Method for preventing condensation in a closed cavity with a breather valve

Abstract

The application discloses a method for preventing condensation in a closed cavity with a breathable valve, which comprises the following steps: obtaining the moisture diffusion coefficient of each measuring point in the closed cavity by setting multiple measuring points in the closed cavity, and fitting the influence function of temperature, humidity and humidity control material on the moisture diffusion coefficient; constructing a simulation model to simulate the distribution of moisture in the closed cavity according to the moisture diffusion coefficient and the influence function; obtaining the internal temperature, humidity of the closed cavity and external environment information; obtaining the condensation condition according to the internal temperature, humidity of the closed cavity and external environment information combined with the moisture diffusion coefficient; obtaining the amount and position distribution of the humidity control material for preventing condensation according to the moisture distribution, the condensation condition and the simulation model. The condensation in the closed cavity with the breathable valve is prevented.

Description

Technical Field

[0001] This invention relates to the field of humidity control technology, and in particular to a method for preventing condensation in a sealed cavity with a vent valve. 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 in their safe and stable operation. Besides inherent design, materials, and quality factors, the insulation capacity of these cavities is frequently compromised by harsh environmental conditions such as temperature, humidity, and condensation. Long-term operational experience with some power cabinets shows that condensation inside the cabinet leads to a decline in internal insulation performance, and various insulation defects can gradually develop into breakdowns, causing accidents and significantly impacting 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, as well as how to prevent condensation. Summary of the Invention

[0004] In view of the above-mentioned shortcomings, the present invention provides a method for preventing condensation in a closed cavity with a vent valve, which can realize the condensation prevention test of a closed cavity with a vent valve.

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

[0006] A method for preventing condensation in a sealed cavity with a vent valve, the method comprising the following steps:

[0007] By setting multiple measurement points in a sealed cavity, the moisture diffusion coefficient of each measurement point in the sealed cavity is obtained, and the influence function of temperature, humidity and humidity-regulating material on the moisture diffusion coefficient is obtained by fitting.

[0008] A simulation model was constructed based on the moisture diffusion coefficient and various influence functions to simulate the distribution of moisture inside a sealed cavity;

[0009] To obtain information on the internal temperature and humidity of a sealed cavity, as well as the external environment;

[0010] The conditions for condensation are obtained by combining information on the internal temperature and humidity of the sealed cavity with information on the external environment and the moisture diffusion coefficient.

[0011] Based on the humidity distribution and condensation conditions, combined with the simulation model, the amount and location distribution of humidity-regulating materials for preventing condensation are obtained.

[0012] According to one aspect of the present invention, the step of setting multiple measurement points within a sealed cavity to obtain the moisture diffusion coefficient at each measurement point within the sealed cavity, and fitting a function to obtain the influence of temperature, humidity, and humidity-regulating materials on the moisture diffusion coefficient includes:

[0013] Obtain the diffusion distance from each measurement point to the vent valve;

[0014] Obtain the temperature and humidity of the external environment of the sealed cavity within a preset time period;

[0015] Obtain the temperature and humidity at various measurement points inside the sealed cavity within a preset time period;

[0016] The moisture diffusion coefficient is calculated 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.

[0017] Obtain the moisture diffusion coefficient under different temperatures and humidity conditions, and fit the influence function of temperature and humidity on the moisture diffusion coefficient;

[0018] Obtain the moisture diffusion coefficient when setting humidity-regulating materials at different locations, and fit the influence function of humidity-regulating materials on the moisture diffusion coefficient.

[0019] According to one aspect of the present invention, obtaining the internal temperature, humidity and external environment information of the sealed cavity includes: obtaining the internal air temperature, relative humidity and surface temperature of the sealed cavity, and obtaining the external air temperature and external air humidity of the sealed cavity.

[0020] According to one aspect of the present invention, the condition for obtaining condensation based on the internal temperature and humidity of the sealed cavity and external environmental information combined with the moisture diffusion coefficient includes: calculating the dew point temperature of the current position of the sealed cavity based on the air temperature and relative humidity inside the sealed cavity; if the surface temperature at the current position is lower than the dew point temperature, there is a risk of condensation.

[0021] According to one aspect of the present invention, the dew point temperature is calculated as follows:

[0022]

[0023] in, T dew denoted as dew point temperature, T as internal air temperature, RH as internal air humidity, and a and b as constants determined based on the Magnus-Tetens formula and its improved formula.

[0024] According to one aspect of the present invention, the simulation model for simulating the distribution of moisture inside a sealed cavity by constructing a simulation model based on the moisture diffusion coefficient and various influence functions includes: the influence of temperature and humidity changes on moisture diffusion; the influence 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 inside the equipment.

[0025] According to one aspect of the present invention, the step of constructing a simulation model based on the moisture diffusion coefficient and various influence functions to simulate the distribution of moisture inside a sealed cavity includes: further optimizing the simulation model by replacing the moisture-regulating materials with different moisture absorption and release rates.

[0026] According to one aspect of the present invention, obtaining the amount and location distribution of the anti-condensation humidity-regulating material based on the humidity distribution, condensation conditions, and simulation model includes: using an optimization algorithm based on the humidity distribution and external environmental information of the sealed cavity to calculate the amount and location distribution of the humidity-regulating material, so as to make the humidity inside the equipment uniform and prevent condensation.

[0027] According to one aspect of the present invention, the calculation of the amount and location distribution of the humidity-regulating material includes: optimizing to obtain the minimum amount and location of the humidity-regulating material.

[0028] According to one aspect of the present invention, the calculation of the amount and location distribution of humidity-regulating material comprises: solving by minimizing a comprehensive objective function: min(α1∙humidity fluctuation + α2∙condensation risk + α3∙humidity-regulating material amount), where α1, α2, and α3 are weighting coefficients.

[0029] The above technical solution enables intelligent dynamic regulation of humidity in enclosed spaces, mitigating the risk of condensation within existing equipment cavities. This is achieved by deploying humidity-regulating materials while maintaining the existing cavity structure. A predictive model is built by integrating internal and external environmental monitoring data, gradually optimizing the material placement scheme to improve the reliability of the equipment cavity while reducing costs. This data-driven optimization mechanism not only ensures the anti-condensation requirements of critical equipment within the cavity but also reduces equipment maintenance costs by 40% through optimized material usage.

[0030] like Figure 1 , Figure 2The structural design shown, combined with a distributed sensor network, can determine the condensation conditions and duration at various key locations within the equipment cavity under different experimental conditions. Further improvements are achieved through the deployment of humidity-regulating materials and simulation calculations. First, by dynamically monitoring the temperature and humidity parameters of the internal and external environments of the sealed cavity, a multi-dimensional data model is established. Combined with a multi-objective optimization algorithm based on weighted coefficients, the optimal configuration of the amount and distribution of humidity-regulating materials is achieved. Second, minimizing the α1∙humidity fluctuation term maintains humidity stability within the cavity, reducing the α2∙condensation risk coefficient effectively prevents condensation, while the constraint of the α3∙humidity-regulating material usage term ensures the economic efficiency of material use. Simultaneously, Figure 1-2 The structural design shown supports a modular layout of the humidity control components, which, together with the real-time data acquisition system, forms a closed-loop control, ultimately verifying the reliability of the equipment cavity under various operating conditions. 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 preventing condensation in a sealed cavity with a vent valve according to the present invention.

[0033] Figure 2 This is a schematic diagram of the blank group motor and the experimental group motor as described in Embodiment 3 of the present invention;

[0034] Figure 3 This is a diagram showing the deployment of measurement points of the blank group motor during the motor anti-condensation test as described in Embodiment 3 of the present invention;

[0035] Figure 4 This is a diagram showing the deployment of measurement points on the motor in the experimental group during the motor anti-condensation test as described in Embodiment 3 of the present invention.

[0036] Figure 5 This is a temperature and humidity change curve at measurement point 11# of the blank group motor vent valve as described in Embodiment 3 of the present invention;

[0037] Figure 6 This is a temperature and humidity change curve at measurement point 01# of the motor vent valve in the experimental group described in Embodiment 3 of the present invention.

[0038] Figure 7 This is a temperature and humidity change curve of measurement point 03# of the blank group motor coil as described in Embodiment 3 of the present invention;

[0039] Figure 8This is a temperature and humidity change curve at measurement point 13# of the motor coil in the experimental group described in Embodiment 3 of the present invention;

[0040] Figure 9 This is a temperature and humidity change curve of the blank group motor high voltage three-phase position measurement point 14# as described in Embodiment 3 of the present invention;

[0041] Figure 10 This is a temperature and humidity change curve of the high-voltage three-phase position measurement point 04# of the experimental group motor described in Embodiment 3 of the present invention. Detailed Implementation

[0042] 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.

[0043] Example 1

[0044] like Figure 1 As shown, a method for preventing condensation in a closed cavity with a vent valve is provided, the method comprising the following steps:

[0045] Step S1: By setting multiple measurement points in the sealed cavity, the moisture diffusion coefficient of each measurement point in the sealed cavity is obtained, and the influence function of temperature, humidity and humidity-regulating material on the moisture diffusion coefficient is obtained by fitting.

[0046] Step S1 involves setting multiple measurement points within the sealed cavity to obtain the moisture diffusion coefficient at each measurement point, and fitting the influence function of temperature, humidity, and humidity-regulating materials on the moisture diffusion coefficient. In specific implementation, this includes the following steps:

[0047] Step S11: Obtain the diffusion distance from each measurement point to the vent valve;

[0048] First, the positions and diffusion distances of multiple measurement points within the sealed cavity are obtained. A three-dimensional coordinate system is established, and the coordinates of the vent valve and each measurement point are acquired. The reference position is set based on the geometric center of the vent valve or the valve port reference point. The coordinates of the measurement point are set as (x, y, z). The straight-line distance from the measurement point to the air valve is calculated as the diffusion distance L from the measurement point to the air valve. The coordinates of the i-th measurement point are (x, y, z). i， y i， z i Define its equivalent diffusion distance:

[0049]

[0050] Because the sensor cannot be installed at the ideal point on the valve port, i.e. At this location, all actual measurement points meet the requirements. .

[0051] 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 of the humidity-regulating materials is measured using an electronic scale. .

[0052] Step S12: Obtain the temperature and humidity of the external environment of the sealed cavity within a preset time period;

[0053] 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.

[0054] 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.

[0055] Step S13: Obtain the temperature and humidity at each measurement point inside the sealed cavity within a preset time period;

[0056] 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.

[0057] Step S14: 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.

[0058] Step S15: Obtain the moisture diffusion coefficient under different temperatures and humidity conditions, and fit the influence function of temperature and humidity on the moisture diffusion coefficient;

[0059] Step S16: Obtain the moisture diffusion coefficient when setting the humidity-regulating material at different locations, and fit the influence function of the humidity-regulating material on the moisture diffusion coefficient.

[0060] Steps S14 to S16 specifically include:

[0061] Step S01: Starting from the error function solution of one-dimensional diffusion, by setting the half-life condition... Relationship obtained:

[0062] ;

[0063] Here, w(x,t) is defined as the moisture state quantity inside the cavity, typically chosen as absolute humidity (AH), with units of g / m³. 3 When the temperature field is approximately uniform, the water vapor partial pressure p can also be used as an equivalent method. v , i.e., the equivalent quantity monotonically corresponding to water vapor concentration; x: one-dimensional equivalent diffusion coordinate, x=0 is the permeable boundary, x>0 is the interior of the cavity; w0 is the initial humidity value, i.e., the initial moisture state quantity inside at t=0; w ext The humidity balance value is given by D, which is the moisture diffusion coefficient, also known as the equivalent moisture diffusion coefficient (m). 2 / s), which are the constant parameters to be identified for "single test / single equivalent path", where L is the equivalent diffusion distance (m) from the measurement point to the permeable boundary; t 1 / 2 The half-life is given by k = erfc. - ¹(1 / 2) is approximately 0.4769, thus yielding:

[0064] ;

[0065] Thus, the moisture diffusion coefficient D at each measurement point is obtained. L D L Let be the equivalent diffusion coefficient corresponding to the measuring point at a distance of L; let D0 at the vent valve be used as the reference moisture diffusion coefficient. D0 is the reference diffusion coefficient, which is the equivalent diffusion capacity parameter (not the value substituted when L=0) corresponding to the reference area near the vent valve under reference temperature and humidity (T0, RH0) and no humidity-regulating material conditions.

[0066] The modeling of the diffusion coefficient is based on:

[0067] (1) Single solution layer: In a specific experiment (temperature T, external humidity program, equipment structure and state are fixed) and an equivalent diffusion path, the equivalent diffusion coefficient D is regarded as a constant parameter, and the constant coefficient diffusion equation is used to solve and obtain the analytical solution of the error function.

[0068] (2) Parameter identification layer: obtained by combining the analytical solution with the half-life condition. This belongs to the "parameter inversion / identification formula", which means "given the L of this experiment and the measured t". 1 / 2 The constant parameter D corresponding to this experiment can be obtained by reverse calculation. This relationship does not mean that D can be written as D(L) or D(x) in the same partial differential equation.

[0069] (3) Cross-test fitting layer: When the measuring point is changed (different L) or the operating conditions are changed (different T, RH), the equivalent diffusion coefficient data points (such as DL, D(T,RH)) under different test objects are obtained, which are used to regress and fit the temperature and humidity influence function. This does not contradict the principle of "taking D as a constant in a single solution".

[0070] In this embodiment, in step S01, the solution is obtained from the error function. The entire derivation process is as follows:

[0071] 01.1 Control Equations and Constant Coefficient Reduction;

[0072] The one-dimensional general form of Fick's second law is:

[0073] (Equation 1) ;

[0074] If D is approximated as a constant in a single trial and along a single equivalent path, then it becomes:

[0075] (Equation 2) ;

[0076] 01.2 Initial and Boundary Conditions (Semi-Infinite One-Dimensional Model);

[0077] Let x be the one-dimensional equivalent diffusion coordinate, x=0 be the air-permeable boundary, and x>0 be the interior of the cavity. Take:

[0078] (Equation 3) Initial conditions: w(x,0)=w0, x>0.

[0079] w0: Initial moisture state quantity inside the unit at time t=0.

[0080] (Equation 4) Boundary condition: w(0,t)=w ext , t>0 (approximately constant within the identification window).

[0081] 01.3 Analytical solution of the error function;

[0082] The standard analytical solution to the above classical semi-infinite diffusion problem is:

[0083] (Equation 5) ;

[0084] Equivalently, it can also be written in the form of a complementary error function:

[0085] (Equation 6) .

[0086] 01.4 Substituting the half-life time condition into the inversion of D;

[0087] Define the half-life t at the measurement point x=L. 1 / 2 :

[0088] (Equation 7) ;

[0089] Substituting equation 5 into equation 7, we get:

[0090] (Equation 8) ;

[0091] make ;

[0092] but:

[0093] (Equation 9) ;

[0094] Summarized as follows:

[0095] (Equation 10) .

[0096] Step S02: 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:

[0097] ;

[0098] 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:

[0099] ;

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The moisture diffusion coefficient at each point can be obtained using the moisture diffusion equation, and the calculation method is as follows:

[0105] ;

[0106] 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; D0 is used to characterize the baseline equivalent moisture exchange capacity between the cavity and the outside world under baseline temperature and humidity conditions. D0 is not obtained by substituting L=0 into the formula. The reason is that the boundary condition w(0,t)=w is satisfied at x=0. ext There is no flow from w0 to w at this location. ext The diffusion response curve shows that the half-life condition degenerates at x=0 (t). 1 / 2 →0), therefore D0 must be obtained from measurable points where L>0.

[0107] D0 can be obtained using the following methods:

[0108] Method A: Definition of nearest measurement point;

[0109] Under baseline conditions (T0, RH0) and without conditioning material, select a set of measuring points close to the vent valve, and take the minimum distance L among them. min For measuring points >0, calculate according to formula 10. and define:

[0110] (Equation 12) D0 = ;

[0111] Alternatively, several measuring points closest to the valve orifice can be selected, and their D values ​​can be measured. L Use the average / weighted average as D0 to improve repeatability.

[0112] Method B: Multi-point regression identification method (avoiding the misinterpretation of "D follows L", directly identifying from the "proportional relationship between half-life and squared distance")

[0113] Under the same reference conditions, select a set S of multiple measuring points close to the valve orifice. From Equation 10, an approximate proportional relationship can be obtained:

[0114] (Equation 13) , ( .

[0115] by For dependent variable, Performing least squares regression on the independent variable, we obtain the slope 'a', then:

[0116] (Equation 14) .

[0117] It is a function of the effect of temperature on the diffusion coefficient;

[0118] It is a function of the effect of humidity on the diffusion coefficient;

[0119] It is a function of the effect of humidity conditioning material on the diffusion coefficient, which depends on the weight of the humidity conditioning material and its moisture absorption / release characteristics.

[0120] in, α is the effect coefficient of temperature on the diffusion coefficient, and T0 is the reference temperature (e.g., 25°C). ; γ is the influence coefficient of humidity on the diffusion coefficient, and RH0 is the reference humidity (e.g., 60%). ; 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 effect of the humidity-regulating material on moisture diffusion.

[0121] 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).

[0122] ;

[0123] 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 obtain the fitted value. The function is given. 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 using 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, we finally obtain... .

[0124] In this embodiment, the specific method for calculating the moisture diffusion coefficient is obtained through the following experimental process:

[0125] Phase 1

[0126] 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).

[0127] 1.2 Experimental conditions:

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Equipment surface temperature: Thermocouples are placed at different locations on the inner surface of the equipment, with a focus on the low-temperature region.

[0132] 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.

[0133] 1.3 Experimental Procedure:

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Calculation of diffusion coefficient: The baseline 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.

[0138] 1.4 Experimental Parameters and Measurement Methods:

[0139] 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.

[0140] External ambient temperature and humidity: The environmental control system is used to adjust and record the external temperature and humidity in real time.

[0141] Humidity change rate: Calculate the half-life of humidity change at each location and calculate the diffusion coefficient D.

[0142] Data synchronization: Use a data acquisition system to synchronously record data from each sensor.

[0143] Fit f T (T)∙f RH (RH):

[0144] Objective: To fit the influence function of temperature and humidity on the diffusion coefficient using the data from the first stage.

[0145] Methods: Regression analysis was used to fit the effects of temperature and humidity on the diffusion coefficient.

[0146] Phase 2: Experiments with humidity-regulating materials:

[0147] 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.

[0148] 2.2 Experimental Conditions

[0149] 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.

[0150] 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.

[0151] 2.3 Experimental Procedure:

[0152] Equipment preparation: In the same equipment as in stage 1, select different test locations and place the same weight of conditioning material.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 2.4 Experimental Parameters and Measurement Methods:

[0157] Mass of humidification material: Use the same weight of humidification material for each experiment.

[0158] Temperature and humidity data: Same as in Phase 1, ensure that temperature and humidity data are collected synchronously at each location.

[0159] 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.

[0160] 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.

[0161] Step S2: Construct a simulation model based on the moisture diffusion coefficient and various influence functions to simulate the distribution of moisture inside the sealed cavity;

[0162] In its specific implementation, the following contents are included:

[0163] 3.1 Fitting f T (T)∙f RH (RH):

[0164] Objective: To fit the influence function of temperature and humidity on the diffusion coefficient using the data from the first stage.

[0165] Methods: Regression analysis was used to fit the effects of temperature and humidity on the diffusion coefficient.

[0166] 3.2 Fitting the influence coefficient f of humidity-regulating materials W (W):

[0167] 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.

[0168] 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.

[0169] 3.3 Establishing a simulation model:

[0170] 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.

[0171] 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.

[0172] Step S3: Obtain information on the internal temperature and humidity of the sealed cavity, as well as the external environment;

[0173] The acquisition of the internal temperature and humidity of the sealed cavity and the external environment information includes: acquiring the internal air temperature of the sealed cavity. relative humidity of air and surface temperature To obtain the temperature of the air outside the sealed cavity and external air humidity .

[0174] Step S4: Obtain the conditions for condensation based on the internal temperature and humidity of the sealed cavity and the external environmental information, combined with the moisture diffusion coefficient.

[0175] The conditions for obtaining condensation based on the internal temperature and humidity of the sealed cavity, external environmental information, and moisture diffusion coefficient include: calculating the dew point temperature at the current location of the sealed cavity based on the air temperature and relative humidity inside the sealed cavity. If the surface temperature at the current location Below dew point temperature If there is a risk of condensation, it is identified as a risk area.

[0176] That is, to identify the condensation risk area: .

[0177] The dew point temperature is calculated as follows:

[0178] ;

[0179] in, T dew denoted as dew point temperature, T as internal air temperature, RH as internal air humidity, and a and b as constants determined based on the Magnus-Tetens formula and its improved formula.

[0180] How to determine the calculation parameters a and b for dew point temperature above 60℃?

[0181] Between 0℃ and 60℃, we often use the Magnus-Tetens approximation formula to calculate the dew point temperature:

[0182] ;

[0183] in, .

[0184] Common parameters (applicable to 0℃~60℃):

[0185] a=17.27, b=237.7. These parameters are derived from the Magnus-Tetens empirical formula, which is accurate in the range of low to medium temperatures (0~60℃).

[0186] The specific values ​​for a and b are shown in the table below:

[0187]

[0188] Basic form of the Antoine equation:

[0189]

[0190] The Antoine equation is an empirical fitting formula. A, B, and C do not have strict "physical constant meanings". A: affects the "overall height" of the vapor pressure curve; B: describes the "driving force of temperature increase on vapor pressure" - the larger the value, the more difficult evaporation; C: is used to adjust the position of the temperature axis to make the fitting function more accurate.

[0191] These are obtained through regression fitting based on the saturated vapor pressure data of actual substances; each substance requires a specific set of A, B, and C parameters; the same substance may have multiple sets of coefficients in different temperature ranges (multi-segment fitting); as shown in the table below:

[0192]

[0193] Step S5: Based on the humidity distribution and condensation conditions, combined with the simulation model, obtain the amount and location distribution of the humidity-regulating material for preventing condensation.

[0194] The process of obtaining the amount and location distribution of humidity-regulating materials for preventing condensation based on moisture distribution, condensation conditions, and simulation models includes: using an optimization algorithm based on moisture distribution and external environmental information of the sealed cavity to calculate the amount and location distribution of humidity-regulating materials, so as to make the humidity inside the equipment uniform and prevent condensation.

[0195] Calculate the surface temperature and dew point temperature of the equipment. Then adjust It improves local moisture absorption capacity.

[0196] 4.1 Optimize the distribution and dosage of humidity-regulating materials:

[0197] Objective: To optimize the minimum amount and distribution of humidity-regulating materials through simulation calculations, so as to ensure uniform humidity inside the equipment and prevent condensation.

[0198] 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.

[0199] In this embodiment, the calculation of the amount and location distribution of humidity-regulating material includes: solving by minimizing a comprehensive objective function: min(α1∙humidity fluctuation + α2∙condensation risk + α3∙humidity-regulating material usage), where α1, α2, and α3 are weighting coefficients.

[0200] in:

[0201] 1) Humidity fluctuation (the smaller the better): Within the optimization time window, measure the deviation between the relative humidity (RH) at each measuring point in the target space and the target set RH: mean square deviation, absolute deviation, peak-to-valley amplitude.

[0202] 2) Condensation risk (the lower the better), which is measured by the relationship between surface temperature Ts and dew point Tdew as “intensity × duration”. It can be expressed in the following three forms: condensation degree per hour (degrees per hour), saturation excess (based on absolute humidity / saturation ratio), probability of condensation occurrence or duration.

[0203] 3) The amount of humidity-regulating material used (the smaller the better) can be characterized by the following methods: material quality, weighted cost (considering material cost, volume occupation, and assembly cost), and engineering penalties (weight, volume, and space occupation).

[0204] To eliminate differences in the dimensions of different indicators, normalization is required:

[0205] Set the target threshold θ (from specifications or customer requirements) and the baseline value b (using no conditioning and historical data), and convert each indicator into a dimensionless indicator of 0 to 1:

[0206] in, ;

[0207] J: Actual indicator value of the current plan;

[0208] The normalized objective function is: .

[0209] Methods for determining weighting coefficients:

[0210] Method A: Monetization empowerment;

[0211] Convert the risk or cost of each indicator into economic loss, and let α1, α2, and α3 be the marginal cost of humidity fluctuation, condensation risk, and usage, respectively. Example: α2:α1:α3 ≈ 10:1:0.2.

[0212] Method B: Analytic Hierarchy Process (AHP);

[0213] A pairwise comparison matrix is ​​constructed using expert scoring, and consistency is calculated and weights are obtained.

[0214] Method C: Pareto front + knee point method;

[0215] The Pareto front is obtained through multi-objective optimization, a compromise solution is selected, and the weights are derived in reverse.

[0216] Method D: Regression / Bayesian method based on historical data;

[0217] Learn implicit weights from the optimal solutions of historical projects.

[0218] Constraints:

[0219] In practical engineering, condensation is typically tolerated with zero tolerance. Treating condensation risk as a constraint, the objective function only balances humidity fluctuations with the amount of humidity-regulating materials used.

[0220] Implementation steps:

[0221] 1. Define the test conditions and time window;

[0222] 2. Select the appropriate indicator scope;

[0223] 3. Set threshold and baseline values;

[0224] 4. Indicator normalization;

[0225] 5. Select initial weight values ​​(method A / B / C / D), usually A is chosen;

[0226] 6. Numerical solution and iterative optimization;

[0227] 7. Actual measurement verification and weight adjustment.

[0228] Example value:

[0229] Assuming the target humidity fluctuation is ≤2%RH, the condensation risk is approximately 0, and the dosage is based on the existing plan. Initial weights can be set as α2=10, α1=1, and α3=0.2.

[0230] Example 2

[0231] like Figure 1As shown, a method for preventing condensation in a closed cavity with a vent valve is provided, the method comprising the following steps:

[0232] Step S1: By setting multiple measurement points in the sealed cavity, the moisture diffusion coefficient of each measurement point in the sealed cavity is obtained, and the influence function of temperature, humidity and humidity-regulating material on the moisture diffusion coefficient is obtained by fitting.

[0233] Step S1 involves setting multiple measurement points within the sealed cavity to obtain the moisture diffusion coefficient at each measurement point, and fitting the influence function of temperature, humidity, and humidity-regulating materials on the moisture diffusion coefficient. In specific implementation, this includes the following steps:

[0234] Step S11: Obtain the diffusion distance from each measurement point to the vent valve;

[0235] First, the positions and diffusion distances of multiple measurement points within the sealed cavity are obtained. A three-dimensional coordinate system is established, and the coordinates of the vent valve and each measurement point are acquired. The reference position is set based on the geometric center of the vent valve or the valve port reference point. The coordinates of the measurement point are set as (x, y, z). The straight-line distance from the measurement point to the air valve is calculated as the diffusion distance L from the measurement point to the air valve. The coordinates of the i-th measurement point are (x, y, z). i， y i， z i Define its equivalent diffusion distance:

[0236]

[0237] Because the sensor cannot be installed at the ideal point on the valve port, i.e. At this location, all actual measurement points meet the requirements. .

[0238] 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 of the humidity-regulating materials is measured using an electronic scale. .

[0239] Step S12: Obtain the temperature and humidity of the external environment of the sealed cavity within a preset time period;

[0240] 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.

[0241] 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.

[0242] Step S13: Obtain the temperature and humidity at each measurement point inside the sealed cavity within a preset time period;

[0243] 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.

[0244] Step S14: 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.

[0245] Step S15: Obtain the moisture diffusion coefficient under different temperatures and humidity conditions, and fit the influence function of temperature and humidity on the moisture diffusion coefficient;

[0246] Step S16: Obtain the moisture diffusion coefficient when setting the humidity-regulating material at different locations, and fit the influence function of the humidity-regulating material on the moisture diffusion coefficient.

[0247] Steps S14 to S16 specifically include:

[0248] Step S01: Starting from the error function solution of one-dimensional diffusion, by setting the half-life condition... Relationship obtained:

[0249] ;

[0250] Here, w(x,t) is defined as the moisture state quantity inside the cavity, typically chosen as absolute humidity (AH), with units of g / m³. 3 When the temperature field is approximately uniform, the water vapor partial pressure p can also be used as an equivalent method. v, i.e., the equivalent quantity monotonically corresponding to water vapor concentration; x: one-dimensional equivalent diffusion coordinate, x=0 is the permeable boundary, x>0 is the interior of the cavity; w0 is the initial humidity value, i.e., the initial moisture state quantity inside at t=0; w ext The humidity balance value is given by D, which is the moisture diffusion coefficient, also known as the equivalent moisture diffusion coefficient (m). 2 / s), which are the constant parameters to be identified for "single test / single equivalent path", where L is the equivalent diffusion distance (m) from the measurement point to the permeable boundary; t 1 / 2 The half-life is given by k = erfc. - ¹(1 / 2) is approximately 0.4769, thus yielding:

[0251] ;

[0252] Thus, the moisture diffusion coefficient D at each measurement point is obtained. L D L Let be the equivalent diffusion coefficient corresponding to the measuring point at a distance of L; let D0 at the vent valve be used as the reference moisture diffusion coefficient. D0 is the reference diffusion coefficient, which is the equivalent diffusion capacity parameter (not the value substituted when L=0) corresponding to the reference area near the vent valve under reference temperature and humidity (T0, RH0) and no humidity-regulating material conditions.

[0253] Step S02: 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:

[0254] ;

[0255] 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 The calculation method is as follows:

[0256] ;

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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.

[0261] The moisture diffusion coefficient at each point can be obtained using the moisture diffusion equation, and the calculation method is as follows:

[0262] ;

[0263] 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; D0 is used to characterize the baseline equivalent moisture exchange capacity between the cavity and the outside world under baseline temperature and humidity conditions. D0 is not obtained by substituting L=0 into the formula. The reason is that the boundary condition w(0,t)=w is satisfied at x=0. ext There is no flow from w0 to w at this location. ext The diffusion response curve shows that the half-life condition degenerates at x=0 (t). 1 / 2 →0), therefore D0 must be obtained from measurable points where L>0.

[0264] D0 can be obtained using the following methods:

[0265] Method A: Definition of nearest measurement point;

[0266] Under baseline conditions (T0, RH0) and without conditioning material, select a set of measuring points close to the vent valve, and take the minimum distance L among them. min For measuring points >0, calculate according to formula 10. and define:

[0267] (Equation 12) D0 = ;

[0268] Alternatively, several measuring points closest to the valve orifice can be selected, and their D values ​​can be measured. L Use the average / weighted average as D0 to improve repeatability.

[0269] Method B: Multi-point regression identification method (avoiding the misinterpretation of "D follows L", directly identifying from the "proportional relationship between half-life and squared distance")

[0270] Under the same reference conditions, select a set S of multiple measuring points close to the valve orifice. From Equation 10, an approximate proportional relationship can be obtained:

[0271] (Equation 13) , ( .

[0272] by For dependent variable, Performing least squares regression on the independent variable, we obtain the slope 'a', then:

[0273] (Equation 14) .

[0274] It is a function of the effect of temperature on the diffusion coefficient;

[0275] It is a function of the effect of humidity on the diffusion coefficient;

[0276] It is a function of the effect of humidity conditioning material on the diffusion coefficient, which depends on the weight of the humidity conditioning material and its moisture absorption / release characteristics.

[0277] in, α is the effect coefficient of temperature on the diffusion coefficient, and T0 is the reference temperature (e.g., 25°C). ; γ is the influence coefficient of humidity on the diffusion coefficient, and RH0 is the reference humidity (e.g., 60%). ; 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 effect of the humidity-regulating material on moisture diffusion.

[0278] ;

[0279] 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 obtain the fitted value. The function is given. 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 using 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, we finally obtain... .

[0280] In this embodiment, the specific method for calculating the moisture diffusion coefficient is obtained through the following experimental process:

[0281] Phase 1:

[0282] 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).

[0283] 1.2 Experimental conditions:

[0284] 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.

[0285] 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.

[0286] 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.

[0287] Equipment surface temperature: Thermocouples are placed at different locations on the inner surface of the equipment, with a focus on the low-temperature region.

[0288] 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.

[0289] 1.3 Experimental Procedure:

[0290] 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.

[0291] 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.

[0292] 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.

[0293] Calculation of diffusion coefficient: The baseline 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.

[0294] 1.4 Experimental Parameters and Measurement Methods:

[0295] 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.

[0296] External ambient temperature and humidity: The environmental control system is used to adjust and record the external temperature and humidity in real time.

[0297] Humidity change rate: Calculate the half-life of humidity change at each location and calculate the diffusion coefficient D.

[0298] Data synchronization: Use a data acquisition system to synchronously record data from each sensor.

[0299] Fit f T (T)∙f RH (RH):

[0300] Objective: To fit the influence function of temperature and humidity on the diffusion coefficient using the data from the first stage.

[0301] Methods: Regression analysis was used to fit the effects of temperature and humidity on the diffusion coefficient.

[0302] Phase 2: Experiments with humidity-regulating materials:

[0303] 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.

[0304] 2.2 Experimental conditions:

[0305] 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.

[0306] 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.

[0307] 2.3 Experimental Procedure:

[0308] Equipment preparation: In the same equipment as in stage 1, select different test locations and place the same weight of conditioning material.

[0309] 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.

[0310] 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.

[0311] 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.

[0312] 2.4 Experimental Parameters and Measurement Methods:

[0313] Mass of humidification material: Use the same weight of humidification material for each experiment.

[0314] Temperature and humidity data: Same as in Phase 1, ensure that temperature and humidity data are collected synchronously at each location.

[0315] 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.

[0316] 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.

[0317] Step S2: Construct a simulation model based on the moisture diffusion coefficient and various influence functions to simulate the distribution of moisture inside the sealed cavity;

[0318] In its specific implementation, the following contents are included:

[0319] 3.1 Fitting f T (T)∙f RH (RH):

[0320] Objective: To fit the influence function of temperature and humidity on the diffusion coefficient using the data from the first stage.

[0321] Methods: Regression analysis was used to fit the effects of temperature and humidity on the diffusion coefficient.

[0322] 3.2 Fitting the influence coefficient f of humidity-regulating materials W (W):

[0323] 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.

[0324] 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.

[0325] 3.3 Establishing a simulation model:

[0326] 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.

[0327] 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.

[0328] Step S3: Obtain information on the internal temperature and humidity of the sealed cavity, as well as the external environment;

[0329] The acquisition of the internal temperature and humidity of the sealed cavity and the external environment information includes: acquiring the internal air temperature of the sealed cavity. relative humidity of air and surface temperature To obtain the temperature of the air outside the sealed cavity and external air humidity .

[0330] Step S4: Obtain the conditions for condensation based on the internal temperature and humidity of the sealed cavity and the external environmental information, combined with the moisture diffusion coefficient.

[0331] The conditions for obtaining condensation based on the internal temperature and humidity of the sealed cavity, external environmental information, and moisture diffusion coefficient include: calculating the dew point temperature at the current location of the sealed cavity based on the air temperature and relative humidity inside the sealed cavity. If the surface temperature at the current location Below dew point temperature If there is a risk of condensation, it is identified as a risk area.

[0332] That is, to identify the condensation risk area: .

[0333] The dew point temperature is calculated as follows:

[0334] ;

[0335] in, T dew denoted as dew point temperature, T as internal air temperature, RH as internal air humidity, and a and b as constants determined based on the Magnus-Tetens formula and its improved formula.

[0336] How to determine the calculation parameters a and b for dew point temperature above 60℃?

[0337] Between 0℃ and 60℃, we often use the Magnus-Tetens approximation formula to calculate the dew point temperature:

[0338] ;

[0339] in, .

[0340] Common parameters (applicable to 0℃~60℃):

[0341] a=17.27, b=237.7. These parameters are derived from the Magnus-Tetens empirical formula, which is accurate in the range of low to medium temperatures (0~60℃).

[0342] The specific values ​​for a and b are shown in the table below:

[0343]

[0344] Basic form of the Antoine equation:

[0345]

[0346] The Antoine equation is an empirical fitting formula. A, B, and C do not have strict "physical constant meanings". A: affects the "overall height" of the vapor pressure curve; B: describes the "driving force of temperature increase on vapor pressure" - the larger the value, the more difficult evaporation; C: is used to adjust the position of the temperature axis to make the fitting function more accurate.

[0347] These are obtained through regression fitting based on the saturated vapor pressure data of actual substances; each substance requires a specific set of A, B, and C parameters; the same substance may have multiple sets of coefficients in different temperature ranges (multi-segment fitting); as shown in the table below:

[0348]

[0349] Step S5: Based on the humidity distribution and condensation conditions, combined with the simulation model, obtain the amount and location distribution of the humidity-regulating material for preventing condensation.

[0350] The process of obtaining the amount and location distribution of humidity-regulating materials for preventing condensation based on moisture distribution, condensation conditions, and simulation models includes: using an optimization algorithm based on moisture distribution and external environmental information of the sealed cavity to calculate the amount and location distribution of humidity-regulating materials, so as to make the humidity inside the equipment uniform and prevent condensation.

[0351] Calculate the surface temperature and dew point temperature of the equipment. Then adjust It improves local moisture absorption capacity.

[0352] 4.1 Optimize the distribution and dosage of humidity-regulating materials:

[0353] Objective: To optimize the minimum amount and distribution of humidity-regulating materials through simulation calculations, so as to ensure uniform humidity inside the equipment and prevent condensation.

[0354] 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.

[0355] In this embodiment, the calculation of the amount and location distribution of humidity-regulating material includes: solving by minimizing a comprehensive objective function: min(α1∙humidity fluctuation + α2∙condensation risk + α3∙humidity-regulating material usage), where α1, α2, and α3 are weighting coefficients.

[0356] in:

[0357] 1) Humidity fluctuation (the smaller the better): Within the optimization time window, measure the deviation between the relative humidity (RH) at each measuring point in the target space and the target set RH: mean square deviation, absolute deviation, peak-to-valley amplitude.

[0358] 2) Condensation risk (the lower the better), which is measured by the relationship between surface temperature Ts and dew point Tdew as “intensity × duration”. It can be expressed in the following three forms: condensation degree per hour (degrees per hour), saturation excess (based on absolute humidity / saturation ratio), probability of condensation occurrence or duration.

[0359] 3) The amount of humidity-regulating material used (the smaller the better) can be characterized by the following methods: material quality, weighted cost (considering material cost, volume occupation, and assembly cost), and engineering penalties (weight, volume, and space occupation).

[0360] To eliminate differences in the dimensions of different indicators, normalization is required:

[0361] Set the target threshold θ (from specifications or customer requirements) and the baseline value b (using no conditioning and historical data), and convert each indicator into a dimensionless indicator of 0 to 1:

[0362] in, ;

[0363] J: Actual indicator value of the current plan;

[0364] The normalized objective function is: .

[0365] Methods for determining weighting coefficients:

[0366] Method A: Monetization empowerment;

[0367] Convert the risk or cost of each indicator into economic loss, and let α1, α2, and α3 be the marginal cost of humidity fluctuation, condensation risk, and usage, respectively. Example: α2:α1:α3 ≈ 10:1:0.2.

[0368] Method B: Analytic Hierarchy Process (AHP);

[0369] A pairwise comparison matrix is ​​constructed using expert scoring, and consistency is calculated and weights are obtained.

[0370] Method C: Pareto front + knee point method;

[0371] The Pareto front is obtained through multi-objective optimization, a compromise solution is selected, and the weights are derived in reverse.

[0372] Method D: Regression / Bayesian method based on historical data;

[0373] Learn implicit weights from the optimal solutions of historical projects.

[0374] Constraints:

[0375] In practical engineering, condensation is typically tolerated with zero tolerance. Treating condensation risk as a constraint, the objective function only balances humidity fluctuations with the amount of humidity-regulating materials used.

[0376] Implementation steps:

[0377] 1. Define the test conditions and time window;

[0378] 2. Select the appropriate indicator scope;

[0379] 3. Set threshold and baseline values;

[0380] 4. Indicator normalization;

[0381] 5. Select initial weight values ​​(method A / B / C / D), usually A is chosen;

[0382] 6. Numerical solution and iterative optimization;

[0383] 7. Actual measurement verification and weight adjustment.

[0384] Example value:

[0385] Assuming the target humidity fluctuation is ≤2%RH, the condensation risk is approximately 0, and the dosage is based on the existing plan. Initial weights can be set as α2=10, α1=1, and α3=0.2.

[0386] Step S17: Verify and adjust through experiments.

[0387] 4.2 Experimental Verification and Adjustment:

[0388] Experimental verification: Experimental data is used to verify the simulation results and ensure the accuracy of the simulation optimization scheme.

[0389] Adjustment and optimization scheme: Based on the experimental results, the parameters in the simulation model were adjusted, and the dosage and distribution of the humidity-regulating material were further optimized. The anti-condensation scheme was optimized by improving the performance of the humidity-regulating material, and an anti-condensation algorithm was established for the application scenario of "equipment cavity with a vent valve".

[0390] Specifically, it includes:

[0391] Objective: To verify the effectiveness of the optimized material distribution scheme in Phase 2 and ensure its anti-condensation effect.

[0392] Experimental steps:

[0393] 1. Based on the simulation optimization results, the humidity-regulating material W(x,y,z) is placed in different positions inside the equipment.

[0394] 2. Record the humidity changes inside the equipment to confirm whether there is condensation.

[0395] 3. Verify the effect of the optimized distribution of humidity-regulating materials to ensure uniform humidity distribution and prevent condensation.

[0396] This solution combines experimental testing, data analysis, and numerical simulation to optimize the distribution strategy of humidity-regulating materials, ensuring uniform humidity inside the equipment and preventing condensation.

[0397] Example 3

[0398] In this embodiment, when the method for preventing condensation in a sealed cavity with a breathable valve described in this invention is applied to a motor, the experimental steps and data are as follows:

[0399] like Figure 2 As shown, blank group motor (HC-T02) and experimental group motor (HC-T01) are prepared.

[0400] Figure 3 This is a test deployment location diagram for the blank group of motors. Figure 4 This is a diagram showing the test deployment locations for the motors in the experimental group; according to... Figure 3 and Figure 4 Deploy test locations; Table 1 below shows the deployment locations and settings for each location.

[0401] Table 1:

[0402]

[0403] As shown in Table 2 below, the key analysis points are:

[0404] Table 2:

[0405]

[0406] like Figure 5 The figure shows the temperature and humidity variation curves at the location of the vent valve on the blank group motor; as shown... Figure 6 The figure shows the temperature and humidity change curves at the location of the motor vent valve in the experimental group. It can be seen that before 14:00 on August 4th, the temperature at the vent valve location remained approximately 40℃, the humidity in the control group continuously rose to 90%, while the humidity in the experimental group remained around 70%. The condensation test data are shown in Table 3 below.

[0407] Table 3:

[0408]

[0409] Figures 7-8 This indicates that from July 29th to August 5th, when the coil temperature was around 40℃, the humidity in the blank group was consistently above 90%, while the humidity in the experimental group remained around 70%. See Table 4 below.

[0410] Table 4:

[0411]

[0412] Figures 9-10 This indicates that from July 29th to August 5th, when the coil temperature was around 40℃, the humidity in the blank group was consistently above 90%, while the humidity in the experimental group remained around 60%. See Table 5 below.

[0413] Table 5:

[0414]

[0415] The conclusions are as follows:

[0416] During the period when condensation occurred, the experimental group (with humidity-regulating materials) showed less fluctuation in temperature and humidity than the control group. During the same time period of condensation (August 4th 20:00 - August 5th 03:30):

[0417] The average internal temperature of the experimental group was 41℃, which was lower than the average temperature of the control group, which was 48℃.

[0418] The relative humidity of the experimental group was 71.9%, and the average temperature was 41℃, resulting in an average absolute humidity of approximately 41.42 g / m³. 3 The control group had a relative humidity of 68.3% and a combined average temperature of 48℃, with an average absolute humidity of approximately 56.15 g / m³. 3 .

[0419] The advantages of this invention are as follows: The above-described solution achieves uniform humidity distribution within the equipment and effectively prevents condensation. The method combining experimental verification and simulation optimization not only improves the accuracy and reliability of the anti-condensation solution but also significantly shortens the optimization cycle and reduces R&D costs. Furthermore, the anti-condensation algorithm established by this solution provides strong technical support and reference for anti-condensation design in application scenarios such as "equipment cavities with vent valves," demonstrating broad application prospects and significant practical value.

[0420] 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 preventing condensation in a sealed cavity with a vent valve, characterized in that, The method includes the following steps: By setting multiple measurement points in a sealed cavity, the moisture diffusion coefficient of each measurement point in the sealed cavity is obtained, and the influence function of temperature, humidity and humidity-regulating material on the moisture diffusion coefficient is obtained by fitting. A simulation model was constructed based on the moisture diffusion coefficient and various influence functions to simulate the distribution of moisture inside a sealed cavity; To obtain information on the internal temperature and humidity of a sealed cavity, as well as the external environment; The conditions for condensation are obtained by combining information on the internal temperature and humidity of the sealed cavity with information on the external environment and the moisture diffusion coefficient. Based on the humidity distribution and condensation conditions, combined with the simulation model, the amount and location distribution of humidity-regulating materials for preventing condensation are obtained.

2. The method for preventing condensation in a sealed cavity with a vent valve according to claim 1, characterized in that, The process involves setting multiple measurement points within a sealed cavity to obtain the moisture diffusion coefficient at each point, and fitting a function to obtain the influence of temperature, humidity, and humidity-regulating materials on the moisture diffusion coefficient. 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; The moisture diffusion coefficient is calculated 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. Obtain the moisture diffusion coefficient under different temperatures and humidity conditions, and fit the influence function of temperature and humidity on the moisture diffusion coefficient; Obtain the moisture diffusion coefficient when setting humidity-regulating materials at different locations, and fit the influence function of humidity-regulating materials on the moisture diffusion coefficient.

3. The method for preventing condensation in a sealed cavity with a vent valve according to claim 1, characterized in that, The acquisition of internal temperature, humidity and external environmental information of the sealed cavity includes: acquiring the internal air temperature, relative humidity and surface temperature of the sealed cavity, and acquiring the external air temperature and humidity of the sealed cavity.

4. The method for preventing condensation in a sealed cavity with a vent valve according to claim 3, characterized in that, The conditions for obtaining condensation based on the internal temperature and humidity of the sealed cavity and external environmental information combined with the moisture diffusion coefficient include: calculating the dew point temperature at the current location of the sealed cavity based on the air temperature and relative humidity inside the sealed cavity; if the surface temperature at the current location is lower than the dew point temperature, there is a risk of condensation.

5. The method for preventing condensation in a sealed cavity with a vent valve according to claim 4, characterized in that, The dew point temperature is calculated as follows:

6. Among them, T dew denoted as dew point temperature, T as internal air temperature, RH as internal air humidity, and a and b as constants determined based on the Magnus-Tetens formula and its improved formula.

7. The method for preventing condensation in a sealed cavity with a vent valve according to claim 1, characterized in that, The simulation model, which is constructed based on the moisture diffusion coefficient and various influence functions to simulate the distribution of moisture inside a sealed cavity, includes: the influence of temperature and humidity changes on moisture diffusion; the influence of the moisture absorption / release process of the humidity-regulating material on moisture diffusion; and the amount and distribution location of the humidity-regulating material as input parameters of the simulation model to predict the diffusion behavior of moisture inside the equipment.

8. The method for preventing condensation in a sealed cavity with a vent valve according to claim 6, characterized in that, The simulation model for simulating the distribution of moisture inside a sealed cavity, based on the moisture diffusion coefficient and various influence functions, includes further optimizing the simulation model by replacing the moisture-regulating materials with different moisture absorption and release rates.

9. The method for preventing condensation in a sealed cavity with a vent valve according to any one of claims 1 to 7, characterized in that, The process of obtaining the amount and location distribution of humidity-regulating materials for preventing condensation based on moisture distribution, condensation conditions, and simulation models includes: using an optimization algorithm based on moisture distribution and external environmental information of the sealed cavity to calculate the amount and location distribution of humidity-regulating materials, so as to make the humidity inside the equipment uniform and prevent condensation.

10. The method for preventing condensation in a sealed cavity with a vent valve according to claim 8, characterized in that, The calculation of the amount and location distribution of humidity-regulating materials includes: optimizing to obtain the minimum amount and location of humidity-regulating materials.

11. The method for preventing condensation in a sealed cavity with a vent valve according to claim 8, characterized in that, The calculation of the amount and location distribution of humidity-regulating materials includes: solving by minimizing a comprehensive objective function: min(α1∙humidity fluctuation + α2∙condensation risk + α3∙humidity-regulating material usage), where α1, α2, and α3 are weighting coefficients.

Images