Satellite cabin load layout optimization method based on quantum algorithm

Abstract

The invention relates to a satellite cabin load layout optimization method based on a quantum algorithm, and the method comprises the following steps: listing a mathematical expression form of an optimization target and a limiting condition according to a satellite cabin load layout requirement, and generating a constraint optimization problem model; wherein the optimization target and the limiting condition at least comprise a satellite quality characteristic requirement, a space geometric compatibility requirement, a thermal control requirement, an electromagnetic compatibility requirement, an installation and maintenance requirement and an installation position requirement of special instrument equipment; converting the layout constraint optimization problem mathematical model into an optimization objective function in a secondary unconstrained binary optimization form; solving the optimization objective function by using a quantum optimization algorithm to obtain an optimization vector; and calculating the load position and attitude of the satellite cabin by optimizing the vector, and carrying out inversion to obtain a final satellite cabin load layout scheme. According to the method, the non-deterministic polynomial problem of satellite cabin load layout optimization can be solved in an accelerated manner by utilizing the characteristics of superposition, entanglement, interference and the like of quantum bits, and a new technical approach is provided for improving the design efficiency of a satellite cabin load layout optimization scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite payload layout optimization, and particularly relates to a satellite cabin payload layout optimization method based on a quantum algorithm. BACKGROUND

[0002] Satellite overall layout design needs to find a space layout scheme meeting the requirements according to the overall performance index of the satellite and the constraints of each subsystem, and satellite cabin payload layout optimization is an important part of satellite overall layout design. Improving the satellite cabin payload layout optimization efficiency is of great significance to the compression of the overall cycle of satellite development. The layout problem has been proved to be a non-deterministic polynomial problem in theory in 1988. Satellite cabin payload layout is also a multidisciplinary research field involving mechanics, thermodynamics, materials science, computer science and other fields. The satellite cabin payload layout optimization problem involves reasonably arranging various devices in a limited space while meeting the requirements of maximum space utilization, system balance, thermal management, heat dissipation and centroid stability. For example, the devices in the satellite cabin may include single machines, radio frequency cables, waveguides and the like, which need to be efficiently laid out in a limited space while considering factors such as heat dissipation and centroid balance. In addition, with the complication of satellite tasks, the scale and difficulty of the layout optimization problem are also increasing. Overall, the satellite cabin payload layout optimization problem belongs to a class of layout problems with performance constraints, which further increases the difficulty of its solution. So far, the targeted solution algorithms can be roughly divided into the following types: heuristic algorithms, human-computer interaction, intelligent optimization algorithms, collaborative optimization algorithms and hybrid optimization algorithms. However, the manufacturing demand of a large number of satellites, especially large-scale constellations, makes the scale of the above optimization problem continuously expand, and the classical algorithm based on the traditional classical computer has fundamental difficulties in solving it.

[0003] Therefore, in order to meet the rapid manufacturing demand of a large number of satellites, it is urgent to explore a new optimization method for the non-deterministic polynomial problem of satellite cabin payload layout. SUMMARY

[0004] In view of the above technical problems, the present application provides a satellite cabin payload layout optimization method based on a quantum algorithm, which utilizes the superposition, entanglement and interference of quantum bits and can accelerate the solution of the non-deterministic polynomial problem of satellite cabin payload layout optimization, thereby providing a new technical approach to improving the design efficiency of the satellite cabin payload layout optimization scheme.

[0005] To achieve the object of the present application, the present application provides a satellite cabin payload layout optimization method based on a quantum algorithm, comprising the following steps:

[0006] comprising the following steps:

[0007] Step S1, listing the mathematical expression forms of optimization objectives and constraint conditions according to the satellite cabin load layout requirements, and generating a constraint optimization problem model;

[0008] Step S2, converting the layout constraint optimization problem mathematical model into an optimization objective function in the form of a quadratic unconstrained binary optimization;

[0009] Step S3, using a quantum optimization algorithm to solve the optimization objective function to obtain an optimization vector;

[0010] Step S4, calculating the satellite cabin load position and attitude through the optimization vector, and inversely deriving a final satellite cabin load layout scheme.

[0011] According to one of the technical solutions of the present application, the optimization objectives and constraint conditions at least include satellite mass characteristic requirements, spatial geometric compatibility requirements, thermal control requirements, electromagnetic compatibility requirements, installation and maintenance requirements, and installation position requirements of special instruments and equipment.

[0012] The satellite mass characteristic requirements include minimization of the satellite moment of inertia, inertia angle, and center of mass deviation parameters.

[0013] According to one of the technical solutions of the present application, in the step S1, specifically comprising:

[0014] Step S11, establishing a load local coordinate system for each load based on each load, and calculating the center of mass position and moment of inertia of each load;

[0015] Step S12, establishing a satellite cabin coordinate system based on the satellite cabin, and calculating the satellite center of mass position after loading the loads by using the center of mass positions and load masses of the loads;

[0016] Step S13, establishing a satellite center of mass coordinate system based on the satellite center of mass position, and calculating the satellite moment of inertia after loading the loads by using the center of mass positions and moment of inertia of the loads;

[0017] Step S14, listing the mathematical expression forms of the optimization objectives and constraint conditions in step S1 in the load local coordinate system, the satellite cabin coordinate system, and the satellite center of mass coordinate system.

[0018] According to one of the technical solutions of the present application, in the step S2, specifically comprising:

[0019] Step S21, constructing an optimization objective function based on one of the satellite mass characteristic requirements;

[0020] Step S22, converting the remaining optimization objectives and constraint conditions in the constraint optimization problem into a penalty function form and adding to the optimization objective function;

[0021] Step S23, converting the optimization objective function into a quadratic function form of binary variables.

[0022] According to one of the technical solutions of the present application, in the step S3, specifically comprising:

[0023] Step S31, initializing the quantum bit state according to the satellite cabin load layout optimization problem model;

[0024] Step S32, designing a quantum circuit to alternately apply a problem Hamiltonian evolution operator and a hybrid Hamiltonian evolution operator;

[0025] Step S33, performing multiple quantum computing iterations based on the optimization target accuracy requirement;

[0026] Step S34, measuring the quantum state and calculating the optimization vector.

[0027] According to one of the technical solutions of the present application, in the step S4, specifically comprising:

[0028] Step S41, according to the conversion relationship between the binary variables and the original variables in the quadratic unconstrained binary optimization model, extracting the spatial coordinate information and the placement angle information of each load from the optimization vector;

[0029] Step S42, inferring the layout pose of the load based on the spatial coordinate information and the placement angle information.

[0030] According to one of the technical solutions of the present application, when the optimization objective function is converted into a quadratic function form of binary variables, the position assignment variable p ij satisfies:

[0031]

[0032] and the optimization objective function is constructed as:

[0033]

[0034] wherein, σ b is a penalty factor, v k is a introduced slack variable, c k = 2 k , and the value range of k is an integer between 0 and log2Δx.

[0035] According to another aspect of the present application, a satellite cabin load layout system is provided, comprising:

[0036] The modeling module is configured to list a mathematical expression form of optimization objectives and constraint conditions according to satellite cabin load layout requirements, and generate a constraint optimization problem model, wherein the optimization objectives and constraint conditions at least include satellite mass characteristic requirements, space geometry compatibility requirements, thermal control requirements, electromagnetic compatibility requirements, installation and maintenance requirements, and special instrument installation position requirements.

[0037] The conversion module is configured to convert the layout constraint optimization problem mathematical model into an optimization objective function in the form of a quadratic unconstrained binary optimization.

[0038] The quantum solving module is configured to solve the optimization objective function using a quantum optimization algorithm to obtain an optimization vector.

[0039] The inversion module is configured to calculate a satellite cabin load position and attitude through the optimization vector, and inversely obtain a final satellite cabin load layout scheme.

[0040] According to another aspect of the present application, an electronic device is provided, comprising one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected with the memory, and the one or more computer programs are stored in the memory; when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes the satellite cabin load layout optimization method based on a quantum algorithm according to any one of the above technical solutions.

[0041] According to still another aspect of the present application, a computer readable storage medium is provided for storing computer instructions, which are executed by a processor to implement the satellite cabin load layout optimization method based on a quantum algorithm according to any one of the above technical solutions.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] According to the concept of the present application, a satellite cabin load layout optimization method based on a quantum algorithm is provided, which converts constraint conditions in a satellite cabin layout optimization problem into a penalty function form added to an optimization objective function, so that a satellite cabin layout problem involving requirements of maximizing space utilization, system balance, thermal management, heat dissipation, and mass center stability is converted from a constraint optimization problem into an unconstrained optimization problem, and an unconstrained optimization method avoids additional calculation burden caused by constraint conditions in a solving process, thereby simplifying algorithm implementation and an optimization process.

[0044] Meanwhile, the unconstrained optimization method can be applied to the case where the objective function is relatively complex and difficult to directly derive, while the constrained optimization needs to consider the feasible region of the constraint condition, which may limit its scope of application. The unconstrained optimization method shows better stability in some cases, so it is easier to converge to the global optimal solution in the iteration process.

[0045] In engineering design, the unconstrained optimization method can be directly used to solve the optimal solution of the design problem without defining complex constraint conditions in advance. The unconstrained optimization method can quickly find the minimum value of the objective function, thereby simplifying the design process.

[0046] Further, the satellite cabin load layout optimization problem is converted into a quadratic unconstrained binary optimization model, which is suitable for quantum hardware solution. Quantum computing uses the superposition, entanglement and interference of quantum bits to accelerate the solution of the quadratic unconstrained binary optimization problem. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 FIG. 1 shows a flowchart of a satellite cabin load layout optimization method based on a quantum algorithm according to an embodiment of the present application;

[0048] Figure 2 FIG. 3 shows a flowchart of a quantum approximate optimization algorithm according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described in the following are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0050] The present application will be described in detail below in conjunction with the drawings and specific embodiments. The embodiments cannot be described one by one here, but the embodiments of the present application are not limited to the following embodiments.

[0051] As shown in FIG. 1, a satellite cabin load layout optimization method based on a quantum algorithm according to an embodiment of the present application includes the following steps: Figure 1 Step S1, list the mathematical expression form of the optimization objective and the constraint condition according to the satellite cabin load layout requirements, and generate a constrained optimization problem model.

[0052]

[0053] The optimization objective and the constraint condition at least include satellite mass property requirements, spatial geometric compatibility requirements, thermal control requirements, electromagnetic compatibility requirements, installation and maintenance requirements, and installation position requirements of special instruments and equipment.​

[0054] The satellite mass characteristic requirements include minimization of satellite moment of inertia, inertia angle, and center of mass deviation parameters.

[0055] By including minimization of satellite moment of inertia, inertia angle, and center of mass deviation parameters in the satellite mass characteristic requirements, accurate control of satellite dynamic performance is achieved. In the constraint optimization problem model generation step, these parameters are optimized through mathematical expression, so that the final layout scheme can reduce the satellite moment of inertia, reduce the inertia angle and the center of mass deviation, help to improve the stability and maneuverability of the satellite in orbit, reduce the consumption of propellant, prolong the service life of the satellite, and improve the attitude control accuracy of the satellite.

[0056] Step S1 specifically includes:

[0057] Step S11, a load local coordinate system O''-x''y''z'' is established for each load based on each load, and the center of mass position and moment of inertia of each load are calculated.

[0058] For example, for a cubic load with uniform mass distribution, the length, width and height are a i ,b i ,h i , and the mass is m i , the moments of inertia I x"i , I y"i and I z"i of the three axes are respectively:

[0059]

[0060] Step S12, a satellite cabin coordinate system O-xyz is established based on the satellite cabin, and the center of mass position of the satellite after carrying the load is calculated using the center of mass position and load mass of each load. Assuming that there are n loads with masses m i and center of mass positions (x i , y i , z i ), the center of mass positions x c , y c and z c of the satellite are respectively:

[0061]

[0062] Step S13, a satellite center of mass coordinate system O ′ -x ′ y ′ z ′, the inertia moment of the satellite after loading the load is calculated by using the center of mass position and the inertia moment of each load, and the assumption is that the placing angle of the i th load in the x' y' plane is α i The three-axis inertia moments of the satellite are respectively:

[0063]

[0064] The inertia angle of the satellite is The inertia angles of the three axes are respectively:

[0065]

[0066]

[0067] The inertia products of the satellite in the x'O'y', x'O'z', and y'O'z' planes are respectively I x′y′ , I x′z′ , and I y′z′ , and the specific values are as follows:

[0068]

[0069] In step S14, the mathematical expression forms of the optimization objectives and the constraint conditions in step S1 are listed in the coordinate system constructed in steps S11, S12, and S13, for example, the requirement that the satellite center of mass deviation δx c , i.e., the deviation of the satellite center of mass x c from the satellite geometric center x0 is less than the limited value Δx can be expressed as:

[0070] δx c = |x c -x0|≤Δx.

[0071] By specifically expressing the optimization objectives and the constraint conditions as the satellite mass property requirements, the spatial geometric compatibility requirements, the thermal control requirements, the electromagnetic compatibility requirements, the installation and maintenance requirements, and the installation position requirements of special instruments and equipment, the comprehensive coverage of the multi-disciplinary and multi-field constraints in the satellite cabin load layout is realized. When the constraint optimization problem model is established, the mechanical properties, the spatial utilization efficiency, the thermal management, the electromagnetic environment, the maintainability, and the working requirements of special instruments of the satellite can be comprehensively considered, so that the final obtained layout scheme can meet various engineering requirements of the satellite in actual operation, and the feasibility and the practicality of the layout scheme are improved.

[0072] By step-by-step establishment of the load local coordinate system, the satellite cabin coordinate system, and the satellite center of mass coordinate system, the construction and the physical quantity calculation of the multi-scale coordinate system are systematically completed, the mechanical property calculation from the micro load to the macro satellite is layer-by-layer accumulated, the mathematical expression of the constraint condition accurately reflects the actual physical relationship, and the model precision is improved, which provides reliable data for subsequent optimization.

[0073] Step S2, converting the layout constraint optimization problem mathematical model into an optimization objective function in the form of a quadratic unconstrained binary optimization.

[0074] The step S2 specifically includes:

[0075] Step S21, constructing an optimization objective function based on one of the satellite mass property requirements;

[0076] For example, taking the maximum total weight of the carried payload as the optimization objective, the optimization objective function is:

[0077]

[0078] Where A is the set of the serial numbers of the carried payloads.

[0079] Step S22, converting the remaining optimization objectives and limit conditions in the constraint optimization problem into a penalty function form and adding them to the optimization objective function;

[0080] For example, the centroid deviation limit condition δx c ≤ Δx can be converted into a penalty function form:

[0081] f p = σ·max(0, δx c - Δx)

[0082] Where σ is a penalty factor, used to control the punishment intensity. When δx c - Δx ≤ 0, the penalty factor σ is 0, and when δx c - Δx > 0, the penalty factor σ significantly increases.

[0083] After adding the penalty function to the optimization objective function, we get

[0084] f obj′ = f obj + f p = -∑ i∈A m i + σ·max(0, δx c - Δx).

[0085] Step S23, converting the optimization objective function into a quadratic function of binary variables.

[0086] For example, the optimization objective function of the maximum total weight of the payload can be converted into:

[0087]

[0088] Where it is assumed that there are n payloads i = 1, …, n and N positions j = 1, …, N, and the position assignment variable p ij satisfies

[0089]

[0090] For example, the centroid deviation constraint penalty function can be converted to

[0091]

[0092] σ b is a penalty factor, v k is a relaxation variable introduced, c k = 2 k and k takes an integer value between 0 and log2Δx.

[0093] By constructing an optimization objective function based on one of the satellite mass property requirements, the remaining optimization objectives and constraints in the constrained optimization problem are converted into penalty function form and added to the optimization objective function, and the optimization objective function is converted into a quadratic function form of binary variables, realizing the conversion of the constrained satellite cabin load layout optimization problem into an unconstrained quadratic unconstrained binary optimization (QUBO) model. The originally complex multi-constraint optimization problem can adapt to the solution requirements of the quantum optimization algorithm, and at the same time, the constraints are integrated into the objective function through the penalty function, ensuring the consistency of the converted optimization problem and the original problem in the solution space, laying a foundation for efficient solution by the quantum algorithm.

[0094] By constructing an optimization objective function based on one of the satellite mass property requirements (such as maximum total weight of carried payloads), other constraints (such as centroid deviation limit) are converted into penalty functions, and the objective function is converted into a quadratic function form of binary variables, realizing the standardization of the optimization model. Specifically, by defining position allocation variables, continuous position and angle variables are discretized into binary variables, and a quadratic objective function is constructed, so that the complex satellite layout problem can be mapped to a quadratic unconstrained binary optimization problem, creating conditions for the direct application of quantum optimization algorithms, while ensuring the mathematical rigor of the conversion process.

[0095] Step S3, using a quantum optimization algorithm to solve the optimization objective function to obtain an optimization vector.

[0096] Figure 2 is a flowchart of a quantum approximation optimization algorithm. The main steps are:

[0097] Problem modeling and initialization, mapping the satellite cabin layout optimization problem to the quantum computing framework, such as representing the mathematical model of the problem through Hamiltonian or quantum circuit. This step includes defining the objective function and the initial state.

[0098] Parametric quantum state preparation generates a parametric quantum state by alternately applying two unitary gates, which act on the problem Hamiltonian (H C ) and the auxiliary Hamiltonian (H M ), respectively. Specifically, the algorithm first initializes a simple quantum state, and then evolves it step by step in the following form:

[0099]

[0100] where γ and β are parameters, and p is the number of evolution layers. These parameters and the number of layers need to be adjusted through classical optimization to improve the quality of the solution.

[0101] Quantum state measurement and classical optimization parameter feedback After generating a parametric quantum state, the quantum state is measured by a quantum computer to obtain a candidate solution. The quality of the optimization result is evaluated to determine whether it meets the requirements. If it does not meet the requirements, the measurement result is passed to a classical computer, which adjusts the parameters γ and β based on the measurement result to iteratively optimize the performance of the quantum state until the optimization result meets the requirements.

[0102] By using quantum approximation optimization algorithms or quantum annealing algorithms suitable for quadratic unconstrained binary optimization models, efficient solutions to the optimization objective function are achieved. Quantum approximation optimization algorithms approximate the optimal solution by designing quantum circuits and iteratively optimizing parameters; quantum annealing algorithms use quantum tunneling effects to jump out of local optima, which can fully exploit the advantages of quantum computing in solving combinatorial optimization problems, avoid the defect of classical optimization algorithms that easily fall into local optima, and thus obtain high-quality optimization solutions within an acceptable time, especially suitable for solving large-scale satellite cabin layout optimization problems.

[0103] Through problem modeling and initialization, parametric quantum state preparation, quantum state measurement and classical optimization parameter feedback, the specific application of the quantum approximation optimization algorithm is realized. Among them, the parametric quantum state preparation is generated by alternately applying the unitary gates of the problem Hamiltonian and the auxiliary Hamiltonian, and the parameters are adjusted through classical optimization. In this way, through the quantum-classical hybrid optimization framework, the quality of the quantum state is continuously iteratively improved, thus obtaining an approximate optimal solution, effectively solving the precision and depth problems that may be faced in direct quantum solving, and improving the success rate and efficiency of solving the satellite cabin layout optimization problem.

[0104] Step S4, calculate the satellite cabin load position and attitude through the optimization vector, and inversely derive the final satellite cabin load layout scheme, specifically including:

[0105] Step S41, according to the transformation relationship between binary variables and original variables in the quadratic unconstrained binary optimization model, extract the spatial coordinate information and placement angle information of each load from the optimization vector;

[0106] Step S42: Estimate the layout posture of the payload based on the spatial coordinate information and the placement angle information.

[0107] The quantum optimization algorithm is implemented through specific steps, including initializing the quantum bit state, designing the quantum circuit, performing multiple quantum computation iterations, and measuring the quantum state. The quantum circuit design is based on the requirements of the satellite cabin payload layout optimization problem model, and the number of iterations is determined by the required optimization accuracy. This allows the quantum computation process to be customized for the specific satellite layout problem, improving the accuracy of the solution through multiple iterations. Quantum measurements are then used to obtain the optimization vector, ensuring that the quantum computation results accurately reflect the solution to the layout optimization problem, thereby improving the reliability of the optimization results.

[0108] According to another aspect of the present invention, a satellite cabin payload layout system is provided, comprising:

[0109] A modeling module is used to list the mathematical expressions of optimization objectives and constraints based on the satellite cabin payload layout requirements and generate a constrained optimization problem model, wherein the optimization objectives and constraints at least include satellite mass characteristic requirements, spatial geometric compatibility requirements, thermal control requirements, electromagnetic compatibility requirements, installation and maintenance requirements, and installation location requirements for special instruments and equipment;

[0110] A conversion module, used to convert the mathematical model of the layout constraint optimization problem into an optimization objective function in the form of a quadratic unconstrained binary optimization;

[0111] The quantum solution module is used to use the quantum optimization algorithm to solve the optimization objective function and obtain the optimization vector;

[0112] The inversion module is used to calculate the satellite cabin payload position and attitude by optimizing the vector, and inversely obtain the final satellite cabin payload layout plan.

[0113] By inferring the satellite capsule payload's center of mass coordinates, placement angle, and other positional information based on the values ​​of the relevant binary variables in the optimization vector, the team achieved the transformation from the optimization vector output by the quantum algorithm to the actual layout plan. By converting the abstract quantum computation results into specific engineering layout parameters, the optimization results can be directly applied to the physical design of the satellite capsule, resulting in an optimal or near-optimal satellite capsule payload layout that satisfies various constraints, completing the closed loop from mathematical optimization to engineering implementation.

[0114] According to another aspect of the present application, an electronic device is provided, comprising one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected with the memory, and the one or more computer programs are stored in the memory; when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes the satellite cabin load layout optimization method based on quantum algorithm according to any one of the above technical solutions.

[0115] According to still another aspect of the present application, a computer readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the satellite cabin load layout optimization method based on quantum algorithm according to any one of the above technical solutions.

[0116] In summary, the present application provides a satellite cabin load layout optimization method based on quantum algorithm, which divides the satellite cabin load layout optimization process into four steps of constraint optimization problem model generation, secondary unconstrained binary optimization model conversion, quantum algorithm problem solving, and satellite cabin load layout scheme inversion, so as to realize systematic solution of the satellite cabin load layout optimization problem. In the step of quantum algorithm problem solving, a quantum optimization algorithm (such as quantum approximate optimization algorithm or quantum annealing algorithm) is used to solve the optimization objective function, and the parallelism and quantum tunneling effect of quantum computing are utilized. The present application solves the problems of easy falling into local optimum and low computing efficiency of traditional classical computer algorithm in solving large-scale satellite cabin load layout optimization problem, so as to efficiently obtain a globally optimal or approximately globally optimal layout scheme, and improve the design efficiency of the satellite cabin load layout optimization scheme.

[0117] In addition, it should be noted that the present application can be provided as a method, device or computer program product. Therefore, the embodiments of the present application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can adopt the form of a computer program product implemented on one or more computer usable storage media containing computer usable program code.

[0118] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams according to the method, terminal device (system) and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, embedded processor or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device produce a machine that implements the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocksFigure 1 means for performing the function specified by that block or blocks.

[0119] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing Figure 1 one or more processes and / or blocks of the flow diagram Figure 1 one or more blocks of the flow diagram. These computer program instructions can also be loaded onto a computer or other programmable data processing Figure 1 one or more processes and / or blocks of the flow diagram Figure 1 one or more blocks of the flow diagram. These computer program instructions can also be loaded onto a computer or other programmable data processing

[0120] It is also noted that the foregoing description is of a preferred embodiment of the application, and that numerous changes and modifications can be made thereto without departing from the spirit and scope of the application as set forth in the appended claims.

[0121] Finally, it is to be noted that the above description is of preferred embodiments of the application, and that numerous changes and modifications will become apparent to those skilled in the art. It is therefore intended that the appended claims cover all such changes and modifications that fall within the scope of the application.

Claims

1. A satellite cabin payload layout optimization method based on quantum algorithm, characterized in that: The following steps are involved: Step S1: List the mathematical expressions of optimization objectives and constraints according to the satellite cabin payload layout requirements to generate a constrained optimization problem model; Step S2: converting the mathematical model of the layout constraint optimization problem into an optimization objective function in the form of a quadratic unconstrained binary optimization; Step S3: using a quantum optimization algorithm to solve the optimization objective function and obtain an optimization vector; Step S4: Calculate the satellite cabin payload position and attitude by optimizing the vector, and inversely calculate the final satellite cabin payload layout plan.

2. The method according to claim 1, characterized in that The optimization objectives and constraints include at least satellite quality characteristics requirements, space geometry compatibility requirements, thermal control requirements, electromagnetic compatibility requirements, installation and maintenance requirements, and installation location requirements for special instruments and equipment; The satellite mass characteristic requirements include minimization of satellite moment of inertia, inertia angle, and center of mass deviation parameters.

3. The method according to claim 1, characterized in that In the step S1, it specifically includes: Step S11: establishing a load local coordinate system for each load based on each load, and calculating the center of mass position and moment of inertia of each load; Step S12: establishing a satellite cabin coordinate system based on the satellite cabin, and calculating the satellite center of mass position after the payload is loaded using the center of mass position and payload mass of each payload; Step S13: establishing a satellite mass center coordinate system based on the satellite mass center position, and calculating the satellite moment of inertia after the payload is loaded using the mass center position and moment of inertia of each payload; Step S14: List the mathematical expressions of the optimization objectives and constraints in step S1 in the payload local coordinate system, the satellite module coordinate system, and the satellite center of mass coordinate system.

4. The method according to claim 2, characterized in that The step S2 specifically includes: Step S21: constructing an optimization objective function based on one of the satellite quality characteristic requirements; Step S22: convert the remaining optimization objectives and constraints in the constrained optimization problem into penalty functions and add them to the optimization objective function; Step S23: convert the optimization objective function into a quadratic function form of binary variables.

5. The method according to claim 1, wherein The step S3 specifically includes: Step S31: Initialize the quantum bit state according to the satellite cabin payload layout optimization problem model; Step S32: designing a quantum circuit to alternately apply the problem Hamiltonian evolution operator and the mixed Hamiltonian evolution operator; Step S33: performing multiple quantum computing iterations based on the optimization target accuracy requirement; Step S34: Measure the quantum state and solve the optimization vector.

6. The method according to claim 1, characterized in that The step S4 specifically includes: Step S41: extracting the spatial coordinate information and placement angle information of each load from the optimization vector according to the transformation relationship between the binary variables and the original variables in the quadratic unconstrained binary optimization model; Step S42: Estimate the layout posture of the payload based on the spatial coordinate information and the placement angle information.

7. The method according to claim 1, characterized in that When the optimization objective function is converted into a quadratic function of binary variables, the position allocation variable p is defined as ij satisfy: And construct the optimization objective function: Among them, σ b is the penalty factor, v k is the introduced slack variable, c k =2 k , and the value range of k is an integer between 0 and log2Δx.

8. A satellite cabin payload layout system, characterized in that: include: A modeling module is used to list the mathematical expressions of optimization objectives and constraints based on the satellite cabin payload layout requirements and generate a constrained optimization problem model, wherein the optimization objectives and constraints at least include satellite mass characteristic requirements, spatial geometric compatibility requirements, thermal control requirements, electromagnetic compatibility requirements, installation and maintenance requirements, and installation location requirements for special instruments and equipment; A conversion module, used to convert the mathematical model of the layout constraint optimization problem into an optimization objective function in the form of a quadratic unconstrained binary optimization; The quantum solution module is used to use the quantum optimization algorithm to solve the optimization objective function and obtain the optimization vector; The inversion module is used to calculate the satellite cabin payload position and attitude by optimizing the vector, and inversely obtain the final satellite cabin payload layout plan.

9. An electronic device, characterized in that: include: One or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory; when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device performs the satellite cabin payload layout optimization method based on quantum algorithm as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store computer instructions, which, when executed by a processor, implement the satellite cabin payload layout optimization method based on quantum algorithm according to any one of claims 1 to 7.

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