Structural parameter optimization design method and system applied to electronic accessories

Abstract

The invention relates to the technical field of structural design of electronic accessories, in particular to a structural parameter optimization design method and system applied to electronic accessories. The method comprises the following steps: obtaining the structure and material parameters of the electronic accessory; performing moisture flow monitoring according to the structure and material parameters of the electronic accessory to obtain a moisture flow vector field; performing permeation path dynamic reconstruction based on the moisture flow vector field to obtain a permeation track spectrogram; acquiring use environment parameters of the electronic accessory; dew point condition calculation at different temperatures is carried out based on the permeation track spectrogram, and a condensation condition difference chart is obtained; and carrying out heat-humidity coupling dynamic analysis on the condensation condition difference chart and the permeation track spectrogram according to electronic accessory use environment parameters to obtain a condensation trigger condition set. According to the invention, the problems of moisture permeation and condensation of the electronic accessory in a complex environment are solved through a structure parameter optimization design technology, the environmental adaptability of the electronic accessory is greatly improved, and the service life of the electronic accessory is greatly prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic accessory structure design, and in particular to a structural parameter optimization design method and system for electronic accessories. Background Art

[0002] Existing electronic component structural designs lack an accurate understanding of the dynamic process of moisture penetration, and mainly rely on static humidity measurements and empirical judgments. They are unable to capture the true movement trajectory of moisture inside complex structures as time and environment change, resulting in a lack of targeted moisture-proof design; existing technologies for condensation risk judgment are inaccurate and non-dynamic, usually relying on simple relative humidity thresholds or static dew point calculations, ignoring the dynamic coupling of temperature gradients and moisture migration, and unable to predict the specific time and spatial conditions for the formation of condensation points, making it difficult to accurately arrange protective measures; the evaluation of structural moisture-proof performance in existing technologies is obviously empirical and non-quantitative, making it difficult to accurately express the moisture-proof performance of different structural units, resulting in the inability to identify real weak links and a lack of scientific basis for moisture-proof design optimization.

[0003] In summary, existing technologies have problems such as inaccurate monitoring of moisture penetration process, inaccurate prediction of condensation risk, and unscientific evaluation of moisture-proof performance, which need to be urgently addressed. Summary of the Invention

[0004] Based on this, it is necessary to provide a structural parameter optimization design method and system for electronic accessories to solve at least one of the above technical problems.

[0005] To achieve the above objectives, a structural parameter optimization design method for electronic accessories includes the following steps:

[0006] Step S1: obtaining the structure and material parameters of the electronic component; monitoring moisture flow based on the structure and material parameters of the electronic component to obtain a moisture flow vector field; dynamically reconstructing the permeation path based on the moisture flow vector field to obtain a permeation trajectory spectrum;

[0007] Step S2: Obtaining the environmental parameters for the electronic component's use; calculating dew point conditions at different temperatures based on the penetration trajectory spectrum to obtain a condensation condition difference map; performing a thermal-humidity coupling dynamic analysis on the condensation condition difference map and the penetration trajectory spectrum based on the environmental parameters for the electronic component's use to obtain a condensation trigger condition set; calculating the condensation risk probability based on the condensation trigger condition set to obtain a moisture-heat risk topology map;

[0008] Step S3: Structural unit risk mapping is performed based on the electronic component structure and material parameters to obtain a structural unit risk correspondence table; material moisture permeability is measured based on the structural unit risk correspondence table to obtain a material moisture permeability coefficient table; barrier strength is quantitatively calculated based on the material moisture permeability coefficient table and the structural unit risk correspondence table, and moisture resistance evaluation is performed to obtain a moisture resistance efficiency index matrix;

[0009] Step S4: performing moisture flow simulation and structural parameter optimization on the electronic component based on the moisture-proof performance index matrix to obtain an optimized configuration set of structural parameters.

[0010] By acquiring the precise structural and material parameters of electronic components and combining them with the spatiotemporal synchronization of multiple micro-humidity sensors, this method can capture the dynamic moisture penetration process within electronic components as the environment changes, rather than simply capturing a static humidity distribution. Reconstructing the moisture flow vector field based on measured data can intuitively and accurately reveal the true migration direction, rate, and path of moisture within complex structures, including identifying key penetration channels and potential moisture accumulation areas. This dynamic, multi-dimensional moisture path tracking capability overcomes the limitations of traditional methods in understanding the moisture penetration process, providing a reliable data foundation for subsequent precise analysis of moisture and heat risks, enabling moisture-proofing designs to be targeted at practical issues rather than based on assumptions. Thermal-humidity coupling analysis is incorporated into condensation risk prediction. By precisely monitoring and reconstructing the temperature field within an electronic component under different operating conditions and combining it with humidity information from the moisture penetration trajectory spectrum, the dynamic dew point temperature and actual temperature difference at each internal point can be calculated. More importantly, this thermal-humidity coupling dynamic analysis simulates the impact of temperature gradients on moisture migration and, taking into account the operating cycle and environmental changes of the electronic component, dynamically predicts the time and spatial conditions for the occurrence of critical condensation states. This method identifies instantaneous or intermittent condensation risk points that are difficult to detect using traditional static dew point calculations or relative humidity thresholds, quantifies their probability of occurrence and potential severity, and ultimately generates a detailed moisture and heat risk topology map, providing precise and dynamic risk distribution information for subsequent targeted moisture-proofing design and optimization. By dividing the electronic component structure into structural units with clear functions and morphologies and associating moisture and heat risks with these units, precise risk localization is achieved. By accurately measuring the moisture permeability of materials and interfaces under different temperature, humidity, and aging conditions, the true moisture permeability coefficient of the materials under various operating conditions is obtained. Based on the material moisture permeability coefficient and structural geometric parameters, the moisture barrier strength of each structural unit is quantitatively calculated, identifying weak links within the structure. This quantitative assessment method breaks away from the limitations of traditional empirical judgments and accurately quantifies the moisture-proofing capacity of each structural unit and matches it with the actual moisture and heat risk. The resulting moisture-proofing effectiveness index matrix clearly identifies the strengths and weaknesses of the moisture-proofing system, providing solid data support for subsequent targeted and informed structural optimization. Based on the key structural units identified by the moisture-proof performance index matrix, a parameter optimization constraint framework was constructed to clarify the adjustable range and limitations of the design. By generating multiple sets of candidate solutions and performing moisture flow simulations, it is possible to predict the moisture-proof performance under different design parameter combinations, and quantify the performance improvement by comparing it with the original design. Extreme condition testing was introduced to evaluate the robustness of the optimized solution in harsh environments. Most importantly, through the multi-objective optimization algorithm, it is possible to balance other important functional requirements such as cost, weight, and structural strength while improving moisture-proof performance, avoiding the "loss of one thing while focusing on another" problem in traditional design.The final output of the optimal configuration set of structural parameters is a specific design solution obtained through scientific evaluation and optimization. It can significantly improve the reliability and environmental adaptability of electronic components in hot and humid environments and extend their service life. Therefore, the present invention provides a structural parameter optimization design method for electronic components. By establishing a complete technical route of "multi-dimensional moisture penetration monitoring-dynamic assessment of condensation risk-structural moisture-proof performance analysis-parametric optimization design", it realizes the dynamic tracking of moisture penetration trajectory, accurate prediction of condensation risk under temperature and humidity coupling conditions, quantitative assessment of structural moisture-proof performance, and structural parameter optimization based on bionic principles, thereby effectively solving the moisture penetration and condensation problems of electronic components in complex environments and greatly improving the environmental adaptability and service life of electronic components.

[0011] Preferably, the present invention further provides a structural parameter optimization design system for electronic accessories, which is used to execute the structural parameter optimization design method for electronic accessories as described above. The structural parameter optimization design system for electronic accessories includes:

[0012] The moisture path analysis module is used to obtain the structure and material parameters of electronic components; monitor moisture flow based on the structure and material parameters of electronic components to obtain a moisture flow vector field; and dynamically reconstruct the infiltration path based on the moisture flow vector field to obtain a infiltration trajectory spectrum;

[0013] The moisture and heat risk assessment module is used to obtain the environmental parameters of electronic components; calculate the dew point conditions at different temperatures based on the penetration trajectory spectrum to obtain a condensation condition difference map; perform a thermal and moisture coupling dynamic analysis on the condensation condition difference map and the penetration trajectory spectrum based on the environmental parameters of the electronic components to obtain a condensation trigger condition set; calculate the condensation risk probability based on the condensation trigger condition set to obtain a moisture and heat risk topology map;

[0014] The structural moisture-proof assessment module is used to map structural unit risks based on the electronic component structure and material parameters to obtain a structural unit risk correspondence table; determine the material moisture permeability based on the structural unit risk correspondence table to obtain a material moisture permeability coefficient table; quantitatively calculate the barrier strength based on the material moisture permeability coefficient table and the structural unit risk correspondence table, and perform moisture-proof assessment to obtain a moisture-proof efficiency index matrix;

[0015] The structural optimization design module is used to simulate moisture flow and optimize structural parameters of electronic components based on the moisture-proof performance index matrix to obtain the optimal configuration set of structural parameters.

[0016] This structural parameter optimization design system for electronic components breaks down complex design methods into four interrelated modules: moisture path analysis, hygrothermal risk assessment, structural moisture-proofing assessment, and structural optimization design. This system achieves systematization and automation of the entire optimization process. The system efficiently integrates multi-source data (structural parameters, material parameters, environmental parameters, and sensor data) and leverages the specialized functions of each module to dynamically monitor and analyze moisture penetration, accurately predict condensation risks, quantitatively evaluate structural moisture-proofing performance, and ultimately achieve parametric optimization design. This modular design provides clear system functionality, ease of maintenance and upgrades, and enables smooth information transfer and collaborative work through data interfaces between modules. The system can significantly improve the efficiency and accuracy of electronic component structural moisture-proofing design, reduce R&D costs and cycles, and continuously optimize design solutions, thereby significantly enhancing the reliability and product competitiveness of electronic components in complex hygrothermal environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flowchart of the steps of a structural parameter optimization design method applied to electronic accessories.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0020] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0021] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0022] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of the structural parameter optimization design method for electronic accessories according to the present invention. In this example, the structural parameter optimization design method for electronic accessories includes the following steps:

[0023] Step S1: obtaining the structure and material parameters of the electronic component; monitoring moisture flow based on the structure and material parameters of the electronic component to obtain a moisture flow vector field; dynamically reconstructing the permeation path based on the moisture flow vector field to obtain a permeation trajectory spectrum;

[0024] In an embodiment of the present invention, first, based on the structural drawings and material parameters of the electronic accessories, its geometric characteristics and material properties are analyzed to identify the key areas for moisture penetration or accumulation. Next, based on the identified key areas, the precise positions of the micro humidity sensors are planned on the electronic accessory samples to form a sensor grid configuration diagram that reflects the spatial distribution and importance of the monitoring points. Then, the sample with the sensors installed is placed in an environmental test chamber and cyclically tested according to a preset environmental condition gradient containing different temperature and humidity changes, and the humidity readings of all sensors at different time points are synchronously recorded to generate a humidity response data set. Finally, the collected humidity response data set is filtered, denoised, and subjected to spatiotemporal correlation analysis. Interpolation and flow field reconstruction techniques are used to convert the discrete humidity data into a moisture flow vector field that varies with time inside the electronic accessory. This vector field describes the flow direction and rate of moisture at various internal locations, laying a data foundation for the subsequent dynamic reconstruction of the penetration path.

[0025] Step S2: Obtaining the environmental parameters for the electronic component's use; calculating dew point conditions at different temperatures based on the penetration trajectory spectrum to obtain a condensation condition difference map; performing a thermal-humidity coupling dynamic analysis on the condensation condition difference map and the penetration trajectory spectrum based on the environmental parameters for the electronic component's use to obtain a condensation trigger condition set; calculating the condensation risk probability based on the condensation trigger condition set to obtain a moisture-heat risk topology map;

[0026] In an embodiment of the present invention, temperature sensors are arranged at key locations of electronic accessory samples and temperature data is collected under different working states and environmental conditions. Combined with the structural and material thermal parameters, the three-dimensional time-varying temperature field inside the electronic accessory is reconstructed through numerical simulation to obtain a temperature gradient distribution map. Next, the humidity information in the penetration trajectory spectrum is mapped to the temperature field grid, and humidity spatial interpolation is performed to obtain a humidity spatial distribution grid aligned with the temperature field. Then, based on the humidity and temperature data, the dew point temperature calculation formula is used to calculate the dew point temperature of each point inside the electronic accessory at each time point to form a dew point temperature distribution map, and the difference between the actual temperature and the dew point temperature is calculated to obtain a condensation condition difference map. At the same time, critical condensation areas with a temperature difference less than or equal to zero are identified to form a condensation risk area map, and the stability of the temperature difference in these areas over time is analyzed to obtain a temperature difference time stability index. Then, the moisture flow information of the infiltration trajectory spectrum and the temperature difference information of the condensation condition difference map are extracted, a temperature-humidity coupling model of the temperature gradient on the moisture flow is established, and the temperature-humidity coupling coefficient map is calculated; the representative standard working cycle set is defined in combination with the environmental parameters of the electronic accessories, and a thermal-humidity coupling dynamic simulation is carried out under this condition to obtain the thermal-humidity dynamic response trajectory of the temperature, humidity and temperature difference inside the electronic accessories changing with time, identify the critical condensation state, form a condensation critical state set, and analyze the accumulation of moisture in the critical area to obtain a moisture accumulation risk map; finally, the moisture accumulation risk map and the condensation critical state set are combined to parameterize the external environment and internal state that lead to condensation, and obtain a condensation trigger condition set. Finally, the historical statistical characteristics of the environmental parameters for the use of electronic accessories are analyzed, the probability of condensation trigger conditions occurring in the actual environment is calculated, and a trigger condition probability table is obtained; the moisture sensitivity of internal components is evaluated in combination with the structure and material parameters of the electronic accessories, and a component sensitivity distribution map is obtained; the trigger probability, component sensitivity, condensation occurrence location, and risk time window are comprehensively evaluated to obtain a moisture and heat risk topology map that identifies the condensation risk level, severity, and time characteristics of each area inside the electronic accessory.

[0027] Step S3: Structural unit risk mapping is performed based on the electronic component structure and material parameters to obtain a structural unit risk correspondence table; material moisture permeability is measured based on the structural unit risk correspondence table to obtain a material moisture permeability coefficient table; barrier strength is quantitatively calculated based on the material moisture permeability coefficient table and the structural unit risk correspondence table, and moisture resistance evaluation is performed to obtain a moisture resistance efficiency index matrix;

[0028] In an embodiment of the present invention, electronic components are divided into multiple independent structural units based on their functional and morphological characteristics, based on their structural drawings and material parameters. Subsequently, the moisture and heat risk topology map is spatially associated with the divided structural units, identifying key structural units that overlap or are adjacent to high-risk areas and forming a structural unit risk correspondence table. The types of materials and interfaces that require evaluation are then determined based on the structural unit risk correspondence table. Standard test samples are prepared, and the water vapor transmission rates of the materials and interfaces are measured using standard methods under standard conditions, different temperatures and humidities, and accelerated aging conditions. This results in a moisture permeability mapping table for the materials at different temperatures and humidities, a table of interface moisture permeability characteristics, and a moisture permeability aging trend chart. Based on the material moisture permeability coefficient table and the structural unit geometric parameters, the moisture permeability resistance of the single-layer material or simple structure within each structural unit is calculated, and the total moisture permeability resistance of complex multi-layer or multi-path structural units is synthesized. Interface weaknesses in the structure are analyzed and identified. Based on the total resistance and weaknesses, the barrier strength coefficient of each structural unit is calculated. Finally, the barrier strength coefficient is matched with the risk environment conditions associated with the moisture-heat risk topology map to evaluate the performance retention ability of the structural unit under the risk environment and obtain an environmental adaptability index table; the barrier strength coefficient and the environmental adaptability index are combined to comprehensively score the moisture-proof performance of each structural unit, and finally form a moisture-proof performance index matrix that quantifies the moisture-proof performance of each structural unit and its correlation with moisture-heat risk.

[0029] Step S4: performing moisture flow simulation and structural parameter optimization on the electronic component based on the moisture-proof performance index matrix to obtain an optimized configuration set of structural parameters;

[0030] In an embodiment of the present invention, based on the moisture-proof efficiency index matrix, key structural units with low moisture-proof performance scores and close association with high-risk areas are identified as optimization targets. Then, for the determined key structural units, the value ranges and mutual constraints of their adjustable design parameters (such as materials, dimensions, and surface treatments) are defined, and a parameter optimization constraint framework is constructed. Then, based on the parameter optimization constraint framework, multiple groups of candidate structural parameter combinations are generated, corresponding simulation models are constructed, and typical moisture penetration boundary conditions and simulation working conditions are set; the moisture transmission simulation tool is used to perform numerical simulation of the moisture penetration process for each candidate solution to obtain a penetration process data set; key penetration features are extracted from the penetration process data set and the penetration trajectory spectrum of the original design for comparison, and the moisture-proof performance improvement of each solution relative to the original design is quantified to obtain a set of performance improvement indicators; and the optimized solution is simulated and tested under extreme environmental conditions to obtain extreme condition response data. Finally, the performance improvement indicators under typical conditions and the robustness data under extreme conditions are comprehensively considered to give each candidate solution a comprehensive performance score to form a solution performance evaluation matrix; a multi-objective optimization algorithm is used to balance the solution performance evaluation matrix and other functional requirements of electronic accessories (such as cost and weight), and the optimal solution is selected from the optimization solution set to generate an optimized configuration set of structural parameters containing detailed information such as material selection and precise geometric dimensions.

[0031] Preferably, the moisture flow monitoring in step S1 is specifically as follows:

[0032] Determine key monitoring areas for moisture penetration points based on electronic component structural data;

[0033] Design the sensor point layout according to the key monitoring areas and obtain the sensor grid configuration diagram;

[0034] According to the sensor grid configuration diagram, the environmental condition gradient of the electronic accessories is sampled to obtain the humidity response data set;

[0035] The humidity response dataset is processed into spatiotemporal series data to obtain the moisture flow vector field.

[0036] In an embodiment of the present invention, based on the computer-aided design drawings of the electronic accessories (the computer-aided design drawings contain the complete three-dimensional geometric model of the electronic accessories, the material type of each component, the assembly relationship and tolerance information) and the physical parameters of each material (such as density, thermal conductivity, water vapor permeability, etc.), the geometric structure characteristics and material combination characteristics of the electronic accessories are analyzed to identify the interfaces where moisture is most likely to pass through, the cavities where moisture is most likely to accumulate, and the areas where key electronic components that are sensitive to moisture are located. These areas generally include the seams of the housing, sealing rings, holes through which cables or connectors pass, heat dissipation holes, and the vicinity of printed circuit boards (PCBs). By performing engineering judgment and preliminary simulation on these potential moisture penetration and accumulation points, a set of key areas that need to be monitored is determined. For example, in a sealed electronic housing, the overlapping surfaces of the upper and lower covers of the housing, the seals of the cable outlets, and the area around the internal main control chip are determined as key monitoring areas.

[0037] Based on the identified critical monitoring areas, a precise sensor layout is designed for the electronic component sample to be tested. Using the 3D model of the electronic component, the placement of micro-humidity sensors within, on, and along the edges of the critical monitoring areas is planned according to specific spatial density and coverage principles. For example, if five potential penetration points and three potential accumulation areas are identified within and on the surface of an electronic component measuring 100 mm in length, 80 mm in width, and 30 mm in height, two to three sensors are placed near each penetration point, three to five sensors are evenly spaced within each accumulation area, and a small number of sensors are placed on the surface and in non-critical areas of the housing as reference points. This creates a sensor grid that covers the critical areas and reflects the overall distribution and flow of moisture. The specific sensor coordinates and the importance weights of their monitoring areas are recorded to generate a sensor grid configuration diagram, such as a table containing sensor IDs, X / Y / Z coordinates, target monitoring areas, and data collection frequency settings.

[0038] Based on the generated sensor grid configuration, actual micro-humidity sensors (e.g., capacitive humidity sensors based on microelectromechanical systems (MEMS) technology, measuring less than 5 mm x 5 mm) are precisely mounted at the planned locations on the electronic component sample. The sensor-mounted electronic component sample is then placed in a programmable temperature and humidity cycling chamber. A series of test cycles with varying temperature and humidity ramp rates and durations are designed based on the actual operating environment and potential humidity failure modes of the electronic component. For example, a test sequence can include a rapid temperature ramp (e.g., from +50°C to +5°C, with 80% relative humidity), a prolonged high-humidity exposure (e.g., 40°C, 95% relative humidity for 24 hours), and a normal temperature and humidity phase (e.g., 25°C, 50% relative humidity). Throughout the test, a data acquisition system (e.g., a multi-channel data logger) synchronously records the humidity readings of all sensors, along with the temperature and humidity data of the environmental chamber, at a frequency specified by the sensor grid configuration (e.g., once per minute). This generates a dataset of the electronic component's humidity response under different environmental conditions. This dataset is a two-dimensional table containing timestamps, sensor IDs, and corresponding humidity values.

[0039] The collected raw humidity response dataset is preprocessed and analyzed. First, a digital filtering algorithm (e.g., median filtering or Kalman filtering) is applied to remove random noise and glitches from the sensor signals. Next, trend analysis and rate of change calculation (e.g., the amount of humidity change per unit time) are performed on the humidity time series of each sensor. Next, using the sensor spatial location information recorded in the sensor grid configuration diagram, spatial correlation analysis is performed on the data from different sensors at the same time point. The humidity differences and distances between adjacent sensors are calculated, thereby preliminarily inferring the spatial gradient of moisture. Furthermore, the time delays between the peak values ​​or significant changes in moisture at different sensor locations are analyzed in conjunction with the time series data. Leveraging this temporal and spatial information, spatial interpolation algorithms (e.g., trilinear interpolation or radial basis function interpolation) and flow field reconstruction techniques are applied to convert the discrete sensor data into a continuous moisture flow vector field within the electronic component. This vector field is a four-dimensional data structure that represents the estimated moisture flow direction and velocity (a three-dimensional vector) at each location (three-dimensional spatial coordinates: X, Y, and Z) within the electronic component at different time points (the fourth dimension). For example, at a certain moment, if the humidity of sensor A (coordinates XA, YA, ZA) rises earlier than and is higher than that of sensor B (coordinates XB, YB, ZB), and there is a known moisture channel between the two, it can be inferred that moisture flows from A to B, and the flow rate can be estimated based on the humidity difference and spatial distance. These local inferences are extended to the entire sensor grid coverage area to form a vector field of moisture flow.

[0040] Preferably, the dynamic reconstruction of the permeation path in step S1 is specifically as follows:

[0041] Extract the flow trajectory of the moisture flow vector field and obtain the moisture particle trajectory set;

[0042] Perform infiltration channel cluster analysis on the moisture particle trajectory set to obtain the infiltration channel characteristic map;

[0043] Calculate the channel morphology parameters on the permeation channel characteristic map to obtain a channel morphology data set;

[0044] Calculate the moisture transfer efficiency table based on the channel morphology dataset and the moisture flow vector field;

[0045] According to the moisture transfer efficiency table and the permeation channel characteristic diagram, the humidity accumulation hot spot analysis is carried out to obtain the humidity accumulation distribution diagram;

[0046] The spatiotemporal dynamic model was analyzed based on the humidity cumulative distribution map, moisture transfer efficiency table and channel morphology dataset to obtain the infiltration trajectory spectrum.

[0047] In an embodiment of the present invention, the moisture flow vector field is processed and regarded as a flow field that varies with time. In this flow field, virtual moisture particles are released at multiple starting points (for example, at the location where moisture enters the outer surface of the electronic component or near a known penetration point). Using a numerical integration method (for example, the Runge-Kutta method), the position of the particle at the next time step is calculated based on the moisture flow vector at the location of the particle at each time point, and this process is repeated to track the movement trajectory of each virtual particle inside the electronic component. For example, if a particle is at position P(x, y, z) at a certain time point, and the moisture flow vector at this position is V = (vx, vy, vz), then after a very small time step Δt, the new position of the particle P'(x', y', z') can be approximately calculated as x' = x + vx × Δt, y' = y + vy × Δt, and z' = z + vz × Δt. By tracking a large number of particles and recording their entire process path from the starting point to the final stable position (for example, the humidity saturation area or the stopped flow area), including the spatial coordinate sequence and the corresponding timestamp, a moisture particle trajectory set is obtained. This is a data set containing multiple particle trajectories, each trajectory consisting of a series of ordered space-time points.

[0048] Cluster analysis is performed on the moisture particle trajectory set to identify trajectories with similar spatial paths and flow characteristics. For example, a density-based clustering algorithm (e.g., DBSCAN) or a distance-based clustering algorithm (e.g., K-means) is applied to classify particle trajectories that are spatially close and have the same flow direction into the same category, representing a moisture penetration channel. The central trajectory, width, shape, and position of each cluster (channel) in the electronic accessory structure are analyzed to obtain a penetration channel feature map. The penetration channel feature map is a spatial distribution map that identifies the main moisture penetration paths inside the electronic accessory, for example, displayed as a pipe-like structure in three-dimensional space, with an identifier for each channel and preliminary spatial range information.

[0049] Quantitative calculations of geometric parameters are performed for each permeation channel identified in the permeation channel characteristic map. For example, the average length, maximum width, minimum cross-sectional area, degree of curvature (for example, by calculating the curvature integral of the centerline) of each channel are calculated, as well as the correlation with the structural features of the electronic component (such as seams and holes). These geometric parameters reflect the physical properties of the channel and have a direct impact on moisture transmission capacity. These calculation results are aggregated to form a channel morphology dataset, which is a table or database that records the unique identifier of each permeation channel and its corresponding numerical parameters such as length, width, cross-sectional area, and curvature.

[0050] Combining the channel morphology dataset and the moisture flow vector field, the moisture transfer efficiency of each infiltration channel is calculated. The moisture transfer efficiency can be defined as the total amount of moisture passing through the channel per unit time or the average moisture flow rate. For example, the moisture flow through the channel can be calculated based on the cross-sectional area of ​​the channel and the average velocity of the moisture flow vector field in the channel. For channels with complex morphology, the principles of computational fluid dynamics can be used to establish a simplified channel model, and the moisture transfer process can be simulated under the action of moisture driving forces (for example, humidity gradients or pressure differences) to quantify the transfer efficiency. The calculated transfer efficiency values ​​for each channel are summarized to form a moisture transfer efficiency table, which is a table containing the channel ID and the corresponding transfer efficiency values.

[0051] Based on the moisture transfer efficiency table and the permeation channel characteristic diagram, the accumulation of moisture inside the electronic accessories is analyzed to identify hot spots of humidity accumulation. During the transmission process, moisture decelerates, stagnates or converges in certain areas, resulting in a significant increase in local humidity. Combining the moisture transfer efficiency (reflecting the ability to "input" moisture) and the geometry of the channel (reflecting the ability to "accommodate" and "exhaust" moisture), as well as the divergence information of the moisture flow vector field (reflecting the convergence or divergence of moisture in local areas), the moisture accumulation potential and accumulation rate of each area inside the electronic accessories are calculated. For example, in areas where the moisture flow rate is significantly reduced or where multiple channels converge, the probability and rate of moisture accumulation are higher. These accumulation potentials or rates are spatially mapped to obtain a humidity accumulation distribution map, which is a three-dimensional spatial distribution map that uses color depth or numerical values ​​to represent the degree or risk of moisture accumulation in each area inside the electronic accessories.

[0052] Combining a moisture cumulative distribution map (providing static accumulation risk information), a moisture transfer efficiency table (providing dynamic channel transfer capacity), and a channel morphology dataset (providing channel physical characteristics), a spatiotemporal dynamic model of moisture infiltration within electronic components is constructed. This model describes how moisture enters through identified infiltration channels, how it flows within, and where it accumulates and settles, accounting for the temporal variations of these processes. For example, the model can be expressed as a set of partial differential equations describing the temporal variation of moisture concentration in different regions, with parameters related to channel morphology and transfer efficiency. By analyzing this spatiotemporal dynamic model, the temporal evolution of moisture concentration at any location within an electronic component, as well as the velocity and final distribution of the moisture front, can be predicted under different environmental conditions. The final output is a penetration trajectory spectrum, a time-varying three-dimensional dataset that records the migration path (channel characteristics), rate (transmission efficiency), and distribution density (cumulative distribution) of moisture within the electronic component. For example, this can be represented as a series of time-evolving moisture concentration contour surfaces or vector visualizations, comprehensively demonstrating the dynamic process of moisture infiltration.

[0053] Preferably, the dew point conditions at different temperatures in step S2 are calculated as follows:

[0054] Collect internal temperature distribution data of electronic components under different working states and environmental conditions, and then reconstruct the temperature field based on the structure and material parameters of the electronic components to obtain a temperature gradient distribution map;

[0055] The humidity space distribution grid is obtained by performing humidity space interpolation calculation based on the infiltration trajectory spectrum and temperature gradient distribution map;

[0056] Calculate the dew point temperature of the humidity space distribution grid according to the temperature gradient distribution map to obtain the dew point temperature distribution map;

[0057] Calculate the temperature difference distribution data set based on the dew point temperature distribution map;

[0058] Identify critical condensation areas on the temperature difference distribution data set and obtain a condensation risk area map;

[0059] Based on the condensation risk area map and the temperature difference distribution data set, the temperature difference time stability analysis is performed to obtain the temperature difference stability index;

[0060] A condensation condition difference map is generated based on the temperature difference distribution dataset, the condensation risk area map, and the temperature difference stability index.

[0061] In an embodiment of the present invention, micro-temperature sensors (e.g., thermocouples or resistance temperature detectors) are placed at key locations within an electronic component sample (e.g., near heat-generating components, heat sinks, the inner surface of the housing, and near moisture penetration channels). The electronic component sample is then tested under conditions simulating its typical operating conditions (e.g., high-speed processor operation, standby mode, and power-on / off processes) and various environmental conditions (e.g., rapid temperature changes, low temperature, high temperature, and high humidity). A data acquisition system records temperature readings at different times and locations. Simultaneously, using the electronic component's structural model and the material's thermophysical parameters (e.g., thermal conductivity, specific heat capacity, thermal convection, and thermal radiation coefficients), combined with numerical simulation techniques such as finite element analysis or the finite difference method, the sensor data is used as boundary conditions or verification points to calculate the temperature at non-measurement points within the electronic component. A complete three-dimensional temperature distribution model of the electronic component's interior is then reconstructed under different times and operating conditions. This model is a time-varying three-dimensional temperature field, representing the temperature value at any location within the electronic component at any moment. For example, a series of three-dimensional grid data can be output, with each grid point containing a timestamp and a corresponding temperature value. Furthermore, the ratio of the temperature difference between any two points to their distance is calculated based on the temperature distribution to obtain a temperature gradient distribution map, which is a three-dimensional vector field that describes the spatial rate of temperature change.

[0062] Use the infiltration trajectory spectrum (containing the humidity information of moisture at various locations inside the electronic accessory) and the temperature gradient distribution map obtained in this step (containing the temperature information of various locations inside the electronic accessory). The infiltration trajectory spectrum provides the areas where moisture arrives and distributes and their humidity levels. Map the discrete or semi-continuous humidity data in the moisture trajectory spectrum to the same spatial grid as the temperature gradient distribution map. For points in the grid that do not have direct humidity monitoring or simulation data, use a spatial interpolation algorithm (for example, Kriging interpolation or radial basis function interpolation) to combine the moisture flow vector field and known humidity data to estimate the humidity values ​​at these locations. Ensure that the humidity data and temperature data are aligned in time and space to form a humidity spatial distribution grid corresponding to the temperature gradient distribution map. This grid is a three-dimensional data structure that represents the humidity value (for example, expressed in absolute humidity or water vapor partial pressure) at any location inside the electronic accessory at any time.

[0063] Based on the humidity spatial distribution grid and the temperature gradient profile obtained in this step, calculate the dew point temperature at each location within the electronic component. Dew point temperature (Tdp) is the temperature at which water vapor begins to condense when air is cooled at a constant pressure. The dew point temperature is directly related to the water vapor content (or water vapor partial pressure) in the air and is independent of the air temperature. Using known humidity values ​​(e.g., water vapor partial pressure Pv) and air pressure (usually assumed to be ambient pressure), a dew point temperature calculation formula, such as the Goff-Gratch equation or a more commonly used approximation (e.g., a variant of the Magnus-Tetens equation), is used to calculate the dew point temperature at each grid point at each time point. For example, a simplified dew point temperature calculation formula is Tdp = 243.12 × ln(Pv / 611.2) / (17.62 - ln(Pv / 611.2)), where Pv is the water vapor partial pressure (in Pa). The calculated dew point temperature values ​​are filled into the corresponding spatial grid to form a dew point temperature distribution map. This is a three-dimensional time-varying data field with the same structure as the temperature distribution map and humidity distribution grid, which represents the dew point temperature at any location inside the electronic component at any time.

[0064] The dew point temperature distribution map and the temperature gradient distribution map are compared point by point. At each spatial grid point and each time point, the difference between the actual temperature (derived from the temperature gradient distribution map) and the dew point temperature (derived from the dew point temperature distribution map) is calculated (ΔT = T_actual - T_dp). This temperature difference directly reflects the likelihood of condensation at that point: when ΔT ≤ 0, condensation theoretically occurs; smaller or negative ΔT values ​​indicate a higher condensation risk. The calculated temperature difference values ​​are stored to form a temperature difference distribution dataset, a time-varying, three-dimensional dataset containing the temperature difference values ​​for each spatial grid point at each time point.

[0065] The temperature difference distribution dataset is analyzed to identify areas with a temperature difference less than or equal to zero, which theoretically meet the temperature conditions for condensation to occur. A small threshold (for example, 0°C or -0.5°C) is set to account for the supercooling effect or measurement error of actual condensation, and areas with a temperature difference less than or equal to this threshold are marked as critical condensation areas. These critical condensation areas are visualized on a three-dimensional structural model of the electronic component to obtain a condensation risk area map. The condensation risk area map is a three-dimensional spatial map that uses different colors or markers to highlight the specific locations and ranges within the electronic component where condensation will occur at different time periods.

[0066] For the critical condensation areas identified in the condensation risk area map, analyze the stability of their temperature difference values ​​over time. Calculate the average, minimum, maximum, standard deviation, and duration of the temperature difference below zero within a time window in each critical condensation area. These indicators reflect the degree to which the temperature in the area is below the dew point temperature, as well as the persistence and volatility of this state. For example, if an area maintains a low negative temperature difference for a long time, its condensation risk is much higher than that of an area where the temperature difference briefly fluctuates below zero. These statistical results are used as indicators of temperature difference time stability, for example, a table that records the ID of each critical area and its corresponding temperature difference statistics (average negative temperature difference, duration ratio, etc.).

[0067] The temperature difference distribution dataset (which provides detailed temperature difference values), the condensation risk area map (which provides potential condensation locations), and the temperature difference stability index (which provides information on the persistence and severity of the risk) are combined to generate a condensation condition difference map. The condensation condition difference map is a comprehensive risk assessment output that not only identifies the location where condensation occurs, but also quantifies the likelihood of condensation occurring, its duration, and its association with the internal structure and moisture distribution of the electronic component. For example, the condensation condition difference map can be a three-dimensional map that, based on the condensation risk area map, uses a color gradient to indicate the magnitude of the negative value of the temperature difference (i.e., the degree below the dew point temperature), and uses a flashing frequency or additional markings to indicate the duration or frequency of the temperature difference below zero, thereby clearly displaying the condensation risk level and dynamic change characteristics of each area inside the electronic component.

[0068] Preferably, the thermal-humidity coupling dynamic analysis in step S2 is specifically as follows:

[0069] Extract the moisture flow direction and velocity data from the infiltration trajectory spectrum, combine it with the temperature difference distribution in the condensation condition difference map, calculate the acceleration or deceleration effect of the temperature gradient on the moisture flow, and form a temperature-humidity coupling coefficient map;

[0070] The working cycle conditions are defined according to the temperature-humidity coupling coefficient diagram and the environmental parameters of the electronic accessories, and a standard working cycle set is obtained;

[0071] The dynamic response trajectory of the standard working cycle set is calculated to obtain the thermal and moist dynamic response trajectory;

[0072] The critical state of the thermal and moist dynamic response trajectory is identified to obtain the condensation critical state set;

[0073] The cumulative effect analysis of the condensation critical state set is performed to obtain the moisture accumulation risk map;

[0074] The trigger conditions are parameterized according to the moisture accumulation risk map and the condensation critical state set to obtain the condensation trigger condition set.

[0075] In the embodiment of the present invention, the flow direction (vector direction) and flow velocity (vector magnitude) data of moisture at various locations and time points inside the electronic component are extracted from the penetration trajectory spectrum. At the same time, the difference between the actual temperature and the dew point temperature (temperature difference ΔT) at the same time and location and the temperature gradient are obtained from the condensation condition difference map. Moisture migration in porous media is driven not only by humidity gradients but also by temperature gradients (e.g., the Soret effect, where temperature gradients induce component migration). The relationship between the direction of the temperature gradient and the direction of moisture flow, as well as the effect of the temperature gradient magnitude on moisture flow velocity, is analyzed. For example, if the temperature gradient is opposite to the direction of moisture flow and has a large negative temperature difference (i.e., the regional temperature is well below the dew point), moisture condenses near cold surfaces or slows down. If the temperature gradient is aligned with the direction of moisture flow and is located at a higher temperature, moisture evaporation or diffusion is accelerated. Based on material properties (e.g., the Soret coefficient) and local temperature and humidity conditions, a mathematical model is developed to calculate the facilitation or inhibition of moisture flow by this temperature gradient. The calculated effect size is quantified as a temperature-humidity coupling coefficient, i.e., a dimensionless multiplier representing the proportional change in moisture flow velocity due to the temperature gradient. These coefficients are mapped onto a three-dimensional spatial grid and time axis within the electronic component to form a temperature-humidity coupling coefficient map. This is a time-varying three-dimensional data field that represents the moisture flow correction factor affected by temperature at each location within the electronic component at each point in time.

[0076] Based on the intended use and actual application scenarios of electronic accessories, analyze their typical operating modes under different environmental conditions. For example, an in-vehicle electronic accessory goes through multiple operating states such as startup, stable operation, and parking and shutdown, while facing rapid changes in external ambient temperature and humidity (such as entering a tunnel or starting the vehicle's air conditioner). Combined with the environmental parameters of the electronic accessory (for example, ambient temperature range, humidity range, vibration, air pressure change data, and typical change curves of various environmental factors over time), define a series of representative, high-risk or extreme working cycle conditions. Each working cycle includes a series of chronological changes in environmental parameters (such as temperature, humidity, and air pressure) and a series of changes in the power consumption or heating state of the electronic accessory itself. These defined environmental and working state sequences are combined to form a standard working cycle set, for example, a data set containing multiple working cycle scenarios, each of which describes in detail how the external environment and internal power consumption change over time within a time span.

[0077] A dynamic simulation of the thermal and humidity conditions within electronic components is performed using a temperature-humidity coupling coefficient map (reflecting internal thermal and humidity interactions) and a set of standard operating cycles (providing information on external driving forces and internal heat sources) as input. For each operating cycle in the standard operating cycle set, a coupled heat conduction and moisture transport model is used to calculate how the temperature and humidity at each location within the electronic component dynamically change over time. The simulation considers the thermal and humidity properties of the material, structural geometry, internal heat sources (heating from components), and heat and moisture exchange with the external environment through permeation channels. The moisture flow rate is corrected based on the temperature-humidity coupling coefficient map. The temporal trajectory of temperature, humidity, and the difference between the actual temperature and the dew point (temperature differential) at each location within the electronic component is tracked. These dynamic changes are recorded to generate a thermal and humidity dynamic response trajectory, a detailed record of the temporal changes in temperature, humidity, temperature differential, and other parameters at all grid points within the electronic component during the standard operating cycle.

[0078] Analyze the thermal and hygroscopic dynamic response trajectory to identify the time points and spatial locations where condensation risk critical states occur. The condensation risk critical state can be defined as the actual temperature being close to or lower than the dew point temperature (for example, the temperature difference is less than or equal to the set threshold, such as 0°C), and the state lasting for a certain period of time or occurring in an area with high moisture concentration. In the thermal and hygroscopic dynamic response trajectory, find all points and time periods that meet these critical conditions. For example, in a working cycle, it is found that during a certain rapid cooling phase, the temperature of the inner surface of the casing drops rapidly below the dew point temperature and remains there for a period of time. Record the specific location, time, temperature difference value, humidity value, and duration of these critical states. Summarize the information of these identified critical states to form a condensation critical state set, which is a list of all identified condensation risk events. Each event includes attributes such as the location of occurrence, start time, end time, and minimum temperature difference value.

[0079] Based on the condensation critical state set and the condensation condition difference map (which provides the location of static condensation risks), the cumulative effect of moisture in critical state areas is analyzed. Condensation depends not only on instantaneous conditions but also on the amount of moisture accumulated in the area. For each area identified in the condensation critical state set, the moisture transfer efficiency and humidity cumulative distribution map from the penetration trajectory spectrum are combined to assess the degree of moisture accumulation in the area before the risk occurs and the likelihood of continued moisture ingress during the risk period. The total moisture accumulation or accumulation rate in the condensation critical area is calculated under different operating cycles. For example, even if the temperature difference in an area briefly drops below the dew point, if moisture is difficult to reach or the accumulation is minimal, the actual condensation risk is low. The moisture accumulation or cumulative risk level is mapped onto the three-dimensional structural model of the electronic component to form a moisture accumulation risk map. This is a three-dimensional spatial distribution map that shows the condensation risk level caused by moisture accumulation in each area within the electronic component.

[0080] Combining the moisture accumulation risk map (reflecting the risk of cumulative effects) and the condensation critical state set (reflecting instantaneous or short-term risks), a parameterized description of condensation trigger conditions is developed. A condensation trigger condition is a combination of external environmental conditions, internal operating conditions, and duration that lead to condensation. For example, a trigger condition might be described as follows: "When the ambient temperature drops from +50°C to +5°C within 30 minutes, the ambient humidity is above 80%, and the electronic components are in a low-power state (low housing temperature), the temperature difference in a certain internal area (for example, the inner surface of the housing near the connector) is less than 0°C for more than 10 minutes." Each risk event identified in the condensation critical state set is converted into a trigger condition description with a clear parameter range and time series. Critical states with similar characteristics are summarized and abstracted to form a representative set of condensation trigger conditions. This resulting condensation trigger condition set is a dataset containing descriptions of multiple condensation trigger scenarios. Each scenario details the external environmental conditions, internal state parameters, and the thresholds or ranges for duration or rate of change that lead to condensation.

[0081] Preferably, the calculation of the condensation risk probability in step S2 is specifically as follows:

[0082] Analyze the environmental parameter statistical feature set of the environmental parameters used by electronic accessories;

[0083] Perform environmental condition probability mapping on the environmental parameter statistical feature set and the condensation trigger condition set to obtain a trigger condition probability table;

[0084] The regional condensation probability is calculated according to the trigger condition probability table and the condensation trigger condition set to obtain the spatial condensation probability distribution;

[0085] Conduct component sensitivity assessment on electronic component structures and material parameters to obtain component sensitivity distribution maps;

[0086] The severity of condensation impact is evaluated based on the component sensitivity distribution map and the spatial condensation probability distribution to obtain a condensation impact severity map;

[0087] Perform time window analysis on the trigger condition probability table and the environmental parameter statistical feature set to obtain the risk time window table;

[0088] A comprehensive assessment of the humid-heat risk is conducted on the spatial condensation probability distribution, condensation impact severity map and risk time window table to obtain a humid-heat risk topology map.

[0089] In an embodiment of the present invention, actual usage environment data of electronic accessories is collected, such as raw data on long-term (e.g., months or years) environmental temperature, humidity, air pressure, and parameters such as electronic accessory power on / off and workload from vehicle-mounted recorders, industrial monitoring systems, or feedback from consumer electronic product users. Statistical analysis is performed on these raw data, outliers are removed, and the frequency distribution, mean, standard deviation, maximum value, minimum value, rate of change (e.g., temperature change per unit time ΔT / Δt) of each environmental parameter, as well as the correlation between different parameters, are calculated. For example, the percentage of time the ambient temperature stays within the range of -40°C to +85°C, the duration distribution of humidity above 90%, and the probability of the combined occurrence of a rapid drop in ambient temperature (e.g., a rate greater than 5°C / minute) and a simultaneous increase in humidity are calculated. These statistical results are organized into a statistical feature set of environmental parameters, such as a database or report containing statistics and probability distribution models for various environmental factors.

[0090] The environmental parameter statistical feature set is matched and mapped to the condensation trigger condition set (which contains the specific external environment and internal state combinations that lead to condensation). For each trigger condition in the condensation trigger condition set, such as "ambient temperature drops from +50°C to +5°C within 30 minutes, while ambient humidity is above 80%, and electronic accessories are in a low-power state," the environmental parameter statistical feature set is queried to calculate the probability of this specific combination of environmental changes and states occurring in the target usage environment. For example, this can be done by looking up the frequency of events in historical data where the temperature drop rate, humidity level, and low-power state simultaneously meet the conditions; or by using a statistical model (e.g., a multivariate probability distribution model) to calculate the probability of this combination of conditions occurring. The calculated probability value for each condensation trigger condition in the target environment is associated with the corresponding condensation risk zone (obtained from the condensation trigger condition set) to form a trigger condition probability table. This table lists each condition description in the condensation trigger condition set and its estimated annual probability of occurrence in the target usage environment.

[0091] According to the trigger condition probability table and the condensation trigger condition set (which contains the condensation risk area inside the electronic accessory corresponding to each trigger condition), the total probability of condensation occurring at each spatial position inside the electronic accessory is calculated. For each small spatial grid unit inside the electronic accessory, determine which condensation trigger conditions (and their occurrence probability in the trigger condition probability table) cause condensation in the grid unit. If a grid unit overlaps with the risk area of ​​multiple condensation trigger conditions, and these trigger conditions are independent, the relevant probabilities can be appropriately combined (for example, using the probability addition formula, considering mutually exclusive or overlapping events). If there is a dependency between the trigger conditions (for example, the occurrence of one event increases the probability of another event), conditional probability needs to be used for calculation. Finally, the cumulative probability of condensation occurring in each spatial grid unit inside the electronic accessory within one year or the entire expected service life is calculated to form a spatial condensation probability distribution, which is a three-dimensional spatial grid data, and each grid point stores the probability value of condensation occurring at that location.

[0092] Analyze the structural and material parameters of electronic assemblies, especially the type, location, and functional importance of the various electronic components contained within them. Consult the component specifications, reliability data sheets, or conduct specialized moisture sensitivity tests to assess the sensitivity of different types of components to moisture and condensation. For example, exposed metal pins, certain types of connectors, non-sealed sensors, and components using moisture-sensitive materials (such as certain types of integrated circuit packages) are generally more sensitive to moisture. Assign a sensitivity score (e.g., a scale from 1 to 5, or a continuous value from 0 to 1) to the component based on its moisture sensitivity. Map these sensitivity scores to the specific locations of the components on the three-dimensional structural model inside the electronic assembly to form a component sensitivity distribution map. This is a three-dimensional spatial distribution map that uses color depth or numerical values ​​to indicate the sensitivity of components in various areas within the electronic assembly to moisture and condensation.

[0093] The severity of damage or functional failure caused by condensation is assessed by combining the component sensitivity distribution map with the spatial condensation probability distribution calculated in this step. The severity of condensation impact depends on two factors: the likelihood of condensation occurring (spatial condensation probability distribution) and the sensitivity of the components to moisture at the location where condensation occurs (component sensitivity distribution map). At each spatial grid point within the electronic component, the condensation probability at that point is combined with the component sensitivity score at that location. For example, a multiplicative model can be used: severity score = condensation probability × component sensitivity score. Thus, even if the probability of condensation is low, the severity of the impact can be high if it occurs near critical components that are extremely sensitive to moisture. The calculated severity scores are mapped into three-dimensional space to form a condensation impact severity map. This is a three-dimensional spatial distribution map that uses color gradients to represent the severity of damage caused by condensation in different areas within the electronic component.

[0094] Based on the trigger condition probability table and the set of statistical characteristics of environmental parameters, the time characteristics of the occurrence of condensation risks are analyzed. In addition to calculating the total probability of risk occurrence, it is also necessary to determine the time window in which the risk is most likely to occur (for example, which season of the year, which time of the day, or after what kind of environmental changes). For example, by analyzing the seasonal or diurnal patterns of specific rapid temperature changes or high humidity durations in the historical data of environmental parameters, and associating them with the conditions in the trigger condition probability table. Calculate the time period and duration of the risk that is most likely to occur for each condensation risk area or each trigger condition. Aggregate this time information to form a risk time window table, which is a table that lists the main condensation risk areas or trigger conditions, as well as the typical time periods in which they are most likely to occur (for example, "in winter, within 1-2 hours after quickly entering a warm and humid indoor environment") and the typical duration of the risk state.

[0095] The spatial condensation probability distribution (answering the question "where and with what probability will it occur"), the condensation impact severity map (answering the question "how severe will it be after it occurs"), and the risk time window table (answering the question "when will it happen") are integrated and visualized. A comprehensive moisture and heat risk score is defined, for example, a weighted average or product model combining probability, severity, and duration (typical duration or frequency information is obtained from the risk time window table). For example, comprehensive risk = spatial condensation probability × condensation impact severity × risk duration factor. The calculated comprehensive moisture and heat risk score is mapped onto the three-dimensional structural model of the electronic component to form a moisture and heat risk topology map. The moisture and heat risk topology map is a three-dimensional spatial visualization map that identifies the comprehensive moisture and heat risk level of each area inside the electronic component (for example, from "low risk" to "extremely high risk") in an intuitive manner (for example, using different colors, transparency, or markings). The map clearly shows the geographical location, likelihood, potential consequences, and time characteristics of the risk, providing accurate risk distribution information for subsequent structural moisture-proof design optimization.

[0096] Preferably, step S3 includes:

[0097] Step S31: Dividing the electronic component structure and material parameters into different structural units according to functional and morphological characteristics;

[0098] Step S32: spatially mapping and associating the moisture-heat risk topology map with the structural units to obtain a structural unit risk correspondence table;

[0099] Step S33: measuring the material moisture permeability according to the structural unit risk correspondence table to obtain a material moisture permeability coefficient table;

[0100] Step S34: Quantitatively calculate the barrier strength based on the material water vapor permeability coefficient table and the structural unit risk correspondence table to obtain a barrier effectiveness score set;

[0101] Step S35: Perform a comprehensive moisture-proof system evaluation on the barrier effectiveness score set and the moisture-heat risk topology map to obtain a moisture-proof effectiveness index matrix.

[0102] In an embodiment of the present invention, the complete three-dimensional structural model of the electronic accessory (derived from the electronic accessory structure and material parameters) is decomposed. According to the functions of the components (for example, housing, sealing ring, connector, printed circuit board, radiator) and morphological characteristics (for example, plane, curved surface, holes, seams, screw holes), the electronic accessory is divided into multiple independent structural units with clear boundaries. For example, an electronic accessory is divided into structural units such as upper cover housing, lower cover housing, mainboard, connector A, connector B, sealing ring 1, sealing ring 2, screw hole M3×6, heat sink, etc. At the same time, the specific material type used for each structural unit and its spatial position and geometric shape in the electronic accessory structure are recorded. This division process can be completed based on the functions of computer-aided design software or by manual analysis of structural drawings.

[0103] Spatially align and associate structural units with the moisture and heat risk topology map. The moisture and heat risk topology map identifies the moisture and heat risk levels of various areas within electronic components. For each structural unit, determine whether its spatial extent overlaps with or is adjacent to a high-risk area in the moisture and heat risk topology map. If a structural unit covers or is immediately adjacent to a high-risk area, the structural unit is considered associated with that risk area, and its moisture-proof performance is critical to reducing the risk in that area. Record the ID, risk level, location, and extent of the moisture and heat risk area associated with each structural unit. For example, a sealing ring structural unit is associated with a high-moisture and heat risk area at the seam of a housing; a printed circuit board area is associated with a condensation risk area near a highly sensitive component on the board. This association information is organized into a structural unit risk correspondence table, which is a table that lists the unique identifier of each structural unit, a list of its associated moisture and heat risk areas, and the corresponding risk information.

[0104] Based on the structural unit risk correspondence table, determine the types of materials requiring moisture permeability evaluation. For each material used in an electronic component (e.g., housing material, sealing material, potting compound, coating material, etc.), perform a moisture permeability test. Moisture permeability is typically measured using the material's water vapor transmission rate (WVTR), which describes the mass of water vapor allowed to pass through a material per unit thickness and unit area per unit time under a unit humidity difference. WVTR is typically a function of temperature and relative humidity. Material samples are tested using standard test methods (e.g., the cup method or film method, as per ASTM E96) under various temperature and humidity conditions to determine their WVTR. Furthermore, the interface moisture permeability of different materials should also be evaluated, for example, by testing the WVTR at the bonding interface between two materials. The measured WVTR parameters for various materials and their interfaces under various temperature and humidity conditions are recorded to create a material WVTR table, a database or table that stores the WVTR values ​​for various materials and their interfaces involved in electronic components under various temperature and humidity conditions.

[0105] Based on the material moisture permeability coefficient table and the structural unit risk correspondence table, the moisture barrier performance of existing electronic component designs is quantitatively calculated to assess the moisture barrier capacity of each structural unit. For each structural unit listed in the structural unit risk correspondence table, the moisture permeability resistance of the structural unit is calculated based on its geometry and material type (derived from the electronic component's structural and material parameters) using the data in the material moisture permeability coefficient table. For example, for a housing wall constructed of a certain material, its moisture permeability resistance is directly proportional to its thickness and inversely proportional to its area and the material's moisture permeability. For a sealing ring, its moisture barrier performance depends on the material's moisture permeability, the sealing ring's cross-sectional shape, and the amount of compression. For complex structural units (such as those with holes or grooves), finite element analysis or computational fluid dynamics simulation can be used to calculate the moisture permeation rate through the structural unit. The calculated moisture barrier capacity of each structural unit is quantified as a barrier strength coefficient (e.g., the inverse of its moisture permeation resistance), and the degradation pattern of its moisture barrier performance under long-term use conditions (taking into account material aging, temperature cycling, etc.) is predicted. These barrier strength coefficients and attenuation predictions are aggregated to form a barrier effectiveness score set, which is a table listing the ID of each structural unit and its corresponding barrier strength coefficient and predicted performance attenuation model.

[0106] The barrier effectiveness score set and the moisture-heat risk topology map are comprehensively analyzed to evaluate the moisture-proof system of the entire electronic accessory. For the high-risk areas identified in the moisture-heat risk topology map, the structural unit risk correspondence table is searched to determine which structural units are responsible for moisture-proof protection of these areas, and the moisture-proof performance data of these structural units are obtained from the barrier effectiveness score set. The risk level of the high-risk areas is compared with the barrier strength of the structural units responsible for protecting these areas, and weak links where the protection capability does not match the risk level are identified. For example, if the moisture-heat risk of an area is very high, but the barrier strength of the structural unit responsible for moisture-proofing the area (such as a sealing ring) is low, then this structural unit is the main moisture-proof weak link. At the same time, the synergy (for example, multi-layer protective structure) and redundancy between different structural units are evaluated. Consider whether the moisture-proof performance of the overall structure meets the expected service life and environmental requirements of the electronic accessories. Through this comprehensive analysis, a moisture-proof effectiveness index matrix is ​​generated. The moisture barrier performance index matrix is ​​a comprehensive assessment result. It not only quantifies the moisture barrier performance (barrier strength, penetration resistance, and lifespan prediction) of each structural unit, but also correlates it with the distribution and level of moisture and heat risk within the electronic component. This clearly identifies the strengths and weaknesses of the moisture barrier system, providing precise improvement directions and performance benchmarks for subsequent optimization designs. For example, the matrix can include the structural unit ID, the associated risk area ID, the risk level, the barrier strength, the risk-barrier match score (e.g., risk level divided by barrier strength), and the recommended improvement priority.

[0107] It is particularly important that the material moisture permeability is determined as follows:

[0108] Prepare and classify material samples according to the structural unit risk correspondence table to obtain a material test sample set;

[0109] Conduct standard moisture permeability test on the material test sample set to obtain the standard moisture permeability benchmark value;

[0110] The temperature-dependent moisture permeability characteristics of the material test sample set are tested according to the standard moisture permeability benchmark value to obtain the temperature-moisture permeability relationship curve;

[0111] Conduct moisture permeability test on the material test sample set according to the temperature-moisture permeability relationship curve to obtain a temperature-moisture permeability mapping table;

[0112] The interface moisture permeability characteristics are analyzed according to the structural unit risk correspondence table and the temperature-humidity-permeability mapping table to obtain the interface moisture permeability characteristics table;

[0113] According to the temperature-humidity-permeability mapping table, the material test sample set is tested for moisture permeability change under aging conditions to obtain a moisture permeability-aging trend graph;

[0114] Comprehensively calculate the moisture permeability coefficient based on the temperature-humidity moisture permeability mapping table, the interface moisture permeability characteristic table, and the moisture permeability aging trend chart to obtain a material moisture permeability coefficient table;

[0115] In an embodiment of the present invention, based on the material types used in each structural unit of the electronic accessory listed in the structural unit risk correspondence table (for example, polycarbonate housing, silicone rubber sealing ring, epoxy resin potting compound, polyimide flexible circuit substrate) and the key interface types involved (for example, housing overlap, sealing interface between connector and housing), test samples with standard sizes and shapes are prepared from batches of these materials. For example, a disc-shaped sample with a diameter of 74 mm and a thickness of 1 mm is prepared for the polycarbonate housing material; a strip-shaped sample with a circular cross-section and a length of 100 mm is prepared for the silicone rubber sealing ring; and a sheet-shaped sample with a thickness of 2 mm is prepared for the epoxy resin potting compound. For interface samples, such as the housing overlap, the actual structure is simulated, and two polycarbonate plates are connected by screws or ultrasonic welding, and a simulated gap is retained at the overlap or a simulated sealant is used. All prepared materials and interface samples are classified and numbered, and information such as the material type, interface type, preparation process, size and thickness corresponding to the sample is recorded to form a material test sample set.

[0116] For each material and interface type in the material test sample set, the water vapor transmission rate is measured under standard temperature and humidity conditions. For example, according to the water cup method or dry cup method of ASTM E96, the test is carried out in a standard test chamber environment at 23°C ± 0.5°C and 85% ± 2% relative humidity. The material sample (for example, a disc-shaped film) is fixed at the opening of the test cup, and a known amount of water (water cup method, forming 100% relative humidity in the cup) or desiccant (dry cup method, forming close to 0% relative humidity in the cup) is placed in the cup. The total mass change of the test cup is measured regularly. When the rate of mass change stabilizes, the water vapor transmission rate (WVTR) is calculated, and the unit is usually g / (m 2 ·d). The water vapor permeability (P) of the material is calculated based on the thickness of the material, the test area, and the water vapor partial pressure difference inside and outside the cup. The unit is usually g·mm / (m 2 ·d·Pa) or similar units. Water vapor partial pressure driving force ΔPv = Psat(T) × (RH_out - RH_in), where Psat(T) is the saturated water vapor partial pressure at temperature T, and RH_out and RH_in are the relative humidity outside and inside the cup, respectively. Permeability P = WVTR × thickness / ΔPv. Record the water vapor permeability values ​​measured under standard conditions for each material and interface to obtain a standard water vapor permeability benchmark value, such as a table containing the material / interface ID and the permeability value under standard conditions.

[0117] While maintaining a constant water vapor partial pressure difference between the inside and outside of the cup (e.g., by maintaining a relative humidity of 85% outside the cup and 0% inside the cup, i.e., a constant relative humidity gradient driving force), the water vapor transmission rate of the material samples in the material test sample set is measured at different temperatures. The test temperature is gradually increased from a low temperature (e.g., -40°C) to a high temperature (e.g., +125°C). After stabilization for a sufficient period of time at each set temperature point (e.g., -40°C, 0°C, 23°C, 50°C, 85°C, 125°C), the water vapor transmission rate at that temperature is measured and the permeability is calculated. The permeability values ​​at different temperatures are recorded and the data are fitted to establish a mathematical relationship between the permeability P and temperature T, such as the Arrhenius equation: P(T) = A × exp(-Ea / (R × T)), where A is the pre-exponential factor, Ea is the diffusion activation energy, R is the gas constant 8.314 J / (mol·K), and T is the absolute temperature (unit: Kelvin). The A and Ea parameters of each material are obtained by fitting, and a temperature-water vapor permeability relationship curve (or its mathematical model) is formed, which describes the variation of permeability with temperature.

[0118] At several representative temperature points (e.g., 23°C, 50°C, and 85°C), the relative humidity gradient between the inside and outside of the cup is varied, and the moisture permeability characteristics of the material samples in the material test sample set are tested. For example, at 23°C, in addition to the standard 85% RH gradient, moisture permeability can also be tested under different humidity gradient conditions, such as a 10% RH gradient (e.g., 90% RH outside the cup, 80% RH inside the cup) or a 50% RH gradient (e.g., 75% RH outside the cup, 25% RH inside the cup). For some hygroscopic materials, it is also necessary to test the material's inherent moisture absorption in high humidity environments and its impact on permeability. Permeability values ​​are recorded under different temperature and humidity gradient conditions. Data from the temperature-moisture permeability curve is integrated to construct a two-dimensional mapping model or lookup table between permeability P, temperature T, and relative humidity RH (or water vapor partial pressure Pv), creating a temperature-humidity-moisture permeability mapping table. This mapping table or model can predict the material's permeability value under any given temperature and relative humidity conditions.

[0119] Based on the critical interface types identified in the structural unit risk correspondence table (e.g., the bonding interface between two different plastics, the sealing interface between metal and plastic, and the lap joint interface secured by screws) and the material information involved, moisture permeability is measured on the corresponding interface samples in the material test sample set, referring to the test temperature and humidity ranges in the temperature-humidity permeability mapping table. For example, a specialized interface moisture permeability test device is used to simulate the geometry and stress state of the actual interface (e.g., the compression of a seal) and measure the rate of moisture vapor passing through the interface under different temperature and humidity conditions. The relationship between the interface moisture permeability and the materials constituting the interface, interface geometric parameters (e.g., gap width), connection method (e.g., bonding strength, screw torque), and external environmental conditions is analyzed. The moisture permeability or permeation resistance per unit length or unit area is calculated for each critical interface. These test results and analytical models are organized into an interface moisture permeability characteristics table, which lists different interface types, related materials, geometric / stress parameters, and interface permeability values ​​or models under different temperature and humidity conditions.

[0120] Accelerated aging tests are conducted on material samples in the material test sample set to simulate the long-term environmental impacts that electronic components experience in actual use. For example, the samples are exposed to high temperature and high humidity environments (for example, 85°C, 85% RH), temperature cycles (for example, -40°C to +85°C cycles), ultraviolet radiation, or corrosive gas environments. At different aging time points (for example, aging for 100 hours, 500 hours, 1000 hours), some samples are taken out and their moisture permeability under standard or representative temperature and humidity conditions is re-measured with reference to the test method in the temperature and humidity moisture permeability mapping table. Compare the changes in moisture permeability after aging with those before aging and at different aging time points. Analyze the change pattern of the material's moisture permeability with aging time, and establish an aging model between permeability P and aging time t, for example, P_aged(t)=P_initial×(1+k×t n ), where k and n are parameters related to the material and aging conditions. These aging models or data are plotted into a moisture permeability aging trend graph, which describes the trend of the material's moisture permeability attenuation or change over time.

[0121] The data and models from the temperature-humidity permeability mapping table (reflecting temperature-humidity dependence), the interface moisture permeability characteristics table (reflecting interface characteristics), and the moisture permeability aging trend chart (reflecting time-dependent aging effects) are integrated to calculate the final moisture permeability coefficient model for each material and interface. The moisture permeability coefficient is a comprehensive parameter that predicts the moisture permeability of a specific material or interface under given temperature, humidity, and usage time (corresponding to aging). For example, for a specific material, its moisture permeability coefficient model can be expressed as a function: P_final(T, RH, t_age) = P_bulk(T, RH) × f_interface(geometry) × f_aging(t_age), where P_bulk(T, RH) comes from the temperature-humidity permeability mapping table, f_interface takes into account interface correction (if applicable, from the interface moisture permeability characteristics table), and f_aging(t_age) comes from the moisture permeability aging trend chart. In this way, a comprehensive model or set of values ​​that can predict the moisture permeability performance under various conditions is generated for each material and key interface involved in the electronic component, forming a material moisture permeability coefficient table, which is a database that stores comprehensive moisture permeability coefficient models or lookup tables for all relevant materials and interfaces.

[0122] Preferably, step S34 is specifically as follows:

[0123] Extracting a set of structural geometric parameters from the electronic component structure and material parameters according to the structural unit risk correspondence table;

[0124] Calculate the moisture permeability resistance of a single layer of material based on the material moisture permeability coefficient table for the structural geometric parameter set to obtain a single layer moisture permeability resistance table;

[0125] According to the single-layer moisture permeability resistance table and the structural geometric parameter set, the multi-layer structural resistance is synthesized to obtain the total structural resistance value;

[0126] Conduct interface weakness analysis based on the total structural resistance value and the structural geometric parameter set to obtain an interface weakness map;

[0127] The barrier strength coefficient is calculated based on the total resistance value of the structure and the interface weak point map to obtain a barrier strength coefficient set;

[0128] Conduct environmental adaptability assessment on the barrier strength coefficient set and the hygrothermal risk topology map to obtain an environmental adaptability index table;

[0129] The barrier strength coefficient set and the environmental adaptability index table are comprehensively scored for barrier effectiveness to obtain a barrier effectiveness score set.

[0130] In an embodiment of the present invention, detailed numerical values ​​related to the geometric shape of each structural unit are extracted from the electronic accessory structure and material parameters according to the structural unit risk correspondence table. For example, for a housing structural unit, its wall thickness, surface area, material type, and the size and position of the existing openings are extracted; for a sealing ring structural unit, its material type, cross-sectional shape parameters (such as diameter or width), circumference, and the amount of compression in the assembled state are extracted; for a connector interface structural unit, its main body material, sealing material, number of pins, pin spacing, and the geometric parameters of the sealing groove that matches the housing are extracted. These geometric parameters are key determinants of the length and cross-sectional area of ​​the moisture penetration path. These extracted numerical parameters are combined to form a structural geometric parameter set, such as a database containing structural unit IDs and corresponding geometric attribute values.

[0131] Using a material vapor permeability coefficient table (which contains models or values ​​for the vapor permeability of various materials and interfaces under different temperature, humidity, and aging conditions) and a set of structural geometric parameters, the vapor permeability resistance of a single material layer or simple structure within each structural unit is calculated. Moisture permeability resistance (R) is the ease with which moisture vapor passes through a material or structure. It is generally proportional to thickness and inversely proportional to permeability and permeability area. For a uniform material layer with thickness d, area A, and material permeability P, its vapor permeability resistance can be approximately calculated as R = d / (P × A). The permeability P is obtained from the material vapor permeability coefficient table or calculated using a model based on the typical operating environment conditions associated with the structural unit (obtained from the structural unit risk correspondence table). For interfaces, the resistance calculation method is different and is related to the interface length or area and the interface permeability. The vapor permeability resistance of all major single material components or simple interfaces within the structural unit is calculated to produce a single-layer vapor permeability resistance table, which lists the vapor permeability resistance values ​​of each component within each structural unit (such as walls, seals, and interfaces).

[0132] Based on the single-layer moisture permeation resistance table and the set of structural geometric parameters, the total moisture permeation resistance of complex multi-layer or multi-path structural units is calculated synthetically. If the structural unit is composed of multiple material layers in series to form a moisture barrier (for example, a multi-layer coating or a composite wall), its total resistance is the sum of the resistances of each layer: R total =∑R i If there are multiple parallel moisture penetration paths in a structural unit (for example, a shell consisting of a wall, a seal and a cable through-hole), the total resistance should be synthesized in parallel: 1 / R total =∑(1 / R j), where the path with the least resistance contributes most to the total resistance. For structures with complex geometries or significant fluid dynamics (e.g., narrow, curved gaps), computational fluid dynamics or finite element analysis can be used to directly simulate the moisture permeation process based on the structural geometry and material permeability. The total moisture flow per unit humidity difference is calculated, and the reciprocal of this value is the total resistance. This method yields the total structural resistance for each unit under typical environmental conditions.

[0133] Based on the total structural resistance value and the set of structural geometric parameters, the moisture penetration weaknesses within each structural unit or at the connections between different structural units are analyzed. The total structural resistance value is an overall assessment, while the weak point analysis focuses on identifying the specific locations or paths with the lowest resistance and the easiest passage of moisture. For example, in a multi-path parallel structure, the path with the smallest resistance value (such as areas with poor sealing, material defects, and gaps formed by tolerance superposition) is the main weak point. Utilizing the detailed dimension and tolerance information in the structural geometric parameter set, combined with the single-layer moisture permeability resistance table, local areas or geometric features with extremely low moisture permeability resistance are found. These identified weak points are visually marked and described on the three-dimensional structural model of the electronic component to form an interface weak point map, which is a three-dimensional spatial map that highlights the specific physical locations and types of moisture in the electronic component structure that are most likely to penetrate (for example, overlap gaps, around screw holes, and local deformation areas of the sealing ring).

[0134] According to the total structural resistance value and the interface weak point map, the barrier strength coefficient of each structural unit is calculated. The barrier strength coefficient is a quantitative indicator of the moisture-proof ability of the structural unit. It is positively correlated with the total structural resistance value. Generally, the greater the resistance, the higher the barrier strength. The barrier strength coefficient can be defined as the total structural resistance value itself, or it can be standardized (for example, taking the logarithm or dividing it by the reference resistance value) for easy comparison. At the same time, the calculation of the barrier strength coefficient also needs to take into account the influence of interface weak points. Even if the overall resistance is high, if there are extreme weak points, the overall barrier function will fail. Therefore, the calculation model of the barrier strength coefficient can add a penalty term for the number, size or resistance ratio of weak points. For example, barrier strength coefficient = f(R total ,WeakPointSeverity), where WeakPointSeverity is a weakness severity index assessed based on the interface weakness map. The calculated barrier strength coefficient values ​​for each structural unit are aggregated to obtain a barrier strength coefficient set, which is a table listing the ID of each structural unit and its corresponding barrier strength coefficient value.

[0135] A correlation analysis is performed between the barrier strength coefficient set and the hygrothermal risk topology to assess the environmental adaptability of each structural unit to its associated hygrothermal risk. The hygrothermal risk topology identifies the range and duration of hygrothermal environmental conditions experienced by different areas. Typical or extreme temperature, humidity, and aging time conditions are extracted from the risk information associated with the hygrothermal risk topology. Using the temperature, humidity, and aging dependencies of the material permeability coefficient table and the strength values ​​calculated based on typical conditions in the barrier strength coefficient set, the degradation or change in the barrier strength of the structural unit under these extreme or typical high-risk environmental conditions is predicted. For example, if the risk area associated with a structural unit is frequently exposed to high temperature and high humidity, and the material used in the structural unit has a significantly increased permeability under high temperature and high humidity, then the environmental adaptability of the structural unit is low. The environmental adaptability index can be defined as the ratio of the predicted barrier strength under the worst risk environmental conditions to the barrier strength under standard conditions (e.g., minimum predicted strength / standard strength), or as an index that scores the performance of the structural unit under different risk environments. The calculated environmental adaptability index of each structural unit is recorded to obtain an environmental adaptability index table, which is a table that lists the ID of each structural unit and its corresponding environmental adaptability index.

[0136] Combining the barrier strength coefficient set (reflecting the inherent moisture-proof ability) and the environmental adaptability index table (reflecting the ability to maintain performance in a risky environment), a comprehensive score is given to the moisture-proof performance of each structural unit. The comprehensive score should fully reflect the moisture-proof performance of the structural unit, taking into account both its moisture-proof ability under ideal conditions and its reliability in actual risk environments. For example, the comprehensive score can be simply multiplied by the barrier strength coefficient and the environmental adaptability index: barrier effectiveness comprehensive score = barrier strength coefficient × environmental adaptability index. A unit with high barrier strength but a sharp drop in performance in harsh environments will not have a better comprehensive score than a unit with moderate barrier strength but can maintain stable performance in various environments. The calculated barrier effectiveness comprehensive scores of each structural unit are aggregated to obtain a barrier effectiveness score set, which is a table that lists the ID of each structural unit and its corresponding final barrier effectiveness comprehensive score. This score is an important component of the subsequent generation of the moisture-proof effectiveness index matrix.

[0137] Preferably, step S4 is specifically:

[0138] Step S41: determining key structural units that need to be optimized based on the moisture-proof performance index matrix;

[0139] Step S42: constructing a parameter optimization constraint framework for key structural units;

[0140] Step S43: performing wet gas flow simulation and evaluation based on the parameter optimization constraint framework and the infiltration trajectory spectrum to obtain a scheme performance evaluation matrix;

[0141] Step S44: Perform multi-objective optimization and solution generation according to the solution performance evaluation matrix to obtain an optimized configuration set of structural parameters.

[0142] In an embodiment of the present invention, based on a moisture-proof efficiency index matrix, the matrix quantifies the moisture-proof performance, associated moisture and heat risks, and the degree of matching between the risk and the barrier of each structural unit in an electronic accessory. The data in the moisture-proof efficiency index matrix is ​​analyzed to identify structural units with low moisture-proof performance scores, close association with high moisture and heat risk areas, or low risk-barrier matching scores. For example, if the moisture-proof efficiency index matrix shows that the barrier effectiveness comprehensive score of a certain connector interface is the lowest among all structural units, and the interface is located near the "extremely high risk" area identified in the moisture and heat risk topology map, then the connector interface structural unit is identified as a key structural unit that needs to be optimized first. At the same time, multiple key units that are ranked high and need to be optimized simultaneously are also identified, such as housing lap seams and cable threading hole seals.

[0143] For the identified key structural units (e.g., connector interfaces), a parameter optimization constraint framework is constructed. First, the designable parameters of the structural unit are defined. These parameters are physical properties or manufacturing process control points that have a significant impact on its moisture-proof performance. For example, for the sealing structure of the connector interface, the adjustable parameters may include: the material type of the sealing ring (e.g., specifying a material library such as nitrile rubber, fluorosilicone rubber, ethylene propylene diene monomer rubber), the cross-sectional shape parameters of the sealing ring (e.g., the diameter or side length of a circular, D-shaped, or square shape, with a numerical range of 1.5 mm to 3.0 mm), the depth of the sealing groove (e.g., a numerical range of 1.8 mm to 2.8 mm), the width of the sealing groove (e.g., a numerical range of 1.9 mm to 2.9 mm), and the torque of the gland (e.g., a numerical range of 0.5 Nm to 1.5 Nm). At the same time, the value ranges of these parameters are defined, which are constrained by the physical limits of the material, the precision of the manufacturing process, cost constraints, and the overall size and functional requirements of the electronic component. For example, the diameter of the sealing ring cannot be larger than the available space in the sealing groove, and the gland torque has a maximum limit to prevent structural damage. Establish mutual constraints between parameters. For example, the width of the seal groove should generally be slightly smaller than or equal to the diameter of the seal ring to ensure compression. Record these parameter definitions, value ranges, and constraints in a structured data format (e.g., a parameter table and constraint rule set) to form a parameter optimization constraint framework.

[0144] Based on the constructed parameter optimization constraint framework, multiple representative combinations of structural parameters are generated as candidate optimized design solutions. For example, experimental design methods (e.g., factorial design, response surface methodology) or random sampling methods (e.g., Monte Carlo sampling) can be used to generate hundreds or even thousands of different parameter configurations within the parameter space. For each candidate design solution, a detailed three-dimensional geometric model of the key structural unit is constructed based on its parameter configuration and integrated into the overall structural model of the electronic component. The performance of each candidate design solution during moisture infiltration is simulated using moisture flow simulation tools based on computational fluid dynamics or finite element methods. During the simulation, external environmental conditions (e.g., high-risk scenarios identified in the moisture and heat risk topology map, such as rapid temperature and humidity changes) are considered. Using the infiltration trajectory spectra obtained in the previous step as a reference or initial condition, the simulation simulates how moisture permeates through the modified structural unit into the interior of the electronic component. The simulation results are quantified, for example, by calculating the total moisture infiltration (e.g., in milligrams) over a specific simulation time (e.g., 24 hours), the time it takes for moisture to reach sensitive internal component areas, and the maximum relative humidity or condensation frequency at key internal locations. Based on these quantitative results, each candidate solution is evaluated for its improved moisture-proofing performance. For example, the percentage reduction in moisture permeability compared to the original design is calculated. Each candidate solution's parameter configuration and its corresponding simulation performance evaluation results (such as moisture permeability and number of condensation events) are recorded to form a solution performance evaluation matrix. This is a table that lists each candidate solution's parameter combination and its numerical value for each moisture-proofing performance indicator.

[0145] Based on the solution performance evaluation matrix and other functional requirements of the electronic component (e.g., total cost, weight constraints, structural strength requirements, heat dissipation performance, electromagnetic compatibility, etc.), a multi-objective optimization is performed. The optimization objective function is defined, such as minimizing moisture permeation, minimizing the number of condensation events, minimizing total cost, and minimizing weight. These objectives are often conflicting. For example, improving moisture resistance requires more expensive materials or a more complex structure, which increases cost and weight. A multi-objective optimization algorithm (e.g., a genetic algorithm or particle swarm optimization algorithm based on the concept of Pareto optimality) is used to search within the parameter space defined by the parameter optimization constraint framework to find a set of solutions that achieve the best balance between all optimization objectives. The algorithm evaluates new parameter combinations based on data provided by the solution performance evaluation matrix or driven by simulation tools, and iteratively improves the solution set. The Pareto frontier (i.e., the set of non-inferior solutions that cannot improve any objective without degrading at least one other objective) generated by the optimization algorithm is analyzed. Based on the actual project requirements and priorities, one or more optimal parameter combinations are selected from the Pareto frontier. For example, if cost is the primary constraint, the solution with the best moisture resistance within an acceptable cost range is selected. Generate detailed structural parameter configurations for the final selected solution, including material types of key structural units, precise geometric dimensions (e.g., sealing ring diameter 2.15 mm, sealing groove depth 2.0 mm, width 2.18 mm), surface treatment requirements, and assembly process parameters (e.g., gland torque 1.2 Nm). At the same time, based on the simulation evaluation results of the solution, predict its expected performance improvement indicators in a hot and humid environment (e.g., moisture permeability reduced by 70%, condensation probability reduced by 90%). Summarize this information to obtain the optimal configuration set of structural parameters, which is a detailed design specification that can be directly used to guide the production and manufacturing of electronic components.

[0146] Of particular importance is the simulation and evaluation of wet gas flow, specifically:

[0147] Generate candidate solution parameter sets based on parameter optimization constraint framework;

[0148] Construct a simulation model based on candidate solution parameters;

[0149] Set the boundary conditions of the simulation model to obtain a simulation condition set;

[0150] The moisture penetration process is simulated according to the simulation working condition set and the simulation model to obtain a penetration process data set;

[0151] Extracting a permeation feature comparison table from the permeation trajectory spectrum and the permeation process dataset;

[0152] Quantify the performance improvement based on the penetration feature comparison table to obtain a set of performance improvement indicators;

[0153] Conduct extreme condition tests on the simulation model based on the performance improvement index set to obtain extreme condition response data;

[0154] Perform comprehensive performance scoring on the performance improvement index set and extreme condition response data to obtain the solution performance evaluation matrix;

[0155] In the embodiment of the present invention, based on the parameter optimization constraint framework (which defines the adjustable parameters of the key structural units and their value ranges and constraint relationships), a certain parameter generation strategy is adopted, for example, uniform distribution sampling, Latin hypercube sampling or a method based on an optimization algorithm (such as the initial population generation of a genetic algorithm), to generate a series of representative parameter combinations in the parameter space. For example, if a key structural unit has three adjustable parameters and each parameter has five discrete value levels, then five representative parameter combinations can be generated. 3 = 125 parameter combinations. If the parameter range is continuous, a certain number (e.g., 100 to 1000 groups) of random or quasi-random sampling points can be generated. Each parameter combination represents a unique structural design solution. These generated parameter combinations are collected to form a candidate solution parameter set, which is a table or list that lists the unique ID of each candidate solution and its corresponding structural parameter value.

[0156] For each parameter combination in the candidate solution parameter set, a precise three-dimensional geometric model of the key structural unit of the electronic accessory corresponding to the candidate solution is generated using computer-aided design software or scripts based on its detailed structural parameter values. For example, based on parameters such as the diameter of the sealing ring, the depth and width of the sealing groove, a three-dimensional model of a seal and a matching groove with a specific size and shape is generated. Then, this modified key structural unit model replaces the corresponding part in the original overall structural model of the electronic accessory to obtain a new version of the three-dimensional structural model of the electronic accessory containing the modified structure. At the same time, according to the material type specified in the candidate solution parameter set, the corresponding material properties (for example, parameters such as moisture permeability and density obtained from the material moisture permeability coefficient table) are assigned to the corresponding components in the model. These constructed three-dimensional geometric models and material property definitions together constitute a simulation model for moisture permeation simulation.

[0157] Based on the actual usage environment of the electronic component and the high-risk scenarios identified in the moisture and heat risk topology map, define boundary conditions and simulation conditions for moisture penetration simulation. Boundary conditions specify environmental parameters at the external surfaces or specific inlets of the simulation model, such as the external air temperature, relative humidity, and air pressure, or the moisture flux in a specific area. For example, a boundary condition could be set as follows: the external ambient temperature increases from 25°C to 50°C, and the relative humidity increases from 50% to 95% within one hour. Simulation conditions define the simulation duration, time step, and the heat source state within the electronic component (for example, the change in component power consumption over time). Multiple simulation conditions can be set for different risk scenarios, for example, one condition simulating rapid temperature and humidity changes, another simulating prolonged high-humidity exposure. These set boundary conditions and simulation parameters are combined to form a simulation condition set, a dataset containing descriptions of multiple simulation scenarios. Each scenario details the external environmental inputs, internal heat source state, and simulation run parameters.

[0158] Using specialized moisture transport simulation tools (e.g., coupled heat and moisture transfer simulation software based on the finite element method or finite difference method), the simulation model of each candidate solution is numerically simulated under the conditions defined by the simulation scenario set. The simulation tool calculates the diffusion, convection (if applicable), adsorption, desorption, and condensation processes of moisture within the electronic component based on the specified boundary conditions, material properties, structural geometry, and internal heat sources. The simulation output is detailed data on the temporal evolution of the moisture content (e.g., water vapor partial pressure or relative humidity) at various locations within the electronic component. For example, a time series of relative humidity at any grid point within the electronic component over the simulation timeframe can be obtained. These detailed simulation results collectively constitute the infiltration process dataset, a large, time-varying, three-dimensional data set that documents the dynamics of moisture distribution and migration within each candidate solution under each simulation scenario.

[0159] Extract key penetration features from the penetration process data set and the penetration trajectory spectrum obtained in the previous step (representing the moisture penetration characteristics of the original design) for comparison. The penetration characteristics may include: total moisture penetration (the total amount of moisture entering per unit time or total simulation time), the time when the moisture front reaches the internal key area, the maximum relative humidity in the key component area, the frequency and duration of condensation, and the distribution range of moisture inside. For each candidate solution's penetration process data set under each simulation condition, calculate or extract the values ​​of these penetration characteristics. Compare these values ​​with the corresponding penetration characteristic values ​​representing the original design in the penetration trajectory spectrum. For example, calculate the percentage reduction in total moisture penetration relative to the original design. Organize these comparison results into a penetration characteristic comparison table, which is a table that lists the values ​​of each penetration characteristic of each candidate solution under each simulation condition and the degree of improvement or deterioration relative to the original design.

[0160] According to the permeability characteristics comparison table, the improvement of each candidate solution in moisture-proof performance is quantified. Based on the degree of improvement or deterioration in the permeability characteristics comparison table, a series of performance improvement indicators are defined. For example, the percentage reduction in moisture penetration, the multiple reduction in the frequency of condensation, and the delay time for moisture to reach the critical area. These indicators directly reflect the degree of improvement in moisture-proof performance of the optimized design scheme compared to the original design. These indicators can be weighted averaged or other comprehensive scoring methods can be used to obtain a comprehensive moisture-proof performance improvement score. The various performance improvement indicators and comprehensive scores of each candidate solution are collected to form a performance improvement indicator set, which is a table that lists the ID of each candidate solution and the values ​​of its various performance improvement indicators under different simulation conditions.

[0161] Based on the performance improvement metrics, each candidate solution is tested under extreme conditions for moisture penetration simulation. Extreme conditions refer to the harshest environmental combinations beyond the standard duty cycle or typical high-risk scenarios, such as extreme temperature change rates, prolonged exposure to saturated humidity, or extreme air pressure fluctuations. These tests are designed to assess the robustness and failure boundaries of the optimized solution under these harshest conditions. Using a simulation model, the boundary conditions and simulation conditions for the extreme conditions are set, and moisture penetration simulations are run. For example, a long-term exposure (e.g., 1000 hours) to -40°C and 100% relative humidity can be simulated, or a transition from a hot, dry environment to a cold, high-humidity environment within a few minutes can be simulated. Under these extreme conditions, the moisture penetration rate, the time it takes for moisture to reach critical areas, and whether a protective failure occurs (e.g., rapid internal humidity saturation or large-scale condensation) are recorded. These simulation results are recorded to generate extreme condition response data, a table or dataset that documents the moisture penetration performance of each candidate solution under different extreme conditions.

[0162] A comprehensive evaluation of the performance improvement metrics (reflecting performance improvements under typical conditions) and the extreme condition response data (reflecting robustness under harsh conditions) is performed to produce a final solution performance evaluation matrix. This comprehensive evaluation should consider the solution's average performance under typical conditions as well as the risk of failure or performance degradation under extreme conditions. For example, a total moisture-proof performance score can be calculated for each candidate solution. This score is the weighted average of the performance improvement metrics under typical conditions, with a penalty term added for performance degradation or failure under extreme conditions. For example, the solution comprehensive performance score = α × (typical performance improvement score) + β × (extreme performance score), where α and β are weighting coefficients. Simultaneously, the solution's stability under different simulation conditions is evaluated; a solution that performs consistently across all conditions is superior to one that performs well only under specific conditions. The final comprehensive performance score of each candidate solution, along with detailed evaluation results (such as various metrics under typical conditions and key response values ​​under extreme conditions), is organized into a solution performance evaluation matrix. This table lists each candidate solution's parameter combination, its detailed moisture-proof performance evaluation results, and the final comprehensive score, providing input for subsequent multi-objective optimization.

[0163] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0164] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A structural parameter optimization design method for electronic accessories, characterized in that: The following steps are involved: Step S1: obtaining the structure and material parameters of the electronic component; monitoring moisture flow based on the structure and material parameters of the electronic component to obtain a moisture flow vector field; dynamically reconstructing the permeation path based on the moisture flow vector field to obtain a permeation trajectory spectrum; Step S2: Obtaining the environmental parameters for the electronic component's use; calculating dew point conditions at different temperatures based on the penetration trajectory spectrum to obtain a condensation condition difference map; performing a thermal-humidity coupling dynamic analysis on the condensation condition difference map and the penetration trajectory spectrum based on the environmental parameters for the electronic component's use to obtain a condensation trigger condition set; calculating the condensation risk probability based on the condensation trigger condition set to obtain a moisture-heat risk topology map; Step S3: performing structural unit risk mapping according to the electronic component structure and material parameters to obtain a structural unit risk correspondence table; The material moisture permeability is measured according to the structural unit risk correspondence table to obtain the material moisture permeability coefficient table; Quantitative calculation of barrier strength is performed based on the material moisture permeability coefficient table and the structural unit risk correspondence table, and moisture-proof evaluation is performed to obtain a moisture-proof performance index matrix; Step S4: performing moisture flow simulation and structural parameter optimization on the electronic component based on the moisture-proof performance index matrix to obtain an optimized configuration set of structural parameters.

2. The structural parameter optimization design method for electronic accessories according to claim 1, characterized in that: The moisture flow monitoring in step S1 is specifically as follows: Determine key monitoring areas for moisture penetration points based on electronic component structural data; Design the sensor point layout according to the key monitoring areas and obtain the sensor grid configuration diagram; According to the sensor grid configuration diagram, the environmental condition gradient of the electronic accessories is sampled to obtain the humidity response data set; The humidity response dataset is processed into spatiotemporal series data to obtain the moisture flow vector field.

3. The structural parameter optimization design method for electronic accessories according to claim 1, characterized in that: The dynamic reconstruction of the permeation path in step S1 is specifically as follows: Extract the flow trajectory of the moisture flow vector field and obtain the moisture particle trajectory set; Perform infiltration channel cluster analysis on the moisture particle trajectory set to obtain the infiltration channel characteristic map; Calculate the channel morphology parameters on the permeation channel characteristic map to obtain a channel morphology data set; Calculate the moisture transfer efficiency table based on the channel morphology dataset and the moisture flow vector field; According to the moisture transfer efficiency table and the permeation channel characteristic diagram, the humidity accumulation hot spot analysis is carried out to obtain the humidity accumulation distribution diagram; The spatiotemporal dynamic model was analyzed based on the humidity cumulative distribution map, moisture transfer efficiency table and channel morphology dataset to obtain the infiltration trajectory spectrum.

4. The structural parameter optimization design method for electronic accessories according to claim 1, characterized in that: The dew point conditions at different temperatures in step S2 are calculated as follows: Collect internal temperature distribution data of electronic components under different working states and environmental conditions, and then reconstruct the temperature field based on the structure and material parameters of the electronic components to obtain a temperature gradient distribution map; The humidity space distribution grid is obtained by performing humidity space interpolation calculation based on the infiltration trajectory spectrum and temperature gradient distribution map; Calculate the dew point temperature of the humidity space distribution grid according to the temperature gradient distribution map to obtain the dew point temperature distribution map; Calculate the temperature difference distribution data set based on the dew point temperature distribution map; Identify critical condensation areas on the temperature difference distribution data set and obtain a condensation risk area map; Based on the condensation risk area map and the temperature difference distribution data set, the temperature difference time stability analysis is performed to obtain the temperature difference stability index; A condensation condition difference map is generated based on the temperature difference distribution dataset, the condensation risk area map, and the temperature difference stability index.

5. The structural parameter optimization design method for electronic accessories according to claim 1, characterized in that: The thermal and moisture coupled dynamic analysis in step S2 is specifically as follows: Extract the moisture flow direction and velocity data from the infiltration trajectory spectrum, combine it with the temperature difference distribution in the condensation condition difference map, calculate the acceleration or deceleration effect of the temperature gradient on the moisture flow, and form a temperature-humidity coupling coefficient map; The working cycle conditions are defined according to the temperature-humidity coupling coefficient diagram and the environmental parameters of the electronic accessories, and a standard working cycle set is obtained; The dynamic response trajectory of the standard working cycle set is calculated to obtain the thermal and moist dynamic response trajectory; The critical state of the thermal and moist dynamic response trajectory is identified to obtain the condensation critical state set; The cumulative effect analysis of the condensation critical state set is performed to obtain the moisture accumulation risk map; The trigger conditions are parameterized according to the moisture accumulation risk map and the condensation critical state set to obtain the condensation trigger condition set.

6. The structural parameter optimization design method for electronic accessories according to claim 1, characterized in that: The calculation of the condensation risk probability in step S2 is specifically as follows: Analyze the environmental parameter statistical feature set of the environmental parameters used by electronic accessories; Perform environmental condition probability mapping on the environmental parameter statistical feature set and the condensation trigger condition set to obtain a trigger condition probability table; The regional condensation probability is calculated according to the trigger condition probability table and the condensation trigger condition set to obtain the spatial condensation probability distribution; Conduct component sensitivity assessment on electronic component structures and material parameters to obtain component sensitivity distribution maps; The severity of condensation impact is evaluated based on the component sensitivity distribution map and the spatial condensation probability distribution to obtain a condensation impact severity map; Perform time window analysis on the trigger condition probability table and the environmental parameter statistical feature set to obtain the risk time window table; A comprehensive assessment of the humid-heat risk is conducted on the spatial condensation probability distribution, condensation impact severity map and risk time window table to obtain a humid-heat risk topology map.

7. The structural parameter optimization design method for electronic accessories according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: Dividing the electronic component structure and material parameters into different structural units according to functional and morphological characteristics; Step S32: spatially mapping and associating the moisture-heat risk topology map with the structural units to obtain a structural unit risk correspondence table; Step S33: measuring the material moisture permeability according to the structural unit risk correspondence table to obtain a material moisture permeability coefficient table; Step S34: Quantitatively calculate the barrier strength based on the material water vapor permeability coefficient table and the structural unit risk correspondence table to obtain a barrier effectiveness score set; Step S35: Perform a comprehensive moisture-proof system evaluation on the barrier effectiveness score set and the moisture-heat risk topology map to obtain a moisture-proof effectiveness index matrix.

8. The structural parameter optimization design method for electronic accessories according to claim 7, characterized in that: Step S34 is specifically as follows: Extracting a set of structural geometric parameters from the electronic component structure and material parameters according to the structural unit risk correspondence table; Calculate the moisture permeability resistance of a single layer of material based on the material moisture permeability coefficient table for the structural geometric parameter set to obtain a single layer moisture permeability resistance table; According to the single-layer moisture permeability resistance table and the structural geometric parameter set, the multi-layer structural resistance is synthesized to obtain the total structural resistance value; Conduct interface weakness analysis based on the total structural resistance value and the structural geometric parameter set to obtain an interface weakness map; The barrier strength coefficient is calculated based on the total resistance value of the structure and the interface weak point map to obtain a barrier strength coefficient set; Conduct environmental adaptability assessment on the barrier strength coefficient set and the hygrothermal risk topology map to obtain an environmental adaptability index table; The barrier strength coefficient set and the environmental adaptability index table are comprehensively scored for barrier effectiveness to obtain a barrier effectiveness score set.

9. The structural parameter optimization design method for electronic accessories according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: determining key structural units that need to be optimized based on the moisture-proof performance index matrix; Step S42: constructing a parameter optimization constraint framework for key structural units; Step S43: performing wet gas flow simulation and evaluation based on the parameter optimization constraint framework and the infiltration trajectory spectrum to obtain a scheme performance evaluation matrix; Step S44: Perform multi-objective optimization and solution generation according to the solution performance evaluation matrix to obtain an optimized configuration set of structural parameters.

10. A structural parameter optimization design system for electronic accessories, characterized in that: For executing the structural parameter optimization design method for electronic accessories according to claim 1, the structural parameter optimization design system for electronic accessories comprises: The moisture path analysis module is used to obtain the structure and material parameters of electronic components; monitor moisture flow based on the structure and material parameters of electronic components to obtain a moisture flow vector field; and dynamically reconstruct the infiltration path based on the moisture flow vector field to obtain a infiltration trajectory spectrum; The moisture and heat risk assessment module is used to obtain the environmental parameters of electronic components; calculate the dew point conditions at different temperatures based on the penetration trajectory spectrum to obtain a condensation condition difference map; perform a thermal and moisture coupling dynamic analysis on the condensation condition difference map and the penetration trajectory spectrum based on the environmental parameters of the electronic components to obtain a condensation trigger condition set; calculate the condensation risk probability based on the condensation trigger condition set to obtain a moisture and heat risk topology map; The structural moisture-proof assessment module is used to map structural unit risks based on the electronic component structure and material parameters to obtain a structural unit risk correspondence table; determine the material moisture permeability based on the structural unit risk correspondence table to obtain a material moisture permeability coefficient table; quantitatively calculate the barrier strength based on the material moisture permeability coefficient table and the structural unit risk correspondence table, and perform moisture-proof assessment to obtain a moisture-proof efficiency index matrix; The structural optimization design module is used to simulate moisture flow and optimize structural parameters of electronic components based on the moisture-proof performance index matrix to obtain the optimal configuration set of structural parameters.

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