Beverage processing production line control method and system based on Internet of Things

Abstract

The invention discloses a beverage processing production line control method and system based on the Internet of Things, and relates to the technical field of production line control. During operation of the system, environment, quality and energy consumption parameters in the beverage processing process are collected in real time by using a distributed Internet of Things sensor network, and data are uploaded and preprocessed through a wireless communication technology; the method comprises the following steps: calculating a quality control consistency coefficient Qc, receiving real-time energy consumption data, analyzing an energy consumption state, calculating an energy consumption utilization efficiency coefficient Ee, dynamically adjusting extraction temperature, time and proportioning parameters, storing historical data of a regulation and control process, optimizing subsequent production batches, and monitoring environmental parameters of a production workshop in real time for calculation. The system performance index SPI is displayed through a visual interface, a multi-platform access interface is provided, a manager is supported to remotely check the state of a production line and adjust production parameters, and an automatic alarm mechanism is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of production line control, and in particular to a control method and system for a beverage processing production line based on the Internet of Things. Background Art

[0002] As one of the globally popular beverages, tea beverages have become an integral part of people's daily lives due to their rich flavors and health benefits. With the continuous improvement of consumers' requirements for the quality of tea beverages, the production process of tea beverages has become increasingly refined and complex. Although traditional tea beverage production lines can complete basic processing tasks, they face many challenges, especially in terms of production efficiency, product consistency, and resource utilization. To address this challenge, modern tea beverage processing production lines have gradually introduced automation and intelligent technologies.

[0003] Traditional production lines usually rely on manual monitoring and regular inspections, resulting in the risk of not promptly detecting quality fluctuations or environmental anomalies during the production process. Existing Internet of Things systems still have deficiencies in aspects such as device connectivity, data processing capabilities, and real-time response. For example, their ability to respond to sudden environmental changes is relatively weak, and there is still room for improvement in the intelligent level of device management, especially for the management and scheduling of high-performance devices.

[0004] The application of the Internet of Things in tea beverage processing production lines has effectively improved the intelligent level of the production line, but some problems and limitations have also emerged. First, due to insufficient power management and system monitoring during the operation of devices, the energy consumption may be too high, especially when the devices are in standby or low-load operation, and it is impossible to achieve sufficient energy conservation. In addition, environmental fluctuations such as temperature and humidity changes or fluctuations in the concentration of air particles are not effectively adjusted in a timely manner, which may lead to fluctuations in the quality of tea beverages. Quality deviations may be manifested in the exceeding of key indicators such as the concentration of tea polyphenols, acidity, and turbidity, seriously affecting the taste and flavor of tea beverages. More importantly, when the production line processes these abnormalities, it lacks timely and effective automatic adjustment capabilities, which may lead to production interruptions, energy consumption waste, and even affect the continuity and stability of production. The production line lacks an adequate intelligent response mechanism, which will directly affect the production efficiency, product quality, and market competitiveness of enterprises. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a control method and system for a beverage processing production line based on the Internet of Things, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A control system for a beverage processing production line based on the Internet of Things includes a data acquisition module, a quality control module, an energy consumption optimization module, an extraction regulation module, an environmental monitoring and control module, and a remote management module; The data acquisition module is used to utilize a distributed Internet of Things sensor network to collect environmental, quality, and energy consumption parameters in the beverage processing process in real time, and upload the data to a cloud platform or edge computing device for preprocessing through wireless communication technology; The quality control module is used to receive quality-related parameters uploaded by the data acquisition module through the Internet of Things platform, perform real-time analysis using cloud computing or edge computing, intelligently evaluate the quality indicators of products, calculate the quality control consistency coefficient Qc to determine whether the quality of the current production batch meets the standards; The energy consumption optimization module is used to receive real-time energy consumption data, intelligently model the energy consumption distribution of equipment through the Internet of Things platform, analyze the energy consumption status using cloud or edge computing, and calculate the energy consumption utilization efficiency coefficient Ee; The extraction regulation module is used to receive instructions provided by the quality control module and the energy consumption optimization module, dynamically adjust the extraction temperature, time, and ratio parameters, store the historical data of the regulation process, and use it to optimize subsequent production batches; The environmental monitoring and control module is used to deploy Internet of Things environmental sensors to monitor the environmental parameters of the production workshop in real time, and regulate the air conditioning system, ventilation equipment, and humidification device through the Internet of Things platform, upload them to the cloud platform or edge computing node for calculation, and obtain the environmental stability coefficient Es; The remote management module is used to calculate the system performance index SPI in real time, display it through a visual interface, provide multi-platform access interfaces, support managers to remotely view the production line status and adjust production parameters, and implement an automated alarm mechanism to push early warning information in a timely manner for environmental, quality, or energy consumption anomalies.

[0007] Preferably, the data acquisition module includes an environmental data acquisition unit, a quality data acquisition unit, and an energy consumption data acquisition unit; The environmental data acquisition unit is used to deploy environmental monitoring sensors to collect data on the temperature, humidity, and air particulate matter concentration in the workshop environment in real time, preprocess the data, and use wireless data transmission methods, including Wi-Fi, LoRa, and NB-IoT, to send the data to the environmental monitoring and control module; The quality data acquisition unit is used to monitor the tea polyphenol concentration, acidity, and turbidity of the beverage in real time through a line spectrum analyzer, pH sensor, and turbidity detector. The collected data is preliminarily filtered and normalized to obtain: tea polyphenol concentration TP, acidity Ac, and turbidity Cl, and transmitted to the quality control module; The energy consumption data acquisition unit is used to monitor the real-time power consumption, standby power consumption, and energy loss of production equipment through an intelligent electricity meter and a power consumption sensor, and obtain: equipment standby power consumption P idle and total equipment power consumption P total, the energy loss E loss and the total energy input E input ; The data of the temperature and humidity of the workshop environment and the concentration of air particles are obtained by installing digital temperature sensors, digital humidity sensors and air quality sensors near the production equipment and the material storage area in the production workshop and preprocessing the data: the ambient temperature Te, the ambient humidity He and the concentration of air particles Pm.

[0008] Preferably, the quality control module includes a data analysis and modeling unit, an anomaly detection unit and an optimization unit; The data analysis and modeling unit is used to establish a prediction model for the quality parameters of tea polyphenol concentration, acidity and turbidity based on historical data and machine learning algorithms, and calculate the quality control consistency coefficient Qc of the current production batch by using big data analysis methods; The quality control consistency coefficient Qc is calculated and obtained through the following formula: ; In the formula, TP act , Ac act and Cl act are respectively the actual tea polyphenol concentration, acidity and turbidity in the current production batch, and TP target , Ac target and Cl target are the set target values or standard values. TP represents the tea polyphenol concentration, Ac represents the acidity, and Cl represents the turbidity; The anomaly detection unit is used to compare the quality data of the current batch with the set standards, determine whether the quality is qualified according to the quality evaluation results, detect quality deviations, immediately send an adjustment instruction to the extraction control module, and at the same time send an alarm signal to the remote management module; The optimization unit is used to adjust the control strategy of the quality parameters according to the product quality evaluation results, and synchronously transmit the quality optimization data to the remote management module for managers to view and analyze.

[0009] Preferably, the determination of the product quality evaluation results: Before the start of the production process, set the target quality parameters according to the specifications of the beverage and determine its qualified range: Tea polyphenol concentration TP: the target value is TP target , and the allowable deviation range is ±5%; Acidity Ac: the target value is Ac target , and the allowable deviation range is ±0.2; Turbidity Cl: the target value is Cl target , and the allowable deviation range is ±10%; Compare the currently measured quality data with the preset standard values: Detection of tea polyphenol concentration: If TP act exceeds TP target by ±5%, it is determined as a deviation; Detection of acidity and alkalinity: If Ac act exceeds Ac target by ±0.2, it is determined as a deviation; Detection of turbidity: If Cl act exceeds Cl target by ±10%, it is determined as a deviation; If any quality data exceeds the deviation range, immediately conduct a quality deviation determination. Once a deviation is found, trigger the following operations: After receiving the quality deviation instruction, the extraction control module automatically adjusts the extraction temperature, time, or ratio to correct the deviation and restore the quality to the target standard. The remote management module sends an alarm signal to the management personnel, indicating that there is a quality abnormality in the current batch and requesting manual inspection or further adjustment.

[0010] Preferably, the energy consumption optimization module includes an energy consumption evaluation unit, an energy consumption optimization decision-making unit, and an intelligent prediction unit; The energy consumption evaluation unit is used to calculate the power consumption of each production device, including real-time power consumption, standby power consumption, and energy loss data, and calculate the overall energy consumption utilization efficiency coefficient Ee; The energy consumption utilization efficiency coefficient Ee is calculated and obtained through the following formula: ; In the formula, P idle represents the standby power consumption of the device, P total represents the total power consumption of the device, E loss represents the energy loss, E input represents the total energy input, and T represents time; The energy consumption optimization unit is used to analyze the energy consumption bottleneck of the production line based on the calculation results of the energy consumption evaluation unit, optimize the device switching time and standby mode strategy, and after calculating the optimal control plan, send the adjustment instruction to the extraction control module; The intelligent prediction unit is used to predict the energy demand of future production batches based on historical energy consumption data, determine and optimize the production schedule according to the energy consumption optimization monitoring, and improve the energy utilization rate; The energy consumption optimization monitoring determination: Before the production process, set the target energy consumption value and reasonable energy consumption range according to different devices and production stages: The standby power consumption P of the device idle : The power consumption in the standby mode, the target value is P idletarget , and the deviation range is ±10%; The total power consumption P of the device total: Total power consumption during equipment operation, with the target value being P totaltarget , and the deviation range is ±15%; Energy loss E loss : Actual energy loss, with the target value being E losstarget , and the deviation range is ±5%; Total energy input E input : Total energy consumed by the equipment, with the target value being E inputtarget , and the deviation range is ±10%; Check whether the energy consumption of the equipment exceeds the set target range through the calculation results of energy consumption assessment: Standby power consumption detection: If P idle exceeds P idletarget by ±10%, it is considered that there is waste of standby energy consumption; Total power consumption detection: If P total exceeds P totaltarget by ±15%, it is considered that the energy efficiency during equipment operation is low; Energy loss detection: If E loss exceeds E losstarget by ±5%, there is a situation of equipment failure or reduced energy efficiency; Energy input quantity detection: If E input exceeds E inputtarget by ±10%, it is considered that too much energy is input; Once abnormal energy consumption is detected, the regulation plan includes: Adjust the switching time of the equipment: including delaying the start time of non-critical equipment and stopping some standby equipment in advance; Adjust the standby mode of the equipment: Switch the equipment in the non-working state to the low-power standby mode, and optimize the equipment usage sequence and load.

[0011] Preferably, the extraction regulation module includes a process parameter regulation unit; The process parameter regulation unit is used to receive instructions from the quality control module, dynamically adjust parameters such as extraction temperature, time, and raw material ratio, store data during the regulation process, and feedback to the data acquisition module to control the operating states of production equipment, including mixers, heaters, and spraying equipment.

[0012] Preferably, the environmental monitoring and control module includes an environmental monitoring unit, an environmental regulation unit, and an abnormal warning unit; The environmental monitoring unit is used to collect temperature, humidity, and air quality, and calculate the environmental stability coefficient Es; The environmental stability coefficient Es is calculated and obtained through the following formula: ; In the formula, Te act 、Heact and Pm act are the measured values of the actual environmental temperature, humidity, and air particulate matter concentration, respectively, 、 、 are the set ideal environmental target values; The environmental control unit is used to control the air conditioner, humidifier, and air purification equipment according to the feedback data of the environmental monitoring unit; The abnormal warning unit is used to monitor environmental fluctuations, including exceeding the set range, and automatically send alarm information to the remote management module.

[0013] Preferably, the environmental abnormal warning monitoring determines that: Compare and analyze the collected environmental data in real time to determine whether it exceeds the set range: Temperature anomaly detection: If Te exceeds Te target ±2 °C, it is determined that the temperature is abnormal; Humidity anomaly detection: If He exceeds He target ±5%, it is determined that the humidity is abnormal; Particulate matter concentration anomaly detection: If Pm exceeds Pm target ±10%, it is determined that the air quality is abnormal; Once any environmental parameter anomaly is detected, the system will automatically trigger the following response: Regulation system adjustment instruction: Based on the detected anomaly, the system will automatically send instructions to the relevant control modules, including the air conditioner, ventilation system, and humidifier, to adjust the production environment and restore it to the normal range. If the temperature is too high, the cooling system will be automatically started. If the humidity is too low, the humidifying equipment will be started. If the air particulate matter concentration is too high, the air purification system will be started.

[0014] Preferably, the remote management module includes a data integration unit, a remote monitoring unit, and a warning unit; The data integration unit is used to receive the data of each module, calculate the system performance index SPI, obtain the strategy plan by comparing with the preset standard threshold Z and the preset standard threshold X, and evaluate the overall production status; The system performance index SPI is calculated and obtained through the following formula: ; In the formula, are the weights of quality, energy efficiency, and environmental factors, respectively; SPI≥X: It indicates that the overall performance of the system is in good condition, and the environment, quality, and energy consumption are all within the set ideal range, and the production line is running smoothly; X > SPI ≥ Z: It indicates that the overall operation of the system is normal, but there are abnormalities within 5%. There are occasional short pauses or abnormal fluctuations in the production line. It is recommended to optimize the switching time and standby mode of production equipment. For high - energy - consuming equipment, perform regular maintenance or adjust the operation mode; SPI < Z: There are serious problems in the overall operation of the system. Abnormalities in the environment, quality, or energy consumption deviate from the standard threshold by more than 35%. The concentration of tea polyphenols or the pH value deviates significantly from the target value. The energy consumption is abnormal. The power consumption of production equipment is too high during standby or operation. The temperature and humidity deviate significantly from the set range. The concentration of airborne particles is too high. Immediately carry out equipment maintenance, start environmental control equipment, suspend the operation of part of the production line, and reduce the production load; The remote monitoring unit is used to provide access interfaces for the PC and mobile terminals, allowing management personnel to remotely view the production status and adjust parameters; The warning unit is used to monitor the abnormal conditions of each module and push warning information to the management terminal.

[0015] The control method for a beverage processing production line based on the Internet of Things includes the following steps: Step 1: Utilize a distributed Internet - of - Things sensor network to collect environmental, quality, and energy - consumption parameters in real - time during the beverage processing process, and upload the data to a cloud platform or edge - computing device for pre - processing through wireless communication technology; Step 2: Receive the quality - related parameters uploaded by the data - acquisition module through the Internet - of - Things platform, perform real - time analysis using cloud computing or edge computing, intelligently evaluate the quality indicators of the product, and calculate the quality - control consistency coefficient Qc to determine whether the quality of the current production batch meets the standards; Step 3: Receive real - time energy - consumption data, perform intelligent modeling of the energy - consumption distribution of equipment through the Internet - of - Things platform, analyze the energy - consumption status using cloud or edge computing, and calculate the energy - utilization efficiency coefficient Ee; Step 4: Receive the instructions provided by the quality - control module and the energy - consumption optimization module, dynamically adjust the extraction temperature, time, and ratio parameters, and store the historical data of the regulation process for optimizing subsequent production batches; Step 5: By deploying Internet - of - Things environmental sensors, monitor the environmental parameters of the production workshop in real - time, and regulate the air - conditioning system, ventilation equipment, and humidification device through the Internet - of - Things platform, and upload them to a cloud platform or edge - computing node for calculation to obtain the environmental stability coefficient Es; Step 6: Calculate the system performance index SPI in real - time, display it through a visualization interface, provide multi - platform access interfaces, support management personnel to remotely view the production - line status and adjust production parameters, and implement an automated alarm mechanism to push warning information in a timely manner for environmental, quality, or energy - consumption abnormalities.

[0016] The present invention provides a control method and system for a beverage processing production line based on the Internet of Things, having the following beneficial effects: (1) During the operation of the system, a distributed Internet of Things sensor network is used to collect environmental, quality, and energy consumption parameters in real time during the beverage processing process. The data is uploaded and preprocessed through wireless communication technology, the quality control consistency coefficient Qc is calculated, real-time energy consumption data is received, the energy consumption status is analyzed, the energy consumption utilization efficiency coefficient Ee is calculated, the extraction temperature, time, and ratio parameters are dynamically adjusted, the historical data of the regulation process is stored and used to optimize subsequent production batches, the environmental parameters of the production workshop are monitored in real time for calculation to obtain the environmental stability coefficient Es, the system performance index SPI is calculated in real time, and is displayed through a visual interface, providing multi-platform access interfaces, supporting managers to remotely view the production line status and adjust production parameters, and implementing an automatic alarm mechanism.

[0017] (2) The control system for the beverage processing production line based on Internet of Things technology realizes comprehensive intelligent and efficient management of the production process by integrating six major modules: data acquisition, quality control, energy consumption optimization, extraction regulation, environmental monitoring, and remote management. First of all, the data acquisition module provides real-time environmental, quality, and energy consumption data, ensuring accurate monitoring and real-time feedback of various indicators of the production line by the system. The quality control module ensures that each batch of tea beverages can strictly meet the standard requirements through intelligent analysis of quality parameters, avoiding errors and lags in manual detection; the energy consumption optimization module greatly improves the energy utilization efficiency of equipment and reduces unnecessary energy consumption losses through efficient energy consumption modeling and analysis.

[0018] (3) Compared with traditional beverage processing production lines, the control system based on the Internet of Things solves problems such as untimely quality detection and serious energy efficiency waste in traditional production processes through fully automated quality monitoring and energy efficiency optimization. In the past, manual operation could not achieve real-time and precise regulation, resulting in unstable quality and large energy waste, especially in workshops with large environmental changes, where the equipment and production environment could not be effectively coordinated. Through the linkage of the extraction regulation module and the environmental monitoring module, the system can timely adjust the extraction temperature, time, and ratio according to changes in quality and energy consumption, ensuring the stability of production quality; at the same time, the environmental monitoring and control module effectively manages the temperature, humidity, and air quality of the workshop, providing a reliable guarantee for the optimization of the production environment.

[0019] (4) Through these technological innovations, the overall efficiency of the beverage processing production line has been significantly improved. The quality consistency is effectively guaranteed, the energy efficiency is greatly enhanced, the environmental fluctuations during the production process are controlled, and production anomalies caused by environmental changes can be effectively avoided. These improvements not only enhance the stability and quality of the products, but also reduce the production costs and energy consumption, thus improving the competitiveness of the enterprise. Compared with traditional technical means, the intelligent control system based on the Internet of Things has successfully achieved the intelligentization, greening, and high-efficiency of the production line through real-time data analysis and optimized decision-making, providing strong support for the modernization upgrade of the beverage industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a block diagram of the control system of the beverage processing production line based on the Internet of Things according to the present invention; Figure 2 It is a schematic diagram of the steps of the control method of the beverage processing production line based on the Internet of Things according to the present invention; Figure 3 It is a simplified flowchart of the control system of the beverage processing production line based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1 The present invention provides a control system for a beverage processing production line based on the Internet of Things. Please refer to Figure 1 , which includes a data acquisition module, a quality control module, an energy consumption optimization module, an extraction regulation module, an environmental monitoring and control module, and a remote management module; The data acquisition module is used to utilize a distributed Internet of Things sensor network to collect environmental, quality, and energy consumption parameters during the beverage processing process in real time, and upload the data to a cloud platform or edge computing device for preprocessing through wireless communication technology; The quality control module is used to receive the quality-related parameters uploaded by the data acquisition module through the Internet of Things platform, perform real-time analysis using cloud computing or edge computing, intelligently evaluate the quality indicators of the products, and calculate the quality control consistency coefficient Qc to determine whether the quality of the current production batch meets the standards; The energy consumption optimization module is used to receive real-time energy consumption data, intelligently model the energy consumption distribution of the equipment through the Internet of Things platform, analyze the energy consumption status using cloud or edge computing, and calculate the energy consumption utilization efficiency coefficient Ee; The extraction control module is used to receive instructions provided by the quality control module and the energy consumption optimization module, dynamically adjust the extraction temperature, time, and ratio parameters, store the historical data of the control process, and use it to optimize subsequent production batches; The environmental monitoring and control module is used to deploy Internet of Things environmental sensors to monitor the environmental parameters of the production workshop in real time, control the air conditioning system, ventilation equipment, and humidification device through the Internet of Things platform, upload them to the cloud platform or edge computing node for calculation, and obtain the environmental stability coefficient Es; The remote management module is used to calculate the system performance index SPI in real time, display it through a visual interface, provide multi-platform access interfaces, support managers to remotely view the production line status and adjust production parameters, and implement an automatic alarm mechanism to push warning information in a timely manner for environmental, quality, or energy consumption anomalies.

[0023] In this embodiment, a distributed Internet of Things sensor network is used to collect environmental, quality, and energy consumption parameters during the beverage processing process in real time, upload the data to the cloud platform or edge computing device for preprocessing through wireless communication technology, receive quality-related parameters uploaded by the data collection module through the Internet of Things platform, perform real-time analysis using cloud computing or edge computing, intelligently evaluate the quality indicators of the product, calculate the quality control consistency coefficient Qc to determine whether the quality of the current production batch meets the standards, receive real-time energy consumption data, intelligently model the energy consumption distribution of the equipment through the Internet of Things platform, analyze the energy consumption status using cloud or edge computing, calculate the energy utilization efficiency coefficient Ee, receive instructions provided by the quality control module and the energy consumption optimization module, dynamically adjust the extraction temperature, time, and ratio parameters, store the historical data of the control process, and use it to optimize subsequent production batches, deploy Internet of Things environmental sensors to monitor the environmental parameters of the production workshop in real time, control the air conditioning system, ventilation equipment, and humidification device through the Internet of Things platform, upload them to the cloud platform or edge computing node for calculation, obtain the environmental stability coefficient Es, calculate the system performance index SPI in real time, display it through a visual interface, provide multi-platform access interfaces, support managers to remotely view the production line status and adjust production parameters, and implement an automatic alarm mechanism to push warning information in a timely manner for environmental, quality, or energy consumption anomalies.

[0024] Embodiment 2 This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The data collection module includes an environmental data collection unit, a quality data collection unit, and an energy consumption data collection unit; The environmental data acquisition unit is used to deploy environmental monitoring sensors, collect the temperature, humidity and air particulate matter concentration data of the workshop environment in real time, preprocess the data, and send the data to the environmental monitoring control module by means of wireless data transmission, including Wi-Fi, LoRa and NB-IoT; The quality data acquisition unit is used to monitor the tea polyphenol concentration, acidity and turbidity of the beverage in real time through a line spectrum analyzer, a pH sensor and a turbidity detector. The collected data is preliminarily filtered and normalized to obtain the tea polyphenol concentration TP, the acidity Ac and the turbidity Cl, and then transmitted to the quality control module; The energy consumption data acquisition unit is used to monitor the real-time power consumption, standby power consumption and energy loss of production equipment through an intelligent electricity meter and a power consumption sensor, and obtain: the standby power consumption P of the equipment idle 、the total power consumption P of the equipment total 、the energy loss E loss and the total energy input E input ; The temperature, humidity and air particulate matter concentration data of the workshop environment are obtained by installing a digital temperature sensor, a digital humidity sensor and an air quality sensor near the production equipment and the material storage area in the production workshop. After preprocessing the data, the environmental temperature Te, the environmental humidity He and the air particulate matter concentration Pm are obtained.

[0025] The quality control module includes a data analysis and modeling unit, an anomaly detection unit and an optimization unit; The data analysis and modeling unit is used to establish a prediction model for the quality parameters of tea polyphenol concentration, acidity and turbidity based on historical data and machine learning algorithms, and calculate the quality control consistency coefficient Qc of the current production batch by using big data analysis methods; The quality control consistency coefficient Qc is calculated and obtained through the following formula: ; In the formula, TP act 、Ac act and Cl act are the actual tea polyphenol concentration, acidity and turbidity in the current production batch respectively, and TP target 、Ac target and Cl target are the set target values or standard values. TP represents the tea polyphenol concentration, Ac represents the acidity, and Cl represents the turbidity; The anomaly detection unit is used to compare the quality data of the current batch with the set standards, judge whether the quality is qualified according to the quality evaluation results, find quality deviations, immediately send adjustment instructions to the extraction control module, and send alarm signals to the remote management module at the same time; The optimization unit is used to adjust the control strategy of quality parameters according to the product quality assessment results, and the quality optimization data is synchronously transmitted to the remote management module for managers to view and analyze.

[0026] The determination of the product quality assessment results: Before the production process starts, set the target quality parameters according to the specifications of the beverage and determine its qualified range: Tea polyphenol concentration TP: The target value is TP target , and the allowable deviation range is ±5%; Acidity and alkalinity Ac: The target value is Ac target , and the allowable deviation range is ±0.2; Turbidity Cl: The target value is Cl target , and the allowable deviation range is ±10%; Compare the currently measured quality data with the preset standard value: Tea polyphenol concentration detection: If TP act exceeds TP target ±5%, it is determined as a deviation; Acidity and alkalinity detection: If Ac act exceeds Ac target ±0.2, it is determined as a deviation; Turbidity detection: If Cl act exceeds Cl target ±10%, it is determined as a deviation; If any quality data exceeds the deviation range, immediately conduct a quality deviation determination. Once a deviation is found, trigger the following operations: After receiving the quality deviation instruction, the extraction control module automatically adjusts the extraction temperature, time or ratio to correct the deviation and restore the quality to the target standard. The remote management module sends an alarm signal to the manager, indicating that there is a quality abnormality in the current batch and requesting manual inspection or further adjustment.

[0027] In this embodiment, the introduction of the data acquisition module greatly improves the real-time monitoring and data accuracy during the production of beverages, providing solid data support for quality control and energy efficiency optimization in the production process. The environmental data acquisition unit collects temperature, humidity, and air particulate matter concentration data in real time by deploying sensors, and transmits the data to the control module using wireless data transmission to ensure that the workshop environment is within the ideal range and avoid the impact of environmental factors on production quality. The quality data acquisition unit monitors the tea polyphenol concentration, acidity, and turbidity in the beverage in real time through high-precision instrument equipment, accurately captures quality fluctuations during the production process, and performs normalization processing to ensure product consistency and stability. The energy consumption data acquisition unit monitors the power consumption status of production equipment in real time, obtains accurate energy efficiency data, and provides a basis for subsequent energy efficiency optimization and equipment regulation. This systematic data acquisition method enables each link to obtain accurate and timely information, greatly improving the controllability of the production process. The intelligent processing of the quality control module in data analysis modeling and anomaly detection can not only evaluate the quality of the current batch in real time, but also automatically trigger regulatory measures when quality deviations are found. The quality prediction model constructed based on historical data and machine learning algorithms can accurately calculate the quality control consistency coefficient Qc, and promptly detect quality deviations by comparing with the preset standard value. The anomaly detection unit can quickly identify quality problems and send adjustment instructions to the extraction control module to ensure quality stability. In addition, the optimization unit adjusts the production strategy based on the quality assessment results, improves production efficiency by intelligently adjusting parameters, and reduces the production of unqualified products.

[0028] Embodiment 3 This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The energy consumption optimization module includes an energy consumption assessment unit, an energy consumption optimization decision-making unit, and an intelligent prediction unit; The energy consumption assessment unit is used to calculate the power consumption of each production device, including real-time power consumption, standby power consumption, and energy loss data, and calculate the overall energy consumption utilization efficiency coefficient Ee; The energy consumption utilization efficiency coefficient Ee is calculated and obtained through the following formula: ; In the formula, P idle represents the standby power consumption of the device, P total represents the total power consumption of the device, E loss represents the energy loss amount, E input represents the total energy input, and T represents time; The energy consumption optimization unit is used to analyze the energy consumption bottleneck of the production line based on the calculation results of the energy consumption assessment unit, optimize the device switching time and standby mode strategy, and send adjustment instructions to the extraction control module after calculating the optimal regulation plan; The intelligent prediction unit is used to predict the energy demand of future production batches based on historical energy consumption data, optimize the production schedule according to the energy consumption optimization monitoring judgment, and improve the energy utilization rate.

[0029] The energy consumption optimization monitoring judgment: Before the production process, set the target energy consumption value and reasonable energy consumption range according to different equipment and production stages: Standby power consumption P of the equipment idle : Power consumption in standby mode, the target value is P idletarget , and the deviation range is ±10%; Total power consumption P of the equipment total : Total power consumption during equipment operation, the target value is P totaltarget , and the deviation range is ±15%; Energy loss E loss : Actual energy loss, the target value is E losstarget , and the deviation range is ±5%; Total energy input E input : Total energy consumed by the equipment, the target value is E inputtarget , and the deviation range is ±10%; Check whether the energy consumption of the equipment exceeds the set target range through the calculation results of energy consumption assessment: Standby power consumption detection: If P idle exceeds P idletarget ±10%, it is considered that there is standby energy consumption waste; Total power consumption detection: If P total exceeds P totaltarget ±15%, it is considered that the energy efficiency during equipment operation is low; Energy loss detection: If E loss exceeds E losstarget ±5%, there is a situation of equipment failure or energy efficiency reduction; Total energy input detection: If E input exceeds E inputtarget ±10%, it is considered that too much energy is input; Once energy consumption anomalies are found, the regulation plan includes: Adjust the switch time of the equipment: including delaying the startup time of non-critical equipment and stopping some standby equipment in advance; Adjust the standby mode of the equipment: Switch the equipment in the non-working state to the low-power standby mode, and optimize the equipment usage order and load.

[0030] The extraction regulation module includes a process parameter regulation unit; The process parameter control unit is used to receive instructions from the quality control module, dynamically adjust parameters such as extraction temperature, time, and raw material ratio, store data during the control process, and feedback to the data acquisition module to control the operating status of production equipment, including mixers, heaters, and spraying equipment.

[0031] The environmental monitoring and control module includes an environmental monitoring unit, an environmental regulation unit, and an abnormal warning unit; The environmental monitoring unit is used to collect temperature, humidity, and air quality, and calculate the environmental stability coefficient Es; The environmental stability coefficient Es is calculated and obtained through the following formula: ; In the formula, Te act , He act and Pm act are the measured values of the actual environmental temperature, humidity, and air particulate matter concentration respectively, , , are the set ideal environmental target values; The environmental regulation unit is used to control air conditioners, humidifiers, and air purification equipment according to the feedback data of the environmental monitoring unit; The abnormal warning unit is used to monitor environmental fluctuations, including exceeding the set range, and automatically send alarm information to the remote management module.

[0032] The determination of environmental abnormal warning monitoring: Compare and analyze the collected environmental data in real time to determine whether it exceeds the set range: Temperature abnormal detection: If Te exceeds Te target ±2 °C, it is determined that the temperature is abnormal; Humidity abnormal detection: If He exceeds He target ±5%, it is determined that the humidity is abnormal; Particulate matter concentration abnormal detection: If Pm exceeds Pm target ±10%, it is determined that the air quality is abnormal; Once any environmental parameter abnormality is detected, the system will automatically trigger the following response: Regulation system adjustment instruction: Based on the detected abnormality, the system will automatically send instructions to relevant control modules, including air conditioners, ventilation systems, and humidifiers, to adjust the production environment and restore it to the normal range. If the temperature is too high, the cooling system will be automatically started. If the humidity is too low, the humidifying equipment will be started. If the air particulate matter concentration is too high, the air purification system will be started.

[0033] In this embodiment, the energy consumption optimization module significantly improves the energy utilization efficiency in the production process through precise energy consumption assessment and intelligent regulation. The energy consumption assessment unit calculates the energy consumption utilization efficiency coefficient Ee by monitoring the power consumption of equipment in real time, including standby power consumption, total power consumption, energy loss, and energy input, providing a data basis for subsequent optimization decisions. Through this assessment, the energy efficiency bottleneck of the equipment can be accurately identified, energy consumption waste can be avoided, and when energy efficiency problems are found, the energy use can be optimized in a timely manner by adjusting the switching time and standby mode of the equipment. For example, the system can delay the startup time of non-critical equipment or stop the standby state of the equipment in advance to reduce unnecessary energy consumption, thereby optimizing production costs and improving overall energy efficiency. The intelligent prediction unit can accurately predict the energy demand of future production batches based on historical energy consumption data and dynamically adjust the energy consumption optimization according to real-time monitoring data. By optimizing the production schedule and equipment usage mode, the intelligent prediction can maximize the energy utilization rate, reduce energy waste, and achieve more efficient production scheduling. This function not only reduces the energy loss during equipment operation but also improves the reasonable allocation of energy resources, optimizes the energy consumption in the production process, especially in the high-load or high-energy consumption state of the equipment, further reducing production costs.

[0034] Embodiment 4 This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The remote management module includes a data integration unit, a remote monitoring unit, and an early warning unit; The data integration unit is used to receive the data of each module, calculate the system performance index SPI, obtain a policy solution by comparing with the preset standard threshold Z and the preset standard threshold X, and evaluate the overall production status; The system performance index SPI is calculated and obtained through the following formula: ; In the formula, are the weights of quality, energy efficiency, and environmental factors respectively; SPI≥X: It indicates that the overall performance of the system is in a good state, and the environment, quality, and energy consumption are all within the set ideal range, and the production line runs smoothly; X>SPI≥Z: It indicates that the overall operation of the system is normal, but there are abnormalities within 5%. The production line occasionally experiences short-term pauses or abnormal fluctuations. It is recommended to optimize the switching time and standby mode of the production equipment. For high-energy consumption equipment, perform regular maintenance or adjust the operation mode; SPI < Z: There are serious problems in the overall operation of the system. Abnormal deviations in the environment, quality, or energy consumption exceed the standard threshold by more than 35%. The concentration of tea polyphenols or the pH value deviates significantly from the target value. The energy consumption is abnormal. The power consumption of the production equipment is too high during standby or operation. The temperature and humidity deviate significantly from the set range. The concentration of air particulate matter is too high. Immediately perform equipment maintenance, start the environmental control equipment, suspend the operation of some production lines, and reduce the production load; The remote monitoring unit is used to provide access interfaces for the PC and mobile terminals, allowing management personnel to remotely view the production status and adjust parameters; The early warning unit is used to monitor the abnormal conditions of each module and push early warning information to the management terminal.

[0035] In this embodiment, the introduction of the remote management module effectively improves the intelligent management level of the production line, especially in the application of data integration, real-time monitoring, and abnormal early warning, making the entire production process more efficient and controllable. The data integration unit can not only evaluate the overall production status by receiving the data of each module in real time and calculating the system performance index SPI, but also dynamically adjust the production strategy by comparing with the preset standard threshold. When SPI is higher than the preset threshold X, it indicates that the production line is operating well, and the environment, quality, and energy efficiency are all within the ideal range, ensuring the efficiency and stability of production; when SPI is in the medium range, the system can prompt management personnel to optimize the production equipment or adjust the operation mode in a timely manner to avoid potential equipment failures or production fluctuations; when SPI is lower than the threshold Z, the system will immediately issue an early warning, indicating that management personnel should start equipment maintenance or adjust the production line, and take timely measures to address major issues in the environment, quality, or energy efficiency, avoiding the occurrence of production accidents and ensuring the safety and continuity of the production process. The design of the remote monitoring unit provides a flexible remote operation method for management personnel, supporting real-time monitoring and parameter adjustment on the PC and mobile terminals, enabling them to grasp the production status immediately even when they are not on site, make quick responses and decisions, and greatly improve the controllability and response ability of the production line. In addition, the early warning unit can monitor the abnormal conditions of each module and push early warning information to the management terminal in a timely manner, helping management personnel discover potential production problems and avoiding the impact of information lag on production. This intelligent early warning mechanism improves the operation safety of the production line and reduces production stagnation or losses caused by emergencies.

[0036] Embodiment 5 For the control method of the beverage processing production line based on the Internet of Things, please refer to Figure 2 , specifically: including the following steps: Step 1: Use a distributed Internet of Things sensor network to collect environmental, quality, and energy consumption parameters in real time during the beverage processing process, and upload the data to the cloud platform or edge computing device for preprocessing through wireless communication technology; Step 2: Receive the quality-related parameters uploaded by the data acquisition module through the Internet of Things platform, perform real-time analysis using cloud computing or edge computing, intelligently evaluate the quality indicators of the product, calculate the quality control consistency coefficient Qc to determine whether the quality of the current production batch meets the standards; Step 3: Receive real-time energy consumption data, intelligently model the energy consumption distribution of the equipment through the Internet of Things platform, analyze the energy consumption status using cloud or edge computing, and calculate the energy consumption utilization efficiency coefficient Ee; Step 4: Receive the instructions provided by the quality control module and the energy consumption optimization module, dynamically adjust the extraction temperature, time, and ratio parameters, store the historical data of the regulation process and use it to optimize subsequent production batches; Step 5: By deploying Internet of Things environmental sensors, monitor the environmental parameters of the production workshop in real time, and regulate the air conditioning system, ventilation equipment, and humidification device through the Internet of Things platform, upload them to the cloud platform or edge computing node for calculation to obtain the environmental stability coefficient Es; Step 6: Calculate the system performance index SPI in real time, display it through a visual interface, provide multi-platform access interfaces, support managers to remotely view the production line status and adjust production parameters, and implement an automated alarm mechanism to push early warning information in a timely manner for environmental, quality, or energy consumption anomalies.

[0037] In this embodiment, through the above six steps, the Internet of Things-based control system for the beverage processing production line realizes the full-range intelligent monitoring and precise regulation of the production process. First of all, the real-time collected environmental, quality and energy consumption data provide complete data support for the production process, enabling managers to timely grasp the production line status and avoiding the lag and limitations of traditional manual inspections. With the support of the cloud platform or edge computing devices, the data is efficiently processed to generate the key quality control consistency coefficient Qc and energy efficiency utilization coefficient Ee, thus realizing the real-time evaluation and optimization of production quality and energy consumption. The system can dynamically adjust parameters such as extraction temperature, time and ratio during the production process, improving the flexibility and precision of the production process, ensuring the stability and consistency of production batches. Through the deployment of environmental sensors, the system can monitor and adjust the environmental parameters of the workshop in real time, effectively avoiding the impact of environmental changes on the production process and improving the stability of the production environment. The calculation of the environmental stability coefficient Es and the linkage adjustment of equipment such as air conditioners and ventilation ensure that the production environment always maintains the best state, avoiding product quality instability caused by temperature and humidity fluctuations and further improving the overall reliability of production. Through intelligent modeling and dynamic adjustment, the system not only optimizes the energy consumption distribution, reduces unnecessary energy waste, but also effectively improves the energy utilization efficiency of equipment through accurate energy efficiency evaluation, bringing significant cost savings and environmental protection benefits to the enterprise. The calculation of the real-time performance index SPI and the visualization display function of the system enable managers to remotely view the production status at any time and adjust production parameters in a timely manner. The automatic alarm mechanism can push warning information immediately when abnormalities occur in the environment, quality or energy consumption, ensuring the safety and efficiency of the production process. Compared with the traditional beverage processing production line, the intelligent control based on Internet of Things technology not only improves production efficiency and product quality, but also optimizes resource utilization, significantly reduces production costs, and further promotes the development of the beverage industry towards a more intelligent, energy-saving and environmental protection direction.

[0038] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made in these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The beverage processing production line control system based on the Internet of Things is characterized by: It includes data acquisition module, quality control module, energy consumption optimization module, extraction control module, environmental monitoring and control module and remote management module; The data acquisition module is used to collect the environment, quality and energy consumption parameters of the beverage processing process in real time using a distributed IoT sensor network, and upload the data to a cloud platform or edge computing device for preprocessing through wireless communication technology; The quality control module is used to receive the quality-related parameters uploaded by the data acquisition module through the Internet of Things platform, use cloud computing or edge computing to perform real-time analysis, intelligently evaluate the quality indicators of the product, and calculate the quality control consistency coefficient Qc to determine whether the quality of the current production batch meets the standards; The energy consumption optimization module is used to receive real-time energy consumption data, intelligently model the energy consumption distribution of the equipment through the Internet of Things platform, analyze the energy consumption status by using cloud or edge computing, and calculate the energy consumption efficiency coefficient Ee; The extraction control module is used to receive instructions provided by the quality control module and the energy consumption optimization module, dynamically adjust the extraction temperature, time and ratio parameters, store historical data of the control process and use it to optimize subsequent production batches; The environmental monitoring control module is used to monitor the environmental parameters of the production workshop in real time by deploying IoT environmental sensors, and to control the air-conditioning system, ventilation equipment and humidification device through the IoT platform, and upload them to the cloud platform or edge computing node for calculation to obtain the environmental stability coefficient Es; The remote management module is used to calculate the system performance index SPI in real time and display it through a visual interface, providing a multi-platform access interface to support managers to remotely view the status of the production line and adjust production parameters, realize an automated alarm mechanism, and promptly push early warning information for abnormal environments, quality or energy consumption.

2. The beverage processing production line control system based on the Internet of Things according to claim 1 is characterized by: The data acquisition module includes an environmental data acquisition unit, a quality data acquisition unit and an energy consumption data acquisition unit; The environmental data acquisition unit is used to deploy environmental monitoring sensors to collect real-time data on workshop environmental temperature and humidity and air particle concentration, and pre-process the data, and use wireless data transmission methods, including Wi-Fi, LoRa and NB-IoT, to send the data to the environmental monitoring control module; The quality data acquisition unit is used to monitor the tea polyphenol concentration, pH value and turbidity of the beverage in real time through a line spectrum analyzer, a pH sensor and a turbidity detector. The collected data is initially filtered and normalized to obtain: tea polyphenol concentration TP, pH value Ac and turbidity Cl, which are then transmitted to the quality control module; The energy consumption data acquisition unit is used to monitor the real-time power consumption, standby power consumption and energy loss of production equipment through smart meters and power consumption sensors, and obtain: equipment standby power consumption P idle 、Total power consumption of equipment P total , Energy loss E loss and the total energy input E input ; The workshop ambient temperature, humidity and air particle concentration data are obtained by installing digital temperature sensors, digital humidity sensors and air quality sensors in the production workshop close to production equipment and material storage areas, and preprocessing the data to obtain: ambient temperature Te, ambient humidity He and air particle concentration Pm.

3. The beverage processing production line control system based on the Internet of Things according to claim 1 is characterized by: The quality control module includes a data analysis and modeling unit, an anomaly detection unit and an optimization unit; The data analysis and modeling unit is used to establish a prediction model for the quality parameters of tea polyphenols concentration, pH, and turbidity based on historical data and machine learning algorithms, and to calculate the quality control consistency coefficient Qc of the current production batch using a big data analysis method; The quality control consistency coefficient Qc is calculated by the following formula: ； Where TP act ,Ac act and Cl act are the actual tea polyphenols concentration, pH and turbidity in the current production batch, TP target ,Ac target and Cl target is the set target value or standard value, TP represents the concentration of tea polyphenols, Ac represents the pH, and Cl represents the turbidity; The abnormality detection unit is used to compare the quality data of the current batch with the set standard, determine whether the quality is qualified according to the quality assessment result, and immediately send an adjustment instruction to the extraction control module if a quality deviation is found, and send an alarm signal to the remote management module at the same time; The optimization unit is used to adjust the control strategy of quality parameters according to the product quality evaluation results, and the quality optimization data is synchronously transmitted to the remote management module for management personnel to view and analyze.

4. The beverage processing production line control system based on the Internet of Things according to claim 3 is characterized by: The product quality assessment results determine: Before the production process begins, target quality parameters are set according to the beverage specifications and their qualified ranges are determined: Tea polyphenols concentration TP: target value is TP target , the allowable deviation range is ±5%; pH Ac: Target value is Ac target , the allowable deviation range is ±0.2; Turbidity Cl: target value is Cl target , the allowable deviation range is ±10%; Compare the currently measured quality data with the preset standard value: Tea polyphenols concentration detection: If TP act Exceed TP target ±5% is considered as deviation; pH test: If Ac act More than Ac target ±0.2, it is considered as deviation; Turbidity detection: If Cl act More than Cl target ±10% is considered as deviation; If any quality data exceeds the deviation range, the quality deviation will be determined immediately. Once a deviation is found, the following operations will be triggered: After receiving the quality deviation instruction, the extraction control module automatically adjusts the extraction temperature, time or ratio to correct the deviation and restore the quality to the target standard. The remote management module sends an alarm signal to the management personnel, indicating that the current batch has quality abnormalities and requires manual inspection or further adjustment.

5. The beverage processing production line control system based on the Internet of Things according to claim 1 is characterized by: The energy consumption optimization module includes an energy consumption evaluation unit, an energy consumption optimization decision unit and an intelligent prediction unit; The energy consumption evaluation unit is used to calculate the power consumption of each production equipment, including real-time power consumption, standby power consumption and energy loss data, and calculate the overall energy consumption efficiency coefficient Ee; The energy efficiency coefficient Ee is calculated by the following formula: ； Where P idle Indicates the standby power consumption of the device, P total Indicates the total power consumption of the device, E loss Indicates the amount of energy loss, E input represents the total amount of energy input, and T represents time; The energy consumption optimization unit is used to analyze the energy consumption bottleneck of the production line based on the calculation results of the energy consumption evaluation unit, optimize the equipment switching time and the standby mode strategy, and after calculating the optimal control plan, send the adjustment instruction to the extraction control module; The intelligent prediction unit is used to predict the energy demand of future production batches based on historical energy consumption data, and optimize production scheduling according to energy consumption optimization monitoring and judgment to improve energy utilization; The energy consumption optimization monitoring determines: Before the production process, set target energy consumption values ​​and reasonable energy consumption ranges according to different equipment and production stages: Equipment standby power consumption P idle : Power consumption in standby mode, target value is P idletarget , the deviation range is ±10%; Total power consumption of the device P total : The total power consumption of the device during operation, the target value is P totaltarget , the deviation range is ±15%; Energy loss E loss : Actual energy loss, target value is E losstarget , the deviation range is ±5%; Total energy input E input : The total energy consumed by the equipment, the target value is E inputtarget , the deviation range is ±10%; Based on the calculation results of energy consumption assessment, check whether the energy consumption of the equipment exceeds the set target range: Standby power consumption detection: If P idle More than P idletarget ±10%, it is considered that there is standby energy waste; Total power consumption detection: If P total More than P totaltarget ±15%, the equipment is considered to be operating with low energy efficiency; Energy loss detection: If E loss More than E losstarget ±5%, there is equipment failure or reduced energy efficiency; Energy input detection: If E input More than E inputtarget ±10%, the energy input is considered excessive; Once abnormal energy consumption is found, the control plan includes: Adjust the on / off time of equipment: including delaying the start-up time of non-critical equipment and stopping certain standby equipment in advance; Adjust device standby mode: Switch non-working devices to low-power standby mode to optimize device usage order and load.

6. The beverage processing production line control system based on the Internet of Things according to claim 1 is characterized by: The extraction control module includes a process parameter control unit; The process parameter control unit is used to receive instructions from the quality control module, dynamically adjust parameters such as extraction temperature, time, and raw material ratio, store data during the control process, and feed back to the data acquisition module to control the production equipment, including the operating status of the mixer, heater, and spray equipment.

7. The beverage processing production line control system based on the Internet of Things according to claim 1 is characterized by: The environmental monitoring and control module includes an environmental monitoring unit, an environmental regulation unit, and an anomaly warning unit; The environmental monitoring unit is used to collect temperature, humidity, and air quality, and calculate the environmental stability coefficient Es; The environmental stability coefficient Es is calculated and obtained through the following formula: ； In the formula, Te act 、He act and Pm act are the measured values ​​of actual ambient temperature, humidity and air particle concentration, , , It is the ideal environmental target value set; The environmental regulation unit is used to control air conditioners, humidifiers, and air purification equipment according to the feedback data of the environmental monitoring unit; The anomaly warning unit is used to monitor environmental fluctuations, including exceeding the set range, and automatically send alarm information to the remote management module.

8. The beverage processing production line control system based on the Internet of Things according to claim 7 is characterized by: The determination of environmental anomaly warning and monitoring: Compare and analyze the collected environmental data in real time to determine whether it exceeds the set range: Temperature anomaly detection: If Te exceeds Te target ±2∘C, it is considered as abnormal temperature; Humidity abnormality detection: If He exceeds He target ±5%, it is considered as abnormal humidity; Abnormal particle concentration detection: If Pm exceeds Pm target ±10%, the air quality is considered abnormal; Once any environmental parameter anomaly is detected, the system will automatically trigger the following response: Regulation system adjustment instruction: Based on the detected anomaly, the system will automatically send instructions to relevant control modules, including air conditioners, ventilation systems, and humidifiers, to adjust the production environment and restore it to the normal range. If the temperature is too high, the cooling system will be automatically started. If the humidity is too low, the humidification equipment will be started. If the concentration of air particles is too high, the air purification system will be started.

9. The beverage processing production line control system based on the Internet of Things according to claim 1 is characterized by: The remote management module includes a data integration unit, a remote monitoring unit, and a warning unit; The data integration unit is used to receive the data of each module, calculate the system performance index SPI, obtain a strategy plan by comparing with the preset standard threshold Z and the preset standard threshold X, and evaluate the overall production status; The system performance index SPI is calculated and obtained through the following formula: ； In the formula, are the weights of quality, energy efficiency and environmental factors respectively; SPI≥X: It indicates that the overall performance of the system is in good condition, and the environment, quality, and energy consumption are all within the set ideal range, and the production line runs smoothly; X>SPI≥Z: It indicates that the overall operation of the system is normal, but there are anomalies within 5%. The production line occasionally experiences short-term pauses or abnormal fluctuations. It is recommended to optimize the switching time and standby mode of production equipment. For high-energy-consuming equipment, conduct regular maintenance or adjust the operation mode; SPI<Z: There are serious problems in the overall operation of the system. The anomalies in the environment, quality, or energy consumption deviate from the standard threshold by more than 35%. The concentration of tea polyphenols or the pH value deviates significantly from the target value. The energy consumption is abnormal. The power consumption of production equipment during standby or operation is too high. The temperature and humidity deviate significantly from the set range. The concentration of air particles is too high. Immediately conduct equipment maintenance, start the environmental regulation equipment, suspend the operation of part of the production line, and reduce the production load; The remote monitoring unit is used to provide access interfaces for the PC side and the mobile side, allowing managers to remotely view the production status and adjust parameters; The warning unit is used to monitor the abnormal conditions of each module and push warning information to the management terminal.

10. A beverage processing production line control method based on the Internet of Things, applied to a beverage processing production line control system based on the Internet of Things according to any one of claims 1 to 9, characterized in that: It includes the following steps: Step 1: Use a distributed Internet of Things sensor network to collect environmental, quality, and energy consumption parameters during the beverage processing process in real time, and upload the data to the cloud platform or edge computing device for preprocessing through wireless communication technology; Step 2: Receive the quality-related parameters uploaded by the data collection module through the Internet of Things platform, perform real-time analysis using cloud computing or edge computing, intelligently evaluate the quality indicators of the product, and calculate the quality control consistency coefficient Qc to determine whether the quality of the current production batch meets the standards; Step 3: Receive real-time energy consumption data, intelligently model the energy consumption distribution of the equipment through the Internet of Things platform, analyze the energy consumption status using cloud or edge computing, and calculate the energy efficiency coefficient Ee; Step 4: Receive instructions from the quality control module and the energy consumption optimization module, dynamically adjust the extraction temperature, time and ratio parameters, store the historical data of the control process and use it to optimize subsequent production batches; Step 5: Deploy IoT environmental sensors to monitor the environmental parameters of the production workshop in real time, and use the IoT platform to control the air conditioning system, ventilation equipment, and humidification devices, upload them to the cloud platform or edge computing node for calculation, and obtain the environmental stability coefficient Es; Step 6: Calculate the system performance index SPI in real time and display it through a visual interface, provide a multi-platform access interface, support managers to remotely view the status of the production line and adjust production parameters, implement an automated alarm mechanism, and push early warning information in a timely manner for abnormal environments, quality or energy consumption.

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