Plastic Foam Particle Film-Coating Drying State Monitoring Method Based on Intelligent Sensors

Abstract

The present application provides a method for monitoring the drying state of plastic foam particles with film coating based on intelligent sensors, which relates to the technical field of sensor monitoring. Through constructing a twin model for drying fitting and establishing a deep balance drying constraint; calling a temperature and humidity sensor to obtain environmental data; conducting surface layer monitoring based on a surface layer sensor to establish a first drying monitoring result; conducting deep node monitoring based on the twin model to establish a second drying monitoring result; using the deep balance drying constraint and the first and second drying monitoring results for drying monitoring and early warning to establish a first monitoring and early warning result; using the environmental data and the first drying monitoring result for surface layer drying analysis to establish a second monitoring and early warning result; and jointly warning and reporting the first and second monitoring and early warning results. The present application solves the technical problem in the prior art that the lack of monitoring means for the deep drying state of foam leads to insufficient monitoring accuracy and reliability, and improves the monitoring accuracy and reliability.

Description

Technical Field

[0001] This application relates to the technical field of sensor monitoring, and particularly to a method for monitoring the drying state of plastic foam particles with film covering based on intelligent sensors. Background Art

[0002] The film covering of plastic foam particles is a common production process, which is widely used in fields such as packaging and thermal insulation. During the film covering process, the plastic foam particles need to be dried to ensure the quality and stability of the film covering. The drying effect directly affects the product quality. At present, the methods for monitoring the drying state of plastic foam particles with film covering mainly rely on traditional temperature and humidity sensors and manual monitoring. Although these methods can provide real-time data during the drying process to a certain extent, they usually ignore the differences between the surface and deep states of the particles during the drying process, and cannot comprehensively reflect the drying state of the particles, resulting in uneven drying or unstable product quality, and also wasting resources. Summary of the Invention

[0003] This application provides a method for monitoring the drying state of plastic foam particles with film covering based on intelligent sensors, which solves the technical problem in the prior art that there is a lack of effective monitoring means for the deep drying state of plastics, resulting in insufficient monitoring accuracy and reliability, thus affecting the product quality, and achieves the technical effect of improving the monitoring accuracy and reliability of the drying process of plastic foam particles with film covering.

[0004] In view of the above problems, this application provides a method for monitoring the drying state of plastic foam particles with film covering based on intelligent sensors. The method includes: performing three-dimensional modeling on the plastic foam particles with film covering to construct a twin model, and the twin model is constructed by reading the data of the plastic foam particles with film covering; performing drying fitting on the plastic foam particles with film covering based on the twin model to establish a depth balance drying constraint; when the drying monitoring of the plastic foam particles with film covering is executed, calling a temperature and humidity sensor to monitor the temperature and humidity in the drying space to establish environmental data; performing surface layer monitoring on the plastic foam particles with film covering based on a surface layer sensor to establish a first drying monitoring result; configuring depth nodes based on the twin model, arranging a wireless sensor network at the depth nodes to establish a second drying monitoring result; using the depth balance drying constraint, the first drying monitoring result, and the second drying monitoring result to perform drying monitoring and early warning to establish a first monitoring and early warning result; using the environmental data and the first drying monitoring result to perform surface layer drying analysis to establish a second monitoring and early warning result; and jointly warning and reporting the first monitoring and early warning result and the second monitoring and early warning result.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] A twin model is constructed by reading the data of the plastic foam particle film coating. This model can dynamically simulate the drying process and adjust in real time according to the actual data, improving the visualization and monitoring accuracy of the drying process. Based on the twin model, drying fitting of the plastic foam particle film coating is carried out to establish a deep balance drying constraint, ensuring the balance of heat and humidity during the drying process and avoiding over-drying or under-drying. When the plastic foam particle film coating performs drying monitoring, temperature and humidity sensors are called to monitor the temperature and humidity of the drying space to establish environmental data; surface monitoring of the plastic foam particle film coating is carried out based on surface sensors to establish the first drying monitoring result; deep nodes are configured based on the twin model, and a wireless sensor network is arranged at the deep nodes to establish the second drying monitoring result. By monitoring the drying process in real time from multiple levels and collecting sensor data, different stages of the drying process can be comprehensively understood, further improving the accuracy of monitoring. By analyzing various sensor data and combining the deep balance drying constraint, drying monitoring warnings are generated in real time, including the first monitoring warning result and the second monitoring warning result, providing important auxiliary decision-making for process control and reducing quality problems caused by unsatisfactory drying states. The first monitoring warning result and the second monitoring warning result are jointly warned and reported, enhancing the reliability of the monitoring result through a multiple warning mechanism.

[0007] In summary, this application uses intelligent sensors and a wireless sensor network to enable real-time monitoring of the drying process. Through multi-sensor data fusion and a joint warning mechanism, it can more accurately monitor the drying state of the plastic foam particle film coating, improve the accuracy and reliability of the monitoring result, and can also promptly detect abnormal situations during the drying process, avoiding product quality problems caused by insufficient or over-drying.

[0008] The above description is only an overview of the technical solution of this application. In order to be able to more clearly understand the technical means of this application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0009] Figure 1 It is a schematic flow chart of the method for monitoring the drying state of plastic foam particle film coating based on intelligent sensors provided by an embodiment of this application.

[0010] Figure 2 It is a schematic flow chart of establishing a deep balance drying constraint in the method for monitoring the drying state of plastic foam particle film coating based on intelligent sensors provided by an embodiment of this application.

[0011] Figure 3Schematic diagram of the process for establishing the second monitoring and early warning result in the plastic foam particle film coating drying state monitoring method provided by the embodiments of the present application. Detailed implementation manners

[0012] By providing a plastic foam particle film coating drying state monitoring method based on intelligent sensors, the embodiments of the present application solve the technical problem in the prior art that there is a lack of effective monitoring means for the deep drying state of plastic foam, resulting in insufficient monitoring accuracy and reliability, thus affecting the product quality, and achieve the technical effect of improving the monitoring accuracy and reliability of the plastic foam particle film coating drying process.

[0013] As Figure 1 shown, the embodiments of the present application provide a plastic foam particle film coating drying state monitoring method based on intelligent sensors, and the method includes:

[0014] Step S1: Perform three-dimensional modeling on the plastic foam particle film coating to construct a twin model, and the twin model is constructed by reading the data of the plastic foam particle film coating.

[0015] Specifically, use three-dimensional modeling software (such as AutoCAD, SolidWorks, etc.) to model the plastic foam particles after film coating to determine the three-dimensional model of the plastic foam particles. These three-dimensional models can reflect the geometric shape of the plastic foam particles, as well as the property characteristics on the surface and inside of the plastic foam particles, such as the density of the foam, surface roughness, etc. Read the data of the plastic foam particle film coating, including the thickness, shape, material properties, etc. of the film coating, as well as the data such as temperature, humidity, pressure, etc. in the actual production process. Associate the three-dimensional model with the data of the plastic foam particle film coating to construct a twin model corresponding to the plastic foam particle film coating. This twin model can accurately reflect the characteristics such as the appearance and internal structure of the film coating, providing a digital research object highly corresponding to the physical object for subsequent operations such as drying analysis of the plastic foam particle film coating.

[0016] Step S2: Perform drying fitting on the plastic foam particle film coating based on the twin model to establish a deep equilibrium drying constraint.

[0017] Specifically, the deep equilibrium drying constraint defines the relationship that various parameters (such as the distribution of humidity, temperature, etc. at different depths) should satisfy when reaching an ideal deep equilibrium state during the drying process. Collect the data in the actual drying process and perform drying process simulation in the twin model to find the humidity change law at different depths inside the plastic foam particles during normal drying, and establish the deep equilibrium drying constraint as an important judgment criterion for subsequent drying monitoring and early warning.

[0018] Step S3: After the drying monitoring of the plastic foam particle film coating is performed, call the temperature and humidity sensor to monitor the temperature and humidity in the drying space and establish environmental data.

[0019] Specifically, the temperature and humidity sensor can convert the physical quantities of temperature and humidity in the environment into electrical signals and then into digital signals that can be read. The temperature and humidity sensor is installed at a suitable position in the drying space, such as in the corner or the center of the drying box. When the drying monitoring of the plastic foam particle film coating starts, call the temperature and humidity sensor to continuously collect the temperature and humidity information in the drying space, for example, collect data every 5 minutes. Then, organize the collected temperature and humidity data in a certain format (such as a table form of time - temperature - humidity) to establish environmental data, which can be used to analyze the influence of environmental factors on the drying process of the plastic foam particle film coating.

[0020] Step S4: Based on the surface sensor, perform surface monitoring of the plastic foam particle film coating and establish the first drying monitoring result.

[0021] Specifically, the surface sensor is a sensor specially used to detect the surface drying condition of the plastic foam particle film coating. This sensor reflects the drying state by detecting surface characteristics such as temperature and humidity, and can be a temperature sensor (such as a K - type thermocouple) and a humidity sensor (such as an RH sensor), etc. The surface sensor is placed on the surface of the plastic foam particle film coating or near the surface. For example, if it is a sensor for detecting surface humidity, it can obtain the humidity information of the film coating surface through contact or non - contact (such as a capacitive humidity sensor) methods. The surface sensor converts the detected signal into corresponding drying information. For example, if the detected surface humidity is 30%, this value can be used as the first drying monitoring result. This result provides basic data on the surface drying condition of the film coating for subsequent analysis and early warning.

[0022] Step S5: Configure depth nodes based on the twin model, deploy a wireless sensor network at the depth nodes, and establish the second drying monitoring result.

[0023] Specifically, depth nodes refer to some pre - set internal points in the twin model, which simulate different depth positions inside the plastic foam particles. Based on the twin model in Step S1, according to the distribution of the plastic foam particles and the requirements of drying analysis, determine the positions of the depth nodes inside the model. For example, if the depth of the plastic foam particles is 50 cm, depth nodes may be set at 10 cm, 20 cm, and 40 cm depths. Deploy a wireless sensor network at the depth nodes to collect the inner - layer humidity and temperature of the plastic foam particles and establish the second drying monitoring result, thereby helping to judge whether the drying is uniform.

[0024] Step S6: Use the depth-balanced drying constraint, the first drying monitoring result, and the second drying monitoring result to perform drying monitoring and early warning, and establish the first monitoring and early warning result.

[0025] Specifically, compare and analyze the depth-balanced drying constraint with the first drying monitoring result (such as the surface humidity value) and the second drying monitoring result (such as the humidity conditions at different depths inside). For example, if the depth-balanced drying constraint requires that the surface humidity is below 30% and the humidity at a specific depth inside is below 40% for a normal drying state, and the actual first drying monitoring result shows that the surface humidity is 35% and the second drying monitoring result shows that the humidity at a specific depth inside is 45%, then it can be determined that the drying process deviates from the normal state. Based on the results of this comparative analysis, the first monitoring and early warning result is obtained.

[0026] Step S7: Use the environmental data and the first drying monitoring result to perform surface drying analysis and establish the second monitoring and early warning result.

[0027] Specifically, comprehensively analyze the environmental data and the first drying monitoring result, and further analyze the uniformity of surface drying. Establish the second monitoring and early warning result based on the analysis results. For example, if it is known that in an environment with a temperature of 50°C and a humidity of 30%, the normal surface humidity should be below 30%, but the first drying monitoring result shows that the surface humidity is 35%, then match the corresponding warning information according to the humidity deviation situation and establish the second monitoring and early warning result. If the surface humidity deviates from the normal range slightly, generate a mild warning message, and if the deviation is large, generate a higher-level warning message.

[0028] Step S8: Jointly report the first monitoring and early warning result and the second monitoring and early warning result.

[0029] Specifically, combine the first monitoring and early warning result (based on the data of the depth and surface sensors) with the second monitoring and early warning result (based on the data of the environmental and surface analysis) to form a comprehensive early warning report. This joint early warning can more accurately identify potential problems in the drying process, provide comprehensive and accurate information about the drying state of the plastic foam particle film coating for the operator, so as to take corresponding measures to reduce quality problems in production.

[0030] Furthermore, as Figure 2 shown, step S2 of the embodiment of the present application further includes:

[0031] Step S21: Obtain depth data based on the twin model, and input the depth data into the grid density distribution channel to establish the calibrated grid density.

[0032] Step S22: Use the calibrated grid density to perform finite element mesh division of the twin model and establish a finite element unit set.

[0033] Step S23: Establish a surface - deep direction for the finite element cell set, and set the heat conduction parameters and humidity diffusion parameters based on the data of the plastic foam particle film coating.

[0034] Step S24: Use the surface - deep direction, the heat conduction parameters, and the humidity diffusion parameters to perform heat conduction and humidity diffusion fitting for each cell in the finite element cell set, establish a temperature - humidity gradient according to the fitting results, and create a depth - balanced drying constraint based on the temperature - humidity gradient and the surface - deep direction.

[0035] Specifically, obtain data of different levels from the surface to the bottom layer of the plastic foam particles from the twin model, that is, depth data. These depth data include the specific distribution information of different depth layers of the plastic foam particles. Input these depth data into the grid density distribution channel. The grid density distribution channel analyzes according to the depth data, determines the appropriate grid density, and outputs it as the calibrated grid density. The calibrated grid density represents the calculation granularity within each grid unit. If the plastic foam particles are relatively large, the grid division may be coarser; for smaller or more complex plastic foam particles, the grid density will be higher to ensure that the characteristics of this area can be accurately represented during subsequent finite element grid division.

[0036] According to the calibrated grid density, use finite element analysis software (such as ANSYS, etc.) to decompose the plastic foam particle film - coated twin model into multiple finite element cells. The set of these finite element cells is the finite element cell set. Each cell in the set represents a small part of the plastic foam particle, and subsequent physical processes such as heat conduction and humidity diffusion will be calculated for each cell respectively.

[0037] In the established finite element cell set, define a direction from the surface of the plastic foam particle to the interior depth, that is, the surface - deep direction. This direction is the direction for subsequent heat conduction and humidity diffusion analysis. Then, set the heat conduction parameters and humidity diffusion parameters according to the actual material characteristics of the plastic foam particle film coating and relevant test data. The heat conduction parameters are used to describe the speed and ease of heat conduction in the plastic foam particle during the heat conduction process. The humidity diffusion parameters reflect the humidity diffusion characteristics in the plastic foam particle.

[0038] For each element within the finite element set, using the set surface - deep direction, heat conduction parameters, and humidity diffusion parameters, simulate the heat conduction and humidity diffusion through the finite element analysis method (such as based on the energy conservation and mass conservation equations). Through the fitting simulation of heat conduction and humidity diffusion for all finite element elements, obtain the temperature and humidity changes in the surface - deep direction, thereby establishing the temperature and humidity gradients, that is, the change rates of temperature and humidity in the surface - deep direction. Create a depth - balanced drying constraint based on the temperature and humidity gradients and the surface - deep direction. This constraint is a mathematical relationship expression regarding the temperature and humidity gradients and the surface - deep direction, used to determine whether the drying state reaches equilibrium in the subsequent process.

[0039] Furthermore, step S5 further includes:

[0040] Step S51: Obtain the set monitoring accuracy for the plastic foam particle film - coating, and generate a first configuration constraint according to the set monitoring accuracy. The first configuration constraint is a distribution quantity constraint.

[0041] Step S52: Perform constraint gradient analysis on the depth - balanced drying constraint, and establish a second configuration constraint based on the constraint gradient analysis result. The second configuration constraint is a density distribution constraint.

[0042] Step S53: Configure the depth nodes based on the first configuration constraint and the second configuration constraint.

[0043] Specifically, the set monitoring accuracy is a pre - determined indicator for measuring the monitoring accuracy of the plastic foam particle film - coating, which determines the required level of detail and error range during the monitoring process. The first configuration constraint is a constraint condition generated according to the set monitoring accuracy, used to limit the distribution quantity of depth nodes. The second configuration constraint is a constraint condition established based on the constraint gradient analysis result, used to limit the density distribution of depth nodes at different depths to better adapt to the characteristic changes at different depths during the drying process.

[0044] Obtain the pre - set monitoring accuracy value. This value can be determined according to product quality requirements and production process standards. Match the appropriate range of depth node distribution quantities according to the monitoring accuracy, and set the first configuration constraint. For example, at least 3 depth nodes are required per cubic centimeter of the film - coating, etc.

[0045] Perform constraint gradient analysis on the deep equilibrium drying constraints to determine the change gradients of temperature and humidity at different depths. Then, based on the results of these constraint gradient analyses, establish the second configuration constraint. For example, in the depth range of 0 to 1 cm, the change gradients of temperature and humidity are relatively large, while in the range of 10 to 20 cm, the change gradients of temperature and humidity are relatively small. In the area with a large humidity change gradient, more dense depth nodes are required to accurately monitor the drying situation. Therefore, it is stipulated that in the depth range of 0 to 10 cm with a large humidity change gradient, the density of depth nodes is one node every 2 cm; while in the depth range of 10 to 20 cm with a small humidity change gradient, the density of depth nodes is one node every 5 cm.

[0046] Configure the depth nodes based on the first configuration constraint and the second configuration constraint. For example, the first configuration constraint stipulates that the total number of depth nodes cannot exceed 20, and the second configuration constraint stipulates that the density of depth nodes in the front half of the plastic foam particles is relatively large, and the density in the rear half is relatively small. Then, in actual configuration, according to the specific shape and size of the plastic foam particles, a certain number of depth nodes (such as 12) will be reasonably allocated at a higher density in the front half, and the remaining depth nodes (such as 8) will be allocated at a lower density in the rear half, thus completing the configuration of the depth nodes.

[0047] Through the above node configuration method, it can be ensured that every key area during the drying process can be fully monitored, making the acquisition of temperature and humidity data during the entire drying process more accurate and comprehensive.

[0048] Furthermore, as Figure 3 shown, step S7 of the embodiment of the present application further includes:

[0049] Step S71: Perform environmental drying fitting based on the twin model to establish an environmental adaptation model.

[0050] Step S72: Input the environmental data and the first drying monitoring result into the environmental adaptation model to establish an adaptation recognition result.

[0051] Step S73: Perform drying rate recognition on the first drying monitoring result to establish a rate recognition result.

[0052] Step S74: Establish a second monitoring and warning result based on the adaptation recognition result and the rate recognition result.

[0053] Specifically, use the twin model to simulate the drying process of plastic foam particles with film coating in different environments, analyze the influence of environmental factors on the drying process, and thus establish an environmental adaptation model. The environmental adaptation model can describe the adaptation relationship between environmental factors (such as temperature and humidity) and the drying process of plastic foam particles with film coating.

[0054] Input the environmental data obtained in step S3 and the first drying monitoring result obtained in step S4 into the environmental adaptation model. The environmental adaptation model calculates the drying state of the plastic foam particles under the current conditions according to the internal algorithms and parameter relationships therein, and generates an adaptation recognition result.

[0055] Analyze the relevant data in the first drying monitoring result, calculate the ratio of the change in humidity to time, and obtain the drying rate as the rate recognition result.

[0056] Establish a second monitoring and warning result based on the adaptation recognition result and the rate recognition result. If the adaptation recognition result shows that the environment does not match well with the surface drying situation, and the rate recognition result shows that the drying rate is slow, then considering these two results comprehensively, a relatively serious second monitoring and warning result (such as a medium warning) can be established. If the adaptation recognition result shows a good match, and the rate recognition result shows that the drying rate is normal, then a normal second monitoring and warning result can be established, indicating that the surface drying process is in a normal state.

[0057] Furthermore, step S6 of the embodiment of the present application further includes:

[0058] Step S61: Establish depth drying data by using the first drying monitoring result and the second drying monitoring result.

[0059] Step S62: Perform drying constraint trigger recognition on the depth drying data based on the depth equilibrium drying constraint, and establish a trigger recognition result.

[0060] Step S63: Establish a first monitoring and warning result according to the trigger recognition result.

[0061] Specifically, obtain the first drying monitoring result obtained in step S4, such as information on the humidity value, temperature value, or drying state of the surface layer, and then obtain the second drying monitoring result obtained in step S5, such as the humidity, temperature, etc. at different depth nodes inside. Then combine these data. For example, a data matrix can be constructed, where the rows represent different monitoring positions (the surface layer and different depth nodes), and the columns represent drying-related data such as humidity and temperature. This combined data matrix is the depth drying data. The depth drying data can comprehensively reflect the drying situation of the plastic foam particles from the surface layer to the inside.

[0062] Analyze the deep drying data with the standard of the deep equilibrium drying constraint. The deep equilibrium drying constraint limits the temperature and humidity ranges at different depths inside. If the temperature and humidity values at this position in the deep drying data exceed this range, it indicates that the constraint conditions are not met. Analyze each relevant data in the deep drying data and each condition of the deep equilibrium drying constraint one by one to determine the deviation degree between the deep drying data and the deep equilibrium drying constraint, and obtain the trigger recognition result. Take the trigger recognition result as the first monitoring and early warning result.

[0063] Further, step S8 of the embodiment of the present application further includes:

[0064] Step S81: If the first monitoring and early warning result is a non-warning result and the second monitoring and early warning result is a warning result, generate a temperature control compensation and an air circulation compensation.

[0065] Step S82: Report the temperature control compensation and the air circulation compensation as a combined early warning.

[0066] Specifically, when the first monitoring and early warning result is a non-warning result and the second monitoring and early warning result is a warning result, it indicates that from the perspective of deep drying monitoring, the drying process of the plastic foam particle film covering is in a normal state, but there are problems in the surface drying process. At this time, it is necessary to generate a temperature control compensation and an air circulation compensation. The temperature control compensation is a compensatory adjustment measure for temperature to improve the drying process, which is used to optimize the temperature according to the early warning result to make the drying process proceed better. The air circulation compensation is an adjustment measure for the air circulation situation, which improves the drying effect by changing the air flow state (such as flow rate, flow direction, etc.).

[0067] Exemplarily, for temperature compensation, if the environmental temperature is too high or too low, resulting in an abnormal drying rate, it is necessary to adjust the temperature control settings of the drying equipment. For example, if it is detected that the surface temperature rises too fast, the temperature needs to be reduced to avoid over-drying. If the temperature and humidity gradient is too large, resulting in ineffective removal of the inner layer moisture, it is necessary to promote the discharge of the inner layer moisture by adjusting the wind speed or increasing the air circulation, so as to ensure the uniformity of the drying process.

[0068] Report the generated temperature control compensation and air circulation compensation as a combined early warning. For example, if the drying process is abnormal, it is recommended to perform a temperature control compensation to increase the temperature by 5°C; at the same time, perform an air circulation compensation to increase the fan speed to 1200 revolutions per minute. Through this combined early warning, it can provide a clear adjustment direction for the operator so that corresponding measures can be taken to improve the drying process of the plastic foam particle film covering.

[0069] Further, the method described in the embodiment of the present application further includes:

[0070] Step S83: If both the first monitoring and warning result and the second monitoring and warning result are warning results, match the warning anomaly level according to the warning values of the first monitoring and warning result and the second monitoring and warning result.

[0071] Step S84: Report the matched warning anomaly level as a combined warning.

[0072] Specifically, when both the first monitoring and warning result and the second monitoring and warning result are warning results, it is first necessary to obtain the warning values in these two results. The warning value in the first monitoring and warning result is calculated based on the deviation degree between the deep drying data and the deep equilibrium drying constraint. For example, when the deviation degree is 30%, the corresponding warning value is 0.3; the warning value in the second monitoring and warning result is obtained through comprehensive evaluation of the adaptation recognition result and the rate recognition result. Then, according to the pre-set matching rules, these two warning values are comprehensively considered to match the warning anomaly level. Exemplarily, the set rule can be that when the sum of the two warning values is less than 0.5, it is a mild warning; when it is between 0.5 and 0.8, it is a moderate warning; and when it is greater than 0.8, it is a severe warning.

[0073] Report the matched warning anomaly level as a combined warning, so that the operator can quickly understand the severity of the problems in the drying process and take corresponding countermeasures.

[0074] Furthermore, the method described in the embodiment of the present application further includes:

[0075] Step S91: Record the combined warning and establish a warning database.

[0076] Step S92: Conduct a bias analysis of the warning trigger through the warning database and establish a bias feedback.

[0077] Step S93: Based on the bias feedback, perform control correction management for the plastic foam particle film laminating drying.

[0078] Specifically, each time a combined warning is generated, record the detailed information of the combined warning, including the time when the warning occurs, the warning anomaly level, and the specific values or states in the first monitoring and warning result and the second monitoring and warning result, etc. Store this information in a relational database (such as MySQL) to establish a warning database.

[0079] Extract data from the early warning database and analyze the relationships between early warning triggers and different factors. Data mining techniques (such as association rule mining) or simple statistical analysis methods can be used to count the occurrence frequencies of different factors (temperature, humidity, air circulation, etc.) when early warnings occur, and calculate the probabilities of early warnings occurring under different factor combinations. Based on the analysis results, identify long-existing operational bias problems, establish bias feedback to remind operators to adjust the settings of the drying process in similar situations. For example, if the early warning database shows that during multiple drying processes, the early warning of "too fast surface drying" is repeatedly triggered when the temperature is set to 85°C, then the bias feedback can be obtained: a temperature of 85°C may cause too fast drying, and it is recommended to lower the temperature.

[0080] According to the historical early warning data and the results of bias analysis, make corresponding adjustments to the various control parameters (such as temperature, humidity, wind speed, etc.) of the drying equipment. For example, if the bias feedback indicates that the problem of uneven drying frequently occurs at certain temperatures, then automatically adjust the temperature setting to avoid triggering the same early warning again. If the bias feedback indicates that a certain specific environmental condition (such as too high humidity, too low wind speed) always leads to incomplete removal of deep moisture, then compensation can be made by increasing air circulation or adjusting the temperature and humidity settings. Through targeted control and correction management, the efficiency and quality of the plastic foam particle film coating drying can be improved, and the frequency of early warnings can be reduced.

[0081] In summary, the method for monitoring the drying state of plastic foam particle film coating based on intelligent sensors provided by the embodiments of the present application has the following technical effects:

[0082] A twin model is constructed by reading the data of the plastic foam particle film coating. This model can accurately reflect various characteristics of the plastic foam particle film coating, providing an accurate data basis for subsequent operations such as drying fitting. The twin model is used for drying fitting to obtain the depth equilibrium drying constraint, providing a theoretical basis for drying monitoring and avoiding over-drying or under-drying situations. The temperature and humidity sensors are called to monitor the temperature and humidity in the drying space, establishing environmental data, which provides real-time basic information for the analysis and optimization of the drying process. The surface layer of the plastic foam particle film coating is monitored based on the surface layer sensor to directly obtain the drying information on the film coating surface, establishing the first drying monitoring result; depth nodes are configured based on the twin model, and a wireless sensor network is arranged at the depth nodes to establish the second drying monitoring result. Complementary to the surface layer monitoring, it can comprehensively understand the overall drying situation and further improve the accuracy of monitoring. The data of various sensors are analyzed, combined with the depth equilibrium drying constraint, and a drying monitoring warning is generated in real time, including the first monitoring warning result and the second monitoring warning result, providing an important auxiliary decision for process control and reducing quality problems caused by unsatisfactory drying states. The first monitoring warning result and the second monitoring warning result are jointly warned and reported, enhancing the reliability of the monitoring results through a multiple warning mechanism.

[0083] Overall, the embodiments of the present application realize comprehensive, accurate, and real-time monitoring and warning of the drying state of the plastic foam particle film coating by constructing a twin model, a depth equilibrium drying constraint, a multi-level sensor monitoring, and a real-time warning mechanism. It not only improves the accuracy and reliability of the drying process monitoring but also can timely detect abnormal situations in the drying process, provides data support for optimizing the drying strategy, avoids quality fluctuations in the drying process, and helps reduce energy waste and resource consumption.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the drying state of plastic foam particles with film coating based on intelligent sensors, characterized in that, The method includes the following steps: Perform three-dimensional modeling on the plastic foam particle film coating to construct a twin model, which is constructed by reading the data of the plastic foam particle film coating; Based on the twin model, perform drying fitting on the plastic foam particle film coating to establish a depth balance drying constraint; After the plastic foam particle film coating performs drying monitoring, call the temperature and humidity sensor to monitor the temperature and humidity in the drying space and establish environmental data; Based on the surface sensor, perform surface monitoring on the plastic foam particle film coating to establish a first drying monitoring result; Based on the twin model, configure depth nodes, deploy a wireless sensor network at the depth nodes, and establish a second drying monitoring result; Use the depth balance drying constraint, the first drying monitoring result, and the second drying monitoring result to perform drying monitoring and early warning, and establish a first monitoring and early warning result; Use the environmental data and the first drying monitoring result to perform surface drying analysis and establish a second monitoring and early warning result; Combine the first monitoring and early warning result and the second monitoring and early warning result for joint early warning and reporting; The step of performing drying fitting on the plastic foam particle film coating based on the twin model to establish a depth balance drying constraint includes: Obtain depth data based on the twin model, input the depth data into the grid density distribution channel, and establish a calibrated grid density; Use the calibrated grid density to perform finite element mesh division on the twin model to establish a finite element unit set; Establish a surface-to-depth direction for the finite element unit set, and set heat conduction parameters and humidity diffusion parameters based on the data of the plastic foam particle film coating; Use the surface-to-depth direction, the heat conduction parameters, and the humidity diffusion parameters to perform heat conduction and humidity diffusion fitting for each unit in the finite element unit set, establish a temperature and humidity gradient according to the fitting results, and create a depth balance drying constraint based on the temperature and humidity gradient and the surface-to-depth direction.

2. The method for monitoring the drying state of plastic foam particles coated with film based on an intelligent sensor according to claim 1, characterized in that, The step of configuring depth nodes based on the twin model includes: Obtain the set monitoring accuracy of the plastic foam particle film coating, generate a first configuration constraint according to the set monitoring accuracy, and the first configuration constraint is a distribution quantity constraint; Perform constraint gradient analysis on the depth balance drying constraint, and establish a second configuration constraint based on the constraint gradient analysis result, and the second configuration constraint is a density distribution constraint; Configure the depth nodes based on the first configuration constraint and the second configuration constraint.

3. The method for monitoring the drying state of plastic foam particles with film coating based on an intelligent sensor according to claim 1, characterized in that, The step of using the environmental data and the first drying monitoring result to perform surface drying analysis and establish a second monitoring and early warning result includes: Perform environmental drying fitting based on the twin model to establish an environmental adaptation model; Input the environmental data and the first drying monitoring result into the environmental adaptation model to establish an adaptation recognition result; Perform drying rate recognition on the first drying monitoring result to establish a rate recognition result; Establish a second monitoring and early warning result according to the adaptation recognition result and the rate recognition result.

4. The method for monitoring the drying state of plastic foam particles with film coating based on intelligent sensors according to claim 1, characterized in that The step of using the depth balance drying constraint, the first drying monitoring result, and the second drying monitoring result to perform drying monitoring and early warning and establish a first monitoring and early warning result includes: Use the first drying monitoring result and the second drying monitoring result to establish depth drying data; Performing drying constraint trigger identification on the deep drying data based on the deep balance drying constraint, and establishing a trigger identification result; Establishing a first monitoring and early warning result according to the trigger identification result.

5. The method for monitoring the drying state of plastic foam particles with film coating based on an intelligent sensor according to claim 1, characterized in that, The joint early warning reporting of the first monitoring and early warning result and the second monitoring and early warning result includes: If the first monitoring and early warning result is a non-early warning result and the second monitoring and early warning result is an early warning result, generating a temperature control compensation and an air circulation compensation; Reporting the temperature control compensation and the air circulation compensation as a joint early warning.

6. The method for monitoring the drying state of plastic foam particle film coating based on an intelligent sensor according to claim 5, wherein The method further includes: If both the first monitoring and early warning result and the second monitoring and early warning result are early warning results, matching an early warning anomaly level according to the early warning values of the first monitoring and early warning result and the second monitoring and early warning result; Reporting the matched early warning anomaly level as a joint early warning.

7. The method for monitoring the drying state of plastic foam particles with film coating based on intelligent sensors according to claim 1, characterized in that, The method further includes: Recording the joint early warning, and establishing an early warning database; Performing a bias analysis of the early warning trigger through the early warning database, and establishing a bias feedback; Performing control correction management on the plastic foam particle film covering drying based on the bias feedback.

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