Intelligent monitoring system and method for beer brewing process

Abstract

The invention relates to the technical field of beer brewing monitoring, and discloses an intelligent monitoring system and method for a beer brewing process. The method comprises the following steps: acquiring real-time production data in a beer brewing process, wherein the real-time production data comprises raw material parameters, fermentation environment parameters and equipment operation parameters; then, based on the raw material parameters and the fermentation environment parameters, key process indexes of the current brewing stage are identified, and monitoring areas of the beer brewing process are divided according to the key process indexes; and finally, according to the equipment operation parameters and the monitoring areas, constructing a multi-dimensional monitoring network of the beer brewing process, and collecting dynamic process data of each monitoring area through the multi-dimensional monitoring network. According to the method, the whole brewing process can be comprehensively covered, monitoring key point dynamic adjustment is achieved, a multi-dimensional monitoring system is constructed, the problems of traditional monitoring data fragmentation, monitoring blind areas and the like are solved, the brewing process is accurately controlled in an assisted mode, and the modern beer brewing production requirement is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of beer brewing monitoring, in particular to an intelligent monitoring system and method for beer brewing process. BACKGROUND

[0002] In the beer brewing industry, the stability of product quality is closely related to the precise control of the production process, and the brewing process involves multiple complex links, from raw material input to fermentation completion. The change of process conditions at each stage will affect the taste, flavor and physicochemical indicators of the final product. Traditional beer brewing monitoring methods rely mainly on manual periodic sampling and detection, which has obvious limitations. On the one hand, manual sampling interval is long, and it is difficult to capture the dynamic changes in the brewing process in real time. When abnormal process parameters are found, it has already caused certain impact on production, resulting in fluctuations in the quality of some batches of products. On the other hand, the results of manual detection are easily affected by factors such as the experience, skill level of the operator and the detection environment, and the accuracy and consistency of the data are difficult to guarantee, which cannot provide reliable basis for the precise adjustment of the brewing process.

[0003] With the continuous improvement of product quality requirements of the beer consumption market and the expansion of production scale, the traditional monitoring method has been difficult to meet the needs of modern brewing production. At present, some beer production enterprises have begun to introduce automatic monitoring equipment to try to collect some parameters in the brewing process in real time, but these devices usually only monitor a single parameter or a single link, lacking systematic coverage of the entire brewing process. For example, some devices only monitor the temperature and pressure in the fermentation tank, ignoring the influence of raw material ratio, equipment running state and other key factors on the brewing process, resulting in fragmented monitoring data and inability to form a complete process data chain. In addition, the existing automatic monitoring system cannot dynamically adjust the monitoring focus according to the process characteristics of different stages in the brewing process, and often uses a unified monitoring standard to cover the entire production process, resulting in excessive concentration of monitoring resources in some links and blind spots in key process nodes, which cannot effectively identify process abnormalities at each stage, thereby affecting the overall control effect of the brewing process.

[0004] There is a complex correlation between various process parameters in the beer brewing process, and subtle changes in raw material parameters may have a significant impact on the quality of the final product through the chain reaction of the fermentation process. Due to the lack of comprehensive analysis capability of multiple parameters, the traditional monitoring method and the existing part of the automatic monitoring system cannot establish the correlation between parameters, and it is difficult to grasp the process state of the brewing process as a whole, which leads to the difficulty and time cost of troubleshooting when the process is abnormal, affecting production efficiency, and may cause raw material waste and production cost increase. Therefore, how to realize the comprehensive, real-time and accurate monitoring of the beer brewing process, and build a multi-dimensional monitoring system covering the entire brewing process, has become a problem to be solved in the current beer brewing industry. SUMMARY

[0005] The purpose of the present application is to provide an intelligent monitoring system and method for beer brewing process to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides an intelligent monitoring method for beer brewing process, which comprises:

[0007] Obtaining real-time production data in the beer brewing process, the real-time production data including raw material parameters, fermentation environment parameters and equipment operation parameters;

[0008] Based on the raw material parameters and fermentation environment parameters, identifying the key process indicators of the current brewing stage, and dividing the monitoring area of the beer brewing process according to the key process indicators;

[0009] According to the equipment operation parameters and the monitoring area, a multi-dimensional monitoring network of the beer brewing process is constructed, and dynamic process data of each monitoring area is collected through the multi-dimensional monitoring network.

[0010] Preferably, the key process indicators of the current brewing stage are identified based on the raw material parameters and fermentation environment parameters, which comprises:

[0011] Conducting component analysis on the raw material parameters to extract malt concentration and yeast activity characteristic values;

[0012] Combining the temperature gradient and humidity distribution in the fermentation environment parameters, calculating the process influence factor of each brewing stage;

[0013] According to the malt concentration characteristic value, yeast activity characteristic value and process influence factor, the key process indicators of the current brewing stage are determined.

[0014] Preferably, the multi-dimensional monitoring network of the beer brewing process is constructed according to the equipment operation parameters and the monitoring area, which comprises:

[0015] Analyzing the pressure change curve and flow fluctuation data in the equipment operation parameters to establish an equipment state correlation matrix;

[0016] Setting the deployment density of the distributed sensor nodes based on the physical distribution characteristics of the monitoring area;

[0017] Combining the equipment state correlation matrix and the deployment density of the distributed sensor nodes to construct a multi-dimensional monitoring network for the beer brewing process.

[0018] Preferably, after collecting the dynamic process data of each monitoring area through the multi-dimensional monitoring network, the method further comprises:

[0019] Performing spatiotemporal alignment processing on the dynamic process data to generate a standardized process data sequence;

[0020] Based on the standardized process data sequence, an anomaly detection model for the beer brewing process is established;

[0021] Identifying process deviation data for each monitoring area through the anomaly detection model and marking abnormal monitoring areas.

[0022] Preferably, after identifying the process deviation data for each monitoring area through the anomaly detection model, the method further comprises:

[0023] Extracting abnormal feature parameters from the process deviation data and calculating deviation influence coefficients for each abnormal monitoring area;

[0024] According to the deviation influence coefficients, a process compensation decision tree for the beer brewing process is established;

[0025] Based on the process compensation decision tree, parameter adjustment instructions for abnormal monitoring areas are generated.

[0026] Preferably, generating parameter adjustment instructions for abnormal monitoring areas based on the process compensation decision tree comprises:

[0027] Analyzing the deviation degree of the abnormal feature parameters from the standard process threshold to determine a compensation priority order;

[0028] Combining the deviation influence coefficients and the compensation priority order to set compensation intensity parameters for each abnormal monitoring area;

[0029] According to the compensation intensity parameters, parameter adjustment instructions for abnormal monitoring areas are generated.

[0030] Preferably, after generating parameter adjustment instructions for abnormal monitoring areas, the method further comprises:

[0031] Real-time monitoring of process response data after the parameter adjustment instructions are executed;

[0032] updating a detection threshold of the anomaly detection model according to a matching degree between the process response data and the expected compensation effect;

[0033] re-evaluating the process state of each monitoring area based on the updated detection threshold.

[0034] Preferably, the matching degree between the process response data and the expected compensation effect comprises:

[0035] extracting a change trend of a key indicator in the process response data, and calculating an actual compensation effect value;

[0036] establishing a difference matrix of the actual compensation effect value and the expected compensation effect value;

[0037] correcting a decision node weight of the process compensation decision tree according to the difference matrix.

[0038] Preferably, the correcting a decision node weight of the process compensation decision tree according to the difference matrix further comprises:

[0039] optimizing a global control strategy of the beer brewing process based on the corrected decision node weight;

[0040] implementing the optimized global control strategy through the multi-dimensional monitoring network;

[0041] collecting strategy execution feedback data in the implementation process for iterative updating of the anomaly detection model;

[0042] the collecting strategy execution feedback data in the implementation process further comprises:

[0043] analyzing a control delay parameter and a response accuracy indicator in the strategy execution feedback data;

[0044] adjusting a data collection frequency of the multi-dimensional monitoring network according to the control delay parameter and the response accuracy indicator;

[0045] establishing an adaptive monitoring mechanism of the beer brewing process based on the adjusted data collection frequency.

[0046] Preferably, the present application further comprises an intelligent monitoring system for a beer brewing process, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor, when executing the computer program, implements the steps of the above-mentioned intelligent monitoring method for a beer brewing process.

[0047] Compared with the prior art, the present application has the following beneficial effects:

[0048] The present application breaks the limitations of incomplete and fragmented data collection in traditional monitoring methods by comprehensively acquiring real-time production data during the beer brewing process, covering raw material parameters, fermentation environment parameters and equipment operation parameters. Traditional monitoring often only focuses on a single type of parameter and cannot take into account the associated effects between raw materials, environment and equipment. However, the present method includes three types of key parameters in the data collection range, which can capture all types of process information during the brewing process, providing a comprehensive data basis for subsequent process analysis and monitoring control. The monitoring process is no longer limited to a single link or a single factor, but covers the entire brewing process from a holistic perspective, allowing workers to clearly understand the process state of the entire brewing process.

[0049] In the process of identifying key process indicators of the current brewing stage based on raw material parameters and fermentation environment parameters, and dividing the monitoring area, the present method realizes the dynamic and precise monitoring of the key points. The beer brewing process includes raw material pretreatment, fermentation, maturation and other stages, and the process requirements and key control points of different stages are significantly different. Traditional monitoring methods use uniform standards for whole-process monitoring, which can easily lead to insufficient monitoring of key stages or waste of monitoring resources in non-key areas. The present method identifies key process indicators for each stage and divides the monitoring area accordingly, which can concentrate monitoring resources on the core process links of each stage, avoid monitoring blind spots, reduce unnecessary monitoring in non-key areas, improve monitoring efficiency, and ensure that key factors affecting product quality are accurately grasped in each brewing stage, and process abnormalities that may occur in the stage are discovered in time.

[0050] The multi-dimensional monitoring network constructed according to the equipment operation parameters and the monitoring area can realize efficient collection and comprehensive analysis of dynamic process data in each monitoring area. The monitoring network is not a simple parameter superposition monitoring, but combines the equipment operation state with the process characteristics of each monitoring area to establish a correlation monitoring mechanism between equipment operation and process. For example, when the process parameters of a monitoring area are abnormal, through the multi-dimensional monitoring network, workers can not only obtain the process data of the area, but also synchronously understand the operation parameters of the related equipment, so as to quickly judge whether the abnormality is caused by process condition change or equipment failure, greatly shortening the problem troubleshooting time and reducing the difficulty of fault troubleshooting. At the same time, the multi-dimensional monitoring network can collect dynamic process data of each monitoring area in real time, which can reflect the process change trend in real time. Workers can predict possible process problems in advance according to data changes, take proactive adjustment measures, avoid process abnormalities from further developing and affecting product quality, and reduce raw material waste and production cost increase caused by process problems.

[0051] Compared with the existing partially automated monitoring system, the multi-dimensional monitoring network constructed by the method has stronger systematicness and correlation, can integrate scattered monitoring data into an organic whole, and form a complete process data chain. Through comprehensive analysis of multi-dimensional data, the staff can deeply mine the internal correlation between raw material parameters, fermentation environment parameters and equipment operation parameters, better understand the influence law of parameter changes on the brewing process, and provide more dimensional reference information for the optimization of brewing process. For example, by analyzing the influence of changes of fermentation environment parameters and equipment operation parameters on product quality under different raw material ratios, it can provide basis for optimizing raw material formula and adjusting fermentation process conditions, thereby promoting the continuous improvement of beer brewing process, improving the stability and consistency of product quality, meeting the needs of modern beer production for efficient and accurate monitoring and control, and helping beer production enterprises to improve production efficiency and market competitiveness. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 A working principle diagram of the intelligent monitoring method for beer brewing process described in the present application;

[0053] Figure 2 A working principle diagram for identifying key process indicators;

[0054] Figure 3 A working principle diagram for dynamic process data processing and abnormality detection. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0056] Please refer to Figure 1The application provides an intelligent monitoring system and method for beer brewing process, which comprises sensors and data acquisition devices deployed on the beer brewing production line to obtain multiple types of data in the production process in real time. These real-time production data include raw material parameters, fermentation environment parameters and equipment operation parameters. The raw material parameters cover indicators such as input amount, moisture content and saccharification degree of malt; the fermentation environment parameters involve variables such as temperature, humidity and pH value in the fermenter; and the equipment operation parameters include state information such as speed of pump, opening degree of valve and frequency of stirrer. On the basis of obtaining real-time production data, the system performs integrated analysis on the raw material parameters and fermentation environment parameters to identify key process indicators of the current brewing stage. The key process indicators are defined according to the standard process of beer brewing, for example, the concentration and temperature of wort in the saccharification stage and the yeast activity and carbon dioxide release amount in the fermentation stage. By analyzing the correlation between these parameters, the system dynamically divides monitoring areas, which correspond to different physical units or process links of brewing equipment, such as raw material pretreatment area, main fermentation area and post-ripening area. The division is based on the spatial distribution and time sequence characteristics of parameters to ensure that each area represents a relatively independent process unit. According to the equipment operation parameters and the divided monitoring areas, the system constructs a multi-dimensional monitoring network. The multi-dimensional monitoring network is composed of distributed sensor nodes, data aggregation units and communication modules. The equipment operation parameters are used to evaluate the real-time state of each device, for example, by analyzing the flow fluctuation of the pump and the pressure change of the valve, the optimal layout of the data acquisition point is determined. The physical boundaries of the monitoring areas and the process correlation guide the deployment of the sensor nodes, for example, increasing the sensor density at the key process points. After the network is constructed, the dynamic process data of each monitoring area are collected in real time through wired or wireless methods, and the dynamic process data include time series variables such as temperature gradient, pressure curve and flow value.

[0057] Example 1: see Figure 2, the system captures multi-parameters of brewing process in real time through high-precision sensor array deployed on key equipment such as mashing kettle and fermentation tank. Raw material parameters include starch content and protein composition of malt, which are collected by online near-infrared analyzer every 30 seconds and transmitted to central processor. Fermentation environment parameters are monitored by temperature sensor group and humidity probe distributed at different heights of tank body. Equipment operation parameters include pump rotating speed and valve opening degree. All data streams are synchronized in millisecond level through industrial Ethernet. On the basis of data integration, ingredient analysis process is started. Malt concentration characteristic value is extracted by measuring absorbance value of wort with ultraviolet spectrophotometry, and optical signal is converted into concentration value by establishing standard curve. Yeast activity characteristic value is quantified by detecting ATP bioluminescence intensity to measure microbial metabolic activity. These biochemical indicators and temperature gradient data collected by physical sensor are input into calculation module. Temperature gradient data are generated into three-dimensional thermal map through spatial interpolation algorithm, and humidity distribution data are filtered by Kalman filter to eliminate measurement noise. The system calculates process influence factor by using multiple regression model, which comprehensively reflects the coupling effect of environmental parameters on mashing efficiency and fermentation rate. The determination process of key process indicators adopts fuzzy reasoning mechanism, taking malt concentration characteristic value, yeast activity characteristic value and process influence factor as input variables, which are converted into fuzzy set through membership function. Rule set constructed by brewing experts' experience is stored in knowledge base, such as "if malt concentration is high and yeast activity is strong, fermentation rate indicator should be adjusted high". The defuzzification link generates accurate key process indicator value by using barycenter method. These indicators dynamically correlate with quality standards at different brewing stages, such as focusing on acetaldehyde concentration indicator in main fermentation stage and double acetyl reduction indicator in post-fermentation stage.

[0058] The division of the monitoring area follows the principle of combining process logic and spatial topology. The system divides the basic monitoring unit according to the physical boundaries of devices such as saccharification pots and fermentation tanks, and determines the material flow between regions according to the pipeline connection relationship. Each region is assigned an independent data acquisition thread, and a 10% overlap is set at the region boundary to avoid monitoring blind spots. The division result is stored in the database in the form of a topology graph, and the region attributes are dynamically adjusted as the production progresses. For example, when the fermentation enters the double acetyl reduction period, the system will automatically mark the corresponding region as a key control point. The analysis of device operating parameters focuses on revealing the internal correlation between mechanical state and process indicators. The pressure change curve is decomposed into different frequency band fluctuation components through wavelet transform, and the flow fluctuation data is extracted through time series analysis. The device state correlation matrix constructed by the system is stored in a graph structure, with the matrix rows and columns corresponding to different device nodes, and the matrix element values representing the correlation coefficients between parameters. The matrix is updated every five minutes, and when an abnormal mutation in the correlation coefficient is detected, a device warning is automatically triggered. The deployment scheme of distributed sensor nodes adopts hierarchical design, with three times the sensor density in the core area of the process such as the bottom of the fermentation tank than in the edge area. The node spacing is determined according to the signal attenuation model, and the core area nodes use a hybrid mode of wired power supply and wireless transmission, while the edge area uses a low-power wireless sensor network. Each node is configured with a self-organizing network protocol that can automatically maintain multi-hop transmission paths. After deployment, the system performs link quality testing and automatically adjusts the position of nodes with a packet loss rate higher than 5%.

[0059] The construction of a multi-dimensional monitoring network is essentially an integration process of physical devices and data protocols. The network layer uses IPv6 protocol to assign a unique address to each sensor, the transmission layer deploys a zero-packet loss retransmission mechanism to ensure data integrity, and the application layer designs a special data packet structure containing time stamp, region code, data value, etc. The network management platform displays the working status of each node in real time, and when a sensor fails, the system automatically starts a redundant node and reroutes the data stream to ensure monitoring continuity. The data acquisition module uses a hybrid working mode of event-driven and periodic scanning, with conventional parameters scanned every ten minutes across the network, and key process parameters with adaptive adjustment of acquisition frequency according to the change rate. The collected data is stored in the real-time database after adding quality labels, and the system will start a re-sampling mechanism for abnormal data. Three consecutive abnormal data will trigger a maintenance alert. The data stream is compressed and encrypted before being transmitted to the monitoring center through the industrial firewall. The entire system uses a dual-machine hot standby architecture to ensure reliability, with the master and slave servers synchronizing data every two seconds. When the network detects a local fault, the control will automatically switch within 100 milliseconds. All configuration parameters are managed centrally through a graphical interface, supporting online modification of sensor range, alarm threshold, etc. The system log records each operation event in detail, meeting the compliance requirements of the pharmaceutical industry for process traceability.

[0060] The calibration maintenance of the monitoring network is integrated into the predictive maintenance strategy. The system predicts the sensor drift trend based on the cumulative running time of the equipment, regularly starts the automatic calibration program, stores the calibration data separately from the production data, generates electronic certificates for calibration records for quality audit, and when the sensor life is approaching, the system will generate a spare parts purchase order in advance and prompt the maintenance plan. The network performance optimization uses reinforcement learning algorithm, by constantly adjusting the data transmission path and collection frequency, the network still maintains more than 95% data integrity rate in the bandwidth limited environment, the optimization algorithm also considers energy balance to avoid individual nodes from being disabled due to overuse, and the network topology supports online reconstruction, which can quickly adapt to the new layout through configuration tools when the brewing production line is transformed. The system realizes two-way data exchange with the upper manufacturing execution system, the key process indicators are uploaded to the production scheduling platform in real time, and the received production plan guides the adjustment of the monitoring strategy in the opposite direction. This two-way integration makes the monitoring network an organic part of the intelligent manufacturing system, rather than an isolated data collection tool. All interfaces comply with the ISA-95 standard to ensure seamless integration with the enterprise resource planning system.

[0061] Example 2: see Figure 3 After the system obtains real-time data of each area through the multi-dimensional monitoring network, it starts the data processing pipeline. The dynamic process data contains time series information from different sensor types. These data have microsecond-level timestamps but have clock drift problems. The space-time alignment process first synchronizes all data points in time using the network time protocol, and adjusts the timestamps to the central server clock reference. The spatial alignment establishes a three-dimensional coordinate mapping table according to the installation position of the sensor on the brewing equipment, and associates each data point to a specific physical coordinate. The standardized processing module performs normalization on the aligned data, converts parameter values of different dimensions to a unified numerical interval, uses the min-max scaling method for continuous parameters such as temperature and pressure, and uses one-hot encoding for discrete state parameters. The missing values in the data sequence are filled by finding the most similar segment in the historical data using a time series similarity-based interpolation algorithm. The standardized process data sequence is stored in groups according to the monitoring area, and each data point is attached with a data quality identifier for marking the credibility. The training of the anomaly detection model is based on the historical normal production data to build a baseline model. The model uses one-class support vector machine algorithm to learn the distribution characteristics of normal process parameters. During the training process, sliding window technology is used to generate sample sequences, each sample contains multiple parameter readings at consecutive time points. The model output is an anomaly score. When the deviation between the incoming standardized data sequence and the baseline model exceeds the dynamic threshold, the system determines it as process deviation data. The deviation detection result is bound to the specific monitoring area, and the area state is marked with different colors on the operation interface.

[0062] The feature extraction process of the process deviation data focuses on the spatial and temporal characteristics of the deviation. The system calculates the percentage value of the deviation amplitude relative to the process allowed range, records the proportion of the deviation duration in the monitoring window, and analyzes the slope characteristics of the deviation trend. These feature parameters are combined into a feature vector input into the deviation influence coefficient calculation module. The calculation process refers to the pre-set process knowledge graph, which defines the influence path and weight coefficient of each parameter deviation on the final beer quality. The construction of the process compensation decision tree uses a rule-based learning method. The decision tree nodes correspond to different deviation scene judgment conditions, and the leaf nodes are associated with specific compensation actions. Each decision node sets the judgment logic, such as "if the temperature deviation influence coefficient is greater than 0.7 and lasts more than 5 minutes, execute the heating power adjustment". The depth and complexity of the decision tree are dynamically adjusted according to the criticality of the monitoring area. For core process areas, a more detailed decision tree with more judgment levels is constructed. The generation process of the parameter adjustment instruction converts the output of the decision tree into device executable commands. The instruction encoding follows the industrial communication protocol standard, including target device address, parameter setting value, execution time window, etc. The system generates independent instruction sequences for each abnormal monitoring area, and sets time correlation constraints between instructions, such as delaying a certain time before adjusting the pressure parameter after adjusting the temperature. All instructions are logically verified and sent to the field controller.

[0063] The instruction execution process is monitored in real time. The system compares the time difference between the instruction sending time and the device response time, and records the degree of agreement between the actual parameter change curve and the expected trajectory. These execution feedback data are sent back to the decision tree module for evaluation of the compensation effect. When it is detected that the instruction execution is blocked or the effect deviates from the expectation, the system will start the alternative decision path to regenerate the adjustment instruction. The update mechanism of the deviation influence coefficient is based on continuous learning. The system periodically re-evaluates the quality influence weight of each process parameter and adjusts the parameters in the influence coefficient calculation formula according to the actual production data. This dynamic update enables the system to adapt to slowly changing factors such as raw material characteristics and equipment aging, maintaining the accuracy of deviation evaluation. The optimization process of the decision tree uses a reinforcement learning framework, taking the instruction execution effect as a reward signal, automatically adjusting the judgment threshold and action selection strategy in the decision node. The optimization process is tested and verified in a simulated environment before being deployed to the online system, ensuring the safety of the changes. Different versions of the decision tree are saved and managed, supporting rollback to previous stable versions when needed. The entire abnormality handling process forms a closed-loop control, from deviation detection to compensation execution and effect evaluation, constituting a complete cycle. The system maintains a handling archive for each abnormal event, including original data, detection results, execution instructions, effect records, and full-link information. These archive data are used for regular audits and analysis, continuously optimizing the abnormality handling strategy.

[0064] The system provides a manual adjustment interface for abnormal handling strategy. The operator can fine-tune the judgment conditions or compensation actions in the decision tree according to actual production experience. These adjustments are recorded and integrated into the automatic decision-making process after safety confirmation, keeping the system decision consistent with human experience. For complex abnormal scenarios, the system supports a multi-level decision-making mechanism. When detecting cross-regional associated abnormalities, it will start the coordinated decision-making process to avoid systemic problems caused by single-point optimization. This coordinated decision-making considers the balance of the entire production line and pursues global optimization rather than local optimization. The parameter adjustment instruction is issued using a safety interlocking mechanism. The system checks the compatibility of the instruction with the current state of the device to prevent conflicting operations. For critical parameter adjustment, a secondary confirmation process is set up. Important instructions require authorization from different levels of operators to ensure production safety. The abnormal handling system is deeply integrated with the production scheduling system. When it is predicted that a long time is needed for parameter adjustment, the production rhythm will be automatically adjusted to minimize the impact of abnormal handling on the overall production plan. This integration unifies quality control and production efficiency management.

[0065] In embodiment 3, after obtaining the process deviation data of the abnormal monitoring area, the compensation decision mechanism is started. The comparison of abnormal characteristic parameters with standard process thresholds uses a multi-dimensional deviation degree evaluation method. Each characteristic parameter has an acceptable floating range upper and lower limit. The deviation degree is quantified by measuring the relative distance between the actual value and the threshold boundary. For continuous parameters such as fermentation temperature, the system calculates the area deviation between the actual temperature curve and the ideal temperature profile. For discrete parameters such as valve state, a state matching algorithm is used to evaluate the conformity. The determination of compensation priority order is based on the comprehensive evaluation of deviation degree and process sensitivity. The system presets a sensitivity weight coefficient for each process parameter, which reflects the influence of parameter change on the final product quality. High sensitivity parameters such as fermentation broth pH value are given a larger weight, and low sensitivity parameters such as environmental humidity have a smaller weight. The product of the deviation degree and the weight coefficient constitutes the parameter priority score. The system arranges the compensation order according to the score, forming a queue to be processed. In special cases, when a critical deviation that may cause a chain reaction is detected, the system will start an emergency channel to raise its priority to the highest.

[0066] The calculation of compensation intensity parameters needs to consider the deviation influence coefficient and priority order comprehensively. The system establishes a compensation intensity calculation model:

[0067]

[0068] Wherein: represents the compensation intensity parameter, is the device response characteristic coefficient, is the standardized deviation degree, is the process sensitivity weight, is the deviation duration, To allow maximum duration. The model ensures that the compensation intensity is positively correlated with the severity of the deviation, while considering the equipment execution capability limit.

[0069] The generation process of parameter adjustment instructions converts abstract compensation intensity into specific device operation quantity, and the system maintains a mapping relationship database between compensation intensity and physical operation quantity. For example, for temperature regulation instructions, the compensation intensity parameter is linearly mapped to the percentage of heater power adjustment or the change amount of cooling valve opening; for flow control instructions, it is converted into pump speed adjustment value. The mapping relationship is dynamically adjusted according to the device type and process stage to ensure the executability of the instructions. The instruction generation module adopts a hierarchical design architecture, the upper layer handles logical decision, and the lower layer is responsible for instruction coding. The logical decision layer determines the instruction execution sequence according to the compensation priority order, and when processing adjustment actions with mutual dependence, the system automatically inserts appropriate delay to ensure the effectiveness of the previous action. The instruction coding layer converts the decision results into data packets conforming to industrial communication protocols, including ModbusTCP, PROFIBUS, etc. Standard formats, each data packet contains target device address, operation instruction, execution timestamp and other necessary fields.

[0070] The system sets a safety interlocking mechanism for key parameter adjustment, which automatically checks the compatibility of the device's current state and the instruction before generating the adjustment instruction. For example, before issuing the instruction to increase the heating power, it will confirm that the liquid level sensor shows that the tank is not empty; before adjusting the speed of the agitator, it verifies that the motor temperature is within the safe range. This preventive check avoids equipment failure or safety accidents caused by instruction conflicts. For complex multi-parameter coordinated adjustment scenarios, the system uses a distributed decision architecture. When detecting that multiple parameters in the same process unit deviate at the same time, the system will start the collaborative compensation algorithm, analyze the coupling relationship between parameters, and generate a set of coordinated adjustment instructions rather than isolated instructions. For example, when adjusting the temperature and stirring speed at the same time, the system will issue instructions in a specific sequence to avoid system oscillation caused by adjusting a single parameter. The instruction issuing channel has a redundant design, the main channel uses real-time industrial Ethernet, and the standby channel uses a wireless communication module. The system monitors the instruction transmission state, and automatically switches channels when it detects that the transmission is timed out or packet loss. Important instructions set a confirmation feedback mechanism, only after receiving the device execution confirmation signal, the instruction is marked as completed; otherwise, it will trigger the retransmission mechanism or start the standby scheme.

[0071] The execution effect of the parameter adjustment instruction is monitored in real time, and the system compares the process parameter change trend before and after the instruction is issued, calculates the degree of coincidence between the actual adjustment effect and the expected effect. These monitoring data are fed back to the compensation decision module to adaptively optimize the parameters in the compensation strength calculation model. When it is found that some types of deviation need to be adjusted repeatedly, the system will automatically increase the monitoring frequency and adjustment sensitivity of the corresponding parameters. Historical instruction data are recorded and associated with specific abnormal events to form an instruction effect knowledge base. The system regularly analyzes these data to find the response characteristics differences of different devices to the same type of instruction, and gradually establishes a personalized compensation parameter mapping table for individual devices. This self-learning capability enables the system to adapt to device aging and process changes, maintaining the consistency of compensation effect. For specific types of complex deviations, the system supports manual experience intervention mechanisms. Operators can manually correct the automatically generated compensation strength parameters or adjust the instruction sequence, and these corrections are recorded and evaluated for safety before being absorbed into the automatic decision rules. This human-machine cooperation mechanism ensures the efficiency of the system's automated operation while retaining the flexibility of human expert experience.

[0072] The instruction generation module interacts with the production planning system to send early warning information to the scheduling system when it is predicted that parameter adjustment may affect production rhythm. For example, when a large temperature adjustment requires a long time to stabilize, the system will suggest adjusting the time arrangement of subsequent processes. This cross-system coordination ensures the smooth operation of the entire production process. The system maintains an instruction template library, storing verified effective instruction combinations. When encountering similar deviation patterns as historical cases, corresponding templates can be quickly called to generate instructions, improving response speed. At the same time, an instruction effectiveness evaluation mechanism is established to regularly check the applicability of each template under current production conditions, eliminate old templates with declining effectiveness, and supplement new templates. The generation process of the parameter adjustment instruction fully considers the characteristics of the actuator, and the system stores parameters such as response speed, accuracy, and dead zone of various actuators to automatically compensate for the inherent nonlinear characteristics of the actuator when calculating the instruction value. For example, for valves with dead zone characteristics, a preset bias amount is added to the instruction value; for heaters with response lag, the instruction includes pre-action compensation. This refined instruction design improves the accuracy of parameter control.

[0073] Example 4: The system initiates the response monitoring mechanism immediately after issuing the adjustment command to the fermenter temperature controller. Temperature sensors installed at different heights of the tank body collect liquid temperature data at a frequency of once per second, while the flow sensor monitors the cooling medium flow rate changes, and the pressure transmitter records the tank pressure fluctuations. These real-time process response data are transmitted to the central processing unit through the industrial network, forming a comparison sequence with the base data before the command is issued. The monitoring system uses a sliding time window to analyze the dynamic characteristics of the process response data, each time window contains a data sequence of sixty consecutive sampling points, and the system calculates the statistical characteristics of the data in the window, including mean, variance and change trend slope. For temperature parameters, the system pays special attention to whether the rate of rise or fall is smooth; for pressure parameters, it focuses on whether there are abnormal fluctuation peaks. These characteristic values are displayed in real time on the trend chart of the operation interface, helping engineers intuitively understand the adjustment effect.

[0074] The calculation of the actual compensation effect value uses a multi-index comprehensive evaluation method, and the system selects three to five key process parameters as effect evaluation indexes, and each index is assigned a weight coefficient according to its importance. The temperature control effect usually occupies the largest weight, followed by the pressure stability index. The actual compensation effect value is obtained by weighted average algorithm, and the numerical range is set between 0 and 1, the closer to 1 indicates that the actual effect is more consistent with the expectation. The calculation process is executed every five minutes, forming an effect evaluation time sequence. The expected compensation effect value comes from the preset targets in the process compensation decision tree, and these target values are dynamically adjusted according to beer varieties and process stages. The system compares the actual effect value with the expected value point by point, generating a difference matrix. This matrix not only records the size of the numerical difference, but also marks the directionality of the difference (whether the actual value is higher or lower than the expected value), as well as the duration and change trend of the difference. Matrix data is stored in a ring buffer, retaining complete records for the last twenty-four hours, see Table 1.

[0075] Table 1: Process response data after temperature adjustment of the fermenter

[0076] Time point (min) Sensor position Temperature actual value (°C) Temperature expected value (°C) Pressure reading (kPa) Cooling valve opening (%) 0 Lower tank body 12.5 12.5 101.3 40 5 Lower tank body 11.8 11.6 101.1 60 10 Lower tank body 10.9 10.7 100.9 75 15 Lower tank body 10.3 10.2 100.8 80 0 Middle tank body 12.8 12.8 101.2 40 5 Middle tank body 12.2 12.0 101.0 60 10 Middle tank body 11.5 11.2 100.8 75 15 Middle tank body 10.9 10.8 100.7 80

[0077] The analysis of the difference matrix reveals the deviation pattern from the expected adjustment effect, and the system classifies the deviation using a pattern recognition algorithm. Common deviation patterns include response lag, excessive adjustment, oscillation, and other types, each of which corresponds to a different decision tree correction strategy. For the response lag pattern, the system will issue an adjustment instruction in advance; for the excessive adjustment pattern, it will reduce the compensation intensity parameter. The pattern recognition results are recorded in the case library together with the current process conditions, forming training samples for the system's self-learning. The correction process of the decision node weights is based on the analysis results of the difference matrix. The system sets the weight adjustment amount as a function of the difference value, and the larger the difference, the greater the weight adjustment amplitude. The weight correction follows the principle of gradualness, and the adjustment amplitude is limited to within 10% of the original weight to avoid system instability caused by excessive single adjustment. The corrected weights need to be verified by simulation before being applied to the actual production environment to ensure the safety of the modification.

[0078] The update mechanism of the anomaly detection model threshold value uses an adaptive algorithm. The system analyzes the accuracy rate indicators of recent deviation detection, and appropriately relaxes the threshold value when the false positive rate is too high, and tightens the threshold value when the false negative rate is too high. Threshold adjustment considers the unique properties of process parameters, and a more stringent threshold value is used for key quality control points, and a wider limit range is set for auxiliary parameters. The updated threshold value needs to be confirmed by quality engineers before it takes effect to ensure that the change is controlled. The system establishes a model performance evaluation system to regularly check the recall rate, accuracy, and other indicators of the anomaly detection model. When the model performance drops below the set standard, the model retraining process is automatically triggered. Retraining uses recently collected production data to maintain consistency between the model and the current equipment state and process conditions. After training is complete, the new model is verified in a test environment, and after confirming its performance improvement through comparative experiments, it is deployed to the production system. The optimization of the process compensation decision tree uses incremental learning. After each parameter adjustment instruction is executed, the system extracts the feature variables and the final results in the decision-making process as new training samples. The decision tree algorithm adjusts the splitting node conditions and leaf node output values based on these samples, and the optimization process focuses on the generalization ability of the decision tree to avoid overfitting to specific scenarios. The system retains multiple versions of the decision tree, and when a new version has problems, it can quickly revert to a stable version.

[0079] The trend extraction of key indicators uses signal processing techniques. The system filters and denoises the process response data, and then uses piecewise linearization to identify trend turning points. Each trend segment is characterized by slope and duration, and the trend change pattern is analyzed in association with context information such as equipment status and raw material characteristics. For example, if a slow-down in the temperature drop trend is detected, it may indicate that the degree of heat exchanger fouling has increased, and the system will adjust the temperature control parameters accordingly. The matching degree calculation takes into account the time dimension. The system not only focuses on the matching degree of the steady-state effect, but also values the consistency of the dynamic response process. For temperature control processes with large inertia, a reasonable time difference between the actual response curve and the expected curve is allowed, but the change trend must be consistent. The matching degree evaluation results are divided into excellent, good, qualified, and unqualified levels, and different levels correspond to different model updating strategies. The system maintains a history record of decision tree correction, including modification time, modification content, modification reason, and execution effect. These records are used to analyze the trajectory of decision tree optimization and summarize the optimization rules. When encountering new deviation patterns, the system can refer to the historical correction records to select similar cases for processing, improving the efficiency of problem solving.

[0080] The entire closed-loop control process forms a virtuous cycle of continuous improvement. The execution effect of parameter adjustment instructions is continuously fed back to the decision system, driving the evolution of the detection model and decision algorithm. This self-optimization capability enables the system to adapt to changes in production conditions and maintain optimal control performance. All automatic modification operations generate change logs for technical personnel to audit and review. The system provides a manual intervention interface, allowing experienced engineers to review automatic correction suggestions and make adjustments. The manual intervention records are learned and absorbed by the system to improve the automatic correction algorithm. This human-machine collaboration mode ensures the efficiency of the system's automated operation while incorporating the experience and wisdom of human experts. The optimized system exhibits good adaptability and maintains stable control performance under conditions such as fluctuations in raw material characteristics and changes in equipment performance. The system continuously accumulates knowledge through continuous learning, forming an enterprise-specific process control knowledge base, which provides the enterprise with a sustained technical competitive advantage.

[0081] Example 5: After the correction of the process compensation decision tree node weights, the system initiates the optimization process of the global control strategy. The optimization process is based on a reinforcement learning framework, considering the entire beer brewing production line as a complex multi-input and multi-output system. The system state includes variables such as process parameters in each monitoring area, equipment operating status, and raw material characteristics. The new decision rules generated after the correction of the decision tree node weights are integrated into the global strategy. The strategy optimization algorithm calculates the long-term impact of each control action on the overall production goal, rather than focusing only on short-term adjustments of local process units. The optimized global control strategy is implemented through a multi-dimensional monitoring network. The system decomposes the strategy into specific control instruction sequences, which are precisely coordinated in the time dimension and cover all monitoring areas in the spatial dimension. The strategy implementation process adopts a distributed execution architecture, where each regional controller receives coordination instructions from the central system while maintaining a certain level of autonomous decision-making ability to handle local emergencies. The instruction issuing mechanism introduces timestamp synchronization technology to ensure that related actions in different regions can be precisely synchronized, such as millisecond-level synchronization between temperature adjustment of the fermentation tank and speed change of the agitator.

[0082] The collection of feedback data for strategy execution covers the entire implementation period. The system sets multiple data collection points before and after the strategy execution, recording indicators such as control instruction transmission delay, equipment response time, and parameter adjustment accuracy. The control delay parameter is measured by the time difference between the instruction generation time and the equipment response time through high-precision clock, and the response accuracy indicator is calculated by comparing the deviation between the set value and the actual value. The collected data is stored in a time series database after filtering for subsequent analysis. The analysis of control delay parameters uses statistical process control methods, and the system calculates the mean, standard deviation, and distribution characteristics of delay data, identifies abnormal delay values, and analyzes their causes. The causes of delay may include network congestion, high controller load, or performance degradation of the actuator, and the system optimizes the network topology or controller configuration based on the analysis results. The analysis of response accuracy indicators focuses on the stability of control effects under different process conditions and identifies key factors affecting accuracy.

[0083] The adjustment of data collection frequency is based on the comprehensive analysis of control delay and response accuracy. The system establishes a correlation model between collection frequency and control performance. When the control delay is long, the collection frequency of non-critical parameters is appropriately reduced to reduce system load; when the response accuracy is insufficient, the collection frequency of critical process points is increased to obtain more detailed process information. The frequency adjustment adopts a smooth transition mode to avoid system oscillation caused by frequent switching. The establishment of the adaptive monitoring mechanism depends on the dynamic adjustment capability of data collection frequency. The system automatically switches the monitoring mode according to real-time process requirements. In the stable running stage, the conventional monitoring mode is adopted to collect data at a lower frequency; in the process conversion or abnormal processing stage, the enhanced monitoring mode is switched to improve the data collection density. The monitoring mode switching rule is based on the process state recognition algorithm, which predicts the upcoming process stage by analyzing the trend of process parameters. The optimization of global control strategy is a continuous iterative process, and the system re-evaluates the strategy effect and makes fine-tuning regularly. The evaluation period is dynamically adjusted according to the stability of the production process. When the characteristics of raw materials change greatly or the equipment undergoes major maintenance, the evaluation period is shortened to speed up the adaptation speed of the strategy. The optimization process preserves historical strategy versions, which can quickly fall back to the previous stable version when the new strategy effect is not ideal.

[0084] The multi-dimensional monitoring network exhibits self-organizing characteristics when implementing optimization strategies. Intelligent sensor nodes in the network can autonomously adjust their working modes according to the importance of monitoring tasks. Important monitoring tasks can obtain more network resources and higher transmission priority. This self-organizing ability improves network resource utilization efficiency. Real-time monitoring data of network performance is used to guide the subsequent optimization direction, forming a continuous improvement cycle. Deep analysis of feedback data from strategy execution reveals potential improvement space for the control system. The system uses association rule mining algorithms to discover implicit relationships between control parameters. These newly discovered association rules are supplemented to the process knowledge base, enriching the system's decision-making basis. The analysis results are also used to predict equipment maintenance needs, scheduling maintenance activities in advance before performance degradation. The learning ability of the adaptive monitoring mechanism enables it to continuously optimize the monitoring strategy. The system records the data value evaluation results under different monitoring frequencies, gradually establishing an optimal collection frequency knowledge base. When encountering new process scenarios, the system can quickly determine appropriate monitoring parameters based on historical data from similar scenarios. This learning ability reduces dependence on human experience and improves system autonomy.

[0085] From monitoring data collection to policy optimization to effect evaluation, the information generated at each link is used to improve system performance. The system maintains detailed operation logs, recording the basis and execution results of all automatic decisions, which are used for system optimization and meet the requirements of product quality traceability. The abnormal handling mechanism in the implementation process ensures the robust operation of the system. When it is detected that there is a large deviation between the expected effect of policy execution and the actual effect, the system will automatically start the abnormal handling process. This process includes cause analysis, policy adjustment scheme generation and safety verification steps to ensure that any adjustment is carried out in a controlled state. Abnormal handling records are learned by the system to improve future decision logic. The information interaction between the system and the upper management system realizes the docking of control strategy and enterprise production target. The optimization of global control strategy considers multiple indicators such as energy consumption, efficiency and quality. The weights of these indicators are dynamically adjusted according to the enterprise strategic target. This docking makes the process control system a powerful tool for optimizing the operation of the enterprise, and promotes the continuous improvement of the level of intelligent manufacturing.

[0086] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A smart monitoring method for the beer brewing process, characterized in that, include: Acquire real-time production data during the beer brewing process, including raw material parameters, fermentation environment parameters, and equipment operating parameters; Based on the raw material parameters and fermentation environment parameters, key process indicators for the current brewing stage are identified, and monitoring areas for the beer brewing process are divided according to these key process indicators. Based on the equipment operating parameters and monitoring areas, a multi-dimensional monitoring network for the beer brewing process is constructed, and dynamic process data of each monitoring area is collected through the multi-dimensional monitoring network. The construction of a multi-dimensional monitoring network for the beer brewing process based on the equipment operating parameters and monitoring area includes: Analyze the pressure change curves and flow fluctuation data in the operating parameters of the equipment to establish an equipment status correlation matrix; Based on the physical distribution characteristics of the monitoring area, the deployment density of distributed sensor nodes is set; By combining the device status correlation matrix and the deployment density of distributed sensor nodes, a multi-dimensional monitoring network for the beer brewing process is constructed. After collecting dynamic process data for each monitoring area through the multi-dimensional monitoring network, the process also includes: The dynamic process data is spatiotemporally aligned to generate a standardized process data sequence; Based on the standardized process data sequence, an anomaly detection model for the beer brewing process is established. The anomaly detection model identifies process deviation data in each monitoring area and marks the anomaly monitoring areas.

2. The intelligent monitoring method for the beer brewing process according to claim 1, characterized in that, The identification of key process indicators for the current brewing stage based on the raw material parameters and fermentation environment parameters includes: The raw material parameters were analyzed to extract malt concentration and yeast activity characteristic values. Based on the temperature gradient and humidity distribution in the fermentation environment parameters, the process influencing factors for each brewing stage are calculated. Based on the malt concentration characteristic value, yeast activity characteristic value, and process influencing factors, the key process indicators for the current brewing stage are determined.

3. The intelligent monitoring method for the beer brewing process according to claim 1, characterized in that, After identifying the process deviation data of each monitoring area through the anomaly detection model, the process further includes: Extract abnormal feature parameters from the process deviation data and calculate the deviation influence coefficient of each abnormal monitoring area; Based on the aforementioned deviation impact coefficient, a process compensation decision tree for the beer brewing process is established; Based on the process compensation decision tree, parameter adjustment instructions are generated for the anomaly monitoring area.

4. The intelligent monitoring method for the beer brewing process according to claim 3, characterized in that, The step of generating parameter adjustment instructions for the anomaly monitoring area based on the process compensation decision tree includes: Analyze the degree of deviation between the abnormal characteristic parameters and the standard process threshold to determine the priority order of compensation; Based on the aforementioned deviation influence coefficient and compensation priority order, the compensation intensity parameters for each anomaly monitoring area are set; Based on the compensation intensity parameters, parameter adjustment instructions are generated for the abnormal monitoring area.

5. The intelligent monitoring method for the beer brewing process according to claim 4, characterized in that, After generating the parameter adjustment instructions for the anomaly monitoring area, the process also includes: Real-time monitoring of process response data after executing the parameter adjustment command; The detection threshold of the anomaly detection model is updated by comparing the matching degree between the process response data and the expected compensation effect. Based on the updated detection thresholds, the process status of each monitoring area is reassessed.

6. The intelligent monitoring method for the beer brewing process according to claim 5, characterized in that, The comparison of the matching degree between the process response data and the expected compensation effect includes: Extract the changing trends of key indicators from the process response data and calculate the actual compensation effect value; Establish a difference matrix between the actual compensation effect value and the expected compensation effect value; Based on the difference matrix, the decision node weights of the process compensation decision tree are adjusted.

7. The intelligent monitoring method for the beer brewing process according to claim 6, characterized in that, After correcting the decision node weights of the process compensation decision tree based on the difference matrix, the process further includes: Based on the revised decision node weights, the global control strategy for the beer brewing process is optimized. The optimized global control strategy is implemented through the multi-dimensional monitoring network. Collect strategy execution feedback data during the implementation process to iteratively update the anomaly detection model; The data collection process, including strategy execution feedback data, also includes: Analyze the control delay parameters and response accuracy indicators in the strategy execution feedback data; The data acquisition frequency of the multi-dimensional monitoring network is adjusted according to the control delay parameter and response accuracy index. An adaptive monitoring mechanism for the beer brewing process was established based on the adjusted data acquisition frequency.

8. An intelligent monitoring system for a beer brewing process, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent monitoring method for the beer brewing process as described in any one of claims 1 to 7.

Images