Rope net system launching parameter optimization method and device, electronic equipment and storage medium

Abstract

The invention relates to a rope net system launching parameter optimization method and device, electronic equipment and a storage medium. According to the method, aiming at the problem of high optimization dimension caused by a nonlinear coupling relationship of rope net launching parameters, an orthogonal experiment sensitivity analysis method is adopted, high-sensitivity launching parameters are selected, and the subsequent optimization and experiment cost is reduced. Then, polynomial chaos expansion is introduced to establish a probability mapping model of parameter randomness and system response, and the problem that the influence of random factors cannot be evaluated by traditional deterministic analysis is solved. And finally, searching is performed in the parameter space after dimension reduction through a multi-objective optimization algorithm, and robustness optimization is performed on the rope net by taking quantized uncertain parameters as optimization objectives, so that the robustness of the rope net is improved. According to the method, through progressive processing of parameter dimension reduction, uncertainty quantification and robust optimization, the reliability of the rope net launching system is systematically improved on the premise of ensuring the calculation efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of parameter optimization, and in particular to a method, device, electronic device and storage medium for optimizing launch parameters of a rope net system. Background Art

[0002] With the rapid development of aerospace technology, the number of global space launch missions has increased exponentially, leading to a sharp increase 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 has posed new challenges to low-altitude security, and the demand for counter-drone systems is becoming increasingly urgent. Against this backdrop, rope net interception technology, with its unique non-contact capture advantages and its low-cost, high-security, and environmentally adaptable nature, has become a research hotspot in the fields of space debris removal and low-altitude security.

[0003] However, existing rope net interception systems face technical bottlenecks, including unstable capture efficiency. Specifically, these bottlenecks are: 1) In terms of parameter sensitivity, key launch parameters such as muzzle velocity, projectile angle, and launch block mass exhibit complex nonlinear coupling relationships, making traditional trial-and-error methods difficult to optimize. 2) In terms of system reliability, uncertainties such as rope net manufacturing errors and material parameter errors affect the capture performance of the rope net. Traditional deterministic analysis methods are unable to effectively quantify the comprehensive impact of parameter uncertainty on the capture success rate.

[0004] The interception and capture effectiveness of a rope net system is significantly sensitive to launch parameters, necessitating a systematic evaluation using a combination of experimental and simulation methods. However, due to the numerous parameters involved, the traditional, piecemeal verification approach is inefficient and resource-intensive. Furthermore, the significant environmental uncertainty inherent in actual launch environments necessitates the development of robust optimization models to enhance the robustness of parameter combinations.

[0005] The existing technology has the following shortcomings: First, traditional parameter optimization methods face the dimensionality disaster problem caused by highly nonlinear coupling parameters (initial velocity, projection angle, mass of the mass block, etc.). The parameter combination space expands exponentially, resulting in low computational efficiency, which seriously restricts the simulation and experimental process; second, the existing model does not quantify uncertain factors such as rope net processing process deviation and material parameter discreteness. The deterministic analysis method does not consider the random distribution characteristics of parameters and the probabilistic characteristics of environmental disturbances, exposing significant robustness defects. Summary of the Invention

[0006] Based on this, it is necessary to provide a method, device, electronic device and storage medium for optimizing the launch parameters of a rope net system in response to the above technical problems.

[0007] A method for optimizing launch parameters of a rope net system, the method comprising: Select the key design parameters and performance indicators of the rope net launching system.

[0008] An orthogonal experimental sensitivity analysis method is used to perform sensitivity analysis on the key design parameters, and parameters that affect the capture performance of the rope net are screened out from the key design parameters as parameters to be optimized.

[0009] According to key design parameters and performance indicators, the polynomial chaos expansion model is used to perform uncertainty analysis and obtain statistical estimation parameters for subsequent uncertainty optimization.

[0010] Taking the statistical estimation parameters as the optimization objectives, the multi-objective optimization method is used to optimize the parameters to be optimized and the optimal rope net launching parameters are obtained.

[0011] In one embodiment, the key design parameters include: inner rope diameter, reinforcing rope diameter, solid unit mass, traction mass launch angle, initial launch velocity and mass.

[0012] Performance indicators include: effective hovering time and effective deployment time.

[0013] In one embodiment, an orthogonal experimental sensitivity analysis method is used to perform sensitivity analysis on the key design parameters, and parameters that affect the capture performance of the rope net are screened out from the key design parameters as parameters to be optimized, including: Each key design parameter is divided into three levels, and a suitable orthogonal table is selected to construct the orthogonal experimental design matrix.

[0014] For each combination of factors, the rope net launch model is run, the corresponding effective hovering time and effective deployment time are calculated and recorded as the response value, and the orthogonal test results are obtained.

[0015] The range analysis of the orthogonal test results was performed to obtain the range of each key design parameter.

[0016] According to the extreme differences of key design parameters, the launching parameters of the rope net launching system are ranked, and a preset number of launching parameters with the highest ranking are selected as the parameters to be optimized.

[0017] In one embodiment, uncertainty analysis is performed using a polynomial chaos expansion model based on transmission parameters and performance indicators to obtain statistical estimation parameters for subsequent uncertainty 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 internal rope diameter.

[0018] A probability distribution model of the initial launch velocity and the internal rope diameter of the traction mass is selected.

[0019] The random response of the random parameters of the objective function is expanded by polynomial chaos; the expression of polynomial chaos expansion is:

[0020] in, is the random response of the system objective function, is an orthogonal polynomial composed of the basis functions of a multivariate orthogonal polynomial. is the polynomial chaos expansion coefficient, P is the number of items after truncation, is the product of orthogonal polynomial basis functions.

[0021] Generate based on the key design parameter distribution of the rope net launching system N Sample points ,in .

[0022] The rope net dynamics model is called at each sampling point to obtain the corresponding rope net launch performance index, and the expression of the polynomial chaos expansion is converted into an overdetermined set of equations.

[0023] The overdetermined equations are solved by the least square method and the polynomial chaos expansion coefficients are obtained.

[0024] According to the polynomial chaos expansion coefficient, the statistical estimation parameters used for subsequent uncertain optimization are obtained; the statistical estimation parameters include expectation and standard deviation.

[0025] 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 parameters with natural variation.

[0026] Uniform distribution: Suitable for situations where the parameter range is known but there is no prior information.

[0027] Beta distribution: Suitable for scenarios where parameters are bounded and asymmetric.

[0028] In one embodiment, the basis functions of the multivariate orthogonal polynomials are Hermite polynomials, Legendre polynomials, or Jacobi polynomials.

[0029] In one embodiment, the statistical estimation parameters are used as optimization targets, and a multi-objective optimization method is used to optimize the parameters to be optimized to obtain the optimal rope net launch parameters, including: Select the uncertain parameters and their ranges among the parameters to be optimized.

[0030] A multi-objective optimization algorithm is used to optimize the parameters to be optimized. The objective function of each optimization is the statistical estimation parameter output by the polynomial chaos expansion model. The frontier solution set is obtained, and the optimal solution is selected from the frontier solution set as the optimal rope net launching parameter.

[0031] A device for optimizing launch parameters of a rope net system, comprising: Design parameter and performance index selection module, used to select key design parameters and performance indicators of the rope net launching system; A sensitivity analysis module is used to perform sensitivity analysis on the key design parameters using an orthogonal experimental sensitivity analysis method, and select parameters that affect the capture performance of the rope net from the key design parameters as parameters to be optimized; A polynomial chaos expansion model uncertainty analysis module is used to perform uncertainty analysis using a polynomial chaos expansion model based on key design parameters and the performance indicators to obtain statistical estimation parameters for subsequent uncertainty optimization; The parameter optimization module is used to optimize the parameters to be optimized by taking the statistical estimation parameters as the optimization target, and adopts the multi-objective optimization method to optimize the parameters to obtain the optimal rope net launching parameters.

[0032] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any of the above-mentioned methods for optimizing the launch parameters of a rope net system are implemented.

[0033] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for optimizing the launch parameters of a rope net system.

[0034] The aforementioned method, device, electronic device, and storage medium for optimizing the launch parameters of a rope net system address the problem of high optimization dimensionality due to the nonlinear coupling relationship between the launch parameters of the rope net. By employing an orthogonal experimental sensitivity analysis method, the method selects highly sensitive launch parameters, thereby reducing subsequent optimization and experimental costs. Subsequently, a polynomial chaos expansion is introduced to establish a probabilistic mapping model between parameter randomness and system response, addressing 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 reduced-dimensional parameter space, using the quantified uncertain parameters as optimization targets to optimize the robustness of the rope net and improve its robustness. This method systematically improves the reliability of the rope net launch system while ensuring computational efficiency through a progressive process of parameter dimensionality reduction, uncertainty quantification, and robust optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of a flow chart of a method for optimizing launch parameters of a rope net system in one embodiment; Figure 2A design diagram of a rope net launcher in another embodiment, wherein (a) is a schematic diagram of a state ready to launch, and (b) is a schematic diagram of a launch state; Figure 3 A structural diagram of a novel rope net in another embodiment; Figure 4 FIG. 1 is a diagram showing the internal structure of an electronic device in one embodiment. DETAILED DESCRIPTION

[0036] 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.

[0037] This application proposes a method for optimizing the launch parameters of a rope-net system. Through a comprehensive process design encompassing "parameter screening → sensitivity analysis → uncertainty modeling → robust optimization," this method addresses the technical shortcomings of traditional methods in nonlinear coupling parameter optimization, random factor quantification, and system robustness enhancement, forming a reusable methodology and engineering tool. This method significantly improves parameter optimization efficiency and system anti-interference capabilities, providing theoretical support for the development of non-cooperative space target capture equipment.

[0038] In one embodiment, Figure 1 As shown, a method for optimizing launch parameters of a rope net system is provided, the method comprising the following steps: Step 100: Select key design parameters and performance indicators of the rope net launching system.

[0039] Specifically, this application is based on the engineering application requirements and technical specifications of the rope net launch system, combined with the system design experience criteria, and determines the key design parameters as follows: internal rope diameter , Reinforcement rope diameter , solid element mass , traction mass launch angle , initial launch velocity and its quality Among them, key design parameters are a subset of launch parameters, which are selected according to the requirements of a specific engineering application. Key design parameters include launch parameters.

[0040] In view of the core requirements of the space interception and capture mission (dynamic deployment performance of the rope net and hovering efficiency), the target parameters of the sensitivity analysis are selected as: effective hovering time and effective expansion time .

[0041] Step 102: Perform sensitivity analysis on the key design parameters using an orthogonal experiment sensitivity analysis method, and select parameters that affect the capture performance of the rope net from the key design parameters as parameters to be optimized.

[0042] Specifically, the existing technology has the following shortcomings: First, the traditional parameter optimization method faces the dimensionality disaster problem caused by highly nonlinear coupling parameters (initial velocity, projection angle, mass of the mass block, etc.). The parameter combination space expands exponentially, resulting in low computational efficiency, which seriously restricts the simulation and experimental process.

[0043] Therefore, this application introduces the orthogonal experimental sensitivity analysis method to perform sensitivity analysis on the rope net launch parameters, realize the screening of key control variables, complete the parameter space dimensionality reduction, and reduce the cost and time required for simulation and experiment.

[0044] In addition to the orthogonal experiment sensitivity analysis method, the Sobol sensitivity analysis method can also be used to perform sensitivity analysis on key design parameters.

[0045] The target parameter of sensitivity analysis is the performance index of the rope net. There are many performance indexes of the rope net. The performance index is selected as the target parameter of sensitivity analysis according to the specific task requirements.

[0046] Step 104: Based on the key design parameters and performance indicators, a polynomial chaos expansion model is used to perform uncertainty analysis to obtain statistical estimation parameters for subsequent uncertainty optimization.

[0047] Specifically, existing models fail to quantify uncertainties such as rope net processing deviations and material parameter discreteness. Deterministic analysis methods also fail to consider the random distribution of parameters and the probabilistic characteristics of environmental disturbances, exposing significant robustness deficiencies. Therefore, we introduce a polynomial chaos expansion uncertainty analysis method to quantify the uncertainty of rope net launches. This establishes an uncertainty propagation model that accurately characterizes the probabilistic response characteristics of random parameters, resolving the problem of traditional deterministic analysis' inability to assess the impact of random factors.

[0048] In addition to the polynomial chaos expansion method, uncertainty analysis methods can also use Monte Carlo method, interval analysis, etc.

[0049] Step 106: Taking the statistical estimation parameters as the optimization targets, a multi-objective optimization method is used to optimize the parameters to be optimized, and the optimal rope net launching parameters are obtained.

[0050] Specifically, a multi-objective optimization algorithm is used to search in the parameter space after dimensionality reduction, and the quantified uncertain parameters are used as optimization targets to optimize the robustness of the rope net and improve the robustness of the rope net.

[0051] In the above-mentioned method for optimizing the launch parameters of a rope net system, the method addresses the problem of high optimization dimensionality caused by the nonlinear coupling relationship between the rope net launch parameters. By using an orthogonal experimental sensitivity analysis method, high-sensitivity launch parameters are selected to reduce subsequent optimization and experimental costs. Subsequently, a polynomial chaos expansion is introduced to establish a probabilistic mapping model between parameter randomness and system response, solving the problem that traditional deterministic analysis cannot evaluate the influence of random factors. Finally, a multi-objective optimization algorithm is used to search within the parameter space after dimensionality reduction. With the quantified uncertain parameters as the optimization target, the rope net is robustly optimized to improve its robustness. This method systematically improves the reliability of the rope net launch system while ensuring computational efficiency through the progressive processing of parameter dimensionality reduction, uncertainty quantification, and robust optimization.

[0052] In one embodiment, the key design parameters in step 100 include: internal rope diameter, reinforcing rope diameter, solid unit mass, traction mass launch angle, initial launch velocity and its mass; performance indicators include: effective hovering time and effective deployment time.

[0053] Specifically, effective airtime : After the rope net is deployed, the time interval during which its projected area in the interception direction continues to exceed the design threshold, indicating the system's ability to maintain an effective capture state.

[0054] Effective expansion time : The time it takes for the rope net to be launched and the projected area to reach the design threshold for the first time reflects the dynamic response characteristics of the system to quickly form an effective interception situation.

[0055] In one embodiment, step 102 includes: dividing each key design parameter into three levels, selecting a suitable orthogonal table to construct an orthogonal experimental design matrix; for each group of factor combinations, running the rope net launch model, calculating the corresponding effective hovering time and effective deployment time and recording them as response values ​​to obtain the orthogonal test results; performing range analysis on the orthogonal test results to obtain the range of each key design parameter; according to the range of the key design parameters, sorting the launch parameters of the rope net launch system, and selecting a preset number of launch parameters with the highest ranking as parameters to be optimized.

[0056] Specifically, the specific steps of parameter sensitivity analysis of the orthogonal experimental method include: (1) Orthogonal experimental design matrix construction Based on the six key design parameters described above, each parameter was divided into three levels, and an appropriate orthogonal array was selected to construct the experimental design matrix. This orthogonal array, containing 18 groups of experiments, comprehensively and evenly covers the parameter space, limiting the number of experiments while maintaining analytical accuracy. The orthogonal design matrix for this experiment is shown in Table 1.

[0057] Table 1 Orthogonal design table of the experiment

[0058] Among them, each factor has three levels, coded as 1, 2, and 3 respectively. According to the actual design, 1, 2, and 3 are mapped to specific physical parameter values. The designed level value mapping table is shown in Table 2.

[0059] Table 2 Designed level value mapping table

[0060] For each factor combination, the rope net launch model was run, and the corresponding effective hang time and effective deployment time were calculated and recorded as the response value. The recorded results are shown in Table 3.

[0061] Table 3 records the results

[0062] (2) Sensitivity index calculation The range analysis of the orthogonal experimental results was performed to evaluate the sensitivity of each parameter to the performance index.

[0063] The range of each parameter xᵢ can be calculated from the mean of the response at different levels: ; in, Representation parameters In the Level The average response value under . The larger the value, the more significant the impact of this parameter on performance.

[0064] The results of range analysis are shown in Table 4.

[0065] Table 4 Range analysis calculation results

[0066] The calculation results of the minimum expansion time are shown in Table 5.

[0067] Table 5 Calculation results of minimum expansion time

[0068] (3) According to the extreme differences of the launch parameters of each rope net launch system calculated in the previous step, the launch parameters of the rope net launch system are sorted, and the parameters with greater influence are selected for subsequent optimization analysis.

[0069] In one embodiment, step 104 includes: dividing the key design parameters into deterministic parameters, namely, the mass of the traction mass block and the launch angle of the traction mass block, and random parameters, namely, the initial launch velocity of the traction mass block and the internal rope diameter; selecting the probability distribution model of the random parameters, namely, the initial launch velocity of the traction mass block and the internal rope diameter, as a uniform distribution; and converting the random response of the random parameters of the objective function into the random response of the random parameters. Perform polynomial chaos expansion, the expression of polynomial chaos expansion is: (9) in, is the random response of the system's objective function, is an orthogonal polynomial composed of the basis functions of a multivariate orthogonal polynomial. is the polynomial chaos expansion coefficient, is the number of items after truncation, is the product of orthogonal polynomial basis functions.

[0070] Generate based on the key design parameter distribution of the rope net launching system N Sample points ,in ; Call the rope net dynamics model at each sampling point to obtain the corresponding rope net launch performance index, and convert the expression of the polynomial chaos expansion into an overdetermined set of equations; solve the overdetermined set of equations by the least squares method to obtain the polynomial chaos expansion coefficient; according to the polynomial chaos expansion coefficient, obtain the statistical estimation parameters used for subsequent uncertain optimization; the statistical estimation parameters include: expectation and standard deviation.

[0071] Specifically, the specific steps of uncertainty analysis of polynomial chaos expansion model include: analysis (1) Probabilistic modeling of uncertain parameters. Specifically, the following are included: Parameter classification: According to the key design parameters of the rope net launch, they are divided into deterministic parameters (including: the mass of the traction mass block, the launch angle of the traction mass block) and random parameters (including: the initial launch velocity of the traction mass block, the internal rope diameter). The probability distribution of the random parameters can be selected as: Normal distribution: Suitable for parameters with natural variation (such as material defects).

[0072] Uniform distribution: Suitable for situations where the parameter range is known but no prior information is available (such as manufacturing tolerances).

[0073] Beta distribution: Applicable to scenarios with bounded and asymmetric parameters (such as ambient temperature range).

[0074] As a preference, the probability distribution models of the random parameter traction mass block initial launch velocity and the internal rope diameter are both uniformly distributed; (2) Polynomial Chaos Expansion (PCE). Specifically, the expression of the polynomial chaos expansion of the random response of the system objective function is shown in the above polynomial chaos expansion expression.

[0075] The basis functions of the orthogonal polynomials can be selected according to the distribution of random parameters: Hermite polynomials for normal distribution, Legendre polynomials for uniform distribution and Jacobi polynomials for Beta distribution. Usually, the truncation strategy for chaotic polynomials can be truncated according to the highest order: limit the total order of the polynomial p , number of items ( is the parameter dimension).

[0076] (3) Random space discretization and sampling. Specifically including: generating Sample points , must meet To meet the fitting accuracy.

[0077] (4) Deterministic model solution and coefficient calculation. Specifically including: Call the rope net dynamics model to obtain the rope net launch performance index . Use regression method to solve overdetermined equations by least squares method.

[0078] (5) Post-processing and uncertainty quantification. Specifically, it includes: using the solved polynomial chaos expansion coefficients to obtain statistical estimation parameters for subsequent uncertainty optimization. The statistical estimation parameters include expectation and standard deviation, specifically: (10) (11) in, For expectations, is the standard deviation.

[0079] In one embodiment, the probability distribution model of the random parameter is normal distribution, uniform distribution or Beta distribution; among them: normal distribution: applicable to naturally varying parameters; uniform distribution: applicable to situations where the parameter range is known but there is no prior information; Beta distribution: applicable to scenarios where the parameters are bounded and asymmetric.

[0080] In one embodiment, the basis functions of the multivariate orthogonal polynomials are Hermite polynomials, Legendre polynomials, or Jacobi polynomials.

[0081] In one embodiment, step 106 includes: selecting uncertain parameters and their ranges among the parameters to be optimized; optimizing the parameters to be optimized using a multi-objective optimization algorithm, wherein the objective function of each optimization is a statistical estimation parameter output by a polynomial chaos expansion model, obtaining a frontier solution set, and selecting the optimal solution from the frontier solution set as the optimal rope net launch parameter.

[0082] Rope net launcher design diagram Figure 2 As shown, Figure 2 (a) is a schematic diagram of the state ready for launch. Figure 2 (b) is a schematic diagram of the launching state. Figure 3 shown.

[0083] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, 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.

[0084] 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 chaos expansion model uncertainty analysis module, and a parameter optimization module, wherein: 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.

[0085] The sensitivity analysis module is used to perform sensitivity analysis on the key design parameters using an orthogonal experimental sensitivity analysis method, and select parameters that affect the capture performance of the rope net from the key design parameters as parameters to be optimized.

[0086] The polynomial chaos expansion model uncertainty analysis module is used to perform uncertainty analysis based on key design parameters and performance indicators using the polynomial chaos expansion model to obtain statistical estimation parameters for subsequent uncertainty optimization.

[0087] The parameter optimization module is used to optimize the parameters to be optimized by taking the statistical estimation parameters as the optimization target and adopting the multi-objective optimization method to obtain the optimal rope net launching parameters.

[0088] 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; performance indicators include: effective hovering time and effective deployment time.

[0089] In one embodiment, the sensitivity analysis module is also used to divide each key design parameter into three levels, select a suitable orthogonal table to construct an orthogonal experimental design matrix; for each group of factor combinations, run the rope net launch model, calculate the corresponding effective hovering time and effective deployment time and record them as response values ​​to obtain the orthogonal test results; perform range analysis on the orthogonal test results to obtain the range of each key design parameter; according to the range of the key design parameters, sort the launch parameters of the rope net launch system, and select a preset number of launch parameters with the highest ranking as parameters to be optimized.

[0090] In one embodiment, the uncertainty analysis module of the polynomial chaos expansion model 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 internal rope diameter; the probability distribution model of the initial launch velocity of the traction mass block and the internal rope diameter is selected; the expression of the polynomial chaos expansion is shown in formula (9); according to the distribution of the key design parameters of the rope net launch system, the random parameters are generated. N Sample points ,in ; Call the rope net dynamics model at each sampling point to obtain the corresponding rope net launch performance index, and convert the expression of the polynomial chaos expansion into an overdetermined set of equations; solve the overdetermined set of equations by the least squares method to obtain the polynomial chaos expansion coefficient; according to the polynomial chaos expansion coefficient, obtain the statistical estimation parameters used for subsequent uncertain optimization; the statistical estimation parameters include: expectation and standard deviation.

[0091] In one embodiment, the probability distribution model of the random parameters in the uncertainty analysis module of the polynomial chaos expansion model is a normal distribution, a uniform distribution or a Beta distribution; among which: the normal distribution is applicable to naturally varying parameters; the uniform distribution is applicable to situations where the parameter range is known but there is no prior information; the Beta distribution is applicable to scenarios where the parameters are bounded and asymmetric.

[0092] In one embodiment, the basis functions of the multivariate orthogonal polynomials in the uncertainty analysis module of the polynomial chaos expansion model are: Hermite polynomials, Legendre polynomials or Jacobi polynomials.

[0093] In one embodiment, the parameter optimization module is also 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 statistical estimation parameter output by the polynomial chaos expansion model, to obtain a frontier solution set, and the optimal solution is selected from the frontier solution set as the optimal rope net launching parameter.

[0094] The specific definitions of the rope net system launch parameter optimization device can be found in the definitions 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 in whole or in part via software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0095] In one embodiment, an electronic device is provided. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The electronic device includes a processor, an internal memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for optimizing the launch parameters of a rope net system is implemented. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a key, trackball, or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad, or mouse.

[0096] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0097] In one embodiment, an electronic device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiment when executing the computer program.

[0098] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0099] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may 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 many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double 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.

[0100] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0101] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the scope of the present application, and such modifications and improvements are all within the scope of protection of the present application.

Claims

1. A method for optimizing launch parameters of a rope net system, characterized in that: The method comprises: Select key design parameters and performance indicators of the rope net launching system; An orthogonal experimental sensitivity analysis method is used to perform sensitivity analysis on the key design parameters, and parameters that affect the capture performance of the rope net are selected from the key design parameters as parameters to be optimized; Based on the key design parameters and the performance indicators, a polynomial chaos expansion model is used to perform uncertainty analysis to obtain statistical estimation parameters for subsequent uncertainty optimization; Taking the statistical estimation parameters as optimization targets, a multi-objective optimization method is adopted to optimize the parameters to be optimized, and the optimal rope net launching parameters are obtained.

2. The method for optimizing the launch parameters of a 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 launch angle, initial launch velocity and mass; The performance indicators include: effective hovering time and effective deployment time.

3. The method for optimizing the launch parameters of a rope net system according to claim 1, characterized in that: An orthogonal experimental sensitivity analysis method is used to perform sensitivity analysis on the key design parameters, and parameters that affect the rope net capture performance are screened out from the key design parameters as parameters to be optimized, including: Divide each of the key design parameters into three levels, and select a suitable orthogonal table to construct an orthogonal experimental design matrix; For each factor combination, run the rope net launch model, calculate the corresponding effective hang time and effective deployment time and record them as response values ​​to obtain the orthogonal test results; Perform range analysis on the orthogonal test results to obtain the range of each key design parameter; According to the extreme differences of key design parameters, the launching parameters of the rope net launching system are ranked, and a preset number of launching parameters with the highest ranking are selected as parameters to be optimized.

4. The method for optimizing the launch parameters of a rope net system according to claim 1, characterized in that: Based on the key design parameters and the performance indicators, a polynomial chaos expansion model is used to perform uncertainty analysis to obtain statistical estimation parameters for subsequent uncertainty 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 internal rope diameter; Selecting a probability distribution model for the initial launch velocity of the traction mass and the internal rope diameter; The random response of the random parameters of the objective function is expanded by polynomial chaos; the expression of polynomial chaos expansion is: in, is the random response of the system objective function, is an orthogonal polynomial composed of the basis functions of a multivariate orthogonal polynomial. is the polynomial chaos expansion coefficient, P is the number of items after truncation, is the product of orthogonal polynomial basis functions; Generate based on the key design parameter distribution of the rope net launching system N Sample points ,in ; The rope net dynamics model is called at each sampling point to obtain the corresponding rope net launch performance index, and the expression of polynomial chaos expansion is converted into an overdetermined equation group; Solving the overdetermined equations by the least square method to obtain polynomial chaos expansion coefficients; According to the polynomial chaos expansion coefficient, statistical estimation parameters for subsequent uncertain optimization are obtained; the statistical estimation parameters include: expectation and standard deviation.

5. The method for optimizing the launch parameters of a rope net system according to claim 4, characterized in that: The probability distribution model of the random parameter is normal distribution, uniform distribution, or Beta distribution; where: Normal distribution: suitable for naturally varying parameters; Uniform distribution: suitable for situations where the parameter range is known but there is no prior information; Beta distribution: Suitable for scenarios where parameters are bounded and asymmetric.

6. The method for optimizing the launch parameters of a rope net system according to claim 4, characterized in that: The basis functions of the multivariate orthogonal polynomials are: Hermite polynomials, Legendre polynomials or Jacobi polynomials.

7. The method for optimizing the launch parameters of a rope net system according to claim 1, characterized in that: Taking the statistical estimation parameters as the optimization targets, a multi-objective optimization method is used to optimize the parameters to be optimized, and the optimal rope net launching parameters are obtained, including: Select the 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. The objective function of each optimization is the statistical estimation parameter output by the polynomial chaos expansion model. The frontier solution set is obtained, and the optimal solution is selected from the frontier solution set as the optimal rope net launching parameter.

8. A device for optimizing launch parameters of a rope net system, characterized in that: The device comprises: Design parameter and performance index selection module, used to select key design parameters and performance indicators of the rope net launching system; A sensitivity analysis module is used to perform sensitivity analysis on the key design parameters using an orthogonal experimental sensitivity analysis method, and select parameters that affect the capture performance of the rope net from the key design parameters as parameters to be optimized; A polynomial chaos expansion model uncertainty analysis module is used to perform uncertainty analysis using a polynomial chaos expansion model based on the key design parameters and the performance indicators to obtain statistical estimation parameters for subsequent uncertainty optimization; The parameter optimization module is used to optimize the parameters to be optimized by using the statistical estimation parameters as the optimization target and adopting a multi-objective optimization method to obtain the optimal rope net launching parameters.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for optimizing the launching parameters of the rope net system according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the launching parameters of a rope net system according to any one of claims 1 to 7 are implemented.

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