Biodegradable plastic tray and preparation method thereof

Abstract

The invention discloses a biodegradable plastic tray and a preparation method thereof.The method comprises the steps that S1, appropriate plastic raw materials are selected according to the use requirement of the tray, the plastic raw materials are dried, moisture and volatile matter in the raw materials are removed, the plastic raw materials and an additive are fully stirred and mixed through a stirrer, and a mixture is obtained; and the additive is uniformly dispersed in the plastic raw material. According to the biodegradable plastic tray and the preparation method, different raw materials, equipment and the like are selected according to different tray requirements and design specifications, quality detection is conducted on the tray in the preparation process, and the production quality of the tray is improved; and intelligent monitoring and predictive maintenance of the production equipment are realized through the intelligent monitoring module, so that the failure shutdown time of the equipment is shortened, and the production continuity is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of pallet preparation, and in particular relates to a biodegradable plastic pallet and a preparation method thereof. Background Art

[0002] Biodegradable plastic pallets are pallets made of bio-based materials or degradable polymers, which have the characteristics of decomposing into harmless substances in the natural environment or under specific conditions.

[0003] Existing pallet preparation methods determine the performance and quality of the prepared pallets, as well as the efficiency and safety of the preparation process. Many existing preparation methods are relatively simple, and the quality of the prepared pallets needs to be improved. In addition, there is a lack of intelligent monitoring during the preparation and production process, and there is no real-time health diagnosis of each production equipment. When a fault occurs, it is impossible to perform diagnostic analysis in the first place, which affects production efficiency.

[0004] Therefore, further improvements are made to the above problems. Summary of the Invention

[0005] The main purpose of the present invention is to provide a biodegradable plastic pallet and a preparation method. Different raw materials and equipment are selected according to different pallet requirements and design specifications, and the pallets are quality-tested during the preparation process to improve the production quality of the pallets. In addition, an intelligent monitoring module is used to realize intelligent monitoring and predictive maintenance of production equipment, thereby reducing equipment downtime and improving production continuity.

[0006] To achieve the above objectives, the present invention provides a method for preparing a biodegradable plastic pallet, comprising the following steps:

[0007] Step S1: Select appropriate plastic raw materials according to the use requirements of the pallet, dry the plastic raw materials to remove moisture and volatiles in the raw materials, and use a blender to fully mix the plastic raw materials and additives to ensure that the additives are evenly dispersed in the plastic raw materials;

[0008] Step S2: Based on the pallet design specifications, a suitable injection mold is selected and the mold is cleaned, inspected, and preheated. The pretreated plastic raw material is added to the injection molding machine hopper and transported to the heating zone by the rotation of the screw, where it is heated to a molten state. The molten plastic raw material is injected into the mold cavity under a certain pressure and the pressure is maintained for a period of time to fill the mold cavity with plastic and compact it. After the pressure is maintained, the cooling system cools the mold to solidify the plastic. When the plastic pallet cools to a preset temperature, the demoulding mechanism is activated to eject the pallet from the mold.

[0009] Step S3: trim the demoulded pallet and use online inspection equipment and visual inspection technology to inspect the appearance quality of the pallet to check whether there are any defects on the surface of the pallet; at the same time, inspect the performance of the pallet to ensure that the quality of the pallet meets the standards, and finally transport and package it for storage.

[0010] As a further preferred technical solution of the above technical solution, in step S3, nano-coating is prepared after the pallet is trimmed, and nano-coating with corresponding functions is applied to the surface of the pallet according to different requirements, which is specifically implemented as follows:

[0011] For the spraying method, a dispersion or solution of the nano-coating material is evenly sprayed onto the pallet surface using a spraying device. The pallet surface needs to be cleaned before spraying. During the spraying process, the pressure, distance, and movement speed of the spraying device are controlled to ensure uniform coating thickness. After spraying, the pallet is placed in a drying oven and dried and cured at an appropriate temperature to ensure that the coating is firmly attached to the pallet surface.

[0012] For the dip coating method, the tray is completely immersed in a container containing the nano-coating material solution for a certain period of time to allow the solution to fully penetrate the surface of the tray. Then the tray is slowly removed to allow the excess solution to drip naturally. The tray is then placed in a dust-free environment to dry and solidify.

[0013] For the chemical vapor deposition method, the tray is placed in a vacuum reactor and a mixed gas containing a nano-coating material precursor is introduced. Under the action of high temperature and catalyst, the gas molecules undergo a chemical reaction on the surface of the tray and are deposited to form a nano-coating.

[0014] As a further preferred technical solution of the above technical solution, during the preparation process of the pallet, the production equipment of the pallet is monitored in real time through an intelligent monitoring module. The intelligent monitoring module includes an edge computing node processor, a cloud server and multiple monitoring sensors. The edge computing node processor is connected to multiple monitoring sensors to realize real-time collection and preliminary processing of monitoring data and transmit the processed monitoring data to the cloud server for fault diagnosis.

[0015] As a further preferred technical solution of the above technical solution, for cloud servers, a distributed storage architecture is adopted to classify and store uploaded data, including real-time monitoring data, historical data, and equipment parameter data. Big data analysis technology is used to clean, integrate and analyze the collected data, and extract key feature parameters. At the same time, machine learning algorithms are used to deeply mine the data, establish an evaluation model for the equipment operation status, and realize real-time evaluation and diagnosis of the equipment health status.

[0016] As a further preferred technical solution of the above technical solution, the evaluation model is specifically implemented as follows:

[0017] First, collect equipment operation data from various monitoring sensors installed on production equipment to form raw time series data. Remove outliers and missing values ​​from the data, fill in missing values ​​using linear interpolation or time series-based prediction methods, and normalize the data.

[0018] Second, divide the preprocessed data into training set, validation set and test set;

[0019] Randomly initialize the weight matrix Wf, WC, Wi, Wo and bias items bf, bC, bi, bo of the LSTM network;

[0020] The training set data is input into the LSTM network in sequence according to the time step, and calculation is performed to obtain the hidden state ht and memory unit Ct of each time step;

[0021] The mean square error is used as the loss function to measure the difference between the model prediction value and the true value. The formula is: Where n is the number of samples, y i is the true value, is the model prediction value;

[0022] The gradient of the loss function with respect to the network parameters is calculated through the back-propagation algorithm, and the weight matrix and bias terms are updated using stochastic gradient descent or its improved algorithm, continuously reducing the loss function value until the model achieves optimal performance on the validation set.

[0023] Third, the pre-processed real-time monitoring data is input into the trained LSTM model. The model predicts the future operating status of the equipment based on the patterns learned from historical data. When the prediction results show that the equipment operating parameters exceed the normal range and reach the preset fault threshold, the equipment is judged to be at risk of failure and a maintenance warning is triggered.

[0024] As a further preferred technical solution of the above technical solution, maintenance warning information is sent to corresponding maintenance personnel, so that they can log in to the cloud management platform to view the detailed operation data and fault diagnosis report of the equipment, and formulate a maintenance plan based on the fault diagnosis results.

[0025] To achieve the above objectives, the present invention also provides a biodegradable plastic pallet, which is applied to a method for preparing the biodegradable plastic pallet. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0027] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0028] In the preferred embodiment of the present invention, those skilled in the art should note that the tray and the like involved in the present invention may be regarded as prior art.

[0029] Preferred embodiment.

[0030] like Figure 1 As shown, the present invention discloses a method for preparing a biodegradable plastic pallet, comprising the following steps:

[0031] Step S1: Select appropriate plastic raw materials according to the use requirements of the pallet, dry the plastic raw materials to remove moisture and volatiles in the raw materials (to avoid defects such as bubbles during the molding process), and use a blender to fully stir and mix the plastic raw materials and additives to ensure that the additives are evenly dispersed in the plastic raw materials;

[0032] Step S2: Select a suitable injection mold based on the pallet design specifications, and perform a process including cleaning, inspection, and preheating the mold (ensure the mold surface is smooth and undamaged, and the preheating temperature is set according to the characteristics of the plastic raw material). The pretreated plastic raw material is added to the injection molding machine hopper, and the raw material is transported to the heating zone by the rotation of the screw and heated to a molten state; under a certain pressure, the molten plastic raw material is injected into the mold cavity, and the pressure is maintained for a period of time to allow the plastic to fill the mold cavity and be compacted. After the pressure is maintained, the cooling system cools the mold to solidify the plastic into shape; when the plastic pallet cools to the preset temperature, the demoulding mechanism is activated to eject the pallet from the mold;

[0033] Step S3: trim the demoulded pallet (remove burrs and fins on the edge of the pallet to make the appearance of the pallet smooth and flat), and use an online inspection device to use visual inspection technology to inspect the appearance quality of the pallet to check whether there are defects (cracks, bubbles, deformation, etc.) on the surface of the pallet; at the same time, test the performance of the pallet to ensure that the quality of the pallet meets the standards, and finally transfer and package it for storage.

[0034] Specifically, in step S3, after the pallet is trimmed, a nano-coating is prepared, and a nano-coating with corresponding functions (such as antibacterial, moisture-proof, anti-fouling, etc.) is applied to the surface of the pallet according to different requirements. The specific implementation is as follows:

[0035] For the spraying method, a dispersion or solution of the nano-coating material is evenly sprayed onto the pallet surface using a spraying device. Before spraying, the pallet surface needs to be cleaned (to remove impurities such as oil and dust to enhance the adhesion of the coating to the pallet). During the spraying process, the pressure, distance, and movement speed of the spraying device are controlled to ensure a uniform coating thickness. After spraying, the pallet is placed in a drying oven and dried and cured at an appropriate temperature (e.g., 60-80°C) to ensure that the coating adheres firmly to the pallet surface.

[0036] For the dip coating method, the pallet is completely immersed in a container containing the nanocoating material solution for a certain period of time (depending on the coating thickness requirements, generally several minutes to several tens of minutes) to allow the solution to fully penetrate the pallet surface. The pallet is then slowly removed to allow the excess solution to drip naturally. The pallet is then placed in a dust-free environment to dry and solidify (the dip coating method is suitable for pallets with simple shapes and mass production, and can obtain a uniform coating. However, for pallets with complex shapes, multiple dip coatings may be required to ensure complete coating coverage).

[0037] For the chemical vapor deposition method, the tray is placed in a vacuum reactor and a mixed gas containing a nano-coating material precursor is introduced. Under the action of high temperature (about 200-400°C) and catalyst, the gas molecules undergo a chemical reaction on the surface of the tray and are deposited to form a nano-coating (the coating prepared by the CVD method has excellent uniformity and density, and has a strong bonding force with the tray surface, but the equipment cost is high and the process conditions are strict. It is suitable for the preparation of special trays with extremely high requirements for coating quality).

[0038] More specifically, during the preparation of the pallet, the production equipment of the pallet is monitored in real time through an intelligent monitoring module. The intelligent monitoring module includes an edge computing node processor, a cloud server and multiple monitoring sensors (installed on each production equipment, for example, a high-precision vibration sensor (such as a piezoelectric vibration sensor) is installed at the bearing of the screw to monitor the vibration frequency and amplitude of the screw during rotation in real time, and determine whether the screw has problems such as wear and eccentricity; a pressure sensor is installed at the inlet and outlet of the hydraulic cylinder of the mold clamping mechanism to monitor the pressure changes during the clamping process to ensure that the clamping force meets the process requirements; a torque sensor is installed at the driving roller and tensioning roller of the conveyor belt to monitor the torque changes during the transmission process and promptly detect problems such as conveyor belt slippage and load abnormality). The edge computing node processor is connected to multiple monitoring sensors to realize real-time collection and preliminary processing of monitoring data and transmit the processed monitoring data to the cloud server for fault diagnosis.

[0039] Furthermore, for cloud servers, a distributed storage architecture is adopted to classify and store uploaded data, including real-time monitoring data, historical data, and equipment parameter data. Big data analysis technology is used to clean, integrate and analyze the collected data, and extract key characteristic parameters (such as vibration characteristic values ​​of equipment operation, temperature change trends, pressure fluctuation range, etc.). At the same time, machine learning algorithms are used to deeply mine the data, establish an evaluation model for equipment operation status, and realize real-time evaluation and diagnosis of equipment health status.

[0040] Furthermore, the evaluation model is specifically implemented as follows:

[0041] First, collect equipment operation data from various monitoring sensors installed on production equipment (such as vibration sensors, temperature sensors, etc.) to form raw time series data. Remove outliers and missing values ​​from the data, fill in missing values ​​using linear interpolation or time series-based prediction methods, and normalize the data.

[0042] Second, divide the preprocessed data into training set, validation set and test set;

[0043] Randomly initialize the weight matrix Wf, WC, Wi, Wo and bias items bf, bC, bi, bo of the LSTM network;

[0044] The training set data is input into the LSTM network in sequence according to the time step, and calculation is performed to obtain the hidden state ht and memory unit Ct of each time step;

[0045] The mean square error (MSE) is used as the loss function to measure the difference between the model prediction value and the true value. The formula is: Where n is the number of samples, y i is the true value, is the model prediction value;

[0046] The gradient of the loss function with respect to the network parameters is calculated through the back-propagation algorithm, and the weight matrix and bias terms are updated using stochastic gradient descent (SGD) or its improved algorithm (such as the Adam optimization algorithm), continuously reducing the loss function value until the model achieves optimal performance on the validation set.

[0047] Third, the pre-processed real-time monitoring data is input into the trained LSTM model. The model predicts the future operating status of the equipment based on the patterns learned from historical data. When the prediction results show that the equipment operating parameters exceed the normal range and reach the preset fault threshold, the equipment is judged to be at risk of failure and a maintenance warning is triggered.

[0048] Preferably, the maintenance warning information is sent to the corresponding maintenance personnel, who can then log in to the cloud management platform to view the detailed operating data and fault diagnosis report of the equipment, and formulate a maintenance plan based on the fault diagnosis results.

[0049] It is worth mentioning that the technical features such as the pallet involved in the patent application of this invention should be regarded as prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected by conventional means in the field and should not be regarded as the inventive point of this patent. This patent will not be further elaborated.

[0050] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for preparing a biodegradable plastic pallet, characterized in that: The following steps are involved: Step S1: Select appropriate plastic raw materials according to the use requirements of the pallet, dry the plastic raw materials to remove moisture and volatiles in the raw materials, and use a blender to fully mix the plastic raw materials and additives to ensure that the additives are evenly dispersed in the plastic raw materials; Step S2: Based on the pallet design specifications, a suitable injection mold is selected and the mold is cleaned, inspected, and preheated. The pretreated plastic raw material is added to the injection molding machine hopper and transported to the heating zone by the rotation of the screw, where it is heated to a molten state. The molten plastic raw material is injected into the mold cavity under a certain pressure and the pressure is maintained for a period of time to fill the mold cavity with plastic and compact it. After the pressure is maintained, the cooling system cools the mold to solidify the plastic. When the plastic pallet cools to a preset temperature, the demoulding mechanism is activated to eject the pallet from the mold. Step S3: trim the demoulded pallet and use online inspection equipment and visual inspection technology to inspect the appearance quality of the pallet to check whether there are any defects on the surface of the pallet; at the same time, inspect the performance of the pallet to ensure that the quality of the pallet meets the standards, and finally transport and package it for storage.

2. The method for preparing a biodegradable plastic pallet according to claim 1, characterized in that: In step S3, after the pallet is trimmed, a nano coating is prepared. Nano coatings with corresponding functions are applied to the surface of the pallet according to different requirements. The specific implementation is as follows: For the spraying method, use a spraying device to evenly spray the dispersion or solution of the nano coating material on the surface of the pallet. Before spraying, the surface of the pallet needs to be cleaned; During the spraying process, control the pressure, distance and moving speed of the spraying equipment to ensure uniform coating thickness; After spraying, place the pallet in a drying oven and dry and solidify it at an appropriate temperature so that the coating adheres firmly to the surface of the pallet. For the dip coating method, the tray is completely immersed in a container containing the nano-coating material solution for a certain period of time to allow the solution to fully penetrate the surface of the tray. Then the tray is slowly removed to allow the excess solution to drip naturally. The tray is then placed in a dust-free environment to dry and solidify. For the chemical vapor deposition method, the tray is placed in a vacuum reactor and a mixed gas containing a nano-coating material precursor is introduced. Under the action of high temperature and catalyst, the gas molecules undergo a chemical reaction on the surface of the tray and are deposited to form a nano-coating.

3. The method for preparing a biodegradable plastic pallet according to claim 2, characterized in that: During the preparation process of the pallet, the production equipment of the pallet is monitored in real time through the intelligent monitoring module. The intelligent monitoring module includes an edge computing node processor, a cloud server and multiple monitoring sensors. The edge computing node processor is connected to multiple monitoring sensors to realize real-time collection and preliminary processing of monitoring data and transmit the processed monitoring data to the cloud server for fault diagnosis.

4. The method for preparing a biodegradable plastic pallet according to claim 3, characterized in that: For cloud servers, a distributed storage architecture is adopted to classify and store uploaded data, including real-time monitoring data, historical data, and equipment parameter data. Big data analysis technology is used to clean, integrate and analyze the collected data, and extract key feature parameters. At the same time, machine learning algorithms are used to deeply mine the data, establish an evaluation model for the equipment operating status, and realize real-time evaluation and diagnosis of the equipment health status.

5. The method for preparing a biodegradable plastic pallet according to claim 4, characterized in that: The specific implementation of the evaluation model is: First, collect equipment operation data from various monitoring sensors installed on production equipment to form raw time series data. Remove outliers and missing values ​​from the data, fill in missing values ​​using linear interpolation or time series-based prediction methods, and normalize the data. Second, divide the preprocessed data into training set, validation set and test set; Randomly initialize the weight matrix Wf, WC, Wi, Wo and bias items bf, bC, bi, bo of the LSTM network; The training set data is input into the LSTM network in sequence according to the time step, and calculation is performed to obtain the hidden state ht and memory unit Ct of each time step; The mean square error is used as the loss function to measure the difference between the model prediction value and the true value. The formula is: Where n is the number of samples, y i is the true value, is the model prediction value; The gradient of the loss function with respect to the network parameters is calculated through the back-propagation algorithm, and the weight matrix and bias terms are updated using stochastic gradient descent or its improved algorithm, continuously reducing the loss function value until the model achieves optimal performance on the validation set. Third, the pre-processed real-time monitoring data is input into the trained LSTM model. The model predicts the future operating status of the equipment based on the patterns learned from historical data. When the prediction results show that the equipment operating parameters exceed the normal range and reach the preset fault threshold, the equipment is judged to be at risk of failure and a maintenance warning is triggered.

6. The method for preparing a biodegradable plastic pallet according to claim 5, characterized in that: The maintenance warning information is sent to the corresponding maintenance personnel, who can then log in to the cloud management platform to view the detailed operating data and fault diagnosis report of the equipment, and formulate a maintenance plan based on the fault diagnosis results.

7. A biodegradable plastic pallet, characterized in that: A method for preparing a biodegradable plastic pallet as claimed in any one of claims 1 to 6.

Images