Methods, apparatus, electronic devices and storage media for optimizing launch parameters of rope-net systems

Abstract

This application relates to a method, apparatus, electronic device, and storage medium for optimizing launch parameters in a rope-net system. The method addresses the problem of high optimization dimensionality caused by nonlinear coupling relationships in the launch parameters of the rope-net system. It employs orthogonal experimental sensitivity analysis to select highly sensitive launch parameters, reducing subsequent optimization and experimental costs. Subsequently, a polynomial chaotic expansion is introduced to establish a probabilistic mapping model between parameter randomness and system response, solving the problem that traditional deterministic analysis cannot assess the influence of random factors. Finally, a multi-objective optimization algorithm is used to search within the dimensionality-reduced parameter space, using quantified uncertain parameters as optimization objectives to perform robust optimization of the rope-net system, thereby improving its robustness. This method, through a progressive process of parameter dimensionality reduction, uncertainty quantification, and robust optimization, systematically improves the reliability of the rope-net launch system while ensuring computational efficiency.

Description

Technical Field

[0001] This application relates to the field of parameter optimization technology, and in particular to a method, apparatus, electronic device and storage medium for optimizing the launch parameters of a rope net system. Background Technology

[0002] With the rapid development of aerospace technology, the number of global space launch missions has increased exponentially, leading to a sharp rise in the density of space debris in low Earth orbit, posing a serious threat to spacecraft in orbit. At the same time, the widespread adoption of drone technology presents new challenges to low-altitude security, making the need for counter-drone systems increasingly urgent. Against this backdrop, rope-net interception technology, which combines low cost, high security, and environmental adaptability, has become a research hotspot in the fields of space debris removal and low-altitude security due to its unique non-contact capture advantages.

[0003] However, existing rope net interception systems suffer from technical bottlenecks related to unstable capture efficiency. Specifically: 1) Regarding parameter sensitivity, key launch parameters such as initial velocity, launch angle, and launch block mass exhibit complex nonlinear coupling relationships, making parameter optimization difficult using traditional trial-and-error methods; 2) In terms of system reliability, uncertainties such as rope net processing errors and material parameter errors affect rope net capture performance, and traditional deterministic analysis methods cannot effectively quantify the comprehensive impact of parameter uncertainties on capture success rate.

[0004] The interception and capture effectiveness of rope-net systems is significantly sensitive to launch parameters, requiring a combination of experimental and simulation methods for system evaluation. However, due to the large number of parameters involved, traditional step-by-step verification methods are inefficient and resource-intensive. Furthermore, the actual launch environment exhibits significant uncertainties, necessitating the development of robust optimization models to enhance the robustness of parameter combinations.

[0005] The existing technology has the following drawbacks: First, traditional parameter optimization methods face the curse of dimensionality caused by highly nonlinear coupled parameters (initial velocity, launch angle, mass of mass block, etc.), and the parameter combination space expands exponentially, resulting in low computational efficiency and severely restricting the simulation and experimental process; Second, existing models do not quantify uncertainties such as rope and net processing deviations and material parameter discreteness, and deterministic analysis methods do not consider the random distribution characteristics of parameters and the probabilistic characteristics of environmental disturbances, revealing significant robustness defects. Summary of the Invention

[0006] Therefore, it is necessary to provide a method, apparatus, electronic device, and storage medium for optimizing the launch parameters of a rope net system to address the aforementioned technical problems.

[0007] A method for optimizing launch parameters of a rope-net system, the method comprising:

[0008] Select key design parameters and performance indicators for the rope net launching system.

[0009] The sensitivity analysis of the key design parameters was performed using an orthogonal experimental sensitivity analysis method, and the parameters that affect the capture performance of the rope net were selected as the parameters to be optimized.

[0010] Based on key design parameters and performance indicators, a polynomial chaotic expansion model is used to conduct uncertainty analysis and obtain statistical estimation parameters for subsequent uncertainty optimization.

[0011] Using the statistical estimation parameters as the optimization objective, a multi-objective optimization method is employed to optimize the parameters to obtain the optimal rope net launch parameters.

[0012] In one embodiment, key design parameters include: internal rope diameter, reinforcing rope diameter, solid unit mass, traction mass block launch angle, initial launch velocity and its mass.

[0013] Performance metrics include: effective hang time and effective deployment time.

[0014] In one embodiment, an orthogonal experimental sensitivity analysis method is used to perform sensitivity analysis on the key design parameters, and parameters affecting the rope net capture performance are selected from the key design parameters as parameters to be optimized, including:

[0015] Each key design parameter is divided into 3 levels, and an orthogonal experimental design matrix is ​​constructed by selecting an appropriate orthogonal array.

[0016] For each combination of factors, the rope-net launch model was run to calculate the corresponding effective hang time and effective deployment time, which were then recorded as response values ​​to obtain the orthogonal test results.

[0017] Range analysis was performed on the orthogonal experimental results to obtain the range of each key design parameter.

[0018] Based on the range of key design parameters, the launch parameters of the rope net launching system are sorted, and the preset number of launch parameters with the highest ranking are selected as the parameters to be optimized.

[0019] In one embodiment, based on the launch parameters and performance indicators, a multinomial chaotic expansion model is used to perform uncertainty analysis, obtaining statistical estimation parameters for subsequent uncertainty optimization, including:

[0020] The key design parameters are divided into deterministic parameters and random parameters; wherein, the deterministic parameters include: the mass of the traction mass block and the launch angle of the traction mass block; the random parameters include: the initial launch velocity of the traction mass block and the diameter of the internal rope.

[0021] The probability distribution model for the initial launch velocity of the traction mass block and the diameter of the internal rope is selected.

[0022] The random response of the random parameters of the objective function is subjected to a polynomial chaotic expansion; the expression for the polynomial chaotic expansion is:

[0023]

[0024] in, The random response of the system objective function. An orthogonal polynomial is formed by combining the basis functions of a multivariable orthogonal polynomial. Here are the coefficients of the polynomial chaotic expansion. P The number of terms after truncation. It is the product of the basis functions of the orthogonal polynomial.

[0025] Generated based on the key design parameter distribution of the rope net launch system N sample points ,in .

[0026] At each sampling point, the rope-net dynamics model is invoked to obtain the corresponding rope-net launch performance index, and the expression of the polynomial chaotic expansion is transformed into an overdetermined system of equations.

[0027] The polynomial chaotic expansion coefficients are obtained by solving the overdetermined system of equations using the least squares method.

[0028] Based on the polynomial chaotic expansion coefficients, statistical estimation parameters for subsequent uncertain optimization are obtained; the statistical estimation parameters include: expectation and standard deviation.

[0029] In one embodiment, the probability distribution model of the random parameters is a normal distribution, a uniform distribution, or a Beta distribution; wherein:

[0030] Normal distribution: applicable to parameters of natural variation.

[0031] Uniform distribution: Applicable to situations where the parameter range is known but there is no prior information.

[0032] Beta distribution: suitable for scenarios with bounded and asymmetric parameters.

[0033] In one embodiment, the basis functions of the multivariable orthogonal polynomial are: Hermite polynomials, Legendre polynomials, or Jacobi polynomials.

[0034] In one embodiment, using statistical estimation parameters as the optimization objective, a multi-objective optimization method is employed to optimize the parameters to obtain the optimal rope net launch parameters, including:

[0035] Select the uncertain parameters and their ranges from the parameters to be optimized.

[0036] A multi-objective optimization algorithm is used to optimize the parameters to be optimized. The objective function of each optimization is the statistically estimated parameters output by the polynomial chaotic expansion model. The front solution set is obtained, and the optimal solution in the front solution set is selected as the optimal rope net launch parameters.

[0037] A device for optimizing launch parameters of a rope net system, the device comprising:

[0038] The design parameter and performance index selection module is used to select the key design parameters and performance indexes of the rope net launching system.

[0039] The sensitivity analysis module is used to perform sensitivity analysis on the key design parameters using the orthogonal experimental sensitivity analysis method, and to select parameters that affect the rope net capture performance as parameters to be optimized from the key design parameters;

[0040] The polynomial chaotic expansion model uncertainty analysis module is used to perform uncertainty analysis using a polynomial chaotic expansion model based on key design parameters and the performance indicators, and to obtain statistical estimation parameters for subsequent uncertainty optimization.

[0041] The parameter optimization module is used to optimize the parameters to be optimized by employing a multi-objective optimization method with the statistical estimation parameters as the optimization objective, so as to obtain the optimal rope net launch parameters.

[0042] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of any of the above-described methods for optimizing the launch parameters of a rope-net system.

[0043] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described methods for optimizing the launch parameters of a rope-net system.

[0044] The aforementioned method, apparatus, electronic device, and storage medium for optimizing launch parameters in a rope-net system address the issue of high optimization dimensionality caused by nonlinear coupling in the launch parameters. The method employs orthogonal experimental sensitivity analysis to select highly sensitive launch parameters, reducing subsequent optimization and experimental costs. Subsequently, a polynomial chaotic expansion is introduced to establish a probabilistic mapping model between parameter randomness and system response, resolving the inability of traditional deterministic analysis to assess the impact of random factors. Finally, a multi-objective optimization algorithm searches within the dimensionality-reduced parameter space, using quantified uncertain parameters as optimization targets to robustly optimize the rope-net system, thereby improving its robustness. This method, through a progressive process of parameter dimensionality reduction, uncertainty quantification, and robust optimization, systematically improves the reliability of the rope-net launch system while ensuring computational efficiency. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating a method for optimizing launch parameters of a rope-net system in one embodiment;

[0046] Figure 2 Here is a design diagram of a rope net launcher in another embodiment, where (a) is a schematic diagram of the launch-ready state and (b) is a schematic diagram of the launch state;

[0047] Figure 3 A diagram of a novel rope net structure in another embodiment;

[0048] Figure 4 This is a diagram of the internal structure of an electronic device in one embodiment. Detailed Implementation

[0049] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0050] This application proposes a method for optimizing launch parameters in a rope-net system. This method, through a complete process of "parameter selection → sensitivity analysis → uncertainty modeling → robust optimization," overcomes the technical shortcomings of traditional methods in areas such as nonlinear coupling parameter optimization, quantification of stochastic factors, and improvement of system robustness, forming a reusable methodological system and engineering tools. This method significantly improves parameter optimization efficiency and system anti-interference capability, providing theoretical support for the development of space non-cooperative target acquisition equipment.

[0051] In one embodiment, such as Figure 1 As shown, a method for optimizing the launching parameters of a rope-net system is provided, which includes the following steps:

[0052] Step 100: Select the key design parameters and performance indicators of the rope net launching system.

[0053] Specifically, based on the engineering application requirements and technical specifications of the rope-net launching system, and combined with system design experience guidelines, this application determines the key design parameter as follows: internal rope diameter. , reinforce rope diameter solid unit mass traction mass block launch angle Initial launch velocity and its quality Critical design parameters are a subset of launch parameters, selected based on specific engineering application requirements. Critical design parameters include launch parameters.

[0054] To address the core requirements of space interception and capture missions (rope and net dynamic deployment performance and loiter effectiveness), the target parameter selected for sensitivity analysis is: effective loiter time. and effective deployment time .

[0055] Step 102: Use the orthogonal experimental sensitivity analysis method to perform sensitivity analysis on the key design parameters, and select the parameters that affect the rope net capture performance from the key design parameters as the parameters to be optimized.

[0056] Specifically, existing technologies have the following drawbacks: First, traditional parameter optimization methods face the curse of dimensionality caused by highly nonlinear coupled parameters (initial velocity, launch angle, mass of mass block, etc.). The parameter combination space expands exponentially, resulting in low computational efficiency and severely restricting the simulation and experimental process.

[0057] Therefore, this application introduces an orthogonal experimental sensitivity analysis method to perform sensitivity analysis on the rope net launch parameters, thereby enabling the screening of key control variables, reducing the dimensionality of the parameter space, and lowering the cost and time required for simulation and experimentation.

[0058] In addition to orthogonal experimental sensitivity analysis, the Sobol sensitivity analysis method can also be used to perform sensitivity analysis on key design parameters.

[0059] The target parameter for sensitivity analysis is the performance index of the rope net. There are many performance indices for rope nets, and the performance index is selected as the target parameter for sensitivity analysis based on the specific task requirements.

[0060] Step 104: Based on the key design parameters and performance indicators, perform uncertainty analysis using a polynomial chaotic expansion model to obtain statistical estimation parameters for subsequent uncertainty optimization.

[0061] Specifically, existing models fail to quantify uncertainties such as deviations in rope and net processing techniques and the discreteness of material parameters. Deterministic analysis methods do not consider the random distribution characteristics of parameters and the probabilistic features of environmental disturbances, revealing significant robustness deficiencies. Therefore, a multinomial chaotic expansion uncertainty analysis method is introduced to quantify the uncertainty of rope and net launch, establish an uncertainty propagation model, accurately characterize the probabilistic response features of random parameters, and solve the problem that traditional deterministic analysis cannot assess the impact of random factors.

[0062] In addition to the polynomial chaos expansion method, uncertainty analysis methods can also include Monte Carlo methods, interval analysis, etc.

[0063] Step 106: Using the statistical estimation parameters as the optimization objective, a multi-objective optimization method is used to optimize the parameters to obtain the optimal rope net launch parameters.

[0064] Specifically, a multi-objective optimization algorithm is used to search within the dimensionality-reduced parameter space, with quantified uncertain parameters as the optimization objective, to perform robustness optimization on the rope net and improve its robustness.

[0065] In the aforementioned method for optimizing launch parameters of the rope-net system, the method addresses the problem of high optimization dimensionality caused by the nonlinear coupling relationship of the launch parameters. It employs orthogonal experimental sensitivity analysis to select highly sensitive launch parameters, reducing subsequent optimization and experimental costs. Subsequently, a polynomial chaotic expansion is introduced to establish a probabilistic mapping model between parameter randomness and system response, solving the problem that traditional deterministic analysis cannot assess the influence of random factors. Finally, a multi-objective optimization algorithm is used to search within the dimensionality-reduced parameter space, using quantified uncertain parameters as optimization objectives to robustly optimize the rope-net system and improve its robustness. This method, through a progressive process of parameter dimensionality reduction, uncertainty quantification, and robust optimization, systematically improves the reliability of the rope-net launch system while ensuring computational efficiency.

[0066] In one embodiment, the key design parameters in step 100 include: internal rope diameter, reinforcing rope diameter, solid unit mass, traction mass block launch angle, initial launch velocity and its mass; performance indicators include: effective hang time and effective deployment time.

[0067] Specifically, effective loiter time The time interval during which the projected area of ​​the rope net in the interception direction continuously exceeds the design threshold after the rope net is deployed represents the system's ability to maintain an effective capture state.

[0068] Effective deployment time The time it takes for the net to reach the design threshold from launch to the first time the projected area reaches the design threshold reflects the dynamic response characteristics of the system in rapidly forming an effective interception posture.

[0069] In one embodiment, step 102 includes: dividing each key design parameter into three levels, constructing an orthogonal experimental design matrix using a suitable orthogonal array; running a rope-net launch model for each combination of factors, calculating the corresponding effective hang time and effective deployment time and recording them as response values ​​to obtain orthogonal experimental results; performing range analysis on the orthogonal experimental results to obtain the range of each key design parameter; and sorting the launch parameters of the rope-net launch system according to the range of the key design parameters, selecting a preset number of launch parameters that are ranked higher as parameters to be optimized.

[0070] Specifically, the steps for parameter sensitivity analysis using the orthogonal experimental method include:

[0071] (1) Construction of orthogonal experimental design matrix

[0072] Based on the six key design parameters mentioned above, each parameter was divided into three levels, and an appropriate orthogonal array was selected to construct the experimental design matrix. This orthogonal array contains 18 sets of experiments, which can comprehensively and evenly cover the parameter space, controlling the number of experiments while maintaining analytical accuracy. The orthogonal design table for this experiment is shown in Table 1.

[0073] Table 1. Orthogonal design table for experiments

[0074]

[0075] Each factor has three levels, coded as 1, 2, and 3 respectively. Based on the actual design, 1, 2, and 3 are mapped to specific physical parameter values. The design level value mapping table is shown in Table 2.

[0076] Table 2 Horizontal Value Mapping Table

[0077]

[0078] For each combination of factors, the rope-net launch model was run to calculate the corresponding effective hang time and effective deployment time, which were then recorded as response values. The results are shown in Table 3.

[0079] Table 3 records the results.

[0080]

[0081] (2) Sensitivity index calculation

[0082] Range analysis was performed on the orthogonal experimental results to evaluate the sensitivity of each parameter to the performance index.

[0083] The range of each parameter xᵢ can be calculated from the mean of its response at different levels:

[0084] ;

[0085] in, Indicates parameters In the Level The average response value under the given conditions. Range The larger the value, the more significant the impact of this parameter on performance.

[0086] The results of the range analysis are shown in Table 4.

[0087] Table 4. Calculation results of range analysis

[0088]

[0089] The results of the minimum time calculation are shown in Table 5.

[0090] Table 5. Calculation results of minimum unfolding time

[0091]

[0092] (3) Based on the range of launch parameters of each rope and net launch system calculated in the previous step, sort the launch parameters of the rope and net launch system and select the parameters with greater influence for subsequent optimization analysis.

[0093] In one embodiment, step 104 includes: dividing the key design parameters into deterministic parameters (traction mass, traction mass launch angle) and random parameters (traction mass initial launch velocity, internal rope diameter); selecting a uniform distribution model for the probability distribution of the random parameters (traction mass initial launch velocity, internal rope diameter); and defining the random response of the random parameters of the objective function. Perform a polynomial chaotic expansion. The expression for the polynomial chaotic expansion is:

[0094] (9)

[0095] in, The objective function of the system is the random response. An orthogonal polynomial is formed by combining the basis functions of a multivariable orthogonal polynomial. Here are the coefficients of the polynomial chaotic expansion. The number of terms after truncation. It is the product of the basis functions of the orthogonal polynomial.

[0096] Generated based on the key design parameter distribution of the rope net launch system N sample points ,in At each sampling point, the rope-net dynamics model is invoked to obtain the corresponding rope-net launch performance index, and the expression of the polynomial chaotic expansion is transformed into an overdetermined system of equations. The overdetermined system of equations is solved by the least squares method to obtain the polynomial chaotic expansion coefficients. Based on the polynomial chaotic expansion coefficients, the statistical estimation parameters for subsequent uncertain optimization are obtained. The statistical estimation parameters include: expectation and standard deviation.

[0097] Specifically, the steps for uncertainty analysis of polynomial chaotic expansion models include: analysis

[0098] (1) Probabilistic modeling of uncertain parameters. Specifically, this includes: parameter classification: based on the key design parameters of the rope net launch, parameters are classified into deterministic parameters (including: traction mass mass, traction mass launch angle) and random parameters (including: traction mass initial launch velocity, internal rope diameter). The probability distribution of random parameters can be selected as follows:

[0099] Normal distribution: Applicable to parameters with natural variability (such as material defects).

[0100] Uniform distribution: suitable for situations where the parameter range is known but there is no prior information (such as manufacturing tolerances).

[0101] Beta distribution: suitable for scenarios with bounded and asymmetric parameters (such as ambient temperature range).

[0102] As a preferred option, the probability distribution models for the initial launch velocity of the random parameter traction mass block and the internal rope diameter are both selected to be uniform distributions;

[0103] (2) Polynomial Chaotic Expansion (PCE). Specifically, the expression for the polynomial chaotic expansion of the random response of the system objective function is shown in the polynomial chaotic expansion expression above.

[0104] The basis functions of orthogonal polynomials can be chosen based on the distribution of the random parameters: Hermite polynomials for a normal distribution, Legendre polynomials for a uniform distribution, and Jacobi polynomials for a Beta distribution. Typically, the truncation strategy for chaotic polynomials is to truncate them according to the highest order: limiting the total order of the polynomial. p Number of items ( (For parameter dimensions).

[0105] (3) Random space discretization and sampling. Specifically, this includes generating data based on the distribution of rope net transmission parameters. sample points , must meet To meet the fitting accuracy.

[0106] (4) Deterministic model solution and coefficient calculation. Specifically, this includes: at sampling points The rope net dynamics model is invoked to obtain the rope net launch performance indicators. The overdetermined system of equations is solved using the regression method and the least squares method.

[0107] (5) Post-processing and uncertainty quantification. Specifically, this includes: using the solved polynomial chaotic expansion coefficients to obtain statistical estimation parameters for subsequent uncertain optimization. The statistical estimation parameters include expectation and standard deviation, specifically:

[0108] (10)

[0109] (11)

[0110] in, For the expectation, The standard deviation is denoted as .

[0111] In one embodiment, the probability distribution model of the random parameter is a normal distribution, a uniform distribution, or a Beta distribution; wherein: normal distribution: suitable for naturally variable parameters; uniform distribution: suitable for situations where the parameter range is known but there is no prior information; Beta distribution: suitable for scenarios where the parameter is bounded and asymmetric.

[0112] In one embodiment, the basis functions of the multivariable orthogonal polynomial are: Hermite polynomials, Legendre polynomials, or Jacobi polynomials.

[0113] In one embodiment, step 106 includes: selecting uncertain parameters and their ranges from the parameters to be optimized; using a multi-objective optimization algorithm to optimize the parameters to be optimized, wherein the objective function for each optimization is the statistically estimated parameters output by the polynomial chaotic expansion model, to obtain a front solution set, and selecting the optimal solution from the front solution set as the optimal rope net launch parameter.

[0114] Rope net launcher design drawing as follows Figure 2 As shown, Figure 2 (a) is a schematic diagram of the launch-ready state. Figure 2 (b) shows a schematic diagram of the launch state. The novel rope-net structure is as follows: Figure 3 As shown.

[0115] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0116] In one embodiment, a device for optimizing launch parameters of a rope-net system is provided, comprising: a design parameter and performance index selection module, a sensitivity analysis module, a polynomial chaotic expansion model uncertainty analysis module, and a parameter optimization module, wherein:

[0117] The design parameter and performance index selection module is used to select the key design parameters and performance indexes of the rope net launching system.

[0118] The sensitivity analysis module is used to perform sensitivity analysis on the key design parameters using the orthogonal experimental sensitivity analysis method, and to select parameters that affect the rope net capture performance as parameters to be optimized.

[0119] The polynomial chaotic expansion model uncertainty analysis module is used to perform uncertainty analysis based on key design parameters and performance indicators using a polynomial chaotic expansion model, and obtain statistical estimation parameters for subsequent uncertainty optimization.

[0120] The parameter optimization module is used to optimize the parameters to be optimized by using statistical estimation as the optimization objective and employing a multi-objective optimization method to obtain the optimal rope and net launch parameters.

[0121] In one embodiment, the key design parameters in the design parameter and performance index selection module include: internal rope diameter, reinforcing rope diameter, solid unit mass, traction mass block launch angle, initial launch velocity and its mass; the performance indexes include: effective hang time and effective deployment time.

[0122] In one embodiment, the sensitivity analysis module is further used to divide each key design parameter into three levels, select an appropriate orthogonal array to construct an orthogonal experimental design matrix; for each combination of factors, run the rope-net launch model, calculate the corresponding effective hang time and effective deployment time and record them as response values ​​to obtain orthogonal experimental results; perform range analysis on the orthogonal experimental results to obtain the range of each key design parameter; and sort the launch parameters of the rope-net launch system according to the range of the key design parameters, and select the preset number of launch parameters with the highest ranking as parameters to be optimized.

[0123] In one embodiment, a polynomial chaotic expansion model uncertainty analysis module is used to divide the key design parameters into deterministic parameters and random parameters; wherein, the deterministic parameters include: the mass of the traction mass block and the launch angle of the traction mass block; the random parameters include: the initial launch velocity of the traction mass block and the diameter of the internal rope; a probability distribution model for the initial launch velocity of the traction mass block and the diameter of the internal rope is selected; the expression of the polynomial chaotic expansion is shown in formula (9); generated according to the distribution of key design parameters of the rope net launch system. N sample points ,in At each sampling point, the rope-net dynamics model is invoked to obtain the corresponding rope-net launch performance index, and the expression of the polynomial chaotic expansion is transformed into an overdetermined system of equations. The overdetermined system of equations is solved by the least squares method to obtain the polynomial chaotic expansion coefficients. Based on the polynomial chaotic expansion coefficients, the statistical estimation parameters for subsequent uncertain optimization are obtained. The statistical estimation parameters include: expectation and standard deviation.

[0124] In one embodiment, the probability distribution model of the random parameters in the uncertainty analysis module of the multinomial chaotic expansion model is a normal distribution, a uniform distribution, or a Beta distribution; wherein: normal distribution: applicable to naturally variable parameters; uniform distribution: applicable to cases where the parameter range is known but there is no prior information; Beta distribution: applicable to scenarios where the parameters are bounded and asymmetric.

[0125] In one embodiment, the basis functions of the multivariable orthogonal polynomial in the uncertainty analysis module of the polynomial chaos expansion model are: Hermite polynomial, Legendre polynomial, or Jacobi polynomial.

[0126] In one embodiment, the parameter optimization module is further used to select uncertain parameters and their ranges among the parameters to be optimized; a multi-objective optimization algorithm is used to optimize the parameters to be optimized, and the objective function of each optimization is the statistically estimated parameters output by the polynomial chaotic expansion model, to obtain the front solution set, and the optimal solution is selected from the front solution set as the optimal rope net launch parameter.

[0127] Specific limitations regarding the rope-net system launch parameter optimization device can be found in the limitations of the rope-net system launch parameter optimization method described above, and will not be repeated here. Each module in the aforementioned rope-net system launch parameter optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0128] In one embodiment, an electronic device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the electronic device includes a processor, internal memory, a network interface, a display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for optimizing the launch parameters of a rope-net system. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0129] Those skilled in the art will understand that Figure 4The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0130] In one embodiment, an electronic device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps in the above method embodiments.

[0131] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0132] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0133] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A method for optimizing launch parameters of a rope-net system, characterized in that, The method includes: Select key design parameters and performance indicators for the rope net launching system; The sensitivity analysis of the key design parameters was performed using the orthogonal experimental sensitivity analysis method, and the parameters that affect the capture performance of the rope net were selected as the parameters to be optimized. Based on the key design parameters and performance indicators, a multinomial chaotic expansion model is used to perform uncertainty analysis and obtain statistical estimation parameters for subsequent uncertainty optimization. Using the estimated parameters of the aforementioned statistics as the optimization objective, a multi-objective optimization method is employed to optimize the parameters to obtain the optimal rope net launch parameters; Specifically, based on the key design parameters and performance indicators, a multinomial chaotic expansion model is used for uncertainty analysis to obtain statistical estimation parameters for subsequent uncertain optimization, including: The key design parameters are divided into deterministic parameters and random parameters; wherein, the deterministic parameters include: the mass of the traction mass block and the launch angle of the traction mass block; the random parameters include: the initial launch velocity of the traction mass block and the diameter of the internal rope; Select the probability distribution model of the initial launch velocity of the traction mass block and the diameter of the internal rope; The random response of the random parameters of the objective function is subjected to a polynomial chaotic expansion; the expression for the polynomial chaotic expansion is: in, The random response of the system objective function. An orthogonal polynomial is formed by combining the basis functions of a multivariable orthogonal polynomial. Here are the coefficients of the polynomial chaotic expansion. P The number of terms after truncation. It is the product of the basis functions of the orthogonal polynomials; Generated based on the key design parameter distribution of the rope net launch system N sample points ,in ; At each sampling point, the rope net dynamics model is called to obtain the corresponding rope net launch performance index, and the expression of the polynomial chaotic expansion is transformed into an overdetermined system of equations. The polynomial chaotic expansion coefficients are obtained by solving the overdetermined system of equations using the least squares method. Based on the polynomial chaotic expansion coefficients, statistical estimation parameters for subsequent uncertain optimization are obtained; the statistical estimation parameters include: expectation and standard deviation.

2. The method for optimizing the launching parameters of the rope-net system according to claim 1, characterized in that, The key design parameters include: internal rope diameter, reinforcing rope diameter, solid unit mass, traction mass block launch angle, initial launch velocity and its mass; The performance indicators include: effective hang time and effective deployment time.

3. The method for optimizing the launching parameters of the rope-net system according to claim 1, characterized in that, Sensitivity analysis of the key design parameters was performed using an orthogonal experimental sensitivity analysis method. Parameters affecting the rope net capture performance were selected from these key design parameters as parameters to be optimized. These parameters include: Each of the key design parameters is divided into 3 levels, and an orthogonal experimental design matrix is ​​constructed by selecting an appropriate orthogonal table. For each combination of factors, the rope-net launch model was run to calculate the corresponding effective hang time and effective deployment time, which were recorded as response values ​​to obtain orthogonal test results. Range analysis was performed on the orthogonal experiment results to obtain the range of each key design parameter; Based on the range of key design parameters, the launch parameters of the rope net launching system are sorted, and the preset number of launch parameters with the highest ranking are selected as the parameters to be optimized.

4. The method for optimizing the launching parameters of the rope-net system according to claim 1, characterized in that, The probability distribution model of the random parameters is a normal distribution, a uniform distribution, or a Beta distribution; where: Normal distribution: applicable to parameters of natural variation; Uniform distribution: suitable for situations where the parameter range is known but there is no prior information; Beta distribution: suitable for scenarios with bounded and asymmetric parameters.

5. The method for optimizing the launching parameters of the rope-net system according to claim 1, characterized in that, The basis functions for multivariable orthogonal polynomials are: Hermite polynomials, Legendre polynomials, or Jacobi polynomials.

6. The method for optimizing the launching parameters of the rope-net system according to claim 1, characterized in that, Using the statistical estimation parameters as the optimization objective, a multi-objective optimization method is employed to optimize the parameters to obtain the optimal rope net launch parameters, including: Select the uncertain parameters and their ranges from the parameters to be optimized; A multi-objective optimization algorithm is used to optimize the parameters to be optimized. The objective function of each optimization is the statistically estimated parameters output by the polynomial chaotic expansion model. The front solution set is obtained, and the optimal solution in the front solution set is selected as the optimal rope net launch parameters.

7. A device for optimizing launch parameters of a rope-net system, characterized in that, The device comprises: The design parameter and performance index selection module is used to select the key design parameters and performance indexes of the rope net launching system. The sensitivity analysis module is used to perform sensitivity analysis on the key design parameters using the orthogonal experimental sensitivity analysis method, and to select parameters that affect the rope net capture performance as parameters to be optimized from the key design parameters; The polynomial chaotic expansion model uncertainty analysis module is used to perform uncertainty analysis using a polynomial chaotic expansion model based on the key design parameters and the performance indicators, and to obtain statistical estimation parameters for subsequent uncertainty optimization. The parameter optimization module is used to optimize the parameters to be optimized by employing a multi-objective optimization method with the statistical estimation parameters as the optimization objective, so as to obtain the optimal rope net launch parameters; The polynomial chaotic expansion model uncertainty analysis module is further used to divide the key design parameters into deterministic parameters and random parameters. The deterministic parameters include: the mass of the traction mass block and the launch angle of the traction mass block; the random parameters include: the initial launch velocity of the traction mass block and the diameter of the internal rope. A probability distribution model for the initial launch velocity of the traction mass block and the diameter of the internal rope is selected; the random response of the random parameters of the objective function is subjected to a polynomial chaotic expansion; the expression for the polynomial chaotic expansion is: in, The random response of the system objective function. An orthogonal polynomial is formed by combining the basis functions of a multivariable orthogonal polynomial. Here are the coefficients of the polynomial chaotic expansion. P The number of terms after truncation. It is the product of the basis functions of the orthogonal polynomials; Generated based on the key design parameter distribution of the rope net launch system N sample points ,in At each sampling point, the rope-net dynamics model is invoked to obtain the corresponding rope-net launch performance index, and the expression of the polynomial chaotic expansion is transformed into an overdetermined system of equations. The overdetermined system of equations is solved by the least squares method to obtain the polynomial chaotic expansion coefficients. Based on the polynomial chaotic expansion coefficients, statistical estimation parameters for subsequent uncertain optimization are obtained. The statistical estimation parameters include: expectation and standard deviation.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the rope-net system launch parameter optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the rope-net system launch parameter optimization method according to any one of claims 1 to 6.

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