A real-time monitoring and data analysis system based on a vacuum potting device

Abstract

The present invention provides a real-time monitoring and data analysis system based on a vacuum encapsulation device, which relates to the technical field of data processing. The method includes: obtaining a device operating condition data set, calculating the change rates of temperature data and pressure data at adjacent time points, and calculating the moisture evaporation rate based on them to obtain a change trend data set; comparing the temperature change rates and pressure change rates at different position points, calculating a drying uniformity parameter, performing cumulative analysis on the moisture evaporation rate, calculating a moisture removal degree parameter; calculating local temperature gradients and local pressure gradients, and dividing the vacuum encapsulation device into regions based on them; extracting the temperature, pressure, drying uniformity parameter and moisture removal degree parameter in different regions, and adjusting the heating time, vacuum maintenance time and temperature setting value of the corresponding regions in the vacuum encapsulation device. The present invention can perform real-time monitoring and data analysis on the vacuum encapsulation device and regulate the drying process.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a real-time monitoring and data analysis system based on a vacuum potting device. Background Art

[0002] Vacuum potting devices are widely used to remove air bubbles inside materials, improve the sealing performance and long-term stability of products. Existing vacuum potting processes usually include multiple stages such as pretreatment, heating, vacuum pumping, potting, and curing. Among them, the drying process is a key step to ensure the final quality of the product. In traditional processes, the drying process mainly relies on set temperature, vacuum degree, and time parameters for control, and the moisture content or solvent residue inside the product is detected by regular sampling to determine whether the drying is sufficient. However, this control method based on fixed process parameters is difficult to adapt to changes in different materials and environmental conditions, resulting in unstable drying effects, and there may be situations of insufficient drying or over-drying.

[0003] Common vacuum drying methods are through infrared heating or hot plate heating for heat conduction, and at the same time, the vacuum environment is used to accelerate the volatilization of moisture or solvents. However, the existing monitoring methods mainly rely on temperature sensors and pressure sensors to collect environmental parameters, and cannot directly monitor the moisture evaporation situation inside the product. Therefore, when problems such as local moisture residue or uneven heat distribution occur during the drying process, the existing monitoring system is difficult to identify in a timely manner, and usually relies on final product inspection to discover quality problems, which may lead to the scrapping of some products, thus affecting production efficiency and product consistency. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time monitoring and data analysis system based on a vacuum potting device, aiming to solve the problems mentioned in the background art.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A real-time monitoring and data analysis system based on a vacuum potting device, the system includes:

[0007] A calculation module, configured to calculate the change rates of temperature data and pressure data at adjacent time points according to the device working condition data set, and calculate the moisture evaporation rate based on them to obtain a change trend data set;

[0008] An analysis module, configured to compare the temperature change rates and pressure change rates at different position points according to the change trend data set, and calculate the drying uniformity parameter; perform cumulative analysis on the moisture evaporation rate, calculate the moisture removal degree parameter, and obtain a key parameter data set;

[0009] The area module is used to calculate the local temperature gradient and local pressure gradient according to the key parameter data set, and divide the vacuum encapsulation equipment according to them to obtain a dry area data set, which includes a normal area, a moisture residue area, and an over-dried area;

[0010] The feature module is used to extract the temperature, pressure, dry uniformity parameter, and moisture removal degree parameter in different areas according to the dry area data set to obtain a dry anomaly data set;

[0011] The regulation module is used to adjust the heating time, vacuum maintenance time, and temperature setting value of the corresponding area in the vacuum encapsulation equipment according to the dry anomaly data to ensure sufficient drying inside the vacuum encapsulation equipment.

[0012] Furthermore, the calculation module includes:

[0013] The rate of change calculation unit is used to calculate the rate of change of temperature data and pressure data at adjacent time points according to the equipment working condition data set to obtain temperature rate of change data and pressure rate of change data;

[0014] The trend analysis unit is used to determine the fluctuation of the temperature rate of change data and pressure rate of change data according to the time parameter to form a trend parameter data set;

[0015] The moisture evaporation calculation unit is used to calculate the moisture evaporation rate according to the trend parameter data set and merge it with the trend parameter data set to obtain a change trend data set.

[0016] Furthermore, the moisture evaporation calculation unit includes:

[0017] The initial rate calculation unit is used to calculate the initial moisture evaporation rate according to the temperature and pressure change conditions in the initial drying stage. The formula for the initial moisture evaporation rate is:

[0018] ,

[0019] Where, is the initial moisture evaporation rate, is the mass transfer coefficient on the surface of the vacuum encapsulation equipment material, is the initial pressure, is the initial temperature, is the gas constant in the ideal gas law, is the latent heat of vaporization of water at normal temperature and pressure, is the temperature on the surface of the vacuum encapsulation equipment material, is the initial saturation vapor pressure;

[0020] A water evaporation rate calculation unit, which is used to calculate the water evaporation rate at any moment according to the initial water evaporation rate. The calculation formula for the water evaporation rate is as follows:

[0021] ,

[0022] where, is the water evaporation rate at the th moment, is the index of the moment, is the temperature at the th moment, is the pressure at the th moment, is the pressure change rate at the th moment, is the temperature change rate at the th moment, is the saturated vapor pressure at the th moment, is a coefficient.

[0023] Furthermore, the analysis module includes:

[0024] A uniformity comparison unit, which is used to extract the temperature change rate and pressure change rate at different position points in the vacuum encapsulation equipment according to the change trend data set, and compare them according to the position points to obtain temperature change comparison data and pressure change comparison data;

[0025] A uniformity parameter calculation unit, which is used to calculate the mean and variance of the temperature change rate and pressure change rate according to the temperature change comparison data and pressure change comparison data, and perform normalization processing on them to obtain temperature uniformity parameters and pressure uniformity parameters;

[0026] A uniformity determination unit, which is used to screen the temperature uniformity parameters and pressure uniformity parameters according to a preset uniformity threshold group. When both the temperature uniformity parameters and pressure uniformity parameters are less than the preset uniformity threshold group, the position point is marked as a dry uniformity point. When any one of the temperature uniformity parameters and pressure uniformity parameters is not less than the preset uniformity threshold group, the position point is marked as a dry non-uniformity point to obtain dry uniformity parameters;

[0027] A water evaporation amount calculation unit, which is used to calculate the water evaporation amount at different position points in different time periods according to the water evaporation rate in the change trend data set, and accumulate the water evaporation amounts of each position point to obtain a water removal degree parameter.

[0028] Furthermore, the calculation formula for the water removal degree parameter is as follows:

[0029] ;

[0030] Among them, is the moisture removal degree parameter of the vacuum encapsulation equipment at the th moment, is the index of time, is the initial moment, is the total area of the drying area of the vacuum encapsulation equipment, is the total moisture content in the entire drying area at the initial moment, is the cumulative moisture removal amount at a certain position point, is the total moisture removal amount of all position points.

[0031] Furthermore, the area module includes:

[0032] A gradient calculation unit, configured to calculate the local temperature gradient and local pressure gradient of different position points according to the temperature change rate and pressure change rate, and obtain a gradient data set;

[0033] A grid processing unit, configured to divide the drying space in the vacuum encapsulation equipment into multiple grid cells according to the position points, and each grid cell contains the temperature gradient and pressure gradient data of the corresponding position points;

[0034] A threshold determination unit, configured to screen the local temperature gradient and local pressure gradient of each grid cell according to a preset gradient threshold group, and when the temperature gradient or pressure gradient exceeds the preset threshold, mark the grid cell as an abnormal candidate area to obtain preliminary area classification data.

[0035] Furthermore, the area module further includes:

[0036] An adjacent area merging unit, configured to check the gradient data set of adjacent grid cells according to the preliminary area classification data, and merge the areas with similar temperature gradients and pressure gradients to obtain optimized area data;

[0037] A moisture removal analysis unit, configured to judge whether the drying states of different areas are uniform according to the moisture removal degree parameter of the optimized area data. When the moisture removal degree parameter of a certain area is lower than the preset moisture removal threshold, adjust the boundary range of the area and reclassify it as a moisture residue area;

[0038] A moisture residue determination unit, configured to identify the areas where the moisture removal degree parameter is lower than the preset moisture removal threshold and the temperature change rate is lower than the preset temperature change rate according to the result of the moisture removal analysis unit, and mark them as moisture residue areas;

[0039] An over-drying determination unit, configured to identify the areas where the temperature change rate is higher than the preset temperature change rate and the drying uniformity parameter is lower than the preset drying uniformity according to the temperature gradient and drying uniformity parameter, and mark them as over-drying areas.

[0040] Further, the feature module includes:

[0041] An abnormal data screening unit, configured to screen and extract the temperature, pressure, drying uniformity parameter, and moisture removal degree parameter of the moisture remaining area and the over-dried area according to the drying area data set, eliminate the data of the normal area, and obtain a feature parameter data set;

[0042] An abnormal feature calculation unit, configured to calculate the temperature fluctuation range, pressure fluctuation range, and moisture evaporation rate change amplitude of each abnormal area according to the feature parameter data set, and obtain an abnormal feature data set;

[0043] An abnormal area feature unit, configured to subdivide the moisture remaining area and the over-dried area according to the abnormal feature data set and a preset feature threshold group to obtain a subdivided area set, and extract abnormal area features according to it to obtain a drying abnormality data set.

[0044] Further, the regulation module includes:

[0045] An abnormal area parameter unit, configured to extract the temperature, pressure, drying uniformity parameter, and moisture removal degree parameter corresponding to the abnormal area according to the drying abnormality data set, and obtain an abnormal area parameter set;

[0046] A regulation parameter calculation unit, configured to calculate the heating time adjustment value, vacuum maintenance time adjustment value, and temperature setting value adjustment value of each abnormal area according to the abnormal area parameter set, and obtain a regulation parameter data set;

[0047] A regulation unit, configured to regulate the vacuum encapsulation device according to the regulation parameter data set to ensure the overall uniformity of the drying process;

[0048] A real-time adjustment unit, configured to perform real-time adjustment on the regulation process according to the real-time monitoring data during the drying process to ensure the uniformity of drying in different areas during the drying process.

[0049] Further, the regulation unit includes:

[0050] A moisture remaining area adjustment unit, configured to increase the temperature setting value of this area and extend the vacuum maintenance time according to the regulation parameters of the moisture remaining area to ensure the moisture removal effect;

[0051] An over-dried area adjustment unit, configured to reduce the heating time of this area and adjust the temperature setting value according to the regulation parameters of the over-dried area to avoid local over-drying;

[0052] A global optimization unit, configured to adjust the drying strategy of the entire vacuum encapsulation device according to the regulation parameter data set of each abnormal area to ensure the overall uniformity of the drying process.

[0053] The above solution of the present invention has at least the following beneficial effects:

[0054] The present invention can extract the temperature change rate and pressure change rate at different positions, calculate the uniformity parameter, and effectively identify the uniformity of different regions during the drying process. The drying uniformity parameter is mainly calculated from the standard deviation of temperature and pressure data. This parameter can intuitively reflect the drying state at different positions in the equipment. When the temperature change rate in a certain area is much higher than that in other areas, it may mean that this area is overheated, while the temperature fluctuation in some areas is small, which may mean that the moisture has not been completely evaporated. By calculating the uniformity parameter and comparing it with the preset threshold, the system can automatically identify the uniformly dried area and the non-uniformly dried area, and optimize the drying strategy during the subsequent regulation process, improve the drying consistency of the entire equipment, reduce product quality differences, and increase the production yield.

[0055] By calculating the local temperature gradient and pressure gradient, the present invention can dynamically divide the drying area and generate a drying area dataset based on the data. This dataset includes normal areas, moisture residue areas, and over-dried areas. The core advantage of the area division lies in its ability to accurately classify according to the thermodynamic characteristics of spatial position points. When the temperature change rate in a certain area is small but the moisture evaporation rate is low, the system will identify this area as a moisture residue area. If the temperature change rate in a certain area is too high but the moisture removal amount is large, it may belong to an over-dried area. Based on this area division technology, the system can achieve precise monitoring and refined control of different areas, avoid local drying non-uniformity problems caused by global parameter settings, and further improve the stability of drying quality.

[0056] The present invention can calculate the moisture evaporation rate through the temperature change rate and pressure change rate and generate a change trend dataset. This trend dataset can reflect the dynamic process of moisture evaporation inside the material, so as to accurately evaluate the moisture removal situation. Compared with the prior art, the core advantage of this method is that it can dynamically adjust the drying time to ensure that the moisture evaporation rate is always maintained within a reasonable range. When the temperature rises rapidly, the moisture evaporation speed is faster, and in the later stage of drying, the evaporation rate slows down. The system can adjust the temperature or vacuum degree to avoid over-drying or under-drying problems. This method can detect abnormal moisture evaporation in real time. The system can immediately identify areas where moisture may remain and adjust parameters for compensation. This adaptive adjustment based on trend analysis makes the present system more intelligent and precise than traditional methods, improving the control accuracy of moisture removal.

[0057] The present invention dynamically adjusts the heating time, vacuum maintenance time, and temperature set value of the vacuum encapsulation equipment through drying abnormal data to ensure the optimal control of the drying process. The core advantage of this control module is that it adopts a closed-loop control logic. First, it calculates the current drying state through a data acquisition and calculation module, then identifies abnormal areas through an analysis module and a region module. Finally, according to the feedback mechanism of the control module, it dynamically adjusts the heating or vacuum parameters and optimizes the drying strategy in real time. This closed-loop control strategy can automatically adjust the drying parameters according to different batches, different materials, and different environmental conditions, avoid the instability of manually set parameters, and improve the automation degree of the production process.

[0058] By analyzing the drying area data set, the present invention can extract the characteristic data of the water residue area and the over-dried area, and generate a drying abnormal data set for accurate diagnosis of drying abnormal conditions. The technical advantage of this function is that it can extract the temperature fluctuation range, pressure fluctuation range, and the change range of the water evaporation rate in the abnormal area, and calculate abnormal characteristic parameters based on these data. These parameters can be used to judge whether there is unevenness in the drying process. When the temperature fluctuation range of a certain area is greater than the threshold, it indicates that there may be excessive local heating in this area. When the water evaporation rate of a certain area is lower than the preset standard, it may mean insufficient water removal. Combining multiple abnormal characteristic parameters can further subdivide different abnormal types and improve the abnormal diagnosis ability of the system. Brief Description of the Drawings

[0059] Figure 1 is a flow block diagram of a real-time monitoring and data analysis system based on a vacuum encapsulation device provided by an embodiment of the present invention. Detailed Embodiments

[0060] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0061] As Figure 1 shown, an embodiment of the present invention provides a real-time monitoring and data analysis system based on a vacuum encapsulation device, and the system includes:

[0062] A calculation module, configured to calculate the change rate of temperature data and pressure data at adjacent time points according to the equipment working condition data set, and calculate the water evaporation rate according to the change rate to obtain a change trend data set;

[0063] An analysis module, configured to compare the temperature change rate and pressure change rate at different position points according to the change trend data set, calculate the drying uniformity parameter; perform cumulative analysis on the water evaporation rate, calculate the water removal degree parameter, and obtain the key parameter data set;

[0064] A region module, configured to calculate the local temperature gradient and local pressure gradient according to the key parameter data set, and perform regional division on the vacuum encapsulation device based on them, to obtain the drying region data set, where the drying region data set includes a normal region, a water residue region, and an over-dried region;

[0065] A feature module, configured to extract the temperature, pressure, drying uniformity parameter, and water removal degree parameter in different regions according to the drying region data set, and obtain the drying anomaly data set;

[0066] A regulation module, configured to adjust the heating time, vacuum maintenance time, and temperature setting value of the corresponding region in the vacuum encapsulation device according to the drying anomaly data, to ensure sufficient drying inside the vacuum encapsulation device.

[0067] In an embodiment of the present invention, a calculation module is configured to calculate the change rates of temperature data and pressure data at adjacent time points according to the device operating condition data set, and calculate the water evaporation rate based on them, to obtain the change trend data set. By calculating the water evaporation rate, this module can judge the water removal situation inside the material and avoid problems of insufficient drying or over-drying; an analysis module is configured to compare the temperature change rate and pressure change rate at different position points according to the change trend data set, calculate the drying uniformity parameter; perform cumulative analysis on the water evaporation rate, calculate the water removal degree parameter, and obtain the key parameter data set, which can identify whether the temperature and pressure distributions in different regions are uniform; a region module is configured to calculate the local temperature gradient and local pressure gradient according to the key parameter data set, and perform regional division on the vacuum encapsulation device based on them, to obtain the drying region data set, where the drying region data set includes a normal region, a water residue region, and an over-dried region, to achieve partition monitoring and accurately locate the drying anomaly region; a feature module is configured to extract the temperature, pressure, drying uniformity parameter, and water removal degree parameter in different regions according to the drying region data set, and obtain the drying anomaly data set, automatically eliminate the data of the normal region, and focus on analyzing the drying anomaly region to improve the accuracy of anomaly detection; a regulation module is configured to adjust the heating time, vacuum maintenance time, and temperature setting value of the corresponding region in the vacuum encapsulation device according to the drying anomaly data, to ensure sufficient drying inside the vacuum encapsulation device, and perform real-time regulation based on data feedback to improve the stability of the drying process.

[0068] Among them, the device operating condition data set includes:

[0069] Obtain the temperature data, pressure data, and time data of the vacuum encapsulation equipment during the drying process to obtain the original data set, and preprocess it to remove redundant data and unify the unit dimensions to obtain the equipment operating condition data set.

[0070] Specifically, at key positions of the vacuum encapsulation equipment, such as the heating area and the inside of the vacuum chamber, temperature sensors and pressure sensors are arranged.

[0071] Adopt a high-frequency sampling mechanism, such as sampling 10 times per second, to obtain the temperature and pressure data during the drying process to ensure the continuity and integrity of the data.

[0072] Record the time stamp and synchronously store the temperature and pressure data with the time for subsequent analysis.

[0073] Denoising process: Eliminate signal noise through mean filtering or median filtering methods to improve data stability.

[0074] Unit standardization: Unify the data of different sensors to the same dimension, such as converting temperature to Kelvin and pressure to Pascal to ensure data consistency.

[0075] Abnormal data elimination: Use the sliding window algorithm to identify and eliminate mutation data, such as the temperature or pressure value at a certain time point is abnormally high or low, to avoid misleading subsequent analysis.

[0076] The preprocessed data is stored in a local database or sent to the central computing system through wireless communication.

[0077] Adopt a real-time streaming processing architecture to ensure the real-time nature of the data and support subsequent calculation modules for dynamic analysis.

[0078] In a preferred embodiment of the present invention, the calculation module includes:

[0079] A rate of change calculation unit for calculating the rate of change of temperature data and pressure data at adjacent time points according to the equipment operating condition data set to obtain temperature rate of change data and pressure rate of change data.

[0080] A trend analysis unit for determining the fluctuation conditions of the temperature rate of change data and pressure rate of change data according to the time parameter to form a trend parameter data set.

[0081] A moisture evaporation calculation unit for calculating the moisture evaporation rate according to the trend parameter data set and merging it with the trend parameter data set to obtain a change trend data set.

[0082] In an embodiment of the present invention, a change rate calculation unit is configured to calculate the change rates of temperature data and pressure data at adjacent time points according to a device operating condition data set, obtain temperature change rate data and pressure change rate data, and quantify the dynamic changes of temperature and pressure during the drying process, providing basic data for subsequent analysis; a trend analysis unit is configured to determine the fluctuation conditions of the temperature change rate data and the pressure change rate data according to time parameters, form a trend parameter data set, reducing the random error during the sensor sampling process and improving data stability; a moisture evaporation calculation unit is configured to calculate the moisture evaporation rate according to the trend parameter data set and merge it with the trend parameter data set to obtain a change trend data set. By calculating the moisture evaporation rate, the moisture removal situation inside the material can be directly evaluated.

[0083] Among them, the trend analysis unit is configured to determine the fluctuation conditions of the temperature change rate data and the pressure change rate data according to time parameters, form a trend parameter data set, and specifically includes:

[0084] Construct time series data according to the temperature change rate data set and the pressure change rate data set;

[0085] Calculate the fluctuation trends of temperature and pressure, and calculate the trend values using the moving average method;

[0086] Calculate the growth rates of the temperature change rate and the pressure change rate to determine whether the current drying stage has entered a steady state;

[0087] Generate a trend parameter data set, recording the temperature trend, pressure trend, and growth rate data at different time points.

[0088] In a preferred embodiment of the present invention, the moisture evaporation calculation unit includes:

[0089] An initial rate calculation unit is configured to calculate the initial moisture evaporation rate according to the temperature and pressure change conditions in the initial drying stage. The formula for the initial moisture evaporation rate is:

[0090] ,

[0091] Among them, is the initial moisture evaporation rate, is the mass transfer coefficient on the surface of the material of the vacuum encapsulation device, is the initial pressure, is the initial temperature, is the gas constant in the ideal gas law, is the latent heat of vaporization of water at normal temperature and pressure, is the temperature on the surface of the material of the vacuum encapsulation device, is the initial saturation vapor pressure;

[0092] A water evaporation rate calculation unit, which is used to calculate the water evaporation rate at any moment according to the initial water evaporation rate. The calculation formula of the water evaporation rate is:

[0093] ,

[0094] where is the water evaporation rate at the moment, is the index of the moment, is the temperature at the moment, is the pressure at the moment, is the pressure change rate at the moment, is the temperature change rate at the moment, is the saturation vapor pressure at the moment, is a coefficient.

[0095] In the embodiment of the present invention, an initial rate calculation unit is used to calculate the initial water evaporation rate according to the temperature and pressure change conditions in the initial stage of drying. This formula can calculate the initial rate of water evaporation based on the actual environmental parameters of the initial temperature and pressure, rather than relying on empirical values or fixed set values, enabling the system to adaptively adjust the drying strategy according to the material characteristics and environmental conditions. By calculating the mass transfer effect on the material surface, this formula can dynamically adapt to different vacuum degrees and temperature change situations, improving the accuracy of water removal in the initial stage; a water evaporation rate calculation unit is used to calculate the water evaporation rate at any moment according to the initial water evaporation rate. This formula can calculate the water evaporation rate by combining the real-time change trends of temperature and pressure, enabling the system to accurately predict the dynamic process of water evaporation, rather than simply relying on single-point measurement values. By combining the temperature change rate and the pressure change rate, this formula can reflect transient changes and can be used to identify abnormal situations.

[0096] In a preferred embodiment of the present invention, the analysis module includes:

[0097] A uniformity comparison unit, which is used to extract the temperature change rate and the pressure change rate at different position points in the vacuum potting equipment according to the change trend data set, and compare them according to the position points to obtain temperature change comparison data and pressure change comparison data;

[0098] A uniformity parameter calculation unit, which is used to calculate the mean and variance of the temperature change rate and the pressure change rate according to the temperature change comparison data and the pressure change comparison data, and perform a normalization process on them to obtain a temperature uniformity parameter and a pressure uniformity parameter;

[0099] The uniformity determination unit is used to screen the temperature uniformity parameter and the pressure uniformity parameter according to a preset uniformity threshold group. When both the temperature uniformity parameter and the pressure uniformity parameter are less than the preset uniformity threshold group, the position point is marked as a dry uniformity point. When any one of the temperature uniformity parameter and the pressure uniformity parameter is not less than the preset uniformity threshold group, the position point is marked as a dry non-uniform point, and the dry uniformity parameter is obtained.

[0100] The water evaporation amount calculation unit is used to calculate the water evaporation amount of different position points at different time periods according to the water evaporation rate in the change trend data set, and accumulate the water evaporation amounts of each position point to obtain the water removal degree parameter.

[0101] In an embodiment of the present invention, the uniformity comparison unit is used to extract the temperature change rate and the pressure change rate of different position points in the vacuum encapsulation device according to the change trend data set, and compare them according to the position points to obtain the temperature change comparison data and the pressure change comparison data. By comparing the temperature and pressure change conditions of different position points, local temperature gradient anomalies can be identified in advance, providing accurate data support for uniformity analysis; the uniformity parameter calculation unit is used to calculate the mean and variance of the temperature change rate and the pressure change rate according to the temperature change comparison data and the pressure change comparison data, and perform standardization processing on them to obtain the temperature uniformity parameter and the pressure uniformity parameter, determining the average characteristics of the overall drying state, improving data interpretability, being able to quantify the uniformity degree between different position points, and ensuring the precise adjustment of the drying process.

[0102] The uniformity determination unit is used to screen the temperature uniformity parameter and the pressure uniformity parameter according to a preset uniformity threshold group. When both the temperature uniformity parameter and the pressure uniformity parameter are less than the preset uniformity threshold group, the position point is marked as a dry uniformity point. When any one of the temperature uniformity parameter and the pressure uniformity parameter is not less than the preset uniformity threshold group, the position point is marked as a dry non-uniform point, and the dry uniformity parameter is obtained. By setting the threshold, the subjectivity of the dry uniformity analysis is avoided, and the determination accuracy is improved. Then, through data screening, the dry non-uniform area can be quickly locked, providing a target area for subsequent precise regulation; the water evaporation amount calculation unit is used to calculate the water evaporation amount of different position points at different time periods according to the water evaporation rate in the change trend data set, and accumulate the water evaporation amounts of each position point to obtain the water removal degree parameter. By calculating the single-point water evaporation, the local water removal efficiency can be identified, and then through global calculation, the water removal state of the entire system can be evaluated.

[0103] In a preferred embodiment of the present invention, the calculation formula of the water removal degree parameter is:

[0104] ;

[0105] Among them, is the moisture removal degree parameter of the vacuum encapsulation device at the th moment, is the index of time, is the initial moment, is the total area of the drying area of the vacuum encapsulation device, is the total moisture content in the entire drying area at the initial moment, is the cumulative moisture removal amount at a certain position point, is the total moisture removal amount of all position points.

[0106] In the embodiment of the present invention, through normalization calculation, the moisture removal degree parameter is always between 0 and 1, thereby providing an intuitive drying progress evaluation method, which can reflect the moisture removal situation at different time points in real time. Compared with the traditional method that relies on setting time and temperature, this calculation method ensures the dynamic visualization of the drying process, enables the system to adjust the drying parameters based on the actual moisture removal situation, and avoids the problems of insufficient drying or over-drying caused by fixed process parameters. Since the calculation of this formula is based on the accumulation of local moisture removal amounts, it can not only reflect the overall drying state, but also quantify the drying conditions in different regions, providing data support for subsequent intelligent regulation.

[0107] In a preferred embodiment of the present invention, the area module includes:

[0108] A gradient calculation unit for calculating the local temperature gradient and local pressure gradient of different position points according to the temperature change rate and pressure change rate to obtain a gradient data set;

[0109] A grid processing unit for dividing the drying space in the vacuum encapsulation device into multiple grid cells according to the position points, and each grid cell contains the temperature gradient and pressure gradient data of the corresponding position points;

[0110] A threshold determination unit for screening the local temperature gradient and local pressure gradient of each grid cell according to a preset gradient threshold group. When the temperature gradient or pressure gradient exceeds the preset threshold, the grid cell is marked as an abnormal candidate area to obtain preliminary area classification data.

[0111] In an embodiment of the present invention, a gradient calculation unit is configured to calculate the local temperature gradient and local pressure gradient at different position points according to the temperature change rate and pressure change rate, so as to obtain a gradient data set. By calculating the gradient, the temperature and pressure distribution during the drying process can be identified, avoiding the limitations of relying only on single-point measurements; a grid processing unit is configured to divide the drying space in the vacuum encapsulation device into multiple grid units according to the position points, and each grid unit contains the temperature gradient and pressure gradient data corresponding to the position points. Through grid processing, complex continuous data can be structured for subsequent analysis; a threshold determination unit is configured to screen the local temperature gradient and local pressure gradient of each grid unit according to a preset gradient threshold group. When the temperature gradient or pressure gradient exceeds the preset threshold, the grid unit is marked as an abnormal candidate area, and preliminary area classification data is obtained. By reasonably setting the threshold, misjudgment or missed judgment of abnormal areas can be avoided, and the accuracy of drying monitoring can be improved.

[0112] Among them, the grid processing unit is configured to divide the drying space in the vacuum encapsulation device into multiple grid units according to the position points, and specifically includes:

[0113] First, it is necessary to establish a reasonable grid division model based on the internal structural characteristics of the vacuum encapsulation device and the physical characteristics of the drying process. To ensure the scientificity and adaptability of the division, first obtain the size data inside the vacuum encapsulation device, including the length, width, and height of the cavity and the specific shape of the drying area. In some devices with special shapes (such as non-regular geometric shapes), three-dimensional modeling methods can be used for spatial data mapping to ensure that the grid units can reasonably cover the entire drying area. On this basis, according to the position distribution of the measurement points, determine the size of the grid, that is, the side length of a single grid unit. The size of the grid unit is a key parameter affecting data accuracy and calculation complexity, and can usually be adjusted according to the measurement density of the sensor, the gradient range of temperature and pressure changes, and the calculation performance requirements. For example, in areas where the drying environment changes violently, such as near heating elements or areas with large air flow, a finer grid division can be adopted to improve data resolution, while in areas where the temperature and pressure change relatively evenly, the size of the grid unit can be appropriately increased to reduce the calculation amount and improve the system response speed.

[0114] After completing the basic parameter settings for mesh generation, it is necessary to map the mesh elements to the actual spatial positions. The core of this step is to assign the data of each measurement point inside the vacuum potting device to a specific mesh element. In practical applications, the data of measurement points are usually discrete, so the nearest neighbor matching or interpolation algorithm can be used for data assignment processing. Specifically, the data of each measurement point determines the number of the mesh element it belongs to according to its spatial coordinates. If there are multiple measurement points in a certain mesh element, the weighted average method can be used to calculate the temperature gradient and pressure gradient of this mesh, so that it can represent the state of the entire mesh area.

[0115] After completing the matching of the measurement data and the mesh elements, it is necessary to establish a mesh data storage structure for subsequent drying area analysis and anomaly detection. Usually, the mesh data can be stored as a three-dimensional array, and each element represents a mesh element, including information such as temperature gradient, pressure gradient, number of measurement points, and mesh center coordinates. The storage format can adopt a sparse matrix to save storage space. Especially in the case of an irregularly shaped drying cavity or irregular data distribution, sparse storage can significantly reduce data redundancy and improve calculation efficiency. At the same time, the system can be updated in real time during the data storage stage, that is, as the drying process progresses, the temperature, pressure and other parameters of the mesh elements are continuously refreshed, so that the mesh data can reflect the dynamic changes of the drying state in real time.

[0116] After the mesh generation and data mapping are completed, it is also necessary to construct the spatial relationship of the mesh elements, that is, to determine the connection relationship between the mesh elements and their adjacent meshes. This process is crucial for subsequent area division and anomaly detection. For example, when judging whether a certain area belongs to the water residue area or the over-dried area, it is necessary to analyze the gradient changes of the adjacent meshes. If the temperature gradient or pressure gradient of a certain mesh is much higher than that of the adjacent meshes, it may mean the non-uniformity of the drying process. In addition, the topological relationship between mesh elements also affects the speed and computational complexity of data processing. For example, when using parallel computing for large-scale data processing, a reasonable mesh topology design can reduce the computational burden and improve the computational efficiency.

[0117] Generally speaking, the implementation of the meshing processing unit can convert the drying space into a structured mesh data model, making the change trends of physical parameters such as temperature and pressure more intuitive, and providing a basis for accurate area division. By reasonably setting the mesh size, optimizing the data interpolation method, constructing an efficient storage structure and spatial relationship network, the meshing processing unit can ensure that the subsequent drying state monitoring, abnormal area identification and intelligent control have higher accuracy and response speed, and ultimately improve the stability of the drying process of the vacuum potting device and the consistency of product quality.

[0118] In a preferred embodiment of the present invention, the area module further includes:

[0119] An adjacent region merging unit, configured to check the gradient data sets of adjacent grid cells according to the preliminary region classification data, and merge regions with similar temperature gradients and pressure gradients to obtain optimized region data;

[0120] A moisture removal analysis unit, configured to judge whether the drying states of different regions are uniform according to the moisture removal degree parameter of the optimized region data. When the moisture removal degree parameter of a certain region is lower than the preset moisture removal threshold, the boundary range of this region is adjusted and it is reclassified as a moisture remaining region;

[0121] A moisture remaining determination unit, configured to identify a region where the moisture removal degree parameter is lower than the preset moisture removal threshold and the temperature change rate is lower than the preset temperature change rate according to the result of the moisture removal analysis unit, and label it as a moisture remaining region;

[0122] An over-drying determination unit, configured to identify a region where the temperature change rate is higher than the preset temperature change rate and the drying uniformity parameter is lower than the preset drying uniformity according to the temperature gradient and the drying uniformity parameter, and label it as an over-drying region.

[0123] In an embodiment of the present invention, the adjacent region merging unit is configured to check the gradient data sets of adjacent grid cells according to the preliminary region classification data, and merge regions with similar temperature gradients and pressure gradients to obtain optimized region data. Through adjacent region merging, over-subdivision caused by measurement errors or local fluctuations is reduced, and the boundaries of abnormal regions are clearer and the classification is more reasonable; the moisture removal analysis unit is configured to judge whether the drying states of different regions are uniform according to the moisture removal degree parameter of the optimized region data. When the moisture removal degree parameter of a certain region is lower than the preset moisture removal threshold, the boundary range of this region is adjusted and it is reclassified as a moisture remaining region. By calculating the moisture removal degree, regions with insufficient moisture removal can be accurately identified; the moisture remaining determination unit is configured to identify a region where the moisture removal degree parameter is lower than the preset moisture removal threshold and the temperature change rate is lower than the preset temperature change rate according to the result of the moisture removal analysis unit, and label it as a moisture remaining region. By analyzing the cause of moisture remaining in combination with the temperature change rate, data support is provided for subsequent precise control, and the reliability of the drying process is improved; the over-drying determination unit is configured to identify a region where the temperature change rate is higher than the preset temperature change rate and the drying uniformity parameter is lower than the preset drying uniformity according to the temperature gradient and the drying uniformity parameter, and label it as an over-drying region. By combining the drying uniformity parameter, the identification of over-drying regions is more accurate.

[0124] Among them, the adjacent region merging unit is used to check the gradient data sets of adjacent grid cells according to the preliminary region classification data, and merge the regions with similar temperature gradients and pressure gradients to obtain optimized region data, specifically including:

[0125] First, according to the preliminary region classification data, this data set contains the temperature gradient, pressure gradient, moisture removal degree, and attribution category of each grid cell inside the device. In actual operation, each grid cell represents a finite space point inside the vacuum potting device. The system needs to screen the temperature and pressure data of all grid cells and construct a spatial adjacency relationship to identify adjacent grid cells that are related to each other. During the construction process, the system will traverse all the grids inside the device and establish six-direction adjacency relationships for each grid cell. For the grid cells at the boundary, if there is no adjacent cell in a certain direction, the data in that direction will be ignored, thereby ensuring the integrity of the adjacency matrix and the efficiency of the calculation.

[0126] After constructing the adjacency relationship, the system will calculate the similarity of the temperature gradient and pressure gradient for adjacent grid cells. Specifically, for two adjacent grid cells, calculate the difference in their temperature gradients and the difference in their pressure gradients respectively, and compare them with a preset threshold. When the gradient changes of the two grid cells are within the set threshold range, it indicates that their thermal distribution and pressure distribution are basically the same and belong to the same region, so they can be merged. This process can not only prevent misclassification caused by local data fluctuations but also ensure more accurate identification of abnormal regions. The system will adopt certain statistical methods, such as calculating the average temperature gradient and average pressure gradient of multiple grids and performing standardization processing based on the standard deviation, so as to ensure the numerical consistency of the merged regions and avoid mismerging caused by some abnormal data points.

[0127] After completing the judgment of adjacent region merging, the system will perform a connected region search to determine all grid cells that can be merged and generate new optimized region data. In the specific implementation process, a depth-first search algorithm can be used to traverse the entire grid structure, and all grid cells that meet the similarity criteria will be grouped into the same region. During the region merging, the system will also recalculate the overall temperature gradient, pressure gradient, and moisture removal degree parameters of the merged region to ensure that the merged region can accurately reflect the current drying state. The merged temperature gradient and pressure gradient are usually calculated using the weighted average method, so that the new region can maintain a reasonable data mean and thus reduce abnormal judgment errors caused by data mutations.

[0128] After the merging is completed, the system will classify the optimized regional data to form a new optimized regional dataset, which is then stored in the regional module. The optimized regional dataset mainly includes three key parts: First, the updated temperature gradient, pressure gradient, and moisture removal degree parameters to ensure data accuracy; Second, the optimized regional boundary information, that is, the spatial coordinate range of the drying area inside the device, enabling the subsequent regulation module to make precise adjustments based on the clear spatial position; Third, the new regional attribution category, that is, marking the area as a normal area, a moisture residue area, or an over-dried area to facilitate subsequent intelligent regulation. During the data storage process, the system will also dynamically mark the optimized areas to ensure that the subsequent analysis module can obtain the latest regional information in a timely manner and further optimize the drying strategy based on this data.

[0129] In a preferred embodiment of the present invention, the feature module includes:

[0130] An abnormal data screening unit for screening and extracting the temperature, pressure, drying uniformity parameters, and moisture removal degree parameters of the moisture residue area and the over-dried area according to the drying area dataset, removing the data of the normal area, and obtaining a feature parameter dataset;

[0131] An abnormal feature calculation unit for calculating the temperature fluctuation range, pressure fluctuation range, and moisture evaporation rate change amplitude of each abnormal area according to the feature parameter dataset, and obtaining an abnormal feature dataset;

[0132] An abnormal area feature unit for subdividing the moisture residue area and the over-dried area according to the abnormal feature dataset and a preset feature threshold group to obtain a subdivided area set, and extracting abnormal area features therefrom to obtain a drying abnormality dataset.

[0133] In an embodiment of the present invention, the abnormal data screening unit is used to screen and extract the temperature, pressure, drying uniformity parameters, and moisture removal degree parameters of the moisture residue area and the over-dried area according to the drying area dataset, remove the data of the normal area, and obtain a feature parameter dataset, avoiding the interference of data in the drying uniform area by accurately screening abnormal areas; the abnormal feature calculation unit is used to calculate the temperature fluctuation range, pressure fluctuation range, and moisture evaporation rate change amplitude of each abnormal area according to the feature parameter dataset, and obtain an abnormal feature dataset. By calculating the fluctuation range of temperature and pressure, the uneven heat distribution or uneven vacuum in the abnormal area can be accurately identified, providing key indicators for regulation; the abnormal area feature unit is used to subdivide the moisture residue area and the over-dried area according to the abnormal feature dataset and a preset feature threshold group to obtain a subdivided area set, and extract abnormal area features therefrom to obtain a drying abnormality dataset. Through area subdivision, the specific reasons for uneven drying can be identified more accurately.

[0134] Among them, the abnormal feature calculation unit is used to calculate the temperature fluctuation range, pressure fluctuation range, and moisture evaporation rate change amplitude of each abnormal area according to the feature parameter data set. The abnormal feature data set specifically includes:

[0135] First, sort the data set according to the time stamp so that all data forms a continuous time series, avoiding calculation deviations caused by data disorder. At the same time, in order to eliminate possible noise interference in the acquisition process, the system uses a moving average filter or a median filter method to smooth the data, so as to reduce the influence of short-term random fluctuations on the final calculation result.

[0136] Extract the temperature data sequence of the abnormal area from the feature parameter data set, and determine the maximum and minimum temperatures at all time points in this area to calculate the temperature fluctuation range. At the same time, in order to further evaluate the stability of the temperature fluctuation, the system calculates the standard deviation of the temperature data and quantitatively analyzes the overall distribution of the temperature in combination with the mean value. While calculating the temperature fluctuation range, the system also calculates the pressure fluctuation range, similar to the processing method of temperature data.

[0137] The system also needs to analyze the change amplitude of the moisture evaporation rate to evaluate the stability of moisture removal in the abnormal area. First, extract the moisture evaporation rate data sequence, and calculate the difference between the maximum evaporation rate and the minimum evaporation rate as the change amplitude of the moisture evaporation rate in this area. At the same time, in order to more accurately judge the stability of moisture evaporation, the system calculates the standard deviation of the moisture evaporation rate and quantitatively analyzes the uniformity of moisture removal during the drying process in combination with the average evaporation rate. If the change amplitude of the moisture evaporation rate is large, it may mean that there are drastic fluctuations in the moisture removal process in this area, resulting in uneven drying effects. If the standard deviation of the moisture evaporation rate is small, it indicates that the moisture removal is relatively stable and the drying process is relatively uniform. By calculating the change characteristics of the moisture evaporation rate, the system can effectively identify possible moisture residue problems during the drying process and optimize and adjust the relevant areas in the subsequent regulation module to ensure uniform moisture removal.

[0138] In a preferred embodiment of the present invention, the regulation module includes:

[0139] The abnormal area parameter unit is used to extract the temperature, pressure, drying uniformity parameter, and moisture removal degree parameter corresponding to the abnormal area according to the drying abnormal data set to obtain the abnormal area parameter set;

[0140] The regulation parameter calculation unit is used to calculate the heating time adjustment value, vacuum maintenance time adjustment value, and temperature setting value adjustment value of each abnormal area according to the abnormal area parameter set to obtain the regulation parameter data set;

[0141] A control unit for controlling the vacuum encapsulation equipment according to the control parameter data set to ensure the overall uniformity of the drying process;

[0142] A real-time adjustment unit for making real-time adjustments to the control process according to the real-time monitoring data during the drying process to ensure the uniformity of drying in different areas during the drying process.

[0143] In an embodiment of the present invention, an abnormal area parameter unit is used to extract the temperature, pressure, drying uniformity parameter, and moisture removal degree parameter corresponding to the abnormal area according to the drying abnormal data set to obtain an abnormal area parameter set, which can accurately identify the areas with moisture residue or over-drying during the drying process and provide detailed parameter information of these areas; a control parameter calculation unit is used to calculate the heating time adjustment value, vacuum maintenance time adjustment value, and temperature setting value adjustment value for each abnormal area according to the abnormal area parameter set to obtain a control parameter data set, which can accurately evaluate the adjustment requirements and avoid errors caused by empirical adjustment; a control unit is used to control the vacuum encapsulation equipment according to the control parameter data set to ensure the overall uniformity of the drying process, solving the problem that the traditional drying process cannot accurately control specific areas; a real-time adjustment unit is used to make real-time adjustments to the control process according to the real-time monitoring data during the drying process to ensure the uniformity of drying in different areas during the drying process, enabling the system to automatically adjust the drying process according to the real-time feedback data.

[0144] Among them, the control parameter calculation unit is used to calculate the heating time adjustment value, vacuum maintenance time adjustment value, and temperature setting value adjustment value for each abnormal area according to the abnormal area parameter set to obtain a control parameter data set, specifically including:

[0145] The system calculates the heating time adjustment value for each abnormal area. The adjustment of the heating time is mainly dynamically calculated based on the relationship between the temperature change rate and the moisture evaporation rate in this area. For the area with moisture residue, the system judges whether to extend the heating time by calculating the difference between the current moisture evaporation rate and the target evaporation rate. If this difference is large, the system will calculate the additional heating duration required for this area according to the heat conduction model and set a reasonable heating time adjustment value in combination with the maximum allowable heating time of the drying equipment; for the over-dried area, the system determines whether to reduce the heating time based on the deviation between the current temperature change rate and the target temperature change rate to avoid changes in material physical properties or unnecessary energy consumption caused by local overheating. During the calculation process, the system also needs to consider the thermal inertia of the drying chamber to prevent new drying unevenness caused by excessive adjustment.

[0146] The system needs to further calculate the adjustment value of the vacuum maintenance time. The setting of the vacuum maintenance time is closely related to the water evaporation rate. Therefore, the system will calculate the vacuum adjustment strategy based on the change trend of the water removal degree parameter. For the water residue area, the system will judge whether it is necessary to extend the vacuum maintenance time by analyzing the current pressure level and historical pressure change data in this area to improve the water evaporation capacity; for the over-dried area, if it is detected that the pressure change rate in this area is large and the water evaporation rate exceeds the normal range, the system will reduce the vacuum maintenance time to reduce the drying intensity. During the calculation process, the system will also combine the exhaust rate and sealing state of the equipment to ensure that the vacuum adjustment will not affect the normal drying of other areas.

[0147] Finally, the system calculates the adjustment value of the temperature setting value, and this step is mainly dynamically optimized based on the temperature deviation analysis. For the water residue area, if it is detected that the temperature in this area is lower than the target temperature, the system will calculate the required temperature compensation value according to the heat transfer model and combine the heating rate of the equipment to set a reasonable temperature increase range; for the over-dried area, the system will calculate the temperature reduction range based on the overheating risk assessment to ensure that the normal drying rate can still be maintained after the temperature adjustment. During the adjustment process of the temperature setting value, the system will combine the historical temperature data to avoid the drying instability phenomenon caused by frequent temperature fluctuations.

[0148] In a preferred embodiment of the present invention, the regulation unit includes:

[0149] The water residue area adjustment unit is used to increase the temperature setting value of this area and extend the vacuum maintenance time according to the regulation parameters of the water residue area to ensure the water removal effect;

[0150] The over-dried area adjustment unit is used to reduce the heating time of this area and adjust the temperature setting value according to the regulation parameters of the over-dried area to avoid local over-drying;

[0151] The global optimization unit is used to adjust the drying strategy of the entire vacuum encapsulation equipment according to the regulation parameter data set of each abnormal area to ensure the overall uniformity of the drying process.

[0152] In the embodiment of the present invention, the water residue area adjustment unit is used to increase the temperature setting value of this area and extend the vacuum maintenance time according to the regulation parameters of the water residue area to ensure the water removal effect. Through the accurate identification of the water residue area and the optimization of local heating, it is ensured that the water in this area is completely removed;

[0153] An over-dry area adjustment unit is used to reduce the heating time of the area and adjust the temperature set value according to the regulation parameters of the over-dry area, avoiding local over-drying phenomena, ensuring temperature balance throughout the drying process through local temperature regulation, and preventing product quality degradation caused by excessive regional temperature.

[0154] A global optimization unit is used to adjust the drying strategy of the entire vacuum encapsulation device according to the regulation parameter data set of each abnormal area, ensuring the overall uniformity of the drying process, avoiding the imbalance of the overall drying environment caused by separate adjustment of local areas through the global optimization strategy, and improving the consistency of the overall product quality.

[0155] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A real-time monitoring and data analysis system based on a vacuum potting device, characterized in that, The system includes: A calculation module, which is used to calculate the change rates of temperature data and pressure data at adjacent time points according to the equipment working condition data set, and calculate the moisture evaporation rate based on them to obtain a change trend data set; An analysis module, which is used to compare the temperature change rate and pressure change rate at different position points according to the change trend data set, and calculate the drying uniformity parameter; conduct cumulative analysis on the moisture evaporation rate, calculate the moisture removal degree parameter, and obtain a key parameter data set; A region module, which is used to calculate the local temperature gradient and local pressure gradient according to the key parameter data set, and divide the vacuum encapsulation equipment based on them to obtain a drying region data set, where the drying region data set includes a normal region, a moisture residue region, and an over-dried region; A feature module, which is used to extract the temperature, pressure, drying uniformity parameter, and moisture removal degree parameter in different regions according to the drying region data set to obtain a drying anomaly data set; A regulation module, which is used to adjust the heating time, vacuum maintenance time, and temperature setting value of the corresponding region in the vacuum encapsulation equipment according to the drying anomaly data to ensure sufficient drying inside the vacuum encapsulation equipment; The analysis module includes: A uniformity comparison unit, which is used to extract the temperature change rate and pressure change rate at different position points in the vacuum encapsulation equipment according to the change trend data set, and compare them according to the position points to obtain temperature change comparison data and pressure change comparison data; A uniformity parameter calculation unit, which is used to calculate the mean and variance of the temperature change rate and pressure change rate according to the temperature change comparison data and pressure change comparison data, and perform standardization processing on them to obtain a temperature uniformity parameter and a pressure uniformity parameter; A uniformity determination unit, which is used to screen the temperature uniformity parameter and pressure uniformity parameter according to a preset uniformity threshold group. When both the temperature uniformity parameter and pressure uniformity parameter are less than the preset uniformity threshold group, mark this position point as a drying uniform point. When any one of the temperature uniformity parameter and pressure uniformity parameter is not less than the preset uniformity threshold group, mark this position point as a drying non-uniform point to obtain the drying uniformity parameter; A moisture evaporation amount calculation unit, which is used to calculate the moisture evaporation amount at different position points in different time periods according to the moisture evaporation rate in the change trend data set, and accumulate the moisture evaporation amounts of each position point to obtain the moisture removal degree parameter; The calculation formula of the moisture removal degree parameter is: ； Among them, is the moisture removal degree parameter of the vacuum encapsulation equipment at the th moment, is the index of the moment, is the initial moment, is the total area of the drying area of the vacuum encapsulation equipment, is the total moisture content in the entire drying area at the initial moment, is the cumulative moisture removal amount at a certain position point, is the total moisture removal amount of all position points, is the initial moisture evaporation rate, is the initial pressure, is the initial temperature, is the gas constant in the ideal gas law, is the latent heat of vaporization of water at normal temperature and pressure, is the temperature on the surface of the material of the vacuum encapsulation equipment, is the th moment temperature, is the th moment pressure, is the th moment pressure change rate, is the th moment temperature change rate, is the coefficient.

2. The real-time monitoring and data analysis system based on a vacuum potting device according to claim 1, characterized in that, The calculation module includes: A change rate calculation unit, which is used to calculate the change rates of temperature data and pressure data at adjacent time points according to the equipment working condition data set to obtain temperature change rate data and pressure change rate data; A trend analysis unit, which is used to determine the fluctuation conditions of the temperature change rate data and pressure change rate data according to the time parameter to form a trend parameter data set; A moisture evaporation calculation unit, which is used to calculate the moisture evaporation rate according to the trend parameter data set, and merge it with the trend parameter data set to obtain a change trend data set.

3. The real-time monitoring and data analysis system based on a vacuum potting device according to claim 2, characterized in that, The moisture evaporation calculation unit includes: The initial rate calculation unit is used to calculate the initial water evaporation rate according to the temperature and pressure changes in the initial stage of drying. The calculation formula for the initial water evaporation rate is: ， Among them, is the initial moisture evaporation rate, is the mass transfer coefficient on the surface of the material of the vacuum encapsulation equipment, is the initial pressure, is the initial temperature, is the gas constant in the ideal gas law, is the latent heat of vaporization of water at normal temperature and pressure, is the temperature on the surface of the material of the vacuum encapsulation equipment, is the initial saturated vapor pressure; The water evaporation rate calculation unit is used to calculate the water evaporation rate at any time according to the initial water evaporation rate. The calculation formula of the water evaporation rate is: ， Among them, is the water evaporation rate at the th moment, is the index of the moment, is the temperature at the th moment, is the pressure at the th moment, is the pressure change rate at the th moment, is the temperature change rate at the th moment, is the saturated vapor pressure at the th moment, is the coefficient.

4. The real-time monitoring and data analysis system based on a vacuum potting device according to claim 3, characterized in that, The regional module includes: A gradient calculation unit is used to calculate the local temperature gradient and the local pressure gradient at different positions according to the temperature change rate and the pressure change rate to obtain a gradient data set; A grid processing unit, used to divide the drying space in the vacuum potting equipment into a plurality of grid units according to the position points, each grid unit containing the temperature gradient and pressure gradient data of the corresponding position point; The threshold determination unit is used to screen the local temperature gradient and local pressure gradient of each grid unit according to the preset gradient threshold group. When the temperature gradient or pressure gradient exceeds the preset threshold, the grid unit is marked as an abnormal candidate area to obtain preliminary area classification data.

5. The real-time monitoring and data analysis system based on a vacuum potting device according to claim 4, characterized in that, The regional module also includes: The adjacent region merging unit is used to check the gradient data sets of adjacent grid cells according to the preliminary region classification data, and merge the regions with similar temperature gradients and pressure gradients to obtain optimized region data; A moisture removal analysis unit is used to determine whether the drying state of different areas is uniform according to the moisture removal degree parameter of the optimized area data. When the moisture removal degree parameter of a certain area is lower than a preset moisture removal threshold, the boundary range of the area is adjusted and the area is reclassified as a moisture residual area. A moisture residual determination unit, for identifying an area where a moisture removal degree parameter is lower than a preset moisture removal threshold and a temperature change rate is lower than a preset temperature change rate according to a result of the moisture removal analysis unit, and marking it as a moisture residual area; The over-drying determination unit is used to identify, based on the temperature gradient and the drying uniformity parameter, an area where the temperature change rate is higher than a preset temperature change rate and the drying uniformity parameter is lower than a preset drying uniformity, and mark it as an over-drying area.

6. The real-time monitoring and data analysis system based on a vacuum potting device according to claim 5, characterized in that The feature module includes: The abnormal data screening unit is used to screen and extract the temperature, pressure, drying uniformity parameters and moisture removal degree parameters of the moisture residual area and the over-drying area according to the drying area data set, and eliminate the data of the normal area to obtain the characteristic parameter data set; An abnormal characteristic calculation unit, used to calculate the temperature fluctuation range, pressure fluctuation range and water evaporation rate change range of each abnormal area and the abnormal characteristic data set according to the characteristic parameter data set; The abnormal area feature unit is used to subdivide the moisture residual area and the over-dry area according to the abnormal feature data set and the preset feature threshold group to obtain a subdivided area set, and extract the abnormal area features therefrom to obtain a dry abnormal data set.

7. A real-time monitoring and data analysis system based on a vacuum potting device according to claim 6, characterized in that, The control module includes: The abnormal area parameter unit is used to extract the temperature, pressure, drying uniformity parameter and moisture removal degree parameter corresponding to the abnormal area according to the drying abnormality data set to obtain the abnormal area parameter set; A regulation parameter calculation unit, which is used to calculate the heating time adjustment value, vacuum maintenance time adjustment value, and temperature setting value adjustment value of each abnormal area according to the abnormal area parameter set, so as to obtain a regulation parameter data set; A regulation unit, which is used to regulate the vacuum encapsulation equipment according to the regulation parameter data set to ensure the overall uniformity of the drying process; A real-time adjustment unit, which is used to make real-time adjustments to the regulation process according to the real-time monitoring data during the drying process to ensure the uniformity of drying in different areas during the drying process.

8. A real-time monitoring and data analysis system based on a vacuum potting device according to claim 7, characterized in that, The regulation unit includes: A moisture residue area adjustment unit, which is used to increase the temperature setting value of this area and extend the vacuum maintenance time according to the regulation parameters of the moisture residue area to ensure the moisture removal effect; An over-drying area adjustment unit, which is used to reduce the heating time of this area and adjust the temperature setting value according to the regulation parameters of the over-drying area to avoid local over-drying; A global optimization unit, which is used to adjust the drying strategy of the entire vacuum encapsulation equipment according to the regulation parameter data set of each abnormal area to ensure the overall uniformity of the drying process.

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