A fully automatic pulp cooking system based on digital twin

Abstract

The present invention relates to the technical field of pulp cooking control, and in particular to a fully automatic pulp cooking system based on digital twins, including a raw bean processing module, a pulp cooking equipment module, a control module, a sensor module, a digital twin module and a user module, wherein the digital twin module includes a digital twin generator and a digital twin engine, and a digital twin model is constructed based on the digital twin generator. The digital twin engine carries the digital twin model to output a signal to the control module, and the control module outputs an operation instruction to the raw bean processing module and the pulp cooking equipment module. The present invention is based on digital twin technology and adopts a continuous pulp cooking method for pulp cooking, which fully guarantees the taste and nutrition of the product. The digital twin model can optimize the production process and plan, and can predict the occurrence of failures, making the operation and control of personnel simple.

Description

Technical Field

[0001] The present invention relates to the technical field of pulp cooking control, and in particular to a fully automatic pulp cooking system based on digital twins. Background Art

[0002] my country's soy products have a history of more than 2,000 years. Soy products are rich in nutrition. Not only are they high in protein, but they are also rich in vitamins, minerals, essential fatty acids, dietary fiber, etc. When making soy milk, it must first be cleaned and then ground. The ground raw pulp is then placed in a cooking tank for cooking. There are four common ways of cooking, namely slow cooking and insulation, slow conduction cooking, fast cooking and insulation, and slow steam cooking. Among them, slow cooking and insulation uses a sandwich steam pot to cook the pulp. After boiling the bean paste, it is kept for 10 minutes; slow conduction cooking: using a sandwich steam pot to boil the bean paste within 20 minutes; fast cooking and insulation: using steam jet cooking, boiling the bean paste for 40 seconds and then maintaining it for 80 seconds; slow steam cooking: using steam jet cooking, boiling the bean paste for 10 to 12 minutes. Experiments have shown that compared with direct heating cooking methods, The soy milk produced by indirect heating has poor color, a strong cooking smell, and a low protein extraction rate. Compared with the cooking methods without heat preservation effect (slow conduction cooking and slow steam cooking), the cooking methods with heat preservation process (slow cooking and heat preservation and fast cooking and heat preservation) have lower trypsin inhibitor residual rates in soy milk, which are 13.89% and 6.70% respectively, both lower than 15%. The steam heating and heat preservation method has better effect, and its residual rate is 60.8% lower than that of direct heating, so the fast steam heat preservation cooking effect is the best.

[0003] In the traditional production process, the liquid level in the buffer tank after the pulping machine is controlled by humans. There are many devices to manage before and after, and people are easily tired and make mistakes. During the pulping process, personnel must strictly follow the requirements of process and equipment operation to ensure quality. Personnel training is very important. Different personnel operations can easily lead to unstable product quality and unqualified product quality due to operational errors. The temperature cannot be detected in real time during pulping. People rely on visual inspection and experience to control the steam valve. The valve is manually operated when the pulp is discharged, and the downstream pulp storage container is always observed to see if it is full or overflowing. It is easy to cause overflow and burns, pollute the food working environment, and cause unnecessary losses.

[0004] Digital twin technology is a digital expression of a physical product, so that we can see what may happen to the physical product on the digital product. In the process of digital design and digital production of digital products, the parameters of the simulation analysis model can be transferred to the full three-dimensional geometric model defined by the digital product, and then transferred to the entity to be processed into the actual product. The actual product is reflected in the digital model of the product definition through the online digital detection and measurement system, and then fed back to the simulation analysis model.

[0005] Traditional pulping equipment has certain limitations in terms of automation, precision control and intelligence, and it is difficult to meet the increasing production needs and quality requirements. The experience of combining digital twin technology with food processing equipment is relatively not rich, and is still in the stage of gradual development and accumulation. It is necessary to design a simple-to-operate, intelligent automatic pulping system based on digital twin technology to ensure the nutritional content and taste of soy milk and reduce human uncontrollable factors by drawing on the experimental research results of better steaming methods. Summary of the invention

[0006] The present invention provides a fully automatic pulp cooking system based on digital twins. In view of the rapid progress of digital technology and the gradual rise of the concept of intelligent manufacturing, digital twins, as an emerging technology that integrates the physical world and the digital world, are combined with food production machinery technology to achieve intelligent control of the food processing flow, so as to solve the problems mentioned in the above background technology that in the traditional pulp cooking process, due to the strict process requirements of the pulp cooking equipment, the complexity of the equipment and the limitations of the technical level of the operators, there are problems such as low production efficiency and difficulty in ensuring product quality.

[0007] In the first aspect, the present invention provides a fully automatic pulp cooking system based on digital twins, which adopts the following technical scheme: it includes a raw bean processing module, a pulp cooking equipment module, a control module, a sensor module, a digital twin module and a user module. The digital twin module includes a digital twin generator and a digital twin engine. The digital twin generator generates a digital twin model. The digital twin engine is equipped with a deep learning algorithm and a digital twin model to process the system operation data and generate calculation results. The control module generates control instructions according to the calculation results and outputs them to the raw bean processing module and the pulp cooking equipment module.

[0008] Furthermore, the green bean processing module includes a soybean container and a pulping machine, and the soybean container and the pulping machine are both provided with visual sensors, and the visual sensors are used to detect the material conditions in the soybean container and the pulping machine.

[0009] Furthermore, the pulp cooking equipment module includes six continuous pulp cooking tanks, a pulp inlet pipe unit, a pulp outlet pipe unit, a CIP cleaning pipe unit and a steam pipe unit. The upper part of the pulp cooking tank is provided with a pulp inlet, the lower part of the pulp cooking tank is provided with a pulp outlet, the steam pipe passes through the upper part of the pulp cooking tank and enters the interior of the tank body. The steam pipe is provided with steam holes on the pipe inside the pulp cooking tank. The upper part of the pulp cooking tank is provided with a pressure reducing valve, the top of the pulp cooking tank is provided with a CIP cleaning pipe inlet, and the top of the pulp cooking tank is provided with a stirring paddle.

[0010] Furthermore, the sensor module includes several sensors, wherein a first temperature sensor is arranged on the upper part of the pulp cooking tank, a second temperature sensor is arranged on the lower part of the pulp cooking tank, a pressure sensor is arranged on the upper part of the pulp cooking tank, a liquid level sensor is arranged on the side wall of the pulp cooking tank, a speed controller is arranged on the agitator, a steam control valve is arranged on the steam pipe unit, a pulp inlet control valve is arranged on the pulp inlet pipe unit, a pulp outlet control valve is arranged on the pulp outlet pipe unit, and a cleaning control valve is arranged on the CIP cleaning pipe unit.

[0011] Furthermore, the digital twin generator includes a data acquisition unit, a data processing unit, a model building unit and a verification and calibration unit. The data acquisition unit is used to acquire system operation data, the data processing unit is used to clean and integrate the equipment operation data, the model building unit is used to build a three-dimensional model and fuse the processed data with the three-dimensional model, and the verification and calibration unit is used to compare the three-dimensional model with the system operation data, and adjust the parameters and algorithms of the three-dimensional model according to the comparison results.

[0012] Furthermore, the constructing of the three-dimensional model and fusing the processed data with the three-dimensional model comprises the following steps:

[0013] Use BIM software to build 3D models and render them;

[0014] Establishing the mapping relationship between operation data and 3D model attributes;

[0015] Use MQTT communication protocol to transmit operation data;

[0016] Modify the 3D model attribute parameters according to the changes in operating data and make visual dynamic adjustments.

[0017] Furthermore, the digital twin engine includes a production process optimization unit, an equipment cleaning optimization unit and an equipment maintenance and repair optimization unit. The production process optimization unit is used to optimize and learn multiple production recipes, the equipment cleaning optimization unit is used to optimize the time and cycle of cleaning equipment, and the equipment maintenance and repair optimization unit is used to optimize the time and frequency of equipment maintenance and repair.

[0018] Furthermore, the digital twin engine carries a deep learning algorithm and a digital twin model to process the system operation data and generate a calculation result, including the following steps:

[0019] Build deep learning algorithm models and optimize them through automatic hyperparameter tuning and regularization;

[0020] Use the dataset to train the model, and use distributed training and cross-validation to evaluate the model performance;

[0021] By integrating the deep learning algorithm model with the digital twin model, the intermediate data interaction format is determined;

[0022] Use the real-time stream data processing framework to access and process real-time data and achieve low-latency reasoning.

[0023] Furthermore, the control module includes a process parameter adjustment unit, an equipment cleaning unit and an equipment maintenance and repair unit. The process parameter adjustment unit is used to generate control instructions for adjusting the temperature, pressure, stirring speed, pulp cooking time, pulp inlet and outlet of the equipment. The equipment cleaning unit is used to generate control instructions for opening the cleaning control valve on the CIP cleaning pipeline unit. The equipment maintenance and repair unit is used to generate control instructions for reminding equipment maintenance and repair.

[0024] Furthermore, the user module includes a terminal control machine, the terminal control machine is provided with a display, and the terminal control machine visualizes the system operation status through a digital twin model.

[0025] In summary, the present invention has the following beneficial technical effects:

[0026] 1. The present invention proposes a fully automatic pulp cooking system based on digital twins. According to a large amount of laboratory data, a deep learning algorithm model is trained, and continuous pulp cooking and steam heating and heat preservation are used for pulp cooking, which fully guarantees the taste and nutrition of the product, reduces the residual rate of soymilk trypsin inhibitors, reduces the beany smell, does not require the addition of defoaming agents, and saves the cost of food additives. The entire pulp cooking equipment module is reasonably designed, and the pulp cooking temperatures of six different pulp cooking tanks are different. The present invention meets the requirements of higher process requirements. The operator can intuitively see the equipment status and set parameters through the digital twin model, and the personnel operation and control become simple;

[0027] 2. The digital twin model in the present invention can store a variety of optimized process formulas, because beans of different qualities and types and their ratios are different, such as the ratio of soybeans and black sesame to make black sesame soy milk, etc., have different production processes. Through deep learning algorithm model training, the production processes of various products are optimized, and the heating temperature, pressure and heating time in different tanks are controlled to achieve ideal and stable product quality. The next round of raw bean grinding process can be started before the pulping is completely completed, reducing downtime in production. The digital twin module can provide optimized production and cleaning and maintenance plans;

[0028] 3. The present invention proposes a fully automatic pulp cooking system based on digital twins, which can detect the occurrence of faults through digital twins, remind maintenance personnel to take corresponding maintenance measures, reduce equipment failures and maintenance costs, and remind maintenance personnel to regularly maintain and replace parts;

[0029] 4. The present invention proposes a fully automatic pulp cooking system based on digital twins. The digital twin module has the ability of automatic model calibration and iteration. During the production process, the model continuously learns and accumulates experience, and can optimize process parameters and adjust control strategies to achieve higher production efficiency and product quality. At the same time, it can provide operators with a visual operation interface, view the status and performance indicators of the pulp cooking system in real time, and generate operation records, fault reports, cleaning records, and maintenance information reports. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a schematic diagram of a pulp cooking process according to Embodiment 1 of the present invention;

[0031] Figure 2 It is a structural schematic diagram of a pulp cooking tank according to Embodiment 1 of the present invention;

[0032] Figure 3 It is a schematic diagram of a fully automatic pulp cooking system based on digital twin according to Example 1 of the present invention. DETAILED DESCRIPTION

[0033] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0034] Example 1

[0035] Reference Figure 1 and Figure 3In this embodiment, a fully automatic pulping system based on digital twins includes a raw bean processing module, a pulping equipment module, a control module, a sensor module, a digital twin module and a user module. The digital twin module includes a digital twin generator and a digital twin engine. The digital twin generator generates a digital twin model. The digital twin engine is equipped with a deep learning algorithm and a digital twin model to process the system operation data and generate calculation results. The control module generates control instructions according to the calculation results and outputs them to the raw bean processing module and the pulping equipment module.

[0036] Reference Figure 2 The raw bean processing module includes a soybean container and a pulping machine. The soybean container and the pulping machine are both provided with visual sensors, and the visual sensors are used to detect the material conditions in the soybean container and the pulping machine.

[0037] Reference Figure 2 The pulp cooking equipment module includes six continuous pulp cooking tanks, a pulp inlet pipe unit, a pulp outlet pipe unit, a CIP cleaning pipe unit and a steam pipe unit. The upper part of the pulp cooking tank is provided with a pulp inlet, the lower part of the pulp cooking tank is provided with a pulp outlet, the steam pipe passes through the upper part of the pulp cooking tank and enters the tank body. The steam pipe is provided with steam holes on the pipe inside the pulp cooking tank. The upper part of the pulp cooking tank is provided with a pressure reducing valve, the top of the pulp cooking tank is provided with a CIP cleaning pipe inlet, and the top of the pulp cooking tank is provided with a stirring paddle.

[0038] A first temperature sensor is arranged on the upper part of the pulp cooking tank, a second temperature sensor is arranged on the lower part of the pulp cooking tank, a pressure sensor is arranged on the upper part of the pulp cooking tank, a liquid level sensor is arranged on the side wall of the pulp cooking tank, a speed controller is arranged on the stirring paddle, a steam control valve is arranged on the steam pipe unit, a pulp inlet control valve is arranged on the pulp inlet pipe unit, a pulp outlet control valve is arranged on the pulp outlet pipe unit, and a cleaning control valve is arranged on the CIP cleaning pipe unit.

[0039] The digital twin generator includes a data acquisition unit, a data processing unit, a model building unit and a verification and calibration unit. The data acquisition unit is used to acquire system operation data, the data processing unit is used to clean and integrate equipment operation data, the model building unit is used to build a three-dimensional model and fuse the processed data with the three-dimensional model, and the verification and calibration unit is used to compare the three-dimensional model with the system operation data, and adjust the parameters and algorithms of the three-dimensional model according to the comparison results.

[0040] The construction of the three-dimensional model and the fusion of the processed data with the three-dimensional model comprises the following steps:

[0041] Use BIM software to build 3D models and render them;

[0042] Establishing the mapping relationship between operation data and 3D model attributes;

[0043] Use MQTT communication protocol to transmit operation data;

[0044] Modify the 3D model attribute parameters according to the changes in operating data and make visual dynamic adjustments.

[0045] When the entire system is running, the sensors collect real-time equipment status information and parameters in the production process, including the temperature of pulp cooking, the pressure value of the pulp cooking tank, the pulp cooking time, and the flow rate of pulp in and out. When the data acquisition unit of the digital twin generator obtains this data, it will sort, screen and conduct preliminary analysis to ensure the accuracy and availability of the data. The digital twin generator will build or update the corresponding digital twin model based on this data. The digital twin generator will use the equipment information in the data to create a digital twin that is highly similar to the actual physical object and reflect the actual status of the pulp cooking system in real time. The digital twin engine will use the received data and use deep learning algorithms for in-depth analysis and calculation to generate corresponding control instructions and performance evaluation results, thereby realizing monitoring, optimization and decision support for the entire pulp cooking process.

[0046] When working, the user module includes a terminal control machine, which is provided with a display. The terminal control machine visualizes the operating status of the equipment through a digital twin model. The terminal control machine can independently operate the start and stop of the machine. The terminal control machine has a built-in control module, which includes a process parameter adjustment unit, an equipment cleaning unit and an equipment maintenance and repair unit. The process parameter adjustment unit is used to generate control instructions for adjusting the temperature, pressure, stirring speed, pulp cooking time, pulp inlet and outlet of the equipment. The equipment cleaning unit is used to generate control instructions for opening the cleaning control valve on the CIP cleaning pipeline unit. The equipment maintenance and repair unit is used to generate control instructions for reminding equipment maintenance and repair.

[0047] The pulping mode is turned on through the control module. The control module automatically controls the operation of the system according to the calculation results of the digital twin engine. The beans in the soybean container 15 are lifted and inverted in the pulper 16 for pulping. The temperatures of the six pulping tanks 1 are 55°C, 65°C, 75°C, 85°C, 95°C, and 105°C, respectively. The first steam valve is opened. After reaching the preset temperature, the first pulp inlet valve is opened, and the stirring paddle 10 rotates and is sucked into the first pulping tank 1 through the pipeline. The steaming times are 80s, 60s, 40s, 30s, 20s, and 10s, respectively. When the preset time is reached, it is sucked to the next pulping tank 1 to continue pulping. The slurry is stirred and heated to reach the preset temperature. At this time, only the valve on the steam pipe 8 and the valve at the exhaust port are opened to control the steam pressure in the pulping tank 1. The pressure is set to zero, and all other valves on the tank are closed. The raw pulp in the tank is cooked by the high-temperature steam discharged from the steam through hole. After cooking, the raw pulp is stewed. All valves are closed, and the residual heat in the tank continues to stew the soybean milk for 60 seconds. At this time, if the pressure sensor in the tank detects that the air pressure exceeds the normal pressure, the pressure reducing valve 9 is opened to release the pressure, and finally the pulp is released. The pulp discharge control valve is opened, and the cooked pulp flows out through the pulp discharge pipeline to the pulp separation equipment 17 for the next process. The system and process in this embodiment do not need to add defoaming agents to reduce the generation of foam during the pulping process. By optimizing the pulping process, adjusting the pulping temperature, time and stirring speed parameters, using physical defoaming methods, and destroying the stability of the foam through mechanical stirring to make it break. The operator can intuitively see the equipment status and performance indicators through the digital twin model, and set parameters, which makes personnel operation and control simple.

[0048] Example 2

[0049] Reference Figure 3 The digital twin engine is also equipped with a deep learning algorithm. The digital twin engine includes a production process optimization unit, an equipment cleaning optimization unit, and an equipment maintenance and repair optimization unit. The production process optimization unit is used to optimize and learn multiple production formulas, and the equipment cleaning optimization unit is used to optimize the time and cycle of cleaning equipment.

[0050] The digital twin engine is equipped with a deep learning algorithm and a digital twin model to process the system operation data and generate calculation results, including the following steps:

[0051] Build deep learning algorithm models and optimize them through automatic hyperparameter tuning and regularization;

[0052] Use the dataset to train the model, and use distributed training and cross-validation to evaluate the model performance;

[0053] The integration of deep learning models and digital twin models to determine the intermediate data interaction format;

[0054] Use the real-time stream data processing framework to access and process real-time data and achieve low-latency reasoning.

[0055] During operation, according to the different ratios of different raw materials in different processes prompted by the system, they are placed in the soybean container in advance, and the pulping machine mixes soybeans and purple rice to make purple rice soy milk; corn and soybeans are mixed to make corn soy milk. Through the digital twin engine, the production process of various products can be optimized. The digital twin engine can accurately calculate and control the temperature, time and stirring speed of each pulping tank 1, and control and adjust the operation of the system through the controller.

[0056] At the same time, the digital twin engine can also intelligently adjust the production plan according to production needs and optimize the conversion time of different soy milk products. The digital twin model simulates the current production status and progress based on real-time data, and uses the processed operation data to train the deep learning model. The model learns the conversion rules, time patterns and influencing factors between the production of different products. Sensors monitor equipment status, production progress, raw material supply and other data in real time. These real-time data are transmitted to the digital twin model and deep learning algorithm. The digital twin model simulates the current production status and progress based on real-time data. The deep learning algorithm calculates the optimal product conversion time based on the learned conversion rules and time patterns combined with the simulation results of the digital twin model. According to the calculated optimal conversion time, the operation order of the equipment in the system is intelligently adjusted. When it is necessary to switch from one soy milk product to another, the system can automatically calculate the optimal conversion time. While boiling the soy milk, the system can automatically arrange the grinding equipment to grind the next batch of raw materials to ensure the continuity of production. Moreover, the visual sensor can detect different products. The deep learning algorithm can intelligently adjust the grinding parameters of the control module according to the needs of different products to improve grinding efficiency and product quality.

[0057] Through the digital twin engine equipped with deep learning algorithms, the time for cleaning equipment has also been optimized. The digital twin engine can reasonably arrange the cleaning time according to the production plan and equipment usage. Before the pulping of a product is completed, the system will enter the pulping process of the next product in advance. At the same time, the pulping equipment will start the cleaning mode and complete the cleaning before the grinding is completed, ensuring that the equipment is always clean and hygienic, while minimizing the impact of cleaning on production and shortening the time from processing to cleaning to product conversion.

[0058] Example 3

[0059] Reference Figure 3 The digital twin engine is equipped with a deep learning algorithm. The digital twin engine also includes: an equipment maintenance and repair optimization unit, which is used to optimize the time and frequency of equipment maintenance and repair.

[0060] Equipment maintenance includes equipment cleaning, adding lubricants, adding lubricants, equipment fixing, equipment adjustment, parts replacement and equipment repair; equipment failures are detected through the sensor digital twin model, including increased friction, increased vibration, imbalance, wear, reduced lubricity, engine failure, bearing failure, shaft failure, part cracks, sensor failure and circuit short circuit. Once a fault is detected, the digital twin model will immediately issue a reminder and provide fault diagnosis and maintenance suggestions.

[0061] In this embodiment, sensors are arranged at key positions of equipment in the pulp cooking system. When the sensor detects that the friction of the equipment increases, the friction data, temperature changes, lubricant consumption rate and other parameters collected by the sensor are analyzed and compared with the preset normal range to determine whether the friction has increased abnormally. The digital twin model will recommend lubrication and cleaning of the equipment. When it is detected that the vibration of the equipment increases, the vibration spectrum data collected by the vibration sensor is combined with the fast Fourier transform signal processing technology to analyze the changes in vibration frequency and amplitude. When the preset value is exceeded, the digital twin model will recommend precise balancing adjustments to the equipment based on the structural characteristics of the equipment, installation method and spectral characteristics of the vibration. At the same time, the digital twin model presets the life and maintenance cycle of parts. The deep learning algorithm can calculate the time for equipment maintenance and repair, thereby ensuring the normal operation of the equipment and extending the service life of the equipment. The digital twin model predicts faults through algorithm analysis and provides fault diagnosis suggestions. According to the actual maintenance situation, feedback is given to the digital twin model to optimize the accuracy and predictive ability of the model, and continuous improvement and optimization.

[0062] Example 4

[0063] The digital twin engine in this embodiment has the ability to calibrate and iterate the model to continuously improve the accuracy and adaptability of the model. The operator adjusts the parameters in the deep learning algorithm model based on product development experience and actual production product quality to improve the accuracy of the model's next execution operation; as time goes by and new data is accumulated, the deep learning algorithm automatically iterates and updates the model to adapt to the evolution and changes of the product.

[0064] The digital twin engine is configured to provide data visualization and generate reports, including equipment operation records, fault reports, cleaning records, and maintenance information. Through an intuitive visualization interface, operators can view the status and performance indicators of the pulping system in real time.

[0065] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A fully automatic pulp cooking system based on digital twin, characterized in that: The invention comprises a raw bean processing module, a pulping equipment module, a control module, a sensor module, a digital twin module and a user module. The digital twin module comprises a digital twin generator and a digital twin engine. The digital twin generator generates a digital twin model. The digital twin engine carries a deep learning algorithm and a digital twin model to process the system operation data and generate a calculation result. The control module generates a control instruction according to the calculation result and outputs it to the raw bean processing module and the pulping equipment module. The pulping equipment module comprises six continuous pulping tanks, a pulp inlet pipeline unit, a pulp outlet pipeline unit, a CIP cleaning pipeline unit and a steam pipeline unit. The upper part of the pulping tank is provided with a pulp inlet, the lower part of the pulping tank is provided with a pulp outlet, the steam pipeline passes through the upper part of the pulping tank into the interior of the tank body, the steam pipeline is provided with a steam hole on the pipeline inside the pulping tank, the upper part of the pulping tank is provided with a pressure reducing valve, and the top of the pulping tank is provided with a A CIP cleaning pipe inlet is provided, and a stirring paddle is provided on the top of the pulp cooking tank. The digital twin generator includes a data acquisition unit, a data processing unit, a model building unit and a verification and calibration unit. The data acquisition unit is used to acquire system operation data, and the data processing unit is used to clean and integrate the equipment operation data. The model building unit is used to build a three-dimensional model and merge the processed data with the three-dimensional model. The verification and calibration unit is used to compare the three-dimensional model with the system operation data, and adjust the parameters and algorithms of the three-dimensional model according to the comparison results. The digital twin engine includes a production process optimization unit, an equipment cleaning optimization unit and an equipment maintenance and repair optimization unit. The production process optimization unit is used to optimize and learn multiple production formulas, the equipment cleaning optimization unit is used to optimize the time and cycle of cleaning equipment, and the equipment maintenance and repair optimization unit is used to optimize the time and frequency of equipment maintenance and repair.

2. According to claim 1, a fully automatic pulp cooking system based on digital twins is characterized in that: The green bean processing module comprises a soybean container and a pulping machine, and both the soybean container and the pulping machine are provided with visual sensors, and the visual sensors are used to detect the material conditions in the soybean container and the pulping machine.

3. The fully automatic pulp cooking system based on digital twin according to claim 1 is characterized in that: The sensor module includes several sensors, wherein a first temperature sensor is arranged on the upper part of the pulp cooking tank, a second temperature sensor is arranged on the lower part of the pulp cooking tank, a pressure sensor is arranged on the upper part of the pulp cooking tank, a liquid level sensor is arranged on the side wall of the pulp cooking tank, a speed controller is arranged on the stirring paddle, a steam control valve is arranged on the steam pipe unit, a pulp inlet control valve is arranged on the pulp inlet pipe unit, a pulp outlet control valve is arranged on the pulp outlet pipe unit, and a cleaning control valve is arranged on the CIP cleaning pipe unit.

4. The fully automatic pulp cooking system based on digital twin according to claim 1 is characterized in that: The construction of the three-dimensional model and the fusion of the processed data with the three-dimensional model comprises the following steps: Use BIM software to build 3D models and render them; Establishing the mapping relationship between operation data and 3D model attributes; Use MQTT communication protocol to transmit operation data; Modify the 3D model attribute parameters according to the changes in operating data and make visual dynamic adjustments.

5. The fully automatic pulp cooking system based on digital twin according to claim 1 is characterized in that: The digital twin engine is equipped with a deep learning algorithm and a digital twin model to process the system operation data and generate calculation results, including the following steps: Build deep learning algorithm models and optimize them through automatic hyperparameter tuning and regularization; Use the dataset to train the model, and use distributed training and cross-validation to evaluate the model performance; By integrating the deep learning algorithm model with the digital twin model, the intermediate data interaction format is determined; Use the real-time stream data processing framework to access and process real-time data and achieve low-latency reasoning.

6. The fully automatic pulp cooking system based on digital twin according to claim 1, characterized in that: The control module includes a process parameter adjustment unit, an equipment cleaning unit and an equipment maintenance and repair unit. The process parameter adjustment unit is used to generate control instructions for adjusting the temperature, pressure, stirring speed, pulp cooking time, pulp inlet and outlet of the equipment. The equipment cleaning unit is used to generate control instructions for opening the cleaning control valve on the CIP cleaning pipeline unit. The equipment maintenance and repair unit is used to generate control instructions for reminding equipment maintenance and repair.

7. The fully automatic pulp cooking system based on digital twin according to claim 1 is characterized in that: The user module includes a terminal control machine, the terminal control machine is provided with a display, and the terminal control machine visualizes the system operation status through a digital twin model.

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