Beverage production monitoring management system based on digital visualization

Abstract

The invention belongs to the technical field of intelligent manufacturing, and provides a beverage production monitoring management system based on digital visualization, which is characterized in that bottle caps are grouped on the basis of materials of the bottle caps, after consistency is ensured through density detection, environment temperature and humidity data are collected, and a pressure attenuation method is used for calculating a sealing leakage rate; combining a Pearson's correlation coefficient with multiple linear regression to obtain a coupling degree index, and judging a leakage-sensitive bottle cap through a leakage-sensitive analysis equation; secondly, decomposing temperature and humidity data flow of the leakage-sensitive bottle cap into steady-state and disturbance components, substituting the steady-state and disturbance components into a prediction equation to obtain a sealing leakage rate, and comparing a fluctuation range to judge whether to trigger early warning; if early warning is carried out, the regulation and control quantity is calculated based on the disturbance peak value and the coupling degree, the standard-reaching rate is verified after adjustment, and different strategies are executed according to the standard-reaching rate; and finally, calculating a regulation and control energy efficiency index, integrating data to construct a dynamic optimization model, and iteratively optimizing parameters by using a Bayesian algorithm to improve the management and control precision.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing technology, specifically a beverage production monitoring and management system based on digital visualization. Background Technology

[0002] In the process of large-scale production in the beverage industry, the sealing performance of bottle caps directly determines the product's shelf life, taste stability, and consumer safety, and is one of the core quality control links. Currently, beverage bottle cap materials in the market are diverse. Different materials have significantly different sensitivities to temperature and humidity in the production environment due to differences in molecular structure and coefficient of thermal expansion and contraction, which may all lead to sealing leakage problems.

[0003] However, existing bottle cap sealing monitoring solutions have significant technical shortcomings: First, they mostly use a single-dimensional leak rate detection method, without establishing a coupled correlation analysis mechanism between environmental temperature and humidity and the leak rate. They can only determine "whether there is a leak" and cannot identify "which bottle cap materials are most sensitive to temperature and humidity fluctuations," resulting in a lag in quality risk positioning. Problems are often only discovered after a batch of defects have occurred. Second, the monitoring methods lack real-time data processing capabilities and cannot dynamically predict the trend of leak rate changes. The early warning response time is usually more than 30 minutes, missing the best time for control.

[0004] Therefore, the present invention provides a beverage production monitoring and management system based on digital visualization. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] Leakage detection module: Acquire bottle caps of different materials and group them, collect environmental temperature and humidity data for each group of bottle caps, and simultaneously calculate the sealing leakage rate and coupling index of each group of bottle caps; determine the leakage-sensitive bottle caps based on the sealing leakage rate and coupling index in a two-dimensional manner.

[0008] Prediction and early warning module: Extracts the ambient temperature and humidity data stream of the leak-sensitive bottle cap and decomposes it into steady-state component sequence and disturbance component sequence; performs dynamic prediction of sealing leakage rate based on steady-state component sequence and disturbance component sequence, and determines whether to trigger sealing early warning signal based on the predicted sealing leakage rate;

[0009] Control and verification module: If the sealing warning signal is triggered, the control amount of the ambient temperature and humidity group is analyzed and controlled; after the control is executed, the sealing leakage rate data of multiple bottle caps are collected to verify the compliance rate of the control effect.

[0010] Parameter optimization module: Based on the compliance rate after regulation, calculate the regulation energy efficiency index; integrate the parameters involved in the regulation process, and construct a dynamic optimization model to iteratively optimize the parameters involved.

[0011] Furthermore, the process of obtaining the coupling index is as follows:

[0012] Based on the multiple linear regression model algorithm, the bottle cap sealing leakage rate is obtained and a coupling degree calculation formula is constructed.

[0013] The Pearson correlation coefficients between ambient temperature and bottle cap leakage rate and ambient humidity and bottle cap leakage rate are obtained and input into the coupling degree calculation formula to obtain the coupling degree index between ambient temperature and humidity group and bottle cap leakage rate.

[0014] Furthermore, the process of obtaining the Pearson correlation coefficients between ambient temperature and bottle cap leakage rate, and between ambient humidity and bottle cap leakage rate, is as follows:

[0015] The sealing leakage rate of each bottle cap in each group was calculated using the pressure decay method.

[0016] The Pearson correlation coefficients for ambient temperature and bottle cap leakage rate, and the Pearson correlation coefficients for ambient humidity and bottle cap leakage rate were calculated using the Pearson correlation coefficient formula.

[0017] Furthermore, the process of performing the aforementioned two-dimensional determination is as follows:

[0018] Based on the average sealing leakage rate and coupling index of each group of bottle caps, a leak-sensitive analysis equation is constructed.

[0019] The leakage index of each group of bottle caps was calculated using the leakage analysis equation.

[0020] Select the maximum value among the leakage indices of each bottle cap and mark the bottle cap with the maximum leakage index as a leakage-sensitive bottle cap.

[0021] Furthermore, the process for determining whether a sealing warning signal has been triggered is as follows:

[0022] The environmental temperature and humidity group data stream of the leak-sensitive bottle cap is decomposed to obtain the steady-state component sequence and the disturbance component sequence.

[0023] Obtain the steady-state and disturbance component sequences of environmental temperature and humidity data and construct a prediction equation for the sealing leakage rate.

[0024] The predicted seal leakage rate is calculated using the seal leakage rate prediction equation.

[0025] Obtain the range of seal leakage fluctuations and compare the predicted seal leakage rate with the range of seal leakage fluctuations. If the predicted seal leakage rate is higher than the maximum value of the range of seal leakage fluctuations, immediately trigger a sealing warning signal.

[0026] Furthermore, the process of performing the decomposition is as follows:

[0027] The environmental temperature and humidity data of the leak-sensitive bottle caps were sorted according to time sequence to obtain the environmental temperature sequence and the environmental humidity sequence, and the environmental temperature and humidity data were preprocessed simultaneously;

[0028] The preprocessed environmental temperature and humidity data were used to calculate the steady-state component sequences of environmental temperature and environmental humidity using the moving average formula.

[0029] The ambient temperature and ambient humidity sequences are subtracted from the ambient temperature steady-state component sequences and ambient humidity steady-state component sequences, respectively, to obtain the ambient temperature perturbation component sequence and the ambient humidity perturbation component sequence.

[0030] Furthermore, the process of controlling the environmental temperature and humidity group is as follows:

[0031] Retrieve the sliding window of the time point that triggered the sealing warning signal, and extract the temperature disturbance component sequence and humidity disturbance component sequence of each time point within the sliding window;

[0032] Extract the maximum absolute value of the temperature and humidity perturbation component sequences within the sliding window, and mark them as the temperature perturbation peak and humidity perturbation peak;

[0033] Temperature and humidity control formulas are constructed based on the extracted temperature and humidity disturbance peaks and the coupling index of the leak-sensitive bottle cap.

[0034] The ambient temperature and humidity control values ​​are obtained through temperature and humidity control formulas.

[0035] The difference between the current actual ambient temperature and humidity group and the ambient temperature and humidity group control value is processed to obtain a new ambient temperature and humidity group set value;

[0036] Input the new ambient temperature and humidity group setpoints into the control system for adjustment.

[0037] Furthermore, the process of verifying the achievement rate of the control effect is as follows:

[0038] After the control was completed and the environmental temperature and humidity data stabilized, multiple bottle cap samples were collected in real time to obtain the sealing leakage rate of each bottle cap sample.

[0039] The range of sealing leakage fluctuations was obtained, and the ratio of the number of bottle cap samples with sealing leakage rates below or within the range of sealing leakage fluctuations to the total number of bottle cap samples was calculated to obtain the compliance rate after environmental temperature and humidity control.

[0040] Based on the compliance rate after environmental temperature and humidity control, different treatment strategies are automatically triggered.

[0041] Furthermore, the process of obtaining the regulation energy efficiency index is as follows:

[0042] Obtain data from the bottle cap samples after regulation, including the peak disturbance of the bottle cap samples and the compliance rate after regulation;

[0043] Based on the data obtained from the bottle cap samples after regulation, a regulation efficiency equation is constructed, and the regulation efficiency index for each regulation is calculated using the regulation efficiency equation.

[0044] If the energy efficiency index is greater than or equal to the energy efficiency threshold, it means that the resource input is at a reasonable or optimal level under the premise of achieving the target effect.

[0045] Furthermore, the iterative optimization process is as follows:

[0046] By integrating historical regulation data with the results of this regulation, a multidimensional dataset is formed that includes disturbance peak, regulation amount, compliance rate and regulation energy efficiency index;

[0047] Based on the aforementioned multidimensional dataset, a dynamic optimization model is constructed using the attenuation coefficient, sliding window length, coupling index, and environmental temperature and humidity disturbance component sequence as key optimization parameters.

[0048] The current production cycle's control data is input into the dynamic optimization model to generate parameter optimization suggestions; key parameters are adjusted based on the parameter optimization suggestions to form a new control strategy; the new control strategy is implemented in the next production cycle, and the optimization effect is verified by real-time collected sealing leakage rate data.

[0049] The beneficial effects of this invention are as follows:

[0050] 1. Acquire bottle caps of different materials and group them, collect environmental temperature and humidity data for each group of bottle caps, and simultaneously calculate the sealing leakage rate and coupling index for each group of bottle caps; determine the leakage-prone bottle caps based on the sealing leakage rate and coupling index in a two-dimensional manner; provide scientific data support for bottle cap material optimization, selection, or sealing process improvement, and reduce product loss or quality problems caused by bottle cap leakage; extract the environmental temperature and humidity data stream of the leakage-prone bottle caps and decompose it into steady-state component sequences and perturbation component sequences; dynamically predict the sealing leakage rate based on the steady-state component sequences and perturbation component sequences, and determine whether to trigger the sealing warning signal based on the predicted sealing leakage rate; capture the leakage rate change pattern, identify sealing hazards in advance and issue timely warnings to avoid subsequent product quality problems or losses caused by sealing failure;

[0051] 2. If a sealing warning signal is triggered, the control amount of the environmental temperature and humidity group is analyzed and adjusted; multiple sets of bottle cap sealing leakage rate data are collected after the control is implemented to verify the compliance rate of the control effect; after the sealing warning is triggered, the environmental temperature and humidity group is analyzed and controlled, and data is collected again to verify the compliance rate of the control effect, forming a closed loop that solves the bottle cap sealing problem caused by temperature and humidity factors, ensuring the stable compliance of subsequent bottle cap sealing performance and reducing quality risks; based on the compliance rate after control, the control energy efficiency index is calculated; the parameters involved in the control process are integrated, and a dynamic optimization model is constructed to iteratively optimize the parameters involved; it can take into account both control effect and efficiency, continuously optimize control parameters to achieve more efficient temperature and humidity control, reduce ineffective control costs, and improve the stability of subsequent bottle cap sealing quality. Attached Figure Description

[0052] The invention will now be further described with reference to the accompanying drawings.

[0053] Figure 1 This is a block diagram of a beverage production monitoring and management system based on digital visualization, as described in an embodiment of the present invention.

[0054] Figure 2 This is a flowchart illustrating the triggering of the sealing warning signal as described in an embodiment of the present invention;

[0055] Figure 3 This is a flowchart illustrating the steps of a beverage production monitoring and management system based on digital visualization, as described in an embodiment of the present invention. Detailed Implementation

[0056] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0057] Example 1: Please refer to Figure 1 As shown in the embodiment of the present invention, a beverage production monitoring and management system based on digital visualization includes the following modules:

[0058] Leakage detection module: Acquire bottle caps of different materials and group them, collect environmental temperature and humidity data for each group of bottle caps, and simultaneously calculate the sealing leakage rate and coupling index of each group of bottle caps; determine the leakage-sensitive bottle caps based on the sealing leakage rate and coupling index in a two-dimensional manner.

[0059] The process of calculating the leakage rate and coupling index of each group of bottle cap seals is as follows:

[0060] Based on the core material of the bottle cap, the bottle caps are grouped according to the type of material.

[0061] The bottle caps are made of different materials, including polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), aluminum-plastic composite material, and silicone sealing ring.

[0062] Collect bottle caps made of different materials from three production cycles and test their material density to ensure the consistency of material within the same group of bottle caps; generally, the material density is tested using the displacement density method, or by measuring the mass and volume of the bottle cap and calculating the ratio to obtain the material density.

[0063] It should be noted that the production cycle means the time period corresponding to a complete production process from the input of raw materials into production, through processing and shaping, to the final production of bottle caps;

[0064] Real-time environmental parameter data for each group of bottle caps are collected synchronously during the production cycle. The environmental parameters include ambient temperature and ambient humidity, which are labeled as ambient temperature and humidity groups.

[0065] The sealing leakage rate of each bottle cap in each group was calculated based on the pressure decay method.

[0066] The method for calculating the seal leakage rate using the pressure decay method is as follows:

[0067] Preferably, according to the pressure decay equation: Obtain the sealing leakage rate Q of the bottle cap;

[0068] Understandably, the pressure decay equation is used to quantify the amount of gas leakage from the cap and calculate the sealing leakage rate of the cap by detecting the change in pressure inside the sealed container over the test cycle time.

[0069] in, The initial pressure at the initial sampling point of the test cycle. The pressure inside the bottle at the sampling point at the end of the test cycle. For the volume of the bottle, The duration of the test period, This represents standard atmospheric pressure, with 1000 being the conversion factor between liters and milliliters.

[0070] Calculate the average seal leakage rate for each group of bottle caps. ;

[0071] Simultaneously, calculate the coupling degree between the bottle cap seal leakage rate and the ambient temperature and humidity group within each group;

[0072] The Pearson correlation coefficients for ambient temperature and bottle cap leakage rate, and the Pearson correlation coefficients for ambient humidity and bottle cap leakage rate were calculated using the Pearson correlation coefficient formula.

[0073] Calculated using Pearson's formula: Obtain the Pearson correlation coefficient between ambient temperature and bottle cap leak rate. ;

[0074] Similarly, the calculation can be performed using Pearson's formula: Obtain the Pearson correlation coefficient between ambient humidity and bottle cap sealing leakage rate. ;

[0075] Wherein, represents the number of bottle caps in each group. This represents the ambient temperature during the production of the i-th bottle cap in each group. This represents the ambient humidity during the production of the i-th bottle cap in each group. This represents the sealing leakage rate of the i-th bottle cap within each group. This represents the average ambient temperature within each group. This represents the average humidity level within each group. This indicates the average leakage rate of the bottle caps within each group.

[0076] The environmental parameters and sealing leakage rate of each group of bottle caps were calculated using the Pearson correlation coefficient formula to obtain the Pearson correlation coefficient between the ambient temperature and the sealing leakage rate of each group. And the Pearson correlation coefficient between ambient humidity and bottle cap sealing leakage rate. ;

[0077] Based on the multiple linear regression model algorithm, a formula for calculating the coupling degree between bottle cap sealing leakage rate and ambient temperature and humidity is constructed, and the normalization interval is set to [0,1].

[0078] The formula for calculating the coupling degree is: ;

[0079] in, and These are the weighting coefficients for ambient temperature and ambient humidity, respectively, and C represents the coupling index between the ambient temperature and humidity group and the bottle cap sealing leakage rate.

[0080] It should be noted that the weighting coefficients for ambient temperature and ambient humidity were determined by those skilled in the art based on historical training data;

[0081] The coupling degree index between the ambient temperature and humidity group and the bottle cap sealing leakage rate is obtained by using the coupling degree calculation formula.

[0082] Compare the coupling index with a preset coupling threshold;

[0083] If the coupling index is greater than the preset coupling threshold, the environmental temperature and humidity group and the bottle cap sealing leakage rate are determined to be strongly coupled.

[0084] If the coupling index is less than or equal to the preset coupling threshold, the environmental temperature and humidity group and the bottle cap sealing leakage rate are determined to be weakly coupled.

[0085] The process of determining leak-sensitive bottle caps based on a dual dimension of sealing leakage rate and coupling degree index is as follows:

[0086] Based on the average sealing leakage rate and coupling index of each group of bottle caps, a leak-sensitive analysis equation is constructed; the sealing leakage rate and coupling index are input into the leak-sensitive analysis equation to obtain the leak-sensitive index of each group of bottle caps;

[0087] Dimensionless processing of QI yields ;

[0088] The sensitive leakage analysis equation is as follows:

[0089] ;

[0090] Where a is b is the active effect coefficient of C, c is the synergistic effect coefficient, and SI is the leakage sensitivity index of a certain group of bottle caps.

[0091] It is understandable that a, b, and c were obtained by those skilled in the art through experiments that integrated historical data;

[0092] a measures the contribution of the average sealing leakage rate of each group of bottle caps to the sensitive leakage index; b measures the contribution of the coupling index to the sensitive leakage index; and c reflects the risk amplification effect of the synergistic effect of the average sealing leakage rate and the coupling index.

[0093] The leakage index of each group of bottle caps was calculated using the leakage analysis equation.

[0094] Select the maximum value among the leakage sensitivity indices of each group of bottle caps, and mark the bottle cap with the maximum leakage sensitivity index as the leakage sensitive bottle cap;

[0095] It should be noted that the physical meaning reflected by the leakage sensitivity index is as follows:

[0096] The leakage sensitivity index is a comprehensive quantification of the sealing risk of bottle caps. It reflects the basic risk of the average leakage rate of each group of bottle caps, as well as the coupling interference between the environmental temperature and humidity group and the leakage rate. It also amplifies the risk of the combined effect of the two through the synergistic effect. The higher the value, the higher the probability of the bottle cap of this material leaking under the fluctuation of the environmental temperature and humidity group.

[0097] It should also be noted that the purpose of obtaining the sensitivity leakage index is:

[0098] It can quickly identify the most risky leak-prone bottle caps in different material groups and clarify the priority targets for quality improvement; at the same time, it can trace the root cause of the risk in reverse. If the high index is due to leakage rate problems, the process can be optimized; if it is due to the coupling of environmental temperature and humidity groups, the environment can be adjusted; in addition, it can also serve as a benchmark for comparing the sealing risks of bottle caps of different production cycles and materials, supporting long-term quality control decisions.

[0099] Prediction and early warning module: Extracts the ambient temperature and humidity data stream of the leak-sensitive bottle cap and decomposes it into steady-state component sequence and disturbance component sequence; performs dynamic prediction of sealing leakage rate based on steady-state component sequence and disturbance component sequence, and determines whether to trigger sealing early warning signal based on the predicted sealing leakage rate;

[0100] The process of extracting the environmental temperature and humidity data stream from the leak-sensitive bottle cap and decomposing it into a steady-state component sequence and a disturbance component sequence is as follows:

[0101] The collected environmental temperature and humidity data of the leak-sensitive bottle caps were sorted and labeled according to the time sequence within the production cycle as environmental temperature sequence M1 and environmental humidity sequence M2.

[0102] The collected environmental temperature and humidity data are preprocessed synchronously to eliminate interference from missing data.

[0103] Those skilled in the art will understand that the preprocessing of the collected environmental temperature and humidity data is as follows: missing values ​​caused by sensor offline are filled with the mean of five adjacent valid data to ensure that they conform to the data trend; outliers that deviate from the overall pattern are replaced with linear interpolation to eliminate interference; and finally, the data is smoothed by the moving average method to reduce the impact of instantaneous fluctuations and obtain stable and reliable environmental temperature and humidity data.

[0104] The preprocessed environmental temperature and humidity data are decomposed into a steady-state component sequence reflecting long-term stable trends and a disturbance component sequence reflecting short-term fluctuations.

[0105] The steady-state component sequence of the environmental temperature and humidity data was calculated using the moving average method.

[0106] Preferably, using the moving average formula: Obtain the steady-state component sequence of ambient temperature ;

[0107] By using the moving average formula: Obtain the steady-state component sequence of ambient humidity ;

[0108] Where d represents the time point, j represents the length of the sliding window, and h w Indicates the sequence number of the collected ambient temperature data, h s M1 indicates the sequence number of the collected ambient humidity data. 稳 M2 represents the steady-state component sequence of ambient temperature. 稳 Represents the steady-state component sequence of ambient humidity;

[0109] Based on the obtained steady-state component sequences of ambient temperature and humidity;

[0110] The ambient temperature sequence M1 is obtained by subtracting the ambient temperature steady-state component sequence from the ambient temperature sequence M1. 扰 ;

[0111] The ambient humidity sequence M2 is obtained by subtracting the ambient humidity steady-state component sequence. 扰 ;

[0112] The process of dynamically predicting the sealing leakage rate based on the steady-state component sequence and the disturbance component sequence is as follows:

[0113] A prediction equation for the sealing leakage rate is constructed based on the steady-state and disturbance component sequences of the environmental temperature and humidity data; the prediction equation for the sealing leakage rate is as follows:

[0114] ;

[0115] Where e is the basic material coefficient of the bottle cap, 0.03 is the influence coefficient of the steady-state component sequence on the sealing leakage rate, 0.05 is the influence coefficient of the disturbance component sequence on the sealing leakage rate, and Q 预 To predict the seal leakage rate;

[0116] For example, the material basis coefficient is preset by those skilled in the art based on historical experience, while the steady-state component sequence influence coefficient and the disturbance component sequence influence coefficient are obtained by those skilled in the art through historical data and multiple linear regression analysis to determine the quantitative relationship between each influencing factor and the leakage rate, thereby fitting their respective correlation coefficients.

[0117] The steady-state component sequence and the disturbance component sequence of the environmental temperature and humidity data are input into the seal leakage rate prediction equation, and the predicted seal leakage rate is obtained by calculation.

[0118] As those skilled in the art will understand, the sealing leakage rate prediction equation can achieve prediction because the material basic coefficient, steady-state component sequence influence coefficient, and disturbance component sequence influence coefficient have been clarified through historical data correlation analysis, which has clarified the correlation rules between temperature and humidity group characteristics and leakage rate. After substituting the steady-state and disturbance component sequences of the environmental temperature and humidity group, the sealing leakage rate can be quantitatively derived through the weighted calculation of preset coefficients.

[0119] The process of determining whether to trigger a sealing warning signal based on the predicted sealing leakage rate is as follows:

[0120] like Figure 2 As shown, the predicted sealing leakage rate is compared with the sealing leakage fluctuation range, and the sealing warning signal is triggered based on the comparison results.

[0121] It should be noted that the range of seal leakage fluctuations is derived from historical benchmark fluctuation data and industry quality standards.

[0122] If the predicted seal leakage rate is lower than the minimum value of the seal leakage fluctuation range, it means that the bottle cap sealing performance is in excellent condition and no intervention measures are required.

[0123] If the predicted seal leakage rate is within the range of seal leakage fluctuations, it means that the bottle cap sealing performance is within a controllable and normal range of random fluctuations, and should be continuously monitored without immediate intervention.

[0124] If the predicted seal leakage rate is higher than the maximum value of the seal leakage fluctuation range, it indicates that the bottle cap sealing performance has deviated from the acceptable fluctuation range, and the sealing warning signal must be triggered immediately and intervention measures must be implemented quickly.

[0125] The technical solution of this embodiment is as follows: Bottle caps of different materials are acquired and grouped; environmental temperature and humidity data for each group of bottle caps are collected; and the sealing leakage rate and coupling index of each group of bottle caps are calculated simultaneously. Based on the sealing leakage rate and coupling index, a dual-dimensional judgment of leak-prone bottle caps is made. This provides scientific data support for bottle cap material optimization, selection, or sealing process improvement, reducing product loss or quality problems caused by leaking bottle caps. The environmental temperature and humidity data stream of leak-prone bottle caps is extracted and decomposed into a steady-state component sequence and a disturbance component sequence. Based on the steady-state component sequence and the disturbance component sequence, the sealing leakage rate is dynamically predicted; based on the predicted sealing leakage rate, a sealing warning signal is triggered. The leakage rate change pattern is captured, sealing hazards are identified in advance, and timely warnings are issued to avoid subsequent product quality problems or losses due to sealing failure.

[0126] Example 2: Please refer to Figure 1 As shown in the embodiment of the present invention, a beverage production monitoring and management system based on digital visualization further includes the following modules:

[0127] Control and verification module: If the sealing warning signal is triggered, the control amount of the ambient temperature and humidity group is analyzed and controlled; after the control is executed, the sealing leakage rate data of multiple bottle caps are collected to verify the compliance rate of the control effect.

[0128] If a sealing warning signal is triggered, the process of adjusting the environmental temperature and humidity control parameters is as follows:

[0129] If a sealing warning signal is triggered, first retrieve the sliding window at the time point when the sealing warning signal is triggered, and extract the temperature disturbance component sequence and humidity disturbance component sequence at each time point within the sliding window;

[0130] Based on the temperature and humidity disturbance component sequences at each time point within the sliding window, the maximum values ​​of the absolute values ​​of the temperature and humidity disturbance component sequences within the sliding window are extracted and marked as the temperature disturbance peak and humidity disturbance peak.

[0131] Based on the extracted temperature and humidity disturbance peaks and the coupling index of the leak-sensitive bottle cap, temperature and humidity control formulas are constructed, and the ambient temperature and humidity control amounts are obtained through these formulas.

[0132] The methods for obtaining the ambient temperature and humidity control values ​​are as follows:

[0133] Through temperature control formula: Obtain ambient temperature regulation amount ;

[0134] Similarly, the humidity control formula is as follows: Obtain environmental humidity control amount ;

[0135] in, This represents the maximum value of the temperature perturbation component sequence. Let C be the maximum value of the humidity disturbance component sequence, C be the coupling degree index of the leak-sensitive bottle cap, and k be the control attenuation coefficient. For temperature control quantity, This refers to humidity control parameters;

[0136] Understandably, k is an empirical constant derived from historical data. This coefficient is introduced to avoid over-regulation, providing a small buffer for the regulation process and ensuring its smoothness.

[0137] Based on the obtained environmental temperature and humidity control parameters and This allows for the difference between the current actual ambient temperature and humidity group and the ambient temperature and humidity group control value to obtain a new ambient temperature and humidity group set value.

[0138] Input the new ambient temperature and humidity group setpoints into the control system for adjustment;

[0139] In some embodiments, the adjustment process is as follows: the central controller sends the new ambient temperature and humidity setpoints to the workshop environmental control unit (such as a PLC-controlled variable frequency chiller, PTC heater, ultrasonic humidifier, or rotary dehumidifier); the control unit adjusts the environmental parameters gradually and non-impactly according to the new ambient temperature and humidity setpoints to prevent secondary impacts on other parts of the production line due to sudden changes in temperature or humidity; the system monitors the environmental data in real time until it stabilizes within the target range;

[0140] The process of collecting and verifying the compliance rate of multiple sets of bottle cap sealing leakage rate data after the control and regulation are implemented is as follows:

[0141] After the control was completed and the environmental temperature and humidity data stabilized, multiple bottle cap samples were collected in real time to obtain the sealing leakage rate of each bottle cap sample.

[0142] The seal leakage rate and seal leakage fluctuation range of each bottle cap sample were compared;

[0143] The ratio of the number of bottle cap samples with a sealing leakage rate lower than or within the sealing leakage fluctuation range to the total number of bottle cap samples was used to obtain the compliance rate F after environmental temperature and humidity control.

[0144] Based on the compliance rate after environmental temperature and humidity control, different treatment strategies are automatically triggered.

[0145] The different processing strategies are as follows:

[0146] If the compliance rate is greater than or equal to 95%, it indicates that the control is successful; it means that the sealing quality has been restored to a controlled state; the system records all data of this warning and control (including disturbance peak, control amount, compliance rate) to enrich the knowledge base, and then exits the emergency control mode and returns to the normal monitoring state;

[0147] If the compliance rate is between 90% and 95%, it indicates that the control is partially effective but not sufficiently effective. The system then activates the fine-tuning mode, which shortens the sliding window, re-extracts the latest perturbation component sequence, and recalculates the control amount with a smaller attenuation coefficient (e.g., k=0.8) for supplementary adjustment. After completion, samples are collected again for verification.

[0148] If the compliance rate is less than 90%, it indicates that the control has failed. The system will immediately trigger the highest level alarm and notify the production engineer to intervene manually. At the same time, the system will package and mark all data streams of this event (environmental data, leakage rate data, control instructions) as "abnormal cases" to provide a data foundation for subsequent model optimization and root cause analysis.

[0149] Parameter optimization module: Based on the compliance rate after regulation, calculate the regulation energy efficiency index; integrate the parameters involved in the regulation process, and construct a dynamic optimization model for iterative optimization;

[0150] The process of calculating the regulation energy efficiency index based on the compliance rate after regulation is as follows:

[0151] Obtain data from the bottle cap samples after regulation, including the peak disturbance of the bottle cap samples and the compliance rate after regulation;

[0152] Based on the data obtained from the bottle cap samples after regulation, a regulation efficiency equation is constructed, and the regulation efficiency index E for each regulation is calculated using the regulation efficiency equation.

[0153] The energy efficiency regulation equation is as follows:

[0154] ;

[0155] Compare the calculated regulation energy efficiency index with the regulation energy efficiency threshold;

[0156] If the energy efficiency index is greater than or equal to the energy efficiency threshold, it means that the resource input is at a reasonable or optimal level under the premise of achieving the target effect.

[0157] If the energy efficiency index is less than the energy efficiency threshold, it means that the resource input exceeds the reasonable range, the regulation process is inaccurate, and the resource utilization rate is low.

[0158] Preferably, the energy efficiency threshold is 1.2;

[0159] It should be noted that the role of obtaining the regulation energy efficiency index is that the regulation energy efficiency index is not only a quantitative feedback of the effect of a single regulation, but also reflects the balance between regulation effect and resource input. For example, if two regulation measures both achieve a 95% compliance rate, but the temperature and humidity adjustment range of one regulation is smaller, then its regulation energy efficiency index is higher, which means that the regulation process is more precise and resource consumption is more reasonable.

[0160] The process of iteratively optimizing key parameters based on multidimensional datasets to improve the accuracy and stability of regulation is as follows:

[0161] By integrating historical regulation data with the results of this regulation, a multidimensional dataset is formed that includes disturbance peak, regulation amount, compliance rate and regulation energy efficiency index;

[0162] It should be noted that historical control data includes, but is not limited to, case data of triggering sealing warning signals in historical production cycles, corresponding environmental temperature and humidity control records, and subsequent compliance rate feedback;

[0163] A dynamic optimization model is constructed based on the aforementioned multidimensional dataset;

[0164] Among them, the dynamic optimization model uses the attenuation coefficient k, sliding window length, coupling degree index and environmental temperature and humidity disturbance component sequence as key optimization parameters;

[0165] Based on the dynamic optimization model, an iterative optimization algorithm is used to optimize the key parameters;

[0166] The iterative optimization algorithm simulates the control effect under different parameter combinations, predicts and selects parameter configurations with higher control energy efficiency index;

[0167] Among them, the iterative optimization algorithm is preferably the Bayesian optimization algorithm;

[0168] The current production cycle control data is input into the dynamic optimization model to generate parameter optimization suggestions; the parameter optimization suggestions include fine-tuning of the control attenuation coefficient k, adaptive adjustment of the sliding window length, or correction of the environmental temperature and humidity group disturbance component sequence.

[0169] Based on the parameter optimization suggestions, key parameters are adjusted to form a new control strategy; the new control strategy is implemented in the next production cycle, and the optimization effect is verified by real-time collected sealing leakage rate data;

[0170] The validated optimization results are fed back to the dynamic optimization model to complete one iteration cycle. The iteration cycle continuously incorporates new data and updates the parameter-performance mapping relationship to gradually improve the model's adaptability to changes in the production environment.

[0171] The technical solution of this embodiment is as follows: If a sealing warning signal is triggered, the control amount of the ambient temperature and humidity group is analyzed and controlled; after the control is executed, the sealing leakage rate data of multiple bottle caps are collected to verify the compliance rate of the control effect; after the sealing warning is triggered, the ambient temperature and humidity group is analyzed and controlled, and data is collected again to verify the compliance rate of the control effect, which can form a closed loop to solve the bottle cap sealing problem caused by temperature and humidity factors, ensure the stable compliance of the subsequent bottle cap sealing performance, and reduce quality risks; based on the compliance rate after control, the control energy efficiency index is calculated; the parameters involved in the control process are integrated, and a dynamic optimization model is constructed to iteratively optimize the parameters involved; it can take into account both the control effect and efficiency, continuously optimize the control parameters to achieve more efficient temperature and humidity control, reduce the cost of ineffective control, and improve the stability of the subsequent bottle cap sealing quality.

[0172] Example 3: Please refer to Figure 3 As shown in the embodiment of the present invention, a beverage production monitoring and management method based on digital visualization includes the following steps:

[0173] Step 1: Obtain bottle caps of different materials and group them. Collect environmental temperature and humidity data for each group of bottle caps and simultaneously calculate the sealing leakage rate and coupling index of each group of bottle caps. Based on the sealing leakage rate and coupling index, determine the leak-prone bottle caps in a two-dimensional manner.

[0174] Step 2: Extract the ambient temperature and humidity data stream of the leak-sensitive bottle cap and decompose it into a steady-state component sequence and a disturbance component sequence; perform dynamic prediction of the sealing leakage rate based on the steady-state component sequence and the disturbance component sequence, and determine whether to trigger the sealing warning signal based on the predicted sealing leakage rate;

[0175] Step 3: If the sealing warning signal is triggered, analyze and adjust the control amount of the environmental temperature and humidity group; collect the sealing leakage rate data of multiple bottle caps after the control is executed to verify the compliance rate of the control effect.

[0176] Step 4: Based on the compliance rate after regulation, calculate the regulation energy efficiency index; integrate the parameters involved in the regulation process, and construct a dynamic optimization model to iteratively optimize the parameters involved.

[0177] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital visualisation based beverage production monitoring management system, characterised in that: The system comprises the following modules: Leakage judgment module: Obtain bottle caps of different materials and group them, collect the environmental temperature and humidity group data of each group of bottle caps, and synchronously calculate the sealing leakage rate and coupling degree index of each group of bottle caps; based on the sealing leakage rate and the coupling degree index, determine the sensitive leakage bottle caps in two dimensions; Prediction and early warning module: extract the environmental temperature and humidity group data stream of the sensitive leakage bottle caps, and decompose it into a steady-state component sequence and a disturbance component sequence; based on the steady-state component sequence and the disturbance component sequence, dynamically predict the sealing leakage rate, and determine whether to trigger the sealing early warning signal according to the predicted sealing leakage rate; Regulation and verification module: if the sealing early warning signal is triggered, analyze the regulation amount of the environmental temperature and humidity group and perform regulation; Collect the sealing leakage rate data of multiple groups of bottle caps after regulation and execution, and verify the compliance rate of the regulation effect; Parameter optimization module: based on the compliance rate after regulation, calculate the regulation energy efficiency index; integrate the parameters involved in the regulation process, construct a dynamic optimization model, and iteratively optimize the involved parameters.

2. The beverage production monitoring and management system based on digital visualization according to claim 1, wherein: The process of obtaining the coupling degree index is: Based on the multivariate linear regression model algorithm, obtain the bottle cap sealing leakage rate and construct a coupling degree calculation formula; Obtain the Pearson correlation coefficient of the environmental temperature and the bottle cap sealing leakage rate and the Pearson correlation coefficient of the environmental humidity and the bottle cap sealing leakage rate, and input them into the coupling degree calculation formula to obtain the coupling degree index of the environmental temperature and humidity group and the bottle cap sealing leakage rate.

3. The beverage production monitoring and management system based on digital visualization according to claim 2, wherein: The process of obtaining the Pearson correlation coefficient of the environmental temperature and the bottle cap sealing leakage rate and the Pearson correlation coefficient of the environmental humidity and the bottle cap sealing leakage rate is: Calculate the sealing leakage rate of each bottle cap in each group by the pressure decay method; Calculate the Pearson correlation coefficient of the environmental temperature and the bottle cap sealing leakage rate and the Pearson correlation coefficient of the environmental humidity and the bottle cap sealing leakage rate in each group by the Pearson correlation coefficient formula.

4. The beverage production monitoring and management system based on digital visualization according to claim 1, wherein: The process of determining in two dimensions is: Construct a sensitive leakage analysis equation according to the average sealing leakage rate and the coupling degree index of each group of bottle caps; Calculate the sensitive leakage index of each group of bottle caps by the sensitive leakage analysis equation; Select the maximum value of the sensitive leakage index of each group of bottle caps, and mark the bottle cap with the maximum sensitive leakage index as the sensitive leakage bottle cap.

5. The beverage production monitoring and management system based on digital visualization according to claim 1, wherein: The process of determining whether to trigger the sealing early warning signal is: Decompose the environmental temperature and humidity group data stream of the sensitive leakage bottle caps to obtain the steady-state component sequence and the disturbance component sequence; Obtain the steady-state component sequence and the disturbance component sequence of the environmental temperature and humidity group data and construct a sealing leakage rate prediction equation; Calculate the predicted sealing leakage rate by the sealing leakage rate prediction equation; Obtain the sealing leakage fluctuation range, and compare the predicted sealing leakage rate with the sealing leakage fluctuation range, if the predicted sealing leakage rate is higher than the maximum value of the sealing leakage fluctuation range, an immediate sealing warning signal is triggered. 6.The beverage production monitoring management system based on digital visualization according to claim 5, characterized in that: The process of performing the decomposition processing is: The environment temperature sequence and the environment humidity sequence are obtained by sequencing the environment temperature and humidity group data of the leaky bottle cap according to time sequence, and the environment temperature and humidity group data is preprocessed synchronously; The environment temperature steady-state component sequence and the environment humidity steady-state component sequence are calculated by the preprocessed environment temperature and humidity group data through the sliding average formula; The environment temperature sequence and the environment humidity sequence are subtracted from the environment temperature steady-state component sequence and the environment humidity steady-state component sequence respectively to obtain the environment temperature disturbance component sequence and the environment humidity disturbance component sequence. 7.The beverage production monitoring management system based on digital visualization according to claim 1, characterized in that: The process of analyzing the control amount of the environment temperature and humidity group and performing control is: The sliding window of the time point of triggering the sealing warning signal is called, and the temperature disturbance component sequence and the humidity disturbance component sequence of each time point in the sliding window are extracted; The maximum values of the absolute values of the temperature and humidity disturbance component sequences in the sliding window are extracted, which are marked as the temperature disturbance peak value and the humidity disturbance peak value; The temperature and humidity control formula is constructed based on the extracted temperature and humidity disturbance peak values and the coupling degree index of the leaky bottle cap; The environment temperature and humidity control amount is obtained through the temperature and humidity control formula; The new environment temperature and humidity group set value is obtained by subtracting the current actual environment temperature and humidity group from the environment temperature and humidity group control amount; The new environment temperature and humidity group set value is input into the control system for adjustment. 8.The beverage production monitoring management system based on digital visualization according to claim 1, characterized in that: The process of verifying the compliance rate of the control effect is: After the control is completed and the environment temperature and humidity group data is stable, a plurality of bottle cap samples are collected in real time, and the sealing leakage rate of each bottle cap sample is obtained; The sealing leakage fluctuation range is obtained, and the number of bottle cap samples with sealing leakage rate lower than or equal to the sealing leakage fluctuation range is compared with the total number of bottle cap samples to obtain the compliance rate of the environment temperature and humidity group after control; According to the compliance rate of the environment temperature and humidity group after control, different processing strategy modes are automatically triggered. 9.The beverage production monitoring management system based on digital visualization according to claim 1, characterized in that: The process of obtaining the control energy efficiency index is: The data of the controlled bottle cap samples are obtained, including the disturbance peak value of the bottle cap sample and the compliance rate after control; The control efficiency equation is constructed according to the data of the controlled bottle cap samples, and the control energy efficiency index of each control is calculated through the control efficiency equation; If the control energy efficiency index is greater than or equal to the control energy efficiency threshold, it means that the resource input is at a reasonable or optimal level under the premise of achieving the compliance effect. 10.The beverage production monitoring management system based on digital visualization according to claim 1, characterized in that: The process of iterative optimization is: Integrate the historical control data and the current control result to form a multi-dimensional data set containing the disturbance peak value, control amount, compliance rate and control efficiency index; Based on the multi-dimensional data set, a dynamic optimization model is constructed with the control attenuation coefficient, sliding window length, coupling degree index and environmental temperature and humidity group disturbance component sequence as the key optimization parameters; The control data of the current production cycle is input into the dynamic optimization model to generate parameter optimization suggestions; According to the parameter optimization suggestions, the key parameters are adjusted to form a new control strategy; the new control strategy is implemented in the next production cycle, and the optimization effect is verified through the real-time collected sealing leakage rate data.

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