A loading and unloading control method for a plastic basket injection molding automated production line

Abstract

The application provides a feeding and discharging control method of a plastic basket injection molding automation production line, and relates to the technical field of injection molding production line cooperative control. The feeding and discharging control method of the plastic basket injection molding automation production line comprises the following steps: S1: collecting feeding rate information of a feeding end and discharging rate information of a discharging end; S2: inputting the feeding rate information and the discharging rate information into a correlation analysis model, wherein the correlation analysis model outputs an intermediate state evaluation result of a plastic basket injection molding process; S3: generating a first control instruction for adjusting running parameters of the feeding end and a second control instruction for adjusting running parameters of the discharging end according to the intermediate state evaluation result; and S4: sending the first control instruction to a feeding end executing mechanism and sending the second control instruction to a discharging end executing mechanism. Through the fusion of feeding and discharging double-end rate information, closed-loop cooperative control is realized, and the production line running stability is improved.

Description

Technical Field

[0001] This invention relates to the field of collaborative control technology for injection molding production lines, specifically a method for controlling the loading and unloading of materials in an automated injection molding production line for plastic crates. Background Technology

[0002] An automated injection molding production line for plastic crates consists of a feeding system, an injection molding machine, and a unloading system. The feeding system delivers plastic granules to the injection molding machine's hopper. After the injection molding machine completes the melting, injection, pressure holding, and cooling molding processes, the unloading system removes the molded plastic crates from the mold and stacks them. Currently, plastic crate manufacturers commonly equip these processes with automatic feeding and unloading devices. The feeding rate at the feeding end and the unloading rate at the unloading end together determine the production line's output efficiency, and the degree of coordination between the two directly affects the stability of the injection molding process.

[0003] Existing technologies already include solutions for applying deep learning to the injection molding loading and unloading process. A published patent (publication number: CN117584412B) discloses a fault early warning method and system embedded in an automatic injection molding loading and unloading machine. This method collects the operating condition information of the loading machine and uses deep learning based on a sensitive convergence learning function to generate a fault detection channel, thus achieving fault early warning. However, this solution only collects operating condition information from one end of the loading machine and does not involve the operating data from the unloading end. Since there is an inherent correlation between the loading rate and the unloading rate, an excessively fast loading rate may lead to insufficient melting of the raw material, while an excessively fast unloading rate may indicate that the product is ejected before it has fully cooled. This correlation information is crucial for determining whether the injection molding process is functioning normally. Existing technologies lack a means to simultaneously utilize the loading and unloading rate information to assess the injection molding process status and implement coordinated control accordingly. Summary of the Invention

[0004] Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a material loading and unloading control method for an automated production line for plastic basket injection molding, which solves the problem of isolated material loading and unloading rate information and lack of coordinated control methods at both ends in existing injection molding material loading and unloading control methods.

[0006] Technical solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for controlling the loading and unloading of materials in an automated production line for injection molding of plastic baskets, comprising the following steps: S1: Collect the feeding rate information at the feeding end and the unloading rate information at the unloading end; S2: Input the feeding rate information and the unloading rate information into the correlation analysis model, and the correlation analysis model outputs the intermediate state evaluation results of the plastic basket injection molding process; S3: Based on the intermediate state evaluation results, generate a first control command for adjusting the operating parameters of the loading end and a second control command for adjusting the operating parameters of the unloading end; S4: Send the first control command to the loading end actuator and the second control command to the unloading end actuator.

[0008] Preferably, in step S1, the feeding rate information and the unloading rate information are collected in a time-synchronized manner, and the correlation analysis model is a time-series neural network model, which takes the time series of the feeding rate information and the unloading rate information as input.

[0009] Preferably, step S1 further includes: collecting environmental parameters obtained by environmental sensors, the environmental parameters including workshop temperature and / or workshop humidity; in step S2, the environmental parameters, together with the feeding rate information and the unloading rate information, are input into the correlation analysis model.

[0010] Preferably, the intermediate state assessment results include whether there is an anomaly in the plastic basket injection molding process and / or the current state category of the plastic basket injection molding process.

[0011] Preferably, in step S3, when the intermediate state evaluation result indicates that there is an abnormality in the plastic basket injection molding process, a first control command to reduce the feeding rate and / or a second control command to delay the unloading action are generated.

[0012] Preferably, step S1 further includes: collecting process parameters of the injection molding machine, the process parameters including at least one of injection pressure, holding time, and cooling temperature; in step S3, generating a third control command for adjusting the process parameters based on the intermediate state evaluation result; and in step S4, sending the third control command to the injection molding machine controller.

[0013] Preferably, in step S3, when the environmental parameters change, the correlation analysis model regenerates the first control command and the second control command based on the changed environmental parameters.

[0014] A loading and unloading control system for an automated production line for injection molding of plastic baskets includes: The feeding end actuator is located on the upstream side of the injection molding machine and is used to perform the feeding operation of raw materials for the production of plastic crates; The unloading end actuator is located on the downstream side of the injection molding machine and is used to perform the unloading operation of the plastic basket molded products; The first data acquisition module is located at the feeding end and is used to collect feeding rate information. The second acquisition module is located at the feeding end and is used to collect feeding rate information; The controller is connected to the feeding end actuator, the unloading end actuator, the first acquisition module, and the second acquisition module, respectively, and the controller is configured to execute the above method.

[0015] Preferably, it also includes an environmental sensor connected to the controller for collecting workshop temperature and / or workshop humidity and sending it to the controller.

[0016] Beneficial effects

[0017] This invention provides a method for controlling the loading and unloading of materials in an automated production line for injection molding of plastic baskets. It has the following beneficial effects: 1. This invention provides a method for controlling the loading and unloading of materials in an automated production line for plastic crate injection molding. This application simultaneously collects loading rate information from both the loading and unloading ends, and fuses this information using a correlation analysis model to obtain an evaluation result of the intermediate state during the plastic crate injection molding process. In contrast, existing technologies only collect information from one end of the automatic loading machine, failing to obtain the correspondence between loading and unloading rates. This application utilizes the correlation between the loading and unloading rate information to determine the operating status of the injection molding process, providing more comprehensive information and more accurate evaluation results.

[0018] 2. This invention provides a material loading and unloading control method for an automated production line for plastic crate injection molding. Based on the intermediate state evaluation results output by a correlation analysis model, this application generates a first control command to adjust the operating parameters of the loading end and a second control command to adjust the operating parameters of the unloading end, respectively, and sends these commands to the corresponding actuators at the loading and unloading ends. In contrast, existing technologies only output fault warning signals for operators' reference, without directly controlling the actuators. This application transforms the state evaluation results into real-time control commands for both the loading and unloading ends, proactively intervening in the loading and unloading rhythm when abnormal tendencies occur during the injection molding process, thus shortening the time from anomaly identification to action response. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the loading and unloading control method in an embodiment of this application. Figure 2 This is a structural block diagram of the loading and unloading control system in an embodiment of this application; Figure 3 This is a timing comparison diagram of the feeding rate and unloading rate in the embodiments of this application; Figure 4 This is a distribution diagram of the identification results of the correlation analysis model for five types of injection molding process states in the embodiments of this application; Figure 5 This is a graph showing the effect of the feeding rate on the unloading cycle under different ambient temperature conditions in the embodiments of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In this application, "feeding end" refers to the upstream side of the injection molding machine, the end used to supply plastic granule raw materials to the injection molding machine hopper; "unloading end" refers to the downstream side of the injection molding machine, the end used to remove and stack the molded plastic baskets from the mold. "Feeding rate information" refers to the mass of raw material conveyed to the injection molding machine hopper per unit time; "unloading rate information" refers to the number of plastic baskets removed and taken away from the mold per unit time. Example 1

[0022] like Figure 1 As shown, the method includes the following steps: S1: Collect the feeding rate information at the feeding end and the unloading rate information at the unloading end.

[0023] The feeding end actuator is a screw conveyor, and there is a definite correlation between the rotational speed of its drive motor and the feeding rate. The feeding rate information can be obtained by collecting the real-time rotational speed of the drive motor through a speed sensor installed at the feeding end. The unloading end actuator is a six-axis industrial robot, and the frequency of its picking-up actions is the unloading rate. The unloading rate information can be obtained by collecting the time interval between each picking-up action completed by the robot through an encoder installed at the unloading end.

[0024] The acquisition of feeding and unloading rate information must be synchronized. If there is a time deviation between the data acquisition time at the feeding end and the data acquisition time at the unloading end, the corresponding relationship between the two rates established by the subsequent correlation analysis model will lose its physical meaning. This embodiment adopts the clock synchronization mechanism of PROFINET industrial Ethernet to control the sampling time deviation between the feeding end acquisition module and the unloading end acquisition module within ±10ms. The sampling frequency is set to 3 times the injection molding cycle. When the injection molding cycle of the plastic basket is 30 seconds, the sampling frequency is 1Hz.

[0025] In a preferred embodiment, step S1 further includes: collecting environmental parameters from environmental sensors, including workshop temperature and workshop humidity. The workshop temperature is collected using a PT100 platinum resistance temperature sensor positioned 2m around the injection molding machine, with a measurement range of -20℃ to 80℃ and an accuracy of ±0.5℃; the workshop humidity is collected using a capacitive humidity sensor, with a measurement range of 0-100%RH and an accuracy of ±3%RH. The sampling frequency of the environmental parameters is consistent with the sampling frequency of the loading / unloading rate information, both being 1Hz.

[0026] S2: Input the feeding rate information and unloading rate information into the correlation analysis model, and the correlation analysis model outputs the intermediate state evaluation results of the plastic basket injection molding process.

[0027] The correlation analysis model is a temporal neural network model, specifically employing a Long Short-Term Memory (LSTM) network. The loading and unloading rates are input into the model in time series format. The input layer has 60 nodes, containing the loading and unloading rates for the past 30 sampling times (60 input nodes in total). The hidden layer consists of two LSTM layers, each containing 128 neurons. The output layer has 5 nodes, corresponding to the probability distribution of the five intermediate state categories. The model is trained using the cross-entropy loss function and the Adam optimizer, with a learning rate of 0.001 and a batch size of 32.

[0028] The training process of the association analysis model is as follows: Time-series data of the feeding and unloading rates of the plastic crate injection molding production line are collected under normal operating conditions and various abnormal conditions. Each data set is labeled with its corresponding intermediate state category. The data is then divided into a training set and a validation set in an 8:2 ratio. The model is trained using the backpropagation algorithm, and training stops when the classification accuracy on the validation set reaches a preset threshold (95% in this embodiment).

[0029] The intermediate state assessment results include whether there are any abnormalities in the plastic crate injection molding process and the current state category of the plastic crate injection molding process. The current state categories include: normal operation, insufficient material supply at the loading end, delayed part removal at the unloading end, abnormal melt temperature of the injection molding machine, and insufficient mold cooling. The judgment criteria corresponding to each category are shown in Table 1.

[0030] Table 1 Criteria for Determining Intermediate State Categories

[0031] When environmental parameters are also collected in step S1, these parameters are also input into the correlation analysis model. The model needs to learn the mapping relationship between the loading and unloading rates and intermediate states under different ambient temperature and humidity conditions. Changes in workshop ambient temperature affect the fluidity and cooling rate of plastic melt, while changes in ambient humidity affect the moisture content of raw materials, thus affecting melt quality. Incorporating environmental parameters into the model input enables the model to maintain high evaluation accuracy under different seasons and weather conditions.

[0032] like Figure 3 As shown, the horizontal axis represents time (in seconds), the left vertical axis represents the feeding rate (in kg / h), and the right vertical axis represents the unloading rate (in pieces / min). In the 0-20 second interval, the feeding rate stabilizes at approximately 200 kg / h, and the unloading rate stabilizes at 4.0 pieces / min, showing a stable correlation. In the 20-48 second interval, the feeding rate gradually decreases from 200 kg / h to 178 kg / h, and the unloading rate correspondingly decreases from 4.0 pieces / min to 2.6 pieces / min. Both curves decrease synchronously, indicating that insufficient material supply at the feeding end directly leads to a reduction in injection molding machine output, forcing the unloading end to reduce its part-picking frequency. In the 48-80 second interval, the feeding rate gradually recovers to 200 kg / h, and the unloading rate synchronously recovers to 4.0 pieces / min. In the 80-100 second interval, both curves return to a stable operating state. Figure 3 The study verified the intrinsic relationship between the feeding rate and the unloading rate, and this relationship can serve as an effective basis for judging the intermediate state during the injection molding process.

[0033] like Figure 4 As shown, the horizontal axis represents the five state categories, the left vertical axis represents the number of correct classifications, and the right vertical axis represents the number of misclassifications. Specifically, the normal operation state had 245 correct classifications and 5 misclassifications; the insufficient material supply state had 245 correct classifications and 12 misclassifications; the delayed part removal state had 245 correct classifications and 9.5 misclassifications; the abnormal melting state had 245 correct classifications and 8.2 misclassifications; and the insufficient cooling state had 245 correct classifications and 7.2 misclassifications. The total number of test samples for each of the five states was 250. The model achieved an accuracy rate of 98% (245 / 250) for the normal operation state, 98% (245 / 250) for the insufficient material supply state, 98% (245 / 250) for the delayed part removal state, 98% (245 / 250) for the abnormal melting state, and 98% (245 / 250) for the insufficient cooling state. The overall accuracy rate for all five states was approximately 98%.

[0034] The test data covered operating conditions under different ambient temperatures (15℃-40℃) and humidity levels (30%-80%RH). In actual production line deployment, the model needs to maintain a stable recognition accuracy under different seasons and weather conditions. The test results verified that the model has good environmental adaptability.

[0035] S3: Based on the intermediate state evaluation results, generate a first control command for adjusting the operating parameters of the feeding end and a second control command for adjusting the operating parameters of the unloading end.

[0036] The intermediate state evaluation results are sent to the decision module. The decision module has preset state-action mapping rules. When the intermediate state evaluation result is "normal operation," the decision module maintains the current control commands. When the intermediate state evaluation result is "insufficient material supply at the feeding end," the decision module generates a first control command to increase the speed of the feeding end drive motor to improve the feeding rate. When the intermediate state evaluation result is "delayed part removal at the unloading end," the decision module generates a second control command to check for fault alarms in the unloading end robotic arm, and simultaneously generates a first control command to reduce the feeding rate to prevent hopper overflow. When the intermediate state evaluation result is "insufficient cooling," the decision module generates a second control command to delay the unloading action, extending the cooling time of the plastic crate within the mold.

[0037] In a preferred embodiment, when the intermediate state evaluation result indicates an abnormality in the plastic crate injection molding process, a first control command to reduce the feeding rate and a second control command to delay the unloading action are generated. Reducing the feeding rate can prevent excessive accumulation of raw material in the hopper and overflow, while delaying the unloading action can provide a time window for eliminating the abnormality.

[0038] In another preferred embodiment, step S1 further includes: acquiring the process parameters of the injection molding machine, including injection pressure, holding time, and cooling temperature. In step S3, a third control command for adjusting the process parameters is generated based on the intermediate state evaluation result. When the intermediate state evaluation result is "abnormal melt temperature state," the third control command is used to adjust the temperature setpoint of the heating section of the injection molding machine; when the intermediate state evaluation result is "insufficient cooling state," the third control command is used to reduce the temperature setpoint of the cooling water. In step S4, the third control command is sent to the injection molding machine controller.

[0039] It should be noted that the generation of the first, second, and third control commands is not independent. When the intermediate state evaluation result shows an anomaly, the generation of the three control commands must satisfy the collaborative constraint conditions. When the feeding rate is reduced, the injection volume of the injection molding machine decreases accordingly. If the holding pressure time and cooling temperature are not adjusted simultaneously, the molding quality of the plastic crate may change. The preset mapping rule in the decision module is a multi-dimensional joint mapping, meaning that the impact on other commands must be considered when generating any control command.

[0040] When environmental parameters are collected in step S1, in step S3, when the environmental parameters change, the correlation analysis model regenerates the first control command and the second control command based on the changed environmental parameters.

[0041] Figure 5 This is a graph showing the effect of the feeding rate on the unloading cycle under different ambient temperatures. Figure 5 As shown, the horizontal axis represents the feeding rate (unit: kg / h), the left vertical axis represents the feeding cycle at 25℃ ambient temperature (unit: s), and the right vertical axis represents the offset of the feeding cycle relative to the 25℃ baseline value at each temperature (unit: s). The graph contains six curves, one corresponding to the left vertical axis (feeding cycle at 25℃), and the other five corresponding to the right vertical axis (offsets at 15℃, 20℃, 30℃, 35℃, and 40℃). Table 2 shows... Figure 5 The corresponding specific data.

[0042] Table 2. Relationship between feeding rate and unloading cycle under different ambient temperatures.

[0043] Table 2 shows that the feeding cycle at 25℃ ambient temperature exhibits a trend of first decreasing and then increasing with the feeding rate. When the feeding rate is 160 kg / h, the feeding cycle is 32.0 s; when the feeding rate increases to 200 kg / h, the feeding cycle decreases to 30.0 s, reaching the minimum value under this temperature condition; when the feeding rate continues to increase to 240 kg / h, the feeding cycle rebounds to 31.5 s. This indicates that at 25℃ ambient temperature, 200 kg / h is the optimal feeding rate setting for this production line.

[0044] From the right-axis offset data, at an ambient temperature of 35℃, the offsets for each feeding rate were 0.25s or 0.26s, the highest among all test temperatures, indicating that high-temperature environments have the most significant impact on cooling efficiency. At an ambient temperature of 15℃, the offset was 0.08s when the feeding rate was 160kg / h, and increased to 0.26s when the feeding rate increased to 220kg / h, indicating that high feeding rates at low temperatures may exacerbate the risk of incomplete melting. At ambient temperatures of 20℃ and 40℃, the offsets for each feeding rate remained at a low level of 0.08s-0.09s, indicating that the system's control capability was relatively stable under these temperature conditions.

[0045] S4: Send the first control command to the loading end actuator and the second control command to the unloading end actuator.

[0046] Control commands are sent via PROFIBUS fieldbus to the driver of the loading actuator and the controller of the unloading actuator. The first control command is in the format of a speed setpoint (in rpm), and the second control command is in the format of a part-picking interval setpoint (in seconds). After receiving the control commands, the loading and unloading actuators adjust their respective operating parameters according to the command values.

[0047] When a third control command is generated in step S3, the third control command is sent to the injection molding machine controller through the OPCUA communication interface of the injection molding machine in step S4. The injection molding machine controller adjusts process parameters such as injection pressure, holding time or cooling temperature according to the command value. Example 2

[0048] The difference between this embodiment and Embodiment 1 is that the correlation analysis model outputs continuous numerical values ​​rather than discrete categories.

[0049] The correlation analysis model is a regression model, and its output is a quantitative index of the intermediate state of the plastic crate injection molding process. This quantitative index is a continuous value between 0 and 1, where 0 indicates that the injection molding process is in a completely normal state, and the closer the value is to 1, the greater the degree to which the injection molding process deviates from the normal state. When the quantitative index exceeds 0.6, it is determined that the injection molding process has an abnormal tendency, a warning signal is generated, and the control command generation action in step S3 is triggered. When the quantitative index exceeds 0.85, it is determined that the injection molding process is in a seriously abnormal state, and in addition to generating control commands, a stop command is also generated and sent to the injection molding machine controller.

[0050] The training process for the regression model is similar to that of the classification model in Example 1, except that the labels for the training data are continuous values ​​rather than discrete categories. The labeling method for continuous values ​​is as follows: process engineers score multiple sets of historical data one by one, and the scoring rules are determined comprehensively based on factors such as the degree of deviation of the loading and unloading rate from the rated value, the duration of the deviation, and whether it is accompanied by product defects. Example 3

[0051] The difference between this embodiment and Embodiment 1 is that the loading end actuator and the unloading end actuator are the same dual-arm robot.

[0052] The dual-arm robot's first arm performs the loading operation (transferring plastic granules from the storage container to the injection molding machine hopper), and the second arm performs the unloading operation (removing the molded plastic baskets from the mold and stacking them). A first data acquisition module is located at the drive joint of the first arm, using an encoder to collect the first arm's movement speed and frequency to obtain the loading rate information. A second data acquisition module is located at the drive joint of the second arm, using an encoder to collect the second arm's movement speed and frequency to obtain the unloading rate information. The controller is connected to both the first and second arm drivers of the dual-arm robot, sending first and second control commands respectively.

[0053] It should be noted that when the loading and unloading ends share the same robot, physical constraints must be met between the first and second control commands. The first and second arms share the same power module; simultaneous high-speed operation of both arms may exceed the system's power limit. The decision module introduces a power constraint when generating control commands: the sum of the power requirements of the first arm corresponding to the first control command and the power requirements of the second arm corresponding to the second control command must not exceed the robot system's rated power. If this is exceeded, the operating speed of one arm is reduced according to priority, with the unloading operation having higher priority than the loading operation. This is because delays in the unloading operation directly affect the molding cycle, while the loading operation can be buffered by preparing materials in advance. Example 4

[0054] This embodiment provides a loading and unloading control system for an automated production line for injection molding of plastic baskets.

[0055] like Figure 2 As shown, the system includes: a feeding end actuator, a discharging end actuator, a first acquisition module, a second acquisition module, and a controller.

[0056] The feeding end actuator is located on the upstream side of the injection molding machine and is used to perform the feeding operation of raw materials for the production of plastic crates. The feeding end actuator is a screw conveyor.

[0057] The unloading end actuator is located on the downstream side of the injection molding machine and is used to perform the unloading operation of the molded plastic crates. The unloading end actuator is a six-axis industrial robot. The end effector of the unloading end actuator is equipped with grippers for grasping the molded plastic crates.

[0058] The first data acquisition module is located at the feeding end and is used to collect feeding rate information. The first data acquisition module is a speed sensor.

[0059] The second acquisition module is located at the unloading end and is used to collect unloading rate information. The second acquisition module is an encoder.

[0060] The controller is connected to the feeding end actuator, the unloading end actuator, the first acquisition module, and the second acquisition module, respectively. The controller is configured to execute the method of any one of Embodiments 1 to 3. The controller is a programmable logic controller (PLC) pre-installed with the parameter file and control algorithm program of the aforementioned correlation analysis model.

[0061] In a preferred embodiment of this invention, the system further includes environmental sensors connected to the controller for collecting workshop temperature and humidity data and transmitting them to the controller. The environmental sensors include a PT100 platinum resistance temperature sensor and a capacitive humidity sensor. The sensor signals are transmitted to the controller's analog input module via a 4-20mA current loop.

[0062] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the loading and unloading of materials in an automated production line for injection molding of plastic baskets, characterized in that, Includes the following steps: S1: Collect the feeding rate information at the feeding end and the unloading rate information at the unloading end; S2: Input the feeding rate information and the unloading rate information into the correlation analysis model, and the correlation analysis model outputs the intermediate state evaluation results of the plastic basket injection molding process; S3: Based on the intermediate state evaluation results, generate a first control command for adjusting the operating parameters of the loading end and a second control command for adjusting the operating parameters of the unloading end; S4: Send the first control command to the loading end actuator and the second control command to the unloading end actuator.

2. The loading and unloading control method for the automated production line of plastic basket injection molding according to claim 1, characterized in that, In step S1, the feeding rate information and the unloading rate information are collected in a time-synchronized manner. The correlation analysis model is a time-series neural network model, which takes the time series of the feeding rate information and the unloading rate information as input.

3. The loading and unloading control method for the automated injection molding production line of plastic baskets according to claim 1, characterized in that, Step S1 further includes: collecting environmental parameters obtained by environmental sensors, the environmental parameters including workshop temperature and / or workshop humidity; in step S2, the environmental parameters, the feeding rate information, and the unloading rate information are input into the correlation analysis model.

4. The loading and unloading control method for the automated injection molding production line of plastic baskets according to claim 1, characterized in that, The intermediate state assessment results include whether there are any abnormalities in the plastic basket injection molding process and / or the current state category of the plastic basket injection molding process.

5. The loading and unloading control method for the automated production line of plastic basket injection molding according to claim 1, characterized in that, In step S3, when the intermediate state evaluation result indicates that there is an abnormality in the plastic basket injection molding process, a first control command to reduce the feeding rate and / or a second control command to delay the unloading action are generated.

6. The loading and unloading control method for the automated production line of plastic basket injection molding according to claim 1, characterized in that, Step S1 further includes: collecting process parameters of the injection molding machine, the process parameters including at least one of injection pressure, holding time, and cooling temperature; in step S3, generating a third control command for adjusting the process parameters based on the intermediate state evaluation result; in step S4, sending the third control command to the injection molding machine controller.

7. The loading and unloading control method for an automated production line for injection molding of plastic baskets according to claim 3, characterized in that, In step S3, when the environmental parameters change, the correlation analysis model regenerates the first control command and the second control command based on the changed environmental parameters.

8. A loading and unloading control system for an automated production line for injection molding of plastic baskets, characterized in that, include: The feeding end actuator is located on the upstream side of the injection molding machine and is used to perform the feeding operation of raw materials for the production of plastic crates; The unloading end actuator is located on the downstream side of the injection molding machine and is used to perform the unloading operation of the plastic basket molded products; The first data acquisition module is located at the feeding end and is used to collect feeding rate information. The second acquisition module is located at the feeding end and is used to collect feeding rate information; The controller is connected to the feeding end actuator, the unloading end actuator, the first acquisition module and the second acquisition module respectively, and the controller is configured to perform the method of any one of claims 1 to 7.

9. The loading and unloading control system of the automated injection molding production line for plastic baskets according to claim 8, characterized in that, It also includes an environmental sensor connected to the controller for collecting workshop temperature and / or workshop humidity and sending it to the controller.

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