A method and system for detecting the waterproof performance of a communication device

Abstract

The present application provides a method and system for detecting the waterproof performance of a communication device, including: during the atomization process, using high-speed imaging technology to monitor in real time the movement trajectory and aggregation state of droplets on the surface of the device, and for the image information obtained by high-speed imaging, identifying the droplet aggregation area through an image processing algorithm; according to the identified aggregation area and its aggregation degree, dynamically adjusting the angle and position of the atomizing nozzle to avoid the situation where water vapor aggregation affects the waterproof performance detection result; arranging a plurality of temperature and humidity sensors in the atomization cavity to collect the temperature and humidity data of the atomization environment in real time and analyze the influence of these data on the atomization effect; through a machine learning algorithm, establishing a correlation model between the temperature and humidity data and the droplet evaporation rate, and this model takes into account both the computational fluid dynamics simulation results and the image monitoring results.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular, to a method and system for detecting the waterproof performance of a communication device. Background Art

[0002] When a communication device is subjected to a waterproofness test, a special spraying device is required to atomize it to simulate the water vapor conditions that the device may encounter in the actual use environment. However, during the atomization process, how to ensure the atomization accuracy is a key technical problem. Insufficient atomization accuracy may lead to the following problems: First, the spray coverage is uneven, with some parts being more affected by the water vapor while others are rarely exposed to it, which will cause deviations in the test results. Second, the particle size distribution of the atomized droplets is unreasonable. Overly large droplets may form a water film on the device surface, resulting in local waterlogging, while overly small droplets are difficult to effectively simulate the actual environment. Third, the spraying angle of the atomizing nozzle is inappropriate, which may cause the water vapor to accumulate on the device surface and form water droplets, unable to truly reflect the waterproof performance of the device in the atomized environment. Therefore, how to reasonably control the various parameters of the atomizing device according to the characteristics of the device to be tested and improve the atomization accuracy is a technical problem that urgently needs to be solved in the waterproofness test of communication devices. Only by solving the atomization accuracy problem can the authenticity and reliability of the waterproofness test results be ensured, providing strong data support for the waterproof design of communication devices. Summary of the Invention

[0003] The present invention provides a method for detecting the waterproof performance of a communication device, mainly including:

[0004] Obtain the three-dimensional model of the communication device to be tested. The three-dimensional model includes the structural characteristics and material property information of the device. According to the three-dimensional model, use the computational fluid dynamics simulation method to simulate the distribution of water vapor on the surface of the device under different atomization parameters; based on the computational fluid dynamics simulation results, determine the optimal combination of atomization parameters. The atomization parameters include the atomization liquid flow rate, the atomization nozzle diameter, and the injection pressure to ensure that the atomization coverage uniformity and the droplet size distribution meet the preset standards, where the droplet size distribution standard includes the average particle size and the particle size distribution range; during the atomization process, use high-speed imaging technology to monitor the movement trajectory and aggregation state of droplets on the surface of the device in real time. For the image information obtained by high-speed imaging, identify the droplet aggregation area through image processing algorithms; according to the identified aggregation area and its aggregation degree, dynamically adjust the angle and position of the atomization nozzle to avoid the situation that water vapor aggregation affects the waterproof performance test results; arrange multiple temperature and humidity sensors in the atomization cavity to collect the temperature and humidity data of the atomization environment in real time and analyze the influence of these data on the atomization effect; through machine learning algorithms, establish a correlation model between the temperature and humidity data and the droplet evaporation rate. This model takes into account both the computational fluid dynamics simulation results and the image monitoring results; use the established correlation model to dynamically predict the evaporation process of droplets and adjust the atomization parameters accordingly to ensure that the adhesion time of droplets on the surface of the device matches the predefined standard usage environment, where the standard usage environment is determined according to the expected usage scenarios and conditions of the device.

[0005] The present invention provides a waterproof performance detection system for a communication device, mainly including: a three-dimensional model acquisition module for obtaining the structural characteristics and material property information of the device; a computational fluid dynamics simulation module for simulating the water vapor distribution and determining the optimal atomization parameters; a high-speed imaging monitoring module for monitoring the movement trajectory and aggregation state of droplets in real time; a temperature and humidity data acquisition module for collecting the temperature and humidity data of the atomization environment; a machine learning modeling module for establishing a correlation model between the temperature and humidity and the droplet evaporation rate; a dynamic adjustment control module for predicting the droplet evaporation and adjusting the atomization parameters. The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0006] The present invention discloses a method for detecting the waterproof performance of a communication device. The method obtains the three-dimensional model of the device, optimizes the atomization parameters by using computational fluid dynamics simulation to achieve uniform coverage. During the detection process, high-speed cameras are used to monitor the movement of droplets, and image processing is used to identify the aggregation areas, and the position of the nozzle is dynamically adjusted. At the same time, temperature and humidity sensors are arranged to collect environmental data, and a machine learning model is established to predict the droplet evaporation process. The present invention combines simulation optimization, real-time monitoring and intelligent prediction to achieve precise control of the waterproof performance detection, ensures that the detection environment matches the actual use scenario, and improves the reliability and representativeness of the detection results. This intelligent detection method not only improves the detection efficiency, but also provides strong support for the evaluation and improvement of the waterproof performance of communication devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a flowchart of a method for detecting the waterproof performance of a communication device according to the present invention.

[0008] Figure 2 It is a schematic diagram of a method and system for detecting the waterproof performance of a communication device according to the present invention.

[0009] Figure 3 It is another schematic diagram of a method and system for detecting the waterproof performance of a communication device according to the present invention.

[0010] Figure 4 It is a schematic structural diagram of a system for detecting the waterproof performance of a communication device according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] Next, the technical solutions in the embodiments of the present invention will be clearly and detailedly described with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention.

[0012] Such as Figures 1-3 , a method for detecting the waterproof performance of a communication device in this embodiment may specifically include:

[0013] Step S101, obtain the three-dimensional model of the communication device to be tested, and the three-dimensional model includes the structural characteristics and material property information of the device; according to the three-dimensional model, adopt the computational fluid dynamics simulation method to simulate the distribution of water vapor on the surface of the device under different atomization parameters.

[0014] Obtain the 3D CAD model data containing the structural characteristics and material property information of the communication device to be tested, and convert it into the STL format; import the converted 3D model data in STL format into the ANSYS Fluent computational fluid dynamics simulation software, and set the boundary conditions and solution parameters, including defining the fluid properties, specifying the inlet and outlet, and meshing; for different environmental temperature and humidity atomization parameters, conduct multiple groups of numerical simulation calculations in the ANSYS Fluent software to obtain the water vapor concentration distribution results under different working conditions; extract the water vapor concentration numerical data on the surface mesh elements of the device in each group of simulation results and save it as a CSV format file; use the Matplotlib library of Python to perform visualization processing on the water vapor concentration data, generate the water vapor distribution cloud map on the device surface, and use a suitable color scheme to represent the concentration level; adopt the K-means clustering algorithm to conduct clustering analysis on the pixel points in the water vapor distribution cloud map, taking the water vapor concentration value as the feature, and divide the pixel points into different aggregation regions; set the water vapor concentration threshold, if in the water vapor aggregation region after clustering, more than 20% of the pixel points have concentration values exceeding this threshold, then it is determined that there is a risk of condensation on the device under this atomization parameter, and it is necessary to further optimize the device structure, including adding drain holes or selecting materials with better surface hydrophobicity.

[0015] Exemplarily, first, use SolidWorks software to open the 3D CAD model of the communication device and export the STL format file. Import the STL file into ANSYS Fluent software, define air as an ideal gas, set the inlet temperature to 25°C, the humidity to 60%, the outlet pressure to 0 Pa, adopt tetrahedral mesh division, and the number of meshes is about 5 million. For 9 combinations of working conditions with environmental temperatures of 15°C, 25°C, 35°C and relative humidities of 40%, 60%, 80%, perform numerical simulations respectively. The solver selects the pressure-based implicit algorithm, and the turbulence model adopts the k-epsilon model. Each working condition iterates 1000 steps to reach convergence. Use the post-processing module to extract the water vapor concentration values of the mesh elements on the device surface and export them in CSV format. Use the Matplotlib library of Python to read the CSV file and draw the water vapor distribution cloud map on the device surface in the form of a 3D scatter plot. The color mapping adopts the viridis scheme, and the concentration range is 0-25 g / m³. Perform K-means clustering on the pixel points in the cloud map, set the number of clusters k = 3 and the maximum number of iterations max_iter = 300, and use the RGB values of the pixel points as feature vectors to obtain 3 aggregation regions. Statistically analyze the proportion of the number of pixel points with a water vapor concentration exceeding 20 g / m³ in each region. If it exceeds 20%, it is considered that there is a risk of condensation under this working condition. For example, under the working condition of temperature 25°C and humidity 80%, the proportion of high-concentration pixel points in the water vapor aggregation region reaches 35%. Therefore, it is necessary to add drainage holes with a diameter of 3 mm at the bottom of the device and spray a hydrophobic nano-coating on the surface of the shell to reduce the surface tension. After the optimized design, perform simulation verification until the risk of condensation is eliminated.

[0016] Step S102, based on the computational fluid dynamics simulation results, determine the optimal combination of atomization parameters. The atomization parameters include the atomization liquid flow rate, the atomization nozzle diameter, and the injection pressure to ensure that the atomization coverage uniformity and the droplet size distribution meet the preset standards, where the droplet size distribution standard includes the average particle size and the particle size distribution range.

[0017] Obtain the physical property parameters and process requirements of the atomization liquid to be optimized. The physical property parameters include viscosity, density, and surface tension, and the process requirements include the atomization liquid flow rate range and the requirements for the droplet size distribution. Based on the physical property parameters and process requirements, establish a computational fluid dynamics simulation model of the atomization process in ANSYS Fluent software, and reasonably set the boundary conditions and solution parameters. For the simulation model, set different combinations of atomization liquid flow rate, nozzle diameter, and injection pressure parameters, and conduct multiple groups of numerical simulation calculations. Extract the droplet size distribution data and spatial coverage distribution data from each group of simulation results, compare the droplet size distribution data with the preset particle size standard, and calculate the root mean square value of the particle size deviation of the parameter combination. If the root mean square of the deviation is less than the preset threshold, output the current parameter combination as the optimal solution for particle size optimization; otherwise, use the particle swarm optimization algorithm to iteratively optimize the parameter combination with the minimum root mean square of the deviation as the goal. On the basis of the optimal parameter combination for particle size, discretize the atomization coverage distribution data, calculate the variance within the coverage area, and use the reciprocal of the variance to characterize the coverage uniformity. If the uniformity index is greater than the preset threshold, determine the current parameter combination as the comprehensive optimal solution; otherwise, use the uniformity index as the optimization goal and further optimize the parameter combination using the genetic algorithm. According to the comprehensive optimal solution, determine the structural parameters of the atomization device, complete the three-dimensional structural design of the atomization device in CAD software, and form the design scheme of the atomization system.

[0018] Exemplarily, a three-dimensional simulation model of the atomized liquid flow is established in ANSYS Fluent software. The atomized liquid is set as water, with a viscosity of 0.001 Pa·s, a density of 998 kg / m³, and a surface tension of 0.072 N / m. The nozzle diameter ranges from 2 - 0 mm, the atomized liquid flow rate ranges from 10 - 100 mL / min, and the injection pressure ranges from 5 - 0 MPa. The pressure-velocity coupling algorithm is adopted, the k-epsilon model is selected for the turbulence model, the PRESTO is used for the pressure discretization format, and the second-order upwind scheme is used for the momentum and turbulence discretization formats. In the Design of Experiments module, a central composite design is adopted to generate 15 groups of parameter combinations. The droplet size distribution data in each group of simulation results is extracted and compared with the target particle size range of 20 μm ± 5 μm, and the root mean square of the deviation is calculated. The particle swarm optimization algorithm is used, with the number of particles set to 20, the learning factors c1 = c2 = 0, the inertia weight w = 8, and the maximum number of iterations set to 50. The optimal parameter combination is output when the root mean square of the deviation is less than 0. On this basis, the atomization coverage uniformity is evaluated. The droplet number density in the cross-section direction is discretized into a 100×100 grid, and the standard deviation of the grid droplet number density is calculated. The uniformity index is the reciprocal of the standard deviation. The genetic algorithm is further used for optimization, with the population number set to 20, the crossover probability set to 8, the mutation probability set to 1, and the maximum number of generations set to 30. When the uniformity index is greater than 1000, the comprehensive optimal parameter combination is determined as: nozzle diameter 6 mm, atomized liquid flow rate 60 mL / min, injection pressure 2 MPa. A three-dimensional structural model of the atomization device is established in SolidWorks, with the nozzle aspect ratio taken as 20 and the divergence angle taken as 60°, forming the final atomization system design scheme.

[0019] Step S103, during the atomization process, the high-speed imaging technology is used to monitor the movement trajectory and aggregation state of the droplets on the device surface in real time; for the image information obtained by the high-speed imaging, the aggregation area of the droplets is identified through the image processing algorithm.

[0020] During the atomization process, a high-speed imaging device is used to monitor the movement trajectory and aggregation state of droplets on the device surface in real time, and image information on droplet movement and aggregation is obtained; the obtained droplet image information is preprocessed, including denoising the image using median filtering and enhancing the image using the histogram equalization algorithm to improve the image quality and prepare for subsequent recognition; according to the characteristics of the droplet movement trajectory and aggregation state, a droplet recognition model is constructed, and the support vector machine algorithm is used to train the model to obtain a trained droplet recognition model; the trained droplet recognition model is used to recognize the preprocessed droplet images to determine whether there is a droplet aggregation area in the image, and if so, the position and range of the aggregation area are determined; according to the recognized droplet aggregation area, the area, droplet number, and distribution density parameters of the aggregation area are calculated to obtain a quantitative description of the droplet aggregation state; the quantitative description of the droplet aggregation state is compared with a preset threshold, and if it exceeds the threshold, it is determined as abnormal aggregation, and an alarm signal is triggered to notify relevant personnel for processing; at the same time, according to the deviation degree of the droplet aggregation state parameters from the preset threshold, the working parameters of the atomization device are dynamically adjusted, including changing the atomization intensity by adjusting the vibration frequency of the atomizer and controlling the droplet size by adjusting the droplet injection pressure, so that the droplet aggregation state is maintained within the normal range, abnormal aggregation is avoided, and the stability and uniformity of the atomization effect are ensured.

[0021] Exemplarily, during the atomization process monitoring, a high-speed camera is used to collect the movement trajectory and aggregation state of droplets in real time. The camera frame rate is set to 1000 fps, and the resolution is 1280×1024. The collected images are successively subjected to median filtering denoising and histogram equalization enhancement processing. The filtering window size is 5×5, and the equalization level number is 256. A feature vector is constructed based on the roundness, area, and movement speed of the droplet contour, and a support vector machine model with a radial basis kernel function is used for training. The sample size of the training set is 500, and the model parameters c = 10 and g = 0.1 are determined through cross-validation. The accuracy of the final model on the test set reaches 98%. The trained model is used to recognize the images frame by frame, and the aggregation area is determined through contour analysis. Parameters such as the number of droplets, average particle size, and distribution density in the area are calculated. When the number of droplets exceeds 50 per cm² or the average particle size is less than 20 μm, it is determined as abnormal aggregation, and an alarm signal is sent to the control system. At the same time, according to the deviation degree of the aggregation parameters from the preset threshold, the fuzzy PID control algorithm is used to dynamically adjust the vibration frequency and injection pressure of the atomizer. The frequency adjustment range is 50 - 200 Hz, and the pressure adjustment range is 1 - 5 MPa. Through real-time monitoring and feedback control, abnormal droplet aggregation is effectively avoided, and the uniformity of the atomized liquid distribution is maintained above 95%, fully ensuring the stability of the atomization effect.

[0022] Step S104: Dynamically adjust the angle and position of the atomizing nozzle according to the identified aggregation area and its aggregation degree to avoid the situation that water vapor aggregation affects the waterproof performance detection result.

[0023] Obtain the water vapor aggregation area image, segment the image using the region-growing-based image segmentation algorithm to obtain the water vapor aggregation area. According to the segmented water vapor aggregation area, estimate the water vapor density by calculating the average gray value of the pixel points in each area to obtain the water vapor aggregation degree. Obtain the angle and position parameters of the current atomizing nozzle. Based on the water vapor aggregation area, aggregation degree, and the current atomizing nozzle angle and position, establish an adjustment model for the atomizing nozzle angle and position based on multiple linear regression. Train the model using the least squares method to obtain the optimized model parameters. Input the water vapor aggregation area, aggregation degree, and the current atomizing nozzle angle and position into the trained adjustment model, and calculate the optimized atomizing nozzle angle and position parameters through the model. Compare the current atomizing nozzle angle and position with the optimized angle and position, and calculate their Euclidean distance. If the distance is greater than the preset threshold, control the atomizing nozzle motor to adjust the angle and position until the adjustment is in place. After completing the dynamic adjustment of the atomizing nozzle angle and position, obtain the product surface image, detect the water vapor aggregation situation on the product surface using the Canny edge detection algorithm, and determine whether there is water vapor aggregation by analyzing the connectivity of the edges. If there is no water vapor aggregation, it is considered that the adjustment effect meets the requirements, and continue with the subsequent waterproof performance detection; if there is water vapor aggregation, return to step 1 to re-obtain the water vapor aggregation area image and perform the next round of dynamic adjustment of the atomizing nozzle angle and position until the water vapor aggregation is eliminated.

[0024] Exemplarily, during the atomization process, images of the water vapor aggregation region are collected by a high-speed camera. The camera frame rate is set to 500 fps, and the resolution is 1024×768. The collected images are preprocessed. Mean filtering is used to remove noise, and the filtering window size is 3×3. Then, the region growing algorithm is used to segment the images. The growing criterion is that the gray difference is less than 10, and the number of growth times is 5 times to obtain the water vapor aggregation region. For the segmented water vapor aggregation region, the average gray value of the pixel points in each region is calculated. The gray value has a linear relationship with the water vapor density. The larger the gray value, the higher the water vapor density. Based on this, the degree of water vapor aggregation is obtained. The pitch angle and horizontal position parameters of the current atomizing nozzle are obtained through the RS-485 bus, denoted as α and d respectively. Based on the water vapor aggregation region, the aggregation degree S, as well as α and d, an atomizing nozzle angle and position adjustment model is established: Δα = k1S + k2α + b1, Δd = k3S + k4d + b2, where Δα and Δd are the angle and position adjustment amounts, and k1, k2, k3, k4 and b1, b2 are model parameters. The least squares method is used to train 100 groups of collected data to obtain the optimized model parameters. The currently collected water vapor aggregation region, aggregation degree, as well as α and d are input into the trained adjustment model to obtain the optimized angle α' and position d'. Calculate the Euclidean distance between the current angle position and the optimized angle position. If the distance is greater than 2° or 20 mm, the atomizing nozzle is controlled by a stepper motor for adjustment, and the adjustment accuracy is 1° and 1 mm until the optimized position is reached. After the dynamic adjustment is completed, edge detection is performed on the product surface image. The Canny algorithm is used, the standard deviation of the Gaussian filter is 4, the high threshold of the double-threshold algorithm is 200, and the low threshold is 100. Whether there is water vapor aggregation is judged through 8-neighborhood connectivity analysis. If not, it is considered that the adjustment meets the requirements. If so, return for readjustment. After 3 iterations of adjustment, the water vapor aggregation region is reduced by 85%, meeting the requirements of the waterproof performance test.

[0025] Step S105, arrange multiple temperature and humidity sensors in the atomization cavity, collect the temperature and humidity data of the atomization environment in real time, and analyze the influence of these data on the atomization effect.

[0026] The temperature and humidity data of the atomization environment are obtained by collecting the temperature and humidity data through the temperature and humidity sensors arranged in the atomization chamber; the temperature and humidity data are preprocessed, and the moving average filtering algorithm is used to eliminate outliers and noise data; the ARIMA time series analysis model is used to model the preprocessed temperature and humidity data to obtain the time trend, periodicity and mutation point characteristics of the temperature and humidity data; the atomization effect evaluation index is obtained, and the atomization effect evaluation index includes the droplet size distribution and the atomization rate; the Pearson correlation coefficient is used to analyze the correlation between the temperature and humidity data and the atomization effect evaluation index, and a multivariate linear regression model of temperature and humidity and atomization effect is established; using A The RIMA time series analysis model makes short-term predictions on temperature and humidity. If the predicted value exceeds the preset threshold range, the alarm mechanism is triggered and the working parameters of the atomization equipment are adjusted according to the preset strategy. The temperature and humidity data under different seasons and weather conditions are obtained, and the temperature and humidity data are divided into different working conditions using the K-means clustering algorithm. For each working condition, a mapping table between temperature and humidity and atomization equipment parameters is established to optimize the control strategy of the atomization equipment. The support vector regression algorithm is used to establish a nonlinear prediction model of temperature and humidity data and atomization effect evaluation indicators. According to the prediction results of the nonlinear prediction model, early warning and dynamic optimization adjustment of the atomization effect are achieved.

[0027] Exemplarily, an SHT31 temperature and humidity sensor is arranged in the atomization cavity, with a sampling frequency of 1 Hz, and temperature and humidity data are collected in real time through the RS-485 bus. The collected temperature and humidity data are preprocessed using a 5-point moving average filtering algorithm with a filtering window size of 5, effectively removing random noise and pulse interference. The ARIMA(1,1,1) model is used to model the filtered temperature and humidity time series data, and the model order is determined through the AIC criterion to obtain the long-term trend, 24-hour periodicity, and mutation points of the temperature and humidity changes. The Malvern Spraytec laser particle size analyzer is used to measure the droplet size distribution, with a sampling time of 10 s, and the average value is taken after repeating 3 times. The atomization rate is measured by the weighing method, the atomization amount within 1 min is collected, and the average value is taken after repeating 3 times. The Pearson correlation coefficients between temperature, humidity, particle size D50, and atomization rate are calculated. Temperature is negatively correlated with D50 (r = -0.85), and humidity is positively correlated with the atomization rate (r = 0.92). Based on this, a multiple linear regression model of temperature, humidity, and atomization effect is established. The ARIMA model is used to perform a rolling prediction of temperature and humidity for the next 15 min. When the predicted temperature value exceeds 35°C or the predicted humidity value exceeds 85%, an alarm is triggered, and the frequency of the atomizer is adjusted from 100 kHz to 80 kHz. Temperature and humidity data for 30 consecutive days under typical working conditions in spring, summer, autumn, and winter are obtained, and are clustered into 4 categories using the K-means algorithm. According to the clustering results, a mapping table of temperature, humidity, atomizer frequency, and nozzle diameter is established to achieve adaptive optimization of the working conditions. The support vector regression algorithm is used to establish a non-linear prediction model of temperature, humidity, particle size distribution, and atomization rate. The Gaussian kernel function is selected, the penalty factor C = 10, the training set sample size is 500, and the predicted mean square error is less than 5%. Based on the prediction results, when the predicted particle size D50 is less than 40 μm or the atomization rate is lower than 1 mL / min, an early warning is given 5 min in advance, and the atomizer frequency and nozzle diameter are dynamically adjusted to continuously optimize the atomization effect.

[0028] Step S106, through a machine learning algorithm, establish an association model between temperature and humidity data and the droplet evaporation rate, which simultaneously considers the computational fluid dynamics simulation results and the image monitoring results.

[0029] Obtain the temperature and humidity data and droplet image monitoring data during the droplet evaporation process, preprocess the data, extract key features, and construct a training dataset; use computational fluid dynamics simulation software to simulate the droplet evaporation process to obtain the theoretical prediction values of the droplet evaporation rate under different temperature and humidity conditions; compare the simulation prediction values with the actual droplet evaporation rate obtained from image monitoring, calculate the error, and use it as the training target of the machine learning model; adopt the support vector machine regression algorithm, with temperature and humidity features as the input and evaporation rate as the output, to construct a droplet evaporation rate prediction model; use the grid search and cross-validation methods to optimize the kernel function type and kernel function parameters of the support vector machine regression algorithm; obtain the real-time data of other influencing factors such as the surface tension and components of the droplet, and introduce them into the droplet evaporation rate prediction model; adopt the feature selection and feature combination methods to screen out the most significant feature subset affecting the evaporation rate and reduce the input dimension of the droplet evaporation rate prediction model; convert the optimized droplet evaporation rate prediction model into a deployable program package and design the input and output interfaces of the prediction model; integrate the prediction model into the online droplet evaporation monitoring system, collect the sensor data of temperature, humidity and other influencing factors in real time, call the prediction model to predict the evaporation rate, and display the prediction results on the human-machine interface in real time; conduct multiple droplet evaporation experiments, collect evaporation data under different environmental conditions and droplet properties, and use them for continuous training and optimization of the droplet evaporation rate prediction model.

[0030] Exemplarily, a high-precision temperature and humidity sensor SHT31 and a high-speed camera are used to collect temperature and humidity data and droplet images in real time during the droplet evaporation process, with a sampling frequency of 10 Hz. Wavelet transform is used to denoise the temperature and humidity time series data. The Daubechies4 wavelet basis function is selected to decompose the signal into 5 layers, and the signal is reconstructed after removing the high-frequency noise. The droplet images are preprocessed. The Canny operator is used to extract the droplet edges, and the droplet contour is fitted by the Hough transform to extract morphological features such as the equivalent diameter and roundness of the droplet. The ANSYS Fluent software is used to numerically simulate the droplet evaporation process. The VOF method is adopted to track the gas-liquid interface, considering the Marangoni effect and thermocapillary flow. The grid size is 10 μm, and the time step is 1 ms. Theoretical values of the droplet evaporation rate under different temperature (20 - 50 °C) and humidity (40% - 90%) conditions are obtained through simulation. The simulation values are compared with the actual evaporation rates obtained by image analysis, and the relative error is calculated as the model training target. The support vector machine regression (SVR) algorithm is used to construct a prediction model. The Gaussian radial basis kernel function is selected, with the penalty factor C = 10 and the kernel function parameter γ = 1. The training sample size is 500 groups. The grid search and 5-fold cross-validation are used to optimize the SVR model parameters. The optimal parameter combination is C = 15 and γ = 0.8, and the average validation error is 2%. Physical property parameters such as the surface tension, viscosity, and density of the droplet are introduced as additional input features, and the recursive feature elimination method is used to screen out three key features: temperature, humidity, and surface tension, reducing the model input dimension. The optimized SVR model is converted into a C++ dynamic link library, the model input and output interfaces are designed, and it is integrated into the online droplet evaporation monitoring system. The system collects temperature, humidity, and droplet image data every 1 s, calls the prediction model to calculate the droplet evaporation rate, and displays it in real-time curves on the human-machine interface. A one-year droplet evaporation experiment is carried out, covering different seasons and day-night working conditions, and more than 2000 sets of evaporation data are collected for continuous optimization of the prediction model. The prediction accuracy of the model can reach over 95%, providing a reliable basis for the optimal control of the droplet evaporation process.

[0031] Step S107, using the established correlation model, dynamically predict the evaporation process of the droplet, and accordingly adjust the atomization parameters to ensure that the attachment time of the droplet on the device surface matches the predefined standard usage environment, where the standard usage environment is determined according to the expected usage scenario and conditions of the device.

[0032] Obtain the expected usage scenario and condition information of the device, and determine the temperature, humidity, and air pressure parameters under the standard usage environment according to the user manual or the environmental parameter range provided by the customer;

[0033] By installing temperature and humidity sensors and barometric pressure sensors on the surface of the device, the temperature, humidity and barometric pressure data of the environment where the device is located are collected in real time, and the collected data are uploaded to the cloud server through the wireless communication module;

[0034] In the cloud server, based on the historical data of the droplet evaporation process under different environmental conditions collected in advance, a regression model between the droplet evaporation time and the environmental temperature, humidity and barometric pressure is established using the multiple linear regression method; using the regression model, the standard environmental parameters and the environmental parameters collected in real time are respectively input, and the predicted evaporation times of the droplets in the two cases are calculated; it is judged whether the difference in the droplet evaporation times in the two environments exceeds the preset threshold. If it exceeds, it is determined that there is an obvious deviation in the droplet evaporation process under the current environment, and a deviation value representing the degree of droplet evaporation deviation is obtained; the deviation value is input into the BP neural network model trained in advance using the droplet evaporation data under different environmental conditions, and the adjustment values of the atomization frequency, droplet diameter and injection speed parameters are calculated through the BP neural network model, and the adjustment values are sent to the device end through the wireless communication module; after the device end control module receives the atomization parameter adjustment value, it controls the piezoelectric ceramic vibration frequency, nozzle diameter and liquid pump pressure of the atomization device according to the adjustment value; after the adjustment is completed, a droplet sample is collected on the surface of the device, and the time from attachment to complete evaporation is measured, and the average evaporation time of the sample is calculated; the average evaporation time is compared with the droplet evaporation time under the standard environment, and the relative error between the two is calculated; if the relative error is within the preset acceptable range, it is determined that the adjusted atomization parameters meet the evaporation requirements of the standard environment; otherwise, the atomization parameter adjustment process is triggered again for the next round of iterative optimization until the relative error meets the requirements or the maximum number of iterations is reached.

[0035] Exemplarily, by referring to the device user manual, the standard ambient temperature is determined to be 25°C, the relative humidity is 50%, and the atmospheric pressure is 10325 kPa. The DHT11 temperature and humidity sensor and the BMP280 pressure sensor are used and connected to the NodeMCU development board through the I2C interface. The ambient data is collected once every 1 s and uploaded to the Alibaba Cloud IoT platform through the MQTT protocol. In the cloud Python program, based on the existing 100 sets of droplet evaporation time data measured under different temperature (20~40°C), humidity (40%~80%), and pressure (90~110 kPa) conditions, the LinearRegression class of the Scikit-learn library is used to construct a multiple linear regression model to fit the relationship between the droplet evaporation time t and temperature T, humidity H, and pressure P: t = aT + bH + cP + d, where the coefficients are a = -5, b = -2, c = 1, and d = 20. Substituting the standard ambient parameters into the model, the expected evaporation time is 15 s; substituting the real-time collected ambient parameters, the current predicted evaporation time is 12 s, and the difference between the two is 3 s, exceeding the preset threshold of 2 s. It is determined that the evaporation process has a deviation, and the deviation value is 20%. This deviation value is input into a three-layer BP neural network pre-trained based on 500 sets of evaporation data. The hidden layer uses the Sigmoid activation function, the output layer uses the linear activation function, the learning rate is 0.1, and the number of iterations is 1000. It is calculated that the atomization frequency needs to be increased by 5 kHz, the droplet diameter needs to be reduced by 10 μm, and the injection speed needs to be increased by 2 m / s. After the control module receives the adjustment value, the PWM frequency of the piezoelectric ceramic is increased from 120 kHz to 125 kHz, the nozzle with an inner diameter reduced from 5 mm to 4 mm is replaced, and the voltage of the liquid pump motor is increased from 5 V to 6 V. After the adjustment, 5 10-μL droplet samples are dropped on the device surface, and the high-speed camera records the evaporation process. The Hough circle transformation algorithm of the OpenCV library is used to track the change of the droplet contour. The calculated average evaporation time of the sample is 18 s, and the relative error from the standard value is 3%, which is less than the acceptable range of 5%. It is determined that the adjusted atomization parameters meet the standard ambient requirements, and the optimization process ends.

[0036] On the other hand, as Figure 4 shown, this embodiment also provides a waterproof performance detection system for a communication device, specifically including:

[0037] A three-dimensional model acquisition module for acquiring device structure characteristics and material property information; a computational fluid dynamics simulation module for simulating water vapor distribution and determining optimal atomization parameters; a high-speed camera monitoring module for real-time monitoring of droplet movement trajectories and aggregation states; a temperature and humidity data acquisition module for acquiring temperature and humidity data of the atomization environment; a machine learning modeling module for establishing a correlation model between temperature and humidity and droplet evaporation rate; a dynamic adjustment control module for predicting droplet evaporation and adjusting atomization parameters.

[0038] Exemplarily, the three-dimensional model acquisition module includes three-dimensional design software such as SolidWorks, UG, Creo, etc.; the computational fluid dynamics simulation module includes fluid simulation software such as ANSYS Fluent, PowerFLOW, etc.; the high-speed camera monitoring module is a high-speed camera with a frame rate of not less than 1000 fps; the temperature and humidity data acquisition module is a sensor array composed of multiple SHT31 temperature and humidity sensors; the machine learning modeling module includes machine learning libraries such as scikit-learn, Keras, etc.; the dynamic adjustment control module includes a cloud server and a piezoelectric atomization device, including hardware facilities such as piezoelectric ceramics, nozzles, and liquid pumps.

[0039] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of the present application. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the present application.

Claims

1. A method for detecting the waterproof performance of communication equipment, characterized in that: The method comprises: Acquire a three-dimensional model of the communication device to be tested, the three-dimensional model including structural characteristics and material property information of the device, and simulate the distribution of water vapor on the surface of the device under different atomization parameters using a computational fluid dynamics simulation method based on the three-dimensional model; Based on the computational fluid dynamics simulation results, determine the optimal atomization parameter combination, wherein the atomization parameters include atomization liquid flow rate, atomization nozzle diameter and injection pressure, so as to ensure that the atomization coverage uniformity and droplet size distribution meet the preset standards, wherein the droplet size distribution standards include average particle size and particle size distribution range; During the atomization process, high-speed camera technology is used to monitor the movement trajectory and aggregation state of the droplets on the surface of the device in real time, and the aggregation area of ​​the droplets is identified through image processing algorithms based on the image information obtained by the high-speed camera; which also includes: During the atomization process, high-speed camera equipment is used to monitor the movement trajectory and aggregation state of droplets on the surface of the equipment in real time to obtain image information of droplet movement and aggregation; Preprocess the acquired droplet image information, including using median filtering to remove noise from the image and using histogram equalization algorithm to enhance the image, so as to improve the image quality and prepare for subsequent recognition; According to the characteristics of droplet motion trajectory and aggregation state, a droplet recognition model is constructed, and the model is trained using a support vector machine algorithm to obtain a trained droplet recognition model; The pre-processed droplet image is identified using the trained droplet recognition model to determine whether there is a droplet aggregation area in the image. If so, the location and range of the aggregation area are determined. According to the identified droplet aggregation area, the area of ​​the aggregation area, the number of droplets and the distribution density parameters are calculated to obtain a quantitative description of the droplet aggregation state; Compare the quantitative description of the droplet aggregation state with the preset threshold. If the threshold is exceeded, it is judged as abnormal aggregation, triggering an alarm signal and notifying relevant personnel to handle it; At the same time, according to the degree of deviation between the droplet aggregation state parameters and the preset threshold, the working parameters of the atomization equipment are dynamically adjusted, including changing the atomization intensity by adjusting the vibration frequency of the atomizer, and controlling the droplet particle size by adjusting the droplet injection pressure, so that the droplet aggregation state is maintained within the normal range, avoiding the occurrence of abnormal aggregation, and ensuring the stability and uniformity of the atomization effect; According to the identified gathering area and its degree of gathering, the angle and position of the atomizing nozzle are dynamically adjusted to avoid the situation where water vapor gathering affects the waterproof performance test results; Arrange multiple temperature and humidity sensors in the atomization chamber to collect temperature and humidity data of the atomization environment in real time, and analyze the impact of these data on the atomization effect; Through machine learning algorithms, a correlation model between temperature and humidity data and droplet evaporation rate is established, which takes into account both computational fluid dynamics simulation results and image monitoring results; The established correlation model is used to dynamically predict the evaporation process of droplets, and the atomization parameters are adjusted accordingly to ensure that the attachment time of droplets on the surface of the device matches a predefined standard usage environment, wherein the standard usage environment is determined according to the expected usage scenarios and conditions of the device.

2. The method according to claim 1, characterized in that The method comprises: obtaining a three-dimensional model of the communication device to be tested, wherein the three-dimensional model includes structural characteristics and material property information of the device; and simulating the distribution of water vapor on the surface of the device under different atomization parameters by using a computational fluid dynamics simulation method according to the three-dimensional model, including: Obtain 3D CAD model data containing structural characteristics and material property information of the communication equipment to be tested, and convert it into STL format; Import the converted STL format 3D model data into ANSYS Fluent computational fluid dynamics simulation software, set boundary conditions and solution parameters, including defining fluid properties, specifying inlets and outlets, and dividing the grid; According to different ambient temperature and humidity atomization parameters, multiple sets of numerical simulation calculations were performed in ANSYS Fluent software to obtain the water vapor concentration distribution results under different working conditions; Extract the numerical data of water vapor concentration on the grid cells on the equipment surface in each group of simulation results and save them as CSV format files; Use Python's Matplotlib library to visualize water vapor concentration data, generate a cloud map of water vapor distribution on the device surface, and use a suitable color scheme to indicate high and low concentrations; The K-means clustering algorithm is used to cluster the pixels in the water vapor distribution cloud map, and the pixels are divided into different clustering areas based on the water vapor concentration value. Set a water vapor concentration threshold. If more than 20% of the pixel concentration values ​​in the clustered water vapor aggregation area exceed the threshold, it is determined that the device is at risk of condensation under the atomization parameters, and the device structure needs to be further optimized, including adding drainage holes or selecting materials with better surface hydrophobicity.

3. The method according to claim 1, characterized in that Based on the computational fluid dynamics simulation results, the optimal atomization parameter combination is determined, and the atomization parameters include atomization liquid flow rate, atomization nozzle diameter and injection pressure to ensure that the atomization coverage uniformity and droplet particle size distribution meet the preset standards, wherein the droplet particle size distribution standard includes the average particle size and the particle size distribution range, including: Obtaining physical property parameters and process requirements of the atomized liquid to be optimized, wherein the physical property parameters include viscosity, density and surface tension, and the process requirements include atomized liquid flow range and droplet size distribution requirements; Based on the physical properties and process requirements, a computational fluid dynamics simulation model of the atomization process is established in ANSYS Fluent software, and boundary conditions and solution parameters are reasonably set; For the simulation model, different combinations of atomizing liquid flow rate, nozzle diameter and injection pressure parameters are set to carry out multiple sets of numerical simulation calculations; Extract droplet size distribution data and spatial coverage distribution data from each set of simulation results, compare the droplet size distribution data with the preset particle size standard, and calculate the root mean square value of the particle size deviation of the parameter combination; If the root mean square of the deviation is less than a preset threshold, the current parameter combination is output as the optimal solution for particle size optimization; Otherwise, the particle swarm optimization algorithm is used to iteratively optimize the parameter combination with the minimum root mean square deviation as the goal; Based on the optimal combination of particle size parameters, the atomization coverage distribution data is discretized, the variance in the coverage area is calculated, and the inverse of the variance is used to characterize the coverage uniformity; If the uniformity index is greater than a preset threshold, the current parameter combination is determined to be a comprehensive optimal solution; Otherwise, the uniformity index is taken as the optimization target, and the genetic algorithm is used to further optimize the parameter combination; According to the comprehensive optimal solution, the structural parameters of the atomization device are determined, and the three-dimensional structural design of the atomization device is completed in the CAD software to form a design scheme of the atomization system.

4. The method according to claim 1, characterized in that: The method of dynamically adjusting the angle and position of the atomizing nozzle according to the identified aggregation area and its aggregation degree to avoid the situation where water vapor aggregation affects the waterproof performance test result includes: S501, obtaining an image of a water vapor gathering area, and segmenting the image using an image segmentation algorithm based on region growing to obtain a water vapor gathering area; S502, estimating the water vapor density by calculating the average gray value of the pixels in each area according to the water vapor accumulation area obtained by segmentation, and obtaining the degree of water vapor accumulation; S503, obtaining the angle and position parameters of the current atomizing nozzle; S504, establishing an atomizing nozzle angle and position adjustment model based on multiple linear regression according to the water vapor gathering area, the degree of gathering and the current angle position of the atomizing nozzle; S505, training the model by the least square method to obtain optimized model parameters; S506, inputting the water vapor gathering area, the degree of gathering and the current angle position of the atomizing nozzle into the trained adjustment model, and obtaining the optimized angle and position parameters of the atomizing nozzle through model calculation; S507, comparing the current angle position of the atomizing nozzle with the optimized angle position, and calculating the Euclidean distance; if the distance is greater than a preset threshold, controlling the atomizing nozzle motor to adjust the angle and position until the angle and position are adjusted to the desired position; S508. After the dynamic adjustment of the angle position of the atomizing nozzle is completed, the product surface image is obtained, and the Canny edge detection algorithm is used to detect the water vapor accumulation on the product surface. By analyzing the connectivity of the edge, it is determined whether there is water vapor accumulation; if there is no water vapor accumulation, it is considered that the adjustment effect meets the requirements, and the subsequent waterproof performance test is continued; S509: If there is water vapor accumulation, return to step S501, reacquire the water vapor accumulation area image, and perform the next round of dynamic adjustment of the angle position of the atomizing nozzle until the water vapor accumulation is eliminated.

5. The method according to claim 1, characterized in that Arranging a plurality of temperature and humidity sensors in the atomization chamber to collect temperature and humidity data of the atomization environment in real time and analyzing the influence of these data on the atomization effect includes: Acquiring temperature and humidity data of the atomization environment, wherein the temperature and humidity data are collected by a temperature and humidity sensor arranged in the atomization cavity; Preprocessing the temperature and humidity data, and using a moving average filtering algorithm to remove outliers and noise data; The ARIMA time series analysis model is used to model the preprocessed temperature and humidity data to obtain the time trend, periodicity and mutation point characteristics of the temperature and humidity data; Obtaining an atomization effect evaluation index, wherein the atomization effect evaluation index includes droplet size distribution and atomization rate; The Pearson correlation coefficient was used to analyze the correlation between the temperature and humidity data and the atomization effect evaluation index, and a multivariate linear regression model of temperature and humidity and atomization effect was established; The ARIMA time series analysis model is used to make short-term predictions of temperature and humidity. If the predicted value exceeds the preset threshold range, an alarm mechanism is triggered, and the working parameters of the atomization equipment are adjusted according to the preset strategy; Acquire temperature and humidity data under different seasons and weather conditions, and use K-means clustering algorithm to divide the temperature and humidity data into different working conditions; For each of the working conditions, a mapping table of temperature, humidity and atomization equipment parameters is established to optimize the control strategy of the atomization equipment; A support vector regression algorithm is used to establish a nonlinear prediction model of the temperature and humidity data and the atomization effect evaluation index; According to the prediction results of the nonlinear prediction model, early warning and dynamic optimization adjustment of the atomization effect are achieved.

6. The method according to claim 1, characterized in that The correlation model between temperature and humidity data and droplet evaporation rate is established through a machine learning algorithm, and the model takes into account both computational fluid dynamics simulation results and image monitoring results, including: Acquire temperature and humidity data and droplet image monitoring data during droplet evaporation, preprocess the data, extract key features, and construct a training data set; The droplet evaporation process was simulated using computational fluid dynamics simulation software to obtain theoretical predictions of droplet evaporation rates under different temperature and humidity conditions; The theoretical prediction of the droplet evaporation rate is compared with the actual droplet evaporation rate obtained by image monitoring, and the error is calculated as the training target of the machine learning model; The support vector machine regression algorithm is used to construct a droplet evaporation rate prediction model with temperature and humidity characteristics as input and evaporation rate as output. Optimizing the kernel function type and kernel function parameters of the support vector machine regression algorithm using grid search and cross validation methods; Acquire real-time data of droplet surface tension and other influencing factors of droplet components, and introduce them into the droplet evaporation rate prediction model; Using feature selection and feature combination methods, the feature subset that has the most significant impact on the evaporation rate is screened out, thereby reducing the input dimension of the droplet evaporation rate prediction model; Convert the optimized droplet evaporation rate prediction model into a deployable program package, and design the input and output interfaces of the prediction model; The prediction model is integrated into an online droplet evaporation monitoring system, temperature and humidity sensor data are collected in real time, the prediction model is called to predict the evaporation rate, and the prediction result is displayed in real time on a human-computer interface; Multiple droplet evaporation experiments were carried out to collect evaporation data under different environmental conditions and droplet properties for continuous training and optimization of the droplet evaporation rate prediction model.

7. The method according to claim 1, characterized in that The established correlation model is used to dynamically predict the evaporation process of the droplets, and the atomization parameters are adjusted accordingly to ensure that the attachment time of the droplets on the surface of the device matches the pre-defined standard use environment, wherein the standard use environment is determined according to the expected use scenario and conditions of the device, including: Obtain the expected usage scenarios and condition information of the equipment, and determine the temperature, humidity and air pressure parameters in the standard usage environment according to the instruction manual or the environmental parameter range provided by the customer; By installing temperature and humidity sensors and air pressure sensors on the surface of the device, the temperature, humidity and air pressure data of the environment in which the device is located are collected in real time, and the collected data is uploaded to the cloud server through the wireless communication module; In the cloud server, based on the historical data of the droplet evaporation process under different environmental conditions collected in advance, a regression model between the droplet evaporation time and the environmental temperature, humidity and air pressure is established using a multivariate linear regression method; Using the regression model, inputting standard environmental parameters and real-time collected environmental parameters respectively, and calculating the predicted evaporation time of the droplets in the two cases; Determine whether the difference in the evaporation time of the droplets under the two environments exceeds a preset threshold value. If so, determine that the evaporation process of the droplets under the current environment has a significant deviation, and obtain a deviation value indicating the degree of deviation of the evaporation of the droplets; The deviation value is input into a BP neural network model pre-trained using droplet evaporation data under different environmental conditions, and the adjustment values ​​of atomization frequency, droplet diameter and injection speed parameters are calculated by the BP neural network model, and the adjustment values ​​are sent to the device end through a wireless communication module; After receiving the atomization parameter adjustment value, the device-side control module controls the piezoelectric ceramic vibration frequency, nozzle diameter and liquid pump pressure of the atomization device according to the adjustment value; After the adjustment is completed, collect droplet samples on the device surface, measure the time from attachment to complete evaporation, and calculate the average evaporation time of the samples; Comparing the average evaporation time with the droplet evaporation time under a standard environment, and calculating the relative error between the two; If the relative error is within a preset acceptable range, it is determined that the adjusted atomization parameters meet the evaporation requirements of the standard environment; Otherwise, the atomization parameter adjustment process is triggered again to perform the next round of iterative optimization until the relative error meets the requirement or reaches the maximum number of iterations.

8. A communication equipment waterproof performance detection system using any one of the detection methods in claims 1-7, characterized in that: The system comprises: 3D model acquisition module, used to obtain equipment structural characteristics and material property information; Computational fluid dynamics simulation module, used to simulate water vapor distribution and determine the optimal atomization parameters; High-speed camera monitoring module, used to monitor the droplet movement trajectory and aggregation state in real time; Temperature and humidity data acquisition module, used to collect temperature and humidity data of atomization environment; Machine learning modeling module, used to establish a correlation model between temperature and humidity and droplet evaporation rate; Dynamic adjustment control module to predict droplet evaporation and adjust atomization parameters.

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