Automatic raw material feeding control system for PVC plastic tile production

Abstract

The invention discloses an automatic raw material feeding control system for PVC plastic tile production, and relates to the technical field of building material manufacturing, and the system comprises a raw material identification module, a PLC central control unit configured on a production line, a bar code and RFID technology for identifying different types of raw materials and obtaining material type information, a preprocessing module, and a control module for controlling the automatic feeding of raw materials based on the material type information. And the weighing module is used for conveying the pretreated raw materials to a weighing assembly at the bottom of the feeding barrel through an automatic conveying belt and an elevator, weighing treatment is carried out, and the weight change trend of the raw materials is obtained. According to the invention, by integrating the dual identification technology of the bar code scanner and the RFID reader-writer, the identification precision is greatly improved, various types of raw materials can be effectively processed, and the adaptability and efficiency of production are improved. A nonlinear mathematical model constructed on the basis of polynomial regression in combination with a neural network is adopted, and a multi-target particle swarm algorithm is matched to perform mixing optimization, so that accurate control of the raw material flow velocity is realized.

Description

Technical Field

[0001] The invention relates to the technical field of building material manufacturing, in particular to an automatic raw material feeding control system for producing PVC plastic tiles. Background Art

[0002] In recent years, with the increasing demand for environmentally friendly and durable materials, PVC (polyvinyl chloride) plastic tiles have become widely used. In the production process of PVC plastic tiles, the loading and mixing of raw materials are critical steps in ensuring product quality. Early loading processes relied primarily on manual labor, which was labor-intensive and inefficient. With the advancement of industrial automation, mechanical conveyor belts, elevators, and other equipment were gradually introduced, enabling the initial automation of raw material transportation. Subsequently, the integrated application of load cells and PLC control systems enabled more precise measurement and control of raw materials, improving the stability of the mixing ratio. Furthermore, some companies began to adopt variable frequency control technology to adjust the motor speed to meet the feeding requirements at different stages. Furthermore, the introduction of PID control algorithms has improved the response speed and accuracy of flow rate regulation, enhancing the system's adaptability to dynamic changes in the continuous production process. Furthermore, the application of multi-sensor fusion technology provides the system with more comprehensive operational status monitoring, further enhancing the reliability and intelligence of the loading system. Traditional methods for raw material identification often rely on a single technology, such as barcodes or RFID. This results in limited recognition accuracy and is ineffective in handling multiple raw material types simultaneously. On the other hand, in the dynamic adjustment module, although the existing PID control algorithm can regulate the raw material flow rate to a certain extent, it is difficult to achieve precise flow control due to the lack of effective modeling capabilities for complex nonlinear relationships, thus affecting the quality consistency of the final product. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides an automatic feeding control system for raw materials used in the production of PVC plastic tiles to solve the problems of insufficient raw material identification accuracy and lack of complex nonlinear relationship modeling capabilities.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides an automatic feeding control system for raw materials used in the production of PVC plastic tiles, which includes a raw material identification module, a PLC central control unit configured on the production line, and uses barcode and RFID technology to identify different types of raw materials and obtain material type information; Pre-processing module, which pre-processes raw materials based on material type information; The weighing module transports the pre-treated raw materials through an automatic conveyor belt and an elevator to the weighing assembly at the bottom of the loading barrel for weighing and obtaining the weight change trend of the raw materials; The dynamic adjustment module calculates the flow rate of the raw materials based on the weight change trend of the raw materials, analyzes it using the PID control algorithm, obtains the valve opening that needs to be adjusted, and fine-tunes the raw material flow rate through the electronically controlled valve; The mixing optimization module monitors and dynamically adjusts the raw material flow rate, which is fine-tuned by electronically controlled valves, through a PLC central control unit. It uses a deep learning model to analyze the raw material flow rate to optimize the mixing process and integrates a reinforcement learning strategy to obtain mixing process parameters. The recording module generates a loading production report based on the mixing process parameters.

[0006] As a preferred solution of the automatic feeding control system for raw materials used in the production of PVC plastic tiles described in the present invention, the PLC central control unit is integrated with a barcode scanner, an RFID reader / writer, multiple sensors, an electric control valve, an automatic conveyor belt and an elevator.

[0007] As a preferred solution of the automatic feeding control system for raw materials used in the production of PVC plastic tiles described in the present invention, the pretreatment includes preliminary inspection and classification, drying using temperature and humidity sensors, screening using a vibrating screen, and impurity removal through a magnetic separator.

[0008] As a preferred solution of the automatic feeding control system for raw materials used in the production of PVC plastic tiles of the present invention, wherein: the pre-treated raw materials are transported to the weighing assembly at the bottom of the feeding barrel through an automatic conveyor belt and an elevator, specifically including the following steps: The pre-treated raw materials are evenly dispersed through a vibrating screen and enter an automatic conveyor belt. A diffuse reflection photoelectric sensor is installed at the end of the automatic conveyor belt to detect the raw material arrival signal at the elevator entrance in real time. After the elevator receives the signal that the raw materials are in place, the internal spiral blades push the raw materials up until they reach the top guide groove and fall into the weighing area at the bottom of the loading barrel.

[0009] As a preferred solution of the automatic feeding control system for raw materials used in the production of PVC plastic tiles of the present invention, the weighing process to obtain the weight change trend of the raw materials specifically includes the following steps: The raw materials falling into the weighing area are weighed by a high-precision piezoelectric crystal sensor, and the sensor signal during the weighing process is transmitted to the conditioning circuit through a shielded cable for processing; The sensor signal processed by the conditioning circuit is converted into a raw material weight value through an analog-to-digital converter, and the difference between the raw material weights of two adjacent samples is calculated using a differential algorithm. At the same time, a zero-point calibration method is used to obtain the net increase in raw material weight; Based on the net increase in raw material weight, the sliding window method is used to smooth short-term weight fluctuations, the average weight within the window is calculated, and the exponential smoothing method is used to eliminate the influence of random noise to generate the raw material weight change trend.

[0010] As a preferred solution of the automatic feeding control system for raw materials used in the production of PVC plastic tiles of the present invention, the flow rate of the raw materials is calculated based on the weight change trend of the raw materials, and the PID control algorithm is used for analysis to obtain the valve opening that needs to be adjusted. The specific steps include: Based on the weight change trend of the raw materials, the weight difference between two adjacent time points is calculated, and the average flow rate of the raw materials over a period of time is obtained according to the corresponding time interval; The sliding window method is used to perform weighted and exponential smoothing on the average flow rate of the raw materials in multiple consecutive time periods to obtain the smoothed raw material flow rate value; Compare the smoothed raw material flow rate value with the target flow rate value, and calculate the error value, error change rate and error integral value; Based on the error value, error change rate and error integral value, PID control instructions are generated through real-time weighted superposition method, and a dead zone compensation mechanism is used in the valve control loop to avoid valve oscillation caused by error; Based on the PID control instruction, the PID control algorithm adopts discretization processing, updates the valve control output once, uses the backward difference method to differentiate the term, and converts the valve control output into valve opening through proportional conversion.

[0011] As a preferred solution of the automatic feeding control system for raw materials used in the production of PVC plastic tiles of the present invention, wherein: the raw material flow rate is fine-tuned by the electric control valve, specifically comprising the following steps: The valve opening that needs to be adjusted is converted into a valve opening instruction through a voltage-current conversion circuit; Based on the valve opening command, the servo motor uses PWM signals to control the motor angle, drives the valve stem through the gear set and guide rail, and fine-tunes the opening of the electronically controlled valve; Use LVDT displacement sensor to feed back the actual opening position of the electronically controlled valve to the PLC central control unit to obtain the valve opening feedback signal; Based on the valve opening feedback signal, the valve opening instruction value is corrected through table lookup method and polynomial interpolation to make the output raw material flow rate reach the target flow rate.

[0012] As a preferred solution of the automatic feeding control system for raw materials used in the production of PVC plastic tiles of the present invention, wherein: the raw material flow rate fine-tuned by the electric control valve is monitored and dynamically adjusted by the PLC central control unit, specifically comprising the following steps: Based on the deviation degree between the raw material flow rate and the target flow rate, a flow rate deviation threshold is defined; Use the distributed I / O function blocks in the PLC central control unit to monitor the flow rate of raw materials; If the raw material flow rate is lower than the flow rate deviation threshold, the PID control algorithm increases the proportional term to increase the valve opening instruction, while the integral term accumulates the negative deviation to push the raw material flow rate up; If the raw material flow rate is higher than the flow rate deviation threshold, the PID control algorithm reduces the proportional term, reduces the valve opening command, and uses the differential term to suppress overshoot in advance.

[0013] As a preferred solution of the automatic feeding control system for raw materials used in the production of PVC plastic tiles of the present invention, a deep learning model is used to analyze the raw material flow rate to optimize the mixing process, specifically including the following operations: Based on the raw material flow rate dynamically adjusted by the PLC central control unit, the flow fluctuation frequency domain characteristics are extracted through time-frequency transformation, and multimodal features are generated by combining the time domain statistical characteristics; Multimodal features are input into the Transformer time series model for trend prediction. Based on the prediction results, the stirring motor speed and the electronically controlled valve opening are dynamically adjusted through a real-time closed-loop control algorithm to optimize the stirring process. As a preferred embodiment of the automatic feeding control system for raw materials used in the production of PVC plastic tiles of the present invention, the mixing process parameters are obtained by integrating a reinforcement learning strategy, which specifically includes the following steps: Define the decision space of hybrid process parameters for integrated reinforcement learning strategy and build a multi-objective optimization framework; Based on the multi-objective optimization framework, energy consumption and mixing uniformity are taken as conflicting objectives and coordinated through the Pareto front. The policy gradient-based PPO algorithm is used to generate candidate parameter combinations, and the final mixing process parameters are obtained through digital twin verification.

[0014] The beneficial effects of the present invention are as follows: the integration of dual recognition technology of a barcode scanner and an RFID reader not only significantly improves recognition accuracy, but also can effectively process various types of raw materials, ensuring that the production line can flexibly respond to raw material inputs of different batches and specifications, thereby improving the adaptability and efficiency of production. A nonlinear mathematical model based on polynomial regression combined with a neural network is used, and a multi-objective particle swarm algorithm is used for mixing optimization to achieve precise control of the raw material flow rate. This advanced control strategy can not only more accurately track the set target flow rate, but also dynamically adjust parameters based on real-time feedback data to ensure that the mixing process parameters are always in the optimal state, thereby significantly improving the quality stability and consistency of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a schematic diagram of the automatic feeding control system for raw materials used in the production of PVC plastic tiles in Example 1.

[0017] Figure 2 This is a flow chart of fine-tuning the raw material flow rate through the electronically controlled valve in Example 1. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0021] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides an automatic feeding control system for raw materials used in the production of PVC plastic tiles, comprising the following steps: S1. Raw material identification module: A PLC central control unit is configured on the production line to use barcode and RFID technology to identify different types of raw materials and obtain material type information.

[0022] The specific operations include the following: The PVC plastic tile production line is equipped with a PLC central control unit. Based on the specific layout and requirements of the production line, high-precision, high-speed industrial-grade barcode scanners and RFID readers and tags are selected and precisely installed at the raw material entrance to ensure that the equipment can cover all possible material entry and feeding paths. These devices are connected to the PLC central control unit via the RS232 standard communication interface. Ensure that the electrical connections between all hardware devices are stable and reliable, and perform necessary grounding to prevent static interference; When raw materials with barcodes or RFID tags enter the production line, the barcode scanner or RFID reader automatically starts and reads the tag information. The read tag information is transmitted to the PLC central control unit in real time through the communication interface. During this process, a CRC error detection mechanism is used to ensure that the data is transmitted completely and accurately. Dedicated software integrated into the PLC central control unit is responsible for parsing the received raw data. For example, it uses barcode parsing algorithms and RFID parsing algorithms to convert the binary data in the barcode or RFID tag into readable text information, including key information such as material number and batch number. Middleware can be deployed to manage communication protocol conversion and data forwarding between different devices, improving the compatibility and scalability of the PLC central control unit. At the same time, electronically controlled valves, automatic conveyor belts and elevators are also integrated into the PLC central control unit to realize automated processing of materials. The electronically controlled valves adjust the valve opening to control the material flow rate. The automatic conveyor belt is driven by a variable frequency speed regulation motor to ensure smooth and efficient transportation of materials. The spiral blades inside the elevator push the raw materials up until they reach the top guide groove and fall into the weighing area at the bottom of the loading barrel. The operation of these devices is coordinated and controlled by the PLC central control unit to ensure seamless connection of the entire process. During this process, the PLC central control unit not only receives data from the barcode scanner and RFID reader, but also monitors the status of multiple sensors and dynamically adjusts the working parameters of each device to ensure that the material enters the next stage of pretreatment in the best condition.

[0023] S2. Pre-process the raw materials based on the material type information.

[0024] The specific operations include the following: After obtaining the material type information, pre-processing is carried out, which usually includes preliminary inspection and classification, drying, screening and impurity removal. During the preliminary inspection and classification stage, photoelectric sensors and visual recognition equipment are used to conduct a preliminary appearance inspection of the raw materials to determine whether there are obvious defects or abnormalities, and the materials are allocated to different processing paths according to their types. During the drying process, appropriate temperature and humidity conditions are set for different material types. Temperature and humidity sensors are used to monitor environmental conditions in real time. Combined with historical parameters in the PLC central control unit, the operating status of the heating and dehumidification equipment is adjusted to ensure that the materials are dried under optimal conditions. This step is crucial to preventing quality issues caused by excessive moisture in subsequent production processes. For example, certain resin materials require storage and processing under specific humidity conditions to ensure the performance of the final product. During the screening and impurity removal stage, the raw materials are graded using a vibrating screen to remove oversized and undersized particles, ensuring that all raw materials entering the production line meet the specified size standards. The design of the vibrating screen and the operating parameters of amplitude and frequency need to be optimized according to the characteristics of the specific raw materials to achieve the best screening effect. At the same time, a magnetic separator is used to remove metallic impurities, not only to prevent them from entering the subsequent process and causing damage to equipment or affecting product quality, but also to ensure the purity of non-metallic raw materials. Through the above operations, not only the consistency and quality of the raw materials are improved, but also the foundation is laid for the subsequent precise weighing and mixing processes, which greatly improves the production efficiency and product quality.

[0025] S3. The pre-treated raw materials are transported to the weighing assembly at the bottom of the loading barrel through an automatic conveyor belt and an elevator for weighing to obtain the weight change trend of the raw materials.

[0026] The specific operations include the following: The pre-treated raw materials are evenly dispersed through a vibrating screen and then fed into an automatic conveyor belt driven by a variable-frequency speed-regulating motor. At the end of the conveyor belt, a diffuse reflection photoelectric sensor is installed. This sensor works based on the principle of light reflection: when the material reaches the front of the sensor, the intensity of the reflected light changes, triggering a signal to the PLC central control unit. To improve detection accuracy, the photoelectric sensor typically uses highly sensitive elements and is equipped with an adaptive threshold adjustment function to cope with materials of different colors or reflective properties. This makes it suitable for real-time detection of material in place. After receiving the signal that the raw materials are in place, the elevator is started. The elevator is equipped with spiral blades, which are driven by a servo motor. The servo motor uses PWM signals to control the motor angle, which allows the motor to accurately adjust the rotation speed and angle to ensure that the raw materials can be pushed up smoothly and efficiently through the elevator. To further improve the stability and accuracy of raw material transportation, the elevator is equipped with a gear set and guide rails. These mechanical components are precisely matched to ensure the stable operation of the spiral blades, avoiding the accumulation or jamming of raw materials due to mechanical failure. When the raw materials fall through the guide trough at the top of the elevator into the weighing area at the bottom of the loading barrel, a high-precision piezoelectric crystal sensor begins to weigh the raw materials. The piezoelectric crystal sensor works based on the piezoelectric effect, that is, when subjected to external force, it generates an electric charge. By measuring the amount of charge, the force applied to it can be accurately calculated, and then the weight of the material can be obtained. To ensure the purity of the sensor signal transmission, the sensor signal is transmitted to the conditioning circuit via a shielded cable. The use of shielded cable effectively prevents external electromagnetic interference and ensures that the signal is not distorted. The grounding design of the shield layer can effectively isolate external electromagnetic noise and ensure signal integrity. In the conditioning circuit, the sensor signal undergoes multi-stage filtering. First, it passes through a hardware RC low-pass filter, which is a simple analog filter used to eliminate low-frequency interference such as mechanical vibration noise. Then, it passes through a software digital filter to suppress high-frequency transient interference and further purify the signal. After the sensor signal is processed by the conditioning circuit, it is converted into a digital signal through an analog-to-digital converter to obtain the specific raw material weight value. Next, a differential algorithm is used to calculate the weight difference between two adjacent raw material samples. The zero-point calibration method is used to eliminate the influence of the cylinder's dead weight and temperature drift, thereby obtaining the net increase in raw material weight. Zero-point calibration is a calibration technique that determines and corrects the baseline error of the measurement by measuring a reference object of known weight to ensure the accuracy of the weighing result. This process usually requires multiple calibrations to ensure the reliability and consistency of the data. Based on the net increase in raw material weight, the sliding window method is used to smooth short-term weight fluctuations. The sliding window method reduces the impact of random fluctuations in a short period of time by performing a weighted average of data within a continuous time period. In specific operations, a fixed time window is selected, such as 10 seconds, and the average value of all sampling points within the window is calculated as the flow rate estimate at the current moment. In addition, the exponential smoothing method is also used to eliminate random noise and generate a more stable and reliable raw material weight change trend. The exponential smoothing method assigns different weights to historical data, making the recent raw material weight change data have a greater impact on the prediction results, thereby better capturing the change trend of the weight data. This not only improves the smoothness of the weight data, but also more accurately reflects the actual flow of the raw materials. Through the above operations, the automated management of precise weighing of raw materials is achieved, which greatly improves production efficiency and product quality, ensures stable operation under various complex working conditions, and provides continuous and reliable performance.

[0027] S4. Based on the weight change trend of the raw materials, the flow rate of the raw materials is calculated and analyzed using the PID control algorithm to obtain the valve opening that needs to be adjusted, and the raw material flow rate is fine-tuned through the electronically controlled valve.

[0028] The specific operations include the following: Based on the weight change trend of the raw materials, by comparing the raw material weights recorded at two adjacent time points, the weight change of the raw materials between the two adjacent time points can be obtained. Then, the time interval between the two adjacent time points can be recorded to calculate the average flow rate of the raw materials. The time interval here is usually a fixed sampling period, such as once per second or once per millisecond, depending on the actual application requirements; The sliding window method is used to perform weighted and exponential smoothing on the average flow rate of raw materials over multiple consecutive time periods to reduce the impact of random fluctuations in a short period of time, so that the average flow rate can more accurately reflect the actual flow of raw materials and improve the accuracy and stability of flow rate data; The smoothed raw material flow rate value is compared with the target flow rate value, and the error value, error change rate and error integral value are calculated. The target flow rate value is defined based on the production process requirements and operating procedures to ensure production stability and product quality. In specific operations, the error value is obtained by subtracting the actual flow rate from the target flow rate. The error value represents the gap between the current flow rate and the target flow rate. The error change rate is calculated based on the error value. The error change rate can reflect the change of the error value over time. The error integral value is calculated based on the error change rate. The error integral value reflects the accumulated error over a long period of time. By identifying and correcting long-term deviations, the final result is prevented from deviating from the target, ensuring that the expected target flow rate is ultimately achieved. According to the proportional, integral and differential coefficients pre-set by the PID control algorithm, the error value, error change rate and error integral value are weighted. The proportional coefficient is used to directly adjust the control output in response to the current error value, the integral coefficient is used to accumulate the error value and adjust the control output accordingly to eliminate the steady-state error value, and the differential coefficient is used to predict the trend of future error values ​​and make adjustments in advance to prevent overshoot in order to obtain PID control instructions. This real-time weighted superposition method ensures a rapid response to error changes while avoiding instability caused by over-adjustment. At the same time, in this process, a dead zone compensation mechanism is introduced to prevent unnecessary oscillation of the valve due to small errors. In specific operations, the dead zone threshold is set based on the minimum allowable error. When the error value is less than the dead zone threshold, no new PID control instruction is issued, thereby avoiding unnecessary fine-tuning of the valve and preventing valve wear. The PID control algorithm uses discretization processing, and the PLC central control unit updates the valve control output every millisecond. The backward difference method is used to approximate the differential term, and the valve control output is converted into the valve opening value through proportional conversion. The specific formula is as follows: ; in, Indicates time The valve opening value, represents the base of natural logarithms, represents the gain coefficient, It represents the PID control output value after dead zone compensation. Indicates the offset; Based on the valve opening value, the valve opening that needs to be adjusted is converted into a valve opening instruction that meets the industrial standard current through a voltage-to-current conversion circuit. This process usually involves analog signal processing technology, such as a D / A converter, to convert digital control instructions into analog signals. In order to drive the servo motor, the analog signal of the valve opening instruction needs to be converted into a PWM signal. The PWM signal is a technology that adjusts the average voltage or current by changing the pulse width. Specifically, the microcontroller generates a series of pulse signals with a specific duty cycle according to the required valve opening. The duty cycle refers to the ratio of the high-level time to the total cycle time in a cycle. By adjusting this ratio, the rotation angle of the servo motor can be controlled. After the servo motor receives the PWM signal, the internal electronic circuit will convert it into a corresponding electrical signal, driving the motor to rotate according to the set angle. Through the gear set and guide rail, the valve stem is driven to fine-tune the opening of the electronically controlled valve, which can reduce friction and other unnecessary energy losses. At the same time, an LVDT displacement sensor is used to feed back the actual opening position of the electronically controlled valve to the PLC central control unit to obtain the valve opening feedback signal. The LVDT displacement sensor has high resolution and stability and can provide reliable displacement measurement data in harsh environments. Based on the valve opening feedback signal, the PLC central control unit corrects the valve opening command value through table lookup and polynomial interpolation to ensure that the output raw material flow rate reaches the target flow rate. In specific operations, the table lookup method can store data tables of optimal valve openings under different conditions, which the PLC central control unit can directly query and apply. Polynomial interpolation constructs a polynomial model based on the current valve opening, actual flow rate and error value, and adjusts the valve opening command value to ensure that the valve opening is sufficiently accurate to meet the requirements of the production process. Through the above operations, automated management of precise control of raw material flow rate is achieved, ensuring stable operation under various complex working conditions. This not only improves the overall performance of the production line, but also reduces resource waste and cost increase caused by operational errors.

[0029] S5. Through the PLC central control unit, the raw material flow rate fine-tuned by the electronically controlled valve is monitored and dynamically adjusted, and the mixing optimization algorithm is used to optimize the mixing process and obtain the mixing process parameters.

[0030] The specific operations include the following: The distributed I / O function block in the PLC central control unit is used to monitor the raw material flow rate in real time. The distributed I / O function block allows the changes in raw material flow rate to be monitored from multiple sensors at the same time, and transmits the monitored data of the raw material flow rate changes to the PLC central control unit for analysis, ensuring rapid response capability. A flow rate deviation threshold is defined based on the degree of deviation between the raw material flow rate and the target flow rate. If the actual flow rate monitored is lower than the set flow rate deviation threshold, the PID control algorithm will start the adjustment mechanism. In specific operations, the valve opening command is increased by increasing the proportional term, thereby increasing the raw material supply. At the same time, the integral term begins to accumulate negative deviations, continuously pushing the raw material flow rate up until the target flow rate is reached. Conversely, if the actual flow rate monitored is higher than the flow rate deviation threshold, the PID control algorithm reduces the valve opening command by reducing the proportional term, and uses the differential term to suppress possible overshoot in advance, ensuring that the raw material flow rate can quickly return to the target flow rate. This dynamic adjustment mechanism not only improves stability and response speed, but also reduces unnecessary fluctuations. To further optimize the flow rate control effect, the parameters of the PID control algorithm can be dynamically adjusted according to actual conditions. For example, when the load changes, the three parameters of proportional, integral, and differential are automatically adjusted through an adaptive adjustment mechanism to adapt to the new operating conditions. This step relies on model prediction technology, such as MPC model predictive control technology, which can predict future error trends and make adjustments in advance, thereby effectively avoiding overshoot and oscillation. The mixing process is optimized based on the raw material flow rate dynamically adjusted by the PLC central control unit. First, the frequency domain characteristics of the flow fluctuations are extracted through time-frequency transformations, such as short-time Fourier transforms. At the same time, time-domain statistical characteristics, including mean and variance, are calculated. These two types of features are combined to generate a multimodal feature vector. Multimodal features can more comprehensively reflect the changing pattern of the raw material flow rate and its potential influencing factors. Next, these multimodal features are input into the Transformer time series model for trend prediction. The Transformer model is selected for its excellent sequence processing capabilities to capture the time dependence and complex patterns of the raw material flow rate. Based on the prediction results of the Transformer time series model, a real-time closed-loop control algorithm is applied to dynamically adjust the speed of the mixing motor and the opening of the electronically controlled valve to ensure the optimal mixing effect. To integrate the reinforcement learning strategy, the decision space for the mixing process parameters is first defined, including but not limited to key parameters such as stirring speed, time, and raw material ratio. A multi-objective optimization framework is then constructed, in which energy consumption and mixing uniformity are considered conflicting objectives. The Pareto frontier approach is used to coordinate the goals of energy consumption and mixing uniformity, aiming to find the optimal solution that minimizes energy consumption without sacrificing mixing quality. On this basis, a policy gradient-based method, such as the proximal policy optimization (PPO) algorithm, is used to generate a series of candidate parameter combinations. Each set of parameters is simulated and verified using digital twin technology, which allows the actual production process to be replicated in a virtual environment and the impact of different parameter settings on the quality of the final product to be evaluated. After multiple iterative optimizations, the mixing process parameters that best meet the multi-objective optimization requirements are selected as the final solution. S6. Based on the mixing process parameters, a loading production report is generated.

[0031] The specific operations include the following: The optimal mixing process parameters are integrated with production data to obtain a data set. The production data includes equipment operating parameters, raw material properties, output, quality indicators, energy consumption statistics, environmental conditions, and maintenance records. The NumPy and SciPy scientific computing libraries are used to calculate key indicators such as average efficiency, energy consumption, and product yield. Exploratory data analysis using EDA can also help understand the potential relationships and trends between key indicators. Matplotlib or Seaborn libraries are used to create visual charts such as histograms and scatter plots to intuitively describe the analysis results. Based on the analysis results, AI algorithms are applied to further explore the value of key indicators. For example, a multi-layer neural network model can be used to predict future production loading efficiency and automatically generate a structured production report containing efficiency, energy consumption, yield, etc. The FineBI tool can be used to design and customize report templates to ensure the professionalism and readability of the information presented in the analysis results. After completing the structured production report, the report can be sent to the management end through the email sending function provided by the Java language to ensure that the management can receive the latest production status report in a timely manner, so as to make timely production decisions.

[0032] In summary, the present invention integrates the dual recognition technology of barcode scanner and RFID reader, which not only greatly improves the recognition accuracy, but also can effectively process various types of raw materials, ensuring that the production line can flexibly respond to the input of raw materials of different batches and specifications, thereby improving the adaptability and efficiency of production. A nonlinear mathematical model based on polynomial regression combined with neural network is adopted, and a multi-objective particle swarm algorithm is used for mixing optimization to achieve precise control of the raw material flow rate. This advanced control strategy can not only track the set target flow rate more accurately, but also dynamically adjust parameters according to real-time feedback data to ensure that the mixing process parameters are always in the optimal state, thereby significantly improving the quality stability and consistency of the product.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An automatic feeding control system for raw materials used in the production of PVC plastic tiles, characterized by: include, The raw material identification module is equipped with a PLC central control unit on the production line, which uses barcode and RFID technology to identify different types of raw materials and obtain material type information; Pre-processing module, which pre-processes raw materials based on material type information; The weighing module transports the pre-treated raw materials through an automatic conveyor belt and an elevator to the weighing assembly at the bottom of the loading barrel for weighing and obtaining the weight change trend of the raw materials; The dynamic adjustment module calculates the flow rate of the raw materials based on the weight change trend of the raw materials, analyzes it using the PID control algorithm, obtains the valve opening that needs to be adjusted, and fine-tunes the raw material flow rate through the electronically controlled valve; The mixing optimization module monitors and dynamically adjusts the raw material flow rate, which is fine-tuned by electronically controlled valves, through a PLC central control unit. It uses a deep learning model to analyze the raw material flow rate to optimize the mixing process and integrates a reinforcement learning strategy to obtain mixing process parameters. The recording module generates a loading production report based on the mixing process parameters.

2. The automatic feeding control system for raw materials used in the production of PVC plastic tiles according to claim 1, characterized in that: The PLC central control unit is integrated with a barcode scanner, an RFID reader / writer, multiple sensors, an electric control valve, an automatic conveyor belt and an elevator.

3. The automatic feeding control system for raw materials used in the production of PVC plastic tiles according to claim 2, characterized in that: The pretreatment includes preliminary inspection and classification, drying using temperature and humidity sensors, screening using a vibrating screen, and impurity removal using a magnetic separator.

4. The automatic feeding control system for raw materials used in the production of PVC plastic tiles according to claim 3, characterized in that: The process of conveying the pre-treated raw materials to the weighing assembly at the bottom of the feeding barrel through an automatic conveyor belt and an elevator specifically includes the following steps: The pre-treated raw materials are evenly dispersed through a vibrating screen and enter an automatic conveyor belt. A diffuse reflection photoelectric sensor is installed at the end of the automatic conveyor belt to detect the raw material arrival signal at the elevator entrance in real time. After the elevator receives the signal that the raw materials are in place, the internal spiral blades push the raw materials up until they reach the top guide groove and fall into the weighing area at the bottom of the loading barrel.

5. The automatic feeding control system for raw materials used in the production of PVC plastic tiles according to claim 4, characterized in that: The weighing process to obtain the weight change trend of the raw materials specifically includes the following steps: The raw materials falling into the weighing area are weighed by a high-precision piezoelectric crystal sensor, and the sensor signal during the weighing process is transmitted to the conditioning circuit through a shielded cable for processing; The sensor signal processed by the conditioning circuit is converted into a raw material weight value through an analog-to-digital converter, and the difference between the raw material weights of two adjacent samples is calculated using a differential algorithm. At the same time, a zero-point calibration method is used to obtain the net increase in raw material weight; Based on the net increase in raw material weight, the sliding window method is used to smooth short-term weight fluctuations, the average weight within the window is calculated, and the exponential smoothing method is used to eliminate the influence of random noise to generate the raw material weight change trend.

6. The automatic feeding control system for raw materials used in the production of PVC plastic tiles according to claim 5, characterized in that: The method of calculating the flow rate of the raw material based on the weight change trend of the raw material and analyzing it using the PID control algorithm to obtain the valve opening that needs to be adjusted specifically includes the following steps: Based on the weight change trend of the raw materials, the weight difference between two adjacent time points is calculated, and the average flow rate of the raw materials over a period of time is obtained according to the corresponding time interval; The sliding window method is used to perform weighted and exponential smoothing on the average flow rate of the raw materials in multiple consecutive time periods to obtain the smoothed raw material flow rate value; Compare the smoothed raw material flow rate value with the target flow rate value, and calculate the error value, error change rate and error integral value; Based on the error value, error change rate and error integral value, PID control instructions are generated through real-time weighted superposition method, and a dead zone compensation mechanism is used in the valve control loop to avoid valve oscillation caused by error; Based on the PID control instruction, the PID control algorithm uses discretization processing to update the valve control output, calculates the differential term using the backward difference method, and converts the valve control output into valve opening through proportional conversion.

7. The automatic feeding control system for raw materials used in the production of PVC plastic tiles according to claim 6, characterized in that: The process of fine-tuning the flow rate of the raw materials by means of the electronically controlled valve specifically comprises the following steps: The valve opening that needs to be adjusted is converted into a valve opening instruction through a voltage-current conversion circuit; Based on the valve opening command, the servo motor uses PWM signals to control the motor angle, drives the valve stem through the gear set and guide rail, and fine-tunes the opening of the electronically controlled valve; Use LVDT displacement sensor to feed back the actual opening position of the electronically controlled valve to the PLC central control unit to obtain the valve opening feedback signal; Based on the valve opening feedback signal, the valve opening instruction value is corrected through table lookup method and polynomial interpolation to make the output raw material flow rate reach the target flow rate.

8. The automatic feeding control system for raw materials used in the production of PVC plastic tiles according to claim 7, characterized in that: The PLC central control unit monitors and dynamically adjusts the flow rate of the raw materials fine-tuned by the electronically controlled valve, specifically including the following steps: Based on the deviation degree between the raw material flow rate and the target flow rate, a flow rate deviation threshold is defined; Use the distributed I / O function blocks in the PLC central control unit to monitor the flow rate of raw materials; If the raw material flow rate is lower than the flow rate deviation threshold, the PID control algorithm increases the proportional term to increase the valve opening instruction, while the integral term accumulates the negative deviation to push the raw material flow rate up; If the raw material flow rate is higher than the flow rate deviation threshold, the PID control algorithm reduces the proportional term, reduces the valve opening command, and uses the differential term to suppress overshoot in advance.

9. The automatic feeding control system for raw materials used in the production of PVC plastic tiles according to claim 8, characterized in that: The deep learning model is used to analyze the raw material flow rate to optimize the mixing process, which specifically includes the following operations: Based on the raw material flow rate dynamically adjusted by the PLC central control unit, the flow fluctuation frequency domain characteristics are extracted through time-frequency transformation, and multimodal features are generated by combining the time domain statistical characteristics; Multimodal features are input into the Transformer time series model for trend prediction. Based on the prediction results, the stirring motor speed and the electronic control valve opening are dynamically adjusted through a real-time closed-loop control algorithm to optimize the stirring process.

10. The automatic feeding control system for raw materials used in the production of PVC plastic tiles according to claim 9, characterized in that: The method of obtaining the mixing process parameters by integrating the reinforcement learning strategy specifically includes the following steps: Define the decision space of hybrid process parameters for integrated reinforcement learning strategy and build a multi-objective optimization framework; Based on the multi-objective optimization framework, energy consumption and mixing uniformity are taken as conflicting objectives. The conflicting objectives are coordinated through the Pareto front, and the policy gradient-based PPO algorithm is used to generate candidate parameter combinations. The final mixing process parameters are obtained through digital twin verification.

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