Satellite payload cabin component layout method and system based on heat pipe constraints

Abstract

The present invention discloses a method and system for layout of satellite payload cabin components under heat pipe constraints. The method comprises: obtaining target component information, constructing an initial layout scheme of components according to the target component information, and generating multiple target constraints; converting the layout problem of satellite payload cabin components under heat pipe constraints into a multi-objective algorithm model according to the initial layout scheme and multiple target constraints; evolving the multi-objective algorithm model according to the first-stage optimization algorithm, and judging whether all current layout schemes are feasible schemes in each evolution process; when all current layout schemes are infeasible schemes, selecting a preset number of layout schemes according to the second-stage optimization algorithm, modifying them, and evolving them; when the evolution reaches the termination condition, taking the best performance scheme among the multiple layout schemes obtained by the last evolution as the target scheme, and outputting them. The present invention can obtain an accurate and effective layout method.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite payload compartment layout, and in particular to a satellite payload compartment component layout method and system based on heat pipe constraints. Background Art

[0002] The rationalization of satellite payload bay layout significantly affects the performance of electronic components. With the increasing requirements for satellite functions, more and more components need to be installed in the limited payload bay space, which will lead to serious heat accumulation problems. Heat pipe-constrained device layout optimization (HDLO) maximizes the performance of electronic components and minimizes heat accumulation by rationalizing the space utilization of satellite payload bays.

[0003] However, because HDLO not only considers the traditional payload bay layout constraints, but also the thermal decomposition capacity constraints caused by heat pipes and the component and heat pipe overlap constraints, the presence of heat pipes introduces additional optimization complexity, resulting in the inability to obtain a suitable layout method.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0005] The main purpose of the present invention is to provide a satellite payload bay component layout method, system, terminal and computer-readable storage medium based on heat pipe constraints, aiming to solve the problem that HDLO in the prior art not only considers the traditional payload bay layout constraints, but also considers the thermal decomposition capacity constraints caused by heat pipes and the overlap constraints between components and heat pipes. Therefore, the existence of heat pipes introduces additional optimization complexity, resulting in the inability to obtain a suitable layout method.

[0006] To achieve the above object, the present invention provides a method for placing satellite payload compartment components under heat pipe constraints, the method comprising the following steps:

[0007] Acquire target component information, construct an initial component layout plan based on the target component information, and generate multiple target constraint conditions;

[0008] According to the initial layout scheme and multiple objective constraints, the satellite payload compartment component layout problem under heat pipe constraints is converted into a multi-objective algorithm model;

[0009] According to the first-stage optimization algorithm, the multi-objective algorithm model is evolved, and in each evolution process, it is determined whether all current layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, according to the second-stage optimization algorithm, a preset number of layout schemes are selected and modified, and evolution is performed again according to the modified results;

[0010] When the evolution reaches the termination condition, the best performing solution among the multiple layout solutions obtained by the last evolution is taken as the target solution and output.

[0011] Optionally, the acquiring of target component information, constructing an initial component layout plan according to the target component information, and generating a plurality of target constraint conditions specifically include:

[0012] Acquire target component information, wherein the target component information includes the number and location information of each component;

[0013] Constructing an initial layout plan of the components according to the number and position information of each component;

[0014] According to the initial layout plan of components, a plurality of target constraints are generated, wherein the plurality of target constraints include non-overlapping constraints, component heat pipe overlapping constraints, static stability constraints and heat dissipation capacity constraints.

[0015] Optionally, the step of converting the satellite payload compartment component layout problem under heat pipe constraints into a multi-objective algorithm model according to the initial layout plan and multiple objective constraints includes:

[0016] According to the initial layout scheme and a plurality of objective constraints, generating an objective function of an overall default value, a crowding distance and a thermal performance corresponding to the initial layout scheme;

[0017] According to the objective functions of the overall default value, the crowding distance and the thermal performance, and with multiple objective constraints as constraints, the satellite payload cabin component layout problem under heat pipe constraints is transformed into a multi-objective algorithm model.

[0018] Optionally, the multi-objective algorithm model is evolved according to the first-stage optimization algorithm, and in each evolution process, it is determined whether all current layout solutions are feasible solutions. When all current layout solutions are infeasible solutions, a current layout solution is selected and modified according to the second-stage optimization algorithm, and evolution is performed again according to the modified result, specifically including:

[0019] According to the first-stage optimization algorithm, the number of initial layout solutions, termination conditions, and preset quantities are set;

[0020] Initialize the population, set auxiliary variables, calculate the current multiple objective constraints and the objective function of thermal performance, and calculate the first parameter according to the current multiple objective constraints, wherein the population includes the number of initial layout solutions;

[0021] Evolution is performed according to the population size, auxiliary variables, current multiple objective constraints, the objective function of thermal performance and the first parameter, and in each evolution, it is determined whether all layout schemes are feasible. When all current layout schemes are infeasible, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm to generate a new population;

[0022] The evolution process is repeated until the value of the auxiliary variable reaches the termination condition.

[0023] Optionally, the evolution is performed according to the population size, auxiliary variables, multiple current objective constraints, the objective function of thermal performance and the first parameter, and in each evolution, it is determined whether all layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm to generate a new population, specifically including:

[0024] According to the mutation strategy and binomial crossover recombination, the population is processed to generate sub-populations, wherein the sub-populations include the number of initial layout schemes and the number of layout schemes;

[0025] Calculating multiple objective constraints and objective functions of thermal performance for each layout solution in the subpopulation, and updating auxiliary variables to obtain first updated auxiliary variables;

[0026] Merge the population and sub-populations to obtain a merged population, and calculate the overall default value, crowding distance, and thermal performance objective function of each layout scheme in the merged population;

[0027] According to the pruning technology and the non-dominated sorting method, the number of initial layout schemes is selected from the merged population as the current population;

[0028] Get the overall default value of each layout scheme in the current population, and judge whether the layout scheme of the current population is a feasible scheme based on the overall default value of each layout scheme. When all current layout schemes are infeasible schemes, select a preset number of layout schemes and modify them according to the second-stage optimization algorithm to generate a new population.

[0029] Optionally, the selecting of the number of initial layout schemes from the merged population as the current population according to the pruning technology and the non-dominated sorting method specifically includes:

[0030] Perform preliminary pruning on the merged population according to the pruning technology to obtain a pruned population;

[0031] The non-dominated sorting method is used to compare any two layout plans according to multiple objective constraints of each layout plan in the pruning population, so as to select the initial number of layout plans as the current population.

[0032] Optionally, selecting a preset number of layout schemes and modifying them according to the second-stage optimization algorithm to generate a new population specifically includes:

[0033] Select one of the selected preset number of layout schemes, calculate the corresponding increment, and update the first update auxiliary variable at the same time;

[0034] According to the increment of the selected layout scheme, the selected layout scheme is updated to obtain an updated layout scheme;

[0035] Calculate multiple objective constraints and thermal performance objective functions of the updated layout solution to determine whether the constraint requirements are met;

[0036] When the condition is satisfied, determine whether the overall default value of the updated layout scheme is better than its corresponding layout scheme. If it is better, replace the corresponding layout scheme in the current population with the updated layout scheme.

[0037] Repeatedly select other layout schemes from the selected preset number of layout schemes, and use the second-stage optimization algorithm for calculation until all the selected preset number of layout schemes are processed to obtain a new population.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also provides a satellite payload compartment component layout system based on heat pipe constraints, wherein the satellite payload compartment component layout system based on heat pipe constraints includes:

[0039] A constraint module, used to obtain target component information, construct an initial component layout plan according to the target component information, and generate multiple target constraint conditions;

[0040] A model building module is used to convert the satellite payload compartment component layout problem under heat pipe constraints into a multi-objective algorithm model according to the initial layout plan and multiple objective constraints;

[0041] An evolution module, used to evolve the multi-objective algorithm model according to the first-stage optimization algorithm, and determine whether all current layout schemes are feasible schemes during each evolution process. When all current layout schemes are infeasible schemes, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm, and evolve again according to the modified results;

[0042] The target output module is used to take the best performance solution among multiple layout solutions obtained by the last evolution as the target solution and output it when the evolution reaches the termination condition.

[0043] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a satellite payload compartment component layout program based on heat pipe constraints stored in the memory and executable on the processor, wherein the satellite payload compartment component layout program based on heat pipe constraints, when executed by the processor, implements the steps of the satellite payload compartment component layout method based on heat pipe constraints as described above.

[0044] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a satellite payload compartment component layout program based on heat pipe constraints, and when the satellite payload compartment component layout program based on heat pipe constraints is executed by a processor, the steps of the satellite payload compartment component layout method based on heat pipe constraints are implemented as described above.

[0045] In the present invention, target component information is obtained, an initial layout scheme of components is constructed according to the target component information, and multiple target constraints are generated; according to the initial layout scheme and multiple target constraints, the layout problem of satellite payload cabin components under heat pipe constraints is converted into a multi-objective algorithm model; according to the first-stage optimization algorithm, the multi-objective algorithm model is evolved, and in each evolution process, it is judged whether all current layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm, and evolution is performed again according to the modified results; when the evolution reaches the termination condition, the best performance scheme among the multiple layout schemes obtained by the last evolution is used as the target scheme and output. The present invention reasonably transforms the layout problem of satellite payload cabin components under heat pipe constraints through a multi-objective algorithm model, and finds a feasible area according to constraint violations through the first-stage optimization algorithm, and introduces a second-stage optimization algorithm to prevent the greedy effect of constraint processing, so that an accurate and effective layout method can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a preferred embodiment of the satellite payload cabin assembly layout method under heat pipe constraints of the present invention;

[0047] Figure 2 It is a flow chart of a method for arranging components of a satellite payload compartment under heat pipe constraints according to the present invention;

[0048] Figure 3It is a schematic diagram of the convergence of the overall default value during the optimization process of the double-layer satellite payload cabin in the satellite payload cabin component layout method under heat pipe constraints of the present invention;

[0049] Figure 4 It is a schematic diagram of the convergence of the actual load peak power during the optimization process of the double-layer satellite payload cabin in the satellite payload cabin component layout method under heat pipe constraints of the present invention;

[0050] Figure 5 It is a structural diagram of a preferred embodiment of the satellite payload cabin component layout system under heat pipe constraints of the present invention;

[0051] Figure 6 It is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] The rationalization of the layout of the satellite payload bay significantly affects the working performance of electronic components. With the increasing requirements for satellite functions, more and more components need to be installed in the limited payload bay space, which will lead to serious heat accumulation problems. The optimization of component layout under heat pipe constraints is to maximize the performance of electronic components and minimize heat accumulation by rationalizing the space utilization of the satellite payload bay. As a constraint, the heat pipe has specific properties, such as high thermal conductivity and huge heat flux, which leads to rapid heat conduction between the heat source and the heat sink. However, because HDLO not only considers traditional payload bay layout constraints, such as non-overlapping constraints and static stability constraints, but also considers the thermal decomposition capacity constraints caused by heat pipes and the overlap constraints between components and heat pipes, the existence of heat pipes introduces additional optimization complexity, resulting in the inability to obtain a suitable layout method. In addition, there may be a large number of local optimal solutions for large-scale HDLO problems. At present, the search for feasible solutions is often given priority, while ignoring a large number of feasible local optimal solutions will cause the algorithm to converge prematurely.

[0054] In response to one or more of the above problems, the present invention obtains target component information, constructs an initial component layout plan based on the target component information, and generates multiple target constraints; based on the initial layout plan and multiple target constraints, the satellite payload cabin component layout problem under heat pipe constraints is converted into a multi-objective algorithm model; according to the first-stage optimization algorithm, the multi-objective algorithm model is evolved, and in each evolution process, it is determined whether all current layout plans are feasible plans. When all current layout plans are infeasible plans, according to the second-stage optimization algorithm, a preset number of layout plans are selected and modified, and evolution is performed again according to the modified results; when the evolution reaches the termination condition, the plan with the best performance among the multiple layout plans obtained by the last evolution is used as the target plan and output.

[0055] The satellite payload cabin component layout method based on heat pipe constraints described in the preferred embodiment of the present invention is as follows: Figure 1 As shown, the satellite payload compartment component layout method based on heat pipe constraints includes the following steps:

[0056] Step S10: Obtain target component information, construct an initial component layout plan according to the target component information, and generate a plurality of target constraint conditions.

[0057] Specifically, in the present invention, after obtaining the corresponding target component information, the initial layout scheme and its corresponding constraints can be generated based on the target component information.

[0058] Furthermore, the step of acquiring target component information, constructing an initial component layout plan according to the target component information, and generating a plurality of target constraint conditions specifically includes:

[0059] Acquire target component information, wherein the target component information includes the number and location information of each component;

[0060] Constructing an initial layout plan of the components according to the number and position information of each component;

[0061] According to the initial layout plan of components, a plurality of target constraints are generated, wherein the plurality of target constraints include non-overlapping constraints, component heat pipe overlapping constraints, static stability constraints and heat dissipation capacity constraints.

[0062] Specifically, the target component information includes the number and position information of each component. The position information includes the horizontal position and vertical position of the centroid of the component. From this information, the corresponding component initial layout scheme L can be obtained, where the component initial layout scheme can be expressed as the following formula (1):

[0063] ; (1)

[0064] in, is the number of the component, N c is the total number of components, Indicates the number The horizontal position of the centroid of the component, Indicates the number The vertical position of the center of mass of the component.

[0065] According to the initial layout plan of the device, the corresponding layout problem of the satellite payload cabin components under the heat pipe constraint can be defined, that is, the objective function of thermal performance Minimize, where the satellite payload cabin component layout problem under the heat pipe constraint can be expressed as the following formula (2):

[0066] ; (2)

[0067] in, is the number of side panels of the load compartment, For the b k Number of heat pipes per side panel; For the The number of components carried by a heat pipe, No. The actual heat generation power of each component is For the The number of heat pipes occupied by each component, For a specific heat pipe.

[0068] Correspondingly, multiple target constraints can be obtained from the initial device layout plan. Preferably, in the present invention, there are four target constraints, namely, non-overlapping constraint, component heat pipe overlap constraint, static stability constraint and heat dissipation capacity constraint, among which the non-overlapping constraint , Component heat pipe overlap constraints , static stability constraints and heat dissipation capacity constraints It can be expressed by the following formulas (3), (4), (5) and (6):

[0069] ; (3)

[0070] Among them, the non-overlapping constraint means that components cannot overlap each other and cannot exceed the layout domain. For components and The overlapping area, N c is the number of components, Indicates the corresponding component number, and are greater than 0, ≠ ;

[0071] ; (4)

[0072] Each component needs to be placed on a heat pipe. When a component uses multiple heat pipes for heat dissipation at the same time, these heat pipes evenly share the thermal power of the component. The corresponding component heat pipe overlap constraint is shown in formula (2), where: For components The distance to the nearest heat pipe is indicated;

[0073] ; (5)

[0074] The static stability constraint states that the center of mass position should be within a given range, where For actual Axis center of mass position, For the The quality of each component, Standard Axis center of mass position, is the maximum allowable position deviation;

[0075] ; (6)

[0076] The heat dissipation capacity constraint means that the heat power on each heat pipe cannot exceed the maximum heat dissipation capacity, where: is the number of heat pipes, is the maximum load power of the heat pipe, For the The number of components carried by a heat pipe, For the The number of heat pipes occupied by each component, No. The actual heat generation power of each component.

[0077] Then the corresponding layout problem of satellite payload cabin components under heat pipe constraints is to minimize And it satisfies multiple objective constraints at the same time, as shown in formula (7):

[0078] ; (7)

[0079] in, Indicates the sequence number of the target constraint. Represents the target constraint corresponding to the layout solution L.

[0080] Step S20: According to the initial layout plan and multiple objective constraints, the satellite payload compartment component layout problem under heat pipe constraints is converted into a multi-objective algorithm model.

[0081] Specifically, in the present invention, for the constructed initial layout plan and multiple objective constraints, the present invention correspondingly converts the payload bay component layout problem into a multi-objective algorithm model.

[0082] Furthermore, according to the initial layout scheme and multiple objective constraints, the satellite payload compartment component layout problem under heat pipe constraints is converted into a multi-objective algorithm model, specifically including:

[0083] According to the initial layout scheme and a plurality of objective constraints, generating an objective function of an overall default value, a crowding distance and a thermal performance corresponding to the initial layout scheme;

[0084] According to the objective functions of the overall default value, the crowding distance and the thermal performance, and with multiple objective constraints as constraints, the satellite payload cabin component layout problem under heat pipe constraints is transformed into a multi-objective algorithm model.

[0085] Specifically, in the present invention, the overall default value, crowding distance and thermal performance objective function corresponding to the initial layout scheme can be generated through the initial layout scheme and multiple target constraints, wherein the thermal performance objective function is .

[0086] The overall default value It can be expressed as the following formula (8):

[0087] ; (8)

[0088] Crowding distance That is, as shown in the following formula (9):

[0089] ; (9)

[0090] in PS is the size of the population, that is, the number of initial layout solutions, Indicates the population number, symbol represents the Euclidean distance, represents the radius of the microhabitat; for L, calculate its relationship with other individuals If the distance is less than The accumulated congestion is greater than Then the crowding degree is recorded as zero, so the crowding degree of L and other individuals can be calculated. In the present invention, in addition to optimizing the objective function of thermal performance In addition, the constraint violation function c(L) is used to evaluate the feasibility of a given layout solution; specifically, is based on the maximum default value found in the initial stage. When it is zero, the layout solution L is considered feasible, otherwise it is infeasible.

[0091] in The size is shown in the following formula (10):

[0092] ; (10)

[0093] in, D is the number of spatial decision variables, that is, the number of variables required to locate all components. N c The coordinates of the components.

[0094] The present invention introduces a unified general multi-objective optimization framework, namely TriGF (Tri-objective General Framework), which transforms the satellite payload compartment component layout problem under heat pipe constraints into a multi-objective algorithm model with three optimization objectives based on the objective functions of overall default value, crowding distance and thermal performance and multiple objective constraints. The multi-objective algorithm model is expressed as the following formula (11):

[0095] ; (11)

[0096] in, , ,

[0097] , is the maximum default value of the non-overlapping constraint, is the maximum default value of the element heat pipe overlap constraint, is the maximum default value of the static stability constraint, is the maximum default value of the heat dissipation capacity constraint, include , , and , D = 2N c , indicating positioning N c The number of decision variables required for each electronic component. nF is a variable in the auxiliary variables, MaxFEs for nF The maximum value of is the first parameter, The scaling factor optimized for the current constraint,k Represents the second auxiliary variable among the auxiliary variables.

[0098] parameter Control the size of the search range while ensuring that when solving the algorithm, the constraint violations are close to 10 in the last 10% of the evolution. -6 .

[0099] In this multi-objective algorithm model, in addition to the objective function of optimizing thermal performance, the overall default value Used to evaluate the feasibility of a given layout solution L. When is zero, the layout solution L is considered feasible, otherwise it is infeasible. In the process of solving the multi-objective algorithm model, the set of all feasible solutions is called the feasible region, which is minimized. , the intermediate solutions generated during the search process will gradually move closer to the feasible area. In addition, the crowding distance Originated from the fitness sharing mechanism, this mechanism effectively solves the problem of the algorithm falling into the local optimum. By minimizing the crowding distance , ensuring that the distance between individual solutions is at least , thereby maintaining the diversity of the population.

[0100] Therefore, TriGF in the present invention integrates various information of the HDLO task and re-models it to cope with different optimization tasks, that is, a multi-objective algorithm model.

[0101] Step S30: According to the first-stage optimization algorithm, the multi-objective algorithm model is evolved, and in each evolution process, it is determined whether all current layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, according to the second-stage optimization algorithm, a preset number of layout schemes are selected and modified, and evolution is performed again according to the modified results.

[0102] Specifically, after constructing the corresponding multi-objective algorithm model, it is evolved to obtain the final evolution result. , and The present invention proposes a first-stage optimization algorithm, and the first-stage optimization algorithm is called AGM-DE algorithm, that is, the method adopted in the present invention adopts Multi-objective constrained optimization operator, namely Domination method is used to make individual comparisons. When facing complex HDLO tasks, on the one hand, the present invention needs to quickly obtain a feasible layout solution within a limited time to adapt to the ever-changing satellite operating environment; on the other hand, the present invention still hopes to find a globally optimal feasible layout solution to achieve the maximum working performance of electronic components. However, in the process of constrained optimization, accelerated convergence may endanger population diversity and then fall into a local optimal solution. This also leads to the fact that although the standard gradient mutation (SGM) has been widely used in complex constrained optimization, it does not have a good global search capability. Therefore, the present invention uses the objective function to alleviate the greedy effect of constraint processing. The present invention introduces the gradient data of the objective function into the traditional SGM and evolves it into an adaptive version, which is the second objective function, which the present invention refers to as AGM.

[0103] Furthermore, according to the first-stage optimization algorithm, the multi-objective algorithm model is evolved, and in each evolution process, it is determined whether all current layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, according to the second-stage optimization algorithm, a current layout scheme is selected and modified, and evolution is performed again according to the modified result, specifically including:

[0104] According to the first-stage optimization algorithm, the number of initial layout solutions, termination conditions, and preset quantities are set;

[0105] Initialize the population, set auxiliary variables, calculate the current multiple objective constraints and the objective function of thermal performance, and calculate the first parameter according to the current multiple objective constraints, wherein the population includes the number of initial layout solutions;

[0106] Evolution is performed according to the population size, auxiliary variables, current multiple objective constraints, the objective function of thermal performance and the first parameter, and in each evolution, it is determined whether all layout schemes are feasible. When all current layout schemes are infeasible, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm to generate a new population;

[0107] The evolution process is repeated until the value of the auxiliary variable reaches the termination condition.

[0108] Specifically, in the present invention, the corresponding multi-objective algorithm model is solved by the first-stage optimization algorithm. When all the layout solutions obtained in the evolution process of the first-stage optimization algorithm are infeasible solutions, the second-stage optimization algorithm is used to modify the current population to obtain a new population, and then the first-stage optimization algorithm is used again to evolve until the auxiliary variables of the evolution are nF achieve MaxFEsWhen , the evolution ends.

[0109] The first stage optimization algorithm can be called AGM-DE. When the algorithm is implemented, the parameters are first input, including PS , that is, the number of initial layout solutions, as the evolution population P size; MaxFEs , i.e., the termination condition of AGM-DE; Nm , that is, the number of layout solutions that need to be modified by AGM. AGM is the second-stage optimization algorithm.

[0110] Initialization is performed before evolution, that is, the initial layout scheme number is randomly generated to initialize the population, and the auxiliary variables are evaluated and set, the current multiple objective constraints and the objective function of thermal performance are calculated, and the first parameter is calculated according to the current multiple objective constraints. The population contains the initial layout scheme number layout schemes, and the auxiliary variables are initialized to set the first auxiliary variable nF = PS , the second auxiliary variable k =0; through the above formulas (2), (3), (4), (5) and (6), the multiple target constraints and the target function of thermal performance corresponding to each layout method in the current population can be calculated; the first parameter is calculated by the following formula (12): The value of , where formula (12) can be expressed as:

[0111] ; (12)

[0112] Then the evolution is carried out until the evolution result is reached.

[0113] Furthermore, the evolution is performed according to the population size, auxiliary variables, the current multiple objective constraints, the objective function of thermal performance and the first parameter, and in each evolution, it is determined whether all layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm to generate a new population, specifically including:

[0114] According to the mutation strategy and binomial crossover recombination, the population is processed to generate sub-populations, wherein the sub-populations include the number of initial layout schemes and the number of layout schemes;

[0115] Calculating multiple objective constraints and objective functions of thermal performance for each layout solution in the subpopulation, and updating auxiliary variables to obtain first updated auxiliary variables;

[0116] Merge the population and sub-populations to obtain a merged population, and calculate the overall default value, crowding distance, and thermal performance objective function of each layout scheme in the merged population;

[0117] According to the pruning technology and the non-dominated sorting method, the number of initial layout schemes is selected from the merged population as the current population;

[0118] Get the overall default value of each layout scheme in the current population, and judge whether the layout scheme of the current population is a feasible scheme based on the overall default value of each layout scheme. When all current layout schemes are infeasible schemes, select a preset number of layout schemes and modify them according to the second-stage optimization algorithm to generate a new population.

[0119] Specifically, in the present invention, when the auxiliary variable nF Not reached MaxFEs When , the first stage optimization algorithm is repeatedly executed. During the execution process, the population is first processed through mutation strategy and binomial crossover recombination to generate sub-populations. The mutation strategy is DE / current-to-rand / 1 mutation. Through DE / current-to-rand / 1 mutation and binomial crossover operation, the population is processed. P In PS Layout schemes are processed to generate PS The subpopulation Q ; Then the corresponding evaluation generated sub-population Q In PS The performance of the child layout scheme is calculated by using the above formulas (2), (3), (4), (5) and (6) to calculate the multiple target constraints and the target function of thermal performance of each layout scheme in the sub-population, and to determine whether the sub-population satisfies the multiple target constraints. At the same time, the auxiliary variables are updated accordingly to obtain the first updated auxiliary variables. The first updated auxiliary variables include nF 1 and k 1, nF 1= nF + PS , k 1= k +1; then merge the population and sub-population to obtain a merged population, where the merged population P 1= P ∪ Q , and calculate the overall default value and crowding distance corresponding to each layout scheme in the merged population according to the multiple target constraints of each population and the objective function of thermal performance, and obtain the objective function of thermal performance of each layout scheme, that is, calculate the overall default value and crowding distance through the above formula (8) and formula (9), judge the performance of each layout scheme in the current merged population, that is, judge the corresponding overall default value, crowding distance and thermal performance objective function value; finally, select the current population from the merged population through the overall default value, crowding distance and thermal performance objective function corresponding to each layout scheme in the merged population.

[0120] Furthermore, the method of selecting the number of initial layout schemes from the merged population as the current population according to the pruning technology and the non-dominated sorting method specifically includes:

[0121] Perform preliminary pruning on the merged population according to the pruning technology to obtain a pruned population;

[0122] The non-dominated sorting method is used to compare any two layout plans according to multiple objective constraints of each layout plan in the pruning population, so as to select the initial number of layout plans as the current population.

[0123] Specifically, when selecting the current population through pruning technology and non-dominated sorting method, the merged population is first pruned by pruning technology to remove the overfitting part to obtain the pruned population; then the pruned population is subjected to non-dominated sorting method to select the initial layout scheme number as the current population. Among them, the non-dominated sorting method is Domination method, using non-dominated sorting method to select the initial layout scheme number layout scheme, where For the scaling factor optimized for the current constraint, the following comparison is made for any two layout solutions L1 and L2: , the target constraint corresponding to the layout scheme L1 Less than or equal to the corresponding scaling factor of the current constraint optimization ,and Less than or equal to the corresponding scaling factor of the current constraint optimization , then the classic Pareto dominance principle is used to compare the advantages and disadvantages of L1 and L2; , Less than or equal to the corresponding scaling factor of the current constraint optimization ,and , Less than or equal to the corresponding scaling factor of the current constraint optimization , then L1 dominates L2; Make Greater than the corresponding scaling factor of the current constrained optimization ,and Greater than the corresponding scaling factor of the current constrained optimization , the total default value in L1 and L2 The smaller one dominates the other. Compare any two layout plans until the initial layout plan number of layout plans are selected, and use the initial layout plan number of layout plans as individuals to obtain the current population.

[0124] Furthermore, according to the second-stage optimization algorithm, a preset number of layout schemes are selected and modified to generate a new population, specifically including:

[0125] Select one of the selected preset number of layout schemes, calculate the corresponding increment, and update the first update auxiliary variable at the same time;

[0126] According to the increment of the selected layout scheme, the selected layout scheme is updated to obtain an updated layout scheme;

[0127] Calculate multiple objective constraints and thermal performance objective functions of the updated layout solution to determine whether the constraint requirements are met;

[0128] When the condition is satisfied, determine whether the overall default value of the updated layout scheme is better than its corresponding layout scheme. If it is better, replace the corresponding layout scheme in the current population with the updated layout scheme.

[0129] Repeatedly select other layout schemes from the selected preset number of layout schemes, and use the second-stage optimization algorithm for calculation until all the selected preset number of layout schemes are processed to obtain a new population.

[0130] Specifically, in the present invention, the overall default value of each layout scheme in the current population is obtained, and each layout scheme in the current population is judged to be a feasible scheme according to multiple target constraints of each layout scheme. When the overall default value is zero, it is a feasible scheme, and when it is not zero, it is an infeasible scheme. When all current layout schemes are infeasible schemes, according to the second-stage optimization algorithm, a preset number of layout schemes are selected and modified, and when a new population is generated, first.

[0131] Furthermore, in the present invention, when a new population is generated by the second-stage optimization algorithm, corresponding parameters need to be input first, wherein the input parameters include: Nm , that is, the preset number, the number of layout solutions that need to be modified by AGM; D , which is the number of variables in the current optimization task, and also the number of spatial decision variables; PS layout scheme and the current first update auxiliary variable nF .

[0132] In the process of selecting a preset number of layout schemes and modifying them to generate a new population according to the second stage optimization algorithm, Nm When modifying a layout scheme, in each modification process, first randomly select one of the layout schemes from the preset number of selected layout schemes, and update the corresponding layout scheme incrementally through the following formula (13), where formula (13) is expressed as:

[0133] ; (13)

[0134] Among them, L′ is the updated layout solution, represents the increment of L required to satisfy the constraint, represents the value of vector G at L, represents the value of vector G at L′, represents the gradient of G at L, is the increment of G at L, and the vector G and ▽V are defined as follows:

[0135] ; (14)

[0136] in, is the unit increment of the corresponding component vector of L, where is the unit increment of the component vector corresponding to the layout scheme L1, is the unit increment of the component vector corresponding to the layout scheme L2, is the unit increment of the component vector corresponding to the layout scheme LD, Represents the general form of the Jacobian matrix. When a specific point L is brought in, it is expressed as , T represents transpose, The objective function of thermal performance is Find the partial derivative, The objective function of thermal performance is Find the partial derivative, The objective function of thermal performance is Find the partial derivative, Represents non-overlapping constraints Find the partial derivative, Represents non-overlapping constraints Find the partial derivative, Represents non-overlapping constraints Find the partial derivative, The heat dissipation capacity constraint Find the partial derivative, The heat dissipation capacity constraint Find the partial derivative, The heat dissipation capacity constraint Find the partial derivative. For the layout scheme L, The matrix element calculation can be expressed as the following formula (15):

[0137] ; (15)

[0138] in, Indicates The layout scheme corresponds to the unit increment of the component vector, is a small variable, which is preferably set to 0.0001 in the present invention. for The matrix elements of It can be calculated by the following formula (16):

[0139] ; (16)

[0140] in, yes The pseudo-inverse matrix of .

[0141] After the increment is calculated, the layout scheme can be updated accordingly to obtain the updated layout scheme L′, and in the process, the first update auxiliary variable is updated simultaneously. nF 1, let the updated nF 2= nF 1+ D ; Then calculate the multiple objective constraints and thermal performance objective functions of the updated layout scheme to determine whether the four constraints are met. If so, calculate whether the overall default value of the updated layout scheme is better than its corresponding layout scheme. If so, replace the corresponding layout scheme in the current population with the updated layout scheme. Nm The layout schemes are modified repeatedly. When the modification is completed, a replaced layout scheme is obtained. A new population is generated through the replaced layout scheme, and the obtained new population is evolved using the first-stage optimization algorithm.

[0142] Step S40: When the evolution reaches the termination condition, the best performance solution among the multiple layout solutions obtained by the last evolution is used as the target solution and output.

[0143] When the evolution reaches the termination condition, that is, the auxiliary variable nF achieve MaxFEs When , the technology evolves, extracts multiple layout schemes obtained from the last evolution, and selects layout schemes that meet multiple target constraints and multiple layout schemes with an overall default value of zero. From these layout schemes, the one with the smallest value of the objective function of thermal performance is selected as the target scheme and output.

[0144] Furthermore, the present invention Figure 2 Further illustrate the implementation process of the present invention, such as Figure 2As shown, at the beginning of the task, after receiving the information from the dispatch center, the sizes of the heat pipe constraint circuit board and electronic components of the target payload cabin will be determined; in the initialization stage, the randomly generated layout schemes are not feasible; at this time, there are multiple target constraints in the optimization task; after the optimization is achieved through the evolution of the present invention, multiple feasible layout schemes will eventually be searched out, and the target scheme can be selected from them.

[0145] The present invention is achieved by Figure 3 and Figure 4 It further shows that the method adopted by the present invention and MG-DE in the optimization process of the double-layer satellite payload compartment have convergence in terms of overall default value convergence and actual load peak power convergence. The gradient information is introduced into the constraint function gradient matrix of the traditional SGM to alleviate the overfitting caused by only processing the constraints. In order to experimentally verify the actual effect of this modification of AGM, the present invention conducted experiments. Specifically, the gradient matrix of SGM only contains the gradient information from the constraints. The algorithm based on SGM is called SGM-DE (Standard Gradient Mutation-based Differential Evolution), that is, differential evolution based on standard gradient mutation. This information is also applied in the TriGF framework, and other parameters are consistent with the parameters of AGM-DE in the present invention. Then the convergence trajectories of the objective function value and the overall default value of the method of the present invention and SGM-DE are respectively as follows: Figure 3 and Figure 4 shown. Figure 3 It is revealed that when the number of evolution iterations is less than 75, the overall default value decreases in a roughly consistent trend, reflecting the effectiveness of the AGM and SGM in the present invention in constraint processing; Figure 4 From the convergence curve, we can see that AGM can converge to a better solution than SGM. This shows that the introduction of the objective function’s gradient information effectively alleviates the premature convergence caused by the greedy algorithm during constraint processing, and guides the search direction to a specific infeasible solution with a smaller objective function value, so as to fully explore the objective function. The above comparative analysis confirms that the gradient-based repair method combined with objective function information is efficient and robust in solving complex HDLO tasks.

[0146] The present invention obtains target component information, constructs an initial layout scheme of components according to the target component information, and generates multiple target constraints; according to the initial layout scheme and multiple target constraints, converts the layout problem of satellite payload cabin components under heat pipe constraints into a multi-objective algorithm model; according to the first-stage optimization algorithm, the multi-objective algorithm model is evolved, and in each evolution process, it is judged whether all current layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, according to the second-stage optimization algorithm, a preset number of layout schemes are selected and modified, and evolution is performed again according to the modified results; when the evolution reaches the termination condition, the best performance scheme among the multiple layout schemes obtained by the last evolution is used as the target scheme, and output. The present invention reasonably transforms the layout problem of satellite payload cabin components under heat pipe constraints through a multi-objective algorithm model, and finds a feasible area according to constraint violations through the first-stage optimization algorithm, and introduces a second-stage optimization algorithm to prevent the greedy effect of constraint processing, so that an accurate and effective layout method can be obtained.

[0147] Furthermore, if Figure 5 As shown, based on the above-mentioned satellite payload cabin component layout method based on heat pipe constraints, the present invention also provides a satellite payload cabin component layout system based on heat pipe constraints, wherein the satellite payload cabin component layout system based on heat pipe constraints includes:

[0148] The constraint module 51 is used to obtain target component information, construct an initial component layout plan according to the target component information, and generate multiple target constraint conditions;

[0149] A model building module 52 is used to convert the satellite payload compartment component layout problem under heat pipe constraints into a multi-objective algorithm model according to the initial layout plan and multiple objective constraints;

[0150] An evolution module 53 is used to evolve the multi-objective algorithm model according to the first-stage optimization algorithm, and judge whether all current layout schemes are feasible schemes in each evolution process. When all current layout schemes are infeasible schemes, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm, and evolve again according to the modified results;

[0151] The target output module 54 is used to take the best performance solution among the multiple layout solutions obtained by the last evolution as the target solution and output it when the evolution reaches the termination condition.

[0152] Furthermore, if Figure 6As shown, based on the above-mentioned satellite payload compartment component layout method and system based on heat pipe constraints, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 6 Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0153] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various data installed on the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores a satellite payload cabin component layout program 40 based on heat pipe constraints, and the satellite payload cabin component layout program 40 based on heat pipe constraints can be executed by the processor 10, thereby realizing the satellite payload cabin component layout method based on heat pipe constraints in the present invention.

[0154] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the satellite payload compartment component layout method based on heat pipe constraints.

[0155] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The components 10-30 of the terminal communicate with each other via a system bus.

[0156] In one embodiment, when the processor 10 executes the satellite payload bay component layout program 40 based on heat pipe constraints in the memory 20, the steps of the above satellite payload bay component layout method based on heat pipe constraints are implemented.

[0157] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a satellite payload compartment component layout program based on heat pipe constraints, and when the satellite payload compartment component layout program based on heat pipe constraints is executed by a processor, the steps of the satellite payload compartment component layout method based on heat pipe constraints are implemented as described above.

[0158] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or terminal including the element.

[0159] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium that can be read by a computer, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a disk, an optical disk, etc.

[0160] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for layout of satellite payload cabin components under heat pipe constraints, characterized in that: The satellite payload compartment component layout method based on heat pipe constraints includes: Acquire target component information, construct an initial component layout plan based on the target component information, and generate multiple target constraint conditions; According to the initial layout scheme and multiple objective constraints, the satellite payload compartment component layout problem under heat pipe constraints is converted into a multi-objective algorithm model; According to the first-stage optimization algorithm, the multi-objective algorithm model is evolved, and in each evolution process, it is determined whether all current layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, according to the second-stage optimization algorithm, a preset number of layout schemes are selected and modified, and evolution is performed again according to the modified results; When the evolution reaches the termination condition, the best performing layout solution among the multiple layout solutions obtained by the last evolution is taken as the target solution and output; The step of converting the satellite payload compartment component layout problem under heat pipe constraints into a multi-objective algorithm model according to the initial layout plan and multiple objective constraints includes: According to the initial layout scheme and a plurality of objective constraints, generating an objective function of an overall default value, a crowding distance and a thermal performance corresponding to the initial layout scheme; According to the objective functions of the overall default value, the crowding distance and the thermal performance, and with multiple objective constraints as constraints, the satellite payload cabin component layout problem under heat pipe constraints is transformed into a multi-objective algorithm model.

2. The method for layout of satellite payload compartment components under heat pipe constraints according to claim 1, characterized in that: The step of acquiring target component information, constructing an initial component layout plan according to the target component information, and generating a plurality of target constraint conditions specifically includes: Acquire target component information, wherein the target component information includes the number and location information of each component; Constructing an initial layout plan of the components according to the number and position information of each component; According to the initial layout plan of components, a plurality of target constraints are generated, wherein the plurality of target constraints include non-overlapping constraints, component heat pipe overlapping constraints, static stability constraints and heat dissipation capacity constraints.

3. The method for layout of satellite payload compartment components under heat pipe constraints according to claim 1, characterized in that: According to the first-stage optimization algorithm, the multi-objective algorithm model is evolved, and in each evolution process, it is determined whether all current layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, according to the second-stage optimization algorithm, a current layout scheme is selected and modified, and evolution is performed again according to the modified result, specifically including: According to the first-stage optimization algorithm, the number of initial layout solutions, termination conditions, and preset quantities are set; Initialize the population, set auxiliary variables, calculate the current multiple objective constraints and the objective function of thermal performance, and calculate the first parameter according to the current multiple objective constraints, wherein the population includes the number of initial layout solutions; Evolution is performed according to the population size, auxiliary variables, current multiple objective constraints, the objective function of thermal performance and the first parameter, and in each evolution, it is determined whether all layout schemes are feasible. When all current layout schemes are infeasible, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm to generate a new population; The evolution process is repeated until the value of the auxiliary variable reaches the termination condition.

4. The method for layout of satellite payload compartment components under heat pipe constraints according to claim 3 is characterized in that: The evolution is performed according to the population size, auxiliary variables, the current multiple objective constraints, the objective function of thermal performance and the first parameter, and in each evolution, it is determined whether all layout schemes are feasible schemes. When all current layout schemes are infeasible schemes, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm to generate a new population, specifically including: According to the mutation strategy and binomial crossover recombination, the population is processed to generate sub-populations, wherein the sub-populations include the number of initial layout schemes and the number of layout schemes; Calculating multiple objective constraints and objective functions of thermal performance for each layout solution in the subpopulation, and updating auxiliary variables to obtain first updated auxiliary variables; Merge the population and sub-populations to obtain a merged population, and calculate the overall default value, crowding distance, and thermal performance objective function of each layout scheme in the merged population; According to the pruning technology and the non-dominated sorting method, the number of initial layout schemes is selected from the merged population as the current population; Get the overall default value of each layout scheme in the current population, and judge whether the layout scheme of the current population is a feasible scheme based on the overall default value of each layout scheme. When all current layout schemes are infeasible schemes, select a preset number of layout schemes and modify them according to the second-stage optimization algorithm to generate a new population.

5. The method for layout of satellite payload compartment components under heat pipe constraints according to claim 4 is characterized in that: The method of selecting the initial layout schemes from the merged population according to the pruning technique and the non-dominated sorting method as the current population specifically includes: Perform preliminary pruning on the merged population according to the pruning technology to obtain a pruned population; The non-dominated sorting method is used to compare any two layout plans according to multiple objective constraints of each layout plan in the pruning population, so as to select the initial number of layout plans as the current population.

6. The method for layout of satellite payload compartment components under heat pipe constraints according to claim 4, characterized in that: According to the second-stage optimization algorithm, a preset number of layout schemes are selected and modified to generate a new population, specifically including: Select one of the selected preset number of layout schemes, calculate the corresponding increment, and update the first update auxiliary variable at the same time; According to the increment of the selected layout scheme, the selected layout scheme is updated to obtain an updated layout scheme; Calculate multiple objective constraints and thermal performance objective functions of the updated layout solution to determine whether the constraint requirements are met; When the condition is satisfied, determine whether the overall default value of the updated layout scheme is better than its corresponding layout scheme. If it is better, replace the corresponding layout scheme in the current population with the updated layout scheme. Repeatedly select other layout schemes from the selected preset number of layout schemes, and use the second-stage optimization algorithm for calculation until all the selected preset number of layout schemes are processed to obtain a new population.

7. A satellite payload cabin component layout system based on heat pipe constraints, characterized in that: The satellite payload cabin component layout system based on heat pipe constraints includes: A constraint module, used to obtain target component information, construct an initial component layout plan according to the target component information, and generate multiple target constraint conditions; A model building module is used to convert the satellite payload compartment component layout problem under heat pipe constraints into a multi-objective algorithm model according to the initial layout plan and multiple objective constraints; An evolution module, used to evolve the multi-objective algorithm model according to the first-stage optimization algorithm, and determine whether all current layout schemes are feasible schemes during each evolution process. When all current layout schemes are infeasible schemes, a preset number of layout schemes are selected and modified according to the second-stage optimization algorithm, and evolve again according to the modified results; The target output module is used to output the best performance solution among multiple layout solutions obtained by the last evolution as the target solution when the evolution reaches the termination condition; The step of converting the satellite payload compartment component layout problem under heat pipe constraints into a multi-objective algorithm model according to the initial layout plan and multiple objective constraints includes: According to the initial layout scheme and a plurality of objective constraints, generating an objective function of an overall default value, a crowding distance and a thermal performance corresponding to the initial layout scheme; According to the objective functions of the overall default value, the crowding distance and the thermal performance, and with multiple objective constraints as constraints, the satellite payload cabin component layout problem under heat pipe constraints is transformed into a multi-objective algorithm model.

8. A terminal, characterized in that: The terminal includes: a memory, a processor, and a satellite payload compartment component layout program based on heat pipe constraints stored in the memory and executable on the processor. When the satellite payload compartment component layout program based on heat pipe constraints is executed by the processor, the steps of the satellite payload compartment component layout method based on heat pipe constraints as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a satellite payload compartment component layout program based on heat pipe constraints. When the satellite payload compartment component layout program based on heat pipe constraints is executed by a processor, the steps of the satellite payload compartment component layout method based on heat pipe constraints as described in any one of claims 1-6 are implemented.

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