CN113761659B - Nested multi-attribute evaluation decision-making method and device for solid-liquid power-like space vehicles - Google Patents
Nested multi-attribute evaluation decision-making method and device for solid-liquid power-like space vehicles Download PDFInfo
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Abstract
The invention provides a nested multi-attribute evaluation decision method and device for a solid-liquid-like power spaceflight carrier, which comprises the steps of firstly establishing a full-attribute continuous model of the solid-liquid-like power spaceflight carrier; based on preset design variables, constraint conditions, optimization targets and full-attribute continuous models, a plurality of alternative schemes and ideal schemes are obtained by adopting a preset optimization algorithm; then determining the closeness of the alternative scheme and the ideal scheme; continuing to execute the preset optimization algorithm to obtain a set number of alternative schemes until the closeness meets the convergence condition; and finally, determining an optimal scheme from a plurality of alternative schemes based on the closeness of the alternative schemes. The invention improves the comprehensiveness of the design scheme evaluation decision of the space vehicle.
Description
Technical Field
The invention relates to the technical field of aircraft design, in particular to a nested multi-attribute evaluation decision method and device for a solid-liquid-like power aerospace carrier.
Background
In the related technology, multidisciplinary parametric modeling and design optimization are generally carried out on continuous attributes of a small carrier rocket, an optimization result is combined with discrete attribute evaluation data obtained by expert scoring, and a multi-attribute decision method is applied to carry out comprehensive evaluation on design schemes of the small carrier rocket, so that the advantages and disadvantages of different design schemes are obtained. However, this approach may miss solutions that better meet the target requirements, and is not comprehensive enough.
Disclosure of Invention
Therefore, the invention aims to provide a nested multi-attribute evaluation decision method and device for a similar solid-liquid power space vehicle, so as to improve the comprehensiveness of evaluation decisions for design schemes of the space vehicle.
In a first aspect, an embodiment of the present invention provides a method for nested multi-attribute evaluation decision-making of a solid-liquid-like power space vehicle, including: establishing a full-attribute continuous model of the solid-liquid-like dynamic space vehicle; based on preset design variables, constraint conditions, optimization targets and full-attribute continuous models, a plurality of alternative schemes and ideal schemes are obtained by adopting a preset optimization algorithm; determining the closeness of the alternative scheme and the ideal scheme; if the closeness does not meet the preset convergence condition, continuing to execute the steps of obtaining a set number of alternative schemes by adopting a preset optimization algorithm based on the preset design variable, constraint condition, optimization target and full-attribute continuous model until the closeness meets the convergence condition; based on the closeness of the alternatives, an optimal solution is determined from among the plurality of alternatives.
With reference to the first aspect, the embodiment of the present invention provides a first possible implementation manner of the first aspect, where the step of establishing a full-attribute continuous model of the solid-liquid like power space vehicle includes: respectively establishing a continuous technical attribute model, a continuous non-technical attribute model and a discrete non-technical attribute model of the solid-liquid power-like space vehicle; converting the discrete non-technical attribute model into a continuous evaluation model by using an uncertainty optimization principle based on a fuzzy theory; based on the continuous technical attribute model, the continuous non-technical attribute model and the continuous evaluation model, a full-attribute continuous model of the solid-liquid power-like space vehicle is generated.
With reference to the first aspect, the embodiment of the present invention provides a second possible implementation manner of the first aspect, wherein the optimization algorithm includes a genetic algorithm; based on preset design variables, constraint conditions, optimization targets and full-attribute continuous models, obtaining a set number of alternative schemes and ideal schemes by adopting a preset optimization algorithm, wherein the method comprises the following steps of: determining a parameter feasible space based on a preset design variable, constraint conditions, an optimization target and a full-attribute continuous model; the parameter feasible space comprises a set number of feasible subspaces corresponding to the parameters; processing the feasible parameter space by adopting a preset genetic algorithm to obtain parameter alternative values of the set number; the group of parameter alternative values comprises alternative values corresponding to a set number of parameters; the alternative value is in a feasible subspace range corresponding to the corresponding parameter; generating a set number of alternatives and an ideal scheme based on the set number of parameter alternatives; the set number matches the set number of groups.
With reference to the second possible implementation manner of the first aspect, the embodiment of the present invention provides a third possible implementation manner of the first aspect, wherein the step of generating the ideal solution based on the parameter alternative values of the set number includes: obtaining the dissimilarity degree of the parameters based on the alternative values of the parameters in the alternative schemes for each parameter; determining the weight of the parameter based on the dissimilarity degree of the parameter; establishing a decision matrix of an alternative scheme based on the weights of the set number of parameters and the alternative scheme; determining an optimal solution for each parameter based on the decision matrix; an ideal solution is generated based on the optimal solution for the set number of parameters.
With reference to the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where each of the foregoing alternatives corresponds to a set number of alternative values corresponding to the parameter; the ideal solution corresponds to an optimal solution for a set number of parameters; the step of determining the closeness of the alternative to the ideal scheme comprises: for each alternative scheme, calculating the closeness of the alternative scheme based on the alternative value and the optimal value of the parameter and the weight corresponding to the parameter.
With reference to the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the step of determining an optimal solution from a plurality of alternative solutions based on the proximity of the alternative solutions includes: and determining the alternative scheme with the maximum closeness as the optimal scheme.
In a second aspect, the embodiment of the present invention further provides a solid-liquid power-like space vehicle nested multi-attribute evaluation decision device, including: the model building module is used for building a full-attribute continuous model of the solid-liquid-like power spaceflight carrier; the optimization module is used for obtaining a plurality of alternative schemes and ideal schemes by adopting a preset optimization algorithm based on preset design variables, constraint conditions, optimization targets and a full-attribute continuous model; the closeness determining module is used for determining the closeness between the alternative scheme and the ideal scheme; if the closeness does not meet the preset convergence condition, continuing to execute the steps of obtaining a set number of alternative schemes by adopting a preset optimization algorithm based on the preset design variable, constraint condition, optimization target and full-attribute continuous model until the closeness meets the convergence condition; the scheme determining module is used for determining an optimal scheme from a plurality of alternatives based on the closeness of the alternatives.
With reference to the second aspect, an embodiment of the present invention provides a first possible implementation manner of the second aspect, where the model building module is configured to: respectively establishing a continuous technical attribute model, a continuous non-technical attribute model and a discrete non-technical attribute model of the solid-liquid power-like space vehicle; converting the discrete non-technical attribute model into a continuous evaluation model by using an uncertainty optimization principle based on a fuzzy theory; based on the continuous technical attribute model, the continuous non-technical attribute model and the continuous evaluation model, a full-attribute continuous model of the solid-liquid power-like space vehicle is generated.
In a third aspect, embodiments of the present invention also provide an electronic device, including a processor and a memory, the memory storing machine-executable instructions capable of being executed by the processor, the processor executing the machine-executable instructions to implement the above-described method.
In a fourth aspect, embodiments of the present invention also provide a machine-readable storage medium storing machine-executable instructions that, when invoked and executed by a processor, cause the processor to perform the above-described method.
The embodiment of the invention has the following beneficial effects:
The embodiment of the invention provides a nested multi-attribute evaluation decision method and device for a solid-liquid-like power spaceflight carrier, which comprises the steps of firstly establishing a full-attribute continuous model of the solid-liquid-like power spaceflight carrier; based on preset design variables, constraint conditions, optimization targets and full-attribute continuous models, a plurality of alternative schemes and ideal schemes are obtained by adopting a preset optimization algorithm; then determining the closeness of the alternative scheme and the ideal scheme; continuing to execute the preset optimization algorithm to obtain a set number of alternative schemes until the closeness meets the convergence condition; and finally, determining an optimal scheme from a plurality of alternative schemes based on the closeness of the alternative schemes. The method improves the comprehensiveness of the design scheme evaluation decision of the space vehicle.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the invention and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a nested multi-attribute evaluation decision method for a solid-liquid-like power aerospace vehicle provided by an embodiment of the invention;
FIG. 2 is a flow chart of another nested multi-attribute evaluation decision method for a solid-liquid power-like space vehicle provided by an embodiment of the invention;
FIG. 3 is a flow chart of another nested multi-attribute evaluation decision method for a solid-liquid power-like space vehicle provided by an embodiment of the invention;
fig. 4 is a flowchart of a TOPSIS process based on an entropy weight method according to an embodiment of the present invention;
Fig. 5 is a schematic structural diagram of a nested multi-attribute evaluation decision device for a solid-liquid-like power space vehicle according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Space engineering is one of the engineering technical fields with the development prospect in the future, and the development and application of space technology to develop socioeconomic performance and military strength are focuses of attention of the aerospace China in the world at present. In recent years, with the continuous improvement of the near-earth orbit launching requirement, the development of a space vehicle scheme with low cost and high reliability becomes urgent requirement, and the small space vehicle with low cost and quick response is hoped to be used in the military and civil fields, so that the aim of accurately and reliably entering the orbit of the payload is fulfilled. The choice of power system is one of the key factors in whether a small space vehicle is economically competitive while ensuring reliable launch.
The solid-liquid rocket engine has high safety and good economy, can realize thrust adjustment and long-time work relative to the solid rocket engine, has simple structure and convenient use relative to the liquid rocket engine, and has great application potential in the development of a small carrier rocket power system from the aspects of technical, safety and economy. In the design process of the solid-liquid-like space carrier, not only the technical indexes influencing the performance of the carrier, such as the total take-off quality, slenderness ratio, total speed increment, thrust-weight ratio and other parameters, but also various non-technical indexes such as low development and manufacturing cost, convenience in operation, safety and reliability in use and the like are considered from the social requirement point of view. Some of these attributes have an explicit (one-to-one) input-response relationship with the design variables, which may be referred to as continuous attributes; some attributes have ambiguity as to the difference in attitudes of the designer's assessment in the decision process, called discrete attributes. Therefore, the development of a multi-attribute evaluation decision method suitable for the solid-liquid power-like space vehicles becomes one of the key problems in the scheme design of the solid-liquid power-like space vehicles.
Wherein, the multi-objective decision is a process of comprehensively selecting by adopting a plurality of evaluation standards in an infinite decision scheme in consideration of two or more targets; multi-attribute decision making refers to the process of making decisions by ordering schemes in a limited number of most optimal alternatives, taking into account a plurality of attributes; the multi-attribute evaluation decision is a process of comprehensively evaluating the scheme under the existing model and giving out the preference sequence by utilizing a multi-objective decision and a multi-attribute decision method and considering continuous and discrete attributes.
In the prior art, multidisciplinary parametric modeling and design optimization are performed on continuous attributes of a small carrier rocket, an optimization result is combined with discrete attribute evaluation data obtained by expert scoring, and a multi-attribute decision method is used for carrying out comprehensive evaluation on the design scheme of the small carrier rocket, so that the advantages and disadvantages of different design schemes are obtained. In some practical cases, aiming at small carrier rockets of three different power systems of solid-liquid, liquid and solid, the design scheme with the lightest take-off quality under the form of the three power systems can be obtained through multidisciplinary design optimization, evaluation data of discrete attributes such as manufacturability, operability, flexibility, safety, environmental protection and the like can be obtained through expert judgment, a decision matrix containing continuous attributes and discrete attributes is formed, the closeness degree between the three schemes and the ideal scheme is calculated by using an ideal dissimilarity sorting method (TOPSIS method) based on entropy weight, and the result shows the advantages of the solid-liquid carrier.
The existing multi-attribute evaluation decision method has certain evaluation and characterization capability on the design scheme of the quasi-solid-liquid power space vehicle, but the evaluation result is performed based on the result of multi-disciplinary design optimization, for example, a solution with the minimum take-off quality in a front solution set obtained by the multi-disciplinary design optimization is selected as a source of continuous attribute in multi-attribute decision, and the method cannot judge all solutions in a feasible domain in the multi-objective decision, so that a scheme which meets target requirements better under potential comprehensive evaluation can be omitted.
Based on the above, the embodiment of the invention provides a nested multi-attribute evaluation decision method and device for a similar solid-liquid power spaceflight carrier, which can be applied to the design process of various similar solid-liquid power spaceflight carriers.
For the convenience of understanding the embodiment, the nested multi-attribute evaluation decision method for the solid-liquid power-like space vehicle disclosed by the embodiment of the invention is first described in detail.
The embodiment of the invention provides a nested multi-attribute evaluation decision method of a solid-liquid power-like space vehicle, which is shown in figure 1 and comprises the following steps:
Step S100, establishing a full-attribute continuous model of the solid-liquid power-like space vehicle.
In a specific implementation, the selection of the form of the spacecraft propulsion system may be made first. The solid-liquid power aerospace vehicle comprises a solid-liquid power vehicle, a solid power vehicle and the like, and then a continuous technical attribute model, a continuous non-technical attribute model and a discrete non-technical attribute model of the corresponding solid-liquid power aerospace vehicle are respectively built based on the selected power system form; then, a discrete non-technical attribute model is converted into a continuous evaluation model by using an uncertainty optimization principle based on a fuzzy theory; and generating a full-attribute continuous model of the solid-liquid power-like space vehicle based on the continuous technical attribute model, the continuous non-technical attribute model and the continuous evaluation model.
The continuous technical attributes may generally include total take-off mass, slenderness ratio, total speed increment, thrust-weight ratio, etc. of the carrier. The continuous non-technical attributes are mainly the cost of the carrier, including the overall and component level costs. Discrete non-technical attributes include reliability, robustness, manufacturability, operability, safety, environmental protection, and the like.
Step S102, based on preset design variables, constraint conditions, optimization targets and a full-attribute continuous model, a plurality of alternative schemes and ideal schemes are obtained by adopting a preset optimization algorithm.
In particular, a part of the technical parameters of the space vehicle can be taken as system parameters, i.e. parameters given by the designer during the design process, which remain unchanged, such as the combustion efficiency of the propellant, the minimum processing thickness of the engine housing, etc.; the other part is used as a design variable, such as the size of the part structure, etc. The constraints may be slenderness ratio of the vehicle, maximum overload during flight, track height to be reached, etc. The optimization target can also be specifically selected according to design requirements, for example, the total take-off quality of the carrier is minimum, the cost is minimum and the like are taken as the optimization target. After combining the design variables, the constraint conditions and the optimization targets with the full-attribute continuous model, attribute parameters in the full-attribute continuous model are all related to the design variables, the constraint conditions and the optimization targets in the design scheme.
And carrying out optimization treatment on the full-attribute continuous model at the moment by using a preset optimization algorithm, obtaining a set number of schemes with better schemes as alternative schemes, and extracting optimal parameters of all attribute parameters in all alternative schemes to form an ideal scheme.
Step S104, determining the closeness of the alternative scheme to the ideal scheme. If the closeness does not meet the preset convergence condition, continuing to execute the steps of obtaining a set number of alternative schemes by adopting a preset optimization algorithm based on the preset design variable, the constraint condition, the optimization target and the full-attribute continuous model until the closeness meets the convergence condition.
Specifically, different weights can be allocated to the attribute parameters through the change conditions of the attribute parameters in different alternatives; and calculating the similarity of the attribute parameter values in the alternative scheme and the attribute parameter values in the ideal scheme based on the weight, so as to determine the closeness of the alternative scheme and the ideal scheme.
And judging whether the closeness meets a preset convergence condition, wherein the convergence condition can be the calculation times (equivalent to the iteration times of the method) of the closeness, or can be that the closeness is larger than a preset threshold value or the change degree of the closeness is smaller than the preset threshold value, and the like. If the convergence condition is not satisfied, continuing to execute step S102 to obtain a new alternative scheme and an ideal scheme, thereby determining the closeness of the new alternative scheme and the new ideal scheme until the closeness converges.
Step S106, determining an optimal scheme from a plurality of alternative schemes based on the closeness of the alternative schemes.
The alternative scheme with the maximum closeness can be selected as the optimal scheme when the closeness converges; the alternative scheme with the maximum closeness in all iterative processes can be selected as the optimal scheme.
The embodiment of the invention provides a nested multi-attribute evaluation decision method of a solid-liquid-like power spaceflight carrier, which comprises the steps of firstly establishing a full-attribute continuous model of the solid-liquid-like power spaceflight carrier; based on preset design variables, constraint conditions, optimization targets and full-attribute continuous models, a plurality of alternative schemes and ideal schemes are obtained by adopting a preset optimization algorithm; then determining the closeness of the alternative scheme and the ideal scheme; continuing to execute the preset optimization algorithm to obtain a set number of alternative schemes until the closeness meets the convergence condition; and finally, determining an optimal scheme from a plurality of alternative schemes based on the closeness of the alternative schemes. The method improves the comprehensiveness of the design scheme evaluation decision of the space vehicle.
The embodiment of the invention also provides another solid-liquid power aerospace vehicle nested multi-attribute evaluation decision method, which is realized on the basis of the method shown in the figure 1, and a genetic algorithm is adopted as an optimization algorithm in the method; as shown in fig. 2, the method comprises the steps of:
Step S200, establishing a full-attribute continuous model of the solid-liquid power-like space vehicle.
Step S202, determining a parameter feasible space based on preset design variables, constraint conditions, an optimization target and a full-attribute continuous model; the parameter feasible space comprises a set number of feasible subspaces corresponding to the parameters. Parameters may include design variables and some or all of the attributes in the full attribute continuous model.
Step S204, processing the feasible parameter space by adopting a preset genetic algorithm to obtain parameter alternative values of the set number; the group of parameter alternative values comprises alternative values corresponding to a set number of parameters; the alternative values are within the feasible subspace range corresponding to the corresponding parameters. The set of parameter alternatives may have a mathematical relationship between them, such as when the alternatives for some design variable are determined, the alternatives for some property in the corresponding full-property continuous model have been determined.
Step S206, generating a set number of alternatives and ideal schemes based on the parameter alternative values of the set number of groups; the set number matches the set number of groups.
In determining the ideal scheme, an ideal solution similarity ordering method (TOPSIS method) based on entropy weight can be adopted: for each parameter, obtaining the dissimilarity degree of the parameter based on the alternative value of the parameter in the alternative scheme, wherein the dissimilarity degree can reflect the change range of the parameter; based on the degree of dissimilarity of the parameters, determining the weight of the parameters, wherein when the degree of dissimilarity is large, the weight can be larger; establishing a decision matrix of an alternative scheme based on the weights of the set number of parameters and the alternative scheme; determining an optimal solution for each parameter based on the decision matrix; an ideal solution is generated based on the optimal solution for the set number of parameters.
Generally, each alternative corresponds to a set number of alternative values for the parameter; the ideal solution corresponds to an optimal solution for a set number of parameters.
Step S208, for each alternative scheme, calculating the closeness of the alternative scheme based on the alternative value of the parameter, the optimal value and the weight corresponding to the parameter.
Step S210, judging whether the closeness meets a convergence condition; if not, executing step S202; if so, step S212 is performed.
Step S212, determining the alternative scheme with the maximum closeness as the optimal scheme.
According to the method, different weights are distributed for parameters based on dissimilarity degree of alternative scheme parameters in the scheme decision process, an ideal scheme is determined, the closeness of the alternative scheme and the ideal scheme is further determined based on the weights, and then when the closeness does not meet the convergence condition, the alternative scheme is obtained by continuously adopting an optimization algorithm until the closeness of the alternative scheme meets the convergence condition, and the optimal scheme is obtained. According to the method, the influence of each parameter in the scheme on the design scheme evaluation decision is comprehensively considered, and the comprehensiveness of the design scheme evaluation decision of the space vehicle is improved.
The embodiment of the invention also provides another nested multi-attribute evaluation decision method of the solid-liquid power aerospace vehicle, which is realized on the basis of the method shown in the figure 1. According to the method, a full-attribute continuous model of the solid-liquid power-like spaceflight carrier is established, a multi-attribute decision module is nested into a multi-objective decision module, so that weight distribution among various attributes is dynamically changed in an iterative process of carrying out multi-disciplinary design optimization on the overall scheme of the carrier, and comprehensive judgment of all technical and non-technical attributes in an infinite scheme set in a design stage is realized.
The attributes of the solid-liquid power-like space vehicle are divided into technical attributes and non-technical attributes, wherein the technical attributes are continuous attributes, and the non-technical attributes comprise continuous non-technical attributes and discrete non-technical attributes. In the method, in order to add discrete non-technical attributes into a model, the discrete non-technical attributes are converted into a continuous model, namely, the discrete model is continuous, and the carrier full-attribute continuous model is finally obtained based on the continuous technical attribute model, the continuous non-technical attribute model and the continuous discrete non-technical attribute model.
The method then proceeds with a multi-objective decision (multidisciplinary design optimization) process based on the vehicle full attribute continuous model. The difficulty of implementing multi-attribute decision in a nested manner in the optimization process is that the distribution of each attribute weight coefficient obtained by the comprehensive evaluation model needs to be calculated based on the parameters of the existing alternative scheme, and the specific numerical value of each attribute changes along with the progress of the optimization process, so that how to select a quantitative key scheme and perform dynamic weight assignment on the quantitative key scheme is the key of the calculation of the closeness in the full-attribute evaluation. In the method, the maximum closeness (the closeness to an ideal scheme) obtained by the full-attribute comprehensive evaluation model is taken as a target, and a part of better solutions (such as 5 solutions or 10 solutions with higher closeness sequences) in feasible scheme solutions obtained by optimization are considered to carry out weight distribution calculation, so that the optimal closeness value in each iteration step is further calculated until the closeness result converges, and the optimal carrier scheme design result is obtained.
Specifically, as shown in fig. 3, a flow example of a nested multi-attribute evaluation decision process for a solid-liquid like power space vehicle is shown.
First, a selection of the form of the spacecraft power system is made. Forms of solid-liquid like powered space vehicles include, but are not limited to, solid-liquid powered vehicles, solid powered vehicles, and the like.
After the form selection of the vehicle power system is determined, the vehicle attributes to be inspected are further divided into three types of continuous technical attributes, continuous non-technical attributes and discrete non-technical attributes.
The continuous technical attributes may generally include total take-off mass, slenderness ratio, total speed increment, thrust-weight ratio, etc. of the carrier. The essence of the carrier rocket is that the carrier rocket is pushed by using the self-propellant, and the quality of the next-stage rocket directly determines the design scale of the engine of the previous-stage, so that the total take-off quality (the quality of the carrier rocket at the take-off moment) is an evaluation attribute capable of directly reflecting the advanced degree of the carrier rocket. A carrier with an excessively large slenderness ratio will cause a worse whole rocket vibration characteristic, and simultaneously weaken the bearing capacity on normal overload, and adversely affect the structural strength, so that the slenderness ratio of the carrier rocket needs to be measured. The total velocity increment directly reflects the ability of the launch vehicle to deliver the payload into orbit and is also an important parameter reflecting the performance of the vehicle. The thrust-weight ratio is the ratio of the thrust of the carrier to the mass, and the rocket can be stably accelerated by the proper thrust-weight ratio, so that the acceleration is too slow if the thrust-weight ratio is too small, the loss of the propellant is large, and the problem of load stress safety is caused if the thrust-weight ratio is too large. The continuous technical attributes can be obtained through a carrier multidisciplinary parameterized design and performance analysis model.
The continuous non-technical attributes are mainly the cost of the carrier, including the overall and component level costs. The overall cost of the vehicle can be studied from both engine and non-engine aspects, and a statistical-based regression method is used to establish a cost estimation relationship for each component of the vehicle to achieve an estimate of the total cost.
Discrete non-technical attributes include reliability, robustness, manufacturability, operability, safety, environmental protection, and the like. Reliability refers to the ability of a system to perform a specified function within a specified time and under specified conditions. Robustness, i.e., the sensitivity of the system to the influence of uncertainty, is the better the less sensitive the system performance is under the influence of the system itself and external environmental uncertainty factors. From a manufacturability standpoint, design efforts should be made to reduce the complexity of manufacturing the components so that they are controlled in a viable or even readily available state. From the viewpoint of operability, the preparation time for transmission should be reduced as much as possible, and the complexity of the operation for transmission should be reduced. Specific evaluation items may include pre-launch preparation time, assembly, propellant charge, propellant storage, propellant pressurization, etc. From the safety point of view, the whole process of production, storage and use of the carrier is subject to the criteria of not jeopardizing personal safety and reducing the additional guarantee cost/possible economic loss caused by safety. From the environmental point of view, the pollution level of the carrier rocket is directly determined by the raw material manufacturing of fuel and oxidant and the chemical reaction products of the flying process.
To evaluate the discrete attributes described above, there is a difficulty in that they tend to have subjective ambiguities, often given at the time of design and use by means of expert subjective experience. Therefore, the evaluation data is quantized into evaluation data, and a continuous model capable of performing data operation and processing is established.
Taking reliability as a non-technical attribute as an example, quantitative evaluation of reliability can be realized through uncertainty optimization process based on fuzzy theory, and the mathematical model is that
Wherein x is a design variable, f is a deterministic model function equation, and f w is a weight coefficient-based solution equation for balancing the mean and standard deviation; pcr is a reliability measure value meeting a certain condition, g (x) is a vector set of inequality constraint conditions, a given reliability expected value set Pcr set between 0 and 1 needs to be met, and elements in the two sets correspond to each other one by one; h (x) is a vector set of equality constraints. The mathematical model shows that after uncertainty analysis is introduced, constraint conditions are changed from strictly greater than a certain fixed value to the fact that the reliability measure is required to be met and greater than a certain proportion, and the capability (namely reliability) of the group of designs for meeting the constraint under the consideration of uncertainty factors is represented. Similarly, the standard deviation of the response in the uncertainty analysis can also be used to measure the robustness of the design, i.e., the sensitivity to system and external uncertainty factors.
And in the multi-objective decision module, the model built above for the solid-liquid-like space vehicle full attribute continuous model is used for selecting design variables, constraint conditions and optimization objectives and developing the vehicle multidisciplinary design optimization. The optimizer chooses a global optimization algorithm that uses a genetic algorithm. After a certain number of feasible schemes are obtained in the optimization process, taking a partial scheme with the highest closeness in the last iteration step (such as a scheme with the top 5 or the top 10 of the ranking) as an alternative scheme, and taking an ideal solution similarity sorting method (TOPSIS method) based on entropy weight as a comprehensive evaluation model to carry out multi-attribute decision, so that each attribute value aiming at dynamic change is realized, and dynamically changing weight distribution is introduced.
The TOPSIS process based on entropy weight method is shown in FIG. 4. The method comprises the following steps:
The first step: firstly, the values of all the attributes of all the schemes are arranged into standardized data so as to eliminate the influence of the difference of the dimensions of all the attributes on decision analysis. The normalization process is expressed by the following formula:
Wherein u is the actual value of a certain attribute, Y is the value normalized by the attribute, i represents the ith scheme, j represents the jth attribute, the subscript ij represents the jth attribute of the ith scheme, max (u j) represents the maximum value of the jth attribute in all schemes, and the same thing min (u j) represents the minimum value of the jth attribute in all schemes.
And a second step of: according to the dissimilarity degree of each attribute under different alternatives, calculating the weight coefficient of the influence of the attribute on the scheme decision, wherein the weight coefficient is expressed by the following formula:
Wherein ω represents the weight coefficient, E represents the information entropy, n represents a total of n attributes to be evaluated, m represents a total of m alternatives to be evaluated, and the meaning of the other symbols is the same as in the first step.
And a third step of: establishing a decision matrix considering the weight influence, wherein the decision matrix is expressed by the following formula:
vij＝Yij·ωj
Wherein v ij is the normalized attribute value multiplied by the weight coefficient of the attribute, i.e. the attribute value after the weight is considered.
Fourth step: searching an ideal solution, namely selecting an optimal value of each attribute in all alternative schemes to form an ideal optimal scheme; is expressed by the following formula:
Wherein, a + is a vector, which is composed of the most ideal value of each column in the weighted decision matrix v ij; conversely, the negative ideal solution A - is made up of the least ideal solution in each column.
Fifth step: calculating the closeness of each alternative scheme relative to the ideal scheme; is expressed by the following formula:
Where η i is the closeness of the ith alternative, d i + is the distance from the ith alternative to the positive ideal solution, and d i - is the distance from the ith alternative to the negative ideal solution.
Sixth step: and (3) performing evaluation decision, and taking the scheme with the highest closeness (namely the scheme closest to the ideal solution) as the optimal scheme. The multi-attribute evaluation of the selected scheme is realized through the method.
Repeating the steps in the optimization process of the multi-objective decision module, and updating the attribute weights in each iteration step, so as to change the influence degree of different attributes on the design of the scheme. And finally obtaining the design scheme of the quasi-solid-liquid power spaceflight carrier with the maximum closeness after the optimization process reaches convergence.
Compared with the prior art, the nested multi-attribute evaluation decision method for the solid-liquid power space vehicle can introduce multi-attribute decision evaluation data into a multi-objective optimization model, consider the influence of decision weight distribution on the optimizing result of a design scheme in the multi-objective optimization process, nest the multi-attribute decision module into the multi-objective decision module, and realize the dynamic weight distribution of the multi-attribute in the optimizing process; by introducing uncertainty theory and concept, an uncertainty multidisciplinary design optimization model of the solid-liquid-like space vehicle is established, discrete non-technical attributes such as reliability, robustness and the like are continuous, and comprehensive judgment of all technical and non-technical attributes in an infinite scheme set in a design stage is realized. With the sustainable development of computer science and technology, the problems of more calculation time consumption and the like after nesting the multi-attribute decision module can be overcome by adopting parallel calculation and other modes, and the method can further improve the possibility of obtaining the full-attribute optimal scheme.
In addition, in the description of the above method, the multi-objective decision module, i.e., the multi-disciplinary design optimization process is implemented by taking a genetic algorithm as an example, but the specific method for implementing the multi-objective decision is not limited to such an optimization algorithm. While the discrete attribute of the reliability of the system is measured by a method of introducing uncertainty measure in the example, the method of quantifying the reliability is not limited thereto, and for example, a reliability evaluation model of the system may be established, and the reliability of the overall system of the vehicle may be calculated by giving the reliability values of the respective component units and taking the correlations between the components into consideration. The comprehensive evaluation process of the multi-attribute decision module in the example is realized by adopting a TOPSIS method based on entropy weight, but other similar multi-attribute decision methods can also realize the weight distribution and comprehensive evaluation process.
Corresponding to the above method embodiment, the embodiment of the present invention further provides a nested multi-attribute evaluation decision device for a solid-liquid power-like space vehicle, as shown in fig. 5, where the device includes:
the model building module 500 is used for building a full-attribute continuous model of the solid-liquid-like power spaceflight carrier;
The optimization module 502 is configured to obtain a plurality of alternatives and ideal schemes by using a preset optimization algorithm based on a preset design variable, constraint conditions, an optimization target and a full-attribute continuous model;
a closeness determination module 504 that determines a closeness of the alternative scheme to the ideal scheme; if the closeness does not meet the preset convergence condition, continuing to execute the steps of obtaining a set number of alternative schemes by adopting a preset optimization algorithm based on the preset design variable, constraint condition, optimization target and full-attribute continuous model until the closeness meets the convergence condition;
The solution determining module 506 is configured to determine an optimal solution from the multiple alternatives based on the closeness of the alternatives.
Further, the model building module is further configured to: respectively establishing a continuous technical attribute model, a continuous non-technical attribute model and a discrete non-technical attribute model of the solid-liquid power-like space vehicle; converting the discrete non-technical attribute model into a continuous evaluation model by using an uncertainty optimization principle based on a fuzzy theory; based on the continuous technical attribute model, the continuous non-technical attribute model and the continuous evaluation model, a full-attribute continuous model of the solid-liquid power-like space vehicle is generated.
The nested multi-attribute evaluation decision device for the quasi-solid-liquid power spaceflight carrier provided by the embodiment of the invention has the same technical characteristics as the nested multi-attribute evaluation decision method for the quasi-solid-liquid power spaceflight carrier provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
The embodiment of the present invention further provides an electronic device, as shown in fig. 6, where the electronic device includes a processor 130 and a memory 131, where the memory 131 stores machine executable instructions that can be executed by the processor 130, and the processor 130 executes the machine executable instructions to implement the above-mentioned method for determining nested multi-attribute evaluation of a solid-liquid power aerospace vehicle.
Further, the electronic device shown in fig. 6 further includes a bus 132 and a communication interface 133, and the processor 130, the communication interface 133, and the memory 131 are connected through the bus 132.
The memory 131 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 133 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc. Bus 132 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 6, but not only one bus or type of bus.
The processor 130 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 130. The processor 130 may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; but may also be a digital signal processor (DIGITAL SIGNAL Processing, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 131, and the processor 130 reads the information in the memory 131, and in combination with its hardware, performs the steps of the method of the foregoing embodiment.
The embodiment of the invention also provides a machine-readable storage medium, which stores machine-executable instructions that, when being called and executed by a processor, cause the processor to implement the multi-attribute evaluation decision method of the solid-liquid power-like space vehicle, and the specific implementation can be referred to the method embodiment and will not be described herein.
The multi-attribute evaluation decision method, device and computer program product of electronic equipment for a solid-liquid power-like space vehicle provided by the embodiment of the invention comprise a computer readable storage medium storing program codes, wherein the instructions included in the program codes can be used for executing the method described in the method embodiment, and specific implementation can be seen in the method embodiment and will not be repeated here.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a gateway electronic device, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (8)
1. A nested multi-attribute evaluation decision method for a solid-liquid-like power spaceflight carrier is characterized by comprising the following steps:
Establishing a full-attribute continuous model of the solid-liquid-like dynamic space vehicle; the full-attribute continuous model is a model containing technical attributes and non-technical attributes of the solid-liquid power-like space vehicle;
Based on preset design variables, constraint conditions, optimization targets and the full-attribute continuous model, a plurality of alternative schemes and ideal schemes are obtained by adopting a preset optimization algorithm;
Determining a proximity of the alternative to the ideal solution; if the closeness does not meet the preset convergence condition, continuing to execute the steps of obtaining a set number of alternatives by adopting a preset optimization algorithm based on a preset design variable, a constraint condition, an optimization target and the full-attribute continuous model until the closeness meets the convergence condition;
determining an optimal scheme from a plurality of alternatives based on the closeness of the alternatives;
The method for establishing the full-attribute continuous model of the solid-liquid power-like space vehicle comprises the following steps of:
Respectively establishing a continuous technical attribute model, a continuous non-technical attribute model and a discrete non-technical attribute model of the solid-liquid power-like space vehicle;
Converting the discrete non-technical attribute model into a continuous evaluation model by using an uncertainty optimization principle based on a fuzzy theory;
And generating a full-attribute continuous model of the solid-liquid power-like space vehicle based on the continuous technical attribute model, the continuous non-technical attribute model and the continuous evaluation model.
2. The method of claim 1, wherein the optimization algorithm comprises a genetic algorithm;
Based on preset design variables, constraint conditions, optimization targets and the full-attribute continuous model, obtaining a set number of alternative schemes and ideal schemes by adopting a preset optimization algorithm, wherein the method comprises the following steps:
determining a parameter feasible space based on a preset design variable, constraint conditions, an optimization target and the full-attribute continuous model; the parameter feasible space comprises feasible subspaces corresponding to a set number of parameters;
Processing the feasible parameter space by adopting a preset genetic algorithm to obtain parameter alternative values of a set number; the group of parameter alternative values comprises alternative values corresponding to a set number of parameters; the alternative value is in a feasible subspace range corresponding to the corresponding parameter;
Generating a set number of alternatives and an ideal scheme based on the set number of parameter alternatives; the set number matches the set number.
3. The method of claim 2, wherein the step of generating an ideal scheme based on the set of parameter alternatives comprises:
For each parameter, obtaining the dissimilarity degree of the parameter based on the alternative value of the parameter in the alternative scheme;
determining the weight of the parameter based on the dissimilarity degree of the parameter;
establishing a decision matrix of an alternative scheme based on the weights of the set number of parameters and the alternative scheme;
determining an optimal solution for each of the parameters based on the decision matrix;
And generating an ideal scheme based on the optimal solution of the set number of parameters.
4. The method of claim 1, wherein each of the alternatives corresponds to a set number of parameter-corresponding alternative values; the ideal solution corresponds to an optimal solution for a set number of parameters;
The step of determining the proximity of the alternative to the ideal solution comprises:
for each alternative scheme, calculating the closeness of the alternative scheme based on the alternative value and the optimal value of the parameter and the weight corresponding to the parameter.
5. The method of claim 1, wherein the step of determining an optimal solution from a plurality of the alternatives based on the proximity of the alternatives comprises:
And determining the alternative scheme with the maximum closeness as the optimal scheme.
6. A solid-liquid-like power aerospace vehicle nested multi-attribute evaluation decision device, comprising:
the model building module is used for building a full-attribute continuous model of the solid-liquid-like power spaceflight carrier; the full-attribute continuous model is a model containing technical attributes and non-technical attributes of the solid-liquid power-like space vehicle;
The optimization module is used for obtaining a plurality of alternative schemes and ideal schemes by adopting a preset optimization algorithm based on preset design variables, constraint conditions, optimization targets and the full-attribute continuous model;
a closeness determining module for determining the closeness of the alternative scheme and the ideal scheme; if the closeness does not meet the preset convergence condition, continuing to execute the steps of obtaining a set number of alternatives by adopting a preset optimization algorithm based on a preset design variable, a constraint condition, an optimization target and the full-attribute continuous model until the closeness meets the convergence condition;
a scheme determining module, configured to determine an optimal scheme from a plurality of alternatives based on a proximity of the alternatives;
The model building module is further configured to:
Respectively establishing a continuous technical attribute model, a continuous non-technical attribute model and a discrete non-technical attribute model of the solid-liquid power-like space vehicle;
Converting the discrete non-technical attribute model into a continuous evaluation model by using an uncertainty optimization principle based on a fuzzy theory;
And generating a full-attribute continuous model of the solid-liquid power-like space vehicle based on the continuous technical attribute model, the continuous non-technical attribute model and the continuous evaluation model.
7. An electronic device comprising a processor and a memory, the memory storing machine-executable instructions executable by the processor, the processor executing the machine-executable instructions to implement the method of any one of claims 1-5.
8. A machine-readable storage medium storing machine-executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of claims 1-5.
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