A method for integrated coupling modeling of spacecraft platform and payload

Abstract

The present invention provides a spacecraft platform and payload integrated coupling modeling method, the spacecraft platform and payload integrated coupling modeling method includes based on multi-body dynamics and control theory, through the numerical integration algorithm to analyze the system dynamic response, identify key parameters, build a sensitivity analysis model, optimize the design parameters, and realize the integrated design optimization of the platform and payload. The present invention can realize the accurate dynamic response analysis and optimization design of the spacecraft platform and payload, improve the system performance and design efficiency, and reduce the design cost and risk.

Description

Technical Field

[0001] The present invention relates to the field of spacecraft design, and more specifically, to a spacecraft platform and payload integrated coupling modeling method. Background Art

[0002] In the field of spacecraft design, traditional design methods often model and analyze the spacecraft platform and payload separately. This method ignores the interaction and influence between the two, resulting in the design results may not accurately reflect the performance under actual working conditions. In addition, the traditional design process lacks effective parameter optimization methods and often relies on experience or single-objective optimization, which makes it difficult to meet the multi-faceted requirements of complex space missions on system performance. In terms of dynamic response analysis, conventional numerical integration algorithms may have low computational efficiency or insufficient accuracy due to fixed step sizes, and cannot adapt to the complex and changeable dynamic characteristics of spacecraft.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: when dealing with the coupling effects between the spacecraft platform and the payload, the prior art often adopts a simplified model or ignores certain key factors, which limits the accuracy and applicability of the model; at the same time, the existing design method lacks systematicity and comprehensiveness in parameter optimization, and it is difficult to achieve the optimal design under multi-objective and multi-constraint conditions; furthermore, when dealing with complex systems, the existing dynamic response analysis method may not be able to provide sufficiently accurate analysis results due to the limitations of the algorithm itself, affecting the accuracy of the design decision. Summary of the invention

[0004] The present invention provides a spacecraft platform and payload integrated coupling modeling method and a spacecraft platform and payload integrated design optimization method.

[0005] In a first aspect of the present invention, a method for integrated coupling modeling of a spacecraft platform and a payload is provided, comprising:

[0006] The spacecraft platform and payload are integrated into a model, and then the dynamic response analysis is performed using a numerical integration algorithm to obtain the system coupling response data;

[0007] Identify key parameters in the system coupling response data, build a sensitivity analysis model based on the key parameters, perform parameter sensitivity analysis on the platform and load respectively, and obtain sensitivity analysis results; set the optimization window based on the sensitivity analysis results to limit the search range of design parameters;

[0008] Under the criterion of meeting system performance indicators, based on multi-objective optimization algorithms and constraints, the optimal design parameters are searched for and the integrated design optimization of the spacecraft platform and payload is performed.

[0009] Furthermore, the numerical integration algorithm is an adaptive step-size Runge-Kutta-Fehlberg method; the mathematical model of the dynamic response analysis is:

[0010]

[0011] k1=Δt·f(x n ,t n )

[0012]

[0013] k4=Δt·f(x n -k1+2k3,t n +Δt)

[0014] Among them, x n and x n+1 At time t n and t n+1 is the system state vector, Δt is the adaptive step size, f is the system dynamic equation, k1, k2, k3, k4 are the intermediate variables in the Runge-Kutta method.

[0015] Furthermore, the integrated modeling of the spacecraft platform and the payload includes:

[0016] The physical parameters and dynamic characteristics of the spacecraft platform and payload are used as modeling inputs. Based on the optimization results of the previous design cycle, the system performance of the current design cycle is estimated, and the optimal modeling parameters are selected based on the performance and design parameters.

[0017] Obtain the corresponding system model according to the modeling parameters, and obtain the prediction curve of the dynamic response according to the system model;

[0018] According to the prediction curve, the parameters of the control law are designed using the numerical integration algorithm. The control law is used to control the platform and the payload respectively, and then the dynamic response coefficient of the system model corresponding to the modeling parameters is used to integrate the spacecraft platform and the payload.

[0019] Furthermore, the optimal modeling parameters are modeling parameters corresponding to the minimum system error under different design parameters.

[0020] Furthermore, the parameter range of the sensitivity analysis model is:

[0021] Δp=[Δp min ,Δp max ]

[0022] Δp min =min(p)-α·std(p)

[0023] Δpmax =max(p)+a·std(p)

[0024] Where p is the parameter vector, min(p) and max(p) are the minimum and maximum values ​​of the parameter vector, std(p) is the standard deviation of the parameter vector, and α is the expansion factor of the sensitivity analysis.

[0025] Furthermore, the integrated design optimization of the spacecraft platform and payload includes: dividing each design parameter along the optimization direction into multiple segments according to the system performance index, first obtaining the system performance before and after the central design parameter is adjusted according to the performance index and the constraint conditions, and then obtaining the system performance and constraint conditions before and after the adjustment of other design parameters.

[0026] In a second aspect of the present invention, a method for optimizing the integrated design of a spacecraft platform and a payload is provided, comprising:

[0027] Acquire system coupling response data obtained by the spacecraft platform and payload integrated coupling modeling method;

[0028] Based on the multi-objective optimization algorithm, the system coupling response data is divided into data blocks of multiple performance indicators;

[0029] According to the system performance indicators, the CPU is used to load the corresponding data blocks into the memory in real time, and the parameter blocks to be optimized are loaded into the search space of the optimization algorithm;

[0030] Based on the constraints, the optimization algorithm is used to search for the optimal solution of multiple objectives in parallel to obtain the optimal design parameters of the spacecraft platform and payload.

[0031] Furthermore, based on the multi-objective optimization algorithm, the system coupling response data is divided into data blocks of multiple performance indicators, including: according to a preset performance indicator threshold, using a multi-objective optimization algorithm, the system coupling response data is divided layer by layer to obtain each sub-block, and according to the performance indicators in adjacent sub-blocks, adjacent sub-blocks are merged until the sub-block performance indicator threshold is reached to obtain multiple data blocks.

[0032] Furthermore, according to the system performance index, the CPU is used to load the corresponding data block into the memory in real time, and the parameter block to be optimized is loaded into the search space of the optimization algorithm, including:

[0033] Calculate the address of the system coupling response data block to be loaded into the memory and cache data according to the current system performance index; construct the memory and cache data using the data structure, and initialize the loading cache and memory system coupling response data block;

[0034] When the system performance index changes, determine the type of performance index change and update the memory data; calculate the cache update threshold, determine whether to clear useless data in the cache and whether to load external memory data into the cache, and perform the required clearing or loading operations.

[0035] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the spacecraft platform and payload integrated coupling modeling method of the present invention can improve the overall performance and reliability of the spacecraft system design. Through integrated modeling and sensitivity analysis, the method can accurately identify and optimize key design parameters, thereby effectively reducing the number of design iterations and costs while meeting system performance indicators. In addition, the method can improve the computational efficiency and accuracy of dynamic response analysis through an adaptive step-size numerical integration algorithm, making the design process more efficient and accurate.

[0036] Furthermore, the implementation of the present invention can enhance the collaborative working ability of the spacecraft platform and the payload, and achieve the optimal design under complex constraints through a multi-objective optimization algorithm, ensuring that the spacecraft can maintain optimal performance in various working modes. The application of this method can not only shorten the research and development cycle of the spacecraft, but also improve the stability and safety of its on-orbit operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0038] Figure 1 A schematic diagram of a flow chart of a method for integrated coupling modeling of a spacecraft platform and a payload provided in one embodiment of the present invention;

[0039] Figure 2 A schematic diagram of a flow chart of a method for optimizing the integrated design of a spacecraft platform and a payload provided in one embodiment of the present invention;

[0040] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0042] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0043] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0044] Reference below Figure 1 , Figure 1 The following is a flow chart of a method for integrated coupling modeling of a spacecraft platform and a payload provided in one embodiment of the present invention. Figure 1 As shown, a spacecraft platform and payload integrated coupling modeling method 100 includes:

[0045] Step 101, based on multi-body dynamics and control theory, the spacecraft platform and the payload are integrated into a model, and then a numerical integration algorithm is used to perform dynamic response analysis to obtain system coupling response data;

[0046] Step 102, identifying key parameters in the system coupling response data, constructing a sensitivity analysis model based on the key parameters, performing parameter sensitivity analysis on the platform and the load respectively, and obtaining sensitivity analysis results; setting an optimization window based on the sensitivity analysis results to limit the search range of the design parameters;

[0047] Step 103, under the criterion of satisfying system performance indicators, based on a multi-objective optimization algorithm and constraints, search for optimal design parameters and perform integrated design optimization of the spacecraft platform and payload.

[0048] Specifically, in the integrated modeling process, the physical parameters and dynamic characteristics of the spacecraft platform and payload need to be used as modeling inputs. This includes but is not limited to mass, moment of inertia, elastic characteristics, damping coefficient, etc. At the same time, it is necessary to estimate the system performance of the current design cycle based on the optimization results of the previous design cycle, and select the optimal modeling parameters based on the performance and design parameters. For example, an adaptive step-size numerical integration algorithm can be used, in which the adaptive step-size can be automatically adjusted according to the changes in the dynamic characteristics of the system to improve the calculation efficiency and accuracy.

[0049] Preferably, in order to further optimize the modeling process, advanced numerical integration algorithms, such as the Runge-Kutta method or the multi-step method, can be introduced to improve the accuracy of the dynamic response analysis. At the same time, advanced parameter identification techniques, such as genetic algorithms or particle swarm optimization algorithms, can be used to search for optimal design parameters under the criteria of meeting system performance indicators. In addition, in order to improve the accuracy of sensitivity analysis, methods such as Monte Carlo simulation or response surface method can be used to perform more detailed analysis and identification of key parameters. Through these optimization measures, the efficiency and quality of the design process can be further improved to ensure that the final designed spacecraft platform and payload can meet the predetermined performance requirements.

[0050] In some embodiments, the numerical integration algorithm is an adaptive step-size Runge-Kutta-Fehlberg method; the mathematical model of the dynamic response analysis is:

[0051]

[0052] k1=Δt·f(x n ,t n )

[0053]

[0054] k4=Δt·f(x n -k1+2k3,t n +Δt)

[0055] Among them, x n and x n+1 At time t n and t n+1 is the system state vector, Δt is the adaptive step size, f is the system dynamic equation, k1, k2, k3, k4 are the intermediate variables in the Runge-Kutta method.

[0056] Specifically, the mathematical model of dynamic response analysis involves the system state vector, adaptive step size, system dynamic equations, and intermediate variables. In practice, the system state vector can be expressed as state information such as the position and velocity of the spacecraft at a certain moment. The adaptive step size is adjusted according to the system dynamic characteristics and error estimation to ensure the stability and accuracy of the integration process. The system dynamic equations describe the evolution of the spacecraft system over time, including the effects of forces and torques. Intermediate variables such as Δt 2 , Δt, etc., are used to calculate predictions and corrections in the Runge-Kutta method.

[0057] Preferably, in order to further improve the accuracy and efficiency of the dynamic response analysis, a higher-order Runge-Kutta method, such as a fourth-order or fifth-order method, can be used to obtain higher accuracy. At the same time, error control mechanisms, such as local error estimation and global step size control, can be introduced to ensure that the required accuracy is maintained throughout the integration process. In addition, parallel computing technology can be used to distribute large-scale numerical integration tasks to multiple processors for simultaneous execution to further improve computational efficiency. These optimization measures help to handle more complex spacecraft systems or complete analysis tasks in a shorter time.

[0058] In some embodiments, the integrated modeling of the spacecraft platform and the payload includes:

[0059] The physical parameters and dynamic characteristics of the spacecraft platform and payload are used as modeling inputs. Based on the optimization results of the previous design cycle, the system performance of the current design cycle is estimated, and the optimal modeling parameters are selected based on the performance and design parameters.

[0060] Obtain the corresponding system model according to the modeling parameters, and obtain the prediction curve of the dynamic response according to the system model;

[0061] According to the prediction curve, the parameters of the control law are designed using the numerical integration algorithm. The control law is used to control the platform and the payload respectively, and then the dynamic response coefficient of the system model corresponding to the modeling parameters is used to integrate the spacecraft platform and the payload.

[0062] It should be noted that the integrated modeling process involves taking the physical parameters and dynamic characteristics of the spacecraft platform and payload as modeling inputs, and estimating the system performance of the current design cycle based on the optimization results of the previous design cycle.

[0063] Specifically, integrated modeling includes a detailed analysis of the physical and dynamic parameters of the spacecraft platform and payload. This may include mass, size, shape, material properties, elasticity and damping characteristics, etc. Modeling inputs need to be determined based on actual physical models and experimental data to ensure the accuracy of the model. In addition, the optimization results of the design cycle can be estimated based on historical design iterations, test data, and performance evaluation, which helps guide parameter selection and performance prediction for the current design cycle.

[0064] Preferably, in order to improve the accuracy and efficiency of integrated modeling, advanced simulation software and computing tools can be used to process complex physical and dynamic parameters. For example, finite element analysis FEA software can be used to simulate the stress and deformation of spacecraft structures, or multi-body dynamics simulation tools can be used to analyze the motion and response of the system. In addition, machine learning algorithms can be introduced to analyze historical data, predict system performance, and optimize design parameters. The application of these tools and techniques can significantly improve the automation and accuracy of modeling, thereby achieving higher efficiency and performance in the design process.

[0065] In some embodiments, the optimal modeling parameters are modeling parameters corresponding to the minimum system error under different design parameters.

[0066] It should be noted that the optimal modeling parameters refer to the modeling parameters that can minimize the system error under different design parameters. This concept is the key to ensuring the accuracy of integrated modeling of spacecraft platform and payload, which involves careful adjustment and optimization of design parameters to achieve the optimization of system performance.

[0067] Specifically, the determination of optimal modeling parameters involves the evaluation and analysis of system errors. System errors may include deviations due to factors such as model simplification, inaccurate parameter estimation, or changes in the external environment. In implementation, these parameters can be determined through simulation experiments, historical data analysis, or expert experience. For example, an error evaluation model can be set up that can calculate the expected system errors based on the input design parameters, and adjust the parameters based on these errors until the parameter configuration corresponding to the minimum error is found.

[0068] Preferably, in order to more accurately determine the optimal modeling parameters, multi-objective optimization algorithms, such as genetic algorithms, particle swarm optimization or simulated annealing algorithms, can be used to automatically search and determine the optimal parameters. These algorithms can efficiently search in the parameter space to find the parameter combination with the smallest system error while meeting specific performance indicators. In addition, sensitivity analysis can be introduced to evaluate the impact of different parameter changes on the system error, so as to adjust the parameters more specifically. Through these methods, the accuracy and efficiency of modeling can be further improved to ensure that the integrated design of the spacecraft platform and payload meets high performance requirements.

[0069] In some embodiments, identifying key parameters in the system coupling response data includes:

[0070] The dynamic response data of the spacecraft platform and payload are sequentially processed through signal processing, feature extraction and identification of each key parameter to obtain the response results in the key parameter domain;

[0071] Search for the maximum response position of the key parameter as the position of each parameter, perform curve fitting on the response positions of multiple adjacent parameters in segments, and calculate the relative deviation of each parameter in the curve fitting result and the average deviation of the parameters in the segment;

[0072] The parameter segment with the smallest average deviation is taken as the segment parameter, and the parameter position is selected according to the relative deviation threshold, the response information of each selected parameter position is extracted, and the parameter position with the maximum response is selected as the key parameter.

[0073] It should be noted that the process of identifying key parameters in the system coupling response data involves an in-depth analysis of the dynamic response data of the spacecraft platform and payload to identify the parameters that have the greatest impact on the system performance.

[0074] Specifically, identifying key parameters includes signal processing, feature extraction, and identification of key parameters. Signal processing may include filtering, denoising, etc. to improve data quality. Feature extraction involves identifying key information that can represent system performance from the processed data, such as frequency, amplitude, etc. The identification of key parameters is to find out the parameters that have the greatest impact on performance by analyzing the relationship between these features and system performance. For example, an algorithm can be set up that can analyze dynamic response data and identify the most critical parameters based on preset performance indicators.

[0075] Preferably, in order to improve the accuracy of key parameter identification, advanced data analysis techniques, such as machine learning or deep learning algorithms, can be used to automatically analyze dynamic response data and identify key parameters. These algorithms can learn and extract useful information from a large amount of complex data, thereby more accurately identifying key parameters. In addition, multivariate analysis techniques, such as principal component analysis PCA or factor analysis, can be introduced to process the relationship between multiple parameters and further optimize the key parameter identification process. Through the application of these technologies, the efficiency and accuracy of parameter identification can be improved, providing a more reliable basis for subsequent design optimization.

[0076] In some embodiments, the parameter range of the sensitivity analysis model is:

[0077] Δp=[Δp min , Δp max ]

[0078] Δp min =min(p)-α·std(p)

[0079] Δp max =max(p)+α·std(p)

[0080] Where p is the parameter vector, min(p) and max(p) are the minimum and maximum values ​​of the parameter vector, std(p) is the standard deviation of the parameter vector, and α is the expansion factor of the sensitivity analysis.

[0081] It should be noted that the parameter range of the sensitivity analysis model is determined by setting the minimum and maximum values ​​of the parameter vector, and considering the standard deviation of the parameter vector and the expansion factor of the sensitivity analysis. This method allows the range of parameter changes to be precisely controlled when performing sensitivity analysis, thereby more effectively identifying the parameters that have the greatest impact on system performance.

[0082] Specifically, the parameter vector in the sensitivity analysis model represents a set of design parameters, which may include the mass, size, material properties, etc. of the spacecraft. The minimum and maximum values ​​of the parameter vector define the feasible range of each design parameter. The standard deviation is a statistic that measures the degree of parameter variation, and the expansion factor is used to further adjust the parameter search range based on the standard deviation to ensure the comprehensiveness and accuracy of the analysis. For example, the expansion factor can be set to 1.1, which means searching for parameters within a range of 1.1 times the standard deviation to find the optimal solution.

[0083] Preferably, in order to improve the accuracy and efficiency of sensitivity analysis, advanced statistical methods or optimization algorithms can be used to dynamically adjust the parameter range. For example, an adaptive sensitivity analysis method can be used, which can dynamically adjust the search range of parameters based on the information found during the analysis. In addition, methods such as Monte Carlo simulation or Bayesian optimization can be introduced, which can effectively explore the parameter space while considering parameter uncertainty, thereby more accurately identifying key parameters. Through the application of these methods, the quality of sensitivity analysis and the effect of design optimization can be further improved.

[0084] In some embodiments, the integrated design optimization of the spacecraft platform and payload includes: dividing each design parameter along the optimization direction into multiple segments according to the system performance index, first obtaining the system performance before and after the central design parameter is adjusted according to the performance index and the constraint conditions, and then obtaining the system performance and constraint conditions before and after the adjustment of other design parameters.

[0085] It should be noted that the process of integrated design optimization of the spacecraft platform and payload involves segmenting the design parameters according to the system performance indicators, and adjusting and optimizing these parameters on the premise of meeting the performance indicators and constraints.

[0086] Specifically, integrated design optimization includes dividing the design parameters into multiple segments according to the requirements of performance indicators, and each segment represents an optimization direction or range. Performance indicators can be the weight, cost, reliability, life, etc. of the spacecraft. Constraints may include material strength, environmental adaptability, operating restrictions, etc. In implementation, it is necessary to first determine the central design parameters, and then evaluate the optimization effect based on the changes in system performance before and after these parameters are adjusted. For example, an optimization algorithm can be set up that can automatically adjust the design parameters according to the performance indicators and constraints until the optimal solution is found.

[0087] Preferably, in order to improve the efficiency and effect of design optimization, multi-objective optimization algorithms, such as genetic algorithms, particle swarm optimization or multi-criteria decision analysis, can be used to simultaneously consider multiple performance indicators and constraints. These algorithms can find the best parameter combination in a complex design space to meet all performance requirements. In addition, parallel computing technology can be introduced to distribute the optimization tasks to multiple processors for simultaneous execution to speed up the optimization process. Machine learning technology can also be used to predict the effect of parameter adjustment by analyzing historical design data, thereby guiding the optimization process.

[0088] The above-mentioned embodiments of the present invention have the following beneficial effects: The spacecraft platform and payload integrated coupling modeling method of the present invention can provide a new analysis and optimization method for the design of spacecraft systems. Through integrated modeling based on multi-body dynamics and control theory, combined with numerical integration algorithm to analyze the dynamic response of the system, this method can more accurately predict and evaluate the performance of the spacecraft in actual operation. At the same time, by identifying key parameters and constructing a sensitivity analysis model, detailed parameter sensitivity analysis of the platform and payload can be performed, so as to more accurately control the design variables and optimize the system performance.

[0089] In addition, this method can improve design efficiency and quality by setting an optimization window to limit the search range of design parameters and using a multi-objective optimization algorithm to search for the optimal design parameters under the criterion of meeting system performance indicators. The application of this method can not only reduce the trial and error cost in the design process, but also ensure that the final designed spacecraft platform and payload achieve the best performance balance while meeting the predetermined performance requirements, thereby improving the reliability and practicality of the entire spacecraft system.

[0090] like Figure 2 As shown, in some embodiments, a spacecraft platform and payload integrated design optimization method 200 includes:

[0091] Step 201, obtaining system coupling response data obtained by the spacecraft platform and payload integrated coupling modeling method;

[0092] Step 202, based on a multi-objective optimization algorithm, dividing the system coupling response data into data blocks of multiple performance indicators;

[0093] Step 203, according to the system performance index, use the CPU to load the corresponding data block into the memory in real time, and load the parameter block to be optimized into the search space of the optimization algorithm;

[0094] Step 204 , based on the constraints, use the optimization algorithm to search for optimal solutions of multiple objectives in parallel to obtain the optimal design parameters of the spacecraft platform and payload.

[0095] It is understandable that the steps described in the spacecraft platform and payload integrated design optimization method 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the spacecraft platform and payload integrated coupling modeling method are also applicable to the spacecraft platform and payload integrated design optimization method 200 and the steps contained therein, and will not be repeated here.

[0096] In some embodiments, the system coupling response data is divided into data blocks of multiple performance indicators based on the multi-objective optimization algorithm, including: according to a preset performance indicator threshold, using a multi-objective optimization algorithm, the system coupling response data is divided layer by layer to obtain each sub-block, and adjacent sub-blocks are merged according to the performance indicators in adjacent sub-blocks until the sub-block performance indicator threshold is reached to obtain multiple data blocks.

[0097] Specifically, the multi-objective optimization algorithm is an optimization method that can consider multiple performance indicators at the same time. It can handle different optimization objectives such as weight, cost, efficiency and reliability. In implementation, the system coupling response data will be divided layer by layer according to the preset performance indicator threshold to form multiple data sub-blocks. Each sub-block represents a set of data with similar performance characteristics. For example, the threshold can be set as a certain percentage of the performance indicator. When the performance indicator in the data block exceeds this threshold, the sub-blocks are merged or split.

[0098] Preferably, in order to further improve the accuracy of data processing and the efficiency of the optimization algorithm, an advanced multi-objective optimization algorithm can be used, such as NSGA-II non-dominated sorting genetic algorithm II or MOEA / D decomposition-based multi-objective evolutionary algorithm. These algorithms can more effectively handle complex multi-objective optimization problems and find the optimal solution. In addition, an adaptive threshold adjustment mechanism can be introduced to dynamically adjust the performance indicator threshold according to the characteristics of the data and the feedback during the optimization process to achieve more flexible and accurate data block division.

[0099] In some embodiments, the method of using a CPU to load corresponding data blocks into a memory in real time according to the system performance index and loading the parameter blocks to be optimized into the search space of the optimization algorithm includes:

[0100] Calculate the address of the system coupling response data block to be loaded into the memory and cache data according to the current system performance index; construct the memory and cache data using the data structure, and initialize the loading cache and memory system coupling response data block;

[0101] When the system performance index changes, determine the type of performance index change and update the memory data; calculate the cache update threshold, determine whether to clear useless data in the cache and whether to load external memory data into the cache, and perform the required clearing or loading operations.

[0102] Specifically, system performance indicators refer to a series of quantitative indicators used to evaluate system performance, such as response time, calculation accuracy, resource consumption, etc. In implementation, the CPU will calculate the addresses of data blocks that need to be loaded into the memory based on these indicators, and then build a data structure to store these data. The parameter blocks to be optimized refer to the parameter sets that have a direct impact on system performance. They will be loaded into the search space of the optimization algorithm for further analysis and optimization. For example, a priority system based on performance indicators can be set up to determine which data blocks and parameter blocks need to be loaded first based on priority.

[0103] Preferably, in order to improve the efficiency of data processing and optimization, advanced data pre-fetching techniques, such as pre-reading algorithms or intelligent cache management systems, can be used to predict and load the data blocks that may be needed in advance. In addition, adaptive optimization algorithms can be introduced, which can dynamically adjust the size and shape of the search space based on real-time performance feedback to better adapt to changes in system performance. Parallel processing technology can also be used to distribute data loading and optimization tasks to multiple CPU cores or processors for simultaneous execution to further increase processing speed. The combined use of these technologies can ensure that the system minimizes resource consumption while meeting performance requirements.

[0104] Reference below Figure 3 , which shows a schematic diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0105] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0106] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0107] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0108] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A spacecraft platform and payload integrated coupling modeling method, characterized in that: The steps include: The spacecraft platform and payload are integrated into a model, and then the dynamic response analysis is performed using a numerical integration algorithm to obtain the system coupling response data; Identify key parameters in the system coupling response data, build a sensitivity analysis model based on the key parameters, perform parameter sensitivity analysis on the platform and load respectively, and obtain sensitivity analysis results; Set the optimization window according to the sensitivity analysis results to limit the search range of design parameters; Under the criterion of meeting system performance indicators, based on multi-objective optimization algorithms and constraints, search for optimal design parameters and perform integrated design optimization of spacecraft platform and payload; The numerical integration algorithm is the adaptive step-size Runge-Kutta-Fehlberg method; the mathematical model of the dynamic response analysis is: k1=Δt·f(x n ,t n ) k4=Δt·f(x n -k1+2k3,t n +Δt) Among them, x n and x n+1 At time t n and t n+1 The system state vector, Δt is the adaptive step size, f is the system dynamic equation, k1, k2, k3, k4 are the intermediate variables in the Runge-Kutta method; The integrated modeling of the spacecraft platform and payload includes: The physical parameters and dynamic characteristics of the spacecraft platform and payload are used as modeling inputs. Based on the optimization results of the previous design cycle, the system performance of the current design cycle is estimated, and the optimal modeling parameters are selected based on the performance and design parameters. Obtain the corresponding system model according to the modeling parameters, and obtain the prediction curve of the dynamic response according to the system model; According to the prediction curve, the parameters of the control law are designed using the numerical integration algorithm. The control law is used to control the platform and the payload respectively, and then the dynamic response coefficient of the system model corresponding to the modeling parameters is used to integrate the spacecraft platform and the payload.

2. The spacecraft platform and payload integrated coupling modeling method according to claim 1 is characterized in that: The optimal modeling parameters are the modeling parameters corresponding to the minimum system error under different design parameters.

3. The spacecraft platform and payload integrated coupling modeling method according to claim 1 is characterized in that: The parameter range of the sensitivity analysis model is: Δp=[Δp min ,Δp max ] Δp min =min(p)-α·std(p) Δp max =max(p)+α·std(p) Where p is the parameter vector, min(p) and max(p) are the minimum and maximum values ​​of the parameter vector, std(p) is the standard deviation of the parameter vector, and α is the expansion factor of the sensitivity analysis.

4. The spacecraft platform and payload integrated coupling modeling method according to claim 1, characterized in that: The integrated design optimization of the spacecraft platform and payload includes: dividing each design parameter along the optimization direction into multiple sections according to the system performance index, first obtaining the system performance before and after the adjustment of the central design parameter according to the performance index and the constraint conditions, and then obtaining the system performance and constraint conditions before and after the adjustment of other design parameters.

5. A spacecraft platform and payload integrated design optimization method, characterized in that: The steps include: Acquire system coupling response data obtained by the spacecraft platform and payload integrated coupling modeling method according to any one of claims 1 to 4; Based on the multi-objective optimization algorithm, the system coupling response data is divided into data blocks of multiple performance indicators; According to the system performance indicators, the CPU is used to load the corresponding data blocks into the memory in real time, and the parameter blocks to be optimized are loaded into the search space of the optimization algorithm; Based on the constraints, the optimization algorithm is used to search for the optimal solution of multiple objectives in parallel to obtain the optimal design parameters of the spacecraft platform and payload.

6. The spacecraft platform and payload integrated design optimization method according to claim 5, characterized in that: The method divides the system coupling response data into data blocks of multiple performance indicators based on a multi-objective optimization algorithm, including: according to a preset performance indicator threshold, using a multi-objective optimization algorithm, dividing the system coupling response data layer by layer to obtain each sub-block, and merging adjacent sub-blocks according to the performance indicators in adjacent sub-blocks until the sub-block performance indicator threshold is reached to obtain multiple data blocks.

7. The spacecraft platform and payload integrated design optimization method according to claim 6, characterized in that: According to the system performance index, the CPU is used to load the corresponding data block into the memory in real time, and the parameter block to be optimized is loaded into the search space of the optimization algorithm, including: Calculate the address of the system coupling response data block to be loaded into the memory and cache data according to the current system performance index; construct the memory and cache data using the data structure, and initialize the loading cache and memory system coupling response data block; When the system performance index changes, determine the type of performance index change and update the memory data; calculate the cache update threshold, determine whether to clear useless data in the cache and whether to load external memory data into the cache, and perform the required clearing or loading operations.

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