A dual-band adaptive infrared stealth structure selection system and method

Abstract

This invention belongs to the field of stealth structure design technology, specifically relating to a dual-band adaptive infrared stealth structure selection system and method. This invention constructs a dual-band coupled emissivity tensor field aligned with multiphysics constraints, combines this with a benchmark structural parameter constraint dataset to complete inverse mapping screening, and then obtains the optimal parameter combination through simulation verification and neighborhood search. This solves the problem that traditional methods cannot accurately characterize the coupling relationship between temperature, heat flux, and multispectral emissivity. It can achieve coordinated control of mid-wave and long-wave infrared emissivity under dynamic thermal boundary conditions, and the output structural parameters are more in line with actual working conditions. This effectively improves the generalization ability of the stealth performance of the dual-band adaptive infrared stealth structure in real thermal environments, while also taking into account the stealth constraints and structural thermal safety requirements of the visible light band. It can adapt to the actual needs of dual-band infrared stealth in complex and ever-changing battlefield environments.

Description

Technical Field

[0001] This invention belongs to the field of stealth structure design technology, specifically relating to a dual-band adaptive infrared stealth structure selection system and method. Background Technology

[0002] With the continuous evolution of infrared detection technology and the accelerated construction of multispectral collaborative reconnaissance systems, traditional single-band infrared stealth structures are no longer able to cope with the severe challenges of simultaneous detection in mid-wave and long-wave infrared dual bands. Especially in complex and ever-changing battlefield environments, the dynamic mismatch between the target surface radiation characteristics and the background infrared radiation field is becoming increasingly prominent, leading to a significant decrease in stealth effectiveness. Therefore, there is an urgent need for a dual-band adaptive infrared stealth structure selection system with real-time environmental perception and dynamic reconstruction capabilities.

[0003] While existing technologies offer some stealth structure design methods based on static material libraries and empirical formulas, they lack the ability to model the thermodynamic evolution of the target. They cannot accurately characterize the coupled response relationship between surface temperature gradient, local heat flux density, and multispectral emissivity, and are even less capable of achieving coordinated control of mid-wave and long-wave infrared emissivity under dynamic thermal boundary conditions. Consequently, the output structural parameters often deviate from actual operating requirements, resulting in severely insufficient generalization ability of stealth performance in real thermal environments. Therefore, this solution provides a dual-band adaptive infrared stealth structure selection method to address these issues. Summary of the Invention

[0004] The purpose of this invention is to provide a dual-band adaptive infrared stealth structure selection system and method, which can achieve coordinated control of mid-wave and long-wave dual-band emissivity through multi-physics field coupling modeling and online inverse mapping optimization.

[0005] The specific technical solution adopted by this invention is as follows: A method for selecting a dual-band adaptive infrared stealth structure includes: Acquire infrared radiation characteristic data and ambient temperature field distribution of the target area, and determine the target emissivity distribution required for each spatial location point in the target area in the mid-wave infrared band and long-wave infrared band based on the infrared background radiation field and the platform's own temperature field; The target emissivity distribution is discretized and sampled in the spatial dimension to construct a dual-band coupled emissivity tensor field in the mid-wave infrared band and the long-wave infrared band, and the visible light reflectance spectral response and local heat flux density constraint boundary at the corresponding location points are extracted simultaneously. Obtain the known available alternative materials and the corresponding range of geometric parameters for each alternative material, and summarize them into a baseline structural parameter constraint dataset; Based on the dual-band coupled emissivity tensor field, visible light reflectance spectral response and local heat flux density constraint boundary, an inverse mapping is performed to the reference structural parameter constraint dataset to generate a parameter candidate set that satisfies the multiphysics constraints; The parameter candidate set is subjected to simulation verification, and the parameters that pass the simulation verification are recorded as the preferred parameter combinations. The parameter candidate set that fails the simulation verification is subjected to neighborhood search until a preferred parameter combination that can pass the simulation verification is found, or the search range reaches the search range threshold and then stops.

[0006] In a preferred embodiment, the step of determining the target emissivity distribution required for each spatial location point within the target area in the mid-wave infrared and long-wave infrared bands based on the infrared background radiation field and the platform's own temperature field includes: Obtain the average background radiation intensity of the infrared background radiation field in the mid-wave infrared band and the long-wave infrared band; The platform's own temperature field is obtained, and the ideal radiation intensity of each spatial location point in the target area at the corresponding temperature in the mid-wave infrared and long-wave infrared bands is calculated based on the platform's own temperature field. Constrained by the radiation characteristics of the preset target blending into the background in the infrared image, the target radiation intensity required for each spatial location point in the mid-wave infrared band and long-wave infrared band is determined by combining the average background radiation intensity data and the ideal radiation intensity. Based on the target radiation intensity and ideal radiation intensity of each spatial location point in the mid-wave infrared band and the long-wave infrared band, the target emissivity required in the mid-wave infrared band and the long-wave infrared band is calculated respectively, and recorded as the first target emissivity and the second target emissivity respectively. Spatial continuity correction and smoothing filtering are applied to the emissivity of the first and second targets to form a continuous and physically achievable target emissivity distribution within the target region.

[0007] In a preferred embodiment, the step of discretizing the target emissivity distribution in the spatial dimension and constructing a dual-band coupled emissivity tensor field in the mid-infrared and long-infrared bands includes: Based on the preset sampling resolution, a spatial sampling grid is established within the target area; At each spatial sampling grid node, the preprocessed target emissivity values ​​in the mid-wave infrared band and the long-wave infrared band are acquired synchronously. The emissivity values ​​of the mid-wave infrared target and the long-wave infrared target under each grid node are combined to form a two-dimensional emissivity vector; Based on all grid nodes of the spatial sampling grid and their corresponding two-dimensional emissivity vectors, a dual-band emissivity tensor field containing the coupling of the mid-wave infrared band and the long-wave infrared band is constructed on each grid node.

[0008] In a preferred embodiment, the step of extracting the visible light reflectance spectral response and the local heat flux density constraint boundary at the corresponding location point includes: Constraint extraction is performed synchronously on the spatial sampling grid nodes of the constructed dual-band coupled emissivity tensor field; At each grid node, obtain the reflectivity requirements of the target surface at the corresponding location within the preset key wavelength in the visible light band. At each grid node, based on preset thermal management requirements and structural thermal safety requirements, a range of steady-state heat flux density that is allowed to pass through the corresponding grid node is set, and the upper and lower limits of steady-state heat flux density are defined as local heat flux density constraint boundaries. The visible light reflectance spectral response data extracted at each grid node is associated with the local heat flux density constraint boundary, and stored in conjunction with the corresponding spatial coordinates and dual-band emissivity vector. The constraints of all grid nodes are summarized to form a multiphysics constraint dataset that is aligned with the dual-band coupled emissivity tensor field in both spatial and physical parameter dimensions.

[0009] In a preferred embodiment, the step of obtaining known selectable alternative materials and the corresponding range of geometric parameters for each alternative material, and summarizing them into a baseline structural parameter constraint dataset, includes: Based on the physical mechanism and structural implementation of dual-band infrared stealth, a multi-layer composite structure model is constructed, and the constituent levels of the multi-layer composite structure model are determined. Candidate materials that meet the constituent hierarchy are collected. Candidate materials include phase change materials, thermochromic materials, electrochromic emissivity materials, semiconductor materials with specific crystal orientations, metasurface configuration materials, and composite materials with specific photonic crystal structures. Define the independent control of candidate materials and the geometric parameters coupled with the structure. The geometric parameters include the thickness of the material layer, the grating period, the unit width, the nanostructure feature size, and the spacing between the multilayer structures. Based on the limits of material preparation process and the mechanical properties of the structure, determine the achievable range of values ​​for each geometric parameter. Based on candidate materials and their corresponding geometric parameter combinations, we pre-calculate or measure their key emissivity values ​​in the mid-infrared and long-infrared bands, as well as their reflectance spectral response in the visible light band. Based on a pre-set structural thermal property database, the thermal conductivity, specific heat capacity and thermal diffusivity of candidate materials within the target operating temperature range are obtained, and the correlation between geometric parameters and equivalent thermal resistance is established. By integrating the types, geometric parameter ranges, thermal conductivity, specific heat capacity, and thermal diffusivity of all candidate materials, a baseline structural parameter constraint dataset is formed.

[0010] In a preferred embodiment, the step of generating a candidate set of parameters that satisfies multiphysics constraints includes: Based on the parameter range of the candidate materials, the dual-band coupled emissivity tensor field, visible light reflectance spectral response and local heat flux density constraint boundary are used as screening conditions for structural parameters. Within the parameter range, by adjusting and combining different physical parameters, the predicted radiation and thermal performance corresponding to each group of physical parameters is evaluated one by one; A set of candidate initial parameters is formed by selecting a combination of physical parameters that simultaneously predict radiation and thermal performance, including the emissivity of mid-wave targets, the emissivity of long-wave targets, the visible light reflectivity, and the upper and lower limits of local heat flux density. Based on the preset processing accuracy standards and the fluctuation range of the usage environment, the system performs inspections, eliminates combinations that are within the allowable manufacturing deviation range and whose performance cannot remain stable under expected environmental changes, and generates a baseline structural parameter constraint dataset for simulation verification.

[0011] In a preferred embodiment, the step of performing simulation verification processing on the candidate parameter set includes: Multiphysics coupling simulation is performed on each combination of physical parameters in the parameter candidate set. Under the boundary conditions of set ambient temperature, convective heat transfer coefficient and internal heat source power, coupled heat transfer calculation is performed, and the temperature values ​​of each discrete spatial location point in the temperature field are output and recorded as the first reference parameter. Based on the first reference parameter, spectral radiance calculations are performed on the mid-wave infrared band and the long-wave infrared band, and the average emissivity value of each discrete spatial location point is output and recorded as the second reference parameter. Multiple sets of different verification conditions are set up, and heat transfer and radiation calculations are repeated under each set of verification conditions to generate a set of baseline parameters; The first and second benchmark parameters in the benchmark parameter set are compared for consistency to identify the dataset combinations that meet the benchmark structural parameter constraints, and the combinations that pass the consistency comparison are recorded as the preferred parameter combinations.

[0012] In a preferred embodiment, the step of performing a neighborhood search on the candidate set of parameters that have not passed simulation verification until a preferred combination of parameters that can pass simulation verification is found includes: Obtain the baseline parameter combination that fails the simulation verification, and output the deviation vectors of the first baseline parameter and the second baseline parameter under the baseline parameter combination, and record them as the first deviation vector and the second deviation vector respectively; Based on the direction and magnitude of the first and second deviation vectors, a parameter neighborhood search space is constructed. Centered on the current reference parameter combination, the parameter perturbation is performed with adaptive step size along the dominant direction of the deviation vector to generate several sets of neighborhood perturbation parameter combinations, and multiphysics coupling simulation and radiation performance evaluation are performed on each set of combinations. Convergence is assessed for simulation results of neighborhood perturbation parameter combinations. If any combination of perturbations satisfies the requirements of the baseline structural parameter constraint dataset, it is marked as a valid preferred parameter combination and the current neighborhood search is terminated. If none of the perturbation combinations meet the requirements, the neighborhood search step size is increased and perturbation combinations are regenerated until the convergence condition is met or the maximum number of iterations is reached.

[0013] This invention also provides a dual-band adaptive infrared stealth structure selection system, using the above-described dual-band adaptive infrared stealth structure selection method, comprising: The data acquisition module is used to acquire infrared radiation characteristic data and ambient temperature field distribution of the target area, and determine the target emissivity distribution required for each spatial location point in the target area in the mid-wave infrared band and long-wave infrared band based on the infrared background radiation field and the platform's own temperature field. The discrete sampling module is used to discretize the target emissivity distribution in the spatial dimension, construct a dual-band coupled emissivity tensor field in the mid-wave infrared band and the long-wave infrared band, and simultaneously extract the visible light reflectance spectral response and local heat flux density constraint boundary at the corresponding location point; The constraint determination module is used to obtain the known selectable alternative materials and the corresponding range of geometric parameters for the alternative materials, and summarize them into a baseline structural parameter constraint dataset. The parameter filtering module is used to perform inverse mapping to the reference structural parameter constraint dataset based on the dual-band coupled emissivity tensor field, visible light reflectance spectral response and local heat flux density constraint boundary, to generate a parameter candidate set that satisfies the multiphysics constraints. The verification module is used to perform simulation verification on the parameter candidate set, record the parameters that pass the simulation verification as the preferred parameter combination, and perform neighborhood search on the parameter candidate set that fails the simulation verification until a preferred parameter combination that can pass the simulation verification is found, or the search range reaches the search range threshold and then stops.

[0014] And, an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the dual-band adaptive infrared stealth structure selection method described above.

[0015] The technical effects achieved by this invention are as follows: This invention constructs a dual-band coupled emissivity tensor field aligned with multiphysics constraints, combines a benchmark structural parameter constraint dataset to complete inverse mapping screening, and then obtains the optimal parameter combination through simulation verification and neighborhood search. This solves the problem that traditional methods cannot accurately characterize the coupling relationship between temperature, heat flux, and multispectral emissivity. It can achieve coordinated control of mid-wave and long-wave infrared emissivity under dynamic thermal boundary conditions, and the output structural parameters are more in line with actual working conditions. It effectively improves the stealth performance generalization ability of dual-band adaptive infrared stealth structures in real thermal environments, while taking into account the stealth constraints and structural thermal safety requirements of the visible light band. It can adapt to the actual needs of dual-band infrared stealth in complex and ever-changing battlefield environments. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the system modules of the present invention; Figure 3 This is a schematic diagram of the electronic device structure of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0020] Please see Figure 1 As shown, this invention provides a method for selecting a dual-band adaptive infrared stealth structure, comprising: S1. Obtain infrared radiation characteristic data and ambient temperature field distribution of the target area, and determine the target emissivity distribution required for each spatial location point in the target area in the mid-wave infrared band and long-wave infrared band based on the infrared background radiation field and the platform's own temperature field. In step S1, during the design process of the stealth structure, it is necessary to first clarify the infrared radiation characteristics and environmental thermodynamic boundary conditions of the target area. This is generally applied to typical platforms such as aircraft, ships, or ground equipment. When selecting materials and parameters for the stealth structure, infrared radiation spectrum data and spatiotemporal evolution data of the environmental temperature field in the target area are collected first. Based on this, the required target emissivity distribution for each spatial location point in the target area is determined. The steps of determining the required target emissivity distribution for each spatial location point in the target area in the mid-wave infrared and long-wave infrared bands based on the infrared background radiation field and the platform's own temperature field include: Obtain the average background radiation intensity of the infrared background radiation field in the mid-wave infrared band and the long-wave infrared band; The platform's own temperature field is obtained, and the ideal radiation intensity of each spatial location point in the target area at the corresponding temperature in the mid-wave infrared and long-wave infrared bands is calculated based on the platform's own temperature field. Constrained by the radiation characteristics of the preset target blending into the background in the infrared image, the target radiation intensity required for each spatial location point in the mid-wave infrared band and long-wave infrared band is determined by combining the average background radiation intensity data and the ideal radiation intensity. Based on the target radiation intensity and ideal radiation intensity of each spatial location point in the mid-wave infrared band and the long-wave infrared band, the target emissivity required in the mid-wave infrared band and the long-wave infrared band is calculated respectively, and recorded as the first target emissivity and the second target emissivity respectively. Spatial continuity correction and smoothing filtering are applied to the emissivity of the first target and the emissivity of the second target to form a continuous and physically achievable target emissivity distribution within the target region; Specifically, when determining the target emissivity distribution at various spatial locations within the target area, it is first necessary to statistically average the radiation intensity of the infrared background in the mid-wave infrared and long-wave infrared bands under the detection viewpoint. The mid-wave infrared band typically selects the 3–5 μm band, and the long-wave infrared band typically selects the 8–14 μm band. Then, based on the platform's own operating status, the real-time temperature of each spatial point in the target area is obtained through infrared thermometry. Next, using Planck's blackbody radiation law, the total radiation intensity in the mid-wave and long-wave infrared bands at each spatial point under its own temperature is calculated and recorded as the ideal radiation intensity. Then, to ensure the target... Since the target is indistinguishable from the background in infrared detection imaging, the outward radiation intensity of the target must be consistent with the background radiation intensity at the same location. Therefore, by combining the obtained average background radiation intensity and the target's intrinsic ideal radiation intensity, the actual emissivity required for each point can be deduced, i.e., the first target emissivity and the second target emissivity. Finally, the spatially discrete emissivity data is smoothed to eliminate unreasonable abrupt changes, resulting in a continuous emissivity distribution that conforms to actual processing and physical laws. When inverting the actual emissivity based on the average background radiation intensity and the target's intrinsic ideal radiation intensity, the specific inversion calculation is based on the following formula: In the formula, where For the target emissivity of the corresponding band, The average background radiation intensity of the corresponding band. It is the Stefan constant. This refers to the platform's own temperature at the corresponding location. To determine the wavelength range width corresponding to the infrared band, the emissivity of the mid-wave and long-wave targets at each spatial location point is finally obtained, providing basic data for the subsequent construction of the discrete tensor field.

[0021] S2. Discretize the target emissivity distribution in the spatial dimension to construct a dual-band coupled emissivity tensor field in the mid-wave infrared band and the long-wave infrared band, and simultaneously extract the visible light reflectance spectral response and local heat flux density constraint boundary at the corresponding location points. In step S2, after the target emissivity distribution in the mid-infrared and long-wave infrared dual bands is determined, grid sampling is performed in the target area with a preset spatial resolution to transform the continuous emissivity distribution into discrete tensor nodes. Each node stores the emissivity values ​​of the mid-infrared and long-wave dual bands and maps them to a three-dimensional spatial coordinate system to form a set of tensor nodes. Simultaneously, the visible light band reflectance spectrum data of the corresponding nodes are collected synchronously, and the local heat flux density at the node location is measured as a thermodynamic boundary constraint condition. The step of discretizing the target emissivity distribution in the spatial dimension and constructing a dual-band coupled emissivity tensor field of the mid-infrared and long-wave infrared bands includes: Based on the preset sampling resolution, a spatial sampling grid is established within the target area; At each spatial sampling grid node, the preprocessed target emissivity values ​​in the mid-wave infrared band and the long-wave infrared band are acquired synchronously. The emissivity values ​​of the mid-wave infrared target and the long-wave infrared target under each grid node are combined to form a two-dimensional emissivity vector; Based on all grid nodes of the spatial sampling grid and their corresponding two-dimensional emissivity vectors, a dual-band emissivity tensor field containing coupled mid-wave infrared and long-wave infrared bands is constructed on each grid node; Specifically, when constructing the dual-band coupled emissivity tensor field, the target area is first divided into grids according to a preset spatial resolution. Each grid node corresponds to a unique spatial coordinate. The target emissivity values ​​of the mid-wave infrared band and the target emissivity of the long-wave infrared band at the coordinates are integrated into a two-dimensional vector. The two-dimensional vectors of all nodes are arranged in the order of spatial coordinates to obtain the complete dual-band coupled emissivity tensor field, which reflects the differentiated requirements of the target area for dual-band emissivity at different locations.

[0022] In addition, the steps of extracting the visible light reflectance spectral response and local heat flux density constraint boundary at the corresponding location points include: Constraint extraction is performed synchronously on the spatial sampling grid nodes of the constructed dual-band coupled emissivity tensor field; At each grid node, obtain the reflectivity requirements of the target surface at the corresponding location within the preset key wavelength in the visible light band. At each grid node, based on preset thermal management requirements and structural thermal safety requirements, a range of steady-state heat flux density that is allowed to pass through the corresponding grid node is set, and the upper and lower limits of steady-state heat flux density are defined as local heat flux density constraint boundaries. The visible light reflectance spectral response data extracted at each grid node is associated with the local heat flux density constraint boundary, and stored in conjunction with the corresponding spatial coordinates and dual-band emissivity vector. The constraints of all grid nodes are summarized to form a multiphysics constraint dataset that is aligned with the dual-band coupled emissivity tensor field in both spatial and physical parameter dimensions. In the above process, after the multiphysics constraint dataset is constructed, constraint conditions are extracted at each grid node. Specifically, the visible light reflectivity requirements at each grid node are first defined to meet the constraint of low detectability of the visible light band in the multispectral stealth scenario. Then, based on the overall thermal management requirements of the equipment and the thermal safety requirements of the structure itself, the range of steady-state heat flux density allowed at each node position is determined, and the upper and lower limits of heat flux density are obtained. After that, the visible light reflection constraint and heat flux density constraint of each node are bound and stored with the spatial coordinates and dual-band emissivity vector of the corresponding grid node. Finally, a multiphysics constraint dataset that is completely aligned with the dual-band coupled emissivity tensor field in space and parameter dimensions is obtained, providing constraint boundaries for subsequent parameter inverse mapping screening.

[0023] S3. Obtain the known selectable alternative materials and the corresponding range of geometric parameters for the alternative materials, and summarize them into a baseline structural parameter constraint dataset. In step S3, when screening candidate materials, it is necessary to ensure that the candidate materials are mature materials that can be industrially mass-produced at present, and that their thermal properties, optical response characteristics, and processing technology adaptability all meet the long-term stability requirements of the equipment in service environment. This is to avoid the attenuation of stealth performance due to material performance drift or uncontrollable processes. At the same time, it is also necessary to clarify the adjustable range of geometric parameters of the candidate materials, including but not limited to dimensions such as thickness, period length, duty cycle, and surface microstructure height, so as to ensure that the current process technology can cover the design requirements. The step of obtaining known selectable candidate materials and the corresponding geometric parameter ranges of the candidate materials, and summarizing them into a baseline structural parameter constraint dataset includes: Based on the physical mechanism and structural implementation of dual-band infrared stealth, a multi-layer composite structure model is constructed, and the constituent levels of the multi-layer composite structure model are determined. Candidate materials that meet the constituent hierarchy are collected. Candidate materials include phase change materials, thermochromic materials, electrochromic emissivity materials, semiconductor materials with specific crystal orientations, metasurface configuration materials, and composite materials with specific photonic crystal structures. Define the independent control of candidate materials and the geometric parameters coupled with the structure. The geometric parameters include the thickness of the material layer, the grating period, the unit width, the nanostructure feature size, and the spacing between the multilayer structures. Based on the limits of material preparation process and the mechanical properties of the structure, determine the achievable range of values ​​for each geometric parameter. Based on candidate materials and their corresponding geometric parameter combinations, we pre-calculate or measure their key emissivity values ​​in the mid-infrared and long-infrared bands, as well as their reflectance spectral response in the visible light band. Based on a pre-set structural thermal property database, the thermal conductivity, specific heat capacity and thermal diffusivity of candidate materials within the target operating temperature range are obtained, and the correlation between geometric parameters and equivalent thermal resistance is established. By integrating the types, geometric parameter ranges, thermal conductivity, specific heat capacity, and thermal diffusivity of all candidate materials, a baseline structural parameter constraint dataset is formed. Specifically, when constructing the baseline structural parameter constraint dataset, the first step is to determine the use of a multi-layer composite structure to achieve adaptive emissivity control based on the physical principle of dual-band infrared stealth. For each layer of the multi-layer structure, candidate materials that meet service requirements are screened. These candidate materials include various material types that can achieve dynamic emissivity adjustment, such as phase change materials and metasurface materials. Then, for each type of candidate material, the geometric parameters that can be adjusted independently or coupled with the structure are identified, such as material layer thickness, microstructure unit size, and interlayer spacing. Combined with the processing limits of existing fabrication processes and structural mechanical requirements, the achievable value range of each geometric parameter is determined. Subsequently, through pre-calculation or experimental measurement, the emissivity of the material in the mid-wave infrared and long-wave infrared bands and the reflectivity in the visible light band under different parameter combinations are obtained. At the same time, the thermal conductivity, specific heat capacity, and thermal diffusivity of the candidate materials within the operating temperature range are extracted from the structural thermophysical property database to establish the correspondence between geometric parameters and the equivalent thermal resistance of nodes. Finally, the types of all materials, the range of geometric parameters, and the thermophysical property parameters are integrated and summarized to obtain the baseline structural parameter constraint dataset, providing a complete space of candidate parameters for subsequent parameter screening.

[0024] S4. Based on the dual-band coupled emissivity tensor field, visible light reflectance spectral response and local heat flux density constraint boundary, perform inverse mapping to the reference structural parameter constraint dataset to generate a parameter candidate set that satisfies the multi-physics constraint. In step S4, after the benchmark structural parameter constraint dataset is output, it is matched with the dual-band coupled emissivity tensor field and the multiphysics constraint dataset. Using the dual-band emissivity requirement, visible light reflectivity requirement, and heat flux density range of each grid node as constraints, a reverse parameter search is performed within the candidate parameter space of the benchmark structural parameter constraint dataset to filter out combinations of geometric parameters and material types that satisfy all constraints, forming a parameter candidate set. The step of generating the parameter candidate set that satisfies the multiphysics constraints includes: Based on the parameter range of the candidate materials, the dual-band coupled emissivity tensor field, visible light reflectance spectral response and local heat flux density constraint boundary are used as screening conditions for structural parameters. Within the parameter range, by adjusting and combining different physical parameters, the predicted radiation and thermal performance corresponding to each group of physical parameters is evaluated one by one; A set of candidate initial parameters is formed by selecting a combination of physical parameters that simultaneously predict radiation and thermal performance, including the emissivity of mid-wave targets, the emissivity of long-wave targets, the visible light reflectivity, and the upper and lower limits of local heat flux density. Based on the preset processing accuracy standards and the fluctuation range of the usage environment, the test is carried out, and combinations that cannot maintain stable performance under expected environmental changes are eliminated, generating a benchmark structural parameter constraint dataset for simulation verification. Specifically, when constructing the parameter candidate set, the parameter value range of the candidate materials is first used as the constraint boundary. The dual-band emissivity requirements, visible light reflectance requirements, and heat flux density boundaries that each grid node needs to meet are all used as screening criteria. The search is traversed through all possible material and parameter combinations. For each combination, the actual achievable medium-wave emissivity and long-wave emissivity are calculated, and the corresponding visible light reflectance data is matched. At the same time, the equivalent thermal resistance of the node is calculated by combining the material's thermophysical parameters and geometric parameters to obtain the actual heat flux density value. Then, each performance index and node requirement is compared one by one, and only parameter combinations that all indices fall within the required range are retained and included in the initial candidate set. Finally, the accuracy error range of actual processing and possible temperature and stress environment fluctuations are considered to evaluate the results. The performance stability of candidate combinations is assessed by eliminating unqualified combinations that require machining precision exceeding current process capabilities or whose performance exceeds constraints due to slight environmental fluctuations. This results in a final set of candidate parameters suitable for subsequent simulation verification. When verifying the initial candidate set, the fluctuation range of each candidate parameter combination within the machining deviation range is calculated, taking into account preset machining tolerances. If the overall fluctuation range still completely covers the constraint range required by the node, the candidate combination is retained; otherwise, it is eliminated. Then, the highest operating temperature, lowest ambient temperature, and mechanical load changes that may occur during equipment service are simulated, and the performance parameters of this combination under extreme environments are recalculated. If the parameters still meet the constraint requirements, they are retained as a candidate parameter set; otherwise, they are eliminated.

[0025] S5. Perform simulation verification on the parameter candidate set, and record the parameters that pass the simulation verification as the preferred parameter combination. Perform neighborhood search on the parameter candidate set that fails the simulation verification until a preferred parameter combination that can pass the simulation verification is found, or the search range reaches the search range threshold and then stops. In step S5, after the parameter candidate set is output, further simulation verification is performed to ensure that the selected parameter combinations are feasible in real physical fields. During this process, parameter combinations that pass simulation verification are recorded as preferred parameter combinations. For parameter combinations that fail verification, an adaptive step-size search is initiated within the neighborhood of the original candidate solutions. Centered on the current parameter, the step-size is dynamically adjusted along key dimensions such as material properties, thickness, and interface roughness to gradually expand the search range until a new solution satisfying all constraints is found, or the search radius reaches a preset threshold and terminates. The step of performing simulation verification on the parameter candidate set includes: Multiphysics coupling simulation is performed on each combination of physical parameters in the parameter candidate set. Under the boundary conditions of set ambient temperature, convective heat transfer coefficient and internal heat source power, coupled heat transfer calculation is performed, and the temperature values ​​of each discrete spatial location point in the temperature field are output and recorded as the first reference parameter. Based on the first reference parameter, spectral radiance calculations are performed on the mid-wave infrared band and the long-wave infrared band, and the average emissivity value of each discrete spatial location point is output and recorded as the second reference parameter. Multiple sets of different verification conditions are set up, and heat transfer and radiation calculations are repeated under each set of verification conditions to generate a set of baseline parameters; The first benchmark parameter and the second benchmark parameter in the benchmark parameter set are compared for consistency to identify the dataset combination that satisfies the benchmark structural parameter constraint, and the combination that passes the consistency comparison is recorded as the preferred parameter combination. Specifically, when performing simulation verification on the parameter candidate set, a multi-physics simulation model containing multi-layered structural details is first built for each parameter candidate combination. Boundary conditions such as ambient temperature, convective heat transfer coefficient, and internal heat source power are set according to the actual service environment of the equipment to perform coupled heat transfer calculations. The actual temperature value at each corresponding grid node location in the temperature field is obtained and recorded as the first reference parameter. Subsequently, based on the actual dual-band emissivity characteristics of this parameter combination and the calculated node temperatures, mid-wave infrared and long-wave infrared spectral radiation calculations are performed to obtain the actual average temperature at each discrete spatial location. The average emissivity value is recorded as the second benchmark parameter. Then, multiple sets of differentiated verification conditions are set to cover scenarios such as low temperature, high temperature and different heat source power that the equipment may encounter during service. The above-mentioned coupled heat transfer and spectral radiation calculations are repeated under each set of verification conditions to obtain a complete set of benchmark parameters. Finally, the node temperature and actual average emissivity obtained from each condition in the benchmark parameter set are compared with the original heat flux density constraint boundary and the target dual-band emissivity requirement. If all parameters of all nodes meet the constraint requirements under all conditions, the corresponding parameter combination is recorded as the preferred parameter combination.

[0026] Secondly, the process of performing a neighborhood search on the candidate parameter set that has not passed simulation verification until an optimal parameter combination that can pass simulation verification is found includes: Obtain the baseline parameter combination that fails the simulation verification, and output the deviation vectors of the first baseline parameter and the second baseline parameter under the baseline parameter combination, and record them as the first deviation vector and the second deviation vector respectively; Based on the direction and magnitude of the first and second deviation vectors, a parameter neighborhood search space is constructed. Centered on the current reference parameter combination, the parameter perturbation is performed with adaptive step size along the dominant direction of the deviation vector to generate several sets of neighborhood perturbation parameter combinations, and multiphysics coupling simulation and radiation performance evaluation are performed on each set of combinations. Convergence is assessed for simulation results of neighborhood perturbation parameter combinations. If any combination of perturbations satisfies the requirements of the baseline structural parameter constraint dataset, it is marked as a valid preferred parameter combination and the current neighborhood search is terminated. If none of the perturbation combinations meet the requirements, the neighborhood search step size is increased and perturbation combinations are regenerated until the convergence condition is met or the maximum number of iterations is reached. In this implementation, for the baseline parameter combination that fails simulation verification, it is first necessary to identify the deviations of the node temperature and target emissivity from the constraint requirements under the corresponding baseline parameter combination, and record them as the first deviation vector and the second deviation vector, respectively. Then, based on the direction and magnitude of the first and second deviation vectors, the priority direction of parameter adjustment is determined, and the corresponding parameter neighborhood search space is constructed. Then, with the current failed baseline parameter combination as the center, along the adjustment direction dominated by the deviation vector, the parameter perturbation is performed with an adaptive step size to generate multiple sets of perturbation parameter combinations within the neighborhood. Then, for each set of perturbation parameter combinations, multiphysics coupling simulation and radiation performance evaluation are performed sequentially to determine whether the results meet all constraint requirements. If any set of perturbation parameter combinations passes the verification, it is directly marked as the preferred parameter combination, and the neighborhood search of the current node is stopped. If none of the currently generated perturbation combinations meet the requirements, the step size of the neighborhood search is expanded, and new perturbation combinations are regenerated to continue verification until an effective solution is found or the preset maximum number of search iterations is reached, and then the search is stopped.

[0027] Please see Figure 2 A dual-band adaptive infrared stealth structure selection system, using the aforementioned dual-band adaptive infrared stealth structure selection method, includes: The data acquisition module is used to acquire infrared radiation characteristic data and ambient temperature field distribution of the target area, and determine the target emissivity distribution required for each spatial location point in the target area in the mid-wave infrared band and long-wave infrared band based on the infrared background radiation field and the platform's own temperature field. The discrete sampling module is used to discretize the target emissivity distribution in the spatial dimension, construct a dual-band coupled emissivity tensor field in the mid-wave infrared band and the long-wave infrared band, and simultaneously extract the visible light reflectance spectral response and local heat flux density constraint boundary at the corresponding location point; The constraint determination module is used to obtain the known selectable alternative materials and the corresponding range of geometric parameters for the alternative materials, and summarize them into a baseline structural parameter constraint dataset. The parameter filtering module is used to perform inverse mapping to the reference structural parameter constraint dataset based on the dual-band coupled emissivity tensor field, visible light reflectance spectral response and local heat flux density constraint boundary, to generate a parameter candidate set that satisfies the multiphysics constraints. The verification module is used to perform simulation verification on the parameter candidate set, record the parameters that pass the simulation verification as the preferred parameter combination, and perform neighborhood search on the parameter candidate set that fails the simulation verification until a preferred parameter combination that can pass the simulation verification is found, or the search range reaches the search range threshold and then stops.

[0028] The execution process of the selected system corresponds to the flow of the aforementioned method, and will not be repeated here.

[0029] Please see Figure 3 An electronic device, comprising: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores a computer program that can be executed by at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the aforementioned dual-band adaptive infrared stealth structure selection method.

[0030] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A method for selecting a dual-band adaptive infrared stealth structure, characterized in that: include: Acquire infrared radiation characteristic data and ambient temperature field distribution of the target area, and determine the target emissivity distribution required for each spatial location point in the target area in the mid-wave infrared band and long-wave infrared band based on the infrared background radiation field and the platform's own temperature field; The target emissivity distribution is discretized and sampled in the spatial dimension to construct a dual-band coupled emissivity tensor field in the mid-wave infrared band and the long-wave infrared band, and the visible light reflectance spectral response and local heat flux density constraint boundary at the corresponding location points are extracted simultaneously. Obtain the known available alternative materials and the corresponding range of geometric parameters for each alternative material, and summarize them into a baseline structural parameter constraint dataset; Based on the dual-band coupled emissivity tensor field, visible light reflectance spectral response and local heat flux density constraint boundary, an inverse mapping is performed to the reference structural parameter constraint dataset to generate a parameter candidate set that satisfies the multiphysics constraints; The parameter candidate set is subjected to simulation verification, and the parameters that pass the simulation verification are recorded as the preferred parameter combinations. The parameter candidate set that fails the simulation verification is subjected to neighborhood search until a preferred parameter combination that can pass the simulation verification is found, or the search range reaches the search range threshold and then stops.

2. The method for selecting a dual-band adaptive infrared stealth structure according to claim 1, characterized in that: The step of determining the target emissivity distribution required for each spatial location point within the target area in the mid-wave infrared and long-wave infrared bands based on the infrared background radiation field and the platform's own temperature field includes: Obtain the average background radiation intensity of the infrared background radiation field in the mid-wave infrared band and the long-wave infrared band; The platform's own temperature field is obtained, and the ideal radiation intensity of each spatial location point in the target area at the corresponding temperature in the mid-wave infrared and long-wave infrared bands is calculated based on the platform's own temperature field. Constrained by the radiation characteristics of the preset target blending into the background in the infrared image, the target radiation intensity required for each spatial location point in the mid-wave infrared band and long-wave infrared band is determined by combining the average background radiation intensity data and the ideal radiation intensity. Based on the target radiation intensity and ideal radiation intensity of each spatial location point in the mid-wave infrared band and the long-wave infrared band, the target emissivity required in the mid-wave infrared band and the long-wave infrared band is calculated respectively, and recorded as the first target emissivity and the second target emissivity respectively. Spatial continuity correction and smoothing filtering are applied to the emissivity of the first and second targets to form a continuous and physically achievable target emissivity distribution within the target region.

3. The method for selecting a dual-band adaptive infrared stealth structure according to claim 1, characterized in that: The step of discretizing and sampling the target emissivity distribution in the spatial dimension to construct a dual-band coupled emissivity tensor field in the mid-infrared and long-infrared bands includes: Based on the preset sampling resolution, a spatial sampling grid is established within the target area; At each spatial sampling grid node, the preprocessed target emissivity values ​​in the mid-wave infrared band and the long-wave infrared band are acquired synchronously. The emissivity values ​​of the mid-wave infrared target and the long-wave infrared target under each grid node are combined to form a two-dimensional emissivity vector; Based on all grid nodes of the spatial sampling grid and their corresponding two-dimensional emissivity vectors, a dual-band emissivity tensor field containing the coupling of the mid-wave infrared band and the long-wave infrared band is constructed on each grid node.

4. The method for selecting a dual-band adaptive infrared stealth structure according to claim 1, characterized in that: The step of extracting the visible light reflectance spectral response and local heat flux density constraint boundary at the corresponding location point includes: Constraint extraction is performed synchronously on the spatial sampling grid nodes of the constructed dual-band coupled emissivity tensor field; At each grid node, obtain the reflectivity requirements of the target surface at the corresponding location within the preset key wavelength in the visible light band. At each grid node, based on preset thermal management requirements and structural thermal safety requirements, a range of steady-state heat flux density that is allowed to pass through the corresponding grid node is set, and the upper and lower limits of steady-state heat flux density are defined as local heat flux density constraint boundaries. The visible light reflectance spectral response data extracted at each grid node is associated with the local heat flux density constraint boundary, and stored in conjunction with the corresponding spatial coordinates and dual-band emissivity vector. The constraints of all grid nodes are summarized to form a multiphysics constraint dataset that is aligned with the dual-band coupled emissivity tensor field in both spatial and physical parameter dimensions.

5. The method for selecting a dual-band adaptive infrared stealth structure according to claim 1, characterized in that: The step of obtaining known selectable alternative materials and the corresponding range of geometric parameters for each alternative material, and summarizing them into a baseline structural parameter constraint dataset, includes: Based on the physical mechanism and structural implementation of dual-band infrared stealth, a multi-layer composite structure model is constructed, and the constituent levels of the multi-layer composite structure model are determined. Candidate materials that meet the constituent hierarchy are collected. Candidate materials include phase change materials, thermochromic materials, electrochromic emissivity materials, semiconductor materials with specific crystal orientations, metasurface configuration materials, and composite materials with specific photonic crystal structures. Define the independent control of candidate materials and the geometric parameters coupled with the structure. The geometric parameters include the thickness of the material layer, the grating period, the unit width, the nanostructure feature size, and the spacing between the multilayer structures. Based on the limits of material preparation process and the mechanical properties of the structure, determine the achievable range of values ​​for each geometric parameter. Based on candidate materials and their corresponding geometric parameter combinations, we pre-calculate or measure their key emissivity values ​​in the mid-infrared and long-infrared bands, as well as their reflectance spectral response in the visible light band. Based on a pre-set structural thermal property database, the thermal conductivity, specific heat capacity and thermal diffusivity of candidate materials within the target operating temperature range are obtained, and the correlation between geometric parameters and equivalent thermal resistance is established. By integrating the types, geometric parameter ranges, thermal conductivity, specific heat capacity, and thermal diffusivity of all candidate materials, a baseline structural parameter constraint dataset is formed.

6. The method for selecting a dual-band adaptive infrared stealth structure according to claim 1, characterized in that: The step of generating a candidate set of parameters that satisfies multiphysics constraints includes: Based on the parameter range of the candidate materials, the dual-band coupled emissivity tensor field, visible light reflectance spectral response and local heat flux density constraint boundary are used as screening conditions for structural parameters. Within the parameter range, by adjusting and combining different physical parameters, the predicted radiation and thermal performance corresponding to each group of physical parameters is evaluated one by one; A set of candidate initial parameters is formed by selecting a combination of physical parameters that simultaneously meet the requirements for emissivity of mid-wave targets, emissivity of long-wave targets, reflectivity of visible light, and upper and lower limits of local heat flux density for predicted radiation and thermal performance. Based on the preset processing accuracy standards and the fluctuation range of the usage environment, the system performs inspections, eliminates combinations that are within the allowable manufacturing deviation range and whose performance cannot remain stable under expected environmental changes, and generates a baseline structural parameter constraint dataset for simulation verification.

7. The method for selecting a dual-band adaptive infrared stealth structure according to claim 1, characterized in that: The step of performing simulation verification on the parameter candidate set includes: Multiphysics coupling simulation is performed on each combination of physical parameters in the parameter candidate set. Under the boundary conditions of set ambient temperature, convective heat transfer coefficient and internal heat source power, coupled heat transfer calculation is performed, and the temperature values ​​of each discrete spatial location point in the temperature field are output and recorded as the first reference parameter. Based on the first reference parameter, spectral radiance calculations are performed on the mid-wave infrared band and the long-wave infrared band, and the average emissivity value of each discrete spatial location point is output and recorded as the second reference parameter. Multiple sets of different verification conditions are set up, and heat transfer and radiation calculations are repeated under each set of verification conditions to generate a set of reference parameters; The first and second benchmark parameters in the benchmark parameter set are compared for consistency to identify the dataset combinations that meet the benchmark structural parameter constraints, and the combinations that pass the consistency comparison are recorded as the preferred parameter combinations.

8. The method for selecting a dual-band adaptive infrared stealth structure according to claim 1, characterized in that: The step of performing a neighborhood search on the candidate parameter set that has not passed simulation verification until a preferred parameter combination that can pass simulation verification is found includes: Obtain the baseline parameter combination that fails the simulation verification, and output the deviation vectors of the first baseline parameter and the second baseline parameter under the baseline parameter combination, and record them as the first deviation vector and the second deviation vector respectively; Based on the direction and magnitude of the first and second deviation vectors, a parameter neighborhood search space is constructed. Centered on the current reference parameter combination, the parameter perturbation is performed with adaptive step size along the dominant direction of the deviation vector to generate several sets of neighborhood perturbation parameter combinations, and multiphysics coupling simulation and radiation performance evaluation are performed on each set of combinations. Convergence is judged for the simulation results of the combination of neighborhood perturbation parameters; If any combination of perturbations satisfies the requirements of the baseline structural parameter constraint dataset, it is marked as a valid preferred parameter combination and the current neighborhood search is terminated. If none of the perturbation combinations meet the requirements, the neighborhood search step size is increased and perturbation combinations are regenerated until the convergence condition is met or the maximum number of iterations is reached.

9. A dual-band adaptive infrared stealth structure selection system, characterized in that: The dual-band adaptive infrared stealth structure selection method according to any one of claims 1 to 8 includes: The data acquisition module is used to acquire infrared radiation characteristic data and ambient temperature field distribution of the target area, and determine the target emissivity distribution required for each spatial location point in the target area in the mid-wave infrared band and long-wave infrared band based on the infrared background radiation field and the platform's own temperature field. The discrete sampling module is used to discretize the target emissivity distribution in the spatial dimension, construct a dual-band coupled emissivity tensor field in the mid-wave infrared band and the long-wave infrared band, and simultaneously extract the visible light reflectance spectral response and local heat flux density constraint boundary at the corresponding location point; The constraint determination module is used to obtain the known selectable alternative materials and the corresponding range of geometric parameters for the alternative materials, and summarize them into a baseline structural parameter constraint dataset. The parameter filtering module is used to perform inverse mapping to the reference structural parameter constraint dataset based on the dual-band coupled emissivity tensor field, visible light reflectance spectral response and local heat flux density constraint boundary, to generate a parameter candidate set that satisfies the multiphysics constraints. The verification module is used to perform simulation verification on the parameter candidate set, record the parameters that pass the simulation verification as the preferred parameter combination, and perform neighborhood search on the parameter candidate set that fails the simulation verification until a preferred parameter combination that can pass the simulation verification is found, or the search range reaches the search range threshold and then stops.

10. An electronic device, characterized in that: The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the dual-band adaptive infrared stealth structure selection method according to any one of claims 1 to 8.

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