Control system and method for automatic beer production line

Abstract

The invention discloses a control system and method for an automatic beer production line, relates to the technical field of automatic control, is used for solving the problems that state judgment is lagged and abnormal trend is difficult to identify in time, identifies electronic tags of key equipment through an RFID reader-writer, imports models and serial numbers into a database, and automatically associates operation parameters. The distributed sensors collect temperature, pressure, flow and vibration data in real time, the analysis unit calculates the operation deviation and the health degree, a multi-condition control strategy is configured through the programmable logic controller based on the health degree, a time window and a task priority algorithm are combined, real-time data and historical data are compared to recognize abnormity, and the real-time data and the historical data are analyzed. And a fuzzy control algorithm is fused to generate an adjustment instruction, the equipment state is automatically adjusted, time sequence modeling is utilized to analyze whole-process data, a start-stop strategy is formulated, energy distribution and process optimization are performed, and intelligent scheduling and self-adaptive optimization of the beer production line are realized.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and more particularly, to a control system and method for an automated beer production line. Background Art

[0002] With the continued development of the beer manufacturing industry and the in-depth application of intelligent manufacturing concepts, more and more beer production companies are beginning to introduce automated control systems to improve production efficiency, ensure product quality, and reduce the risk of manual intervention. Traditional beer production line control methods rely primarily on local PLC control and manual parameter setting. This method has limited awareness of the operating status of production equipment and is unable to achieve coordinated optimization between equipment and systematic resource allocation. In response to the production demands of multiple batches, multiple varieties, and rapid changeovers, this control model has gradually exposed problems such as slow response, poor operational stability, and insufficient exception handling capabilities.

[0003] The existing technology has the following deficiencies: Currently, some automated production lines are attempting to introduce sensor-based real-time monitoring modules and industrial Internet architectures. However, these efforts still face challenges with inconsistent parameter collection at the equipment level and a lack of standardized data interfaces, making it difficult to create technical documentation for the entire equipment lifecycle. Real-time monitoring data is not deeply integrated with historical operation data, resulting in delayed status judgment and difficulty in timely identification of abnormal trends; The collaborative control strategy lacks a basis for operational health assessment, equipment linkage and coordination are insufficient, and the overall system operation efficiency is low; Anomaly detection and response methods rely on static threshold judgments, lack intelligent adjustment capabilities, and cannot achieve targeted control; Whole-process optimization is usually based on empirical rules and lacks a global modeling method based on time series data. It is difficult to adapt to dynamic decision-making in complex scenarios. Therefore, a control system and method for an automated beer production line are proposed.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a control system and method for an automated beer production line, which solves the problems raised in the above-mentioned background technology by utilizing RFID equipment identification and parameter import, real-time monitoring by distributed sensors, collaborative control driven by operation health assessment, abnormal adaptive adjustment based on fuzzy control algorithm, and a full-process optimization mechanism combined with time series modeling.

[0006] To achieve the above objectives, the present invention provides the following technical solution for a control system of an automated beer production line, comprising the following modules: The equipment identification and parameter acquisition module is configured to read the electronic tag information of key equipment on the production line through an RFID reader and import the equipment model into the central database to obtain technical parameters; The real-time status monitoring module is configured to collect temperature, pressure, flow and vibration data of each device using a distributed sensor network, and calculate the operation deviation value and evaluate the operation health through the data analysis unit; The collaborative control module is configured to dynamically adjust the collaborative operation mode between devices according to the operational health, and prioritize the allocation of resources to high-priority tasks through the logic judgment unit; The abnormal warning and adaptive adjustment module is configured to detect abnormal conditions by comparing real-time monitoring data with historical benchmark data, and generate adjustment instructions through fuzzy control algorithms and send them to the actuators; The full-process optimization module is configured to analyze the overall operating data of the production line through time series modeling methods to generate equipment start-up and shutdown plans, energy allocation strategies, and process parameter adjustment plans.

[0007] In a preferred embodiment, the electronic tag in the device identification and parameter acquisition module uses a high-frequency RFID chip, and the RFID reader communicates with the central database via an RS485 interface, and the communication protocol is the Modbus RTU standard.

[0008] In a preferred embodiment, the distributed sensor network in the real-time status monitoring module is composed of multiple sub-nodes, and each sub-node includes a temperature sensor, a pressure sensor, and a vibration sensor.

[0009] In a preferred embodiment, the calculation formula of the running deviation value is: ; Where, 、 and Respectively represent the actual measured temperature, pressure and flow values, 、 and Indicates the baseline value.

[0010] In a preferred embodiment, the logic judgment unit in the collaborative control module adopts a programmable logic controller PLC, and the internal program of the PLC is written in ladder diagram language, which supports multi-conditional branch judgment and loop control. The time window mechanism divides the operation sequence between devices by setting a fixed time interval.

[0011] In a preferred embodiment, the calculation formula of the relative deviation rate in the abnormal warning and adaptive adjustment module is: ; If R exceeds the preset threshold, an abnormal warning is triggered and the output adjustment instruction is sent to the actuator in the form of a digital signal.

[0012] In a preferred embodiment, the time series modeling method in the full-process optimization module adopts the ARIMA model, the model parameters are obtained through historical data training, the prediction results are displayed in the form of charts, and the optimization suggestions are output in the form of text.

[0013] The control method for the beer automated production line comprises the following steps: Step S1: Use an RFID reader to read the electronic tag information of each device in the production line, and import the device model into the central database to obtain technical parameters; Step S2: Utilize the distributed sensor network to collect temperature, pressure, flow, and vibration data of each device, calculate the operation deviation value and evaluate the operation health through the data analysis unit; Step S3: Dynamically adjust the collaborative operation mode between devices according to the operational health, and prioritize the allocation of resources to high-priority tasks; Step S4: Detecting abnormal situations through the abnormal warning module. If an abnormality occurs, adjusting the equipment operating parameters through the adaptive adjustment module; Step S5: Use the full-process optimization module to analyze the overall operation data, generate optimization suggestions and guide subsequent operations.

[0014] In a preferred embodiment, in step S2, the scoring formula for the running health is: ; Where, and are the weight coefficients of temperature deviation and vibration frequency, and They are the maximum allowable deviation value and the maximum allowable vibration value respectively.

[0015] In a preferred embodiment, in step S5, the full-process optimization module predicts future operating trends through the ARIMA model, and the generation of optimization suggestions is based on the gap between the predicted results and the target value. When the predicted energy consumption is higher than the target value, it is recommended to reduce the operating time of non-essential equipment.

[0016] Control method for beer automated production line Technical effects and advantages of the present invention: The present invention uses RFID readers to identify the electronic tag information of key equipment, and imports the acquired equipment model, serial number, etc. into a central database, automatically associates operating parameters with standards, and uses a distributed sensor network to collect parameters such as temperature, pressure, flow and vibration during equipment operation in real time. The data analysis unit calculates operating deviations and health indicators, and based on the health of each device, configures a multi-condition control strategy through a programmable logic controller, combines the time window mechanism with the task priority algorithm, calculates the relative deviation rate by comparing real-time data with historical benchmark data, identifies potential abnormal situations, and integrates the fuzzy control algorithm to generate adjustment instructions, automatically adjust the equipment operation status, and at the same time, uses time series modeling methods to perform trend analysis on the entire production process data, formulate start-stop strategies, energy distribution and process parameter optimization plans, and realize full-process intelligent scheduling and deep adaptive optimization of the beer production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a structural block diagram of the control system of the present invention for the beer automated production line.

[0018] Figure 2 The figure is a flow chart of the control method for an automated beer production line according to the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] Example 1 The present invention provides a control system and method for a beer automated production line. Figure 1 and attached Figure 2 Provide detailed explanation.

[0021] Attachment Figure 1 A structural block diagram of a control system for an automated beer production line provided in an embodiment of the present invention shows the connection relationship between the equipment identification and parameter acquisition module, the real-time status monitoring module, the collaborative control module, the abnormal warning and adaptive adjustment module, and the full-process optimization module, and marks the main functional interaction process of each module.

[0022] Attachment Figure 2 The flow chart of the control method for an automated beer production line provided by an embodiment of the present invention shows the complete steps from reading equipment information to generating optimization suggestions for the entire process.

[0023] In this embodiment, the device identification and parameter acquisition module communicates with key devices on the production line through an RFID reader.

[0024] The RFID reader is installed close to the device with its antenna facing the device surface to ensure the signal strength for reading the electronic tag.

[0025] The electronic tag uses a high-frequency RFID chip with an operating frequency of 13.56 MHz and a read and write distance of 0-10 cm.

[0026] The RFID reader is connected to the central database via the RS485 interface, and the communication protocol adopts the Modbus RTU standard to ensure the stability and reliability of data transmission.

[0027] The central database stores the technical parameter range, historical maintenance records and process-related information of each device, and classifies and labels the equipment according to its function in the production line.

[0028] The central database adopts a distributed architecture, supports concurrent access by multiple users, and has data backup and recovery functions. Its server is deployed in a dedicated cabinet in the production line control room and is connected to other modules of the production line through a local area network.

[0029] The real-time status monitoring module consists of a distributed sensor network, and each sub-node contains a temperature sensor, a pressure sensor, and a vibration sensor.

[0030] The temperature sensor adopts thermocouple design with a measurement range of -50℃ to 300℃ and an accuracy of ±0.5℃. It is installed in key parts of the equipment such as the pump casing or the outer wall of the pipeline; The pressure sensor adopts the piezoresistive principle, with a measuring range of 0-10MPa and an accuracy of ±0.1%. It is installed at the pressure measuring point of the pipeline or container. The vibration sensor is designed as an accelerometer with a frequency response range of 1 Hz to 10 kHz and a sensitivity of 10mV / g. It is installed on the bearing seat or motor housing of the equipment.

[0031] Each sub-node of the distributed sensor network is connected to the data analysis unit through wired or wireless means. The data analysis unit is deployed in the control center of the production line and is responsible for processing and analyzing the collected data. The calculation formula of the operating deviation value is: .

[0032] Where, 、 and Respectively represent the actual measured temperature, pressure and flow values, 、 and Indicates the baseline value.

[0033] Furthermore, the scoring formula for running health is: .

[0034] Where, and are the weight coefficients of temperature deviation and vibration frequency, and They are the maximum allowable deviation value and the maximum allowable vibration value respectively.

[0035] The core logic judgment unit of the collaborative control module adopts a programmable logic controller PLC, whose input end is connected to the output end of the distributed sensor network through a shielded cable, and the output end is connected to the actuator through a drive circuit.

[0036] The PLC's internal program is written in ladder diagram language, supporting multiple conditional branching and loop control. A time window mechanism uses a fixed time interval, such as 5 seconds, to divide the operation sequence between devices and avoid resource competition.

[0037] For example, when a certain device needs to complete a task first, the PLC will dynamically adjust the operating time of other devices according to the priority of the current task to ensure that the resource allocation of high-priority tasks will not be interfered with.

[0038] The abnormal warning and adaptive adjustment module determines whether an abnormal situation has occurred by comparing real-time monitoring data with historical benchmark data. The calculation formula for the relative deviation rate is: .

[0039] Where, is the relative deviation rate, To monitor data in real time, This is historical benchmark data.

[0040] If R exceeds a preset threshold, such as 10%, an abnormal warning is triggered.

[0041] The adaptive adjustment module has a built-in fuzzy control algorithm, and the input variables include temperature deviation ΔT, pressure fluctuation ΔP and vibration frequency ΔF; The fuzzy control rule base contains multiple rules, such as: If ΔT is large and ΔP is small, reduce the pump speed. Each rule has been verified experimentally.

[0042] The output adjustment instruction is sent to the actuator in the form of a digital signal. The actuator includes a frequency converter and a servo motor, which are used to adjust the speed of the pump and the opening of the valve respectively.

[0043] The full-process optimization module analyzes the overall operational data of the production line and provides optimization recommendations. This data analysis uses time series modeling to combine historical equipment operating data and current status to predict future operating trends.

[0044] The time series modeling method uses the ARIMA model, and the model parameters are trained using historical data. The forecast results are displayed in graphical form, for example, including the equipment operating load curve and energy consumption trend for the next 7 days.

[0045] Optimization suggestions are output in text form, for example, "It is recommended to start the backup pump in the 3rd hour to balance the load."

[0046] Optimization recommendations are generated based on the difference between the predicted results and the target values. For example, when the predicted energy consumption is higher than the target value, it is recommended to reduce the operating time of non-essential equipment.

[0047] The specific operation process of this embodiment is as follows: first, the electronic tag information of each device in the production line is read through the RFID reader, and the device model is imported into the central database to obtain technical parameters.

[0048] Subsequently, a distributed sensor network is used to collect temperature, pressure, flow and vibration data of each device, and the operation deviation value is calculated and the operation health is evaluated through the data analysis unit.

[0049] Dynamically adjust the collaborative operation mode between devices based on operational health, and prioritize resource allocation to high-priority tasks.

[0050] The abnormal warning module detects abnormal situations. If an abnormality occurs, the adaptive adjustment module adjusts the equipment operating parameters.

[0051] Finally, the full-process optimization module is used to analyze the overall operation data, generate optimization suggestions and guide subsequent operations.

[0052] In the above embodiment, the connection relationship and position relationship between each module are clear, the equipment identification and parameter acquisition module is connected to the central database through an RFID reader, the real-time status monitoring module is connected to the data analysis unit through a distributed sensor network, the collaborative control module is connected to the actuator through a PLC, the abnormal warning and adaptive adjustment module is connected to the actuator through a fuzzy control algorithm, and the full-process optimization module is connected to the central database through an ARIMA model.

[0053] Data exchange between modules is achieved through the local area network to ensure efficient operation and collaborative work of the system.

[0054] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the implementation principle of the present invention is supplemented below with reference to a specific application scenario.

[0055] In the beer automated production line, the device information initialization operation is first completed through the device identification and parameter acquisition module.

[0056] The RFID reader is installed near key equipment on the production line, with its antenna facing the surface of the equipment to ensure stable reading of the electronic tag signal.

[0057] When the production line starts, the RFID reader reads the high-frequency RFID chip information on the equipment and transmits the read equipment model to the central database.

[0058] The central database matches the stored technical parameter range, historical maintenance records, and process-related information according to the equipment model, and classifies and marks the relevant information and feeds it back to the collaborative control module and real-time status monitoring module to provide benchmark data support for subsequent operations.

[0059] Subsequently, the real-time status monitoring module begins to collect multi-dimensional data on the equipment in the production line.

[0060] Each sub-node of the distributed sensor network is installed at a key part of the equipment, such as a temperature sensor on the pump casing, a pressure sensor on the pipeline pressure measuring point, and a vibration sensor on the bearing seat.

[0061] These sensors transmit the collected temperature, pressure, flow and vibration data to the data analysis unit via wired or wireless means.

[0062] The data analysis unit calculates the running deviation value based on the preset algorithm, for example, by the formula Evaluate whether the actual operating status of the equipment deviates from the baseline value. At the same time, the operating health score is calculated by the formula Perform quantitative analysis, where the weight coefficient and Dynamically adjust based on the importance of the equipment.

[0063] If the operational health score of a device is lower than the preset threshold, further processing of the abnormal warning module is triggered.

[0064] The collaborative control module dynamically adjusts the collaborative operation mode between devices based on the operational health feedback from the real-time status monitoring module.

[0065] As the core logic judgment unit, the PLC receives data from the distributed sensor network and determines whether there is a high-priority task through a program written in ladder diagram language.

[0066] For example, during the cooling process of a fermentation tank, if insufficient cooling water flow is detected, the PLC will prioritize allocating resources to the cooling water pump and use a time window mechanism to ensure that the operations of other equipment do not conflict with it.

[0067] The time window mechanism divides the operation sequence between devices by setting a fixed time interval (such as 5 seconds), avoiding the decline in production efficiency caused by resource competition.

[0068] In the saccharification process of beer brewing, the abnormal warning and adaptive adjustment module plays an important role.

[0069] When the temperature sensor of the saccharification pot detects that the actual temperature deviates from the reference value, the abnormal warning module uses the formula Calculate the relative deviation rate.

[0070] If the relative deviation rate exceeds the preset threshold (such as 10%), an abnormal warning is triggered and the abnormal information is transmitted to the adaptive adjustment module.

[0071] The adaptive adjustment module has a built-in fuzzy control algorithm that generates corresponding output adjustment instructions according to the change amplitude of the input variable.

[0072] For example, if it is detected that the temperature of the saccharification pot is too high and the pressure fluctuation is small, the rule "If ΔT is large and ΔP is small, reduce the heating power" in the fuzzy control rule base is activated, and the output adjustment instruction is sent to the actuator in the form of a digital signal. The frequency converter in the actuator reduces the power of the heater, thereby restoring the stable operation of the system.

[0073] The full-process optimization module analyzes the overall operating data of the production line and makes optimization suggestions to improve production efficiency and resource utilization.

[0074] For example, in the beer filling process, the ARIMA model predicts the equipment operating load curve and energy consumption trend in the next 7 days based on historical operating data and current status.

[0075] When the prediction results show that the energy consumption of the filling machine is higher than the target value, the optimization module generates optimization suggestions, such as "it is recommended to start the backup pump in the 3rd hour to balance the load."

[0076] The suggestion is transmitted to the collaborative control module via the local area network. The collaborative control module adjusts the equipment start and stop plan based on the suggestion to ensure the efficient operation of the production line.

[0077] In the above embodiment, the connection relationship and position relationship between each module are clear, the equipment identification and parameter acquisition module is connected to the central database through an RFID reader, the real-time status monitoring module is connected to the data analysis unit through a distributed sensor network, the collaborative control module is connected to the actuator through a PLC, the abnormal warning and adaptive adjustment module is connected to the actuator through a fuzzy control algorithm, and the full-process optimization module is connected to the central database through an ARIMA model.

[0078] Data exchange between modules is achieved through the local area network to ensure efficient operation and collaborative work of the system.

[0079] Example 2 See also Figure 2 , a control method for an automated beer production line, comprising the following steps: Step S1: Use an RFID reader to read the electronic tag information of each device in the production line, and import the device model into the central database to obtain technical parameters; Step S2: Utilize the distributed sensor network to collect temperature, pressure, flow, and vibration data of each device, calculate the operation deviation value and evaluate the operation health through the data analysis unit; Step S3: Dynamically adjust the collaborative operation mode between devices according to the operational health, and prioritize the allocation of resources to high-priority tasks; Step S4: Detecting abnormal situations through the abnormal warning module. If an abnormality occurs, adjusting the equipment operating parameters through the adaptive adjustment module; Step S5: Use the full-process optimization module to analyze the overall operation data, generate optimization suggestions and guide subsequent operations.

[0080] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0081] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0082] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0083] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0084] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0085] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0086] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0088] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0089] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0090] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0091] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The control system for beer automated production line is characterized by: Includes the following modules: The equipment identification and parameter acquisition module is configured to read the electronic tag information of key equipment on the production line through an RFID reader and import the equipment model into the central database to obtain technical parameters; The real-time status monitoring module is configured to collect temperature, pressure, flow and vibration data of each device using a distributed sensor network, and calculate the operation deviation value and evaluate the operation health through the data analysis unit; The collaborative control module is configured to dynamically adjust the collaborative operation mode between devices according to the operational health, and prioritize the allocation of resources to high-priority tasks through the logic judgment unit; The abnormal warning and adaptive adjustment module is configured to detect abnormal conditions by comparing real-time monitoring data with historical benchmark data, and generate adjustment instructions through fuzzy control algorithms and send them to the actuators; The full-process optimization module is configured to analyze the overall operating data of the production line through time series modeling methods to generate equipment start-up and shutdown plans, energy allocation strategies, and process parameter adjustment plans.

2. The control system for the beer automated production line according to claim 1, characterized in that: The electronic tags in the equipment identification and parameter acquisition module use high-frequency RFID chips. The RFID reader communicates with the central database through the RS485 interface, and the communication protocol is the Modbus RTU standard.

3. The control system for the beer automated production line according to claim 2, characterized in that: The distributed sensor network in the real-time status monitoring module consists of multiple sub-nodes, each of which contains a temperature sensor, a pressure sensor, and a vibration sensor.

4. The control system for the beer automated production line according to claim 3, characterized in that: The calculation formula for the running deviation value is: ； Where, 、 and Respectively represent the actual measured temperature, pressure and flow values, 、 and Indicates the baseline value.

5. The control system for the beer automated production line according to claim 1, characterized in that: The logic judgment unit in the collaborative control module adopts a programmable logic controller (PLC). The internal program of the PLC is written in ladder diagram language, which supports multi-condition branch judgment and loop control. The time window mechanism divides the operation sequence between devices by setting a fixed time interval.

6. The control system for the beer automated production line according to claim 5, characterized in that: The calculation formula for the relative deviation rate in the abnormal warning and adaptive adjustment module is: ； If R exceeds the preset threshold, an abnormal warning is triggered and the output adjustment instruction is sent to the actuator in the form of a digital signal.

7. The control system for the beer automated production line according to claim 6, characterized in that: The time series modeling method in the full-process optimization module adopts the ARIMA model. The model parameters are obtained through historical data training. The prediction results are displayed in graphical form, and the optimization suggestions are output in text form.

8. A control method for an automated beer production line, for implementing the control system for an automated beer production line according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step S1: Use an RFID reader to read the electronic tag information of each device in the production line, and import the device model into the central database to obtain technical parameters; Step S2: Utilize the distributed sensor network to collect temperature, pressure, flow, and vibration data of each device, calculate the operation deviation value and evaluate the operation health through the data analysis unit; Step S3: Dynamically adjust the collaborative operation mode between devices according to the operational health, and prioritize the allocation of resources to high-priority tasks; Step S4: Detecting abnormal situations through the abnormal warning module. If an abnormality occurs, adjusting the equipment operating parameters through the adaptive adjustment module; Step S5: Use the full-process optimization module to analyze the overall operation data, generate optimization suggestions and guide subsequent operations.

9. The control method for an automated beer production line according to claim 8, characterized in that: In step S2, the scoring formula for running health is: ； Where, and are the weight coefficients of temperature deviation and vibration frequency, and They are the maximum allowable deviation value and the maximum allowable vibration value respectively.

10. The control method for an automated beer production line according to claim 8, characterized in that: In step S5, the full-process optimization module predicts future operating trends through the ARIMA model. The generation of optimization suggestions is based on the gap between the predicted results and the target values. When the predicted energy consumption is higher than the target value, it is recommended to reduce the operating time of non-essential equipment.

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