 1,2, and n, σ (j) is the ranking of the evaluation information, and θ (Ir)iσ(j) c) For the language items in the jth evaluation information arranged according to the descending principle in the ith satellite observation scheme in the aggregated evaluation information,
for the membership degrees in the j evaluation information arranged according to the descending principle in the ith satellite observation scheme in the aggregated evaluation information,
the non-membership degree, w, in the j evaluation information arranged according to the descending principle in the ith satellite observation scheme in the aggregated evaluation informationiFor the ith satellite observerThe weight corresponding to the case;
a score function of the evaluation information of each satellite observation scenario is calculated according to formula (24),
S(QGDDi)＝θ(QGDDi)(1+μi-vi)， (24)
wherein, θ (QGDD)i) In the form of a number, mu, of terms corresponding to the evaluation information of the ith satellite observation planiMembership degree, v, corresponding to the evaluation information of the ith satellite observation schemeiAnd the non-membership degree corresponding to the evaluation information of the ith satellite observation scheme.
8. A satellite observation scenario screening system based on intuitive language preference particles, the screening system comprising a processor configured to perform the screening method of any one of claims 1 to 7.
9. A computer readable storage medium having stored thereon instructions for reading by a machine to cause the machine to perform a screening method according to any one of claims 1 to 7.
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