Humidity Gradient Compensation Method and Device for Damp Heat Test Chamber

Abstract

The present invention relates to the technical field of humidity compensation, and particularly to a humidity gradient compensation method and device for an alternating damp heat test chamber. The method includes the following steps: obtaining state monitoring parameters of the test chamber based on a multi-dimensional sensor array; analyzing the spatial humidity gradient distribution of the state monitoring parameters of the test chamber, and performing discrete interpolation fitting to construct a three-dimensional humidity gradient trend network; performing internal dynamic airflow diffusion analysis on the alternating damp heat test chamber to generate dynamic airflow diffusion data of the test chamber; mining dynamic humidity diffusion paths for the three-dimensional humidity gradient trend network based on the dynamic airflow diffusion data of the test chamber to construct a dynamic humidity diffusion twin model; obtaining external environment monitoring parameters of the test chamber; mining the association between environmental changes for the three-dimensional humidity gradient trend network based on the external environment monitoring parameters of the test chamber to generate humidity gradient change association data under environmental fluctuations. The present invention realizes efficient and accurate humidity gradient compensation.

Description

Technical Field

[0001] The present invention relates to the technical field of humidity compensation, and particularly to a humidity gradient compensation method and device for an alternating temperature and humidity test chamber. Background Art

[0002] As a device widely used in environmental testing and reliability assessment, an alternating temperature and humidity test chamber is usually used to simulate the working state of equipment under different environmental conditions, especially to evaluate the durability of materials, electronic components, mechanical equipment, etc. under high temperature and high humidity conditions. In the alternating temperature and humidity test, humidity is a key environmental factor, and the fluctuation of humidity directly affects the accuracy and reliability of test results. However, in practical applications, there is often a gradient effect in the humidity distribution inside the alternating temperature and humidity test chamber, that is, there are differences in humidity at different positions inside the test chamber. This humidity gradient problem is mainly caused by factors such as the local non-uniformity of the temperature and humidity control system, the position of the test samples, and the air flow inside the chamber.

[0003] The existence of the humidity gradient not only leads to errors in test results, but also affects the true performance of the test samples, thereby having an adverse impact on the product quality assessment. For example, some test pieces are exposed to too high or too low humidity environments, resulting in uneven moisture absorption or drying, affecting their physical properties or electrical characteristics. Therefore, during the alternating temperature and humidity test process, how to effectively compensate for the humidity gradient and ensure the uniformity and accuracy of humidity inside the test chamber has become a key issue in improving the performance of the test chamber and the test accuracy.

[0004] Currently, traditional humidity gradient compensation methods mostly rely on increasing the stability of the humidity control system or reducing the non-uniformity of humidity distribution by adjusting the internal structure of the test chamber. However, these methods often have disadvantages such as limited adjustment effect, high cost, and complex implementation. Therefore, there is an urgent need for a more intelligent, precise, and efficient humidity gradient compensation method to meet the requirements of modern alternating temperature and humidity test chambers in high-precision and high-efficiency environmental testing. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a humidity gradient compensation method and device for an alternating temperature and humidity test chamber to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a humidity gradient compensation method for an alternating temperature and humidity test chamber, including the following steps:

[0007] Step S1: Obtain the test chamber state monitoring parameters based on a multi-dimensional sensor array; analyze the spatial humidity gradient distribution of the test chamber state monitoring parameters, and perform discrete interpolation fitting to construct a three-dimensional humidity gradient trend network;

[0008] Step S2: Conduct an internal dynamic airflow diffusion analysis on the damp heat test chamber to generate the dynamic airflow diffusion data of the test chamber;

[0009] Step S3: Based on the dynamic airflow diffusion data of the test chamber, conduct dynamic humidity diffusion path mining on the three-dimensional humidity gradient trend network to construct a dynamic humidity diffusion twin model;

[0010] Step S4: Obtain the external environment monitoring parameters of the test chamber; based on the external environment monitoring parameters of the test chamber, conduct environmental change correlation mining on the three-dimensional humidity gradient trend network to generate humidity gradient change correlation data under environmental fluctuations;

[0011] Step S5: According to the humidity gradient change correlation data under environmental fluctuations, conduct humidity compensation flow simulation and humidity gradient compensation diffusion evolution on the dynamic humidity diffusion twin model to generate the humidity gradient compensation diffusion law under environmental fluctuations;

[0012] Step S6: Based on the humidity gradient compensation diffusion law under environmental fluctuations, conduct regional adaptive humidity gradient compensation on multiple regional twin models to construct an intelligent humidity compensation strategy.

[0013] Through the multi-dimensional sensor array, the present invention obtains the state monitoring parameters of the test chamber, which can comprehensively understand the internal humidity distribution characteristics of the test chamber. Constructing the three-dimensional humidity gradient trend network helps to reveal the spatial characteristics of the humidity distribution and provides a basis for subsequent humidity compensation. The internal dynamic airflow diffusion analysis can reveal the dynamic characteristics of the internal humidity change of the test chamber and provide dynamic airflow diffusion data, which are crucial for understanding the propagation path and speed of humidity inside the test chamber and lay the foundation for the establishment of the humidity compensation model. Through dynamic humidity diffusion path mining, a dynamic humidity diffusion twin model can be constructed to help simulate the propagation process of humidity inside the test chamber. This model helps to understand the law of humidity diffusion and provides a basis for the subsequent humidity compensation strategy. By analyzing the external environment monitoring parameters, the correlation between the environment and the humidity gradient change can be mined to generate humidity gradient change correlation data, which helps to understand the impact of the external environment on the humidity of the test chamber and provides more comprehensive information for the humidity compensation model. Through humidity compensation flow simulation and humidity gradient compensation diffusion evolution, the humidity gradient compensation diffusion law under environmental fluctuations can be generated to understand the change law of humidity under environmental fluctuations and provide a basis for formulating the humidity compensation strategy. Through the research on regional adaptive humidity gradient compensation, an intelligent humidity compensation strategy can be constructed to improve the humidity compensation effect of the test chamber. The formulation of the intelligent strategy can dynamically adjust the humidity according to the real-time environmental change, improving the humidity control accuracy and efficiency of the test chamber.

[0014] Preferably, step S1 includes the following steps:

[0015] Step S11: Obtain the test chamber status monitoring parameters based on the multi-dimensional sensor array;

[0016] Step S12: Perform temperature change calculation on the test chamber status monitoring parameters to identify the test chamber temperature change data;

[0017] Step S13: Conduct spatial humidity gradient distribution analysis on the test chamber status monitoring parameters to obtain the three-dimensional humidity gradient distribution characteristics;

[0018] Step S14: Mine the humidity change trend from the three-dimensional humidity gradient distribution characteristics according to the test chamber temperature change data, so as to obtain the humidity gradient change trend data;

[0019] Step S15: Perform discrete interpolation fitting on the humidity gradient change trend data to construct a three-dimensional humidity gradient trend network.

[0020] The present invention obtains the test chamber status monitoring parameters through a multi-dimensional sensor array, which can comprehensively understand the internal temperature, humidity and other parameters of the test chamber. These data are the basis for subsequent analysis, providing necessary information for humidity gradient compensation. Identifying the test chamber temperature change data helps to understand the dynamic change of the internal temperature of the test chamber. The temperature change data provides an important reference for subsequent analysis and supports the formulation of humidity gradient compensation strategies in terms of temperature. Through spatial humidity gradient distribution analysis, the three-dimensional humidity gradient distribution characteristics inside the test chamber can be obtained. These characteristics help to understand the distribution of humidity inside the test chamber and provide a basis for subsequent humidity gradient change trend analysis. Mining the humidity change trend from the humidity gradient distribution characteristics according to the test chamber temperature change data can obtain the humidity gradient change trend data. These data help to understand the change law of humidity at different temperatures and provide important information for the establishment of the humidity compensation model. By discrete interpolation fitting to construct a three-dimensional humidity gradient trend network, the spatial distribution characteristics of the humidity gradient can be more intuitively displayed. This network helps to deeply understand the change trend of the humidity gradient and provides visual support for the formulation of humidity compensation strategies.

[0021] Preferably, the specific steps of step S13 are as follows:

[0022] Extract all sensor nodes based on the multi-dimensional sensor array;

[0023] Perform spatial position calculation on all sensor nodes to obtain the spatial position coordinates of each node;

[0024] Conduct sensor node distribution analysis on the spatial position coordinates of each node to obtain the sensor node distribution data;

[0025] Based on the sensor node distribution data, the monitoring parameters of the test chamber state are segmented and identified by region parameters, so as to obtain multiple monitoring parameters of the test chamber regions;

[0026] Calculate the humidity of each test chamber region one by one for the multiple monitoring parameters of the test chamber regions, and extract the humidity parameters of each test chamber region;

[0027] Calculate the humidity difference between regions for the humidity parameters of each test chamber region to generate a humidity difference value between regions;

[0028] Conduct a humidity difference gradient analysis on the humidity difference value between regions to generate humidity difference gradient data;

[0029] Conduct a three-dimensional humidity gradient distribution analysis on the humidity difference gradient data to obtain the three-dimensional humidity gradient distribution characteristics.

[0030] By extracting all sensor nodes, the present invention ensures that all key positions inside the test chamber are covered by monitoring. By calculating the spatial position coordinates of each sensor node, the accurate positions of the sensor nodes in three-dimensional space are established, which provides a spatial reference for subsequent region parameter segmentation and humidity difference analysis. By analyzing the spatial position distribution of the sensor nodes, the density and distribution of the sensor nodes inside the test chamber can be understood, and the uniformity of the monitoring coverage can be evaluated, providing a basis for further parameter segmentation. By identifying the monitoring parameters of different regions, the monitoring and differentiation of different regions inside the test chamber are realized, which provides basic information for humidity compensation strategies for different regions. Extracting the humidity parameters of each test chamber region enables humidity control and adjustment for each region, providing data support for more refined humidity compensation. Generating the humidity difference value between regions helps to understand the humidity differences between different regions, providing a basis for humidity gradient analysis and compensation, making the humidity control more accurate. By analyzing the humidity difference gradient data, the gradient situation of the humidity change between regions can be understood, providing more in-depth data support for the formulation of humidity compensation strategies. Obtaining the three-dimensional humidity gradient distribution characteristics helps to comprehensively understand the spatial distribution of the humidity inside the test chamber, providing an important reference basis for formulating more effective humidity compensation schemes.

[0031] Preferably, the specific steps of step S2 are as follows:

[0032] Step S21: Conduct an internal geometric morphology analysis on the alternating humidity and heat test chamber to obtain the internal geometric morphology data of the test chamber;

[0033] Step S22: Conduct a spatial topological structure identification on the internal geometric morphology data of the test chamber and extract the spatial topological structure data;

[0034] Step S23: Perform 3D topological point cloud modeling on the spatial topological structure data to construct a 3D structure model of the test chamber;

[0035] Step S24: Based on the test chamber status monitoring parameters, conduct internal air flow excavation on the 3D structure model of the test chamber to extract the internal air flow characteristics of the test chamber;

[0036] Step S25: Conduct dynamic air flow diffusion analysis on the internal air flow characteristics of the test chamber to generate dynamic air flow diffusion data of the test chamber.

[0037] Through geometric morphology analysis, the present invention can obtain the geometric morphology data inside the test chamber, including the spatial layout and structure. Obtaining the geometric morphology data inside the test chamber helps to understand the spatial characteristics inside the test chamber and provides basic data for subsequent analysis. Through spatial topological structure identification, the spatial topological structure data inside the test chamber can be extracted, including the connections and relationships between various components. Extracting the spatial topological structure data helps to understand the organizational mode of the internal structure of the test chamber and provides a basis for modeling and analysis. Through 3D topological point cloud modeling, a 3D structure model of the test chamber can be constructed to display the spatial layout and morphology inside the test chamber. Constructing the 3D structure model helps to visualize the internal structure of the test chamber and provides visual support for subsequent analysis and optimization. Through air flow excavation, the internal air flow characteristics of the test chamber can be analyzed to understand the air flow situation and path inside the test chamber. Extracting the air flow characteristics helps to optimize the internal air flow of the test chamber and improve the uniformity and stability of the test environment. Through air flow diffusion analysis, the dynamic diffusion process of the air inside the test chamber can be simulated to generate dynamic air flow diffusion data. Generating the air flow diffusion data helps to understand the propagation and change of humidity inside the test chamber and provides an important reference for humidity gradient compensation.

[0038] Preferably, the specific steps of step S3 are as follows:

[0039] Step S31: Based on the dynamic air flow diffusion data of the test chamber, perform humidity diffusion simulation on the 3D humidity gradient distribution characteristics to generate humidity diffusion simulation data of the test chamber;

[0040] Step S32: Track the spatio-temporal changes of the humidity diffusion simulation data of the test chamber to generate a spatio-temporal change trajectory of humidity diffusion;

[0041] Step S33: Conduct dynamic humidity diffusion path excavation on the spatio-temporal change trajectory of humidity diffusion to obtain dynamic humidity diffusion path data;

[0042] Step S34: Based on the dynamic humidity diffusion path data, perform dynamic humidity diffusion rendering on the 3D humidity gradient trend network to construct a dynamic humidity diffusion twin model.

[0043] Through humidity diffusion simulation, the present invention can generate humidity diffusion simulation data inside the test chamber based on dynamic air flow diffusion data, simulate the distribution of humidity inside the test chamber, and generating humidity diffusion simulation data helps to understand the change trend and distribution characteristics of humidity inside the test chamber, providing an important reference for humidity gradient compensation. Through spatio-temporal change tracking, a spatio-temporal change trajectory of humidity diffusion can be generated to track the change process of humidity inside the test chamber. Generating the spatio-temporal change trajectory helps to understand the dynamic changes of humidity diffusion, providing time-series data support for subsequent analysis and optimization. Through humidity diffusion path mining, dynamic humidity diffusion path data can be extracted from the humidity diffusion trajectory to reveal the propagation path of humidity inside the test chamber. Extracting the humidity diffusion path data helps to understand the propagation mode of humidity inside the test chamber, providing support for humidity control and regulation. Through humidity diffusion rendering, a three-dimensional humidity gradient trend network can be rendered based on the dynamic humidity diffusion path data to construct a dynamic humidity diffusion twin model. Constructing the dynamic humidity diffusion twin model helps to visualize the propagation path and trend of humidity inside the test chamber, providing visual support for the formulation and optimization of humidity gradient compensation strategies.

[0044] Preferably, the specific steps of step S4 are as follows:

[0045] Step S41: Obtain the monitoring parameters of the external environment of the test chamber;

[0046] Step S42: Conduct an analysis of environmental parameter changes on the monitoring parameters of the external environment of the test chamber to generate environmental parameter change data;

[0047] Step S43: Fit the time-series changes of the environmental parameter change data to construct an environmental parameter time-series change curve;

[0048] Step S44: Identify the parameter mutation points of the environmental parameter time-series change curve and extract multiple parameter mutation points;

[0049] Step S45: Conduct environmental change correlation mining on the three-dimensional humidity gradient trend network based on multiple parameter mutation points to generate humidity gradient change correlation data under environmental fluctuations.

[0050] By obtaining the monitoring parameters of the external environment of the test chamber, data related to the external environment of the test chamber can be obtained, such as monitoring parameters like temperature and humidity. Obtaining the external environment monitoring parameters helps to understand the changes in the environment around the test chamber and provides basic data for subsequent analysis. Through the analysis of environmental parameter changes, change data of environmental parameters can be generated, such as the time-series changes of temperature and humidity. Generating the change data of environmental parameters helps to understand the change trend of the external environment and provides data support for subsequent analysis and comparison. Through time-series change fitting, a time-series change curve of environmental parameters can be constructed to reveal the change law of environmental parameters over time. Constructing the time-series change curve helps to understand the trend change of environmental parameters and provides a basis for subsequent analysis and prediction. Through parameter mutation point identification, mutation points in the environmental parameter curve can be extracted, that is, the time points when the environment changes rapidly. Extracting the parameter mutation points helps to identify the critical moments of environmental changes and provides important clues for subsequent analysis and the formulation of response strategies. Based on multiple parameter mutation points, environmental change correlation mining is carried out on the three-dimensional humidity gradient trend network to generate humidity gradient change correlation data under environmental fluctuations. Environmental change correlation mining helps to understand the impact of external environmental changes on the humidity gradient and provides support for the optimization and adjustment of the humidity gradient compensation strategy.

[0051] Preferably, the specific steps of step S5 are as follows:

[0052] Step S51: Divide the dynamic humidity diffusion twin model into regions to obtain multiple regional twin models;

[0053] Step S52: Based on the humidity gradient change correlation data under environmental fluctuations, perform humidity compensation flow simulation on multiple regional twin models to generate humidity compensation flow simulation data between multiple regions;

[0054] Step S53: Perform humidity gradient compensation diffusion evolution on the humidity compensation flow simulation data between multiple regions to generate the humidity gradient compensation diffusion law under environmental fluctuations;

[0055] Step S54: Perform humidity gradient compensation diffusion evolution on the humidity compensation delay response characteristics of each region to generate the humidity gradient compensation diffusion law under environmental fluctuations.

[0056] Through regional division, the dynamic humidity diffusion twin model of the present invention is divided into multiple regional twin models, and individual processing is performed on different regions. Regional division helps to study the characteristics and variation laws of the humidity diffusion model in different regions more precisely. Based on the correlation data of humidity gradient changes under environmental fluctuations, humidity compensation flow simulation is performed on multiple regional twin models to generate humidity compensation flow simulation data between multiple regions. Humidity compensation flow simulation helps to simulate the mutual relationship of humidity changes between different regions and provides data support for further analysis of humidity gradient compensation. Humidity gradient compensation diffusion evolution is performed on the humidity compensation flow simulation data between multiple regions to generate the humidity gradient compensation diffusion law under environmental fluctuations. Humidity gradient compensation diffusion evolution helps to understand the propagation mode and law of humidity compensation between different regions and provides support for the optimization of humidity gradient compensation strategies. Humidity gradient compensation diffusion evolution is performed on the humidity compensation delay response characteristics of each region to generate the humidity gradient compensation diffusion law under environmental fluctuations. Analyzing the humidity compensation delay response characteristics helps to understand the humidity adjustment speed and effect of different regions and provides a reference basis for the real-time adjustment of humidity gradient compensation.

[0057] Preferably, the specific steps of step S6 are as follows:

[0058] Step S61: Calculate the current humidity distribution of multiple regional twin models, and extract the humidity distribution values of each regional model;

[0059] Step S62: Calculate the humidity deviation of the humidity distribution value of each regional model based on the preset humidity value of the test chamber, so as to generate the humidity deviation value of each region;

[0060] Step S63: Perform humidity compensation calculation on the humidity deviation value of each region to obtain the humidity compensation value of each region;

[0061] Step S64: Perform regional adaptive humidity gradient compensation on the test chamber based on the humidity gradient compensation diffusion law under environmental fluctuations and the humidity compensation value of each region, so as to generate adaptive humidity gradient compensation data;

[0062] Step S65: Perform multi-region collaborative optimization calculation on the adaptive humidity gradient compensation data to obtain adaptive humidity compensation optimization parameters;

[0063] Step S66: Perform iterative compensation learning on the adaptive humidity compensation optimization parameters to construct an intelligent humidity compensation strategy.

[0064] Through calculating the current humidity distribution of multiple regions, the present invention can obtain the humidity distribution values of each regional model, and understand the humidity distribution of each region. Extracting the humidity distribution values helps to determine the actual humidity conditions in each region, providing basic data for subsequent humidity compensation calculations. Through humidity deviation calculation, the humidity deviation values of each regional model can be calculated based on a preset humidity value, analyzing the difference between the actual humidity and the preset value. Calculating the humidity deviation helps to understand the degree of deviation of the humidity in each region, providing a basis for formulating humidity compensation strategies. Through humidity compensation calculation, humidity compensation calculations can be performed on each region according to the humidity deviation values, obtaining the humidity compensation values for each region to adjust the humidity distribution. Calculating the humidity compensation values helps to achieve humidity adjustment for each region, improving the accuracy and stability of humidity control. Through regional adaptive humidity gradient compensation, the test chamber can be humidity-compensated according to environmental fluctuations and the humidity compensation values of each region, generating adaptive humidity gradient compensation data. Implementing adaptive humidity gradient compensation helps to improve the uniformity and stability of the humidity in the test chamber, ensuring the accuracy of the test environment. Through multi-region collaborative optimization calculation, the adaptive humidity gradient compensation data can be optimized to obtain more accurate adaptive humidity compensation parameters. Collaborative optimization calculation helps to improve the efficiency and accuracy of the humidity compensation strategy, optimizing the overall performance of humidity control in the test chamber. Through iterative compensation learning, an intelligent humidity compensation strategy can be constructed based on the adaptive humidity compensation optimization parameters, realizing the intelligence and continuous optimization of the humidity compensation strategy. Iterative learning helps to continuously improve the humidity compensation strategy, enhancing the stability and accuracy of humidity control in the test chamber and adapting to changes in different environmental conditions.

[0065] In this specification, a humidity gradient compensation device for an alternating damp and heat test chamber is provided, which is used to execute the humidity gradient compensation method for an alternating damp and heat test chamber as described above, and includes:

[0066] A humidity gradient trend module, configured to obtain test chamber status monitoring parameters based on a multi-dimensional sensor array; perform spatial humidity gradient distribution analysis on the test chamber status monitoring parameters, and perform discrete interpolation fitting to construct a three-dimensional humidity gradient trend network;

[0067] An air flow diffusion module, configured to perform internal dynamic air flow diffusion analysis on the alternating damp and heat test chamber, thereby generating test chamber dynamic air flow diffusion data;

[0068] A humidity diffusion path module, configured to perform dynamic humidity diffusion path mining on the three-dimensional humidity gradient trend network based on the test chamber dynamic air flow diffusion data to construct a dynamic humidity diffusion twin model;

[0069] An environmental change correlation module, configured to obtain the external environmental monitoring parameters of the test chamber; perform environmental change correlation mining on the three-dimensional humidity gradient trend network based on the external environmental monitoring parameters of the test chamber, so as to generate humidity gradient change correlation data under environmental fluctuations;

[0070] A humidity compensation diffusion module, configured to perform humidity compensation flow simulation and humidity gradient compensation diffusion evolution on the dynamic humidity diffusion twin model according to the humidity gradient change correlation data under environmental fluctuations, so as to generate humidity gradient compensation diffusion rules under environmental fluctuations;

[0071] An adaptive humidity compensation module, configured to perform regional adaptive humidity gradient compensation on multiple regional twin models based on the humidity gradient compensation diffusion rules under environmental fluctuations, and construct an intelligent humidity compensation strategy.

[0072] The present invention obtains the test chamber state monitoring parameters through a multi-dimensional sensor array, realizes the comprehensive monitoring of the internal state of the test chamber, conducts spatial humidity gradient distribution analysis and discrete interpolation fitting, constructs a three-dimensional humidity gradient trend network, which helps to understand the spatial distribution characteristics of the humidity gradient, analyzes the dynamic airflow diffusion inside the test chamber, generates dynamic airflow diffusion data, provides a detailed understanding of the airflow movement inside the test chamber, optimizes the airflow movement mode inside the test chamber, improves the uniformity and stability of humidity and temperature, based on the dynamic airflow diffusion data, conducts dynamic humidity diffusion path mining on the three-dimensional humidity gradient trend network, constructs a dynamic humidity diffusion twin model, understands the propagation path of humidity inside the test chamber, provides a basis for formulating a humidity compensation strategy, obtains the external environmental monitoring parameters of the test chamber, conducts environmental change correlation mining on the three-dimensional humidity gradient trend network, generates humidity gradient change correlation data under environmental fluctuations, understands the influence of external environmental changes on the humidity inside the test chamber, provides a basis for adjusting the humidity compensation strategy, according to the humidity gradient change correlation data under environmental fluctuations, performs humidity compensation flow simulation and humidity gradient compensation diffusion evolution on the dynamic humidity diffusion twin model, generates humidity gradient compensation diffusion rules under environmental fluctuations, realizes the real-time adjustment of the humidity gradient, ensures the stability and uniformity of the humidity inside the test chamber, based on the humidity gradient compensation diffusion rules under environmental fluctuations, performs regional adaptive humidity gradient compensation on multiple regional twin models, constructs an intelligent humidity compensation strategy, and performs personalized humidity compensation according to the humidity requirements of different regions, improving the accuracy and efficiency of humidity control inside the test chamber. Description of the Drawings

[0073] Figure 1 It is a schematic step flow diagram of a humidity gradient compensation method for an alternating damp heat test chamber of the present invention;

[0074] Figure 2 It is a schematic detailed implementation step flow diagram of step S1;

[0075] Figure 3 It is a schematic diagram of the detailed implementation steps of step S2;

[0076] Figure 4 It is a schematic diagram of the detailed implementation steps of step S3. Specific implementation manners

[0077] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] The embodiments of the present application provide a method and a device. The execution subjects of the method and the device include but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that carry this system, which can be regarded as general computing nodes of the present application. The data processing platform includes but is not limited to at least one of: audio and image management systems, information management systems, and cloud data management systems.

[0079] Please refer to Figures 1 to 4 , the present invention provides a humidity gradient compensation method for an alternating temperature and humidity test chamber. The humidity gradient compensation method for the alternating temperature and humidity test chamber includes the following steps:

[0080] Step S1: Obtain the test chamber state monitoring parameters based on a multi-dimensional sensor array; analyze the spatial humidity gradient distribution of the test chamber state monitoring parameters, and perform discrete interpolation fitting to construct a three-dimensional humidity gradient trend network;

[0081] Step S2: Perform internal dynamic airflow diffusion analysis on the alternating temperature and humidity test chamber to generate test chamber dynamic airflow diffusion data;

[0082] Step S3: Mine the dynamic humidity diffusion path of the three-dimensional humidity gradient trend network based on the test chamber dynamic airflow diffusion data to construct a dynamic humidity diffusion twin model;

[0083] Step S4: Obtain the external environment monitoring parameters of the test chamber; perform environmental change correlation mining on the three-dimensional humidity gradient trend network based on the external environment monitoring parameters of the test chamber to generate humidity gradient change correlation data under environmental fluctuations;

[0084] Step S5: Perform humidity compensation flow simulation and humidity gradient compensation diffusion evolution on the dynamic humidity diffusion twin model according to the humidity gradient change correlation data under environmental fluctuations to generate humidity gradient compensation diffusion laws under environmental fluctuations;

[0085] Step S6: Perform regional adaptive humidity gradient compensation on multiple regional twin models based on the humidity gradient compensation diffusion laws under environmental fluctuations to construct an intelligent humidity compensation strategy.

[0086] The present invention obtains the test chamber state monitoring parameters through a multi-dimensional sensor array, can comprehensively understand the humidity distribution characteristics inside the test chamber, constructing a three-dimensional humidity gradient trend network helps to reveal the spatial characteristics of the humidity distribution, provides a basis for subsequent humidity compensation, and the internal dynamic airflow diffusion analysis can reveal the dynamic characteristics of the humidity change inside the test chamber, providing dynamic airflow diffusion data, and these data are crucial for understanding the propagation path and speed of humidity inside the test chamber, laying a foundation for the establishment of the humidity compensation model. Through dynamic humidity diffusion path mining, a dynamic humidity diffusion twin model can be constructed to help simulate the propagation process of humidity inside the test chamber, and this model helps to understand the law of humidity diffusion, providing a basis for subsequent humidity compensation strategies. Through the analysis of external environment monitoring parameters, the correlation between the environment and humidity gradient changes can be mined to generate humidity gradient change correlation data, and these data help to understand the influence of the external environment on the humidity of the test chamber, providing more comprehensive information for the humidity compensation model. Through humidity compensation flow simulation and humidity gradient compensation diffusion evolution, the humidity gradient compensation diffusion law under environmental fluctuations can be generated to understand the change law of humidity under environmental fluctuations, providing a basis for formulating humidity compensation strategies. Through the research on regional adaptive humidity gradient compensation, an intelligent humidity compensation strategy can be constructed to improve the humidity compensation effect of the test chamber, and the formulation of the intelligent strategy can dynamically adjust the humidity according to real-time environmental changes, improving the humidity control accuracy and efficiency of the test chamber.

[0087] In an embodiment of the present invention, refer to Figure 1 , which is a schematic flow chart of the steps of a humidity gradient compensation method for an alternating damp heat test chamber of the present invention. In this example, the steps of the method include:

[0088] Step S1: Obtain the test chamber state monitoring parameters based on a multi-dimensional sensor array; perform spatial humidity gradient distribution analysis on the test chamber state monitoring parameters, and perform discrete interpolation fitting to construct a three-dimensional humidity gradient trend network;

[0089] In this embodiment, a multi-dimensional sensor array is installed inside the test chamber. The types of sensors can include temperature sensors, humidity sensors, barometric pressure sensors, etc. Ensure that the sensors are evenly distributed to comprehensively monitor the environmental conditions inside the test chamber. A data acquisition system is established. For example, Arduino, Raspberry Pi or other microcontrollers are used to connect the sensors and write programs to read sensor data at regular intervals. Set up the data acquisition program to regularly read the status monitoring parameters (such as humidity, temperature, barometric pressure, etc.) from the sensors and store the data in a local database or a cloud database for subsequent analysis. Preprocess the collected data, including removing noise, handling missing values and outliers, to ensure the accuracy and integrity of the data. Smoothing processing can be performed through the moving average method or interpolation method. Organize the preprocessed humidity data into a structured format, such as a CSV file or a database table, containing the ID of each sensor, the position coordinates (such as x, y, z) and the corresponding humidity value. Use visualization tools (such as Matplotlib, Seaborn or Plotly) to draw a two-dimensional heat map of the humidity distribution to intuitively display the spatial distribution of humidity inside the test chamber. Calculate the spatial gradient of humidity. Numerical differentiation methods or interpolation methods (such as the finite difference method) can be used to estimate the rate of change of humidity in space. Record the calculated humidity gradient data in the dataset for subsequent analysis and modeling. Select a suitable interpolation method. Common ones include Kriging interpolation, spline interpolation or inverse distance weighted interpolation (IDW). Kriging interpolation is usually used for the smoothing of geospatial data and is suitable for the spatial interpolation of humidity data. Use the selected interpolation method to fit the humidity gradient data to generate a continuous humidity distribution model. This can be achieved through the SciPy library in Python or the gstat package in R language. Based on the interpolation results, establish a three-dimensional humidity gradient trend network. This usually involves converting the interpolation results into three-dimensional grid data and using three-dimensional modeling software (such as MATLAB, Blender or ParaView) for visualization to generate a visualization map of the three-dimensional humidity gradient trend network to show the distribution trend of humidity inside the test chamber. Identify the patterns and abnormal areas of the humidity distribution. Record the parameters of the constructed three-dimensional humidity gradient trend network in the dataset, including the coordinates of the grid points, humidity values and gradient information. This will provide a basis for subsequent analysis and decision-making.

[0090] Step S2: Conduct an internal dynamic airflow diffusion analysis on the alternating temperature and humidity test chamber to generate the dynamic airflow diffusion data of the test chamber;

[0091] In this embodiment, a three-dimensional model of the test chamber is established using CAD or 3D modeling software (such as AutoCAD, SolidWorks, or Blender) to ensure that the model accurately reflects the internal structure of the test chamber (such as ventilation openings, fan positions, internal partitions, etc.). According to the actual operating conditions of the test chamber, boundary conditions are set, which include the flow rate at the inlet, the pressure at the outlet, the ambient temperature and humidity, etc. A suitable computational fluid dynamics (CFD) software is selected for dynamic airflow diffusion analysis, such as ANSYS Fluent, COMSOL Multiphysics, or OpenFOAM. In the CFD software, the physical properties of the fluid (such as the density and viscosity of air) are set, and a suitable turbulence model (such as the k-ε model or LES model) is selected to better simulate the behavior of the airflow. The established three-dimensional model is meshed to ensure that the mesh is fine enough to accurately capture the changes in the airflow. The software's built-in meshing tool is used to select a suitable mesh type (such as structured mesh or unstructured mesh), and the quality of the generated mesh is checked, including the distortion, uniformity, and refinement degree of the mesh, to ensure that the mesh is suitable for numerical simulation. Initial conditions are set in the CFD software, including the initial temperature, humidity, and flow rate of the internal air, etc. A suitable solver (such as a steady-state or transient solver) is selected, and the solution parameters (such as convergence criteria, time step size, etc.) are set. The simulation is started, and the software will calculate the diffusion of the airflow inside the test chamber according to the set boundary conditions and initial conditions. The key parameters during the simulation process (such as pressure, flow rate, and temperature) are monitored to ensure the stability and convergence of the simulation. After the simulation is completed, the airflow diffusion data is extracted from the CFD software, including the velocity field, pressure field, and temperature field, etc. These data can be exported in various formats (such as CSV, VTK). Visualization tools (such as ParaView, Tecplot, or MATLAB) are used to visualize the airflow diffusion results, display the flow path, velocity distribution, and temperature changes of the airflow, analyze the dynamic characteristics of the airflow, identify the main channels, stagnant areas, and turbulent areas of the airflow, evaluate the efficiency and uniformity of the airflow diffusion, record the generated dynamic airflow diffusion data in the database, including key parameters such as airflow velocity, pressure, and temperature, summarize the analysis results, write a report, and provide conclusions and suggestions for the airflow diffusion analysis to guide the design improvement or operation optimization of the test chamber.

[0092] Step S3: Based on the dynamic airflow diffusion data of the test chamber, mine the dynamic humidity diffusion path of the three-dimensional humidity gradient trend network, and construct a dynamic humidity diffusion twin model;

[0093] In this embodiment, dynamic airflow diffusion data, including flow velocity, pressure, temperature, and humidity distribution, etc., are extracted from the previous steps. These data will serve as the basis for analysis. The extracted data is sorted and cleaned to ensure data integrity and accuracy, noise and outliers are removed, and it is prepared for further analysis. A suitable path mining algorithm is selected, such as the particle tracking method based on hydrodynamics, dynamic time warping (DTW), or the shortest path algorithm. These algorithms can effectively identify the diffusion path of humidity. The selected algorithm is used to analyze the dynamic airflow diffusion data to identify the key path of humidity diffusion. The specific steps include: combining the humidity data with the airflow data, calculating the change of humidity over time and space, tracking the change path of humidity through the algorithm, identifying the main diffusion channels and stagnant areas, recording the mined dynamic humidity diffusion path data in the dataset, including the starting point, ending point, humidity value, and change situation of the path. A suitable dynamic model is selected to represent the humidity diffusion process. Usually, a model based on hydrodynamics or a machine learning model (such as a recurrent neural network RNN, long short-term memory network LSTM) is used. Necessary parameters, such as diffusion coefficient, boundary conditions, and initial conditions, are set in the model to ensure that the model can accurately simulate the dynamic behavior of humidity. The recorded dynamic humidity diffusion path data is used to train the model, and the model parameters are adjusted to improve its prediction accuracy. Methods such as cross-validation can be used to evaluate the performance of the model. The framework of the dynamic humidity diffusion twin model is designed, and the actual humidity distribution is compared and verified with the simulated humidity diffusion path. The real-time monitoring data is combined with the existing dynamic model to ensure that the twin model can reflect the humidity change in the test chamber in real time. The constructed dynamic humidity diffusion twin model is verified, the differences between the simulation results and the actual monitoring data are compared, and necessary model optimizations are carried out. The parameters and results of the dynamic humidity diffusion twin model are recorded in the database, including humidity distribution, diffusion path, and model performance indicators. The results of humidity diffusion path mining and twin model construction are summarized, a report is written, providing suggestions and improvement directions for humidity control in the test chamber. Visualization tools (such as ParaView or Matplotlib) are used to display the results of the dynamic humidity diffusion path and the twin model, facilitating the analysis and understanding of the dynamic behavior of humidity.

[0094] Step S4: Obtain the external environment monitoring parameters of the test chamber; perform environmental change correlation mining on the three-dimensional humidity gradient trend network based on the external environment monitoring parameters of the test chamber, so as to generate humidity gradient change correlation data under environmental fluctuations;

[0095] In this embodiment, a variety of environmental monitoring devices are installed outside the test chamber, including temperature sensors, humidity sensors, barometric pressure sensors, light sensors, etc., to comprehensively monitor environmental changes. A microcontroller (such as Arduino or Raspberry Pi) or a data acquisition system is used to connect the monitoring devices and write programs to collect data of each parameter, ensuring that the devices can record data in real time and have data storage or transmission functions. The acquisition frequency is set, and the external environmental monitoring parameters are read regularly, and the data is stored in a local database or a cloud database for subsequent analysis. The collected environmental monitoring data is preprocessed, including denoising, handling missing values and outliers, to ensure the accuracy and integrity of the data. The moving average method or interpolation method can be used for smoothing processing. The preprocessed external environmental monitoring parameters are organized into a structured format (such as CSV or a database table), including the timestamp, value, and relevant location of each parameter. The three-dimensional humidity gradient trend network constructed in the previous steps is utilized to ensure that the network can reflect the humidity changes inside the test chamber. The external environmental parameters are combined with the humidity gradient trend network for correlation analysis. A suitable association mining method is selected, such as correlation analysis, regression analysis, time series analysis, or machine learning methods (such as random forest, support vector machine, etc.), to mine the relationship between external environmental changes and humidity gradient changes. The selected method is used to analyze the association between the external environmental monitoring parameters and humidity gradient changes. For example: calculate the correlation coefficient between the environmental parameters and the humidity gradient, identify the external factors that significantly affect humidity, use regression analysis for modeling, and explore the impact of external environmental changes on the humidity gradient inside the test chamber. The analysis results, relevant parameters, and model performance indicators are recorded in the dataset and report for subsequent use. Summarize the impact of environmental changes on the humidity gradient, write a report, and provide suggestions and improvement directions for humidity management in the test chamber. Based on the results of the correlation analysis, corresponding humidity control strategies are formulated to cope with external environmental changes and improve the stability and reliability of humidity control in the test chamber.

[0096] Step S5: Perform humidity compensation flow simulation and humidity gradient compensation diffusion evolution on the dynamic humidity diffusion twin model according to the associated data of humidity gradient changes under environmental fluctuations, so as to generate the humidity gradient compensation diffusion law under environmental fluctuations;

[0097] In this embodiment, from the associated data of humidity gradient changes under environmental fluctuations, these data include information on the impact of the external environment on the humidity gradient, providing a basis for subsequent simulations. Ensure that the dynamic humidity diffusion twin model has been constructed to be able to reflect the dynamic characteristics of humidity changes in the test chamber. The model should be able to receive external environment data and humidity gradient information, and select a suitable computational fluid dynamics (CFD) software for humidity compensation flow simulation, such as ANSYS Fluent, COMSOL Multiphysics, or OpenFOAM. Input the associated data of humidity gradient changes into the CFD software, and set the initial conditions and boundary conditions, which include: the initial state of humidity distribution, external environment parameters (such as temperature, humidity, air flow velocity, etc.). Start the humidity compensation flow simulation and observe the flow of humidity inside the test chamber. During the simulation process, the following aspects need to be concerned: the flow path and velocity distribution of humidity compensation, the changes in humidity in different regions, identify the main humidity flow channels, record the simulation results, extract humidity compensation flow data, including humidity values and flow conditions in different regions, for subsequent analysis. Select a suitable mathematical model to describe the evolution of humidity gradient compensation diffusion. Common models include diffusion equations, convection-diffusion equations, or heat conduction equations. Set necessary parameters in the diffusion model, for example: diffusion coefficient (determined according to humidity and air flow characteristics), boundary conditions (such as solid boundaries, convective boundaries, etc.). Use numerical methods (such as finite difference method, finite element method) to simulate the evolution of humidity gradient compensation diffusion. The specific steps include: taking the humidity compensation flow simulation results as the initial conditions and inputting them into the diffusion evolution model, running the simulation, observing the dynamic process of humidity compensation diffusion, recording the evolution of humidity in different regions, analyzing the generated humidity gradient compensation diffusion data, identifying the diffusion rate and range, generating visualization charts (such as heat maps, three-dimensional distribution maps) to intuitively display the law of humidity diffusion, recording the key data of the humidity gradient compensation diffusion law in the database, including diffusion rate, humidity distribution, and influencing factors. Summarize the results of humidity compensation flow simulation and compensation diffusion evolution, write a detailed report, provide suggestions and improvement directions for humidity management in the test chamber. According to the analysis results, formulate corresponding humidity control strategies to improve the efficiency and stability of humidity management in the test chamber.

[0098] Step S6: Based on the law of humidity gradient compensation diffusion under environmental fluctuations, perform regional adaptive humidity gradient compensation on multiple regional twin models to construct an intelligent humidity compensation strategy.

[0099] In this embodiment, ensure that the humidity twin models of multiple regions have been constructed and can reflect the changes in the actual environment, and are ready for adaptive humidity gradient compensation. Select a suitable adaptive control algorithm, such as fuzzy control, PID control, or adaptive gain control. These algorithms can automatically adjust the humidity compensation strategy according to environmental changes. Determine the input parameters required for the compensation algorithm, including: the current humidity value, the preset humidity target value, external environmental parameters (such as temperature, humidity, air flow velocity, etc.). Develop a regional adaptive humidity gradient compensation strategy to ensure that the compensation operations for each region can be carried out independently while working in coordination. For example, increase the compensation intensity in regions with large humidity deviations and reduce the compensation in regions where the humidity is close to the target value. Real-time monitor the humidity values and external environmental parameters of each region through sensors, input the real-time data into the adaptive compensation algorithm, and start the corresponding humidity control devices (such as humidifiers, dehumidifiers, fans, etc.) for compensation operations according to the adaptive humidity gradient compensation strategy. Implement dynamic adjustment within each region to ensure that the humidity can quickly respond to environmental changes. Record the effects of each compensation operation, including humidity changes, equipment operating status, and environmental parameters, for subsequent analysis and optimization. Select a suitable machine learning algorithm (such as neural networks, decision trees, or reinforcement learning) to construct an intelligent humidity compensation strategy. This strategy can automatically optimize the compensation scheme based on historical data and real-time feedback. Organize the historical humidity compensation data, including compensation values, humidity changes, and environmental states, and use it to train the intelligent algorithm. Train the intelligent algorithm using the prepared dataset and optimize the model parameters to improve the accuracy and response speed of humidity compensation. Verify the model through methods such as cross-validation to ensure its effectiveness under different environmental conditions.

[0100] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of the said step S1 include:

[0101] Step S11: Obtain the test chamber status monitoring parameters based on the multi-dimensional sensor array;

[0102] Step S12: Calculate the temperature change of the test chamber status monitoring parameters to identify the test chamber temperature change data;

[0103] Step S13: Analyze the spatial humidity gradient distribution of the test chamber status monitoring parameters to obtain the three-dimensional humidity gradient distribution characteristics;

[0104] Step S14: Mine the humidity change trend of the three-dimensional humidity gradient distribution characteristics according to the test chamber temperature change data to obtain the humidity gradient change trend data;

[0105] Step S15: Perform discrete interpolation fitting on the humidity gradient change trend data to construct a three-dimensional humidity gradient trend network.

[0106] In this embodiment, a multi-dimensional sensor array is arranged in the test chamber. These sensors include temperature sensors, humidity sensors, barometric pressure sensors, light sensors, etc. Ensure that the sensors are evenly distributed at different positions to obtain comprehensive monitoring data. Select a suitable data acquisition system (such as Arduino, Raspberry Pi, or a dedicated monitoring device) to collect and process sensor signals. Configure data acquisition software, set the sampling frequency and data storage format, and start the data acquisition system to obtain the status monitoring parameters of the test chamber in real time, including temperature, humidity, barometric pressure, etc. Record the data for subsequent analysis. Organize the obtained temperature monitoring data into time series data to ensure the integrity and consistency of the data. Calculate the temperature change rate, ΔT = T(t)−T(t−1), where T(t) is the temperature at the current moment and T(t−1) is the temperature at the previous moment. Identify the trend of the temperature change data and generate a temperature change data set, including statistical information such as the maximum value, minimum value, and change amplitude. Organize the humidity data obtained by the humidity sensor into a data set in a three-dimensional coordinate system to ensure that the data can reflect the humidity levels at different positions. Select a suitable spatial analysis method, such as Kriging interpolation or inverse distance weighting (IDW), to generate a spatial distribution model of humidity. Analyze the humidity data using the selected method to generate a three-dimensional humidity gradient distribution feature map. It can be visualized using GIS software (such as ArcGIS or QGIS) or scientific computing software (such as MATLAB, Python). Select a suitable time series analysis method, such as moving average, exponential smoothing, or ARIMA model, to explore the trend of humidity changes. Analyze the humidity gradient distribution feature data using the selected method to extract the humidity change trend data, including trends such as rising, falling, or remaining stable. Record the humidity gradient change trend data and generate a visualization chart to help understand the dynamic characteristics of humidity changes. Select a suitable discrete interpolation method, such as cubic spline interpolation or radial basis function interpolation (RBF), to fit the humidity gradient change trend data. Input the humidity gradient change trend data into the selected interpolation method to construct a three-dimensional humidity gradient trend network and generate a change model of humidity. Verify the accuracy of the fitting model by comparing it with the actual measurement data and make necessary adjustments. Use a visualization tool (such as Matplotlib, Plotly, or three-dimensional graphics software) to display the three-dimensional humidity gradient trend network to ensure its intuitiveness and understandability.

[0107] In this embodiment, the specific steps of step S13 are as follows:

[0108] Extract all sensor nodes based on the multi-dimensional sensor array;

[0109] Perform spatial position calculations on all sensor nodes to obtain the spatial position coordinates of each node;

[0110] Analyze the distribution of sensor nodes based on the spatial position coordinates of each node to obtain sensor node distribution data;

[0111] Based on the sensor node distribution data, perform regional parameter segmentation and identification on the test chamber status monitoring parameters to obtain multiple test chamber regional monitoring parameters;

[0112] Calculate the humidity for each test chamber region one by one from the multiple test chamber regional monitoring parameters, and extract the humidity parameter of each test chamber region;

[0113] Calculate the humidity difference between regions for the humidity parameter of each test chamber region to generate an inter-regional humidity difference value;

[0114] Perform humidity difference gradient analysis on the inter-regional humidity difference value to generate humidity difference gradient data;

[0115] Perform three-dimensional humidity gradient distribution analysis on the humidity difference gradient data to obtain three-dimensional humidity gradient distribution characteristics.

[0116] In this embodiment, ensure that the multi-dimensional sensor array is correctly installed in the test chamber. The sensor types include temperature, humidity, air pressure, etc. Use a data acquisition system (such as Arduino or Raspberry Pi) to extract real-time monitoring data from each sensor node, including node identification, sensor type, and its measured value. Organize the extracted sensor node information into a structured data format (such as CSV or database), ensuring the completeness of the basic information of each node, including location identification and sensor type. Determine the geometric layout of the sensor array, usually determining the spatial coordinates of the sensors according to the physical dimensions of the test chamber. Calculate the spatial position coordinates of each sensor node through the known sensor installation positions and spatial coordinate systems (such as Cartesian coordinate system). Record the spatial coordinates of each node in the dataset to form a node coordinate list. Select a suitable distribution analysis method, such as statistical analysis, visualization tools (such as heat maps), or spatial analysis software (such as GIS). Use the selected method to analyze the spatial position coordinates of each sensor node, generate a distribution feature map of the sensor nodes, identify the concentrated and sparse areas of the sensors, generate sensor node distribution data, including the number of nodes, density distribution, and its spatial layout within the test chamber. According to the sensor node distribution data and the physical structure of the test chamber, divide the test chamber into multiple regions. For example, it can be divided based on node density or functional areas, and divide the test chamber state monitoring parameters (such as temperature and humidity) into regions, ensuring that the parameters within each region can be analyzed independently. Generate a monitoring parameter dataset for each region, ensuring that it contains region identification and its corresponding state monitoring parameters. Extract the humidity parameter from the sensor nodes in each divided region, ensuring the completeness of the humidity data in each region. Calculate the average humidity value of each region, record the calculated humidity parameter of each region in the dataset. Calculate the humidity difference between each region: Humidity difference = Humidity of region A - Humidity of region B. Calculate the humidity difference for each pair of regions to generate a list of inter-region humidity difference values. Select a suitable gradient analysis method, such as numerical differentiation or smoothing interpolation method, to calculate the spatial gradient of the humidity difference. Calculate the gradient of the humidity difference through the already calculated humidity difference values, identify the regions with large humidity changes, generate a humidity difference gradient dataset, and record the gradient values of each region. Select a suitable three-dimensional analysis and visualization tool (such as MATLAB, Matplotlib of Python, or Mayavi) to generate a three-dimensional distribution map. Use the three-dimensional visualization method to display the humidity difference gradient data, generate a three-dimensional humidity gradient distribution feature map, showing the distribution of humidity within the test chamber. Record the generated three-dimensional humidity gradient distribution feature map, and interpret and analyze the results to provide a basis for subsequent research or experiments.

[0117] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of the said step S2 include:

[0118] Step S21: Conduct an internal geometric morphology analysis on the damp heat test chamber to obtain the internal geometric morphology data of the test chamber;

[0119] Step S22: Identify the spatial topological structure of the internal geometric morphology data of the test chamber and extract the spatial topological structure data;

[0120] Step S23: Perform three-dimensional topological point cloud modeling on the spatial topological structure data to construct a three-dimensional structure model of the test chamber;

[0121] Step S24: Based on the test chamber status monitoring parameters, conduct internal air flow excavation on the three-dimensional structure model of the test chamber to extract the internal air flow characteristics of the test chamber;

[0122] Step S25: Conduct dynamic air flow diffusion analysis on the internal air flow characteristics of the test chamber to generate the dynamic air flow diffusion data of the test chamber.

[0123] In this embodiment, geometric shape data inside the test chamber is obtained using a laser scanner, a 3D measuring instrument, or photogrammetry technology. These devices can generate high-precision 3D point cloud data. The acquired original geometric data is cleaned and preprocessed, including noise removal, data alignment, and coordinate transformation, to ensure the accuracy and consistency of the data. The cleaned geometric data is organized into a structured format (such as an XYZ coordinate file or a mesh format), recording the geometric features inside the test chamber, including dimensions, shapes, and surface features. The topological structure features inside the test chamber are determined, including channels, cavities, and connection points, etc. Topological data analysis (TDA) tools or algorithms (such as Alpha shape, Voronoi diagram) are used to identify and extract spatial topological structure data. The selected method is used to analyze the geometric shape data, extract the spatial topological structure of the test chamber, identify different regions and their interconnection relationships. The extracted topological structure data is recorded in a database, including the properties and connection information of each topological element. The extracted spatial topological structure data is converted into a 3D point cloud model and processed using point cloud processing software (such as CloudCompare or Meshlab). A suitable 3D modeling software (such as Blender, AutoCAD, SolidWorks) is selected to model the topological point cloud. In the selected software, a 3D structure model of the test chamber is constructed based on the processed point cloud data, ensuring the accuracy and visualization effect of the model. The generated 3D model is optimized, including mesh simplification, surface smoothing, and ensuring geometric accuracy, for subsequent analysis and simulation. The status monitoring parameters of the test chamber are integrated, including temperature, humidity, air flow velocity, and pressure, etc., for a comprehensive analysis of air flow. A computational fluid dynamics (CFD) software (such as ANSYS Fluent, COMSOL Multiphysics, or OpenFOAM) is selected for internal air flow excavation. The constructed 3D structure model is imported into the CFD software, and boundary conditions and initial conditions are set, including inflow and outflow positions, flow velocities, and physical properties. The air flow simulation is run, observing the air flow distribution and flow characteristics inside the test chamber, and extracting data such as air flow velocity, pressure change, and flow direction. The air flow characteristic data obtained from the CFD simulation is organized, including time series data, for dynamic analysis. A suitable analysis method, such as the Laplace equation, diffusion model, or particle tracking method, is selected to analyze the diffusion characteristics of the air flow inside the test chamber. The dynamic air flow data is subjected to diffusion analysis, generating diffusion paths, velocity distributions, and concentration change diagrams of the air flow inside the test chamber. The generated dynamic air flow diffusion data is recorded, including diffusion coefficients, diffusion ranges, and dynamic change trends, and visualized to help understand the air flow behavior.

[0124] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of the said step S3 include:

[0125] Step S31: Perform humidity diffusion simulation on the three-dimensional humidity gradient distribution characteristics based on the dynamic airflow diffusion data of the test chamber to generate test chamber humidity diffusion simulation data;

[0126] Step S32: Track the spatio-temporal changes of the test chamber humidity diffusion simulation data to generate a humidity diffusion spatio-temporal change trajectory;

[0127] Step S33: Mine the dynamic humidity diffusion path from the humidity diffusion spatio-temporal change trajectory to obtain dynamic humidity diffusion path data;

[0128] Step S34: Perform dynamic humidity diffusion rendering on the three-dimensional humidity gradient trend network based on the dynamic humidity diffusion path data to construct a dynamic humidity diffusion twin model.

[0129] In this embodiment, the dynamic airflow diffusion data generated previously in the test chamber is collected, including air flow characteristics and related parameters (such as air flow velocity, pressure distribution, etc.). A suitable numerical simulation software (such as COMSOL Multiphysics, ANSYS Fluent, or MATLAB) is selected to perform humidity diffusion simulation. In the selected simulation software, a physical model including humidity diffusion is established, the airflow diffusion data is input, and the boundary conditions and initial conditions of humidity diffusion are set. The simulation is run to observe the diffusion process of humidity inside the test chamber, and the humidity diffusion simulation data is recorded, including humidity distribution and changes. The humidity diffusion simulation data is organized into time series data to ensure that the data includes the humidity distribution at each time point. An algorithm for tracking humidity changes is determined, such as using interpolation methods or time series analysis techniques, to analyze the humidity distribution at different time points. The spatio-temporal changes in the humidity diffusion simulation data are tracked to generate the spatio-temporal change trajectory of humidity diffusion, and the changes in humidity at different regions and time points are recorded. Visualization tools (such as Matplotlib, Plotly, or 3D visualization software) are used to display the spatio-temporal change trajectory of humidity diffusion for easy understanding and analysis. A suitable path mining algorithm is selected, such as dynamic time warping (DTW), the shortest path algorithm, or a path tracking algorithm based on hydrodynamics. Based on the spatio-temporal change trajectory of humidity diffusion, the selected algorithm is used to mine the dynamic humidity diffusion path to identify the main paths and characteristics of humidity diffusion. The mined dynamic humidity diffusion path data is recorded in the dataset, including the starting point, ending point, and humidity values at each key node of the path. Visualization tools are used to display the dynamic humidity diffusion path to help understand the flow and diffusion characteristics of humidity in the test chamber. The dynamic humidity diffusion path data is integrated with the three-dimensional humidity gradient trend network to ensure that the data can be correlated with each other. A suitable rendering engine or visualization software (such as Unity, Blender, or Unreal Engine) is selected to perform the rendering of dynamic humidity diffusion. In the rendering tool, a three-dimensional humidity gradient trend network is constructed, and the rendering parameters of the dynamic humidity diffusion path are set, including color, transparency, and dynamic effects. The rendering program is run to display the process of dynamic humidity diffusion in real time, generating a dynamic humidity diffusion twin model for easy observation and analysis of the changes in humidity diffusion. The consistency between the rendering result and the actual data is verified, and the rendering effect is optimized according to the feedback and actual observation results.

[0130] In this embodiment, step S4 includes the following steps:

[0131] Step S42: Analyze the changes in environmental parameters of the external environment monitoring parameters of the test chamber to generate environmental parameter change data;

[0132] Step S43: Fit the time series changes of the environmental parameter change data to construct an environmental parameter time series change curve;

[0133] Step S44: Identify the parameter mutation points in the environmental parameter time series change curve and extract multiple parameter mutation points;

[0134] Step S45: Conduct environmental change correlation mining on the three-dimensional humidity gradient trend network based on multiple parameter mutation points, so as to generate humidity gradient change correlation data under environmental fluctuations.

[0135] In this embodiment, environmental monitoring parameter data outside the test chamber is collected, including temperature, humidity, air pressure, light, etc. It can be obtained through a sensor array or a monitoring system. The collected environmental monitoring parameters are organized into a structured data format (such as CSV or a database) to ensure the integrity of the data for each parameter and the consistency of the timestamps. Calculate the change amounts of each environmental parameter, and use basic statistical analysis methods (such as mean, variance) to evaluate the fluctuation of the parameters. Generate environmental parameter change data, including the change trend and amplitude of each parameter within a specific time period. Organize the environmental parameter change data into time series data, ensuring that the data is arranged in chronological order and includes timestamps. Select a suitable fitting method, such as linear regression, locally weighted regression, or spline interpolation, to construct the time series change curve of the environmental parameters. Use the selected fitting method to analyze the time series data of the environmental parameters and generate the time series change curve of the environmental parameters. This can be achieved by using data analysis software (such as SciPy in Python, R, or MATLAB). Visualize the fitting results to generate a time series change curve graph for easy observation of the change trends of each environmental parameter. Select a suitable algorithm for mutation point identification, such as CUSUM (Cumulative Sum Control Chart), Bayesian Change Point Detection, or the moving average method. Input the time series change data of the environmental parameters into the selected algorithm for mutation point analysis. Run the algorithm to identify the mutation points existing in the environmental parameter change curve. These mutation points indicate significant changes in environmental conditions, such as a sudden increase in temperature or a decrease in humidity. Record the identified multiple parameter mutation points in a dataset, including the positions (times) of the mutation points and their corresponding environmental parameter values. Select a suitable correlation mining method, such as association rule learning, time series association analysis, or regression analysis, to study the relationship between the mutation points and the humidity gradient change. Organize the data of the humidity gradient trend network, including the humidity gradient change situation and the corresponding environmental parameter change data, for correlation analysis. Run the selected correlation mining algorithm to analyze the relationship between multiple parameter mutation points and the humidity gradient change, and generate the correlation data of the humidity gradient change under environmental fluctuations. Record the generated environmental fluctuation and humidity gradient change correlation data, and use a visualization tool (such as Matplotlib, Seaborn, or PowerBI) to display the correlation results for in-depth analysis and understanding.

[0136] In this embodiment, step S5 includes the following steps:

[0137] Step S51: Divide the dynamic humidity diffusion twin model into regions to obtain multiple regional twin models;

[0138] Step S52: Based on the humidity gradient change correlation data under environmental fluctuations, perform humidity compensation flow simulation on multiple regional twin models to generate humidity compensation flow simulation data between multiple regions;

[0139] Step S53: Perform humidity gradient compensation diffusion evolution on the humidity compensation flow simulation data between multiple regions to generate the humidity gradient compensation diffusion law under environmental fluctuations;

[0140] Step S54: Perform humidity gradient compensation diffusion evolution on the humidity compensation delay response characteristics of each region to generate the humidity gradient compensation diffusion law under environmental fluctuations.

[0141] In this embodiment, analyze the existing dynamic humidity diffusion twin model, identify different regions in the model. These regions are based on physical characteristics (such as size, shape) or functional characteristics (such as different humidity environments), determine the criteria for region division. For example, based on humidity distribution, air flow path, or temperature gradient, ensure that the division can reflect the characteristics of the actual environment. Conduct region division in the dynamic humidity diffusion twin model, use modeling software (such as Blender, Unity, or MATLAB) to generate multiple region twin models, record the twin model information of each region in the database, including region identification, geometric features, and initial humidity conditions. Select a suitable CFD simulation software (such as ANSYS Fluent, COMSOL Multiphysics) to perform humidity compensation flow simulation, import multiple region twin models into the CFD software, set the initial conditions and boundary conditions, input the associated data of humidity gradient changes under environmental fluctuations, run the humidity compensation flow simulation, observe the humidity flow situation between different regions, record the humidity compensation flow simulation data, including humidity changes and flow paths, organize the simulation results, generate a humidity compensation flow dataset between multiple regions, including humidity distribution and changes between different regions. Based on the humidity compensation flow simulation data, establish a humidity gradient compensation diffusion evolution model, select a suitable mathematical model (such as diffusion equation or convection-diffusion equation), run the compensation diffusion evolution simulation, observe the diffusion evolution process of the humidity gradient in different regions, record the humidity gradient compensation diffusion data, analyze the generated humidity gradient compensation diffusion law, use a visualization tool to display the diffusion process, ensure that the results are intuitive and easy to understand, record the humidity gradient compensation diffusion law in the dataset, including diffusion rate, range, and influencing factors. Determine the delay response characteristics of humidity gradient compensation, including humidity response time, compensation effect, and mutual influence between regions, select a suitable dynamic response model (such as time-delay model, linear or nonlinear dynamic system model), conduct compensation delay response analysis on the humidity gradient of each region, analyze the delay response characteristics of the humidity gradient of each region, record the relationship between humidity changes and time, generate humidity gradient compensation response data, organize and analyze the results, and generate a visualization chart of the humidity gradient compensation delay response characteristics to help understand the response characteristics of different regions under environmental fluctuations.

[0142] In this embodiment, step S6 includes the following steps:

[0143] Step S61: Calculate the current humidity distribution of multiple region twin models, and extract the humidity distribution values of each region model;

[0144] Step S62: Calculate the humidity deviation of the humidity distribution value of each region model based on the preset humidity value in the test chamber, so as to generate the humidity deviation value of each region;

[0145] Step S63: Perform humidity compensation calculation on the humidity deviation value of each area to obtain the humidity compensation value of each area;

[0146] Step S64: Perform regional adaptive humidity gradient compensation on the test chamber based on the humidity gradient compensation diffusion law under environmental fluctuations and the humidity compensation value of each area, so as to generate adaptive humidity gradient compensation data;

[0147] Step S65: Perform multi-region collaborative optimization calculation on the adaptive humidity gradient compensation data to obtain the adaptive humidity compensation optimization parameters;

[0148] Step S66: Perform iterative compensation learning on the adaptive humidity compensation optimization parameters to construct an intelligent humidity compensation strategy.

[0149] In this embodiment, the current humidity state data is extracted from the twin models of each region, including the simulated or real-time monitored humidity values. Numerical calculation tools (such as MATLAB, NumPy library of Python) are used to calculate the humidity of each region to generate humidity distribution values. The calculated humidity distribution values of each region are organized into a structured data format (such as a table or database) for subsequent analysis. The preset humidity value of the test chamber is determined, which can be based on standard operating conditions or experimental requirements. Humidity deviation = current humidity value - preset humidity value. The humidity deviation calculation is performed on the current humidity distribution value of each region to generate the humidity deviation value of each region. The generated humidity deviation values are recorded in the dataset for subsequent compensation calculation. A suitable humidity compensation model is selected, such as linear compensation, proportional control, or PID control algorithm, to calculate the humidity compensation value of each region. According to the humidity deviation value, the compensation model is applied to calculate the humidity compensation value of each region. The humidity compensation values of each region are recorded in the dataset for subsequent regional adaptive humidity adjustment. According to the humidity gradient compensation diffusion law under environmental fluctuations, the humidity compensation values of each region are integrated to generate a regional adaptive compensation scheme. A suitable adaptive control algorithm (such as fuzzy control, PID control, or adaptive gain adjustment) is selected to implement the humidity gradient compensation. The adaptive humidity gradient compensation is applied in the test chamber, and the humidity of each region is adjusted according to the calculated humidity compensation value. The adaptive humidity gradient compensation data is recorded, including the adjusted humidity value and the corresponding control parameters. A suitable optimization algorithm (such as genetic algorithm, particle swarm optimization, or gradient descent) is selected to achieve the collaborative optimization of multiple regions. The adaptive humidity gradient compensation data and the humidity compensation values of each region are used as inputs for optimization calculation. The collaborative optimization algorithm is run to calculate the adaptive humidity compensation optimization parameters to ensure the coordinated humidity adjustment of each region. The optimized humidity compensation optimization parameters are recorded and analyzed to verify the optimization effect. A suitable machine learning algorithm (such as reinforcement learning, supervised learning, or neural network) is selected to perform the learning and optimization of humidity compensation. The historical humidity compensation data, including compensation values, deviations, and environmental conditions, is organized to train the learning model. Through an iterative approach, the compensation strategy is continuously adjusted to optimize the humidity compensation model to adapt to different environmental conditions. Based on the learning results, an intelligent humidity compensation strategy is constructed to ensure automatic adjustment of humidity compensation during environmental fluctuations to achieve optimal humidity control. The constructed intelligent humidity compensation strategy is verified, feedback is collected, and necessary optimization adjustments are made to ensure its effectiveness and reliability.

[0150] In this embodiment, a humidity gradient compensation device for an alternating temperature and humidity test chamber is provided, which is used to execute the humidity gradient compensation method for the alternating temperature and humidity test chamber as described above, including:

[0151] Humidity gradient trend module, used to obtain the test chamber status monitoring parameters based on a multi-dimensional sensor array; perform spatial humidity gradient distribution analysis on the test chamber status monitoring parameters, and perform discrete interpolation fitting to construct a three-dimensional humidity gradient trend network;

[0152] Airflow diffusion module, used to perform internal dynamic airflow diffusion analysis on the alternating humidity and heat test chamber, so as to generate the test chamber dynamic airflow diffusion data;

[0153] Humidity diffusion path module, used to mine the dynamic humidity diffusion path of the three-dimensional humidity gradient trend network based on the test chamber dynamic airflow diffusion data, and construct a dynamic humidity diffusion twin model;

[0154] Environmental change correlation module, used to obtain the test chamber external environment monitoring parameters; perform environmental change correlation mining on the three-dimensional humidity gradient trend network based on the test chamber external environment monitoring parameters, so as to generate the humidity gradient change correlation data under environmental fluctuations;

[0155] Humidity compensation diffusion module, used to perform humidity compensation flow simulation and humidity gradient compensation diffusion evolution on the dynamic humidity diffusion twin model according to the humidity gradient change correlation data under environmental fluctuations, so as to generate the humidity gradient compensation diffusion law under environmental fluctuations;

[0156] Adaptive humidity compensation module, used to perform regional adaptive humidity gradient compensation on multiple regional twin models based on the humidity gradient compensation diffusion law under environmental fluctuations, and construct an intelligent humidity compensation strategy.

[0157] The present invention obtains the state monitoring parameters of the test chamber through a multi-dimensional sensor array, realizes the comprehensive monitoring of the internal state of the test chamber, conducts spatial humidity gradient distribution analysis and discrete interpolation fitting, constructs a three-dimensional humidity gradient trend network, which helps to understand the spatial distribution characteristics of the humidity gradient, analyze the dynamic airflow diffusion inside the test chamber, generate dynamic airflow diffusion data, provides a detailed understanding of the airflow movement inside the test chamber, optimizes the airflow movement mode inside the test chamber, improves the uniformity and stability of humidity and temperature. Based on the dynamic airflow diffusion data, it mines the dynamic humidity diffusion path of the three-dimensional humidity gradient trend network, constructs a dynamic humidity diffusion twin model, understands the propagation path of humidity in the test chamber, provides a basis for formulating a humidity compensation strategy, obtains the external environment monitoring parameters of the test chamber, conducts environmental change correlation mining on the three-dimensional humidity gradient trend network, generates humidity gradient change correlation data under environmental fluctuations, understands the influence of external environmental changes on the humidity inside the test chamber, provides a basis for adjusting the humidity compensation strategy, conducts humidity compensation flow simulation and humidity gradient compensation diffusion evolution on the dynamic humidity diffusion twin model according to the humidity gradient change correlation data under environmental fluctuations, generates the humidity gradient compensation diffusion law under environmental fluctuations, realizes the real-time adjustment of the humidity gradient, ensures the stability and uniformity of the humidity inside the test chamber, conducts regional adaptive humidity gradient compensation on multiple regional twin models based on the humidity gradient compensation diffusion law under environmental fluctuations, constructs an intelligent humidity compensation strategy, conducts personalized humidity compensation according to the humidity requirements of different regions, and improves the accuracy and efficiency of humidity control inside the test chamber.

[0158] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application document within the present invention.

[0159] As described above, this is only the specific implementation manner of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A humidity gradient compensation method for an alternating damp heat test chamber, characterized in that: The following steps are involved: Step S1: Acquire the test chamber state monitoring parameters based on the multi-dimensional sensor array; perform spatial humidity gradient distribution analysis on the test chamber state monitoring parameters, and perform discrete interpolation fitting to construct a three-dimensional humidity gradient trend network; Step S2: performing internal dynamic airflow diffusion analysis on the alternating humidity and heat test chamber, thereby generating dynamic airflow diffusion data of the test chamber; Step S3: mining the dynamic humidity diffusion path of the three-dimensional humidity gradient trend network based on the dynamic airflow diffusion data of the test chamber, and constructing a dynamic humidity diffusion twin model; Step S4: Acquire external environment monitoring parameters of the test chamber; perform environmental change correlation mining on the three-dimensional humidity gradient trend network based on the external environment monitoring parameters of the test chamber, thereby generating humidity gradient change correlation data under environmental fluctuations; Step S5: According to the humidity gradient change correlation data under environmental fluctuations, the dynamic humidity diffusion twin model is subjected to humidity compensation flow simulation and humidity gradient compensation diffusion evolution, thereby generating a humidity gradient compensation diffusion law under environmental fluctuations; Step S6: Based on the diffusion law of humidity gradient compensation under environmental fluctuations, regional adaptive humidity gradient compensation is performed on multiple regional twin models to construct an intelligent humidity compensation strategy; Among them, the specific steps of step S1 are: Step S11: Acquire test chamber status monitoring parameters based on a multi-dimensional sensor array; Step S12: Calculate the temperature change of the test chamber status monitoring parameters and identify the temperature change data of the test chamber; Step S13: performing spatial humidity gradient distribution analysis on the test chamber state monitoring parameters to obtain three-dimensional humidity gradient distribution characteristics; Step S14: mining the humidity change trend of the three-dimensional humidity gradient distribution characteristics according to the test chamber temperature change data, thereby obtaining humidity gradient change trend data; Step S15: performing discrete interpolation fitting on the humidity gradient change trend data to construct a three-dimensional humidity gradient trend network; The specific steps of step S5 are: Step S51: dividing the dynamic humidity diffusion twin model into regions to obtain multiple regional twin models; Step S52: performing humidity compensation flow simulation on multiple regional twin models based on the humidity gradient change correlation data under environmental fluctuations, thereby generating humidity compensation flow simulation data between multiple regions; Step S53: performing humidity gradient compensation diffusion evolution on the humidity compensation flow simulation data among multiple regions, thereby generating humidity compensation delayed response characteristics of each region; Step S54: Generate a humidity gradient compensation diffusion law under environmental fluctuations based on the humidity compensation delayed response characteristics of each area.

2. The humidity gradient compensation method of the alternating damp heat test chamber according to claim 1, characterized in that: The specific steps of step S13 are: Extract all sensor nodes based on multi-dimensional sensor array; Calculate the spatial position of all sensor nodes to obtain the spatial position coordinates of each node; Perform sensor node distribution analysis on the spatial position coordinates of each node to obtain sensor node distribution data; Based on the sensor node distribution data, the regional parameter segmentation and identification of the test chamber state monitoring parameters are performed to obtain multiple test chamber regional monitoring parameters; Calculate humidity for each area of ​​multiple test chambers and extract humidity parameters for each area of ​​the test chamber; Calculate the humidity difference between the regions for the humidity parameters of each test chamber area to generate the humidity difference value between the regions; Conduct humidity difference gradient analysis on humidity difference values ​​between regions to generate humidity difference gradient data; The humidity difference gradient data is subjected to three-dimensional humidity gradient distribution analysis to obtain the three-dimensional humidity gradient distribution characteristics.

3. The humidity gradient compensation method of the alternating damp heat test chamber according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: performing internal geometry analysis on the alternating damp heat test chamber to obtain internal geometry data of the test chamber; Step S22: performing spatial topological structure recognition on the internal geometric data of the test box to extract spatial topological structure data; Step S23: performing three-dimensional topological point cloud modeling on the spatial topological structure data to construct a three-dimensional structure model of the test box; Step S24: performing internal air flow mining on the three-dimensional structure model of the test box based on the test box state monitoring parameters, thereby extracting the internal air flow characteristics of the test box; Step S25: Perform dynamic airflow diffusion analysis on the air flow characteristics inside the test box, thereby generating dynamic airflow diffusion data of the test box.

4. The humidity gradient compensation method of the alternating damp heat test chamber according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: performing humidity diffusion simulation on the three-dimensional humidity gradient distribution characteristics based on the dynamic airflow diffusion data of the test chamber to generate humidity diffusion simulation data of the test chamber; Step S32: Tracking the spatiotemporal changes of the test chamber humidity diffusion simulation data to generate a spatiotemporal change trajectory of humidity diffusion; Step S33: mining the dynamic humidity diffusion path of the spatiotemporal change trajectory of humidity diffusion, thereby obtaining dynamic humidity diffusion path data; Step S34: Based on the dynamic humidity diffusion path data, the three-dimensional humidity gradient trend network is dynamically rendered to construct a dynamic humidity diffusion twin model.

5. The humidity gradient compensation method of the alternating damp heat test chamber according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Acquire external environment monitoring parameters of the test chamber; Step S42: performing environmental parameter change analysis on the external environmental monitoring parameters of the test chamber to generate environmental parameter change data; Step S43: Perform time series variation fitting on the environmental parameter variation data to construct an environmental parameter time series variation curve; Step S44: identifying parameter mutation points on the environmental parameter time series variation curve, and extracting multiple parameter mutation points; Step S45: Perform environmental change association mining on the three-dimensional humidity gradient trend network based on multiple parameter mutation points, thereby generating humidity gradient change association data under environmental fluctuations.

6. The humidity gradient compensation method of the alternating damp heat test chamber according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: Calculate the current humidity distribution of multiple regional twin models and extract the humidity distribution value of each regional model; Step S62: Calculating the humidity deviation of the humidity distribution value of each regional model based on the preset test chamber humidity value, thereby generating a humidity deviation value for each region; Step S63: performing humidity compensation calculation on the humidity deviation value of each area to obtain a humidity compensation value of each area; Step S64: performing regional adaptive humidity gradient compensation on the test box based on the humidity gradient compensation diffusion law under environmental fluctuations and the humidity compensation value of each area, thereby generating adaptive humidity gradient compensation data; Step S65: performing multi-region collaborative optimization calculation on the adaptive humidity gradient compensation data to obtain adaptive humidity compensation optimization parameters; Step S66: Perform iterative compensation learning on the adaptive humidity compensation optimization parameters to construct an intelligent humidity compensation strategy.

7. A humidity gradient compensation device for an alternating damp heat test chamber, characterized in that: The method for performing the humidity gradient compensation method of the alternating damp heat test chamber as claimed in claim 1 comprises: The humidity gradient trend module is used to obtain the test chamber state monitoring parameters based on the multi-dimensional sensor array; the spatial humidity gradient distribution analysis of the test chamber state monitoring parameters is performed, and discrete interpolation fitting is performed to construct a three-dimensional humidity gradient trend network; The air flow diffusion module is used to analyze the internal dynamic air flow diffusion of the alternating humidity and heat test chamber, thereby generating dynamic air flow diffusion data of the test chamber; The humidity diffusion path module is used to mine the dynamic humidity diffusion path of the three-dimensional humidity gradient trend network based on the dynamic airflow diffusion data of the test chamber, and to build a dynamic humidity diffusion twin model; The environmental change association module is used to obtain the external environmental monitoring parameters of the test chamber; based on the external environmental monitoring parameters of the test chamber, the three-dimensional humidity gradient trend network is mined for environmental change associations, thereby generating humidity gradient change association data under environmental fluctuations; The humidity compensation diffusion module is used to simulate the humidity compensation flow and the humidity gradient compensation diffusion evolution of the dynamic humidity diffusion twin model according to the humidity gradient change correlation data under environmental fluctuations, so as to generate the humidity gradient compensation diffusion law under environmental fluctuations; The adaptive humidity compensation module is used to perform regional adaptive humidity gradient compensation for multiple regional twin models based on the diffusion law of humidity gradient compensation under environmental fluctuations, and to build an intelligent humidity compensation strategy.

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