A method, system, device and medium for system architecture trade-off analysis

Abstract

The present invention discloses a method, a system, a device and a medium for system architecture trade-off analysis, which relates to the field of aerospace system architecture design. The method includes: obtaining decision items and alternative items of the rocket first-stage and second-stage separation system architecture to be trade-off analyzed; performing formal representation on the decision items and the alternative items to obtain a morphological matrix; the morphological matrix includes a plurality of alternative architectures; determining alternative architectures that meet the constraints among the plurality of alternative architectures according to the morphological matrix; the constraints include option mutual exclusion and option association; using a multi-objective optimization search method to determine alternative architectures on the Pareto front among the alternative architectures that meet the constraints; using a weight sensitivity analysis technique to determine the weight values of the system indicators of each alternative architecture on the Pareto front, and using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to determine the final system architecture. The present invention improves the efficiency of system architecture trade-off analysis.

Description

Technical Field

[0001] The present invention relates to the field of aerospace system architecture design, and particularly to a system architecture trade-off analysis method, system, device and medium. Background Art

[0002] With the development of technology and the improvement of human needs, the products and systems constructed by humans are becoming more and more complex, and the difficulty of conceiving, designing and implementing complex systems is also increasing, resulting in huge production costs. In order to meet the needs of different stakeholders as much as possible and reflect their values, system thinking has become an important mode to solve complex systems, and a closely related concept is "architecture". Architecture design belongs to the preliminary design stage of complex systems and greatly affects the final performance of products.

[0003] System architecture can be expressed as a series of decision-making processes. From the perspective of system thinking, the important work of an architect is to train his thinking to understand complex systems, convert a series of requirements and goals into a system architecture, find out the decision points for confirming the system architecture, and then make satisfactory decisions carefully and reasonably to obtain a system with greater value. However, for complex systems, the functional implementation between systems is not a simple linear superposition of each component. The intricate relationships between numerous system components and design parameters, as well as various alternative solutions related thereto, make the decision-making space extremely large, posing challenges to the decision-making of complex system architecture design.

[0004] The advantages and disadvantages of the architecture will directly affect the quality of complex systems, the progress of subsequent detailed design, and the cost of the entire design process. The trade-off problem of complex system architecture has always been the focus and difficulty of product design. A wrong architecture may even ruin the entire project. However, in the face of a large number of alternative solutions generated by architecture generation, it is difficult for designers to make a quick and effective choice. Therefore, architecture trade-off analysis is required. The purpose of architecture trade-off analysis is to select the optimal solution from the architecture design space that meets the requirements as the basis for subsequent design. How to select the optimal solution in the case of increasingly complex systems has become a difficult problem faced by designers.

[0005] Currently, the methods for architecture trade-off analysis often directly adopt the multi-attribute decision-making method. For multiple alternative solutions generated in the architecture design space, they are directly scored and compared through relevant sorting algorithms. However, there are two problems with this. One is that as the complexity increases, the scale of the system architecture design space also increases sharply, generating a large number of infeasible architectures. Solving and weighing all architectures will increase the workload and waste computing power. The second is that the determination of the weight values of multiple indicators is currently rather subjective and it is difficult to meet the needs of multiple stakeholders simultaneously. In this context, a new architecture trade-off analysis method is needed to reduce the trade-off workload and scientifically determine the indicator weights. Summary of the Invention

[0006] The object of the present invention is to provide a method, system, device and medium for system architecture trade-off analysis, so as to improve the efficiency of trade-off analysis of system architecture.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A method for system architecture trade-off analysis, comprising:

[0009] Obtain the decision items and alternative items of the rocket first-stage and second-stage separation system architecture to be trade-off analyzed; the decision items are the problems to be decided in the rocket first-stage and second-stage separation system architecture to be trade-off analyzed; the alternative items are the discrete option sets corresponding to the problems to be decided; the problems to be decided include a selection signal conversion device, a selection signal transmission device, a selection connection and separation device, and a selection separation thrust device; the discrete option set corresponding to the selection signal conversion device includes an electric igniter, an electric detonator, a semiconductor bridge igniter, a mechanical detonator, a diaphragm detonator, and a delay igniter; the discrete option set corresponding to the selection signal transmission device includes a restrictive detonating component and a plastic detonating tube component; the discrete option set corresponding to the selection connection and separation device includes an explosive bolt, a separation nut, and a connecting pin type separation device; the discrete option set corresponding to the selection separation thrust device includes a pyrotechnic actuator and a separation rocket;

[0010] Perform formal representation on the decision items and the alternative items to obtain a morphological matrix; the morphological matrix has the decision items as columns and the alternative items as rows; the morphological matrix includes multiple alternative architectures; the alternative architectures are composed of any one item in the discrete option set corresponding to the signal conversion device, any one item in the discrete option set corresponding to the signal transmission device, any one item in the discrete option set corresponding to the connection and separation device, and any one item in the discrete option set corresponding to the separation thrust device;

[0011] Determine the alternative architectures that meet the constraints among the multiple alternative architectures according to the morphological matrix; the constraints include option mutual exclusion and option association;

[0012] Use a multi-objective optimization search method to determine the alternative architectures on the Pareto front among the alternative architectures that meet the constraints;

[0013] According to the alternative architectures on the Pareto front, use the technique for order preference by similarity to ideal solution (TOPSIS) method to determine the final system architecture.

[0014] Optionally, determining the alternative architectures that meet the constraints according to the morphological matrix specifically includes:

[0015] Establish a constraint satisfaction problem model according to the morphological matrix; the constraint satisfaction problem model includes variables, value ranges, and the constraints; the variables are the decision items; the value ranges are the alternative items;

[0016] Based on the constraint satisfaction problem model, use the backtracking algorithm to determine the alternative architectures that meet the constraints among the multiple alternative architectures.

[0017] Optionally, use the multi-objective optimization search method to determine the alternative architectures on the Pareto front among the alternative architectures that meet the constraints, specifically including:

[0018] Calculate the system metric values of the alternative architectures that meet the constraints;

[0019] According to the system metric values, use the multi-objective optimization search method to determine the alternative architectures on the Pareto front.

[0020] Optionally, according to the alternative architectures on the Pareto front, use the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to determine the final system architecture, specifically including:

[0021] Use the weight sensitivity analysis method to determine the weight values of each system metric of the alternative architectures on the Pareto front;

[0022] According to the alternative architectures on the Pareto front and the weight values of each system metric of the alternative architectures on the Pareto front, use the TOPSIS method to determine the final system architecture.

[0023] Optionally, it further includes:

[0024] Determine the system metrics of the system architecture to be subject to trade-off analysis.

[0025] Optionally, determining the system metrics of the system architecture to be subject to trade-off analysis specifically includes:

[0026] Obtain the decision metrics of each decision item of the system architecture to be subject to trade-off analysis; the decision metrics include inherent attributes and functional attributes; the inherent attributes include architecture quality, architecture size, and architecture cost; the functional attributes include architecture load-bearing strength, architecture output power, and architecture reliability;

[0027] According to the decision metrics, use the value function to determine the system metrics of the system architecture to be subject to trade-off analysis.

[0028] A system architecture trade-off analysis system, comprising:

[0029] A data acquisition module, configured to acquire decision items and alternative items of the rocket first-stage and second-stage separation system architecture to be weighed and analyzed; the decision items are the problems to be decided in the rocket first-stage and second-stage separation system architecture to be weighed and analyzed; the alternative items are the discrete option sets corresponding to the problems to be decided; the problems to be decided include a selection signal conversion device, a selection signal transmission device, a selection connection and separation device, and a selection separation thrust device; the discrete option set corresponding to the selection signal conversion device includes an electric igniter, an electric detonator, a semiconductor bridge igniter, a mechanical detonator, a diaphragm detonator, and a delay igniter; the discrete option set corresponding to the selection signal transmission device includes a restricted detonating component and a plastic detonating tube component; the discrete option set corresponding to the selection connection and separation device includes an explosive bolt, a separation nut, and a connection pin type separation device; the discrete option set corresponding to the selection separation thrust device includes a pyrotechnic actuator and a separation rocket;

[0030] A data characterization module, configured to perform formal characterization on the decision items and the alternative items to obtain a morphological matrix; the morphological matrix uses the decision items as columns and the alternative items as rows; the morphological matrix includes multiple alternative architectures; the alternative architectures are composed of any one item in the discrete option set corresponding to the signal conversion device, any one item in the discrete option set corresponding to the signal transmission device, any one item in the discrete option set corresponding to the connection and separation device, and any one item in the discrete option set corresponding to the separation thrust device;

[0031] A constraint screening module, configured to determine the alternative architectures that meet the constraints among the multiple alternative architectures according to the morphological matrix; the constraints include option mutual exclusion and option association;

[0032] A search module, configured to use a multi-objective optimization search method to determine the alternative architectures that are on the Pareto front among the alternative architectures that meet the constraints;

[0033] A final system architecture determination module, configured to determine the final system architecture according to the alternative architectures that are on the Pareto front by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS).

[0034] An electronic device, including: a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above system architecture trade-off analysis method.

[0035] A computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above system architecture trade-off analysis method is implemented.

[0036] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0037] A method, system, device, and medium for system architecture trade-off analysis provided by the present invention first characterize the rocket first-stage and second-stage separation system architecture to be trade-off analyzed through a morphological matrix; then reduce the morphological matrix through constraints to determine alternative architectures that meet the constraints, realizing the reduction of the architecture design space; use a multi-objective optimization search method to obtain alternative architectures on the Pareto front, and further rank and screen the alternative architectures on the Pareto front through the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to obtain the final system architecture. The trade-off method proposed by the present invention solves the problem that it is difficult to solve and trade off satisfactory architectures for complex systems at present. Especially for complex system architectures with a large design space scale, many alternatives, many indicators, and involving multiple stakeholders, making it difficult to determine weights. Through the method of the present invention, architecture decisions can be made quickly, effectively, comprehensively, and reliably, improving the efficiency of system architecture trade-off analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a flowchart of the system architecture trade-off analysis method provided by the present invention;

[0040] Figure 2 It is a schematic diagram of the overall process of the system architecture trade-off analysis method of the present invention;

[0041] Figure 3 It is a schematic diagram of the morphological matrix in Embodiment 1;

[0042] Figure 4 It is a schematic diagram of the relationship among decision-making indicators, system indicators, and value functions in Embodiment 1;

[0043] Figure 5 It is a flowchart of the backtracking algorithm in Embodiment 1;

[0044] Figure 6 It is a schematic diagram of the CSP model of the first-stage and second-stage separation system architecture in Embodiment 1;

[0045] Figure 7 It is a flowchart of the multi-objective optimization search method in Embodiment 1;

[0046] Figure 8 It is a schematic diagram of the Pareto front determined by the multi-objective optimization search method in Embodiment 1;

[0047] Figure 9It is the flowchart of weight sensitivity analysis in the first embodiment;

[0048] Figure 10 It is the ternary diagram for the trade - off of the primary and secondary separation systems in the first embodiment; among which, Figure 10 (a) is the ternary diagram of mass M; Figure 10 (b) is the ternary diagram of cost C; Figure 10 (c) is the ternary diagram of reliability R; Figure 10 (d) is the ternary diagram after the superposition of system indicators;

[0049] Figure 11 It is the schematic diagram of the multi - objective optimization search results of the primary and secondary separation system architectures in the first embodiment;

[0050] Figure 12 It is the schematic diagram of the TOPSIS sorting results of the primary and secondary separation systems in the first embodiment;

[0051] Figure 13 It is the structural diagram of the system architecture trade - off analysis system provided by the present invention. Specific implementation manners

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0053] The purpose of the present invention is to provide a system architecture trade - off analysis method, system, device and medium to improve the efficiency of trade - off analysis of system architectures.

[0054] To make the above - mentioned objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0055] Embodiment 1

[0056] The separation carried out between a multi - stage rocket and its adjacent sub - stages is called "inter - stage separation". The inter - stage separation system is an important sub - system in the design of multi - stage rockets. Its function is to separate and discard the part that has completed the predetermined work during the rocket flight and is useless in the subsequent flight, thereby improving the mass characteristics of the rocket, increasing the rocket flight speed and payload capacity. In the development of rockets, the research on inter - stage separation technology is very important, and all countries attach great importance to it.

[0057] At present, there are two situations for the stage separation system of rockets: the first and second stage separation system, and the second and third stage separation system (some rockets only have the first and second stages and no third stage). This invention takes the design of the first and second stage separation system of a launch vehicle as an example to further describe the engineering process of the system architecture trade-off analysis method of this invention.

[0058] The first and second stage separation system generally includes the following steps during operation: receiving a separation signal → disconnecting the connection between the first stage and the second stage of the interconnected rocket → relative movement of each stage under the action of separation power → within a specified time, the distance of the separation section reaches a safe distance.

[0059] As Figure 1 shown, the system architecture trade-off analysis method of this embodiment includes:

[0060] Step 101: Obtain the decision items and alternative items of the first and second stage separation system architecture of the rocket to be trade-off analyzed.

[0061] Step 102: Perform formal characterization on the decision items and the alternative items to obtain a morphological matrix; the morphological matrix has the decision items as columns and the alternative items as rows. The morphological matrix includes multiple alternative architectures; each alternative architecture is composed of any item from the discrete option set corresponding to the signal conversion device, any item from the discrete option set corresponding to the signal transmission device, any item from the discrete option set corresponding to the connection and separation device, and any item from the discrete option set corresponding to the separation thrust device.

[0062] In practical applications, as Figure 2 shown, the overall process of the system architecture trade-off analysis method includes three steps: architecture formal characterization S1, architecture design space reduction S2, and architecture optimization and trade-off S3, and the final output result is a satisfactory architecture (i.e., the final system architecture).

[0063] The following is a detailed discussion of each step:

[0064] Step S1, architecture formal characterization, uses a morphological matrix to perform architecture decision characterization on the first and second stage separation system architecture of the rocket to be trade-off analyzed, and determines system indicators and decision indicators through architecture indicator analysis.

[0065] Step S11: Architecture decision representation. Use a morphological matrix to represent the decision items and alternative options of the rocket first and second stage separation system architecture to be weighed and analyzed. The morphological matrix is a way to display and organize decisions in a table. Among them, the decision item refers to the problem to be decided in the rocket first and second stage separation system architecture to be weighed and analyzed, and the alternative option is the discrete option set corresponding to each problem to be decided. In this embodiment, the problems to be decided include selecting a signal conversion device, a signal transmission device, a connection and separation device, and a separation thrust device. The discrete option set corresponding to the signal conversion device includes an electric igniter, an electric detonator, a semiconductor bridge igniter, a mechanical detonator, a diaphragm detonator, and a delay igniter. The discrete option set corresponding to the signal transmission device includes a restricted detonating component and a plastic detonating tube component. The discrete option set corresponding to the connection and separation device includes an explosive bolt, a separation nut, and a connection pin type separation device. The discrete option set corresponding to the separation thrust device includes a pyrotechnic actuator and a separation rocket.

[0066] As Figure 3 shown, it is a schematic diagram of the morphological matrix. The example has 4 decision items, and each decision item has corresponding alternative options. By selecting each alternative option of the morphological matrix, an alternative architecture is formed. For example, the combination {H4, L2, M5, N3} forms an alternative architecture, and the scale of the design space of this architecture is 6×6×6×5, a total of 1080 alternative architectures.

[0067] According to the separation steps of the first and second stage separation system, the following four functional requirements can be extracted: signal conversion, signal transmission, unlocking of the connection surface between the first and second sub-stages, and providing separation thrust. The corresponding system architecture can be constructed in sequence according to the requirements and functional decomposition of the first and second stage separation system.

[0068] Based on the four functions, devices that can achieve each function are designed or selected respectively. Using the functions as decision items and the alternative devices as alternative options, a morphological matrix is constructed as shown in Table 1.

[0069] Table 1 Morphological matrix table of the first and second stage separation system

[0070]

[0071]

[0072] The morphological matrix shown in Table 1 constitutes the first and second stage separation system architecture. It is necessary to select a suitable device model from each column to form a solution (alternative architecture). For example, S = {electric igniter A, restricted detonating component B, explosive bolt C, separation rocket B} constitutes a set of definite separation system solutions, and there are a total of 8×4×11×6 = 2112 alternative architectures.

[0073] Step S12: Architecture metric analysis. Metrics are set to evaluate the quality of the architecture. The metrics include decision metrics, system metrics, and a value function that links the two.

[0074] (1) The decision metrics are the attributes of a single decision item. The attributes can be inherent characteristics such as quality, size, cost, etc.; they can also be functional attributes such as load-bearing strength, output power, reliability, etc. The decision metrics are obtained by designers through relevant design manuals, parameter tables, or calculations.

[0075] (2) The system metrics refer to the overall metrics that the architecture needs to consider. These metrics require consideration from a global system level to measure the quality of the entire architecture, such as factors like the reliability, security, and weight of the entire system architecture. These are considered during the system design requirements and are determined by designers according to the task. For example, when designing a car, the overall metric is lightweight; when decomposing and designing each component of the car, the mass distribution of each component will be considered; but some may consider high load-bearing, so when designing each component, it will serve the load-bearing value.

[0076] (3) The value function is the functional relationship that links the decision metrics and the system metrics. As shown in, it is a schematic diagram of the relationship among the decision metrics, system metrics, and value function; denote A = {A1, A2,..., A Figure 4} as the set of alternative architectures, where A n represents a specific alternative architecture. For example, A i = {H4, L2, M5, N3}. For the example architecture, each decision item constructs the corresponding decision metrics, denoted as {H(i), L(i), M(i), N(i)}. The decision metrics provide the original data for the construction of the value function. Through the mapping of the value function f, the system metric S is calculated as follows: i = {H4, L2, M5, N3}. For the example architecture, each decision item constructs the corresponding decision metrics, denoted as {H(i), L(i), M(i), N(i)}. The decision metrics provide the original data for the construction of the value function. Through the mapping of the value function f, the system metric S is calculated as follows: Figure 3 For the example architecture, each decision item constructs the corresponding decision metrics, denoted as {H(i), L(i), M(i), N(i)}. The decision metrics provide the original data for the construction of the value function. Through the mapping of the value function f, the system metric S is calculated as follows:

[0077] S = f(x H(i) , x L(j) , x M(p) , x N(q) )

[0078] Through step S11, the architecture is formally characterized. The mathematical representation form facilitates the subsequent further processing of the architecture; through the metric analysis in step S12, a measurement basis is provided for the optimization and trade-off of the architecture.

[0079] Perform architecture metric analysis on the morphological matrix in Table 1:

[0080] (1) Decision metrics

[0081] Decision-making indicators generally come from general specifications, product performance, usage requirements, etc., and are determined by designers. Taking the signal conversion device (SC) as an example, the selection of this device needs to consider factors such as firing time, device weight, reliability, output power, device cost, etc.; as shown in Table 2, the data for each model are presented.

[0082] Table 2 Decision-making Indicator Values of Each Model of Signal Conversion Device

[0083]

[0084] Similarly, the decision-making indicators of the signal transmission device (ST), connection and separation device (CS), and separation thrust device (SP) can be obtained in sequence.

[0085] (2) System Indicators

[0086] When selecting the devices of the first and second stage separation system, the overall factors to be considered include lightweight, low cost, high reliability, low pollution, high load-bearing, etc.

[0087] In this case, the overall mass M, cost C, and system reliability R are taken as system indicators.

[0088] (3) Value Function

[0089] The value function is a functional relationship constructed to calculate system indicators, connecting system indicators with decision-making indicators. The generation of this function comes from domain knowledge, empirical formulas, or approximate simulations.

[0090] Value functions for the three system indicators are constructed respectively.

[0091] ① Overall Mass M

[0092]

[0093] Among them, the signal conversion device adopts redundant technology to improve working reliability, that is, two initiators are used to ensure that the failure of a single initiator will not cause the failure of the pyrotechnic device function. Therefore, a1 is taken as 2.

[0094] The length of the signal transmission device is generally about 1 - 10 meters. In this case, the value is taken as 8 meters, and the detonating device generally considers redundancy and is set as a double detonating device. Therefore, a2 is taken as 16.

[0095] For the connection and separation device, the number range of the connection and separation device is generally determined according to the weight of the sub-stage to be connected. Here, a3 is taken as 14.

[0096] The number of separation thrust devices is generally related to the required impulse. In this case, the number a4 of the separation thrust device is taken as 4.

[0097] ② Cost C

[0098] C = ∑c SC + Σc ST + Σc CS + Σc SP = a1c C + a2c ST + a3c CS + a4c SP

[0099] ③Reliability R

[0100] R = R SC ·R ST ·R CS ·R SP

[0101] Referring to the actual design experience of the separation system pyrotechnic device, the reliability of the pyrotechnic device consists of 4 links in a series model, so the overall reliability is the product of each link.

[0102] When calculating the reliability inside each structure, both the initiator and the detonating device adopt redundant design, that is, the redundant components are in a parallel relationship; the explosive bolts are in a series relationship. For the reliability of the parallel relationship, for example, for a double initiator, its reliability calculation formula is:

[0103]

[0104] Through the construction of the above value function, the mathematical function relationship between the system index and each decision index is obtained, providing a basis for the optimization and trade-off of the architecture.

[0105] Step 103: Determine the alternative architectures that meet the constraints among the multiple alternative architectures according to the morphological matrix; the constraints include option mutual exclusion and option association.

[0106] Further, the step 103 specifically includes:

[0107] Establish a constraint satisfaction problem model according to the morphological matrix.

[0108] Based on the constraint satisfaction problem model, use the backtracking algorithm to determine the alternative architectures that meet the constraints among the multiple alternative architectures.

[0109] Step S2, architecture design space reduction, specifically includes:

[0110] Step S21: Construct a constraint satisfaction problem (CSP) model according to the morphological matrix.

[0111] The CSP is an important branch in the field of artificial intelligence and can be used to quickly eliminate the huge search space. The CSP consists of three elements: variables (V), value domains (D), and constraints (C), where:

[0112] (1) The variable V is a decision item of the form matrix.

[0113] (2) The value range D is an alternative item of the form matrix.

[0114] (3) The constraint C is a series of restrictions such as the specifications and empirical rules satisfied by the architecture, and two forms of constraints are defined: option mutual exclusion and option association; the option mutual exclusion means that there are some options that cannot coexist in different decision items, and the option association means that after selecting a certain option, another decision item must select a specific option.

[0115] Step S22: Use the backtracking algorithm to search for alternative architecture solutions that satisfy the constraints.

[0116] As Figure 5 shown, it is a flowchart of the backtracking algorithm, and the following will detail each execution step:

[0117] (1) Build a CSP model using the said step S21.

[0118] (2) Initialize i = 1.

[0119] (3) Determine whether i traverses all variables, and execute different steps according to the determination result; if the determination result is "yes", execute step (4), otherwise execute step (8).

[0120] If the number of variables is n, the judgment unit is:

[0121] 0 ≤ i ≤ n?

[0122] (4) Assign a value to the variable V i in its corresponding value range D i and sequentially allocate all possible values in it, D ij represents the j-th value in the value range D i ;

[0123] (5) Determine whether the assigned value satisfies the constraint C, and execute different steps according to the determination result; if the determination result is "yes", execute step (6), otherwise execute step (7).

[0124] The constraint sets two situations, option mutual exclusion, and option association. For example: variables A and B are set, and the corresponding domains are A = {A1, A2,..., A8}, B = {B1, B2, B3, B4}.

[0125] Set the constraints: mutual exclusion: A1 ≠ B1; association: A2 = B1, A2 = B2.

[0126] Then during the retrieval and judgment, when generating the A1B1 scheme, it will judge the mutual exclusion and does not meet the constraint requirements.

[0127] Similarly, when generating solutions A2B1 and A2B2, the association requirements are met and they can be output.

[0128] However, A2B3 will be excluded; because after A2 is determined, it can only be combined with B1 or B2.

[0129] (6) Execute i = i + 1, that is, start to assign a value to the next variable V i+1 Assign a value.

[0130] (7) Execute i = i - 1, that is, the assignment of this variable does not meet the constraint requirements, and return to re-assign the previous variable.

[0131] (8) Determine whether there is a solution that meets all constraints C in this assignment loop. If the determination result is "yes", then the CSP model has no solution; if the determination result is "no", output the architecture solution.

[0132] According to the set number of constraints. For example, if three constraints are set, then judge in turn whether they meet the requirements of the three constraints. For example, for the constraints set in (5), when generating A3B1, it does not violate the three constraints, so it meets the requirements.

[0133] Through the backtracking algorithm in steps (1)-(8), a feasible architecture solution that meets the constraint requirements is obtained, realizing the reduction of the architecture design space.

[0134] Perform step S2 architecture design space reduction on the morphological matrix in Table 1.

[0135] Step S21, CSP model construction:

[0136] CSP includes three elements: variable V, value range D, and constraint C. As Figure 6 shown, it is the CSP model of a first- and second-level separation system architecture; for the convenience of calculation, each decision item and alternative item are represented by letters.

[0137] (1) Variable V

[0138] In this embodiment, the variable is four decision items to be determined, V = {V1, V2, V3, V4}.

[0139] (2) Value range D

[0140] The value range D is the specific device type that the variable can take, and can be expressed as: D = {SC, ST, CS, SP}.

[0141] Among them, SC = {SC1, SC2,..., SC8}, ST = {ST1, ST2, ST3, SC4}, CS = {CS1, CS2,..., CS11}, SP = {SP1, SP2,..., SP6}.

[0142] Each variable can only select values within its corresponding value range. For example, V1 can only be selected from the value range SC = {SC1, SC2,..., SC8}.

[0143] (3) Constraint C

[0144] In the design of the primary and secondary separation system architecture, the selection of each device has corresponding usage specifications and precautions, and designers set corresponding constraint rules accordingly.

[0145] In this embodiment, three constraints are set.

[0146] Constraint 1: The diaphragm initiator is used for devices that generate a large backpressure after initiation and require no leakage, and is often used simultaneously with a restrictive detonating component.

[0147] Constraint 2: The accuracy of the delay igniter is greatly affected by temperature and is generally not used simultaneously with a plastic detonating component.

[0148] Constraint 3: The plastic detonating component is suitable for the explosive transfer link with a low ambient temperature, low impact, no pollution, and synchronous initiation. However, when multiple pyrotechnic actuators are used simultaneously, the synchronism is poor and they are generally not used simultaneously.

[0149] Based on the above, the constraint relationship of this case is constructed as shown in Table 3.

[0150] Table 3 Schematic Table of Constraint C

[0151]

[0152] Through the above steps, a CSP model of the primary and secondary separation system can be constructed.

[0153] Step S22, backtracking algorithm search:

[0154] Adopt Figure 5 The backtracking algorithm search process shown in the figure to search the constructed CSP model. Through backtracking search, the size of the architecture space is reduced from 2112 to 1452.

[0155] Step 104: Use the multi-objective optimization search method to determine the alternative architectures on the Pareto front among the alternative architectures that meet the constraints.

[0156] Further, step 104 specifically includes:

[0157] Calculate the system index values of the alternative architectures that meet the constraints.

[0158] According to the system index values, use the multi-objective optimization search method to determine the alternative architectures on the Pareto front.

[0159] Step 105: Determine the final system architecture according to the alternative architectures on the Pareto front by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS).

[0160] Further, Step 105 specifically includes:

[0161] Determine the weight values of the system indicators of the alternative architectures on the Pareto front by using the weight sensitivity analysis method.

[0162] Determine the final system architecture according to the alternative architectures on the Pareto front and the weight values of the system indicators of the alternative architectures on the Pareto front by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS).

[0163] In practical applications, Step S3, architecture optimization and trade-off, specifically includes:

[0164] Step S31: Obtain alternative architectures on the Pareto front by using the multi-objective optimization method, specifically including:

[0165] Calculate all the system indicator values of the alternative architectures generated in Step S22, perform Pareto dominance sorting on them, obtain the Pareto front, and obtain the alternative architectures on the Pareto front. The system indicators are the objectives in the multi-objective optimization search.

[0166] The Pareto dominance is defined as follows: for a minimization multi-objective problem, for any decision vectors x1 and x2, if the following two conditions are satisfied, then x1 is said to dominate x2, denoted as x1 < x2.

[0167] ①

[0168] ②

[0169] In the above formula, there are n objectives, f i (x1) represents the value of the objective i corresponding to a set of decision vectors x1; the same applies to others.

[0170] In the present invention, for the generated feasible architecture space, assume that there are m architecture solutions in total, denoted as:

[0171] A = {A1, A2,..., A m}.

[0172] Set n system indicators, denoted as:

[0173] f = {f1, f2,..., f n}.

[0174] Then for each architecture A i, there are corresponding n index values, all aiming to minimize the index. (If maximizing is pursued, a negative sign is added in front to convert it into a minimization index), denoted as:

[0175] f(A i ) = {f1(A i ), f2(A i ),..., f n (A i )}.

[0176] As Figure 7 shown, perform Pareto sorting, where R represents the number of current candidate Pareto solutions, m represents the number of all architectures to be sorted; n represents the number of system indicators (objectives); j represents the architecture number currently being looped and compared; r represents the architecture number used for comparison; q represents the number of solutions removed from the current candidate Pareto solution set.

[0177] Calculate all index values, starting from A1 to A m : f(A1), f(A2)... f(A m ).

[0178] Initialize R = 1, j = 1; starting from A1, take A1 as the candidate Pareto solution.

[0179] Let j = j + 1.

[0180] Judge whether j ≤ m; if yes, execute step (5); if no, the algorithm stops because all architectures have been considered, and output all results.

[0181] Let r = 1, q = 0.

[0182] Judge whether A j dominates A r ; if yes, execute step (8); otherwise, execute step (7). There are three relationships between Aj and Ar: ① Ar dominates Aj; ② Aj dominates Ar; ③ Aj and Ar are non-dominant to each other. Among them, ① means Aj is dominated and must not be on the Pareto front; because the definition of the Pareto front is that the solutions on the front are not dominated by any other solutions; and the solutions on the front dominate all non-front solutions; so it can be excluded. For ② and ③, further comparison and judgment should be made.

[0183] Method for judging Pareto dominance:

[0184] If for all i = 1, 2,..., n, there is: f i (A j ) ≤ f i (A r ), and there exists k ∈ 1, 2,..., n, f k(A j )<f k (A j ), then it is said that A j dominates A r .

[0185] Judge whether A r dominates A j ; if yes, return to step (3), because A j is dominated by other architectures and is not on the Pareto front, so it can be excluded; otherwise, execute step (9).

[0186] Let q = q + 1, f(A R ) = f(A j ), that is, add architecture A j to the candidate Pareto solutions, and mark A r as the solution to be removed.

[0187] Let r = r + 1.

[0188] Judge whether r ≤ R; if yes, return to step (6); if not, execute step (11); this step judges whether the Pareto dominance comparison has been performed on all candidate architectures.

[0189] Judge whether q ≠ 0; if yes, execute step (12); if not, execute step (13).

[0190] Remove the solution marked in step (8) from the candidate Pareto solutions, and use A j as the new candidate Pareto solution and add it to the Pareto solution set.

[0191] Let R = R + 1, A R = A j , f(A R ) = f(A j ), that is, the number of candidate solutions increases by one.

[0192] If the candidate solution x (obtained after Pareto sorting comparison) is not dominated by any other solution, then the candidate solution x is called a Pareto optimal solution. And the set composed of all Pareto optimal solutions becomes the Pareto optimal solution set, and its corresponding objective vector set is the Pareto front.

[0193] Here, the Pareto front of the architecture is illustrated with a diagram. As Figure 8 shown, it is an example of an architecture optimization problem with two objectives (system metrics). Figure 8 In it, the horizontal and vertical coordinates S1 and S2 respectively represent two objectives (such as cost and quality); A1 - A9 represent 9 architectures, and each point represents an architecture scheme. Each scheme corresponds to the results of two index values.

[0194] Figure 8 Among them, the area between the solid line and the dashed line is called the feasible region. Among them, the solid line represents the Pareto front, which consists of multiple Pareto-optimal solutions (architectures A1, A2, A3, A4 in the figure), and these architectures are not dominated by any other architecture. There is a dominated relationship for A5 - A9. Figure 8 For A1, A2, A3, A4, both Equation ① and Equation ② are satisfied.

[0195] For the system index values of each alternative architecture, perform a dominance comparison with other values according to Equation ① and Equation ②, record all Pareto-optimal solutions, and obtain the Pareto front.

[0196] Step S32: Use the weight sensitivity analysis method to determine the weight values of each system index; as Figure 9 shown, it is the flowchart of weight sensitivity analysis, and the specific process includes:

[0197] Step S321: Based on the preferences of multiple stakeholders, set trade-off indicators and different weight scenarios. In practical applications, the preference situation refers to the weights of different system indicators. For example: The implementation of a certain system architecture considers three system indicators: cost, risk, and time. These three are often in conflict. In the case of low risk, the cost is often high and the time consumption is long; the one with short time consumption often has high risk.

[0198] However, different stakeholders pursue different benefits. Some pursue low risk, and some pursue low cost, etc. Then when weighing the three indicators, it is necessary to comprehensively consider their respective weight values. Each stakeholder gives the acceptable weight range and expected value of their own. The purpose of weight sensitivity analysis is to find a common weight range to meet the needs of each stakeholder as much as possible.

[0199] Step S322: Update the weight scenario, that is, assign weight values to each system indicator in this round of iteration.

[0200] Step S323: Rank the scores of each architecture under this weight scenario through the TOPSIS method.

[0201] Step S324: Determine whether the weight scenario has been traversed. If not, retain the current result and return to Step S322; if it has been traversed, execute Step S325.

[0202] Step S325: Calculate the target deviation matrix D d =[d1, d2, …, d n , and use the formula to calculate the deviation d ij of the optimal architecture of index i in scenario j, where {S i} represents the set of index values of index i under different scenarios.

[0203] The deviation value indicates the degree of deviation of the result from the ideal value. The smaller the deviation value, the closer it is to the ideal value.

[0204] Step S326: Generate a weight matrix based on the preferences of multiple stakeholders. The weight matrix is a specific quantitative manifestation of all weight scenarios.

[0205] Step S327: According to the target deviation matrix and the weight matrix, adopt a visual analysis method to represent the optimal architecture results in each scenario graphically.

[0206] Step S328: The designer combines the visual analysis results, sets the maximum allowable deviation value for each system indicator, and comprehensively obtains a satisfactory weight interval. Within this interval, the values of each system indicator of the architecture are all within the deviation range.

[0207] Step S33: According to the alternative architectures on the Pareto front and the final indicator weights, combine the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to obtain the final satisfactory architecture (the final system architecture). The TOPSIS method is as follows:

[0208] The sorting principle of TOPSIS is that the optimal solution should be the closest to the ideal solution and the farthest from the worst solution; and a satisfactory architecture should not only have the highest benefit but also be able to withstand the lowest risk. Therefore, the TOPSIS method is very suitable for the trade-off sorting of architectures. The specific calculation method is as follows:

[0209] (1) Suppose there are m feasible architecture solutions and 3 system indicators, denoted as S = {S1, S2, S3}. Normalize the indicator S through Equation (1) and standardize it through Equation (2).

[0210] (2) According to the obtained system indicator weight values W = (ω1, ω2, ω3), ∑ω j = 1, and further perform weighted processing through Equation (3).

[0211] The weight values are obtained from the previous weight sensitivity analysis; the weight sensitivity analysis will obtain a result that meets the requirements of multiple stakeholders (i.e., the achievement requirements of each indicator); taking Figure 10 (d) as an example, its intersection (shaded area) is the result of the weight sensitivity analysis. In this area, the achievement degree of each indicator is above 70%; the so-called achievement degree is the ratio of the indicator value of this solution to the indicator value of the most ideal situation. In this area, the designer sets the weight values.

[0212] (3) Obtain the positive ideal solution and the negative ideal solution through Equation (4).

[0213] (4) Calculate the distances between the architecture indicator values and the positive and negative ideal solutions through Equation (5).

[0214] (5) Calculate the relative proximity through formula (6).

[0215] (6) If solution i is the ideal solution, then the corresponding C i = 1; conversely, the closer it is to the negative ideal solution, the closer C i is to 0.

[0216] (7) Sort the C i values of all solutions and select the satisfactory architecture.

[0217] (8) Formulas (1)-(6) are as follows:

[0218]

[0219]

[0220] T = (ω j · r ij ) m×3 (3)

[0221]

[0222]

[0223]

[0224] In the above formula, S ij is the j-th index value of architecture Ai, and S' ij represents the value after S ij is normalized; r ij represents the result of normalizing S ij . As shown in Table 4, it is the content corresponding to the system index decision matrix S = {S1, S2, S3}.

[0225] That is:

[0226] T represents the result after weighting the normalized index decision matrix S = {S1, S2, S3} through weight combination. T + represents the positive ideal solution, that is, the solution that each index most expects to achieve; T - represents the negative ideal solution, that is, the worst solution for each index; represents the distance between architecture Ai and the positive ideal solution, represents the distance between architecture Ai and the negative ideal solution. C i represents the relative proximity between architecture Ai and the ideal solution.

[0227] Table 4 Statistical table of system index values of the decision matrix and the corresponding normalization results

[0228]

[0229] Execute step S3 for architecture optimization and trade-off on the alternative architectures that meet the constraints reduced in step S2.

[0230] Step S31, multi-objective optimization search:

[0231] Perform multi-objective optimization search on the 1452 architectures generated by the CSP method.

[0232] This case has three objectives, quality M, cost C, and reliability R, which can be expressed as:

[0233]

[0234] Calculate the Pareto front of the architectures according to the value function.

[0235] As Figure 11 shown, it is the result of multi-objective optimization search for a primary-secondary separation system architecture; Figure 11 Among them, each point represents an architecture solution, and the one in the lower left corner is the Pareto front.

[0236] Through Pareto sorting, the size of the alternative architecture space is reduced from 1542 to 52 again.

[0237] Step S32, weight sensitivity analysis:

[0238] Adopt Figure 9 the process of weight sensitivity analysis shown, construct 10 design scenarios, and calculate the deviation values of the optimal architecture solutions corresponding to each scenario through iterative calculation. The calculation results are shown in Table 5.

[0239] Table 5 Statistical table of design scenarios and deviation results

[0240]

[0241]

[0242] According to the weight matrix W = [ω1, ω2, ω3] and deviation matrix D d = [d M , d C , d R in Table 5, conduct visual analysis.

[0243] As Figure 10 shown, it is a ternary diagram for the trade-off of a primary-secondary separation system. In the ternary diagram, the three sides respectively represent the weight values of the three objectives, Figure 10The colors in it represent the deviation values of each target, and their magnitudes can be read from the color bar beside. The deviation value represents the degree of deviation of the result from the ideal value. The smaller the deviation value, the closer it is to the ideal value.

[0244] Take Figure 10 (a) as an example to illustrate the reading method of the ternary diagram. Figure 10 (a) is the ternary diagram of mass M. Take the deviation value less than 0.3 as the ideal weight scheme, that is, the blue area in the figure. In this area, the weight value of mass ranges from 0.2 to 1, the weight value of cost ranges from 0 to 0.58, and the weight value of reliability ranges from 0 to 0.85. In this area, only the expected solution of minimizing the first target cost is guaranteed. Similarly, Figure 10 (b), Figure 10 (c) The corresponding areas also satisfy that the deviations of cost and reliability are within 30%. To find a solution that satisfies multiple stakeholders, the ternary diagrams of each target are overlapped to obtain Figure 10 (d).

[0245] In Figure 10 (d), the shaded area is the result where all three targets reach a relatively satisfactory level, that is, the deviation ranges of the three targets from their respective optima are all within 30%. The weight range of the shaded area is shown in Table 6.

[0246] Table 6 Statistical Table of Satisfactory Weight Ranges

[0247] Goal Weight <![CDATA[Mass M(ω1)]]> 0.12-0.38 <![CDATA[Cost C(ω2)]]> 0.3-0.43 <![CDATA[Reliability R(ω3)]]> 0.3-0.58

[0248] The weight regions for each target are determined through the weight sensitivity analysis method, and the weight values within this range can all meet the deviation requirements. If this region is not found, the designer can re - search for the intersection by adjusting the weight values.

[0249] Within this range, in this case, ω=(0.2, 0.3, 0.5) is selected as the final weight.

[0250] Step S33, TOPSIS ranking:

[0251] Based on the 52 architectures on the Pareto front and the weight combination ω=(0.2, 0.3, 0.5), comprehensive ranking is carried out through the TOPSIS method. As Figure 12 shown, it is the TOPSIS ranking result of the primary - secondary separation system. The 48th position on the Pareto front is the satisfactory architecture.

[0252] The device results adopted by the satisfactory architecture scheme are shown in Table 7.

[0253] Table 7 Schematic Table of Satisfactory Architecture Scheme

[0254]

[0255] The target values corresponding to this solution and their deviation results from the ideal values are shown in Table 8.

[0256] Table 8 Statistical Table of Targets and Deviation Results of the Satisfactory Architecture

[0257] Solution Result Quality Cost Reliability Goal Value 33.56 23920 0.99942 Deviation 0.237 0.147 0.108

[0258] As can be seen from Table 8, the deviation of each system index value of this solution from the ideal architecture is within 0.3, meeting the design requirements.

[0259] Through the method of the present invention, the trade-off of the primary and secondary separation system architecture is realized; for the formal characterization of the S1 architecture, the entire architecture design space is constructed through the morphological matrix, with a total of 2112 solutions; for the reduction of the S2 architecture design space, the architecture design space is reduced to 1452 through the CSP method. For the optimization and trade-off of the S3 architecture, the Pareto front is determined through the method of multi-objective optimization search, and the number of alternative architectures is further determined to be 52. Through the weight sensitivity analysis and TOPSIS sorting, finally, among the 52 architecture solutions, a satisfactory architecture that meets the requirements of multiple stakeholders is found.

[0260] The trade-off method for complex system architecture proposed by the present invention has strong applicability and has application potential in various complex product design fields such as aviation, aerospace, and vehicles. It can also help designers explore satisfactory architectures in a huge architecture design space. It has the following advantages:

[0261] 1. The present invention formally characterizes the system architecture in the form of a morphological matrix, enabling subsequent decision-making analysis to be carried out based on the morphological matrix, without the need to integrate architecture modeling software and system analysis software.

[0262] 2. The present invention constructs a CSP model based on the morphological matrix, thereby being able to delete a large number of infeasible architectures through the backtracking algorithm, avoiding the useless work of analyzing and weighing infeasible architectures, realizing the reduction of the architecture design space, and reducing the workload of trade-off analysis.

[0263] 3. The weight sensitivity analysis method for the architecture proposed by the present invention is used to help designers determine the index weights, reducing the subjectivity and uncertainty in the process of weight determination, so that the finally generated solutions can meet the needs of multiple stakeholders.

[0264] Example Two

[0265] In order to execute the method corresponding to the above Example One to achieve the corresponding functions and technical effects, the following provides a system architecture trade-off analysis system, as Figure 13 shown, including:

[0266] A data acquisition module 1301, configured to acquire decision items and alternative items of the rocket first-stage and second-stage separation system architecture to be weighed and analyzed; the decision items are the problems to be decided in the rocket first-stage and second-stage separation system architecture to be weighed and analyzed; the alternative items are discrete option sets corresponding to the problems to be decided; the problems to be decided include a selection signal conversion device, a selection signal transmission device, a selection connection and separation device, and a selection separation thrust device; the discrete option set corresponding to the selection signal conversion device includes an electric igniter, an electric detonator, a semiconductor bridge igniter, a mechanical detonator, a diaphragm detonator, and a delay igniter; the discrete option set corresponding to the selection signal transmission device includes a restricted detonating component and a plastic detonating tube component; the discrete option set corresponding to the selection connection and separation device includes an explosive bolt, a separation nut, and a connection pin type separation device; the discrete option set corresponding to the selection separation thrust device includes a pyrotechnic actuator and a separation rocket.

[0267] A data characterization module 1302, configured to perform formal characterization on the decision items and the alternative items to obtain a morphological matrix; the morphological matrix uses the decision items as columns and the alternative items as rows; the morphological matrix includes a plurality of alternative architectures; the alternative architectures are composed of any one item in the discrete option set corresponding to the selection signal conversion device, any one item in the discrete option set corresponding to the selection signal transmission device, any one item in the discrete option set corresponding to the selection connection and separation device, and any one item in the discrete option set corresponding to the selection separation thrust device.

[0268] A constraint screening module 1303, configured to determine alternative architectures that meet the constraints among the plurality of alternative architectures according to the morphological matrix; the constraints include option mutual exclusion and option association.

[0269] A search module 1304, configured to use a multi-objective optimization search method to determine alternative architectures that are on the Pareto front among the alternative architectures that meet the constraints.

[0270] A final system architecture determination module 1305, configured to determine a final system architecture according to the alternative architectures that are on the Pareto front by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method.

[0271] Embodiment III

[0272] This embodiment provides an electronic device, including: a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the system architecture trade-off analysis method of Embodiment I.

[0273] Embodiment IV

[0274] This embodiment provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the system architecture trade-off analysis method of Embodiment I.

[0275] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0276] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea. At the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A system architecture trade-off analysis method, characterized in that Including: Obtaining decision items and alternative options for the rocket first-stage and second-stage separation system architecture to be weighed and analyzed; the decision items are the problems to be decided in the rocket first-stage and second-stage separation system architecture to be weighed and analyzed; the alternative options are the discrete option sets corresponding to the problems to be decided; the problems to be decided include a selection signal conversion device, a selection signal transmission device, a selection connection and separation device, and a selection separation thrust device; the discrete option set corresponding to the selection signal conversion device includes an electric igniter, an electric detonator, a semiconductor bridge igniter, a mechanical detonator, a diaphragm detonator, and a delay igniter; the discrete option set corresponding to the selection signal transmission device includes a restricted detonating component and a plastic detonating tube component; the discrete option set corresponding to the selection connection and separation device includes an explosive bolt, a separation nut, and a connection pin type separation device; The discrete option set corresponding to the selection separation thrust device includes a pyrotechnic actuator and a separation rocket; Formally representing the decision items and the alternative options to obtain a morphological matrix; The morphological matrix has the decision items as columns and the alternative options as rows; the morphological matrix includes multiple alternative architectures; the alternative architectures are composed of any one item in the discrete option set corresponding to the signal conversion device, any one item in the discrete option set corresponding to the signal transmission device, any one item in the discrete option set corresponding to the connection and separation device, and any one item in the discrete option set corresponding to the separation thrust device; Determining the alternative architectures that meet the constraints among the multiple alternative architectures according to the morphological matrix; the constraints include option mutual exclusion and option association; Using a multi-objective optimization search method to determine the alternative architectures on the Pareto front among the alternative architectures that meet the constraints; According to the alternative architectures on the Pareto front, using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to determine the final system architecture.

2. The system architecture trade-off analysis method according to claim 1, wherein Determining the alternative architectures that meet the constraints according to the morphological matrix, specifically including: Establishing a constraint satisfaction problem model according to the morphological matrix; the constraint satisfaction problem model includes variables, value ranges, and the constraints; the variables are the decision items; the value ranges are the alternative options; Based on the constraint satisfaction problem model, using a backtracking algorithm to determine the alternative architectures that meet the constraints among the multiple alternative architectures.

3. The system architecture trade-off analysis method according to claim 1, wherein Using a multi-objective optimization search method to determine the alternative architectures on the Pareto front among the alternative architectures that meet the constraints, specifically including: Calculating the system index values of the alternative architectures that meet the constraints; According to the system index values, using a multi-objective optimization search method to determine the alternative architectures on the Pareto front.

4. The system architecture trade-off analysis method according to claim 1, characterized in that According to the alternative architectures on the Pareto front, using the TOPSIS method to determine the final system architecture, specifically including: Using a weight sensitivity analysis method to determine the weight values of each system index of the alternative architectures on the Pareto front; According to the alternative architectures on the Pareto front and the weight values of each system index of the alternative architectures on the Pareto front, using the TOPSIS method to determine the final system architecture.

5. The system architecture trade-off analysis method according to claim 1, wherein Also including: Determining the system indexes of the system architecture to be weighed and analyzed.

6. The system architecture trade-off analysis method according to claim 5, wherein Determining the system indexes of the system architecture to be weighed and analyzed, specifically including: Obtain the decision metrics for each decision item of the system architecture to be analyzed for trade - off; the decision metrics include inherent attributes and functional attributes; the inherent attributes include architecture quality, architecture size, and architecture cost; the functional attributes include architecture load - bearing strength, architecture output power, and architecture reliability; Based on the decision metrics, use the value function to determine the system metrics of the system architecture to be analyzed for trade - off.

7. A system architecture trade-off analysis system, characterized in that It includes: A data acquisition module, configured to obtain decision items and alternative options of the rocket first - stage and second - stage separation system architecture to be analyzed for trade - off; the decision items are the problems to be decided in the rocket first - stage and second - stage separation system architecture to be analyzed for trade - off; the alternative options are the discrete option sets corresponding to the problems to be decided; the problems to be decided include selecting a signal conversion device, a signal transmission device, a connection and separation device, and a separation thrust device; the discrete option set corresponding to the signal conversion device includes an electric igniter, an electric detonator, a semiconductor bridge igniter, a mechanical detonator, a diaphragm detonator, and a delay igniter; the discrete option set corresponding to the signal transmission device includes a restricted detonating assembly and a plastic detonating tube assembly; the discrete option set corresponding to the connection and separation device includes an explosive bolt, a separating nut, and a connection pin - type separation device; the discrete option set corresponding to the separation thrust device includes a pyrotechnic actuator and a separation rocket; A data characterization module, configured to perform formal characterization on the decision items and the alternative options to obtain a morphological matrix; The morphological matrix has the decision items as columns and the alternative options as rows; the morphological matrix includes multiple alternative architectures; the alternative architectures are composed of any one item from the discrete option set corresponding to the signal conversion device, any one item from the discrete option set corresponding to the signal transmission device, any one item from the discrete option set corresponding to the connection and separation device, and any one item from the discrete option set corresponding to the separation thrust device; A constraint screening module, configured to determine the alternative architectures that meet the constraints among the multiple alternative architectures according to the morphological matrix; the constraints include option mutual exclusion and option association; A search module, configured to use a multi - objective optimization search method to determine the alternative architectures on the Pareto front among the alternative architectures that meet the constraints; A final system architecture determination module, configured to determine the final system architecture according to the alternative architectures on the Pareto front by using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

8. An electronic device, characterized in that, It includes: A memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the system architecture trade - off analysis method according to any one of claims 1 - 6.

9. A computer-readable storage medium, characterized in that, The computer - readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the system architecture trade - off analysis method according to any one of claims 1 - 6.

Images