Design method and device of spaceflight optical load and computer program product

Abstract

The invention relates to the technical field of spaceflight, in particular to a design method and device of a spaceflight optical load and a computer program product. The design method of the spaceflight optical load comprises the following steps: determining the emphasis dimension of a to-be-designed optical load according to a received task demand, and determining an optical weight, a mechanical weight and a thermal control weight based on the emphasis dimension; determining a performance optimization objective function according to the optical weight, the mechanical weight, the thermal control weight, the optical index, the mechanical index and the thermal control index; and performing simulation design on the optical load based on a simulation model and the task demand, and optimizing the simulation design at least based on the performance optimization objective function.

Description

Technical Field

[0001] This disclosure relates to the field of aerospace technology, and in particular to a design method, apparatus and computer program product for aerospace optical payloads. Background Technology

[0002] As the core equipment for acquiring space information, aerospace optical payloads need to work stably in extreme space environments such as vacuum, alternating high and low temperatures, and strong radiation.

[0003] In related technologies, the design phase of optical payloads primarily focuses on optical performance design, while mechanical and thermal design are considered separately. This leads to a situation where, for optical payloads, adjustments to the mechanical design may meet requirements, but the thermal design may not; conversely, adjustments to the thermal design may meet requirements, but the mechanical or optical design may fail to meet them. This results in frequent conflicts between lightweighting and thermal stability of the optical payload structure, and between thermal control effectiveness and optical component deformation.

[0004] The relevant technologies make it difficult to design optical payloads from multiple dimensions simultaneously, resulting in a complex, time-consuming, and costly process for designing suitable optical payloads. Summary of the Invention

[0005] To overcome the problems existing in related technologies, this disclosure provides a design method, device and computer program product for aerospace optical payloads, which can solve the above problems.

[0006] According to a first aspect of the present disclosure, a design method for aerospace optical payloads is provided. The method includes: determining the emphasis dimension of the optical payload to be designed based on received mission requirements, and determining optical weights, mechanical weights, and thermal control weights based on the emphasis dimension; determining a performance optimization objective function based on the optical weights, mechanical weights, and thermal control weights, as well as optical indicators, mechanical indicators, and thermal control indicators; performing simulation design on the optical payload based on a simulation model and the mission requirements, and optimizing the simulation design at least based on the performance optimization objective function.

[0007] According to a second aspect of the present disclosure, a design apparatus for aerospace optical payloads is provided. The apparatus includes: a weight determination unit configured to determine the emphasis dimension of the optical payload to be designed based on received mission requirements, and to determine optical weights, mechanical weights, and thermal control weights based on the emphasis dimension; a function determination unit configured to determine a performance optimization objective function based on the optical weights, mechanical weights, and thermal control weights, as well as optical indicators, mechanical indicators, and thermal control indicators; and an optimization unit configured to perform simulation design of the optical payload based on a simulation model and the mission requirements, and to optimize the simulation design at least based on the performance optimization objective function.

[0008] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor and a memory; the memory being used to store a computer program; and the processor being used to execute the design method for aerospace optical payloads as described in the first aspect by invoking the computer program.

[0009] According to a fourth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in the first aspect.

[0010] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0011] This disclosure allows for the determination of the emphasis dimensions of the optical payload based on the received task requirements, and the determination of the weights of the optical payload in the three dimensions of optical design, mechanical design, and thermal control design based on these emphasis dimensions. After determining the weights, a performance optimization objective function is determined based on the optical, mechanical, and thermal control indicators, and then simulation design is performed based on this performance optimization objective function. Since this performance optimization objective function is determined based on three dimensions, when designing the optical payload, its performance in the optical, mechanical, and thermal control aspects can be comprehensively considered, enabling the designed optical payload to simultaneously meet the requirements of all three aspects. This simplifies the design process, saves design time, reduces the number of modifications and adjustments, and lowers costs.

[0012] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0013] The accompanying drawings, which are incorporated in and form part of this disclosure, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0014] Figure 1 This disclosure is a schematic flowchart illustrating a design method for an aerospace optical payload according to an exemplary embodiment.

[0015] Figure 2 This disclosure is a schematic flowchart illustrating the design of an aerospace optical payload according to an exemplary embodiment.

[0016] Figure 3 This disclosure is a block diagram illustrating a design apparatus for an aerospace optical payload according to an exemplary embodiment.

[0017] Figure 4 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0018] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0019] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0020] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0021] With the rapid development of aerospace remote sensing, deep space exploration, and other fields towards high resolution, long on-orbit lifespan, and large-scale applications, the synergistic optimization of performance, energy consumption, and cost of aerospace optical payloads, as core equipment for space information acquisition, has become a key factor in the success or failure of space missions. These payloads need to operate stably in extreme space environments such as vacuum, alternating high and low temperatures, and intense radiation, while simultaneously meeting engineering requirements for lightweight design, low energy consumption, and low cost. This poses stringent challenges to the collaborative design of multiple disciplines, including optics, mechanics, and thermal control.

[0022] There is a strong coupling relationship between optical imaging performance, mechanical structure reliability, and thermal control environment adaptability, while energy consumption level and total life cycle cost directly constrain the on-orbit operating time and engineering feasibility of the payload. Different optical system architectures, mechanical structure design schemes, and thermal control strategies will result in payloads exhibiting drastically different performance characteristics, energy consumption features, and cost investments. On the other hand, the differences in energy consumption in various scenarios such as on-orbit operation / standby and ground testing / environmental trials, as well as the cumulative costs of research and development, manufacturing, and operation and maintenance, further increase the design difficulty.

[0023] However, the relevant design methods have significant limitations: the traditional design method's separate process of "optical design-mechanical design-thermal control design" lacks a systematic consideration of the multidisciplinary coupling relationship, leading to frequent conflicts between structural lightweighting and thermal stability, and between thermal control effect and optical component deformation; thermal control design mostly adopts a single passive or active strategy, making it difficult to balance temperature control accuracy and energy consumption optimization. Although some infrared remote sensing cameras achieve multi-load temperature control, they suffer from weight redundancy and high cost; energy consumption control only focuses on a single on-orbit operating condition, lacking full-scenario coverage of ground testing and on-orbit operation, making it difficult to accurately predict the risk of energy consumption exceeding limits; cost assessment is limited to the single aspect of manufacturing costs, failing to cover the entire life cycle costs such as simulation tool investment in the R&D stage and on-orbit measurement and control energy consumption, resulting in prominent cost overruns.

[0024] Meanwhile, cost assessment, as a core evaluation indicator of the economics of aerospace engineering, is indispensable for payload design optimization. However, in current integrated optical-mechanical-thermal design, cost assessment is often limited to the single aspect of manufacturing costs, lacking full lifecycle coverage of simulation tool investment during the R&D phase, design manpower costs, and on-orbit measurement and control energy consumption and fault maintenance costs, leading to significant cost overruns. Furthermore, existing design iterative optimization lacks a targeted feedback mechanism, resulting in a vague understanding of the root causes of performance, energy consumption, and cost deficiencies, and strong tendency for blind design modifications.

[0025] Multiphysics coupled simulation and multidimensional constraint-based collaborative optimization are key technical approaches to solving the aforementioned problems. However, in aerospace optical payload design, the accuracy of simulation analysis based on the full-link coupling of optical-mechanical-thermal systems is currently insufficient, and a closed-loop system of "design-simulation-verification-evaluation-iteration" has not yet been formed. Research on related methods for quantifying coupling effects and energy consumption-cost collaborative optimization mechanisms remains scarce. The reliance on high-precision manufacturing in traditional design leads to long payload development cycles and high costs. How to break the industry pain point of "high-precision manufacturing = high cost + long cycle" through design innovation has become an urgent problem to be solved.

[0026] To address the aforementioned technical problems, this disclosure proposes a design method for aerospace optical payloads.

[0027] Figure 1 This is a schematic flowchart illustrating a design method for aerospace optical payloads according to embodiments of the present disclosure. The design method for aerospace optical payloads can be performed by electronic devices.

[0028] like Figure 1 As shown, the design methods for aerospace optical payloads include: In step S101, the emphasis dimension of the optical payload to be designed is determined according to the received task requirements, and the optical weight, mechanical weight and thermal control weight are determined based on the emphasis dimension. In step S102, the performance optimization objective function is determined based on the optical weight, mechanical weight, and thermal control weight, as well as the optical index, mechanical index, and thermal control index. In step S103, the optical payload is simulated and designed based on the simulation model and the task requirements, and the simulation design is optimized based at least on the performance optimization objective function.

[0029] In some embodiments, mission requirements include space mission requirements.

[0030] Customers can specify requirements and needs for various parameters of the optical payload to be designed based on the task requirements.

[0031] Based on mission requirements, the orbital parameters, imaging performance indicators, and on-orbit lifetime of the optical payload can be clearly defined, forming a quantified list. For example, the orbital type of the optical payload (e.g., sun-synchronous orbit, low Earth orbit, etc.) can be determined, the panchromatic / multispectral imaging resolution can be specified, and the on-orbit design lifetime can be defined. The quantified mission indicator list may include MTF ≥ 0.2 (Modulation Transfer Function) and a wavelength range of 450–900 nm, etc.

[0032] Based on mission requirements, constraint thresholds such as launch mechanical environment and on-orbit thermal environment can be defined to clarify the constraints on optical payloads. For example, the mechanical environment and on-orbit thermal environment can be defined, including the range of external heat flux variation and orbital period. Environmental adaptability thresholds can also be specified, such as structural fundamental frequency ≥100Hz and optical component temperature fluctuation ≤±1℃.

[0033] Based on mission requirements, the aerospace industry standards and interface protocols to be followed with the satellite platform can be determined. By adhering to aerospace industry standards such as GJB and ECSS, and combining them with satellite platform interface protocols (e.g., mechanical, electrical, and thermal interfaces), the design compliance of optical payloads can be ensured.

[0034] In some embodiments, the emphasis dimension of the optical payload to be designed is determined according to the received task requirements, and optical weight, mechanical weight and thermal control weight are determined based on the emphasis dimension.

[0035] Based on the received task requirements, the emphasis of the optical payload can be determined. The task requirements proposed by the customer have multiple requirements for the optical payload in multiple dimensions (such as optics, mechanics, and thermal control). Therefore, based on the customer's task requirements, the emphasis among these multiple dimensions can be clarified when designing the optical payload.

[0036] And determine the optical weights based on the emphasis dimension. Mechanical weight and thermal control weight These three weights are used to couple optics, mechanics, and thermal control during the design phase, so that the simulation design for optical loads can simultaneously take into account the needs of these three aspects.

[0037] In some embodiments, a performance optimization objective function is determined based on the optical weights, mechanical weights, and thermal control weights, as well as the optical, mechanical, and thermal control indices.

[0038] Optical metrics are used to measure the performance of the optical load in the optical dimension, mechanical metrics are used to measure the performance of the optical load in the mechanical dimension, and thermal control metrics are used to measure the performance of the optical load in the thermal control dimension.

[0039] Based on optical, mechanical, and thermal performance indicators, the performance of optical payloads in these three dimensions can be reflected to a certain extent.

[0040] Different mission requirements impose different performance demands on optical payloads across various dimensions. Therefore, a performance optimization objective function for the optical payload can be determined based on the weights and indices of the three dimensions identified from the mission requirements. This function is used to optimize the performance of the optical payload, ensuring that the designed optical payload meets the needs of the space mission.

[0041] In some embodiments, the optical payload is designed based on a simulation model and the task requirements, and the simulation design is optimized at least based on the performance optimization objective function.

[0042] The optical payload is designed through simulation, and the simulation results are optimized based on the performance optimization objective function.

[0043] In related technologies, the optimization of optical payload simulation design is performed from a single dimension. This involves optimizing the simulation design results separately using optical, mechanical, and thermal optimization objective functions, followed by adjustments. However, this optimization method means that while the simulation design may reach its optimal design in one dimension, the other two dimensions may not have met their optimization goals. Furthermore, optimizing the other two dimensions can cause changes in the objective function values ​​for any of those dimensions, meaning the optimized result may not be optimal for any single dimension. Therefore, the entire design optimization process is quite complex and requires repeated modifications and adjustments.

[0044] The method proposed in this disclosure couples the three dimensions, so that the optimization goal of the simulation design of the optical payload is not that all three dimensions are optimal, but that the optimization goal is achieved in the whole after the three dimensions are coupled. This reduces the design difficulty, reduces the complexity of the optimization design process, and improves the optimization efficiency.

[0045] In some embodiments, the optical payload is designed based on a simulation model.

[0046] A parallel collaborative design model can be adopted, combined with multi-physics coupling simulation, to achieve deep integration between disciplines.

[0047] In the optical dimension, Zemax or Code V software can be used to complete the initial architecture design of the optical system to meet the optical requirements of the mission. For example, parameters such as lens / mirror shape, focal length, and F-number can be optimized, and an optical system report and ray tracing model can be output.

[0048] In the mechanical dimension, three-dimensional structural models can be created using SOLIDWORKS or UGNX. For example, lightweight, high-stiffness materials such as aluminum alloys and carbon fiber composites can be selected to achieve lightweight design, perform optimization and mechanical simulation, and output structural analysis reports and lightweight assessment results.

[0049] In terms of thermal control, ANSYS or SINDA / FLUINT can be used to build thermal control models. Thermal control design can employ active and passive collaborative strategies such as distributed heating films, multi-layer insulation components, and heat pipes, outputting temperature field distribution reports and thermal control power consumption predictions.

[0050] In some embodiments, the simulation design of the optical payload is optimized based on multiphysics coupling simulation.

[0051] A full-link coupled model of "optics-mechanics-thermal" can be established to simulate changes in optical image quality, structural deformation and thermal control system response under extreme on-orbit conditions, and a genetic algorithm can be used to solve the multi-objective optimization problem.

[0052] The simulation design can be optimized using the performance optimization objective function based on the method proposed in this disclosure. The optimization objective is to achieve the optimal simulation design across the three comprehensive dimensions of optics, mechanics, and thermal control.

[0053] In some embodiments, the optical parameters include image quality degradation, the mechanical parameters include structural bending stiffness, and the thermal control parameters include thermal power consumption; the expression for the performance optimization objective function F is:

[0054] in, For optical weights, For mechanical weight, For thermal control weights; denoted as , where is the image quality degradation caused by thermal-structural coupling; K is the structural bending stiffness. E is the elastic modulus, I is the moment of inertia, and L is the support length; This refers to the thermal control power consumption (total power consumption of the thermal control system).

[0055] The performance optimization objective function can be constructed by weighted summation with the optimization objectives of minimizing image quality degradation, maximizing structural bending stiffness, and minimizing thermal control power consumption.

[0056] Alternatively, preset parameters can be determined based on task requirements, and then substituted into the performance optimization objective function based on the preset parameters to determine the corresponding optimization target value, and then optimization can be performed using the optimization target value as the target.

[0057] In this performance optimization objective function, The weighting coefficients are determined based on task requirements normalization. For example, if the task requirements emphasize the optical dimension, the weighting coefficients can be set as follows: , , When the task requirements focus on thermal control, it can make , , .

[0058] It should be noted that the method for determining the weight coefficients based on the emphasis of the task requirements can be based on the experience of technical personnel or on model algorithms, and this disclosure does not impose any restrictions on this.

[0059] In some embodiments, multiphysics coupling simulation also includes thermal-structural coupling relationships.

[0060] Structural thermal deformation caused by thermal load satisfy:

[0061] in, This refers to the coefficient of linear expansion of the material, such as the coefficient of thermal expansion of aluminum alloy 6061. . The change in temperature The length is the structural feature length.

[0062] In some embodiments, multiphysics coupling simulation also includes structure-optical coupling relationships.

[0063] Optical wavefront error W and MTF degradation caused by structural deformation Relationship:

[0064] Where k is an empirical coefficient (e.g., it can be 3.2), and W is the RMS wavefront error.

[0065] Based on any of the multi-physics coupled simulation designs of the present disclosure, the optical payload can be optimized using a performance optimization objective function. Since the performance optimization objective function includes indices for optical, mechanical, and thermal control dimensions, its value can be used to perform coupled optimization of the simulation design in these three dimensions.

[0066] In some embodiments, optimizing the simulation design based on the performance optimization objective function includes: determining an optimization threshold for the performance optimization objective function according to the task requirements; optimizing the simulation design such that the value of the performance optimization objective function of the optimized simulation design is not greater than the optimization threshold.

[0067] Based on the quantified parameters in the task requirements, an optimization threshold for the performance optimization objective function can be determined. This ensures that the simulation design reaching the optimization threshold meets the task requirements.

[0068] For example, in the quantitative input indicators of task requirements, the image quality degradation of optical payload (e.g., less than 0.1), structural bending stiffness, and thermal control power consumption (e.g., less than 50 watts) are clearly limited. These thresholds are then substituted into the performance optimization objective function to determine the optimization threshold (e.g., the optimization threshold is calculated to be 0.6).

[0069] When the values ​​of the simulation design parameters determined based on the performance optimization objective function are not greater than the optimization threshold, it can be considered that the simulation design has met the task requirements and performance optimization can be stopped.

[0070] In some embodiments, the method further includes: determining the energy consumption and / or cost of the simulation design; optimizing the simulation design at least based on the performance optimization objective function includes: optimizing the simulation design according to a comprehensive deviation function, wherein the comprehensive deviation function is determined by summing the energy consumption deviation and / or cost deviation with the performance deviation, wherein the performance deviation is determined according to the performance optimization objective function, the energy consumption deviation is determined based on the energy consumption of the simulation design and the target energy consumption, and the cost deviation is determined based on the cost of the simulation design and the target cost.

[0071] For aerospace optical payloads, some scenarios may not only have requirements for the performance of the optical payload, but also for other aspects of the optical payload (such as energy consumption and cost). For example, commercial satellites have certain cost requirements; if the cost is too high, they cannot be profitable and may even result in losses.

[0072] Therefore, after the optical payload is designed based on performance, the energy consumption and / or cost of the simulation design can be determined to further optimize and improve the simulation design in terms of energy consumption and / or cost.

[0073] In some embodiments, performance, energy consumption, and cost can be coupled to determine a comprehensive deviation function, and then the simulation design can be optimized based on the comprehensive deviation function.

[0074] A comprehensive deviation function can be determined, and the simulation design can be optimized in multiple aspects based on the comprehensive deviation function. For example, the comprehensive deviation function may include energy consumption deviation and performance deviation, or it may include cost deviation and performance deviation, or it may include energy consumption deviation, cost deviation and performance deviation, and this disclosure does not impose any limitations.

[0075] In some embodiments, the comprehensive deviation function The expression is:

[0076] in, These are weighting coefficients, corresponding to performance, energy consumption, and cost, respectively. .

[0077] The performance deviation is determined based on the performance optimization objective function. The energy consumption and target energy consumption are determined based on the simulation design to account for performance deviations. Cost deviation is determined based on the cost and target cost of the simulation design.

[0078] It should be noted that the weighting coefficients of the comprehensive deviation function can also be determined based on the type of task requirements. For example, if the task requirement is a high-resolution task, then the emphasis is on performance, and the corresponding weighting coefficients will be determined accordingly. , , If the task requirement is a business task, then the focus is on cost, and the corresponding... , , For missions requiring long-term on-orbit operation, the focus is on energy consumption, and the corresponding... , , .

[0079] Based on the comprehensive deviation function, the three indicators of performance, energy consumption and cost can be coordinated as a whole, and the target equilibrium point can be approached through closed-loop iterative optimization.

[0080] Figure 2 This is a schematic flowchart illustrating the design of an aerospace optical payload according to an embodiment of the present disclosure.

[0081] like Figure 2 As shown, based on the mission requirements, the performance requirements of the optical payload to be designed are determined. The performance requirements include three aspects: optics, mechanics, and thermal control.

[0082] Based on the determined performance requirements of the optical payload, the optical payload is designed through simulation, including optical design, mechanical design and thermal control design.

[0083] During the simulation design process, the simulation design is optimized based on the determined performance target optimization function until the determined simulation design can meet the task requirements in terms of performance and achieve the performance target.

[0084] If the performance meets the requirements, determine the cost and energy consumption of the simulation design, and determine the comprehensive deviation function based on the cost, performance and energy consumption. Then, optimize the simulation design again using the comprehensive deviation function.

[0085] If the overall deviation function does not meet the standard, the optical, mechanical and thermal control design of the optical load is adjusted based on the overall deviation function until the overall deviation function meets the standard and the optimization target is met, and then the simulation design results are output.

[0086] As can be seen, this disclosure first couples the optical, mechanical, and thermal control dimensions to comprehensively consider the relationships between these three dimensions during the performance optimization process of the optical payload, optimizing all three dimensions together and simplifying the process. Once the performance optimization meets the standards, a comprehensive deviation function is used to further optimize the optical, mechanical, and thermal control aspects of the optical payload simulation design, taking into account cost and energy consumption. This ensures that the simulation design results meet the performance requirements of the optical payload in the mission, as well as the cost and energy consumption requirements of the optical payload mission. It achieves synergistic minimization of energy consumption and cost while ensuring performance compliance, significantly improving the economy and sustainability of space missions.

[0087] The following section provides a detailed explanation of how to determine energy consumption, cost, energy consumption deviation, cost deviation, and performance deviation.

[0088] In some embodiments, the energy consumption of the simulation design can be determined based on the temperature field simulation model of the thermal control system.

[0089] Based on the temperature field simulation model of the thermal control system, and combined with the operating state parameters of each component of the payload, the power consumption level in the on-orbit operating state and / or standby state is quantitatively calculated, providing data basis for satellite energy allocation. After completing the energy consumption calculation, a prediction report (e.g., called the "On-Orbit Operating Condition Energy Consumption Prediction Report") can be output to clarify the energy consumption demand curves for different orbital stages.

[0090] Under operating conditions, core components such as optics, mechanics, and thermal control are all running at full load. The total energy consumption is the sum of the energy consumption of each component, calculated using the following formula:

[0091] The parameters were selected based on the rated operating power consumption of the optical payload assembly; P opt For the optical system, including the lens drive and filter switching unit, the operating power consumption is P. mech The mechanical drive unit, including the pointing mechanism and focusing mechanism, consumes energy; P thermal For thermal control systems, including the temperature control energy consumption of heating elements and chillers.

[0092] It should be noted that once the power consumption P is determined, the energy consumption E can be determined based on the duration t, and there is a correlation between power consumption P and energy consumption E. Therefore, the energy consumption discussed later is also determined based on power consumption, and the process and method of determining energy consumption based on power consumption will not be elaborated further.

[0093] In standby mode, core functions such as thermal control and mechanical posture maintenance are retained, while non-core components switch to low-power mode. The power consumption formula is as follows:

[0094] Among them, the low energy consumption parameter is based on the component's sleep mode test data; P mech,standby Energy consumption for attitude maintenance of the mechanical drive unit; P thermal,standby This refers to the basic insulation energy consumption of the thermal control system.

[0095] Based on the power consumption in standby mode and the power consumption in standby mode, combined with the working time and standby time of the optical payload, the total power consumption of the simulation design of this optical payload can be determined. .

[0096] Energy consumption deviation in the comprehensive deviation function The formula is:

[0097] in, The total energy consumption for the nth round of optimization design of the optical payload. The target energy consumption.

[0098] It should be noted that this disclosure can use the comprehensive deviation function to optimize the design during the simulation design phase, and it can also be used to optimize the design during the verification phase after producing a physical sample. Therefore, the total energy consumption can be the total energy consumption determined by the algorithm during the simulation phase, and it can be the actual total energy consumption obtained by experimental measurement during the verification phase. This disclosure does not impose any restrictions on this.

[0099] In some embodiments, the determination of the energy consumption of the simulation design is based on a first formula, and the method further includes: producing a sample according to the simulation design and measuring the actual energy consumption of the sample; determining an energy consumption verification error based on the energy consumption in the simulation design stage and the actual energy consumption; and adjusting the first formula if the energy consumption verification error is greater than a first threshold.

[0100] The energy consumption determined during the simulation design phase is based on the first formula, which may contain errors. Therefore, after producing a sample of the simulation design, the sample can be tested experimentally to verify the design, and the first formula for determining the energy consumption can be optimized based on the results of the experimental tests.

[0101] For example, in the verification phase, including ground-based energy consumption verification, specialized equipment such as high- and low-temperature vacuum chambers are needed to simulate the complex on-orbit environment. Measured data is used to calibrate the simulation model and verify its accuracy. Testing equipment such as vacuum thermal test chambers and high-precision power analyzers can be configured, and samples can be tested according to different scenarios. For instance, integrated testing can be completed at a normal temperature of 25°C to obtain basic ground operating / standby energy consumption data. Then, thermal cycling tests from -10°C to 45°C can simulate on-orbit temperature fluctuations, testing the power consumption variation at different temperatures. Finally, vibration-thermal coupling tests are conducted to simultaneously replicate the on-orbit thermal environment and verify the power consumption stability under extreme conditions. After completing the tests and obtaining the measured data, the energy consumption verification error formula can be used for further verification.

[0102] Based on this formula, the deviation between simulation design and actual measurement can be quantified. If the result... If the value is ≤ the first threshold (e.g., 10%), then the design requirements are met (e.g., the design requirement that the energy consumption prediction accuracy is greater than or equal to 90%), which further improves the matching degree between the simulation model and the actual working conditions.

[0103] And if the result If the first threshold is reached, the first formula can be optimized based on the result, so that the first formula for determining energy consumption is closer to the actual test results. This makes the energy consumption determined in the simulation design stage more in line with the actual situation, thereby reducing the problem of substandard energy consumption of produced samples, which leads to increased costs and longer processing time.

[0104] In some embodiments, an energy consumption report can be output after the energy consumption is determined.

[0105] After integrating on-orbit simulation and ground test data, an energy consumption report (such as the "Full-Condition Energy Consumption Report") is output. The core content covers multi-dimensional information: including specific power consumption values ​​for operation and standby in on-orbit mode, as well as power consumption variation curves of the payload at different orbital stages, providing a basis for orbital energy scheduling; comparing measured power consumption under normal temperature and thermal cycling environments in ground tests; and providing energy consumption optimization suggestions. The report must clearly define the energy demand thresholds for each mode, with an energy consumption prediction accuracy of no less than 90%, providing a precise reference for satellite platform energy allocation. This is the core basis for ensuring payload-platform energy matching and guaranteeing stable on-orbit energy adaptation.

[0106] In some embodiments, the cost of simulation design can be determined based on a three-dimensional cost model (second formula).

[0107] Based on the core principle of full-process cost breakdown and quantitative model calculation, this system constructs a three-dimensional cost model covering "R&D-manufacturing-on-orbit operation and maintenance" to achieve precise cost control of the entire lifecycle of the payload from design to on-orbit operation. This three-dimensional cost model can break down the cost of the entire lifecycle into three independent yet related dimensions, integrate the costs of each dimension through formulaic calculation, and verify the accuracy of the calculation to ensure data reliability.

[0108] For example, a three-dimensional cost model can break down the entire life cycle cost into three key stages: research and development, manufacturing, and on-orbit operation and maintenance, with each stage corresponding to a clear cost accounting scope.

[0109] During the R&D phase, cost accounting focuses on resource investment in the design and verification stages. For example, the accounting scope includes licensing fees for professional design software such as optical / mechanical / thermal control software, procurement costs of multiphysics simulation tools, human resource costs of the design team, and costs for processing and testing prototypes, which are the upfront investments required to ensure that load performance meets standards.

[0110] During the manufacturing phase, cost accounting covers all production stages, from parts processing to load integration. For example, the accounting scope includes the processing costs of precision optical components, the manufacturing and lightweighting costs of mechanical structures, the procurement costs of thermal control components, and the assembly, adjustment, and performance testing costs of optomechanical systems. These are the direct costs of transforming design schemes into physical loads.

[0111] During the on-orbit operation and maintenance phase, cost accounting focuses on the ongoing investment in optical payloads during their on-orbit operation. For example, the accounting scope includes the energy consumption costs of measurement and control in the payload's on-orbit working / standby modes, the costs of fault diagnosis and remote maintenance, the cost of spare parts reserves and on-orbit replacement of key components, etc., which are the subsequent support costs to ensure the payload's on-orbit lifespan.

[0112] Total lifecycle cost (TWC) is the sum of the costs of the three phases: research and development, manufacturing, and on-orbit operation and maintenance. The formula is as follows:

[0113] in, For research and development costs, To manufacturing costs, This refers to the cost of on-orbit operation and maintenance; however, the costs at each stage need to be further broken down into the sum of specific input items.

[0114] For example, R&D costs correspond to ,in For tool procurement costs, To design labor costs, Cost of experimental verification.

[0115] For example, manufacturing costs correspond ,in For parts processing costs, To reduce assembly and adjustment costs, For component procurement costs.

[0116] For example, the cost of on-orbit operation and maintenance corresponds to ,in For on-orbit energy consumption costs, To maintain costs, For spare parts costs.

[0117] Cost deviation in the overall deviation function The calculation formula is:

[0118] in, The target cost can be determined based on task requirements; Let be the cost of the nth round.

[0119] It should be noted that, similar to the energy consumption deviation, this disclosure can use the comprehensive deviation function to optimize the design during the simulation design phase, and it can also be used to optimize the design during the verification phase after producing a physical sample. Therefore, the total cost can be the total cost determined by the algorithm during the simulation phase, and it can be the actual total cost obtained from experimental calculations during the verification phase; this disclosure does not impose any restrictions on this.

[0120] In some embodiments, the determination of the cost of the simulation design is based on a second formula, and the method further includes: producing a sample according to the simulation design and determining the actual cost of the sample; determining a cost verification error based on the cost in the simulation design stage and the actual cost; and adjusting the second formula if the cost verification error is greater than a second threshold.

[0121] To ensure the reliability of cost accounting results, the error between the calculated value and the actual cost (cost verification error) can be quantified using a cost accuracy verification formula. The formula is as follows:

[0122] in, The cost value is calculated based on the second formula. The cost value determined by actual measurements during the verification phase.

[0123] If the cost verification error is no greater than the second threshold (e.g., 5%), it indicates that the corresponding accounting accuracy is no less than 95%. After completing the accounting and accuracy verification, a cost report (e.g., a "Life Cycle Cost Analysis Report") can be output. The report can cover the accounting details of costs at each stage, the distribution of cost proportions, and the analysis of deviation ranges. Based on the cost structure, it can also propose targeted optimization suggestions, such as the reuse strategy of tools in the R&D stage and the direction of process cost reduction in the manufacturing stage. This provides a feasible reference for cost control throughout the entire life cycle of the load.

[0124] If the cost verification error is greater than the second threshold, it indicates that the second formula has a large error in calculating the cost. The second formula needs to be adjusted so that the calculation result of the second formula is closer to the actual cost, thereby avoiding the situation where the cost meets the standard in the simulation design stage, but the cost of the actual produced sample exceeds the standard.

[0125] In some embodiments, the performance of the simulation design can be determined based on a performance objective optimization function.

[0126] The performance of a simulation design can be determined through performance metrics. These metrics may include, but are not limited to, parameters such as MTF, distortion, signal-to-noise ratio, and noise-equivalent temperature difference (NETD). Furthermore, performance can be represented using a performance objective optimization function.

[0127] Performance Deviation The formula is:

[0128] in, The performance value is a simulation design value, which can be a normalized value of multiple performance indicators. For target performance.

[0129] During the simulation design phase, this performance value is determined based on the third formula; during the sample verification phase, this performance value is determined based on the measured data of the sample.

[0130] During the verification phase, if the difference between the measured performance value and the value determined by the third formula exceeds a certain threshold, the third formula needs to be adjusted.

[0131] Given the performance deviation, energy consumption deviation, and cost deviation, these three are normalized to determine the comprehensive deviation function.

[0132]

[0133] In typical technical scenarios, performance has a higher weighting, therefore , and You can take values ​​of 0.4, 0.3, and 0.3 in sequence.

[0134] It should be noted that the three deviation values ​​are only retained when the standard is not met; when the standard is met, the deviation value is 0.

[0135] The corresponding indicators for the target performance, target energy consumption, and target cost are determined as follows: Performance indicators, including whether MTF, distortion, and signal-to-noise ratio meet the standards; energy consumption indicators, including whether the total on-orbit energy consumption does not exceed the design limit; and cost indicators, including whether the total cost over the entire life cycle is within the budget.

[0136] The simulation design is optimized based on the comprehensive deviation function value.

[0137] In some embodiments, optimizing the simulation design based on the comprehensive deviation function includes: optimizing the mechanical and thermal control designs of the simulation design when the energy consumption deviation exceeds the optimization target; optimizing the mechanical and optical designs of the simulation design when the cost deviation exceeds the optimization target; and optimizing the optical design of the simulation design when the performance deviation exceeds the optimization target.

[0138] It should be noted that the simulation design of optical payloads, including the design of optical, mechanical, and thermal dimensions, will affect performance, energy consumption, and cost. Therefore, if any deviation exceeds the optimization target, optimization can be achieved by adjusting the design of these three dimensions.

[0139] However, the degree to which performance, energy consumption, and cost are affected by optical design, mechanical design, and thermal control design varies.

[0140] For example, the performance of an optical payload is mainly determined by the optical design. Therefore, if the performance deviation exceeds the optimization target, it can be fed back to the optical design unit to optimize the optical system parameters and improve the optical design. Energy consumption is mainly determined by thermal control and mechanical design. Therefore, if the energy consumption deviation exceeds the optimization target, the thermal control strategy or mechanical structure layout can be adjusted to achieve a better optimization effect. And if the cost deviation exceeds the optimization target, it can be fed back to the mechanical and optical design modules to promote the lightweighting of the optical payload or the simplification of the process, thereby reducing the cost deviation.

[0141] In some embodiments, if any deviation exceeds the optimization target, the sensitivity coefficient corresponding to any deviation can be determined, and then the deviation can be optimized based on the sensitivity coefficient.

[0142] Sensitivity coefficient (These correspond to performance P, energy consumption E, and cost C, respectively) Control the adjustment range of each indicator. For example, the sensitivity coefficient can be set to a range of 0.1 to 0.3.

[0143] The optimization formula for any deviation is:

[0144] Here, n+1 represents the (n+1)th round, and the value of this round is determined by optimization based on the value of the nth round.

[0145] In some embodiments, the method further includes: terminating optimization and outputting the simulation design when the value of the comprehensive deviation function is less than a convergence threshold; and / or terminating optimization and outputting the simulation design when the change in the optimized index of the simulation design is less than a set threshold.

[0146] The iteration terminates when all three indicators—performance, energy consumption, and cost—meet the task requirements, and the overall deviation function value is [value missing]. (ε is the convergence threshold, which can be 0.05~0.1), or the change of each index in two consecutive iterations is less than the set threshold, such as performance change <2%, cost change <3%, energy consumption change <3%.

[0147] After optimization, the final design results are output. Simultaneously, the optimization process should focus on non-compliant indicators by normalizing the deviation, and the weights and sensitivity coefficients should be dynamically adjusted based on the task context and technical feasibility.

[0148] Based on the above embodiments, this disclosure has the following beneficial technical effects: Deep multidisciplinary collaborative innovation: Breaking through the limitations of traditional separate design, the interaction between disciplines is quantified through full-link coupled simulation. For the first time, a collaborative optimization criterion for "optics-mechanical-thermal control" parameters is established, effectively solving the performance conflict problem and improving the overall reliability of the load by more than 30%. Full-scenario energy consumption management system: For the first time, it has achieved full coverage verification of energy consumption in both on-orbit and ground scenarios, established a dynamic mapping relationship between energy consumption and design parameters, and achieved an energy consumption prediction accuracy of over 90%, providing a scientific basis for on-orbit energy consumption control; Full lifecycle cost control: Construct a three-dimensional cost assessment model that covers the entire process from R&D to operation and maintenance, with a cost accounting accuracy of 95%, which is more comprehensive than the traditional single manufacturing cost assessment and significantly reduces the risk of cost overruns; Iterative feedback efficiency: Based on the ternary constraint judgment, a precise feedback path is established to avoid blind modifications, shorten the R&D cycle by 20%-25%, and greatly improve the efficiency of engineering applications.

[0149] Corresponding to the embodiments of the design method for aerospace optical payloads disclosed herein, this disclosure also provides embodiments of the design apparatus for corresponding aerospace optical payloads.

[0150] Please see Figure 3 , Figure 3 This is a block diagram of a design device for aerospace optical payloads according to one embodiment of this disclosure. Figure 3 As shown, the design device for aerospace optical payloads includes: The weight determination unit 310 is configured to determine the emphasis dimension of the optical payload to be designed according to the received task requirements, and to determine the optical weight, mechanical weight and thermal control weight based on the emphasis dimension. The function determination unit 320 is configured to determine the performance optimization objective function based on the optical weights, mechanical weights, and thermal control weights, as well as the optical, mechanical, and thermal control indices. The optimization unit 330 is configured to perform simulation design of the optical payload based on the simulation model and the task requirements, and to optimize the simulation design based at least on the performance optimization objective function.

[0151] In some embodiments, the optical parameters include image quality degradation, the mechanical parameters include structural bending stiffness, and the thermal control parameters include thermal power consumption; the expression for the performance optimization objective function F is:

[0152] in, For optical weights, For mechanical weight, For thermal control weights; Where K is the image quality degradation, and K is the structural bending stiffness. This refers to the power consumption for thermal control.

[0153] In some embodiments, optimizing the simulation design based on the performance optimization objective function includes: determining an optimization threshold for the performance optimization objective function according to the task requirements; optimizing the simulation design such that the value of the performance optimization objective function of the optimized simulation design is not greater than the optimization threshold.

[0154] In some embodiments, the apparatus is further configured to: determine the energy consumption and / or cost of the simulation design; the optimization of the simulation design based at least on the performance optimization objective function includes: optimizing the simulation design according to a comprehensive deviation function, wherein the comprehensive deviation function is determined by summing the energy consumption deviation and / or cost deviation with the performance deviation, wherein the performance deviation is determined according to the performance optimization objective function, the energy consumption deviation is determined based on the energy consumption of the simulation design and the target energy consumption, and the cost deviation is determined based on the cost of the simulation design and the target cost.

[0155] In some embodiments, the determination of the energy consumption of the simulation design is based on a first formula, and the method further includes: producing a sample according to the simulation design and measuring the actual energy consumption of the sample; determining an energy consumption verification error based on the energy consumption in the simulation design stage and the actual energy consumption; and adjusting the first formula if the energy consumption verification error is greater than a first threshold.

[0156] In some embodiments, the determination of the cost of the simulation design is based on a second formula, and the apparatus is further configured to: produce a sample according to the simulation design and determine the actual cost of the sample; determine a cost verification error based on the cost in the simulation design stage and the actual cost; and adjust the second formula if the cost verification error is greater than a second threshold.

[0157] In some embodiments, optimizing the simulation design based on the comprehensive deviation function includes: optimizing the mechanical and thermal control designs of the simulation design when the energy consumption deviation exceeds the optimization target; optimizing the mechanical and optical designs of the simulation design when the cost deviation exceeds the optimization target; and optimizing the optical design of the simulation design when the performance deviation exceeds the optimization target.

[0158] In some embodiments, the apparatus is further configured to: terminate optimization and output the simulation design when the value of the comprehensive deviation function is less than a convergence threshold; and / or terminate optimization and output the simulation design when the change in the performance index after the simulation design optimization is less than a set threshold.

[0159] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0160] Embodiments of this disclosure also provide an electronic device, including: a processor and a memory; the memory for storing a computer program; and the processor for executing a design method for aerospace optical payloads as described in any of the above embodiments by invoking the computer program.

[0161] Embodiments of this disclosure also propose a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the design method for aerospace optical payloads as described in any of the above embodiments.

[0162] Embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements the methods described in any of the foregoing embodiments.

[0163] Please see Figure 4 , Figure 4This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system).

[0164] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0165] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0166] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0167] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0168] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0169] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0170] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

[0171] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0172] The methods and apparatus provided in the embodiments of this disclosure have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this disclosure. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this disclosure. Therefore, the content of this specification should not be construed as a limitation of this disclosure.

Claims

1. A method of designing a space optical payload, characterized by, The method comprises: determining a dimension of emphasis of the optical payload to be designed according to the received task requirement, and determining optical weight, mechanical weight and thermal control weight based on the dimension of emphasis; determining a performance optimization objective function according to the optical weight, the mechanical weight and the thermal control weight, and optical indicators, mechanical indicators and thermal control indicators; performing simulation design on the optical payload based on a simulation model and the task requirement, and optimizing the simulation design based on at least the performance optimization objective function.

2. The method of claim 1, wherein: the optical indicators comprise image quality degradation, the mechanical indicators comprise structural bending stiffness, and the thermal control indicators comprise thermal control power consumption; an expression of the performance optimization objective function F is: wherein, is an optical weight, is a mechanical weight, is a thermal control weight; K is the amount of image degradation, K is the structural bending stiffness, Pth is the thermal control power consumption.

3. The method of claim 1, wherein, the optimization of the simulation design based on the performance optimization objective function comprises: determining an optimization threshold of the performance optimization objective function according to the task requirement; optimizing the simulation design, and a value of the performance optimization objective function of the optimized simulation design is not greater than the optimization threshold.

4. The method of claim 1, wherein: the method further comprises determining energy consumption and / or cost of the simulation design; the optimization of the simulation design based on at least the performance optimization objective function comprises: optimizing the simulation design according to a comprehensive deviation function, wherein the comprehensive deviation function is determined according to summation of energy consumption deviation and / or cost deviation and performance deviation, wherein the performance deviation is determined according to the performance optimization objective function, the energy consumption deviation is determined based on energy consumption of the simulation design and target energy consumption, and the cost deviation is determined based on cost of the simulation design and target cost.

5. The method of claim 4, wherein, the determination of the energy consumption of the simulation design is based on a first formula, and the method further comprises: producing a sample according to the simulation design, and measuring actual energy consumption of the sample; determining an energy consumption verification error based on the energy consumption in the simulation design stage and the actual energy consumption, and adjusting the first formula if the energy consumption verification error is greater than a first threshold.

6. The method of claim 4, wherein, the determination of the cost of the simulation design is based on a second formula, and the method further comprises: producing a sample according to the simulation design, and determining actual cost of the sample; determining a cost verification error based on the cost in the simulation design stage and the actual cost, and adjusting the second formula if the cost verification error is greater than a second threshold.

7. The method of claim 4, wherein, the optimization of the simulation design according to the comprehensive deviation function comprises: optimizing mechanical design and thermal control design of the simulation design if the energy consumption deviation exceeds an optimization target; optimizing mechanical design and optical design of the simulation design if the cost deviation exceeds an optimization target; optimizing optical design of the simulation design if the performance deviation exceeds an optimization target.

8. The method of claim 4, wherein, the method further comprises: ending optimization and outputting the simulation design if a value of the comprehensive deviation function is less than a convergence threshold; and / or, In a case where the index of the simulation design after optimization changes less than a set threshold, the optimization is ended, and the simulation design is output.

9. An apparatus for designing a space optical payload, characterized by, The device comprises: a weight determination unit configured to determine a dimension of emphasis of the optical payload to be designed according to the received task requirement, and determine optical weight, mechanical weight and thermal control weight based on the dimension of emphasis; a function determination unit configured to determine a performance optimization objective function according to the optical weight, the mechanical weight and the thermal control weight, and optical indexes, mechanical indexes and thermal control indexes; an optimization unit configured to perform simulation design on the optical payload based on a simulation model and the task requirement, and perform optimization on the simulation design based on at least the performance optimization objective function.

10. A computer program product, characterised in that, The computer program is executed by a processor to implement the design method of the space optical payload as described in any one of claims 1 to 8.

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