Automatic control and information management system for new-generation fruit juice production line

Abstract

The invention relates to the technical field of data processing and big data, in particular to a new-generation fruit juice production line automatic control and information management system, which comprises a control module, a data acquisition module, a data analysis processing module and a man-machine interaction module, the analysis module processes data through the steps of cleaning, standardization and the like, short-term and long-term dynamic threshold values are set according to a sliding window technology, early warning is triggered through real-time comparison, the control module adjusts equipment according to the early warning, and the man-machine interaction module is used for management personnel to operate and check information; the objective of the invention is to solve the problems that fruit juice production line equipment control is not intelligent, production data cannot be effectively managed and product quality is difficult to monitor and early warn in time.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing and big data, and relates to a new generation of automatic control and information management system for a juice production line. Background Art

[0002] In the field of juice production, with the intensification of market competition and the continuous improvement of consumers' requirements for juice quality and safety, juice production companies are facing huge challenges. Modern juice production lines cover multiple complex links such as raw material processing, juicing, blending, sterilization, filling, etc., and each link is interrelated and affects each other. In order to ensure stable product quality, improve production efficiency and reduce costs, it is crucial to achieve automated control and efficient information management of the production line. At present, automation technology and information technology are widely used in industrial production, providing technical support for the upgrading and transformation of juice production lines, but in actual applications, there are still many problems in juice production lines that need to be solved.

[0003] The equipment control of traditional juice production lines mostly relies on manual operation or simple program settings. Manual operation is prone to errors, and it is difficult to adjust the equipment operating parameters in a timely and accurate manner according to the actual production situation. For example, in the juicing process, it is impossible to dynamically adjust the speed of the juicer according to the real-time status of the raw materials, which affects the juice yield and juice quality. At the same time, there is a lack of effective collaborative control between different equipment, and the overall optimization of the production process cannot be achieved, resulting in low production efficiency; and various types of data generated on the production line, such as raw material information, equipment operation data, product quality data, etc., are stored in a scattered manner and in an inconsistent format. These data have not been effectively integrated and deeply analyzed, and cannot provide strong support for production decisions. For example, it is difficult for companies to dig out the potential relationship between raw material characteristics and product quality from a large amount of raw material quality data, and it is difficult to optimize the production process in a targeted manner.

[0004] The existing system mainly relies on manual sampling and post-testing to monitor product quality, and cannot monitor product quality changes in real time and comprehensively. When there are problems with product quality, it is often difficult to detect and take effective measures in a timely manner, resulting in a large number of substandard products, increased production costs, and damage to corporate reputation. For example, in the sterilization process, if the sterilization temperature and time are not properly controlled, the problem of excessive microorganisms may not be discovered until after filling, causing a large number of products to be scrapped.

[0005] The main solution to the above problems is currently available in the patent titled "A refined energy management method, system, IoT cloud management server and storage medium thereof", with the authorization announcement number CN116048023B. By obtaining the historical energy consumption inspection data set, splitting it into multiple management subsets to determine the energy consumption standard, combining the production plan to predict energy use, obtaining the estimated and real-time energy consumption curves, comparing the differences to determine the management strategy and implement it, and revising the energy consumption standard. The distributed monitoring equipment is used to collect data and upload it to the management cloud server for analysis and processing to achieve refined energy management. A high-precision energy management model that is not affected by production has been established, which can achieve minute-level precision division and automatically give management recommendation strategies. By continuously iterating and optimizing the energy demand and supply curves, the goal of energy saving and carbon reduction is achieved, providing an effective means for energy management in the production process.

[0006] However, the above solutions mainly focus on energy management and control, and involve less on the intelligent control of equipment on the juice production line, production data management, and product quality monitoring and early warning.

[0007] Another example is the patent titled Product Optimization Production Method and System Combined with pH Control, with authorization announcement number CN117311298B. It obtains the process flow nodes of the target product and the added ingredients and operating environment information of each node, establishes an ingredient and environment correlation matrix, evaluates the pH fluctuation index, and establishes a pH stabilization identification unit through historical production record data. It determines whether to optimize the process for abnormal nodes based on the probability of fluctuation stabilization. It optimizes the production process through correlation analysis and probability evaluation for the pH control problem in the product production process, which can effectively improve product quality and has certain reference significance in product quality control. However, the above solutions only focus on product quality optimization related to pH value, and cannot comprehensively solve problems such as equipment control, data management, and other quality indicator monitoring and early warning of juice production lines, and have a narrow scope of application.

[0008] The patent name is "A method and equipment for deep processing of cherries based on the industrial Internet", with publication number CN119292195A. Sensors and intelligent devices are deployed on cherry planting, picking and processing production lines to collect data, and big data analysis and machine learning technologies are used to deeply mine data. Product formulas and production processes are adjusted based on market demand, and comprehensive digital management of the processing process is achieved through the industrial Internet platform. An industrial Internet platform is built to achieve intelligent, automated and digital management, with functions such as digital traceability and personalized customization, which improves production efficiency and product quality. However, this patent is for the deep processing of cherries, and its processing technology and equipment are different from those of juice production. It lacks adaptability in terms of equipment control details and data management, quality monitoring and early warning for juice production.

[0009] In comprehensive comparison with the above schemes, the new generation of juice production line automation control and information management system proposed in the present invention integrates control module, data acquisition module, data analysis and processing module and human-computer interaction module according to the characteristics of juice production. The control module realizes intelligent control of equipment; the data acquisition module comprehensively collects multi-link data; the data analysis and processing module deeply analyzes data, sets thresholds and issues warnings; the human-computer interaction module facilitates personnel operation and monitoring. The system can effectively solve the problems of unintelligent control of juice production line equipment, ineffective management of production data, and difficulty in timely monitoring and warning of product quality, and is more in line with the actual needs of juice production. Summary of the invention

[0010] The present invention provides a new generation of automatic control and information management system for juice production lines, which solves the problems of unintelligent equipment control of juice production lines, inability to effectively manage production data, and difficulty in timely monitoring and warning of product quality.

[0011] In order to solve the above problems, the technical solution adopted by the invention is: The new generation of juice production line automation control and information management system, including: Control module, used for starting, stopping and adjusting operating parameters of equipment in each link; The data acquisition module is arranged at each node of the juice production line, including the raw material processing area, the juicing area, the mixing area, the sterilization area and the filling area, and is used to collect raw material information, equipment operation status information and product quality parameter information. The raw material information includes raw material type, weight and freshness. The equipment operation status information includes equipment speed, temperature, pressure and energy consumption. The product quality parameter information includes juice concentration, acidity and microbial content. The data acquisition module is electrically connected to the data analysis module to transmit the collected data to the data analysis module in real time. The data analysis and processing module is used to receive and analyze the data from the data acquisition module, and after data analysis, when the monitoring parameters deviate from the preset normal range, send adjustment instructions to the central control unit; The specific steps of the data analysis are: The S01 data analysis and processing module obtains the original data X from the data acquisition module, cleans the original data X, removes abnormal values ​​caused by sensor failure and electromagnetic interference during the acquisition process, and uses the 3σ criterion based on statistics combined with the adjacent data smoothing comparison method to process the data. If the fluctuation of the data at a certain moment and the data before and after exceeds the set smoothing coefficient, it is marked as abnormal and removed; S02 uses the Z-score standardization method to convert the weight, freshness, rotation speed, temperature, pressure, energy consumption, juice concentration, acidity, and microbial content into standard normal distribution data with a mean of 0 and a standard deviation of 1. The formula is: Where X is the original data processed in S01, and Z is the standardized data. is the mean, is the standard deviation; the raw material types are converted into binary vector form using one-hot encoding; S03 is based on sliding window technology. It sets a short-term time window and calculates the dynamic threshold for the standardized data in the window. For the equipment speed, it calculates the mean of the speed data in the window. and standard deviation , set the upper threshold to , the lower threshold is ,in The sensitivity coefficient is pre-set according to the stability of production line equipment and process requirements; short-term dynamic thresholds are established for weight, freshness, temperature, pressure, energy consumption, juice concentration, acidity, and microbial content; S04 sets a long-term time window and calculates the median M and standard deviation of the interquartile range IQR of the data in the window , for juice acidity, the upper threshold is set to , the lower threshold is , The value is determined by the strictness of the product quality standards and historical quality fluctuations, and long-term dynamic thresholds are established for weight, freshness, rotation speed, temperature, pressure, energy consumption, juice concentration, and microbial content; S05 compares the collected parameters with the corresponding short-term and long-term dynamic thresholds in real time. If the current measured value is higher than the short-term upper threshold or lower than the short-term lower threshold, a first-level warning is triggered; if the current measured value is higher than the long-term upper threshold and the short-term upper threshold or lower than the long-term lower threshold and the short-term lower threshold, it is upgraded to a second-level warning, and the warning information is sent to the control module for adjustment instructions, and the warning information is highlighted in the human-computer interaction module; The human-computer interaction module is connected to the control module in a two-way communication manner and is used by production managers to input control instructions, view real-time status information of the production line, historical production data reports, and receive warning information pushed by the system.

[0012] The principles and advantages of this solution are: through the coordinated work of the control module, data acquisition module, and data analysis and processing module, the automatic control and information management of the juice production line are realized. The control module is responsible for starting and stopping the equipment in each link and adjusting the operating parameters to ensure the normal progress of the production process. The data acquisition module is distributed at various key nodes of the production line to collect various information such as raw materials, equipment operating status and product quality parameters, and transmit these data to the data analysis and processing module in real time. The module cleans the collected raw data, removes outliers, and then standardizes and encodes the data to make it easier to analyze. Then, based on the sliding window technology, short-term and long-term time windows are set respectively, and the dynamic thresholds of various parameters are calculated. The collected parameters are compared with the thresholds in real time, and different levels of warnings are triggered according to the comparison results. Adjustment instructions are sent to the control module, thereby realizing real-time monitoring and adjustment of the production process.

[0013] Compared with the existing technology, the early warning mechanism usually relies on a single short-term or long-term dynamic threshold, which has obvious defects. If only the short-term dynamic threshold is used, although it is sensitive to the immediate changes in data and can quickly capture instantaneous fluctuations, it is very susceptible to interference from accidental factors because it is too sensitive, and false alarms are frequently generated. For example, in actual production, when the equipment is running, it may occasionally cause the equipment speed to exceed the short-term threshold momentarily due to short-term power fluctuations, thereby triggering an early warning. However, the production line as a whole is actually in a stable operating state, which will undoubtedly interfere with the normal production order for no reason and waste manpower and time to verify these false alarms. On the contrary, if only the long-term dynamic threshold is set, although the long-term trend of the data can be effectively grasped, it will be slow to react and difficult to detect quickly when facing sudden abnormal changes in the short term. For example, in the raw material supply link, if the freshness of a batch of fruits suddenly decreases, since the long-term threshold reflects the average situation over a period of time, it may not be able to capture this short-term anomaly in time, resulting in the discovery of product quality problems in the subsequent production process. At this time, a certain production loss has been caused, and the best adjustment time has been missed. This solution innovatively combines short-term and long-term dynamic thresholds to build a more complete and accurate early warning system. When the collected parameters are higher than the short-term upper threshold or lower than the short-term lower threshold, the system can quickly detect the immediate abnormality and immediately trigger a level one warning, promptly reminding staff to pay attention to the current production situation and quickly troubleshoot possible emergencies; if the parameters deviate from the long-term threshold at the same time, the system will upgrade the warning to level two, which means that the problem is not only immediate, but may also involve long-term accumulated systemic risks in the production line, requiring in-depth investigation and comprehensive rectification. Taking juice acidity monitoring as an example, through the dual comparison of short-term and long-term dynamic thresholds, the system can accurately determine whether it is a short-term process fluctuation caused by an instantaneous mixing error, or a quality risk caused by the accumulation of deviations in the production process over a long period of time, effectively avoiding frequent production stoppages or product quality out of control problems caused by misjudgment, and greatly improving the accuracy and reliability of early warnings.

[0014] The existing technology often has problems of incomplete coverage and in-depth analysis in monitoring the operating status of equipment and product quality parameters. This solution uses the data acquisition module to realize the all-round and in-depth collection of multi-dimensional information of raw materials, equipment, and products. The data analysis and processing module can accurately dig out the potential failure risks of equipment. Taking equipment energy consumption monitoring as an example, the equipment energy consumption is continuously monitored through long-term dynamic thresholds. If it is found that the equipment energy consumption is higher than the threshold for a long time, it is very likely to indicate that there are problems such as component wear and line aging inside the equipment. The system will issue an early warning to remind the staff to arrange maintenance in time, thereby effectively ensuring the stable operation of the equipment, greatly reducing the risk of production interruption caused by sudden equipment failure, and reducing the time and economic costs caused by equipment maintenance. At the same time, in terms of product quality control, taking the key parameter of microbial content as an example, this solution performs dual monitoring based on short-term and long-term dynamic thresholds. The short-term threshold can timely detect the instantaneous fluctuations in microbial content caused by accidental factors such as environmental changes and operational errors during the production process, while the long-term threshold can continuously evaluate product quality to ensure that the product always meets strict quality standards throughout the entire production cycle. Through this dual guarantee mechanism, the economic losses and brand reputation damage caused by product quality problems to the enterprise are effectively avoided, the product quality is fundamentally improved, and it helps the enterprise gain advantages in market competition and achieve steady growth in economic benefits.

[0015] Furthermore, the control module is also capable of presetting a variety of equipment operating parameter combination modes according to different juice product formulas. Each juice product has its own specific production process requirements. Different formulas require different equipment operating parameters to achieve the best production effect. The preset parameter combination mode is carefully debugged and verified, which can ensure that the quality of each product produced remains highly stable and consistent, thereby enhancing consumers' trust and satisfaction with the product.

[0016] Furthermore, the sensors in the data acquisition module adopt a redundant design. At least two sensors of the same type are equipped at the acquisition node. Multiple sensors collect the same parameter at the same time, and the collected data can be mutually verified and compared. If there is a large deviation in the data of different sensors, the problem can be discovered and checked in time. It may be that a certain sensor is faulty or the measurement environment is abnormal. In this way, more accurate and reliable data can be screened out, reducing the impact of measurement errors on production decisions.

[0017] Furthermore, the data analysis and processing module also adds a manual review interface. When the data is complex or there are anomalies, humans can review it based on their professional knowledge and experience, and promptly discover and correct errors or omissions in the system analysis to ensure that the data is accurate and reliable.

[0018] Furthermore, the data analysis and processing module is provided with a data storage unit, which adopts a distributed storage architecture and classifies and stores data according to data type, collection time, and production line area. When specific data needs to be found, it can be quickly located through the corresponding classification without blindly searching in the data, saving time and resources; at the same time, data is stored in a dispersed manner on multiple nodes, reducing the risk of data loss due to single point failure. Even if a node has a problem, the data on other nodes is still available, ensuring the integrity and availability of the data, and ensuring that the monitoring and management of the production line will not be interrupted due to data storage problems.

[0019] Furthermore, the human-computer interaction module also displays the dynamic changes of equipment operating parameters, raw material information, and product quality parameters in each link in real time in the form of visual charts. Compared with complex data tables or text descriptions, intuitive charts can present large amounts of data in a concise and clear manner. Production managers do not need to laboriously interpret cumbersome data, and can quickly grasp the key information of each link, thereby greatly improving the efficiency and accuracy of decision-making.

[0020] Furthermore, the short-term time is 1 hour, and the long-term time is 24 hours. A 1-hour short-term time window and a 24-hour long-term time window are set. In terms of production rhythm control, the 1-hour short-term window can track production in real time, and promptly discover problems such as sudden equipment failures and short-term fluctuations in raw materials, and quickly issue warnings and make adjustments to ensure smooth production. The 24-hour long-term window can analyze production stability and trends as a whole, such as evaluating the operating efficiency of equipment within a day, the overall consumption of raw materials, etc., to provide a basis for production planning. The short-term window has a small amount of data, which is convenient for rapid processing and can detect anomalies in a timely manner. The long-term window has a large amount of data, which can more comprehensively reflect production rules, reduce interference from abnormal data, and improve analysis accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0022] The embodiment is basically as shown in the attached Figure 1 As shown, the new generation of juice production line automatic control and information management system is characterized by including: Control module, used for starting, stopping and adjusting operating parameters of equipment in each link; The data acquisition module is arranged at each node of the juice production line, including the raw material processing area, the juicing area, the mixing area, the sterilization area and the filling area, and is used to collect raw material information, equipment operation status information and product quality parameter information. The raw material information includes raw material type, weight and freshness. The equipment operation status information covers equipment speed, temperature, pressure and energy consumption. The product quality parameter information includes juice concentration, acidity and microbial content. The data acquisition module is electrically connected to the control module to transmit the collected data to the control module in real time. The data analysis and processing module is used to receive and analyze the data from the data acquisition module, conduct real-time monitoring and early warning of the production process after data analysis, and send adjustment instructions to the central control unit in a timely manner when the monitored data deviates from the preset normal range; The specific steps of the data analysis are: The S01 data analysis and processing module obtains the original data X from the data acquisition module. First, the original data X is cleaned to remove abnormal values ​​caused by sensor failure, electromagnetic interference, etc. during the acquisition process. The 3σ criterion based on statistics is combined with the adjacent data smoothing comparison method. If the fluctuation of the data at a certain moment and the data before and after exceeds the set smoothing coefficient, it is marked as abnormal and removed; S02 uses the Z-score standardization method to convert the weight, freshness, rotation speed, temperature, pressure, energy consumption, juice concentration, acidity, and microbial content into standard normal distribution data with a mean of 0 and a standard deviation of 1. The formula is: Where X is the original data processed in S01, and Z is the standardized data. is the mean, is the standard deviation; the raw material types are converted into binary vector form using one-hot encoding.

[0023] S03 is based on sliding window technology. It sets a 1-hour time window and calculates the dynamic threshold for the standardized data in the window. For the device speed, it calculates the mean of the speed data in the window. and standard deviation , set the upper threshold to , the lower threshold is ,in The sensitivity coefficient is pre-set according to the stability of production line equipment and process requirements; similarly, corresponding short-term dynamic thresholds are established for weight, freshness, temperature, pressure, energy consumption, juice concentration, acidity, and microbial content; S04 sets a 24-hour time window and calculates the median M and the standard deviation of the interquartile range IQR of the data in the window , for juice acidity, the upper threshold is set to , the lower threshold is , The value is determined according to the strictness of the product quality standards and historical quality fluctuations. Similarly, corresponding long-term dynamic thresholds are established for weight, freshness, rotation speed, temperature, pressure, energy consumption, juice concentration, and microbial content. S05 compares the collected data with the corresponding short-term and long-term dynamic thresholds in real time. If the current measured value is higher than the short-term upper threshold or lower than the short-term lower threshold, a first-level warning is triggered immediately; if it is higher than the long-term upper threshold or lower than the long-term lower threshold at the same time, it is upgraded to a second-level warning, and the warning information is sent to the control module for adjustment instructions, and the warning information is highlighted in the human-computer interaction module; The human-computer interaction module is connected to the control module in a two-way communication manner and is used by production managers to input control instructions, view real-time status information of the production line, historical production data reports, and receive warning information pushed by the system.

[0024] The control module is used to control the equipment in each link of the juice production line, and realize the first-level and second-level warnings. The data acquisition module is distributed at each node of the production line to collect data information and transmit these real-time data to the data analysis module. After receiving the data, the data analysis processing module first cleans the original data, and then standardizes and encodes the data to make it have a unified measurement and a form that is easy to analyze. Based on the sliding window technology, the short-term and long-term time windows are set respectively to calculate the short-term and long-term dynamic thresholds. By comparing the collected data with these thresholds in real time, the abnormal situation in the production process can be discovered in time. When the data deviates from the short-term threshold, the first-level warning is triggered, and when it deviates from the long-term threshold, it is upgraded to the second-level warning, and the warning information is sent to the control module. When the control module receives the first-level warning, because the parameters only deviate from the short-term threshold, it will make a small adjustment to the parameters of the relevant equipment, and at the same time increase the data collection frequency of the corresponding node to monitor the data changes more closely. It will also record the warning information and notify the production management personnel to pay attention; when the second-level warning is received, because the parameters deviate from the long-term threshold, the control module will immediately stop the production process, urgently adjust the equipment, start the product traceability and isolation mechanism, prevent the problem products from entering the market, and continuously display the warning information in a striking way in the human-computer interaction module.

[0025] When the system receives the production instructions, the control module will start the equipment in each link in sequence according to the preset production process. For example, the equipment in the raw material processing area will be started first. According to the type and weight of the raw materials, the appropriate operating parameters will be calculated through precise algorithms, and the equipment speed will be adjusted to ensure that the raw materials can be processed evenly and efficiently. In the juice extraction area, the equipment pressure and speed are adjusted according to the freshness and weight of the raw materials to ensure the juice yield and juice quality. In the blending area, the start and stop and flow parameters of the ingredient addition equipment are controlled in combination with the juice concentration, acidity and other data for precise blending. When the operating parameters of a certain link of equipment are abnormal, such as temperature and pressure exceeding the set range, the control module will immediately take corresponding measures according to the warning level. If it is a level 1 warning, the equipment operating parameters will be fine-tuned. If it is a level 2 warning, the equipment operation of this link will be stopped immediately. After the fault is found and repaired, it will be restarted and adjusted according to the established process to ensure the safe and efficient operation of the entire production process, making the production process more compact and smooth, thereby greatly shortening the production cycle. The data acquisition module collects raw material type, weight, freshness, equipment speed, temperature, pressure, energy consumption, juice concentration, acidity, microbial content, and raw material type. The determination provides a basic basis for subsequent production process selection and formula adjustment. Accurate weight collection helps to accurately control the input of raw materials, thereby ensuring product consistency and quality stability. Freshness monitoring can ensure the use of high-quality raw materials for production, avoiding the quality and taste of juice affected by raw material deterioration. It also helps to optimize procurement plans and inventory management, reduce costs and reduce waste.

[0026] The collection of speed, temperature, pressure and energy consumption in the equipment operating status information brings many benefits. The monitoring of speed can reflect the working efficiency and operating stability of the equipment. Timely detection of abnormal speed can help prevent equipment failure and production interruption. The collection of temperature and pressure is crucial to ensure the process conditions in the production process. They directly affect the processing effect and quality of the juice. The monitoring of energy consumption not only helps to evaluate the operating efficiency of the equipment, but also provides data support for energy-saving optimization, thereby reducing production costs and achieving sustainable production.

[0027] Taking the change of temperature parameters as an example, when the control module receives a level 1 warning triggered by temperature, it means that the temperature has deviated from the short-term dynamic threshold. At this time, the control module will make small adjustments to the power of the refrigeration or heating equipment according to the preset fine-tuning strategy. For example, in the juice sterilization area, if the sterilization temperature is slightly higher than the short-term upper limit threshold, the control module will increase the power of the refrigeration equipment by 5% to slowly reduce the sterilization temperature. At the same time, the control module will closely monitor temperature changes, collect temperature data every 30 seconds, and observe the adjustment effect.

[0028] When the temperature triggers the second-level warning, that is, exceeds the long-term dynamic threshold, the control module will take more radical measures. It will immediately stop the current sterilization operation to avoid affecting the quality of the juice due to too high or too low temperature. The control module will then recalculate the appropriate cooling or heating power based on historical temperature data and equipment performance parameters. For example, the cooling power is directly increased by 20%, and after the equipment is restarted, the temperature data is collected at a high frequency of 15 seconds to ensure that the temperature can quickly stabilize within a reasonable range. During the entire adjustment process, the control module will also work in conjunction with other related equipment, such as adjusting the material conveying speed to prevent the material from staying in the sterilization area for too long or too short due to temperature adjustment.

[0029] Accurate control of juice concentration determines the taste and flavor of the product, meeting consumer expectations and market demand. Acidity monitoring helps to ensure the taste balance and quality stability of the product, and avoids the adverse effects of abnormal acidity on product quality. The detection of microbial content is an important part of ensuring food safety. It can timely detect potential contamination and deterioration risks and ensure the health and safety of consumers. These parameter characteristics comprehensively cover the key aspects of raw materials, equipment operation and product quality, and provide necessary data support for production process optimization, quality control, cost management, equipment maintenance and food safety assurance, which helps to achieve efficient, stable, high-quality and safe juice production; through data cleaning and standardization methods, interference and erroneous data are effectively removed, making the analysis results more reliable. Based on the dynamic threshold setting and graded early warning mechanism of sliding window technology, sliding window technology combines the setting of short-term and long-term dynamic thresholds to realize the monitoring of the production process. The short-term dynamic threshold can timely capture abnormal fluctuations in the short term and quickly trigger an early warning; the long-term dynamic threshold evaluates the stability and quality trend of production from a more macro perspective, further improving the accuracy and comprehensiveness of the early warning. Compared with directly setting a fixed threshold, the dynamic threshold setting based on the sliding window technology and the hierarchical early warning mechanism have unique advantages. Fixed thresholds are often unable to adapt to the complex and changeable actual conditions in the production process. In some scenes where the working conditions change gradually, false alarms or missed alarms are prone to occur. The dynamic threshold setting can dynamically analyze and evaluate the data based on the real-time data changes in the production process using the sliding window, and adjust the threshold adaptively. Once an abnormality occurs in production, the mechanism can issue an accurate alarm in the first time, allowing managers to take measures quickly to effectively avoid the expansion of the problem, thereby effectively ensuring the stability of product quality.

[0030] The control module also has the function of presetting a variety of equipment operating parameter combination modes according to different juice product formulas. Each juice product has its own specific production process requirements. Different formulas require different equipment operating parameters to achieve the best production effect. The preset parameter combination mode is carefully debugged and verified, which can ensure that the quality of each product produced remains highly stable and consistent, thereby enhancing consumers' trust and satisfaction with the product.

[0031] The sensors in the data acquisition module adopt a redundant design. At least two sensors of the same type are equipped at the acquisition node. Multiple sensors collect the same parameter at the same time, and the collected data can be mutually verified and compared. If there is a large deviation in the data of different sensors, the problem can be discovered and checked in time. It may be that a sensor is faulty or the measurement environment is abnormal. In this way, more accurate and reliable data can be screened out, reducing the impact of measurement errors on production decisions.

[0032] The data analysis and processing module also adds a manual review interface. When the data is complex or there are anomalies, humans can review it based on their professional knowledge and experience, and promptly discover and correct errors or omissions in the system analysis to ensure that the data is accurate and reliable.

[0033] The data analysis and processing module is provided with a data storage unit, which adopts a distributed storage architecture and classifies and stores data according to multiple dimensions such as data type, acquisition time, and production line area. When specific data needs to be found, it can be quickly located through the corresponding classification without blindly searching in the data, saving time and resources; at the same time, data is dispersedly stored on multiple nodes, reducing the risk of data loss due to single point failure. Even if a node has a problem, the data on other nodes is still available, ensuring the integrity and availability of the data, and ensuring that the monitoring and management of the production line will not be interrupted due to data storage problems. The human-computer interaction module also displays the dynamic changes of equipment operating parameters, raw material information, and product quality parameters in each link in real time in the form of visual charts. Compared with complex data tables or text descriptions, intuitive charts can present large amounts of data in a concise and clear manner. Production managers do not need to laboriously interpret cumbersome data, and can quickly grasp the key information of each link, thereby greatly improving the efficiency and accuracy of decision-making.

[0034] The short-term time is 1 hour, and the long-term time is 24 hours. A 1-hour short-term time window and a 24-hour long-term time window are set. In terms of production rhythm control, the 1-hour short-term window can track production in real time, promptly discover problems such as sudden equipment failures and short-term fluctuations in raw materials, and quickly issue warnings and make adjustments to ensure smooth production. The 24-hour long-term window can analyze production stability and trends as a whole, such as evaluating the operating efficiency of equipment within a day, the overall consumption of raw materials, etc., to provide a basis for production planning. The short-term window has a small amount of data, which is convenient for rapid processing and can detect anomalies in a timely manner. The long-term window has a large amount of data and can more comprehensively reflect production rules, reduce interference from abnormal data, and improve analysis accuracy. Specific embodiments Data acquisition modules are installed at key nodes of the juice production line, including the raw material processing area, juicing area, mixing area, sterilization area and filling area. Each node is equipped with sensors for collecting raw material type, weight, freshness, equipment speed, temperature, pressure, energy consumption and juice concentration, acidity and microbial content as needed. These sensors are electrically connected to the control module to ensure real-time transmission of data.

[0035] The control module is installed in the central control room of the production line and connected to each device to start, stop and adjust the operating parameters of the equipment. At the same time, the data analysis and processing module and the human-computer interaction module are also installed and configured in the control room.

[0036] During the production process, in the raw material processing area, when a batch of fresh fruits arrives, the sensor collects information such as the type, weight and freshness of the fruits, and transmits this data to the control module and data analysis and processing module in real time.

[0037] In the juicing area, sensors such as equipment speed, temperature and pressure continuously monitor the operating status of the equipment, while in the blending area, quality parameters such as juice concentration and acidity are accurately collected. Similarly, relevant data from the sterilization area and filling area are also acquired and transmitted in a timely manner.

[0038] After the data analysis and processing module receives the data transmitted by the data acquisition module, it processes it according to the following steps: First, data cleaning is performed. The statistically based 3σ criterion is combined with the adjacent data smoothing comparison method to remove abnormal values ​​caused by sensor failure, electromagnetic interference, etc. For example, if the temperature data at a certain moment fluctuates significantly beyond the set smoothing coefficient compared with the data at the previous and next moments, the data is marked as abnormal and removed.

[0039] Next, the weight, freshness, rotation speed, temperature, pressure, energy consumption, juice concentration, acidity and microbial content data were Z-score standardized. At the same time, the raw material types were converted into binary vector form through one-hot encoding to make the data have a unified measurement and a form that is easy to analyze.

[0040] Then, based on the sliding window technology, a short-term time window of 1 hour and a long-term time window of 24 hours are set. For acidity, the mean and standard deviation of the speed data in the 1-hour window are calculated, and the upper and lower thresholds are set. Similarly, corresponding short-term dynamic thresholds are established for other parameters. For juice acidity, the corresponding long-term threshold is set by calculating the median M and interquartile range IQR of the data in the 24-hour window. , collected juice acidity data from production lines over the past two years, totaling 15,000 samples. After preliminary analysis, the median Standard deviation (pH value) and interquartile range (IQR) =0.3 (pH value). Based on the strictness of product quality standards, the target acidity range of juice is set at 3.4-3.8 (pH value). Through text mining and analysis of consumer online reviews and offline survey data, it is found that when the acidity of juice deviates from the target range of 0.12 (pH value), consumers' negative evaluation of taste increases significantly. After cost accounting and production efficiency evaluation, the acceptable balance point between the cost increase caused by the adjustment and the quality improvement is determined. The corresponding acidity fluctuation range is 0.13 (pH value). Combining the above market feedback and cost-benefit analysis, the average value of the allowable fluctuation range of the two is taken as a reference. =0.125, interquartile range IQR=0.3, then , so, for the long-term dynamic threshold of juice acidity, the upper threshold is , the lower threshold is When the real-time monitored juice acidity exceeds this range, the system can issue timely warnings and make adjustments.

[0041] Compare the collected data with the short-term and long-term dynamic thresholds in real time. Once the current measured value is found to be higher than the short-term upper threshold or lower than the short-term lower threshold, a first-level warning is triggered immediately; if it is higher than the long-term upper threshold or lower than the long-term lower threshold at the same time, it is upgraded to a second-level warning. The warning information is quickly sent to the control module and highlighted in the human-computer interaction module.

[0042] The control module accurately controls the equipment in each link of the production line based on the preset parameters and the received data analysis results.

[0043] When producing apple juice, the control module automatically switches to the preset apple juice production equipment operating parameter combination mode. During the production process, if the data analysis and processing module issues a warning that the equipment speed is too high, the control module immediately reduces the speed to ensure production stability and product quality.

[0044] In the pressure monitoring of the filling area, two pressure sensors of the same type are installed to work simultaneously. During a production run, the data collected by the two sensors showed a large deviation, and the system quickly issued an alarm. After investigation, the technicians found that one of the sensors had a measurement error due to loose installation. The system repaired it in time and ensured the accuracy of the data.

[0045] When processing the production data of a batch of new raw materials, the system analysis results were slightly abnormal. Through the newly added manual review interface, experienced engineers reviewed the data and found that the characteristics of the new raw materials caused the system's original analysis algorithm to be inaccurate. The engineers corrected the algorithm in time to ensure the normal progress of subsequent production.

[0046] At the same time, the data storage unit adopts a distributed storage architecture, and classifies and stores data according to multiple dimensions such as raw material data, equipment operation data, product quality data, collection time, production line area, etc. When it is necessary to query the equipment energy consumption data of the filling area last month, the staff can quickly locate and obtain accurate data through classification, providing a strong basis for optimizing energy consumption management.

[0047] The human-computer interaction module also displays the dynamic changes of equipment operating parameters, raw material information and product quality parameters in each link in real time in the form of visual charts. Production managers can intuitively see the real-time data of each area in front of the large monitoring screen.

[0048] The juice concentration in the mixing area showed a downward trend in the visual chart in the human-computer interaction module. The manager immediately entered adjustment instructions through the human-computer interaction module, increased the amount of concentrated juice added, corrected the concentration deviation in time, and ensured the consistency of product quality.

[0049] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field are aware of all the common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A new generation of juice production line automation control and information management system, characterized by: include: Control module, used for starting, stopping and adjusting operating parameters of equipment in each link; The data acquisition module is arranged at each node of the juice production line, including the raw material processing area, the juicing area, the mixing area, the sterilization area and the filling area, and is used to collect raw material information, equipment operation status information and product quality parameter information. The raw material information includes raw material type, weight and freshness; the equipment operation status information includes equipment speed, temperature, pressure and energy consumption; the product quality parameter information includes juice concentration, acidity and microbial content; the data acquisition module is electrically connected to the data analysis module, and transmits the collected data to the data analysis module in real time; The data analysis and processing module is used to receive and analyze the data from the data acquisition module, and after data analysis, when the monitoring parameters deviate from the preset normal range, send adjustment instructions to the central control unit; The specific steps of the data analysis are: The S01 data analysis and processing module obtains the original data X from the data acquisition module, cleans the original data X, removes abnormal values ​​caused by sensor failure and electromagnetic interference during the acquisition process, and uses the 3σ criterion based on statistics combined with the adjacent data smoothing comparison method to process the data. If the fluctuation of the data at a certain moment and the data before and after exceeds the set smoothing coefficient, it is marked as abnormal and removed; S02 uses the Z-score standardization method to convert weight, freshness, rotation speed, temperature, pressure, energy consumption, juice concentration, acidity, and microbial content into standard normal distribution data with a mean of 0 and a standard deviation of 1. The formula is: Where X is the original data processed in S01, and Z is the standardized data. is the mean, is the standard deviation; the raw material types are converted into binary vector form using one-hot encoding; S03 is based on sliding window technology. It sets a short-term time window and calculates the dynamic threshold for the standardized data in the window. For the equipment speed, it calculates the mean of the speed data in the window. and standard deviation , set the upper threshold to , the lower threshold is ,in The sensitivity coefficient is pre-set according to the stability of production line equipment and process requirements; and short-term dynamic thresholds are established for weight, freshness, temperature, pressure, energy consumption, juice concentration, acidity, and microbial content; S04 sets a long-term time window and calculates the median M and standard deviation of the interquartile range IQR of the data in the window , for juice acidity, the upper threshold is set to , the lower threshold is , The values ​​are determined by the strictness of product quality standards and historical quality fluctuations, and long-term dynamic thresholds are established for weight, freshness, rotation speed, temperature, pressure, energy consumption, juice concentration, and microbial content; S05 compares the collected parameters with the corresponding short-term and long-term dynamic thresholds in real time. If the current measured value is higher than the short-term upper threshold or lower than the short-term lower threshold, a first-level warning is triggered; if the current measured value is higher than the long-term upper threshold and the short-term upper threshold or lower than the long-term lower threshold and the short-term lower threshold, it is upgraded to a second-level warning, and the warning information is sent to the control module and an adjustment instruction is generated, and the warning information is highlighted in the human-computer interaction module; The human-computer interaction module is connected to the control module in a two-way communication manner and is used by production managers to input control instructions, view real-time status information of the production line, historical production data reports, and receive warning information pushed by the system.

2. The new generation juice production line automation control and information management system according to claim 1 is characterized in that: The control module is also capable of presetting a plurality of equipment operation parameter combination modes according to different juice product formulas.

3. The new generation juice production line automation control and information management system according to claim 1 is characterized in that: The sensors in the data acquisition module adopt a redundant design, and at least two sensors of the same type are equipped at the acquisition node.

4. The new generation juice production line automation control and information management system according to claim 1 is characterized in that: The data analysis and processing module is also provided with a manual review interface.

5. The new generation juice production line automation control and information management system according to claim 1 is characterized in that: The data analysis and processing module is provided with a data storage unit, which adopts a distributed storage architecture and classifies and stores data according to data type, collection time, and production line area.

6. The new generation juice production line automation control and information management system according to claim 1 is characterized in that: The human-computer interaction module also displays the dynamic changes of equipment operating parameters, raw material information, and product quality parameters in each link in real time in the form of visual charts.

7. The new generation juice production line automation control and information management system according to claim 1 is characterized in that: The short-term time is 1 hour, and the long-term time is 24 hours.

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