CN112529241A - Remote sensing satellite cost effectiveness balance optimization method - Google Patents
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Abstract
The invention relates to a remote sensing satellite cost balancing and optimizing method, which comprises the following four steps: designing a remote sensing satellite efficiency evaluation model; designing a remote sensing satellite multi-dimensional cost model; on the basis, a remote sensing satellite efficiency cost balance optimization model is designed by taking a performance index, a reliability index and a weight index of the remote sensing satellite as optimization parameters; and designing a fitness function based on the efficiency cost balance optimization model, solving the model by using a genetic algorithm to obtain an optimal solution, and feeding back the optimal solution to the engineering unit. The invention forms a comprehensive system which integrates an efficiency model, a multi-dimensional cost model and an efficiency cost balance optimization model of the remote sensing satellite, and can quickly and accurately obtain the optimal design scheme of the remote sensing satellite.
Description
Technical Field
The invention provides a remote sensing satellite cost-effectiveness balance optimization method which can analyze and balance a remote sensing satellite design scheme to obtain an optimal design scheme with high efficiency and low cost.
Background
One of the most important tasks in the early days of remote sensing satellite development was to make optimal tradeoffs between performance and cost requirements for various designs. The efficiency is the degree to which the remote sensing satellite meets a set of specific task requirements under specified conditions and within specified time, and is a comprehensive measure of the performance and reliability of the satellite. The cost is mainly the development cost of the remote sensing satellite and is closely related to the design parameters of the satellite. The balance between the efficiency and the cost is optimal, the method is a necessary means for realizing the shortening, the rapidness and the occurrence on demand of the satellite development period, and is an effective way for supporting the further improvement of the overall design capability of the satellite system. Efficiency cost balance optimization is used as an important link of a remote sensing satellite, and a design scheme for realizing high efficiency and low cost is increasingly concerned at home and abroad.
At present, the research of carrying out cost balance optimization directly aiming at remote sensing satellites abroad is not sufficient, but a great deal of research is carried out in the fields of weapon equipment and the like. ADC performance models proposed by the American Industrial weapons systems Performance advisory Committee (WSEIAC) are widely applied and accepted, and a series of derivative models such as ARINC model, AN model and AAM model are developed by American aviation radio research corporation, American navy and American army respectively for the performance evaluation of weaponry. On the other hand, with the increasingly large scale and complex structure of the engineering system, the financial resources consumed are huge, and the phenomenon of cost overload frequently occurs, the economic affordability of equipment acquisition at home and abroad is more and more important. Foreign institutions establish a series of expense evaluation models aiming at the characteristics of different systems on the basis of a large amount of historical expense data. For example, the U.S. national aerospace administration (NASA) proposes Unmanned Spacecraft Cost Models (USCM) and Small Spacecraft Cost Models (SSCM) using weight as a core element for spacecraft cost estimation. The United states department of defense adopts a cost independent variable (CAIV) method to comprehensively optimize the combat effectiveness, cost and performance parameters of novel weaponry, determine a cost target in a life cycle and perform cost control. The domestic research work on the aspect of cost balance optimization starts late, and although certain results are obtained, a certain gap still exists so far, so that the problem of remote sensing satellite cost balance optimization is necessarily researched.
Compared with a weapon system, the remote sensing satellite cost optimization problem has the difficulty that due to the fact that the satellite system has the characteristic of being not maintainable, degradation use conditions exist in the operation process, the working mode and the actual operation condition are complex, and the existing efficiency evaluation model cannot meet the actual engineering requirements. Meanwhile, the evaluation dimension of the satellite cost model is single, cost influence factors in the satellite development process cannot be covered comprehensively, a series of continuous constraint and discrete constraint limits are often applied to the remote sensing satellite design process, and an efficiency cost balance optimization model aiming at the characteristics of the remote sensing satellite is not provided at present. In addition, the engineering design usually depends on the experience of designers to try and optimize the design scheme of the remote sensing satellite, and is lack of automatic means support, so that not only is the optimal solution difficult to approach, but also the waste of design resources can be caused.
Disclosure of Invention
The technical problem solved by the invention is as follows: the remote sensing satellite efficiency cost balance optimization method is characterized in that an efficiency cost balance model is constructed by utilizing remote sensing satellite performance parameters, reliability parameters and weight parameters, and the model is solved by utilizing a genetic algorithm, so that an optimal design scheme with high efficiency and low cost is obtained.
The technical scheme of the invention is as follows: a remote sensing satellite cost-effectiveness balance optimization method is realized by the following steps:
the method comprises the following steps: designing a remote sensing satellite efficiency model for comprehensively measuring the performance and reliability of the satellite by taking a remote sensing satellite subsystem as minimum granularity;
step two: designing a remote sensing satellite multi-dimensional cost model;
step three: designing a remote sensing satellite efficiency cost balance model, wherein the remote sensing satellite efficiency model and the remote sensing satellite multi-dimensional cost model established in the first step and the second step are used as objective functions, and the remote sensing satellite performance index, the reliability index and the weight index are used as optimization parameters;
step four: designing a remote sensing satellite cost-effectiveness balance model based on the third step, designing a fitness function, and determining a remote sensing satellite design scheme with the optimal cost-effectiveness ratio by utilizing a genetic algorithm according to the optimized parameters.
Preferably, the remote sensing satellite efficiency model E comprises a remote sensing satellite availability model A, a remote sensing credibility model D and a remote sensing satellite capacity model C,
then, E ═ a · D · C;
the remote sensing satellite availability model A is a model for describing the probability of different working states when the remote sensing satellite starts to work, and the working state of the remote sensing satellite is a combination of normal or fault states of different subsystems;
the remote sensing satellite credibility model D is a model for describing the transition probability of the remote sensing satellite in different working states;
the remote sensing satellite capability model C is a model for measuring the capability of the remote sensing satellite for completing a given task under different working states.
Preferably, the remote sensing satellite availability model a is specifically:
in the formula, ajIs the availability of the remote sensing satellite in the jth working state, m ═ n2+ n +2)/2 is the total number of working states of the remote sensing satellite, n is the number of subsystems of the remote sensing satellite, Fi(0) Probability of failure, x, before execution of a mission for the ith subsystem of a remote sensing satellitej,iAnd e {0,1} represents a binary state variable of the ith subsystem when the remote sensing satellite is in the jth working state, 0 represents normal, and 1 represents fault.
Preferably, the remote sensing satellite credibility model D is:
D＝[djk]m×m，
in the formula (d)jkRepresenting the transfer of the remote sensing satellite from the jth operating state to the kth operating state over time tProbability, FiAnd (t) the failure probability of the ith subsystem of the remote sensing satellite at the moment t.
Preferably, the remote sensing satellite capability model C is established by:
s1, establishing an efficiency hierarchical structure index system, wherein the system comprises attitude control capability, imaging capability and information transmission capability, and determining performance indexes of the three capabilities; the performance indexes of the attitude control capability comprise three-axis measurement precision, three-axis pointing precision and three-axis stability; the performance indexes of the imaging capability comprise target positioning precision, imaging width, imaging time and ground resolution; the performance indexes of the information transmission capacity comprise signal bandwidth, information transmission rate and information transmission error rate;
s2, obtaining each performance index p by using an analytic hierarchy processlTotal sort weight of
Wherein the three capabilities are in the same level, and the performance index under each capability is in the same level;
s3, determining each performance index p when the remote sensing satellite subsystem operates normallylIs evaluated byl0Evaluation value ul0∈[0,10]Is defined as the index value range [ p ]l,L,pl,U](1, 2, …, q), where q is the number of performance indicators;
s4, regarding the capability of the remote sensing satellite subsystem in the abnormal operation state as a reduced capability of the system capability in normal operation, and needing to evaluate the value ul0On the basis, the evaluation values of all performance indexes when the remote sensing satellite subsystems are in different states are further calculated;
s5, calculating the corresponding capacity c of the j working state of the remote sensing satellite according to the results of S2 and S4j；
The resulting capability model C is:
C＝[c1,c2,…,cm]T，
wherein m is the number of the working states of the remote sensing satellite, xiIs the normal state of the ith subsystem, ρli∈[0,1]Represents the i-th subsystem fault to the performance index plInfluence.
Preferably, the evaluation value in S3 is determined as follows:
if p is the forward direction indexl<pl,LThen u isl00; if p isl>pl,UThen u isl010; if p isl,L≤pl≤pl,U，ul0According to piThe value is changed linearly; if p is the reverse index when the performance index isl<pl.LThen u isl010; if p isl>pl,UThen u isl00; if p isl,L≤pl≤pl.U，ul0According to plThe value is changed linearly.
Preferably, the remote sensing satellite multidimensional expense model is expressed in the following way:
in the formula, M is the total cost of the remote sensing satellite; wiIs the weight of the ith subsystem, αi、βl,i、βi,r、βi,wThe cost correction coefficient of the ith subsystem is solved by a regression method;
represents the capability index p of the ith subsystem pair l1 indicates a relationship, 0 indicates no relationship,
is a ceiling function and when the state of the ith subsystem does not affect the index plWhen is pili＝0， βl,i＝0(l＝1,2,…,q)；FiAnd (t) represents the failure probability of the remote sensing satellite at the moment t when the remote sensing satellite works.
Preferably, the remote sensing satellite cost tradeoff model is as follows:
max E/M
s.t.M≤MU
pl∈{pl,L,pl,1,…,pl,U},l＝1,2,…,q；p＝(p1,p2,…,pq)T
Ri∈{Ri,L,Ri,1,…,Ri,U},i＝1,2,…,n；R＝(R1,R2,…,Rn)T
Wi∈{Wi,L,Wi,1,…,Wi,U},i＝1,2,…,n；W＝(W1,W2,…,Wn)T
wherein, { pl,L,pl,1,…,pl,UExpressing a set of value ranges of the first individual performance index; { Ri,L,Ri,1,…,Ri,URepresenting a set of reliability value ranges of the ith subsystem at the end of the service life; { Wi,L,Wi,1,…,Wi,URepresents a set of i-th subsystem weight measurement value ranges; rLIs the lower limit of reliability at the end of the whole satellite life; wUIs the upper limit of the whole star weight; mUIs the upper limit of the whole satellite cost; the failure probability of the ith subsystem at the end of the service life of the whole satellite meets the relation Fi(t)＝1-Ri。
Preferably, the step four is realized by the following steps:
step four, appointing a coding rule: the code expression adopts an extended expression mode of performance indexes, the reliability of each subsystem and 3 vectors of weight; encoding a design into a chromosome;
step four, designing a fitness function: for the kth chromosome xi(k)Fitness function of (xi)(k)) Is defined as follows:
in the formula, xi(k)The k-th chromosome representing the genetic algorithm,
and Wi (k)(1, 2, …, n) respectively represents the reliability and weight values of the ith subsystem on the kth chromosome; mUIs the upper limit of the cost of the whole satellite, WLIs the lower limit of the weight of the whole star, RLThe lower limit of the reliability at the end of the life of the whole star, wherein E represents the corresponding effective value of the chromosome, and M represents the corresponding cost value of the chromosome;
determining genetic operators, performing cross operation and mutation operation on each chromosome respectively, and calculating the corresponding fitness of each chromosome;
step four, searching the maximum fitness and the corresponding chromosome in the population of the current generation, judging whether the maximum iteration times is reached, if so, finishing the optimization, and outputting an optimal design scheme; if not, carrying out selection, crossing and mutation operations to generate a new optimization point, and carrying out a new iteration until all iteration times are finished;
preferably, the chromosome expression in step four-one is as follows:
in the formula,
and the value of the l individual performance index on the k chromosome is shown.
Compared with the prior art, the invention has the beneficial effects that:
the method constructs a remote sensing efficiency model, considers the characteristics of irreparability, degradation use and the like of the remote sensing satellite, and can support the evaluation of the whole life cycle efficiency of the remote sensing satellite.
The invention constructs a multi-dimensional cost model of the remote sensing satellite, considers the performance index, the reliability and the weight of the remote sensing satellite and avoids the excessive dependence of the existing model on the weight index.
The method designs the remote sensing satellite cost-effectiveness weight balance optimization model on the basis of the performance parameters, the reliability parameters and the weight parameters, constructs the continuity constraint and the discreteness constraint, and effectively solves the problem of optimization solution of the multi-variable coupling condition by using a genetic algorithm. When conditions such as the lower limit of the reliability of the whole satellite of the remote sensing satellite, the upper limit of the cost, the upper limit of the weight, the value ranges of the performance parameters, the reliability parameters and the weight parameters and the like are given, a design scheme with the maximum efficiency and the lowest cost can be given. The model is wide in application conditions and is not limited to a specific remote sensing satellite.
By way of example, the method for optimizing the cost balance can automatically search the optimal design scheme meeting the constraint condition, and compared with the traditional method which tries to get together by relying on experience of designers, the method can quickly and accurately support the design of the remote sensing satellite scheme.
Drawings
FIG. 1 is a schematic diagram of the basic steps of the present invention;
FIG. 2 is a diagram illustrating the relationship of steps of the present invention;
FIG. 3 is an index system for the remote sensing satellite performance hierarchy according to the present invention;
FIG. 4 is a schematic diagram of a partial cross-mapping of the present invention;
FIG. 5 is a graph of simulation results of the method of the present invention, i.e., an iterative curve of satellite cost effectiveness ratio optimization calculations of the present invention.
Detailed Description
The invention is further illustrated by the following examples.
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments.
The scheme discloses a remote sensing satellite efficiency cost balance optimization method. The method has the specific steps shown in figure 1. Firstly, establishing a remote sensing satellite efficiency model; secondly, establishing a remote sensing satellite multi-dimensional cost model; on the basis, a remote sensing satellite balance optimization model is constructed; and finally, solving the model by using a genetic algorithm to obtain an optimal design scheme and feed back an engineering unit. If the design scheme meets the requirements, ending the process; if not, further constraining the balance space according to development requirements, and solving the optimal design scheme again.
The relationship among the steps of the remote sensing satellite efficiency cost balance optimization method is shown in figure 2. The remote sensing satellite efficiency model is related to performance parameters and reliability parameters; the remote sensing satellite multi-dimensional cost model is related to performance parameters, reliability parameters and weight parameters; the efficiency model and the cost model form an objective function of the efficiency cost balance optimization model, and the performance parameter, the reliability parameter and the weight parameter form an optimization parameter of the efficiency cost balance optimization model. By inputting continuous constraints such as whole satellite reliability constraint, whole satellite weight constraint, whole satellite cost and the like, and discrete constraints such as performance parameters, reliability parameters and weight parameter value ranges, and utilizing a genetic algorithm, an optimal design scheme of the remote sensing satellite can be obtained.
The method comprises the following steps: establishing remote sensing satellite efficiency model
The efficiency of the remote sensing satellite is the degree that the remote sensing satellite meets a group of specific task requirements under specified conditions and within specified time, and is the comprehensive measurement of the performance and the reliability of the satellite. The remote sensing satellite efficiency model takes a remote sensing satellite subsystem as minimum granularity and comprises a remote sensing satellite availability model part, a credibility model part and an ability model part.
The remote sensing satellite has similarity in structure, is composed of several subsystems (or systems), and defines X1，X2，Xn. Wherein n is the number of subsystems. Definition of XiIn the normal state of the ith subsystem,
is the fault condition of the ith subsystem. The full star state may be represented as a combination of normal or fault states for different subsystems.
According to engineering experience, a remote sensing satellite failure criterion is defined as more than two subsystem failures. The remote sensing satellite has m as n 22+ n/2+2 working states.
Without loss of generality, the working state of the remote sensing satellite is recorded as Sj(j is 1,2 …, m), and let S1The Sm is in a failure state when all subsystems of the satellite work normally.
Defining the binary state variable of ith subsystem in jth working state as xj,iIs provided with
The remote sensing satellite availability model is a model for describing the probability of different working states when the remote sensing satellite starts to work, and the working state of the remote sensing satellite is the combination of normal or fault states of different subsystems and is represented by availability vectors. Defining the availability vector of the remote sensing satellite as A ═ a1,a2,…,am]. Wherein, ajAnd the availability of the remote sensing satellite in the jth working state is represented. a isjCalculated using the formula:
in the formula, Fi(0) And (i is 1,2, …, n) is the failure probability of the ith remote sensing satellite subsystem before the task is executed.
The remote sensing satellite credibility model is a model for describing the transition probability of the remote sensing satellite between different working states and can be expressed as a credibility matrix D ═ Djk]m×m. Wherein d isjk＝P(Sk|Sj) Representing the probability of a transition from the jth operating state to the kth operating state over time t. djkCalculated using the formula:
in the formula, FiAnd (t) represents the failure probability of the remote sensing satellite at the moment t when the remote sensing satellite works.
The remote sensing satellite capability model is a model for describing transition probability of the remote sensing satellite between different working states, and can use a capability vector of C ═ C1,c2,…,cm]T. Wherein, cjAnd the measurement represents the mission-completion degree of the remote sensing satellite in the jth working state. The method for establishing the remote sensing satellite capability model comprises the steps of establishing an efficiency hierarchical structure index system and multi-state capability evaluation, and specifically comprises the following steps:
1) establishing a hierarchy of performance indices
The performance hierarchy index system includes attitude control capability, imaging capability, and information transfer capability.
The performance indexes of the attitude control capability include three-axis measurement precision, three-axis pointing precision and three-axis stability.
The imaging capability comprises performance indexes such as target positioning precision, imaging width, imaging time and ground resolution.
The performance indexes of the information transmission capability include signal bandwidth, information transmission rate and information transmission error rate.
Wherein, the three abilities are in the same level, and the performance index under each ability is in the same level. The performance hierarchy index system is shown in figure 3.
2) Multistate capability assessment
And constructing a judgment matrix according to the remote sensing satellite efficiency index system, and comparing the importance degrees of all indexes at the same level. For the criterion matrix with the order greater than 2, hierarchical sequencing and consistency test need to be carried out. Then respectively calculating the maximum eigenvalue of the judgment matrix and the corresponding eigenvector, and calculating the eigenvectorAnd (5) normalization processing is carried out, and the ranking weight of the importance of each evaluation index of the same layer relative to a certain index of the upper layer is obtained. According to the hierarchical relation of the index system, multiplying the sorting weight of the capability layer by the sorting weight of the index layer to which the capability layer belongs to obtain the total sorting weight of each index
Index plIs evaluated byl0∈[0,10]Is defined as the index value range [ p ]l,L,pl,U](l ═ 1,2, …, q) linearly varying function values, where q is the total number of performance indicators. When the index is a forward index (index of expected characteristics), if pl<pl,LThen u isl00; if p isl>pl,UThen u isl010; if p isl,L≤pl≤pl,U，ul0According to plThe value is changed linearly; when the index is a reverse index (index of small characteristic), if pl<pl,LThen u isl010; if p isl>pl,UThen u isl00; if p isl,L≤pl≤pl,U，ul0According to plThe value is changed linearly.
The capability of the system in the abnormal operation state can be regarded as a reduction of the capability of the system in the normal operation state according to the definition that the satellite is in different operation states. Definition of pli∈[0,1]Indicating the i-th subsystem failure pair capability index p l0 means no influence and 1 means a decisive influence.
When the satellite is in different working states, an index p is definedlIs evaluated bylComprises the following steps:
the capability corresponding to the jth working state of the remote sensing satellite is defined as:
according to the availability model, the credibility model part and the capability model of the remote sensing satellite, the efficiency model of the remote sensing satellite is as follows:
E＝A·D·C
step two: design of multi-dimensional cost model of remote sensing satellite
The total cost of the remote sensing satellite can represent the sum of the costs of all the subsystems, namely:
in the formula, M is the total cost of the remote sensing satellite; miIs the basic cost of the ith subsystem.
The subsystem cost model is constructed from multiple dimensions of weight, performance, reliability, and the like. The basic costs of the ith subsystem are:
in the formula, WiIs the weight of the ith subsystem, αi、βli(j＝1,2,…,q)、βi,r、βi,wIs the i-th subsystem cost correction factor.
Represents the capability index p of the ith subsystem pair l1 indicates a relationship, 0 indicates no relationship,
is a ceiling function; and when the status of the ith branch does not affect the index plWhen (i.e. pi)li＝0)，βl,i＝0(l＝1,2,…,q)；FiAnd (t) represents the failure probability of the remote sensing satellite at the moment t.
Equation (7) can be solved by a regression method. Suppose the ithSubsystem has NiIndividual data sample
When the number of samples
Then, the correction coefficient can be solved by the least quadratic regression method, i.e.
Therefore, the calculation formula of the total cost of the remote sensing satellite is as follows:
in the formula,
the coefficients of the fitted ith subsystem cost model,
n required for cost fitting of ith subsystemiAnd (4) sampling.
Step three: and designing a remote sensing satellite efficiency cost balance model (17) by taking the performance index, the reliability index and the weight index of the remote sensing satellite as optimization parameters on the basis of the efficiency model and the cost model established in the first step and the second step, wherein the remote sensing satellite efficiency cost balance model comprises an optimization target and a balance space.
Firstly, according to the efficiency model obtained in the second stage and the cost model obtained in the third stage, an efficiency-cost ratio (abbreviated as "efficiency-cost ratio") is established as follows:
ε＝E/M (11)
the invention aims at maximizing the cost-effectiveness ratio, and hopefully improves the satellite efficiency and reduces the satellite cost by adjusting design parameters such as satellite capacity indexes, the reliability of each subsystem, the weight and the like. This requires finding the optimal design solution in a trade-off space determined by constraints such as satellite capability indicators, subsystem reliability and weight.
Second, a trade-off space, i.e., model constraints, is constructed. The constraints comprise whole satellite reliability constraint, whole satellite weight constraint, whole satellite cost constraint, performance parameter, reliability parameter, weight parameter value range and the like. Considering that the reliability of the remote sensing satellite is reduced along with the increase of the on-orbit running time, the reliability (or failure probability) at the end of the service life of each subsystem is taken as a trade-off optimization object. Wherein,
the whole satellite reliability constraint is that the multiplication of the reliability indexes of the subsystems is not less than a given value;
the whole satellite weight constraint is that the sum of the weights of all the subsystems does not exceed a given value;
the whole satellite cost constraint is that the sum of the costs of all the subsystems does not exceed a given value;
the constraints are all continuous constraints.
According to engineering practice, the constraints of performance parameters, reliability parameters and weight parameters generally reserve decimal 3-4 bits, and belong to discrete constraints.
The remote sensing satellite cost tradeoff optimization model can be expressed in the following mathematical form:
wherein, { pl,L,pl,1,L,pl,UExpressing a set of value ranges of the first individual performance index; { Ri,L,Ri,1,L,Ri,URepresenting a set of reliability value ranges of the ith subsystem at the end of the service life; { Wi,L,Wi,1,L,Wi,URepresents a set of i-th subsystem weight measurement value ranges; rLIs the lower limit of reliability at the end of the whole satellite life; wUIs the upper limit of the whole star weight; mUIs the upper limit of the whole satellite cost. It should be noted that the whole starThe failure probability of the ith subsystem at the end of the life satisfies the relation Fi(t)＝1-Ri。
Step four, designing a remote sensing satellite cost-effectiveness balance model based on the step three, designing a fitness function, and determining a remote sensing satellite design scheme with the optimal cost-effectiveness ratio by utilizing a genetic algorithm aiming at an optimized space, wherein the process is as follows:
1) specifying the encoding rule: the coding expression adopts an expansion expression mode of capability index, system reliability of each subsystem and 3 vectors of weight, and a design scheme is coded from a chromosome.
In the formula, xi(k)The k-th chromosome representing the genetic algorithm,
the value of the l-th performance index on the k-th chromosome is shown,
and Wi (k)(l ═ 1,2, …, n) represents the reliability and weight values of the ith subsystem on the kth chromosome, respectively.
2) Design fitness function definition: for chromosome xi(k)The fitness function of (a) is defined as follows.
3) And determining genetic operators, including initial population number, convergence criterion, cross probability, mutation probability and the like.
4) Respectively carrying out cross operation and mutation operation on each chromosome, and calculating the corresponding adaptability of each chromosome;
5) searching the maximum fitness and the corresponding chromosome (the optimal design scheme of the generation) in the population of the generation;
6) judging whether the maximum iteration times is reached, if so, finishing the optimization, and outputting an optimal design scheme;
7) and if not, carrying out selection, crossing and mutation operations. Where the selection is the basic operation of the genetic algorithm, it can be obtained from published data.
And (3) crossing: the report part maps the intersection method, namely two intersection points are randomly selected, the intersection parts are needed in the two inner positions of the two points, and then the intersection is carried out one by one according to the sequence, as shown in fig. 4. According to practical experience, the cross probability is generally 0.4-0.8.
Mutation: the variation point Delta is specified according to the variation probability, h is a given integer and is recorded
Is chromosome xi(k)The corresponding variable at the variation point Δ. If the constraint of the variation point delta corresponding to the variable is a discrete constraint, the set of values is subject to the constraint, namely { xiΔ,L,ξΔ,1,L,ξΔ,U}. Randomly drawing h values in the set
By replacing the mutation points Δ, h new chromosomes can be generated, and the best one of the h new chromosomes is selected to replace the original chromosome, see formula (15). If the constraint of the variable corresponding to the variation point delta is continuous constraint, h values are randomly extracted in the value range and then processed with the discrete constraint condition. According to practical experience, the cross probability is generally 0.01-0.2.
After crossing and mutation operations, a new optimization point is generated, and a new iteration is performed until all iteration times are completed. Where the selection is the basic operation of the genetic algorithm, it can be obtained from published material.
And feeding back the optimized optimal design scheme to the engineering design unit.
Examples illustrate that:
by utilizing the remote sensing satellite efficiency cost balancing optimization method provided by the invention, the remote sensing satellite capacity index, the reliability and the weight of each subsystem are optimized, the satellite efficiency is improved, and the satellite cost is reduced.
The composition and code of each subsystem of the composition structure of a certain remote sensing satellite are shown in a table 1.
TABLE 1 typical remote sensing satellite composition and code
| Serial number | Subsystem system | (Code) | Serial number | Subsystem system | (Code) | Serial number | Subsystem system | (Code) | ||
| 1 | Optical | X | 1 | 4 | Propulsion subsystem | X4 | 7 | Measurement and | X | 7 |
| 2 | Data transmission subsystem | X2 | 5 | Power supply and | X | 5 | 8 | Number pipe subsystem | X8 | |
| 3 | | X | 3 | 6 | Thermal | X | 6 | 9 | Structure and mechanism subsystem | X9 |
The working state of the remote sensing satellite is defined in table 2. Wherein, all subsystems of the satellite normally work in a state 1; recording the satellite adjusting failure state as a state 47; the states 2-46 represent the respective cases in which only one subsystem fails or two subsystems fail.
TABLE 2 typical remote sensing satellite operating State definition
And establishing a remote sensing satellite efficiency model according to the first step.
According to the second step, cost data samples of 8 remote sensing satellites of the same type are collected, and a multidimensional cost model of the remote sensing satellites is obtained through fitting as follows:
and designing a remote sensing satellite cost optimization model according to the third step.
According to design requirements, the reliability of the whole star is not lower than 0.68, the total weight is not lower than 2800kg, and the total cost is not more than 40 million. Meanwhile, the value ranges of the weight, the reliability and the performance indexes of all the subsystems of the constraint satellite are shown in a table 3.
According to engineering practice, the capability index, reliability and weight parameter are taken from a set of 10-20 discrete points which are uniformly distributed in the upper and lower limit ranges.
TABLE 3 remote sensing satellite weight, reliability and performance index value ranges
And according to the fourth step, determining the design scheme of the remote sensing satellite with the optimal cost-effectiveness ratio. Wherein the operating parameters are as follows:
population quantity: 200. cross probability: 0.6, mutation probability: 0.2, evolution algebra: 200.
the relationship between the evolution times of the genetic algorithm and the cost-effectiveness ratio, the efficiency and the cost are respectively shown in FIG. 5. It can be seen from the figure that as the number of evolutions increases, the efficiency and cost ratio of the satellite increases, the cost of the satellite decreases, and finally an optimal solution is converged.
The original design scheme is shown in table 4, and the satellite optimization design scheme and the constraint of each subsystem are specifically shown in table 4. The satellite cost-effectiveness ratio is improved by 23.35 percent after optimization. And the satellite capacity index, the subsystem reliability and the weight distribution are fed back to a design unit.
TABLE 4 Pre-and post-optimization schemes for satellites
The invention has not been described in detail in part in the common general knowledge of a person skilled in the art.
Claims (10)
1. A remote sensing satellite cost-effectiveness balance optimization method is characterized by comprising the following steps: the method is realized by the following steps:
the method comprises the following steps: designing a remote sensing satellite efficiency model for comprehensively measuring the performance and reliability of the satellite by taking a remote sensing satellite subsystem as minimum granularity;
step two: designing a remote sensing satellite multi-dimensional cost model;
step three: designing a remote sensing satellite efficiency cost balance model, wherein the remote sensing satellite efficiency model and the remote sensing satellite multidimensional cost model established in the first step and the second step are used as objective functions, and the performance index, the reliability index and the weight index of the remote sensing satellite are used as optimization parameters;
step four: designing a remote sensing satellite cost-effectiveness balance model based on the third step, designing a fitness function, and determining a remote sensing satellite design scheme with the optimal cost-effectiveness ratio by utilizing a genetic algorithm according to the optimized parameters.
2. The method of claim 1, wherein: the remote sensing satellite efficiency model E comprises a remote sensing satellite availability model A, a remote sensing satellite credibility model D and a remote sensing satellite capacity model C,
then, E ═ a · D · C;
the remote sensing satellite availability model A is a model for describing the probability of different working states when the remote sensing satellite starts to work, and the working state of the remote sensing satellite is the combination of normal or fault states of different subsystems;
the remote sensing satellite credibility model D is a model for describing the transition probability of the remote sensing satellite in different working states;
the remote sensing satellite capability model C is a model for measuring the capability of the remote sensing satellite to complete a given task under different working states.
3. The method of claim 2, wherein: the remote sensing satellite availability model A specifically comprises the following steps:
in the formula, ajIs the availability of the remote sensing satellite in the jth working state, m ═ n2+ n +2)/2 is the total number of working states of the remote sensing satellite, n is the number of subsystems of the remote sensing satellite, Fi(0) Probability of failure, x, before execution of a mission for the ith subsystem of a remote sensing satellitej,iAnd e {0,1} represents a binary state variable of the ith subsystem when the remote sensing satellite is in the jth working state, 0 represents normal, and 1 represents fault.
4. The method of claim 2, wherein: the remote sensing satellite credibility model D is as follows:
in the formula (d)jkRepresenting the probability of the remote sensing satellite transferring from the jth working state to the kth working state over time t, FiAnd (t) the failure probability of the ith subsystem of the remote sensing satellite at the moment t.
5. The method of claim 2, wherein: the remote sensing satellite capability model C is established in the following way:
s1, establishing an efficiency hierarchical structure index system, wherein the system comprises attitude control capability, imaging capability and information transmission capability, and determining performance indexes of the three capabilities; the performance indexes of the attitude control capability comprise three-axis measurement precision, three-axis pointing precision and three-axis stability; the performance indexes of the imaging capability comprise target positioning precision, imaging width, imaging time and ground resolution; the performance indexes of the information transmission capacity comprise signal bandwidth, information transmission rate and information transmission error rate;
s2, obtaining each performance index p by using an analytic hierarchy processlTotal sort weight of
Wherein the three capabilities are in the same level, and the performance index under each capability is in the same level;
s3, determining each performance index p when the remote sensing satellite subsystem operates normallylIs evaluated byl0Evaluation value ul0∈[0,10]Is defined as the index value range [ p ]l,L,pl,U](1, 2, …, q), where q is the number of performance indicators;
s4, regarding the capability of the remote sensing satellite subsystem in the abnormal operation state as a reduced capability of the system capability in normal operation, and needing to evaluate the value ul0On the basis, the evaluation values of all performance indexes when the remote sensing satellite subsystems are in different states are further calculated;
s5, calculating the corresponding capacity c of the j working state of the remote sensing satellite according to the results of S2 and S4j；
And further obtaining a remote sensing satellite capability model C as follows:
where m is the number of remote sensing satellite operating states, xiIs the normal state of the ith subsystem, ρli∈[0,1]Indicates the ith subsystem isBarrier pair performance index plThe influence coefficient of (c).
6. The method of claim 5, wherein: the evaluation value in S3 is determined as follows:
if p is the forward direction indexl<pl,LThen u isl00; if p isl>pl,UThen u isl010; if p isl,L≤pl≤pl,U，ul0According to piThe value is changed linearly; if p is the reverse index when the performance index isl<pl.LThen u isl010; if p isl>pl,UThen u isl00; if p isl,L≤pl≤pl.U，ul0According to plThe value is changed linearly.
7. The method of claim 1, wherein: the remote sensing satellite multi-dimensional cost model expression mode is as follows:
in the formula, M is the total cost of the remote sensing satellite; wiIs the weight of the ith subsystem, αi、βl,i、βi,r、βi,wThe cost correction coefficient of the ith subsystem is solved by a regression method;
represents the capability index p of the ith subsystem pairl1 indicates a relationship, 0 indicates no relationship,
is a ceiling function and when the state of the ith subsystem does not affect the index plWhen there is pili＝0，βl,i＝0(l＝1,2,…,q)；FiAnd (t) represents the failure probability of the remote sensing satellite at the moment t when the remote sensing satellite works.
8. The method of claim 1, wherein: the remote sensing satellite cost tradeoff model is as follows:
max E/M
s.t.M≤MU
pl∈{pl,L,pl,1,…,pl,U},l＝1,2,…,q；p＝(p1,p2,…,pq)T
Ri∈{Ri,L,Ri,1,…,Ri,U},i＝1,2,…,n；R＝(R1,R2,…,Rn)T
Wi∈{Wi,L,Wi,1,…,Wi,U},i＝1,2,…,n；W＝(W1,W2,…,Wn)T
wherein, { pl,L,pl,1,…,pl,UExpressing a set of value ranges of the first individual performance index; { Ri,L,Ri,1,…,Ri,URepresenting a set of reliability value ranges of the ith subsystem at the end of the service life; { Wi,L,Wi,1,…,Wi,URepresenting a set of weight value ranges of the ith subsystem; rLIs the lower limit of reliability at the end of the whole satellite life; wUIs the upper limit of the whole star weight; mUIs the upper limit of the whole satellite cost; the failure probability of the ith subsystem at the end of the service life of the whole satellite meets the relation Fi(t)＝1-Ri。
9. The method of claim 1, wherein: the fourth step is realized by the following mode:
step four, appointing a coding rule: the code expression adopts an extended expression mode of 3 vectors of performance indexes, system reliability and weight of each subsystem; encoding a design into a chromosome;
step four, designing a fitness function: for the kth chromosome xi(k)Fitness function of (xi)(k)) Is defined as follows:
in the formula, xi(k)The k-th chromosome representing the genetic algorithm,
and Wi (k)(1, 2, …, n) represents the reliability and weight values of the ith subsystem on the kth chromosome; mUIs the upper limit of the cost of the whole satellite, WLIs the lower limit of the weight of the whole star, RLThe lower limit of the reliability at the end of the whole star life, wherein E represents the corresponding effect value of the chromosome, and M represents the corresponding cost value of the chromosome;
determining genetic operators, performing cross operation and mutation operation on each chromosome respectively, and calculating the corresponding fitness of each chromosome;
step four, searching the maximum fitness and the corresponding chromosome in the population of the current generation, judging whether the maximum iteration times is reached, if so, finishing the optimization, and outputting an optimal design scheme; if not, carrying out selection, crossing and mutation operations to generate a new optimization point, and carrying out a new iteration until all iteration times are finished;
10. the method of claim 9, wherein: in the fourth step, the chromosome expression mode is as follows:
in the formula,
and the value of the l individual performance index on the k chromosome is shown.
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