Method for predicting moisture diffusion of closed cavity under influence of humidity control material

Abstract

The invention discloses a method for predicting the moisture diffusion of a closed cavity under the influence of a humidity control material, which is based on the closed cavity with a ventilation valve, and comprises the following steps: acquiring the diffusion distance from each measurement point to the ventilation valve; acquiring the temperature and humidity of the external environment of the closed cavity in a preset time period; acquiring the temperature and humidity of each measurement point in the closed cavity in a preset time period; according to the diffusion distance, the temperature and the humidity of the external environment of the closed cavity and the temperature and the humidity of each measurement point, a moisture diffusion coefficient is calculated through a half-decay time method, and an influence function of the temperature and the humidity on the moisture diffusion coefficient is obtained through fitting; setting a humidity adjusting material at each measuring point, constructing a dynamic moisture absorption / release model of the humidity adjusting material in the closed cavity, and obtaining the moisture adjusting capability of the humidity adjusting material in the closed cavity; and according to the moisture diffusion coefficient, each influence function and the moisture adjusting capability of the moisture adjusting material, constructing a simulation model to simulate the distribution of moisture in the closed cavity so as to predict the moisture diffusion of the closed cavity.

Description

Technical Field

[0001] This invention relates to the field of humidity control technology, and in particular to a method for predicting the diffusion of moisture in a closed cavity under the influence of humidity control materials. Background Technology

[0002] Many applications now require equipment cavities with vent valves (such as base stations, motors, electrical controls, vehicle lights, batteries, power cabinets, and lidar systems). The insulation capacity of these cavities is a crucial factor for their safe and stable operation. Besides inherent design, materials, and quality factors, the insulation capacity of these cavities is frequently compromised by harsh environmental factors such as temperature, humidity, and condensation within the operating environment, leading to frequent insulation failures. Long-term operational experience with some power cabinets shows that condensation inside the cabinet degrades internal insulation performance, and various insulation defects can gradually develop into breakdowns, resulting in accidents that significantly impact the stability of the entire power system. Therefore, addressing condensation in switchgear is vital for the safe operation of switchgear and the stability of the power grid.

[0003] Currently, the understanding of condensation phenomena in switchgear is not in-depth. The physical process of condensation, especially the energy and mass exchange mechanism in condensation, is not clear. There is also a lack of systematic analysis of the factors affecting condensation under different operating conditions. It is urgent to conduct in-depth research on its condensation process and influencing factors. Summary of the Invention

[0004] In view of the above-mentioned shortcomings, the present invention provides a method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials, which can predict moisture diffusion in a closed cavity, thereby providing reference data for simulation calculation of condensation in equipment cavities.

[0005] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0006] A method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials, based on a closed cavity with a vent valve, wherein multiple measurement points are provided within the closed cavity, and the method includes the following steps:

[0007] Obtain the diffusion distance from each measurement point to the vent valve;

[0008] Obtain the temperature and humidity of the external environment of the sealed cavity within a preset time period;

[0009] Obtain the temperature and humidity at various measurement points inside the sealed cavity within a preset time period;

[0010] Based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point, the moisture diffusion coefficient is calculated by the half-life method, and the influence function of temperature and humidity on the moisture diffusion coefficient is obtained by fitting.

[0011] Humidity-regulating materials were set at each measurement point to construct a dynamic moisture absorption / release model of the humidity-regulating materials in a closed cavity, thereby obtaining the humidity regulation capability of the humidity-regulating materials in a closed cavity.

[0012] A simulation model is constructed based on the moisture diffusion coefficient, various influence functions, and the moisture regulation capability of the moisture-regulating material to simulate the distribution of moisture inside a sealed cavity and predict the moisture diffusion in the sealed cavity.

[0013] According to one aspect of the present invention, obtaining the diffusion distance from each measurement point to the vent valve includes: establishing a three-dimensional coordinate system, obtaining the coordinate points of the vent valve and each measurement point, and calculating the straight-line distance from the coordinate points of the measurement points to the coordinate points of the vent valve as the diffusion distance from the measurement points to the vent valve.

[0014] According to one aspect of the present invention, the step of calculating the moisture diffusion coefficient by means of the half-life method based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measuring point includes: combining different external environmental temperatures and humidity to calculate the moisture diffusion coefficient by means of the half-life method.

[0015] According to one aspect of the present invention, the step of calculating the moisture diffusion coefficient by means of the half-life method based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point includes: setting the external environment temperature to be the same, obtaining the moisture diffusion coefficient under different external environment humidity, calculating the humidity influence function, and thus fitting the moisture diffusion coefficient.

[0016] According to one aspect of the present invention, the step of calculating the moisture diffusion coefficient by means of the half-life method based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point includes: setting the external environment humidity to be the same, obtaining the moisture diffusion coefficient at different external environment temperatures, calculating the temperature influence function, and thus fitting the moisture diffusion coefficient.

[0017] According to one aspect of the present invention, the method further includes the following steps: the effect of temperature and humidity changes on moisture diffusion; the effect of the moisture absorption / release process of the humidity-regulating material on moisture diffusion; and using the amount and distribution location of the humidity-regulating material as input parameters of a simulation model to predict the diffusion behavior of moisture within the equipment.

[0018] According to one aspect of the present invention, the fitting of the influence function of temperature and humidity on the moisture diffusion coefficient includes the following steps: after changing the temperature and / or humidity, repeating the steps to obtain the temperature and humidity of the external environment of the sealed cavity within a preset time period; repeating the steps to obtain the temperature and humidity of each measurement point inside the sealed cavity within the preset time period; repeating the steps to calculate the moisture diffusion coefficient using the half-life method based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point, so as to obtain the moisture diffusion coefficient under different temperature and humidity conditions, and fitting the influence function of temperature and humidity on the moisture diffusion coefficient.

[0019] According to one aspect of the present invention, the dynamic moisture absorption / release model of the constructed moisture-regulating material in a closed cavity is as follows:

[0020]

[0021] m is the moisture content of the conditioning material in grams, and t is the time taken for the conditioning material to be used.

[0022] S abs (T, RH) is the moisture absorption rate of the humidity-regulating material, expressed as mass flow rate, and depends on the current temperature T and relative humidity RH.

[0023] S des (T, RH) is the moisture release rate of the humidity conditioning material, with the unit being mass flow rate, which depends on the current temperature T and relative humidity RH;

[0024] k abs (T) is a function of temperature, indicating how temperature affects the moisture absorption capacity of the moisture-regulating material;

[0025] f abs (RH) is a function of humidity, representing the relationship between moisture concentration and moisture absorption capacity;

[0026] k des (T) is a function of temperature, indicating how temperature affects the moisture release capacity of the moisture-regulating material;

[0027] f des (RH) is a function of humidity, representing the relationship between moisture concentration and moisture release capacity.

[0028] According to one aspect of the invention, the method includes the following steps: the weight of the humidity-regulating material placed at each measuring point is the same.

[0029] According to one aspect of the present invention, the method includes the following steps: further optimizing the simulation model by replacing the humidity-regulating materials with different humidity-regulating capabilities.

[0030] The advantages of this invention are as follows: First, by fitting experimental data from multiple locations and with multiple material conditions, an influence function model is established, which can quantitatively characterize the dynamic influence of different humidity-regulating materials on moisture transfer within the cavity. Second, by standardizing the configuration of humidity-regulating materials at measurement points, measurement deviations caused by uneven material distribution are effectively eliminated, improving the accuracy of moisture diffusion coefficient measurements. Third, adopting a uniform weight configuration principle not only simplifies the experimental operation process but also ensures the comparability of data between different measurement points, providing a reliable parameter basis for establishing a three-dimensional humidity field simulation. Based on the moisture distribution and the operating conditions of the equipment, the optimal amount and location distribution of humidity-regulating materials are calculated, and the moisture distribution under each configuration can be predicted through simulation model calculations. Finally, a reusable optimized configuration scheme for humidity-regulating materials is formed, providing precise theoretical support for humidity control engineering in fields such as electrical cabinets and electrical equipment. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of a method for predicting moisture diffusion in a closed cavity under the influence of a humidity-regulating material, as described in this invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1

[0035] like Figure 1 As shown, a method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials is based on a closed cavity with a vent valve. Multiple measurement points are located within the closed cavity. The method includes the following steps:

[0036] Step S1: Obtain the diffusion distance from each measurement point to the vent valve;

[0037] First, obtain the positions and diffusion distances of multiple measurement points within the sealed cavity, establish a three-dimensional coordinate system, and obtain the coordinates of the vent valve and each measurement point. Set the vent valve position as the reference position (x0, y0, z0), and set the coordinates of the measurement points as (x, y, z). Calculate the straight-line distance from the measurement point to the vent valve coordinates as the diffusion distance L from the measurement point to the vent valve. The calculation formula is as follows:

[0038]

[0039] Step S2: Obtain the temperature and humidity of the external environment of the sealed cavity within a preset time period;

[0040] Temperature and humidity sensors and surface temperature sensors are deployed outside the sealed cavity of the equipment to acquire temperature and humidity data of the external environment. The acquired external environmental temperature and humidity can be actual temperature and humidity obtained through measurement, or it can be set as needed based on predefined conditions. The preset time is t.

[0041] In this embodiment, various combinations of external environmental temperature and humidity are set according to the actual working scenario, working state, and start / stop state of the equipment to form different external temperature and humidity conditions. For example: Group 1: T out =15℃,RH out =30%; Group 2: T out =25℃,RH out =60%; Group 3: T out =35℃,RH out =90%; Group 4: T out =50℃,RH out =50%, etc.; where T out Represents the external ambient temperature, RH out This represents the humidity of the external environment.

[0042] Step S3: Obtain the temperature and humidity at various measurement points inside the sealed cavity within a preset time period;

[0043] Temperature and humidity sensors and surface temperature sensors are deployed at different locations inside the sealed cavity of the equipment, such as the vent valve and measurement points. The temperature and humidity data of the vent valve and multiple measurement points inside the equipment are obtained through the deployed temperature and humidity sensors and surface temperature sensors.

[0044] Step S4: Based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point, the moisture diffusion coefficient is calculated using the half-life method, and the influence function of temperature and humidity on the moisture diffusion coefficient is obtained by fitting.

[0045] The step described above calculates the moisture diffusion coefficient using the half-life method based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity at each measurement point.

[0046] Step S41: Starting from the error function solution of one-dimensional diffusion, by setting the half-life time condition w(L,t) 1 / 2 ) = w ext +(w0-w ext ) / 2 yields the relation:

[0047]

[0048] Where w0 is the initial humidity value, w ext Here, D is the humidity balance value, L is the moisture diffusion coefficient, and t is the diffusion distance. 1 / 2 The half-life is given by k = erfc. -1 (1 / 2) is approximately 0.4769, thus yielding:

[0049]

[0050] Thus, the moisture diffusion coefficient D at each measurement point is obtained. L The moisture diffusion coefficient D0 at the vent valve is used as the reference moisture diffusion coefficient.

[0051] Step S42: Due to the influence of humidity and temperature in various scenarios, the diffusion coefficient will be different. Therefore, it needs to be calculated using the following formula:

[0052]

[0053] The above equation is the moisture diffusion equation, where AH(x,y,z,t) is the absolute humidity at position (x,y,z) and time t, with units of g / m³. 3 The calculation method is as follows:

[0054]

[0055] divergence term This describes the net effect of moisture flow or diffusion, i.e., the net amount of moisture flowing into or out of a volume element. This term reflects the process of moisture diffusing from a region of high concentration to a region of low concentration in space.

[0056] D(T,RH) is the diffusion coefficient under the influence of temperature and humidity. It depends on temperature T and relative humidity RH and changes dynamically over time. The diffusion coefficient reflects the rate of moisture diffusion: the larger the diffusion coefficient, the faster the moisture diffuses.

[0057] This is the spatial gradient of moisture concentration, which describes how moisture concentration changes in space. The direction of the gradient points in the direction of the fastest increase in moisture concentration, while the magnitude represents the rate of change of moisture concentration per unit distance in that direction.

[0058] This is the spatial gradient of moisture diffusion, representing the change and diffusion process of moisture concentration in space. Specifically, the change in moisture concentration is related not only to the spatial diffusion rate but also closely related to the temporal change. According to the diffusion equation, the temporal change of moisture is reflected through spatial diffusion.

[0059] The moisture diffusion coefficient at each point can be obtained using the moisture diffusion equation. The calculation method is as follows:

[0060] D(T,RH)=D0·f T (T)·f RH (RH);

[0061] D0 is the baseline moisture diffusion coefficient, with the reference point selected at the location of the vent valve. It represents the diffusion coefficient without any moisture-regulating material, and is expressed in meters (m). 2 / s, obtained through step S41;

[0062] f T (T) is the function of the effect of temperature on the diffusion coefficient;

[0063] f RH (RH) is the effect of humidity on the diffusion coefficient.

[0064] in, α is the effect coefficient of temperature on the diffusion coefficient, T0 is the reference temperature (e.g., 25℃), and ln(D0) = ln(D0). ref +α(T-T0); γ is the effect coefficient of humidity on the diffusion coefficient, RH0 is the reference humidity (e.g., 60%), and ln(D0) = ln(D0). ref +γ(RH-RH0).

[0065] The influence coefficients α and γ of temperature and humidity on the diffusion coefficient were obtained by fitting the measured AH gradient and the corresponding D (derived through deduction).

[0066] In other words, the influence coefficients mentioned above are obtained by using regression analysis to fit the effects of temperature and humidity on the diffusion coefficient through the data from the first stage.

[0067]

[0068] By following the steps above, the moisture diffusion coefficient of a sealed cavity with a breathable valve under the influence of temperature and humidity can be obtained, thus providing a basis for the temperature and humidity control of the equipment.

[0069] Step S5: Set humidity-regulating materials at each measurement point, construct a dynamic moisture absorption / release model of the humidity-regulating materials in a closed cavity, and obtain the humidity regulation capability of the humidity-regulating materials in a closed cavity;

[0070] The dynamic moisture absorption / release model of the humidity-regulating material in a closed cavity is constructed as follows:

[0071]

[0072] m is the moisture content of the conditioning material in grams, and t is the time taken for the conditioning material to be used.

[0073] S abs (T, RH) is the moisture absorption rate of the humidity-regulating material, expressed as mass flow rate in g / s, which depends on the current temperature T and relative humidity RH.

[0074] S des (T, RH) is the moisture release rate of the humidity conditioning material, with the unit being mass flow rate, g / s, which depends on the current temperature T and relative humidity RH;

[0075] k abs (T) is a function of temperature, indicating how temperature affects the moisture absorption capacity of the moisture-regulating material;

[0076] f abs (RH) is a function of humidity, representing the relationship between moisture concentration and moisture absorption capacity;

[0077] k des (T) is a function of temperature, indicating how temperature affects the moisture release capacity of the moisture-regulating material;

[0078] f des (RH) is a function of humidity, representing the relationship between moisture concentration and moisture release capacity.

[0079] In practical applications, the dynamic moisture absorption / release model of humidity-regulating materials is indeed mainly determined by the inherent properties of the humidity-regulating materials, but factors such as changes in ambient temperature and humidity and equipment operating conditions can affect their actual performance.

[0080] In different devices, especially those with large temperature and humidity fluctuations, it may be necessary to adjust or calibrate the model of the humidity-regulating material to ensure that its moisture absorption and release behavior is accurately reflected.

[0081] The basic moisture absorption / release properties of humidity-regulating materials can be obtained through experiments. However, in practical applications, refining and modifying the model according to different operating conditions can ensure the optimal performance of humidity-regulating materials in different devices.

[0082] • Moisture absorption process: When S abs (T, RH) > S des (T, RH), that is, when the moisture absorption rate is greater than the moisture release rate, the moisture content m of the humidity conditioning material will increase.

[0083] • Moisture release process: When S abs (T, RH) des (T, RH), that is, when the moisture release rate is greater than the moisture absorption rate, the moisture content m of the humidity conditioning material will decrease.

[0084] • Equilibrium state: when S abs (T, RH) = S des (T, RH), the humidity-regulating material is in a state of moisture equilibrium, and the moisture content remains constant.

[0085] Combining the moisture absorption and release rates, the complete dynamic moisture absorption / release model is as follows:

[0086]

[0087] • Moisture-absorbing part: When temperature T <T max The humidity-regulating material absorbs moisture, increasing the humidity concentration.

[0088] • Moisture release portion: When temperature T≥T max The humidity-regulating material releases moisture, reducing the humidity concentration.

[0089] Using experimental data, you can fit the various parameters of the above model (such as A, B, C, D, C1, C2, α1, α2, etc.). The specific steps are as follows:

[0090] 1. Experimental procedure: Record the rate of humidity change of the humidity-regulating material under different temperature and humidity conditions.

[0091] 2. Moisture absorption rate measurement: Measure the moisture absorption rate of the moisture-conditioning material at low temperatures and calculate the moisture absorption rate S. abs .

[0092] 3. Moisture release rate measurement: At higher temperatures, measure the moisture release rate of the moisture-regulating material and calculate the moisture release rate S. des .

[0093] 4. Data Fitting: Using regression analysis, the temperature and humidity dependence of moisture absorption and release rates is fitted to obtain the parameters of the above function.

[0094] Step S6: Based on the moisture diffusion coefficient, various influence functions, and the moisture regulation capability of the moisture-regulating material, a simulation model is constructed to simulate the distribution of moisture inside the sealed cavity in order to predict the moisture diffusion in the sealed cavity.

[0095] ​Establish a simulation model to simulate the distribution of moisture inside the equipment. The model includes: the effect of temperature and humidity changes on moisture diffusion; and the effect of the moisture absorption / release process of the humidity-regulating material on moisture diffusion.

[0096] The amount and distribution location of humidity-regulating materials are used as input parameters to predict the diffusion behavior of moisture within the equipment. Optimization algorithms (such as particle swarm optimization and genetic algorithms) are used to calculate the optimal amount and location distribution of the humidity-regulating materials based on the moisture distribution and the equipment's operating conditions. Simulation calculations can predict the moisture distribution under each configuration. Experimental data are used to verify the simulation results and ensure the accuracy of the simulation optimization scheme.

[0097] Adjustment and optimization scheme: Adjust the parameters in the simulation model based on the experimental results, and further optimize the amount and distribution of humidity-regulating materials.

[0098] In this embodiment, the specific method for calculating the moisture diffusion coefficient is obtained through the following experimental process:

[0099] Phase 1:

[0100] 1.1 Objective: To obtain the diffusion coefficient under conditions without humidity control materials and to fit the influence functions of temperature f(T) and humidity f(RH).

[0101] 1.2 Experimental conditions:

[0102] Equipment: Select equipment chambers with vent valves (such as base stations, motors, electrical control systems, power cabinets, etc.) to ensure that there are multiple measurement points inside.

[0103] External temperature and humidity: Set different combinations of external temperature and humidity to ensure different environmental conditions when the equipment is in working, dormant, or start-stop states.

[0104] Internal temperature and humidity: Temperature and humidity sensors are deployed in different locations inside the equipment to record the air humidity and temperature at each location.

[0105] Equipment surface temperature: Thermocouples are placed at different locations on the inner surface of the equipment, with a focus on the low-temperature region.

[0106] Equipment operating state, hibernation state, and start-stop state: Simulates the actual working conditions of the equipment, including changes in temperature and humidity during the start-up and shutdown process.

[0107] 1.3 Experimental Procedure:

[0108] Equipment preparation: Ensure that there are no humidity-regulating materials inside the equipment (consistent with the actual application scenario), and install temperature and humidity sensors and thermocouples in several key locations.

[0109] External environment control: Set and adjust the external temperature and humidity, record the external and internal temperature and humidity data of the equipment, and set the equipment's working state and start / stop state.

[0110] Temperature and humidity data recording: Record the humidity and temperature data of the air inside the equipment and the temperature of the inner surface of the equipment at different locations to ensure the time synchronization of the data.

[0111] Calculation of diffusion coefficient: The baseline moisture diffusion coefficient D0 was calculated using the half-life method, and the influence functions of temperature f(T) and humidity f(RH) were fitted using experimental data.

[0112] 1.4 Experimental Parameters and Measurement Methods:

[0113] Internal temperature and humidity of the equipment: Temperature and humidity sensors and thermocouples are placed in multiple key locations to ensure the acquisition of humidity and temperature data of the air inside the equipment, as well as the temperature of the internal surfaces of the equipment.

[0114] External ambient temperature and humidity: The environmental control system is used to adjust and record the external temperature and humidity in real time.

[0115] Humidity change rate: Calculate the half-life of humidity change at each location and calculate the diffusion coefficient D.

[0116] Data synchronization: Use a data acquisition system to synchronously record data from each sensor.

[0117] Fit f T (T)·f RH (RH):

[0118] Objective: To fit the influence function of temperature and humidity on the diffusion coefficient using the data from the first stage.

[0119] Methods: Regression analysis was used to fit the effects of temperature and humidity on the diffusion coefficient.

[0120] This method has a sound theoretical basis and correct dimensions, and can utilize the half-life t. 1 / 2 The diffusion coefficient was inverted, and the respective influencing parameters were further fitted by combining temperature and humidity data, thereby providing a quantitative basis for the subsequent design of internal humidity dynamics and anti-condensation of equipment.

[0121] Phase 2: Experiments with humidity-regulating materials

[0122] 2.1 Objective: Through multiple tests, the same mass of humidity-regulating material is placed at different locations each time, and its influence on the moisture diffusion coefficient D is measured to fit the influence coefficient of the humidity-regulating material.

[0123] 2.2 Experimental Conditions

[0124] Humidity conditioning material: Select a humidity conditioning material with known moisture absorption / release properties, and maintain the same weight of humidity conditioning material for each experiment. The initial dosage is calculated at 600 g / m³, which will also be the amount to be optimized later.

[0125] Equipment operating state and start / stop state: Repeat the experimental conditions of stage 1 to ensure that the experiment is carried out under different external temperature and humidity conditions.

[0126] 2.3 Experimental Procedure

[0127] Equipment preparation: In the same equipment as in stage 1, select different test locations and place the same weight of conditioning material.

[0128] External environment control: Set and record external temperature and humidity conditions in the equipment's working, dormant, and start / stop states to ensure consistency with stage 1.

[0129] Data recording: Real-time recording of temperature and humidity data at different locations inside the equipment, with particular attention to the location where humidity-regulating materials are placed.

[0130] Diffusion coefficient calculation: The diffusion coefficient of the conditioned material is calculated using the half-life method, and the difference between the diffusion coefficients of the conditioned material and the non-conditioned material is compared.

[0131] 2.4 Experimental Parameters and Measurement Methods

[0132] Mass of humidification material: Use the same weight of humidification material for each experiment.

[0133] Temperature and humidity data: Same as in Phase 1, ensure that temperature and humidity data at each measurement point are collected synchronously.

[0134] Moisture diffusion coefficient: The diffusion coefficient was calculated using the half-life method, and the results were compared with experimental data without moisture conditioning materials.

[0135] 3. Data Analysis and Model Fitting:

[0136] 3.1 Fitting f T (T)·f RH (RH):

[0137] Objective: To fit the influence function of temperature and humidity on the diffusion coefficient using the data from the first stage.

[0138] Methods: Regression analysis was used to fit the effects of temperature and humidity on the diffusion coefficient.

[0139] 3.2 Fitting the influence coefficient f of humidity-regulating materials W (W):

[0140] Objective: Using the data from Phase 2, fit the influence function of the humidity-regulating material to describe the effect of humidity-regulating materials at different locations and with different weights on moisture diffusion.

[0141] Method: Based on the difference in moisture diffusion coefficients under conditions with and without moisture-regulating materials, a function relating the moisture-regulating material to the diffusion coefficient was fitted. The model can be further optimized by replacing the moisture-regulating material with different moisture absorption and desorption rates.

[0142] 4. Simulation Calculation and Optimization

[0143] 4.1 Establishing a simulation model

[0144] Using experimental data from Phase 1 and Phase 2, a simulation model was established to model the distribution of moisture inside the equipment. The model includes: the effect of temperature and humidity changes on moisture diffusion; and the effect of the moisture absorption / release process of the humidity-regulating material on moisture diffusion.

[0145] The specific model for the dynamic moisture absorption / release of humidity-regulating materials in a closed cavity is as follows:

[0146]

[0147] m is the moisture content of the conditioning material in grams, and t is the time taken for the conditioning material to be used.

[0148] S abs (T, RH) is the moisture absorption rate of the humidity-regulating material, expressed as mass flow rate in g / s, which depends on the current temperature T and relative humidity RH.

[0149] S des (T, RH) is the moisture release rate of the humidity conditioning material, with the unit being mass flow rate, g / s, which depends on the current temperature T and relative humidity RH;

[0150] k abs (T) is a function of temperature, indicating how temperature affects the moisture absorption capacity of the moisture-regulating material;

[0151] f ads (RH) is a function of humidity, representing the relationship between moisture concentration and moisture absorption capacity;

[0152] k des (T) is a function of temperature, indicating how temperature affects the moisture release capacity of the moisture-regulating material;

[0153] f des (RH) is a function of humidity, representing the relationship between moisture concentration and moisture release capacity.

[0154] The amount and distribution location of the humidity-regulating material are used as input parameters for the model to predict the diffusion behavior of moisture inside the equipment.

[0155] 4.2 Optimize the distribution and dosage of humidity-regulating materials

[0156] Objective: To optimize the minimum amount and distribution of humidity-regulating materials through simulation calculations to ensure uniform humidity inside the equipment.

[0157] Method: Optimization algorithms (such as particle swarm optimization and genetic algorithms) are used to calculate the optimal amount and location distribution of humidity-regulating materials based on humidity distribution and equipment operating conditions. Simulation calculations can predict the humidity distribution for each configuration.

[0158] 4.3 Experimental Verification and Adjustment

[0159] Experimental verification: Experimental data is used to verify the simulation results and ensure the accuracy of the simulation optimization scheme.

[0160] Adjustment and optimization scheme: Adjust the parameters in the simulation model based on the experimental results, and further optimize the amount and distribution of humidity-regulating materials.

[0161] This embodiment successfully established a quantitative influence model of temperature and humidity on the diffusion coefficient by combining multi-sensor collaborative monitoring with regression analysis. This approach not only achieves non-destructive in-situ measurement but also effectively reflects the transient characteristics of the moisture diffusion process by introducing a dynamic parameter of half-life. The f-value is constructed based on the principle of dimensional consistency. T (T)·f RH The (RH) two-factor equation allows for the mathematical separation of temperature and humidity effects, ensuring both the clarity of physical meaning and significantly improving the accuracy of parameter identification. Simultaneously, its standardized data processing workflow ensures the engineering reproducibility of experimental results.

[0162] Example 2

[0163] like Figure 1 As shown, a method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials is based on a closed cavity with a vent valve. Multiple measurement points are located within the closed cavity. The method includes the following steps:

[0164] Step S1: Obtain the diffusion distance from each measurement point to the vent valve;

[0165] First, obtain the positions and diffusion distances of multiple measurement points within the sealed cavity, establish a three-dimensional coordinate system, and obtain the coordinates of the vent valve and each measurement point. Set the vent valve position as the reference position (x0, y0, z0), and set the coordinates of the measurement points as (x, y, z). Calculate the straight-line distance from the measurement point to the vent valve coordinates as the diffusion distance L from the measurement point to the vent valve. The calculation formula is as follows:

[0166]

[0167] During this process, humidity-regulating materials are placed at each measuring point, and the mass W of the humidity-regulating materials at each measuring point is the same. The position (x) of the humidity-regulating materials is measured using an electronic scale. W y W , z W ).

[0168] Step S2: Obtain the temperature and humidity of the external environment of the sealed cavity within a preset time period;

[0169] Temperature and humidity sensors and surface temperature sensors are deployed outside the sealed cavity of the equipment to acquire temperature and humidity data of the external environment. The acquired external environmental temperature and humidity can be actual temperature and humidity obtained through measurement, or it can be set as needed based on predefined conditions. The preset time is t.

[0170] In this embodiment, various combinations of external environmental temperature and humidity are set according to the actual working scenario, working state, and start / stop state of the equipment to form different external temperature and humidity conditions. For example: Group 1: T out =15℃,RH out =30%; Group 2: T out =25℃,RH out =60%; Group 3: T out =35℃,RH out =90%; Group 4: T out =50℃,RH out =50%, etc.; where T out Represents the external ambient temperature, RH out This represents the humidity of the external environment.

[0171] Step S3: Obtain the temperature and humidity at each measurement point inside the sealed cavity within a preset time period;

[0172] Temperature and humidity sensors and surface temperature sensors are deployed at different locations inside the sealed cavity of the equipment, such as the vent valve and measurement points. The temperature and humidity data of the vent valve and multiple measurement points inside the equipment are obtained through the deployed temperature and humidity sensors and surface temperature sensors.

[0173] Step S4: Based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point, the moisture diffusion coefficient is calculated by the half-life method, and the influence function of temperature, humidity and humidity-regulating material on the moisture diffusion coefficient is obtained by fitting.

[0174] The step described above calculates the moisture diffusion coefficient using the half-life method based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity at each measurement point.

[0175] Step S41: Starting from the error function solution of one-dimensional diffusion, by setting the half-life time condition w(L,t) 1 / 2 ) = w ext +(w0-w ext ) / 2 yields the relation:

[0176]

[0177] Where w0 is the initial humidity value, w ext Here, D is the humidity balance value, L is the moisture diffusion coefficient, and t is the diffusion distance. 1 / 2 The half-life is given by k = erfc. -1 (1 / 2) is approximately 0.4769, thus yielding:

[0178]

[0179] Thus, the moisture diffusion coefficient D at each measurement point is obtained. L The moisture diffusion coefficient D0 at the vent valve is used as the reference moisture diffusion coefficient.

[0180] Step S42: Due to the influence of humidity and temperature in various scenarios, the diffusion coefficient will be different. Therefore, it needs to be calculated using the following formula:

[0181]

[0182] The moisture diffusion equation, AH(x,y,z,t), represents the absolute humidity at location (x,y,z) and time t, with units of g / m³. 3 ;

[0183] divergence term This describes the net effect of moisture flow or diffusion, i.e., the net amount of moisture flowing into or out of a volume element. This term reflects the process of moisture diffusing from a region of high concentration to a region of low concentration in space.

[0184] D(W,T,RH) is the diffusion coefficient under the influence of humidity-regulating materials. It depends on the weight W of the humidity-regulating materials, the temperature T, and the relative humidity RH, and changes dynamically over time. The diffusion coefficient reflects the rate of moisture diffusion: the larger the diffusion coefficient, the faster the moisture diffuses.

[0185] This is the spatial gradient of moisture concentration, which describes how moisture concentration changes in space. The direction of the gradient points in the direction of the fastest increase in moisture concentration, while the magnitude represents the rate of change of moisture concentration per unit distance in that direction.

[0186] This is the spatial gradient of moisture diffusion, representing the change and diffusion process of moisture concentration in space. Specifically, the change in moisture concentration is related not only to the spatial diffusion rate but also closely related to the temporal change. According to the diffusion equation, the temporal change of moisture is reflected through spatial diffusion.

[0187] The moisture diffusion coefficient at each point can be obtained using the moisture diffusion equation. The calculation method is as follows:

[0188] D(W, T, RH) = D0·f T (T)·f RH (RH)·f W (W);

[0189] D0 is the baseline moisture diffusion coefficient, with the reference point selected at the location of the vent valve. It represents the diffusion coefficient without any moisture-regulating material, and is expressed in meters (m). 2 / s;

[0190] f T (T) is the function of the effect of temperature on the diffusion coefficient;

[0191] f RH (RH) is the function of the effect of humidity on the diffusion coefficient;

[0192] f W (W) is the effect function of the humidity conditioning material on the diffusion coefficient, which depends on the weight of the humidity conditioning material and its moisture absorption / release characteristics.

[0193] in, α is the effect coefficient of temperature on the diffusion coefficient, T0 is the reference temperature (e.g., 25℃), and ln(D0) = ln(D0). ref +α(T-T0); γ is the effect coefficient of humidity on the diffusion coefficient, RH0 is the reference humidity (e.g., 60%), and ln(D0) = ln(D0). ref +γ(RH-RH0); f W (W)=e -βW β is a constant representing the effectiveness factor of the humidity-regulating material, which determines the material's ability to inhibit moisture diffusion. The larger the β value, the more significant the influence of the humidity-regulating material on moisture diffusion. The influence coefficients α and γ of temperature and humidity on the diffusion coefficient are obtained by fitting the measured AH gradient with the corresponding D (derived through deduction).

[0194]

[0195] To ultimately obtain the influence function of the humidity-regulating material on the diffusion coefficient, a multi-stage experiment can be designed. In the first stage, inside a device without the humidity-regulating material, multiple test points are deployed (measuring air temperature and humidity at different locations, as well as the device surface temperature at different locations). Then, in both the device's operating and start-stop states, different external temperature and humidity data are combined to obtain real-time data, and the fitted f is obtained. T (T)·f RH The diffusion coefficient D is a function of (RH). In the second stage, under the same conditions as in the first stage, the same mass of humidity-regulating material is placed at a different location each time, and the diffusion coefficient D is calculated from the experimental data. This allows us to obtain the difference in diffusion coefficient at different locations under the same external temperature and humidity combination, with and without the humidity-regulating material, in both the operating and start-up / shutdown states of the equipment. Through the above experimental design, f is finally obtained. W (W).

[0196] Step S5: Set humidity-regulating materials at each measurement point, construct a dynamic moisture absorption / release model of the humidity-regulating materials in a closed cavity, and obtain the humidity regulation capability of the humidity-regulating materials in a closed cavity;

[0197] The dynamic moisture absorption / release model of the humidity-regulating material in a closed cavity is constructed as follows:

[0198]

[0199] m is the moisture content of the conditioning material in grams, and t is the time taken for the conditioning material to be used.

[0200] S abs (T, RH) is the moisture absorption rate of the humidity-regulating material, expressed as mass flow rate in g / s, which depends on the current temperature T and relative humidity RH.

[0201] S des (T, RH) is the moisture release rate of the humidity conditioning material, with the unit being mass flow rate, g / s, which depends on the current temperature T and relative humidity RH;

[0202] k abs (T) is a function of temperature, indicating how temperature affects the moisture absorption capacity of the moisture-regulating material;

[0203] f ads (RH) is a function of humidity, representing the relationship between moisture concentration and moisture absorption capacity;

[0204] k des (T) is a function of temperature, indicating how temperature affects the moisture release capacity of the moisture-regulating material;

[0205] f des (RH) is a function of humidity, representing the relationship between moisture concentration and moisture release capacity.

[0206] In practical applications, the dynamic moisture absorption / release model of humidity-regulating materials is indeed mainly determined by the inherent properties of the humidity-regulating materials, but factors such as changes in ambient temperature and humidity and equipment operating conditions can affect their actual performance.

[0207] In different devices, especially those with large temperature and humidity fluctuations, it may be necessary to adjust or calibrate the model of the humidity-regulating material to ensure that its moisture absorption and release behavior is accurately reflected.

[0208] The basic moisture absorption / release properties of humidity-regulating materials can be obtained through experiments. However, in practical applications, refining and modifying the model according to different operating conditions can ensure the optimal performance of humidity-regulating materials in different devices.

[0209] • Moisture absorption process: When S abs (T, RH) > S des (T, RH), that is, when the moisture absorption rate is greater than the moisture release rate, the moisture content m of the humidity conditioning material will increase.

[0210] • Moisture release process: When S abs (T, RH) des (T, RH), that is, when the moisture release rate is greater than the moisture absorption rate, the moisture content m of the humidity conditioning material will decrease.

[0211] • Equilibrium state: when S abs (T, RH) = S des (T, RH), the humidity-regulating material is in a state of moisture equilibrium, and the moisture content remains constant.

[0212] Combining the moisture absorption and release rates, the complete dynamic moisture absorption / release model is as follows:

[0213]

[0214] • Moisture-absorbing part: When temperature T <T max The humidity-regulating material absorbs moisture, increasing the humidity concentration.

[0215] • Moisture release portion: When temperature T≥T max The humidity-regulating material releases moisture, reducing the humidity concentration.

[0216] Using experimental data, you can fit the various parameters of the above model (such as A, B, C, D, C1, C2, α1, α2, etc.). The specific steps are as follows:

[0217] 1. Experimental procedure: Record the rate of humidity change of the humidity-regulating material under different temperature and humidity conditions.

[0218] 2. Moisture absorption rate measurement: Measure the moisture absorption rate of the moisture-conditioning material at low temperatures and calculate the moisture absorption rate S. abs .​

[0219] 3. Moisture release rate measurement: At higher temperatures, measure the moisture release rate of the moisture-regulating material and calculate the moisture release rate S. des .

[0220] 4. Data Fitting: Using regression analysis, the temperature and humidity dependence of moisture absorption and release rates is fitted to obtain the parameters of the above function.

[0221] Step S6: Based on the moisture diffusion coefficient, various influence functions, and the moisture regulation capability of the moisture-regulating material, a simulation model is constructed to simulate the distribution of moisture inside the sealed cavity in order to predict the moisture diffusion in the sealed cavity.

[0222] Establish a simulation model to simulate the distribution of moisture inside the equipment. The model includes: the effect of temperature and humidity changes on moisture diffusion; and the effect of the moisture absorption / release process of the humidity-regulating material on moisture diffusion.

[0223] The amount and distribution location of humidity-regulating materials are used as input parameters to predict the diffusion behavior of moisture within the equipment. Optimization algorithms (such as particle swarm optimization and genetic algorithms) are used to calculate the optimal amount and location distribution of the humidity-regulating materials based on the moisture distribution and the equipment's operating conditions. Simulation calculations can predict the moisture distribution under each configuration. Experimental data are used to verify the simulation results and ensure the accuracy of the simulation optimization scheme.

[0224] Adjustment and optimization scheme: Adjust the parameters in the simulation model based on the experimental results, and further optimize the amount and distribution of humidity-regulating materials.

[0225] In this embodiment, the specific method for calculating the moisture diffusion coefficient is obtained through the following experimental process:

[0226] Phase 1:

[0227] 1.1 Objective: To obtain the diffusion coefficient under conditions without humidity control materials and to fit the influence functions of temperature f(T) and humidity f(RH).

[0228] 1.2 Experimental conditions:

[0229] Equipment: Select equipment chambers with vent valves (such as base stations, motors, electrical control systems, power cabinets, etc.) to ensure that there are multiple measurement points inside.

[0230] External temperature and humidity: Set different combinations of external temperature and humidity to ensure different environmental conditions when the equipment is in working, dormant, or start-stop states.

[0231] Internal temperature and humidity: Temperature and humidity sensors are deployed in different locations inside the equipment to record the air humidity and temperature at each location.

[0232] Equipment surface temperature: Thermocouples are placed at different locations on the inner surface of the equipment, with a focus on the low-temperature region.

[0233] Equipment operating state, hibernation state, and start-stop state: Simulates the actual working conditions of the equipment, including changes in temperature and humidity during the start-up and shutdown process.

[0234] 1.3 Experimental Procedure:

[0235] Equipment preparation: Ensure that there are no humidity-regulating materials inside the equipment (consistent with the actual application scenario), and install temperature and humidity sensors and thermocouples in several key locations.

[0236] External environment control: Set and adjust the external temperature and humidity, record the external and internal temperature and humidity data of the equipment, and set the equipment's working state and start / stop state.

[0237] Temperature and humidity data recording: Record the humidity and temperature data of the air inside the equipment and the temperature of the inner surface of the equipment at different locations to ensure the time synchronization of the data.

[0238] Calculation of diffusion coefficient: The baseline moisture diffusion coefficient D0 was calculated using the half-life method, and the influence functions of temperature f(T) and humidity f(RH) were fitted using experimental data.

[0239] 1.4 Experimental Parameters and Measurement Methods:

[0240] Internal temperature and humidity of the equipment: Temperature and humidity sensors and thermocouples are placed in multiple key locations to ensure the acquisition of humidity and temperature data of the air inside the equipment, as well as the temperature of the internal surfaces of the equipment.

[0241] External ambient temperature and humidity: The environmental control system is used to adjust and record the external temperature and humidity in real time.

[0242] Humidity change rate: Calculate the half-life of humidity change at each location and calculate the diffusion coefficient D.

[0243] Data synchronization: Use a data acquisition system to synchronously record data from each sensor.

[0244] Fit f T (T)·f RH (RH):

[0245] Objective: To fit the influence function of temperature and humidity on the diffusion coefficient using the data from the first stage.

[0246] Methods: Regression analysis was used to fit the effects of temperature and humidity on the diffusion coefficient.

[0247] Phase 2: Experiments with humidity-regulating materials:

[0248] 2.1 Objective: Through multiple tests, the same mass of humidity-regulating material is placed at different locations each time, and its influence on the moisture diffusion coefficient D is measured to fit the influence coefficient of the humidity-regulating material.

[0249] 2.2 Experimental conditions:

[0250] Humidity conditioning material: Select a humidity conditioning material with known moisture absorption / release properties, and maintain the same weight of humidity conditioning material for each experiment. The initial dosage is calculated at 600 g / m³, which will also be the amount to be optimized later.

[0251] Equipment operating state and start / stop state: Repeat the experimental conditions of stage 1 to ensure that the experiment is carried out under different external temperature and humidity conditions.

[0252] 2.3 Experimental Procedure:

[0253] Equipment preparation: In the same equipment as in stage 1, select different test locations and place the same weight of conditioning material.

[0254] External environment control: Set and record external temperature and humidity conditions in the equipment's working, dormant, and start / stop states to ensure consistency with stage 1.

[0255] Data recording: Real-time recording of temperature and humidity data at different locations inside the equipment, with particular attention to the location where humidity-regulating materials are placed.

[0256] Diffusion coefficient calculation: The diffusion coefficient of the conditioned material is calculated using the half-life method, and the difference between the diffusion coefficients of the conditioned material and the non-conditioned material is compared.

[0257] 2.4 Experimental Parameters and Measurement Methods:

[0258] Mass of humidification material: Use the same weight of humidification material for each experiment.

[0259] Temperature and humidity data: Same as in Phase 1, ensure that temperature and humidity data at each measurement point are collected synchronously.

[0260] Moisture diffusion coefficient: The diffusion coefficient was calculated using the half-life method, and the results were compared with experimental data without moisture conditioning materials.

[0261] Using the data from the second stage, the influence function of the humidity-regulating material was fitted to describe the effect of humidity-regulating materials at different locations and with different weights on moisture diffusion. Based on the difference in moisture diffusion coefficients under conditions with and without humidity-regulating materials, the influence function of the humidity-regulating material on the diffusion coefficient was fitted.

[0262] 3. Data Analysis and Model Fitting:

[0263] 3.1 Fitting f T (T)·f RH(RH):

[0264] Objective: To fit the influence function of temperature and humidity on the diffusion coefficient using the data from the first stage.

[0265] Methods: Regression analysis was used to fit the effects of temperature and humidity on the diffusion coefficient.

[0266] 3.2 Fitting the influence coefficient f of humidity-regulating materials W (W):

[0267] Objective: Using the data from Phase 2, fit the influence function of the humidity-regulating material to describe the effect of humidity-regulating materials at different locations and with different weights on moisture diffusion.

[0268] Method: Based on the difference in moisture diffusion coefficients under conditions with and without moisture-regulating materials, a function relating the moisture-regulating material to the diffusion coefficient was fitted. The model can be further optimized by replacing the moisture-regulating material with different moisture absorption and desorption rates.

[0269] 4. Simulation Calculation and Optimization

[0270] 4.1 Establishing a simulation model

[0271] Using experimental data from Phase 1 and Phase 2, a simulation model was established to model the distribution of moisture inside the equipment. The model includes: the effect of temperature and humidity changes on moisture diffusion; and the effect of the moisture absorption / release process of the humidity-regulating material on moisture diffusion.

[0272] The specific model for the dynamic moisture absorption / release of humidity-regulating materials in a closed cavity is as follows:

[0273]

[0274] m is the moisture content of the conditioning material in grams, and t is the time taken for the conditioning material to be used.

[0275] S abs (T, RH) is the moisture absorption rate of the humidity-regulating material, expressed as mass flow rate in g / s, which depends on the current temperature T and relative humidity RH.

[0276] S des (T, RH) is the moisture release rate of the humidity conditioning material, with the unit being mass flow rate, g / s, which depends on the current temperature T and relative humidity RH;

[0277] k abs (T) is a function of temperature, indicating how temperature affects the moisture absorption capacity of the moisture-regulating material;

[0278] f abs (RH) is a function of humidity, representing the relationship between moisture concentration and moisture absorption capacity;

[0279] kdes (T) is a function of temperature, indicating how temperature affects the moisture release capacity of the moisture-regulating material;

[0280] f des (RH) is a function of humidity, representing the relationship between moisture concentration and moisture release capacity.

[0281] The amount and distribution location of the humidity-regulating material are used as input parameters for the model to predict the diffusion behavior of moisture inside the equipment.

[0282] 4.2 Optimize the distribution and dosage of humidity-regulating materials

[0283] Objective: To optimize the minimum amount and distribution of humidity-regulating materials through simulation calculations to ensure uniform humidity inside the equipment.

[0284] Method: Optimization algorithms (such as particle swarm optimization and genetic algorithms) are used to calculate the optimal amount and location distribution of humidity-regulating materials based on humidity distribution and equipment operating conditions. Simulation calculations can predict the humidity distribution for each configuration.

[0285] 4.3 Experimental Verification and Adjustment

[0286] Experimental verification: Experimental data is used to verify the simulation results and ensure the accuracy of the simulation optimization scheme.

[0287] Adjustment and optimization scheme: Adjust the parameters in the simulation model based on the experimental results, and further optimize the amount and distribution of humidity-regulating materials.

[0288] This method has a sound theoretical basis and correct dimensions, and can utilize the half-life t. 1 / 2 The diffusion coefficient was inverted, and combined with temperature and humidity data and humidity-regulating materials, their respective influence parameters were further fitted, thus providing a quantitative basis for the subsequent design of internal humidity dynamics and anti-condensation of equipment.

[0289] The technical advantages of this embodiment are mainly reflected in the following aspects: First, by precisely controlling the mass and temperature / humidity data of the humidity-regulating material, the stability and repeatability of the experimental conditions are ensured, providing a foundation for the accurate calculation of the diffusion coefficient. Second, the half-life method is used to calculate the diffusion coefficient, which is not only simple but also accurate and reliable, truly reflecting the influence of the material on moisture diffusion. Finally, by fitting the influence function of the humidity-regulating material, the influence law of humidity-regulating materials at different locations and weights on moisture diffusion is successfully described, providing important quantitative basis for the dynamics of internal humidity and anti-condensation design of equipment. In summary, this embodiment has significant technical advantages and practical value.

[0290] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting moisture diffusion in a sealed cavity under the influence of humidity-regulating materials, based on a sealed cavity with a vent valve, wherein multiple measurement points are provided within the sealed cavity, characterized in that... The method includes the following steps: Obtain the diffusion distance from each measurement point to the vent valve; Obtain the temperature and humidity of the external environment of the sealed cavity within a preset time period; Obtain the temperature and humidity at various measurement points inside the sealed cavity within a preset time period; Based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point, the moisture diffusion coefficient is calculated by the half-life method, and the influence function of temperature and humidity on the moisture diffusion coefficient is obtained by fitting. Humidity-regulating materials were set at each measurement point to construct a dynamic moisture absorption / release model of the humidity-regulating materials in a closed cavity, thereby obtaining the humidity regulation capability of the humidity-regulating materials in a closed cavity. A simulation model is constructed based on the moisture diffusion coefficient, various influence functions, and the moisture regulation capability of the moisture-regulating material to simulate the distribution of moisture inside a sealed cavity and predict the moisture diffusion in the sealed cavity.

2. The method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials according to claim 1, characterized in that, The process of obtaining the diffusion distance from each measurement point to the vent valve includes: establishing a three-dimensional coordinate system, obtaining the coordinates of the vent valve and each measurement point, and calculating the straight-line distance from the coordinates of the measurement point to the coordinates of the vent valve as the diffusion distance from the measurement point to the vent valve.

3. The method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials according to claim 1, characterized in that, The process of calculating the moisture diffusion coefficient using the half-life method based on diffusion distance, temperature and humidity of the external environment of the sealed cavity, and temperature and humidity of each measurement point includes: combining different external environmental temperatures and humidity to calculate the moisture diffusion coefficient using the half-life method.

4. The method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials according to claim 3, characterized in that, The process of calculating the moisture diffusion coefficient using the half-life method based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point includes: setting the external environment temperature to be the same, obtaining the moisture diffusion coefficient under different external environment humidity, calculating the humidity influence function, and thus fitting the moisture diffusion coefficient.

5. The method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials according to claim 3, characterized in that, The process of calculating the moisture diffusion coefficient using the half-life method based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point includes: setting the external environment humidity to be the same, obtaining the moisture diffusion coefficient at different external environment temperatures, calculating the temperature influence function, and thus fitting the moisture diffusion coefficient.

6. The method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials according to claim 1, characterized in that, The method further includes the following steps: the effect of temperature and humidity changes on moisture diffusion; the effect of the moisture absorption / release process of the humidity-regulating material on moisture diffusion; and using the amount and distribution location of the humidity-regulating material as input parameters of the simulation model to predict the diffusion behavior of moisture in the equipment.

7. The method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials according to any one of claims 1 to 6, characterized in that, The fitting of the influence function of temperature and humidity on the moisture diffusion coefficient includes the following steps: after changing the temperature and / or humidity, repeating the steps to obtain the temperature and humidity of the external environment of the sealed cavity within a preset time period; repeating the steps to obtain the temperature and humidity of each measurement point inside the sealed cavity within a preset time period; repeating the steps to calculate the moisture diffusion coefficient using the half-life method based on the diffusion distance, the temperature and humidity of the external environment of the sealed cavity, and the temperature and humidity of each measurement point, so as to obtain the moisture diffusion coefficient under different temperature and humidity conditions, and fit the influence function of temperature and humidity on the moisture diffusion coefficient.

8. The method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials according to claim 7, characterized in that, The dynamic moisture absorption / release model of the constructed moisture-regulating material in a closed cavity is as follows: m is the moisture content of the conditioning material in grams, and t is the time taken for the conditioning material to be used. S abs (T, RH) is the moisture absorption rate of the humidity-regulating material, expressed as mass flow rate, and depends on the current temperature T and relative humidity RH. S des (T, RH) is the moisture release rate of the humidity conditioning material, with the unit being mass flow rate, which depends on the current temperature T and relative humidity RH; k abs (T) is a function of temperature, indicating how temperature affects the moisture absorption capacity of the moisture-regulating material; f abs (RH) is a function of humidity, representing the relationship between moisture concentration and moisture absorption capacity; k des (T) is a function of temperature, indicating how temperature affects the moisture release capacity of the moisture-regulating material; f des (RH) is a function of humidity, representing the relationship between moisture concentration and moisture release capacity.

9. The method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials according to claim 8, characterized in that, The method includes the following steps: the weight of the humidity-regulating material placed at each measurement point is the same.

10. The method for predicting moisture diffusion in a closed cavity under the influence of humidity-regulating materials according to claim 1, characterized in that, The method includes the following steps: further optimizing the simulation model by replacing the humidity-regulating materials with different humidity-regulating capabilities.

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