A thermal stress analysis method based on a low-light-level night vision device

Abstract

The application discloses a thermal stress analysis method based on a low-light night vision device, and relates to the technical field of thermal stress analysis. The method comprises the following steps: obtaining spatial topography data of the low-light night vision device through scanning, constructing a three-dimensional entity model, and segmenting to form a plurality of original areas with independent functional properties; collecting independent functional modules corresponding to the original areas, and determining an importance degree quantitative value of the independent functional modules on the overall performance of the low-light night vision device; collecting geometric constraint features and material parameters of the original areas, and analyzing to obtain a probability of thermal stress phenomenon of the original areas; screening and determining a target area for thermal stress analysis from the original areas; obtaining environmental data of the target area, combining the material parameters of the target area, establishing a dynamic constraint condition equation, and simulating to obtain a thermal stress analysis result of the target area. The application improves the applicability of the thermal stress analysis based on the low-light night vision device.

Description

Technical Field

[0001] This application relates to the technical field of thermal stress analysis, and in particular to a thermal stress analysis method based on a low-light night vision device. Background Technology

[0002] Low-light night vision devices utilize the faint natural light of the night sky, such as moonlight, starlight, atmospheric glow, and the Milky Way, for illumination. A light intensifier amplifies the weak photons reflected from the target and converts them into a visible image, enabling nighttime observation. Thermal stress refers to the stress generated when an object expands and contracts freely due to external constraints and internal constraints between its parts as temperature changes. Thermal stress analysis of low-light night vision devices can simulate the physical deformation of the equipment under temperature gradients and assess the thermal stability of key components such as optical elements and electronic devices. Furthermore, analyzing the distribution of thermal stress can identify vulnerable components (such as photocathodes and microchannel plates) and optimize heat dissipation design or material selection. However, existing thermal stress analysis methods for low-light night vision devices merely apply conventional thermal stress analysis to these devices. Since conventional thermal stress analysis was not specifically developed for low-light night vision, its applicability to these devices is limited. Summary of the Invention

[0003] The purpose of this invention is to provide a thermal stress analysis method based on a low-light night vision device to solve the problems mentioned in the background art.

[0004] This application provides a thermal stress analysis method based on a low-light night vision device, which adopts the following technical solution:

[0005] The spatial topography data of the low-light night vision device is obtained by scanning, a three-dimensional solid model of the low-light night vision device is constructed, and the three-dimensional solid model is divided into several original regions with independent functional attributes.

[0006] Collect data on the independent functional modules corresponding to the original area, and determine the quantitative value of the importance of the independent functional modules to the overall performance of the low-light night vision device.

[0007] The geometric constraint features of the original region are collected, the material parameters of the material used to construct the original region are obtained, and the probability of thermal stress phenomena occurring in the original region is obtained by combining the geometric constraint features and material parameters.

[0008] Based on the quantification value of importance and the probability of thermal stress phenomena, the target area for thermal stress analysis is selected from the original area.

[0009] By acquiring environmental data of the target area and combining it with the material parameters of the target area, dynamic constraint equations are established, and the thermal stress analysis results of the target area are obtained through simulation.

[0010] Preferably, the step of determining the quantitative value of the importance of the independent functional modules corresponding to the original acquisition area to the overall performance of the low-light night vision device specifically includes:

[0011] To obtain the core performance of a low-light night vision device, and to simulate the performance degradation ratio of different core performance components when an independent functional module of the low-light night vision device is removed. , where m is the number of different core performance;

[0012] The simulation yielded a quantitative value of the impact of core performance on the imaging of low-light night vision devices. , where m is the number of different core performance;

[0013] Based on performance correlation function The performance level quantification value XL is calculated, where m is the number of different core performance and n is the maximum number of different core performance.

[0014] Collect user usage habit data and simulate the degree of usage based on the usage habit data;

[0015] The importance of each independent functional module to the overall performance of the low-light night vision device is obtained by combining the performance level quantification value XL and the usage level quantification value.

[0016] Preferably, the step of collecting user usage habit data and simulating a quantitative value of usage degree based on the usage habit data specifically includes:

[0017] Collect faulty low-light night vision devices, scan faulty low-light night vision devices, and establish a three-dimensional model of the fault.

[0018] Wear data was extracted from the three-dimensional fault model to simulate user habits when using low-light night vision devices;

[0019] Collect fault data of faulty low-light night vision devices, and determine whether the cause of the fault is related to the user based on usage habit data;

[0020] If the cause of the failure is unrelated to the user, a simulation evaluation of the fault resistance capability of the independent functional module is performed to quantify the robustness level of the independent functional module in the scenario where the cause of the failure is unrelated to the user, which is used as a quantitative value of the degree of use.

[0021] Preferably, the steps of acquiring the geometric constraint features of the original region, obtaining the material parameters of the material used to construct the original region, and combining the geometric constraint features and material parameters to analyze the probability of thermal stress phenomena occurring in the original region are as follows:

[0022] Collect the geometric constraint features of the original region, and extract the movement gap displacement of the original region based on the geometric constraint features;

[0023] Collect the operating temperature range of the low-light night vision device, and calculate the temperature change based on the operating temperature range;

[0024] Obtain the material parameters of the original region material, and calculate the expansion displacement by combining the temperature change.

[0025] The displacement difference between the moving clearance displacement and the expansion displacement is calculated.

[0026] The location information of the original region is extracted from the 3D solid model, and the probability of change of geometric constraint features is determined based on the location information of the original region.

[0027] Based on a comprehensive analysis of displacement difference and change probability, the probability of thermal stress occurring in the original region is derived.

[0028] Preferably, the step of extracting the location information of the original region based on the 3D solid model and determining the probability of change of the geometric constraint features based on the location information of the original region specifically includes:

[0029] Based on the 3D solid model and wear data, wear points in the original area are marked in the 3D solid model;

[0030] Determine whether the wear at the wear point is caused by direct contact with an external object. If the wear is caused by direct contact, then extract the degree of wear based on the wear data. q represents the wear point number in the original region;

[0031] The statistical cause of wear is the number of wear points (MS) obtained from direct contact with external objects;

[0032] Correlation function based on wear change The probability of change of the geometric constraint feature, BH, is calculated, where q is the wear point number in the original region, and p is the maximum wear point number in the original region. , It is a scaling factor and is greater than 0;

[0033] If the cause of wear and tear is not due to direct contact with external objects, then the area with the attribute of direct contact with the original area is obtained as the base area;

[0034] Obtain material information for the basic region and assess the probability of changes in the obtained geometric constraint features.

[0035] Preferably, the step of obtaining material information of the basic region and evaluating the probability of change of geometric constraint features specifically includes:

[0036] Determine whether the base region and the original region are in direct contact, and count the base regions that are not in direct contact as the first region;

[0037] Count the number of material types (CS) in the first region and obtain the historical thermal stress failure probability of different material types in the first region. , where M is the code for different material types;

[0038] Collect the minimum distance between different material types and the original area and record it as the material distance. , where M is the code for different material types;

[0039] Association function based on feature changes The probability of change of the geometric constraint feature, BH, is calculated, where M is the number of different material types and N is the maximum number of different material types. , It is a scaling factor and is greater than 0.

[0040] Preferably, the step of acquiring environmental data of the target area, combining it with material parameters of the target area, establishing dynamic constraint equations, and simulating the thermal stress analysis results of the target area specifically includes:

[0041] Collect environmental data of the target area, and extract the temperature boundary range based on the environmental data;

[0042] Establish dynamic constraint equations and calculate the dynamic expansion ratio based on the dynamic constraint equations.

[0043] A dynamic material strength function is established, material information of the target area is collected, and dynamic material strength is calculated by combining the dynamic material strength function.

[0044] The dynamic thermal stress in the target region is obtained by combining dynamic expansion ratio and dynamic material strength simulation within the temperature boundary range;

[0045] The dynamic distribution law of thermal stress of the low-light night vision device was confirmed based on dynamic thermal stress and used as the result of thermal stress analysis.

[0046] Preferably, the step of establishing dynamic constraint equations and calculating the dynamic expansion ratio based on the dynamic constraint equations specifically includes:

[0047] The region with direct contact with the target area is identified and designated as the first region. The material expansion coefficient of the first region is then collected. , where h is the number of the first region;

[0048] The minimum distance between the first region and the target region is used as the original gap distance. , where h is the number of the first region;

[0049] Establish dynamic constraint equations:

[0050]

[0051] Where g(T) is the real-time gap distance. It is the coefficient of thermal expansion of the target region. It is the coefficient of thermal expansion of the material in the first region. It is the effective length of the target region in the direction of influencing the gap. It is the effective length of the first region influencing the direction of the void. It is the change in temperature;

[0052] The dynamic gap distance is obtained by simulating the real-time gap distance of the target region under different temperatures based on the dynamic constraint equation;

[0053] The material expansion is calculated based on the material expansion coefficient and temperature change of the target area. The dynamic expansion ratio is obtained by the ratio of the material expansion to the dynamic void distance.

[0054] Preferably, the step of establishing a dynamic material strength function, collecting material information of the target area, and calculating the dynamic material strength using the dynamic material strength function specifically includes:

[0055] Establish the dynamic function of material strength:

[0056]

[0057] Where DQ is the real-time strength of the material. It is the yield strength term. It is a hardening term. It is a correction term for strain rate and Poisson effect. It is a time decay term. This is the time decay coefficient;

[0058] The functional form of the yield strength term is:

[0059]

[0060] in, The room temperature yield strength, The temperature decay coefficient of yield strength. It is the change in temperature. Critical creep failure time, where t is real-time;

[0061] The functional form of the strain rate and Poisson effect correction term is:

[0062]

[0063] in, For strain rate, Here, j is the reference strain rate, v is the strain sensitivity coefficient, and v is Poisson's ratio.

[0064] The dynamic material strength is calculated based on the material strength dynamic function.

[0065] In summary, this application includes at least one of the following beneficial technical effects:

[0066] 1. A scanning model of the low-light night vision device is created, and then segmented into multiple original regions with independent functional attributes. Based on the influence of each original region on the core performance, and considering the combined impact of the core performance on the imaging of the low-light night vision device, a quantitative value for the performance level of the independent functional modules corresponding to each original region is obtained. User habits are extracted from wear data of faulty low-light night vision devices. Then, under fault conditions unrelated to user usage, simulation evaluation of the fault resilience of the independent functional modules is conducted. The robustness level of the independent functional modules in scenarios where the fault cause is unrelated to user usage is quantified as a usage-related quantitative value. The importance-related quantitative value is obtained by combining the combined performance-related quantitative values. Further, the probability of thermal stress phenomena is determined, and a target area is selected for thermal stress analysis. The thermal stress analysis results are obtained by constructing dynamic constraint equations and analyzing the dynamic function of material strength. This thermal stress analysis is more closely aligned with the usage of low-light night vision devices, improving the applicability of thermal stress analysis based on low-light night vision devices.

[0067] 2. The displacement difference is calculated based on the geometric constraint characteristics and expansion of the original region. The probability of thermal stress phenomena occurring in the original region is then confirmed by combining this with the probability of changes in the geometric constraint characteristics. For regions directly exposed to external objects, the probability of changes in geometric constraint characteristics is simulated based on the quantity and degree of wear caused by these objects. For regions not directly exposed to external objects, the probability is simulated based on the types and quantities of surrounding materials, the historical thermal stress failure probability of each material, and the distance between the materials. This provides a more accurate probability of thermal stress phenomena, improving the accuracy of thermal stress analysis based on low-light night vision devices.

[0068] 3. By constructing dynamic constraint equations based on whether the original gap distance is zero, the dynamic expansion ratio can be calculated. The dynamic function of material strength is obtained through multi-factor coupling, which helps to obtain more accurate material strength. The establishment of dynamic constraint equations and the dynamic function of material strength facilitates obtaining more accurate data, making the results obtained in subsequent thermal stress calculations and analyses more reliable, thus improving the reliability of thermal stress analysis based on low-light night vision devices. Attached Figure Description

[0069] Figure 1 This is a schematic diagram illustrating the specific steps of an embodiment of the thermal stress analysis method based on a low-light night vision device according to the present invention. Detailed Implementation

[0070] The following examples and... Figure 1 The present invention will be described in further detail, but the embodiments of the present invention are not limited thereto.

[0071] This invention discloses a thermal stress analysis method based on a low-light night vision device, specifically including the following steps:

[0072] Step S1: Scan the spatial topography data of the low-light night vision device, construct a three-dimensional solid model of the low-light night vision device, and divide the three-dimensional solid model into several original regions with independent functional attributes.

[0073] Existing 3D scanning technologies, such as near-infrared laser technology, can be used to precisely scan the low-light night vision device to obtain comprehensive spatial topographic data, and based on this, a high-precision 3D solid model of the device can be constructed. Spatial topographic data includes point cloud coordinates, normal vectors, curvature, and other information to describe the device's outline. Reverse engineering software (such as Geomagic and MeshLab) is used to convert the scanned point cloud into a NURBS surface or triangular mesh model, focusing on optimizing the geometric accuracy of the image intensifier channel (micrometer-scale diameter) and fiber optic plate arrangement. Parametric modeling is performed using CAD software (such as SolidWorks), for example, the F1.2 aperture structure of the objective lens and the diopter adjustment mechanism of the eyepiece. An interactive attention mechanism (such as the iSeg model) can be used to divide the infrared auxiliary light source, battery compartment, and other independent modules based on user click marks. Deep learning models are used to identify specific components (such as the S25 cathode surface of the super-second-generation image intensifier).

[0074] Step S2: Collect the independent functional modules corresponding to the original area and determine the quantitative value of the importance of the independent functional modules to the overall performance of the low-light night vision device.

[0075] Step S3: Collect the geometric constraint features of the original region, obtain the material parameters of the material used to construct the original region, and analyze the probability of thermal stress phenomena occurring in the original region by combining the geometric constraint features and material parameters.

[0076] Step S4: Based on the importance quantification value and the probability of thermal stress phenomena, select and determine the target area for thermal stress analysis from the original area.

[0077] The target regions for thermal stress analysis can be determined by filtering from the original regions using a risk matrix method. For example, the horizontal axis represents the probability of thermal stress (low / medium / high), and the vertical axis represents the importance (low / medium / high), mapping the regions onto a matrix. Then, regions with high probability and high importance (such as solder joints of power devices) are listed as target regions.

[0078] Step S5: Obtain environmental data of the target area, combine it with the material parameters of the target area, establish dynamic constraint equations, and simulate the thermal stress analysis results of the target area.

[0079] In practical applications, thermal stress analysis of low-light night vision devices is beneficial for assessing the thermal stability of key components such as optical elements and electronic devices, and for optimizing heat dissipation design or material selection. However, conventional thermal stress analysis typically analyzes the entire low-light night vision device, but not all areas are subject to thermal stress interference, nor do all areas require thermal stress analysis. Therefore, performing thermal stress analysis on the entire low-light night vision device results in resource waste, consumes more time, and yields some invalid results. Selecting specific areas for thermal stress analysis based on the quantified probability and importance of thermal stress phenomena effectively reduces resource waste, makes the thermal stress scheme more compatible with the low-light night vision device, and improves the adaptability of thermal stress analysis. Furthermore, establishing dynamic constraint equations during thermal stress analysis helps obtain more accurate constraint conditions, leading to more accurate thermal stress analysis results and improving the accuracy of thermal stress analysis for low-light night vision devices.

[0080] The steps for collecting data on the independent functional modules corresponding to the original area and determining the quantitative values ​​of the importance of each independent functional module to the overall performance of the low-light night vision device are as follows:

[0081] Step S21: Obtain the core performance of the low-light night vision device and simulate the performance degradation ratio of different core performances when the independent functional modules of the low-light night vision device are removed. , where m is the number of different core performance.

[0082] The core performance characteristics of a low-light night vision device include image intensifier sensitivity, resolution, signal-to-noise ratio, dynamic range, and operating life, all of which can be configured by the user. Finite element simulation modeling can be used to assess performance degradation in low-light environments, such as when removing an infrared auxiliary light source or an automatic anti-glare module. An optical-electrical-thermal coupling model can be built using ANSYS or COMSOL to simulate the decrease in photon capture efficiency after removing the infrared light source (e.g., a 60% reduction). Zemax optical simulation can be used to analyze field distortion caused by objective lens removal or magnification adjustment failure (e.g., distortion rate increasing from 0.1% to 1.2%). In other words, the degree of performance degradation of various core components when a single functional module is removed can be simulated as the performance degradation ratio: Performance degradation ratio = (Original performance value - Performance value after module removal) / Original performance value × 100%.

[0083] Step S22: Simulate and quantify the impact of core performance on low-light night vision imaging. , where m is the number of different core performance.

[0084] The core function of a low-light night vision device is imaging, and different core performance characteristics have varying degrees of impact on imaging. For example, operating life generally only affects the duration of use of a low-light night vision device, with relatively little interference to its imaging. Historical data can be collected, and corresponding correlation curves can be constructed based on this data to quantify the degree of impact. For instance, a correlation curve can be established between operating life and imaging quality of a low-light night vision device, and the slope of the curve can be calculated as the quantification of the degree of impact. The larger the absolute value, the greater the degree of impact.

[0085] Step S23, based on the performance correlation function The performance level quantification value XL is calculated, where m is the number of different core performance and n is the maximum number of different core performance.

[0086] For example, core performance includes image intensifier sensitivity and resolution. The performance degradation ratios of independent functional module A are 10% and 5%, respectively. The quantified values ​​of the influence of image intensifier sensitivity and resolution on low-light night vision imaging are 2 and 3, respectively. Then the quantified value of performance XL = 10% × 2 + 5% × 3 = 0.35.

[0087] Step S24: Collect user usage habit data and simulate the degree of usage based on the usage habit data to obtain a quantitative value.

[0088] Step S25: Combine the performance level quantification value XL and the usage level quantification value to obtain the quantification value of the importance of the independent functional module to the overall performance of the low-light night vision device.

[0089] In practical applications, the importance quantification value is obtained by combining the performance quantification value XL and the usability quantification value using a weighted scoring method. For example, if the weight ratios of the performance quantification value XL and the usability quantification value are set to 50% and 50% respectively, and the performance quantification value XL and the usability quantification value are 0.5 and 10 respectively, then the importance quantification value is 0.5 × 50% + 10 × 50% = 5.25. Quantifying the importance of independent functional modules helps to identify which areas are more important to the low-light night vision device, facilitating the subsequent selection of more important areas for thermal stress analysis and reducing resource waste.

[0090] The steps for collecting user usage habit data and quantifying usage levels based on that data are as follows:

[0091] Step S241: Collect faulty low-light night vision devices, scan the faulty low-light night vision devices, and establish a three-dimensional model of the fault.

[0092] By scanning the low-light night vision device used by the user and which has malfunctioned, a three-dimensional model of the malfunctioning low-light night vision device is obtained using the scanning method in step S1.

[0093] Step S242: Extract wear data from the three-dimensional fault model to simulate user usage habits of the low-light night vision device.

[0094] Laser grating projection technology can be used to detect faulty 3D models, collecting phase information from the undeformed areas of the grating pattern on the object under test. The data is then fitted using a least-squares iterative method to obtain the phase distribution parameters of an ideal reference surface, allowing for the extraction of 3D information about the wear area. The topological structure information of the 3D model (such as branches and connectivity) can be used to identify wear characteristics. For example, Reeb plots can be used for skeleton extraction to describe the spatial topological relationships of the model. Wear data can also be extracted by comparing the visual image similarity of the 3D model in various directions. Using finite element software (such as Abaqus) combined with the Archard wear formula (wear amount = K × load × sliding distance / material hardness), the contact wear of mechanical components in low-light night vision devices (such as eyepiece adjustment mechanisms and battery compartment latches) can be simulated through dynamic updates of node wear amounts. For example, after obtaining wear morphology data through industrial CT scanning, the frequency and force of the user adjusting the eyepiece can be deduced.

[0095] Step S243: Collect fault data of the faulty low-light night vision device, and determine whether the cause of the fault is related to the user based on the usage habit data.

[0096] Different malfunctions have different causes. We need to obtain the usage standards for low-light night vision devices and determine whether these standards are violated based on usage habit data. If they are violated, the cause of the malfunction is determined to be related to user habits. This means determining whether these malfunctions are caused by user habits. If they are caused by user habits, then malfunctions not caused by user habits are selected as the reference standard.

[0097] In step S244, if the cause of the fault is unrelated to the user, a simulation evaluation of the fault resistance capability of the independent functional module is performed to quantify the robustness level of the independent functional module in the scenario where the cause of the fault is unrelated to the user, which is used as the quantitative value of the degree of use.

[0098] In practical applications, the stronger the regional resilience of an independent functional module against failures, the greater its usability quantification value. Therefore, it is more necessary to focus on its thermal stress analysis to reduce the impact of thermal stress. This is because the weaker the fault resistance of an independent functional module, the more prone it is to failure even after thermal stress analysis. Three-dimensional visual simulation technology can be used to establish physical-digital twin models of each module. For example, ANSYS Mechanical can be used to construct a thermal stress distribution model of a photocathode, and finite element analysis can be used to simulate material deformation at extreme temperatures (-40℃ to 70℃).

[0099] The steps involved in collecting the geometric constraint features of the original region, obtaining the material parameters of the material used to construct the original region, and combining the geometric constraint features and material parameters to analyze the probability of thermal stress phenomena occurring in the original region are as follows:

[0100] Step S31: Collect the geometric constraint features of the original region, and extract the movement gap displacement of the original region based on the geometric constraint features.

[0101] In the existing 3D model of low-light night vision device, the distance between the original area and other areas is statistically calculated, that is, the gap distance that the original area can move is used as the movement gap displacement.

[0102] Step S32: Collect the operating temperature range of the low-light night vision device, and calculate the temperature change based on the operating temperature range.

[0103] The operating environment of the low-light night vision device is collected or set, and the maximum and minimum temperature values ​​are extracted from the operating environment to form a temperature range. The difference between the maximum and minimum temperature values ​​is calculated as the temperature change.

[0104] Step S33: Obtain the material parameters of the original region material and calculate the expansion displacement by combining the temperature change.

[0105] The material parameters include the initial length of the material and the coefficient of thermal expansion. For example, the coefficient of thermal expansion of a carbon fiber support used in the objective lens assembly of a certain low-light night vision device is 1.0 × 10⁻⁶. -6 ℃ -1 The initial length of the material is 200 mm. If the temperature increases by 60°C (i.e., the temperature change is 60°C), then the expansion displacement is 1.0 × 10⁻⁶. -6 ×200×60=0.012mm.

[0106] Step S34: Calculate the displacement difference between the moving gap displacement and the expansion displacement.

[0107] For example, if the expansion displacement is 0.012 mm and the movement gap displacement is 0.2 mm, then the displacement difference is 0.2 - 0.012 = 0.188 mm.

[0108] Step S35: Extract the location information of the original region based on the 3D solid model, and determine the change probability of the geometric constraint features based on the location information of the original region.

[0109] Step S36: Based on the comprehensive analysis of displacement difference and change probability, the probability of thermal stress phenomenon occurring in the original region is derived.

[0110] In practical applications, when the displacement difference is not greater than 0, the probability of thermal stress is considered 100%. When the displacement difference is greater than 0, a regression model can be established by finding a linear or nonlinear relationship between the independent variables (displacement difference, probability of change) and the dependent variable (probability of thermal stress). The regression model is trained using historical data to predict the probability of thermal stress under new data. For example, if the regression model's probability of thermal stress is 0.6 for displacement difference and 0.4 for probability of change, then substituting these values ​​into the regression model yields the probability of thermal stress. Because a smaller displacement difference means the material is more easily constrained, and the greater the probability of change in the constraint characteristics, the more likely thermal stress will occur due to changes in these characteristics, thus increasing the probability of thermal stress.

[0111] The steps of extracting the location information of the original region from the 3D solid model and determining the probability of change of geometric constraint features based on the location information of the original region are as follows:

[0112] Step S351: Based on the three-dimensional solid model and wear data, mark the wear points of the original area in the three-dimensional solid model.

[0113] The location that has reached a certain degree of wear can be set as the wear point, and the wear data can be used to determine whether the preset wear level has been reached.

[0114] Step S352: Determine whether the wear point is caused by direct contact with an external object. If the wear point is caused by direct contact with an external object, then extract the degree of wear based on the wear data. q represents the wear point number in the original region.

[0115] Some wear and tear is caused by external contact, while some is caused by internal use. If there is material blocking the wear point, it cannot directly contact external objects. For example, the outer shell of a low-light night vision device can directly contact external objects, such as people or backpacks. However, the image intensifier of a low-light night vision device is a critical and sensitive part that cannot directly contact external objects unless it is disassembled.

[0116] Step S353: Count the number of wear points MS caused by direct contact with external objects.

[0117] Step S354, based on the wear change correlation function The probability of change of the geometric constraint feature, BH, is calculated, where q is the wear point number in the original region, and p is the maximum wear point number in the original region. , It is a scaling factor and is greater than 0.

[0118] Greater wear indicates a higher frequency of changes in constraints, and a greater number of wear points indicates more variations in the geometric constraints of the original area, thus increasing the probability of change. For example, theoretically, the outer shell of a low-light night vision device is unconstrained, but when the shell is placed in a backpack or used by a user, it inevitably comes into contact with the user, at which point the constraints of the shell change.

[0119] Step S355: If the wear is not caused by direct contact with external objects, then obtain the area with the attribute of direct contact with the original area as the base area.

[0120] Step S356: Obtain material information for the basic region and assess the probability of change of the obtained geometric constraint features.

[0121] In practical applications, if the geometric constraints of the original region are not caused by external changes, they are considered to be caused by the expansion of other internal materials. For example, region A cannot directly contact external objects, but region A can directly contact region B. There is initially a 0.1 mm gap between regions A and B, but if region B expands by more than 0.1 mm, it will directly contact region A, and the constraints of region A will change. A region with the property of directly contacting the original region refers to a region that can directly contact the original region through movement or expansion; that is, the two regions are directly connected without any physical obstruction other than air.

[0122] The steps for obtaining material information of the basic region and evaluating the probability of changes in the obtained geometric constraint features are as follows:

[0123] Step S3551: Determine whether the base region and the original region are in direct contact, and count the base regions that are not in direct contact as the first region.

[0124] Direct contact refers to a situation where, without expansion, two regions are in direct contact with each other, with no gaps between them.

[0125] Step S3552: Count the number of material types CS in the first region and obtain the historical thermal stress failure probability of different material types in the first region. , where M is the code for different material types.

[0126] The historical thermal stress failure probability can be obtained by statistically analyzing the ratio of the number of thermal stress failures of a material in history to the total number of uses.

[0127] Step S3553: Collect the minimum distance between different material types and the original area and record it as the material distance. , where M is the code for different material types.

[0128] Step S3554, based on the feature change correlation function The probability of change of the geometric constraint feature, BH, is calculated, where M is the number of different material types and N is the maximum number of different material types. , It is a scaling factor and is greater than 0.

[0129] In practical applications, the more diverse the materials surrounding the original region, the more factors influence the geometric constraint characteristics, leading to a higher probability of change. Furthermore, the smaller the minimum distance between the material type and the original region, the higher the probability of historical thermal stress failure, and consequently, the greater the probability of change in the combined constraint characteristics. For example, if region A is surrounded by regions B, C, and D, any expansion or thermal stress failure in any of these regions will cause a change in the geometric constraint characteristics of region A. If region B is only 0.1 mm away from region A, even slight expansion of region B will bring it into contact with region A, changing its geometric constraint characteristics. However, if the distance is 2 cm, even slight expansion of region B will not bring it into contact with region A, so region A will not experience thermal stress, and the probability of a change in its geometric constraint characteristics is even lower. Even if region B is far from region A, thermal stress failure in region B can still easily alter its geometric constraint characteristics; therefore, a higher probability of thermal stress failure leads to a higher probability of changes in geometric constraint characteristics.

[0130] The steps for obtaining environmental data of the target area, combining it with material parameters of the target area, establishing dynamic constraint equations, and simulating the thermal stress analysis results of the target area are as follows:

[0131] Step S51: Collect environmental data of the target area and extract the temperature boundary range based on the environmental data.

[0132] Step S52: Establish dynamic constraint equations and calculate the dynamic expansion ratio based on the dynamic constraint equations.

[0133] Step S53: Establish a dynamic function for material strength, collect material information of the target area, and calculate the dynamic material strength by combining the dynamic function for material strength.

[0134] Step S54: Within the temperature boundary range, the dynamic thermal stress of the target region is obtained by combining the dynamic expansion ratio and dynamic material strength simulation calculation.

[0135] Material information includes the material's coefficient of thermal expansion z and elastic modulus E. Based on the obtained dynamic expansion ratio B, the dynamic thermal stress is calculated using the formula for calculating thermal stress. The formula is as follows: Dynamic thermal stress is .

[0136] Step S55: The dynamic distribution law of thermal stress of the low-light night vision device is confirmed based on the dynamic thermal stress and used as the result of thermal stress analysis.

[0137] In practical applications, because the constraints are influenced by other materials, and these materials also change with time and temperature, the dynamic constraints of the target region are constantly changing. Therefore, the allowable expansion ratio (i.e., the dynamic expansion ratio) varies, and this allowable expansion ratio affects the thermal stress in the target region. Thus, constructing dynamic constraint equations is beneficial for calculating more accurate thermal stress. Simultaneously, if the thermal stress exceeds the material strength, thermal stress failure will occur. The dynamic distribution of thermal stress requires analysis of the magnitude of the thermal stress, while thermal stress failure occurs when there is no thermal stress, indicating material damage. Therefore, it is necessary to simulate the material strength to confirm when thermal stress failure occurs, obtaining thermal stress analysis results, i.e., thermal stress at different temperatures and times, including the case of thermal stress failure. Since material strength also changes with temperature and time, constructing a dynamic function of material strength is also beneficial for obtaining more accurate thermal stress analysis results.

[0138] The steps for establishing dynamic constraint equations and calculating the dynamic expansion ratio based on these equations are as follows:

[0139] Step S521: Obtain the region with direct contact with the target area and designate it as the first region; collect the material expansion coefficient of the first region. , where h is the number of the first region.

[0140] Step S522: Collect the minimum distance between the first region and the target region as the original gap distance. , where h is the number of the first region.

[0141] Step S523, establish dynamic constraint equations:

[0142]

[0143] Where g(T) is the real-time gap distance. It is the coefficient of thermal expansion of the target region. It is the coefficient of thermal expansion of the material in the first region. It is the effective length of the target region in the direction of influencing the gap. It is the effective length of the first region influencing the direction of the void. It is the change in temperature.

[0144] Step S524: Simulate the real-time gap distance of the target area at different temperatures based on the dynamic constraint equation to obtain the dynamic gap distance.

[0145] Step S525: Calculate the material expansion amount based on the material expansion coefficient and temperature change of the target area, and obtain the dynamic expansion ratio by the ratio of the material expansion amount to the dynamic void distance.

[0146] In practical applications, the amount of material expansion can be calculated using the method for calculating expansion displacement. The dynamic expansion ratio is obtained by comparing the amount of material expansion with the dynamic gap distance. When the two regions are not in direct contact, temperature changes affect their respective dimensions through the material expansion coefficients of the first and target regions, leading to dynamic changes in the gap. Initially, when the two regions are in contact, temperature changes may induce contact pressure or plastic deformation, but the gap remains zero (unless the material yields).

[0147] The steps for establishing a dynamic material strength function, collecting material information from the target region, and calculating the dynamic material strength using the dynamic material strength function are as follows:

[0148] Step S531, establish the material strength dynamic function:

[0149]

[0150] Where DQ is the real-time strength of the material. It is the yield strength term. It is a hardening term. It is a correction term for strain rate and Poisson effect. It is a time decay term. This is the time decay coefficient.

[0151] The hardening term describes the change in strength of a material during the plastic stage due to changes in its microstructure. In materials mechanics and thermal stress analysis, Plastic strain refers to the irreversible deformation of a material after the stress exceeds its elastic limit (yield strength). It can be described in software such as ABAQUS using the parameters PEEQ (equivalent plastic strain) and PEMAG (instantaneous strain). The hardening factor K(T) characterizes the initial hardening rate of plastic deformation and is related to the microstructure, such as grain size and dislocation density. Grain coarsening (dynamic recrystallization) or phase transformations (such as the precipitation of intermetallic compounds) at high temperatures decrease K(T). For example, in nickel-based alloys at 750℃, increased grain size leads to a decrease in K(T) of approximately 40% compared to room temperature. The hardening exponent w(T) describes the rate at which plastic strain contributes to strength growth, reflecting the material's strain hardening capability. Increased dislocation mobility at high temperatures leads to a decrease in the hardening rate (a decrease in w(T)). For example, in aluminum alloys, w≈0.15 at 200℃ and w≈0.25 at room temperature, indicating significant high-temperature softening.

[0152] Power-law hardening models (such as the Ludwik equation) describe the strength increase caused by dislocation accumulation during plastic deformation. The effect of temperature on the hardening coefficient K(T) and the exponent w(T) stems from dynamic recovery and recrystallization (such as softening during high-temperature deformation of metals). Uniaxial cyclic loading tests (such as tensile-unloading cycles in thin metal sheets) determine the hardening curve and fit the K and w values. Compression tests of aluminum alloys at high temperatures verify the decreasing trend of K(T) with increasing temperature. The Ramberg-Osgood model parameters (hardening coefficient K and exponent w) are calibrated. Uniaxial tensile tests are conducted using aluminum alloy AA6061, and the fitting results are K = 450 MPa, w = 0.15, with an error <3%. Some experimental data are shown below:

[0153] Plastic strain Flow stress (MPa) <![CDATA[Strain rate (s -1 )]]> 0.05 300 0.001 0.10 360 0.001 0.20 420 0.001

[0154] Stress relaxation tests (e.g., ASTM E328) were used to determine relaxation curves, and the time decay coefficient β was fitted. The Prony series model parameters (relaxation time and modulus decay coefficient) were calibrated, with the following fitting parameters: relaxation time 50 seconds, modulus decay coefficient 0.3; relaxation time 500 seconds, modulus decay coefficient 0.2. Some experimental data using epoxy resin are as follows:

[0155] Time (s) Shear modulus G(t) (MPa) Temperature (°C) 0 1.0 25 100 0.65 25 1000 0.45 25

[0156] Step S532, the functional form of the yield strength term is:

[0157]

[0158] in, The room temperature yield strength, The temperature decay coefficient of yield strength. It is the change in temperature. Critical creep failure time, where t is the real-time time.

[0159] The relationship between yield strength and temperature (T) and time (t) originates from thermal activation theories (such as the Arrhenius equation), where enhanced dislocation movement at high temperatures leads to a decrease in yield strength. The time dependence corresponds to creep or relaxation effects, such as the creep deformation of polymer materials under constant stress. Yield strength at different temperatures is measured through high-temperature tensile tests (e.g., the yield strength of steel decreases by 50% at 500°C). Constant-load creep tests (such as ASTM E139) record the change in material yield strength over time.

[0160] Step S533, the functional form of the strain rate and Poisson effect correction term is:

[0161]

[0162] in, For strain rate, is the reference strain rate, j is the strain sensitivity coefficient, and v is Poisson's ratio.

[0163] The Poisson effect (lateral shrinkage rate v) corrects for volume changes under complex stress states (such as the incompressibility of rubber-like materials). The Hopkinson bar test (SHPB) measures the stress response under different strain rates to verify this effect. The nonlinear characteristics of the material are shown in the Hopkinson bar test conducted using a zirconium-based amorphous alloy. Some experimental data are as follows:

[0164] <![CDATA[Strain rate (s -1 ).]]> Dynamic stress (MPa) Temperature (°C) Experimental conditions 300 1850 25 Low strain rate, partial reaction 800 2100 100 Medium strain rate, shear fracture 1400 2450 200 High strain rate, violent reaction

[0165] In dynamic compression tests of polymer materials, the transverse strain is measured using DIC technology to calibrate the effect of Poisson's ratio v on strength.

[0166] Step S534: Calculate the dynamic material strength based on the material strength dynamic function.

[0167] In practical applications, obtaining the dynamic function of material strength by coupling multiple factors such as time and temperature is beneficial for obtaining more accurate calculation results and more accurate thermal stress analysis results.

[0168] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A thermal stress analysis method based on a low-light night vision device, characterized in that, Includes the following steps: The spatial topography data of the low-light night vision device is obtained by scanning, a three-dimensional solid model of the low-light night vision device is constructed, and the three-dimensional solid model is divided into several original regions with independent functional attributes. Collect data on the independent functional modules corresponding to the original area, and determine the quantitative value of the importance of the independent functional modules to the overall performance of the low-light night vision device. The geometric constraint features of the original region are collected, the material parameters of the material used to construct the original region are obtained, and the probability of thermal stress phenomena occurring in the original region is obtained by combining the geometric constraint features and material parameters. Based on the quantification value of importance and the probability of thermal stress phenomena, the target area for thermal stress analysis is selected from the original area. By acquiring environmental data of the target area and combining it with the material parameters of the target area, dynamic constraint equations are established to simulate and obtain the thermal stress analysis results of the target area. The steps of acquiring environmental data of the target area, combining it with material parameters of the target area, establishing dynamic constraint equations, and simulating the thermal stress analysis results of the target area are as follows: Collect environmental data of the target area, and extract the temperature boundary range based on the environmental data; Establish dynamic constraint equations and calculate the dynamic expansion ratio based on the dynamic constraint equations. A dynamic material strength function is established, material information of the target area is collected, and dynamic material strength is calculated by combining the dynamic material strength function. The dynamic thermal stress in the target region is obtained by combining dynamic expansion ratio and dynamic material strength simulation within the temperature boundary range; The dynamic distribution law of thermal stress of the low-light night vision device was confirmed by dynamic thermal stress and used as the result of thermal stress analysis. The steps of establishing dynamic constraint equations and calculating the dynamic expansion ratio based on these equations are as follows: The region with direct contact with the target area is identified and designated as the first region. The material expansion coefficient of the first region is then collected. , where h is the number of the first region; The minimum distance between the first region and the target region is used as the original gap distance. , where h is the number of the first region; Establish dynamic constraint equations: ； Where g(T) is the real-time gap distance. It is the coefficient of thermal expansion of the target region. It is the coefficient of thermal expansion of the material in the first region. It is the effective length of the target region in the direction of influencing the gap. It is the effective length of the first region influencing the direction of the void. It is the change in temperature; The dynamic gap distance is obtained by simulating the real-time gap distance of the target region under different temperatures based on the dynamic constraint equation; The material expansion is calculated based on the material expansion coefficient and temperature change of the target area. The dynamic expansion ratio is obtained by the ratio of the material expansion to the dynamic void distance.

2. The thermal stress analysis method based on a low-light night vision device according to claim 1, characterized in that, The step of determining the quantitative value of the importance of the independent functional modules corresponding to the original acquisition area to the overall performance of the low-light night vision device is as follows: To obtain the core performance of a low-light night vision device, and to simulate the performance degradation ratio of different core performance components when an independent functional module of the low-light night vision device is removed. , where m is the number of different core performance; The simulation yielded a quantitative value of the impact of core performance on the imaging of low-light night vision devices. , where m is the number of different core performance; Based on performance correlation function The performance level quantification value XL is calculated, where m is the number of different core performance and n is the maximum number of different core performance. Collect user usage habit data and simulate the degree of usage based on the usage habit data; The importance of each independent functional module to the overall performance of the low-light night vision device is obtained by combining the performance level quantification value XL and the usage level quantification value.

3. The thermal stress analysis method based on a low-light night vision device according to claim 2, characterized in that, The step of collecting user usage habit data and quantifying the degree of usage based on the usage habit data is as follows: Collect faulty low-light night vision devices, scan faulty low-light night vision devices, and establish a three-dimensional model of the fault. Wear data was extracted from the three-dimensional fault model to simulate user habits when using low-light night vision devices; Collect fault data of faulty low-light night vision devices, and determine whether the cause of the fault is related to the user based on usage habit data; If the cause of the failure is unrelated to the user, a simulation evaluation of the fault resistance capability of the independent functional module is performed to quantify the robustness level of the independent functional module in the scenario where the cause of the failure is unrelated to the user, which is used as a quantitative value of the degree of use.

4. The thermal stress analysis method based on a low-light night vision device according to claim 3, characterized in that, The steps of acquiring the geometric constraint features of the original region, obtaining the material parameters of the material used to construct the original region, and combining the geometric constraint features and material parameters to analyze the probability of thermal stress phenomena occurring in the original region are as follows: Collect the geometric constraint features of the original region, and extract the movement gap displacement of the original region based on the geometric constraint features; Collect the operating temperature range of the low-light night vision device, and calculate the temperature change based on the operating temperature range; Obtain the material parameters of the original region material, and calculate the expansion displacement by combining the temperature change. The displacement difference between the moving clearance displacement and the expansion displacement is calculated. The location information of the original region is extracted from the 3D solid model, and the probability of change of geometric constraint features is determined based on the location information of the original region. Based on a comprehensive analysis of displacement difference and change probability, the probability of thermal stress occurring in the original region is derived.

5. The thermal stress analysis method based on a low-light night vision device according to claim 4, characterized in that, The steps of extracting the location information of the original region from the 3D solid model and determining the probability of change of geometric constraint features based on the location information of the original region are as follows: Based on the 3D solid model and wear data, wear points in the original area are marked in the 3D solid model; Determine whether the wear at the wear point is caused by direct contact with an external object. If the wear is caused by direct contact, then extract the degree of wear based on the wear data. q represents the wear point number in the original region; The statistical cause of wear is the number of wear points (MS) obtained from direct contact with external objects; Correlation function based on wear change The probability of change of the geometric constraint feature, BH, is calculated, where q is the wear point number in the original region, and p is the maximum wear point number in the original region. , It is a scaling factor and is greater than 0; If the cause of wear and tear is not due to direct contact with external objects, then the area with the attribute of direct contact with the original area is obtained as the base area; Obtain material information for the basic region and assess the probability of changes in the obtained geometric constraint features.

6. The thermal stress analysis method based on a low-light night vision device according to claim 5, characterized in that, The steps of obtaining material information of the basic region and evaluating the probability of change of the obtained geometric constraint features are as follows: Determine whether the base region and the original region are in direct contact, and count the base regions that are not in direct contact as the first region; Count the number of material types (CS) in the first region and obtain the historical thermal stress failure probability of different material types in the first region. , where M is the code for different material types; Collect the minimum distance between different material types and the original area and record it as the material distance. , where M is the code for different material types; Association function based on feature changes The probability of change of the geometric constraint feature, BH, is calculated, where M is the number of different material types and N is the maximum number of different material types. , It is a scaling factor and is greater than 0.

7. The thermal stress analysis method based on a low-light night vision device according to claim 1, characterized in that, The steps of establishing a dynamic material strength function, collecting material information of the target area, and calculating the dynamic material strength using the dynamic material strength function are as follows: Establish the dynamic function of material strength: ； Where DQ is the real-time strength of the material. It is the yield strength term. It is a hardening term. It is a correction term for strain rate and Poisson effect. It is a time decay term. This is the time decay coefficient; The functional form of the yield strength term is: ； in, The room temperature yield strength, The temperature decay coefficient of yield strength. It is the change in temperature. Critical creep failure time, where t is real-time; The functional form of the strain rate and Poisson effect correction term is: ； in, For strain rate, Here, j is the reference strain rate, v is the strain sensitivity coefficient, and v is Poisson's ratio. The dynamic material strength is calculated based on the material strength dynamic function.

Images