Processing technology of natural fruit honey black tea

Abstract

The invention discloses a processing technology of natural fruit honey black tea, which mainly comprises the following steps: S1, selecting a cleaned and screened black tea raw material, and determining the soaking temperature and time by using a molecular dynamics simulation system; s2, the thermodynamic state of the soak solution is monitored in real time, and the temperature and pressure are dynamically adjusted through a non-equilibrium thermodynamic inversion control system; s3, cooling the soaked black tea soup to a target temperature, and mixing the cooled black tea soup with the concentrated fruit honey; s4, in the mixing process, the mixing conditions are adjusted through unbalanced thermodynamic inversion control and the probability fluctuation theory; s5, performing pasteurization treatment on the mixed fruit honey black tea; s6, performing filling and sealing in a sterile environment; s7, the product quality is detected after rapid cooling; and S8, storing the fruit honey black tea which is detected to be qualified, and monitoring storage conditions. According to the process, the flavor and the stability of the black tea and the fruit honey are improved by optimizing treatment conditions.

Description

Technical Field

[0001] The invention relates to the technical field of food processing, in particular to a processing technology of natural nectar black tea. Background Art

[0002] First, natural fruit nectar black tea has been favored by consumers in recent years due to its unique flavor and health benefits. However, the traditional processing technology of natural fruit nectar black tea has many challenges in terms of quality control, flavor retention and nutritional stability. Traditional methods usually rely on experience and simple physical parameter adjustments, and cannot accurately control key variables in the processing process, such as temperature, pressure and mixing conditions, resulting in unstable product quality and large batch-to-batch differences. In addition, due to the lack of advanced control technology, the extraction efficiency of active ingredients in black tea (such as tea polyphenols) is low, and the fusion of fruit nectar and black tea is not sufficient, which affects the taste and nutritional value of the final product.

[0003] Secondly, the existing black tea soaking process usually adopts fixed temperature and time parameters, and does not fully consider the dynamic behavior of black tea ingredients under different conditions. Due to the complexity of black tea ingredients, traditional processes cannot optimize the extraction of active substances such as tea polyphenols at the molecular level, thereby failing to maximize their health benefits and flavor. In addition, the traditional process only uses simple mechanical stirring to achieve the fusion of tea soup and fruit nectar during the mixing stage. This method easily leads to uneven mixing and affects the taste consistency of the product.

[0004] Third, most traditional fruit nectar processing methods use conventional high-temperature concentration. Although this method can remove some water, it also destroys the nutrients and flavor substances in the fruit nectar, ultimately leading to a decline in product quality. Existing fruit nectar and black tea mixing technologies usually do not have real-time monitoring and control capabilities, and cannot dynamically adjust process parameters according to the real-time thermodynamic state and physical properties of the mixed liquid, making it difficult to ensure product stability.

[0005] In summary, the existing natural fruit nectar black tea processing technology has significant deficiencies in temperature and time control, extraction efficiency of active ingredients, fruit nectar processing and mixing uniformity. The existing technology is difficult to effectively solve these problems, resulting in unstable product quality, inconsistent flavor and nutritional value, and insufficient market competitiveness. Therefore, how to provide a natural fruit nectar black tea processing technology is a problem that technicians in this field urgently need to solve. Summary of the invention

[0006] One purpose of the present invention is to provide a processing technology for natural fruit honey black tea. The present invention makes full use of molecular dynamics simulation technology, non-equilibrium thermodynamic inversion control system and probability wave theory, and describes in detail how to optimize temperature, pressure and mixing conditions in the process of fusion of black tea and fruit honey, so as to achieve efficient extraction of black tea active ingredients and uniform fusion of fruit honey and black tea. The process has the advantages of good flavor retention, stable nutritional ingredients and high product quality consistency.

[0007] A processing technology of natural nectar black tea according to an embodiment of the present invention comprises the following steps:

[0008] S1. Select cleaned and screened black tea raw materials, simulate the behavior trajectories of tea polyphenols and other active molecules during the black tea soaking process through a molecular dynamics simulation system, and determine the soaking temperature and time parameters;

[0009] S2. During the black tea soaking process, the thermodynamic state of the soaking liquid is monitored in real time, and the temperature and pressure are dynamically adjusted by applying a non-equilibrium thermodynamic inversion control system;

[0010] S3, cooling the soaked black tea soup to a target temperature under specified conditions, and mixing it with the fruit honey that has been vacuum-low-temperature concentrated in an optimized volume ratio, so that the tea soup and the fruit honey are evenly integrated at the molecular level;

[0011] S4. During the mixing process, a non-equilibrium thermodynamic inversion control system is used to monitor and adjust the thermodynamic parameters of the mixed liquid in real time, and the probability fluctuation theory is used to analyze the small fluctuations in the mixed liquid to automatically adjust the stirring speed and pressure;

[0012] S5, pasteurizing the mixed fruit honey black tea, optimizing the sterilization temperature and time to ensure microbial safety and maintain the active ingredients and flavors in the black tea and fruit honey;

[0013] S6. The sterilized fruit honey black tea is filled in a sterile environment and sealed by automatic filling equipment to prevent the product from being contaminated;

[0014] S7. After filling, the fruit honey black tea is rapidly cooled and tested for pH value, sugar content and microbiological indexes to ensure that it meets product quality standards;

[0015] S8. Store the qualified fruit honey black tea in a constant temperature environment of 6℃ to 8℃, and monitor the storage conditions through Internet of Things technology to maintain the stability of the product's flavor and nutritional ingredients.

[0016] Optionally, the S1 includes the following steps:

[0017] S11, selecting black tea raw materials that have been physically cleaned, and screening them through a standard sieve to ensure that the particle size of the tea leaves is uniform and meets the requirements of the soaking process;

[0018] S12. Establish a simulation system based on molecular dynamics, construct molecular models of active ingredients of black tea such as tea polyphenols and caffeine, and initialize simulation parameters, including temperature, pressure, and solution concentration, to truly simulate the dynamic behavior of molecules during the soaking process;

[0019] S13. In the simulation system, the molecular dynamics method is used to simulate the behavior trajectories of tea polyphenols and other active molecules during the soaking process of black tea, and the molecular diffusion rate and interaction under different temperature and pressure conditions are evaluated;

[0020] S14, determining the optimal temperature and time parameters for soaking black tea based on the simulation results;

[0021] S15, applying the determined optimal soaking temperature and time parameters to the actual black tea soaking process, and monitoring the state of the soaking liquid through a temperature sensor and a pressure sensor;

[0022] S16. During the entire soaking process, continuously monitor the tea polyphenols concentration in the black tea soaking liquid.

[0023] Optionally, S2 includes the following steps:

[0024] S21. Use temperature sensors and pressure sensors to monitor the temperature T(t) and pressure P(t) of the soaking liquid in real time during the soaking process of black tea. The sampling frequency of the sensor is 1000 times per second to capture small fluctuations in temperature and pressure. The concentration of tea polyphenols C(t) in the soaking liquid is monitored online by an analytical instrument using ultrafast laser spectroscopy technology. The real-time data of temperature, pressure and tea polyphenol concentration are collected and stored.

[0025] S22. Construction of non-equilibrium thermodynamic inversion model:

[0026] S221. Establish a non-equilibrium thermodynamic inversion model. The model is based on the extended entropy generation equation and combines the changes in the microscopic state function to describe the evolution of the thermodynamic state of the system:

[0027]

[0028] Among them, σ(t) is the entropy generation rate at time t, which describes the thermodynamic behavior of the system in a non-equilibrium state; It represents the heat rate per unit time of the system in time t; It represents the power per unit time of the system in time t; T ( t) and P ( t) respectively represent the real-time temperature and pressure; is the correction factor for the entropy generation term, taking into account the gradient effect of the internal material flow J(t) and the energy density E(t); It is the divergence of material flow, which indicates the change in the distribution of material in the system; is the divergence of energy density, reflecting the spatial variation of energy distribution in the system;

[0029] S222. According to the extended entropy generation equation, calculate the sensitivity of entropy change to the system temperature and pressure response, and generate the control factor λ T ( t) and λ P ( t):

[0030]

[0031] Among them, λ T (t) is the temperature control factor, which describes the response of entropy change to temperature change; λ P (t) is the pressure regulation factor, which describes the response of entropy change to pressure change; and are the spatial gradients of temperature and pressure, respectively, indicating the distribution changes of temperature and pressure in the system;

[0032] S223, based on control factor λ T The calculation result of (t) is used to automatically adjust the temperature T of the immersion liquid using the nonlinear control equation. ( t):

[0033]

[0034] Where T0 is the initial temperature; λ T (t') is the temperature control factor at time t'; is the second-order derivative of temperature, indicating the acceleration of temperature change; α T is the third-order nonlinear control coefficient, describing the nonlinear effect of temperature change;

[0035] S224, according to λ P (t), the pressure P(t) of the immersion liquid is dynamically adjusted, and the coupling term is combined to reflect the mutual influence of temperature and pressure:

[0036]

[0037] Where P0 is the initial pressure; λ P (t') is the pressure control factor at time t'; is the rate of change of pressure over time; TP is the coupling coefficient between temperature and pressure, describing the nonlinear interaction between the two;

[0038] S23, feedback control is performed through real-time monitoring of temperature T(t), pressure P(t) and tea polyphenol concentration C(t) data, and the control factor λ is updated every second T ( t) and λ P The value of (t);

[0039] S24, based on the feedback control data, an adaptive control strategy is used to fine-tune the temperature and pressure control formulas;

[0040] S25. A nonlinear multivariable control model is introduced to describe the complex dynamic relationship between temperature and pressure through the following equation:

[0041]

[0042] Among them, γ is the nonlinear control coefficient, which describes the coupling relationship between temperature and pressure; δ is the second-order term coefficient of nonlinear control, which describes the high-order effect;

[0043] S26. Continuously collect data from each batch of soaking processes, extract features from the soaking process data using a convolutional neural network, and identify key variables that affect the extraction efficiency of tea polyphenols; use a recurrent neural network to model inter-batch data and optimize the soaking conditions for subsequent batches;

[0044] S27, perform data analysis and automatically optimize the soaking conditions for subsequent batches to ensure that the quality of each batch of products is consistent and optimal:

[0045]

[0046] Where N is the total number of batches; C k (t) represents the tea polyphenols concentration of the kth batch; ΔT k (t) and ΔP k (t) represents the deviation of the temperature and pressure of the kth batch from the optimal state; β is the control parameter to adjust the balance between different batches;

[0047] S28. Monitor the long-term stability of the soaking process, especially during long-term operation, by detecting progressive changes that affect extraction efficiency and product quality.

[0048] Optionally, S3 includes the following steps:

[0049] S31, introducing the soaked black tea soup into the cooling system, setting the target temperature T(t); the temperature change during the cooling process is controlled by the optimized cooling curve;

[0050] S32. During the cooling process, the temperature T(t) of the tea soup is monitored in real time using a temperature sensor, and the cooling parameters are dynamically adjusted through the feedback mechanism of the control system;

[0051] S33, preliminarily filtering and purifying the fruit honey, and then placing it in a vacuum low-temperature concentration device; setting the vacuum degree and concentration temperature, and the conditions in the concentration process are predicted and adjusted by the aforementioned optimization model;

[0052] S34, during the concentration process, the vacuum degree P(t) and temperature T(t) are monitored in real time, and the vacuum degree and temperature are dynamically adjusted based on the feedback data;

[0053] S35, determining the optimal mixing ratio of tea soup and concentrated fruit nectar based on a molecular dynamics simulation system; the mixing process is performed by an automated device to uniformly mix the tea soup and the fruit nectar according to the optimal ratio;

[0054] S36. During the mixing process, a high-speed shear mixer is used to process the tea soup and the fruit nectar so that they are fully integrated at the molecular level; the physical properties of the mixed liquid, such as viscosity and density, are monitored in real time, and the mixing conditions are adjusted through an intelligent control system;

[0055] S37. Use online sensors to monitor the key physical and chemical properties of the mixed liquid, collect real-time data, and fine-tune the mixing speed, time and other parameters based on the real-time data to maintain the stability and uniformity of the mixed liquid; use intelligent optimization algorithms to continuously optimize the mixing process;

[0056] S38. Conduct comprehensive quality testing on the mixed fruit nectar black tea, including pH value, sugar content, tea polyphenol content and microbial indicators; the test results must meet the predetermined quality standards, otherwise the production parameters will be automatically adjusted for remedial measures. Qualified fruit nectar black tea will be placed in a sterile environment for filling and storage; monitor storage conditions to maintain product stability and safety.

[0057] Optionally, S4 includes the following steps:

[0058] S41. During the mixing process, use an integrated sensor network to monitor key thermodynamic parameters of the mixed liquid in real time, including temperature T(t), pressure P(t), and energy density E(t) in the mixed liquid; generate real-time monitoring data;

[0059] S42. Based on real-time monitoring data, the thermodynamic control factor α is calculated using the non-equilibrium thermodynamic inversion model. T (t) and α P (t) to dynamically adjust the temperature and pressure of the mixed liquid:

[0060]

[0061] Among them, ΔS(t) is the entropy change at time t, describing the change of energy state during the mixing process; and are the spatial gradients of temperature and pressure; η and ζ are the control coefficients, which are related to the material flow J(t) and energy distribution E(t), respectively, and characterize the influence of these effects on the overall thermodynamic state.

[0062] S43. Use sensor networks to capture microscopic fluctuations in the mixed liquid in real time during the mixing process, mainly monitoring local density fluctuations, temperature micro-oscillations and microscopic pressure fluctuations caused by changes in intermolecular forces; perform spectrum analysis and time domain analysis on real-time data to extract fluctuation characteristics;

[0063] S44. Based on the captured microscopic fluctuation data, the probabilistic fluctuation theory is applied to construct a dynamic fluctuation model; the dynamic fluctuation model is based on the time-dependent fluctuation dissipation theorem to describe the fluctuation evolution process in the mixed liquid:

[0064]

[0065] Among them, ψ(x,t) is the wave function at position x and time t, describing the probability distribution of the fluctuation; ψ0 is the initial wave function; ΔE(t') is the energy deviation of the system at time t'; σ(t') is the standard deviation at time t', indicating the intensity of the fluctuation; λ(t') is the viscous dissipation coefficient at time t', indicating the speed of the fluctuation decay; is the density gradient, which describes the density change when the wave propagates;

[0066] S45. Analyze the evolution of the wave function ψ(x, t) to evaluate the impact of local fluctuations in the mixture on the overall fluid stability, especially the impact of the wave propagation speed and amplitude on the mixing uniformity. By calculating the vorticity Ω in the mixture ( t) to quantify the impact of fluctuations:

[0067]

[0068] Where v(x,t) is the velocity field of the mixed liquid at position x and time t; Ω(t) is the vorticity, which describes the fluid rotation effect caused by the wave;

[0069] S46. According to the results of the wave model, the stirring speed ω(t) during the mixing process is fine-tuned:

[0070]

[0071] Among them, Δω ( t) is the stirring speed adjustment at time t; γ is the control coefficient, which indicates the sensitivity of the system to the fluctuation response; Ω(t) is the vorticity caused by the fluctuation, which reflects the rotation effect of the fluid; αT (t) and α P (t is the temperature and pressure thermodynamic control factor at time t; and Respectively represent the rate of change of temperature and pressure with time; is the time derivative of the wave function, describing the evolution speed of the fluctuation;

[0072] S47. During the mixing process, the uniformity of the mixed liquid is maintained by adjusting the pressure P(t) to prevent uneven mixing caused by local pressure fluctuations; the pressure regulation is based on a non-equilibrium thermodynamic model and probability fluctuation analysis to dynamically optimize the pressure changes during the control process;

[0073] S48, through the feedback loop, continuously monitor and record thermodynamic parameters, stirring speed, pressure and physical properties of the mixed liquid; according to the real-time feedback data, adaptively adjust the control strategy to ensure fluid stability and uniformity during the mixing process;

[0074] S49. The system accumulates mixing process data over a long period of time and analyzes the changing trends of thermodynamic parameters and fluid stability during the mixing process. It uses historical data for pattern recognition and optimizes future mixing conditions. After the mixing process is completed, the system conducts a quality assessment of the mixed liquid and optimizes the mixing strategy for the next batch based on the quality assessment results.

[0075] The beneficial effects of the present invention are:

[0076] (1) The present invention proposes a processing technology for natural nectar black tea. The molecular dynamics simulation technology is used to simulate the behavior of tea polyphenols and other active molecules during the soaking process of black tea, and the optimal soaking temperature and time parameters are determined to achieve efficient extraction of active ingredients in black tea. Compared with traditional processes, the present invention greatly improves the extraction efficiency of key ingredients such as tea polyphenols, and enhances the flavor and health benefits of black tea.

[0077] (2) The present invention introduces a non-equilibrium thermodynamic inversion control system to monitor and dynamically adjust temperature and pressure parameters in real time during the process of soaking black tea and mixing tea soup with fruit honey. By precisely controlling the thermodynamic state of the soaking liquid, the uniform fusion of black tea and fruit honey at the molecular level is ensured, and the uneven mixing phenomenon caused by traditional stirring methods is avoided, thereby maintaining the quality consistency of the product and extending the shelf life of the product.

[0078] (3) The present invention uses probability fluctuation theory to analyze the tiny fluctuations in the mixed liquid, and combines real-time data to fine-tune the stirring speed and pressure during the mixing process to prevent product quality problems caused by local fluctuations. Through this refined control method, the present invention effectively improves the stability of the mixture of black tea and fruit nectar, ensuring the consistency of flavor and nutritional components of each batch of products.

[0079] (4) The present invention uses vacuum low-temperature concentration technology to process fruit honey, which retains the natural flavor and nutritional components of the fruit honey to the greatest extent and efficiently blends it with black tea. Compared with traditional high-temperature concentration, the present invention reduces the damage to the quality of fruit honey and improves the flavor and health attributes of the final product. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0081] Figure 1 This is a flow chart of a processing technology of natural nectar black tea proposed by the present invention;

[0082] Figure 2 This is a schematic diagram of the application of the non-equilibrium thermodynamic inversion control system proposed in the present invention to monitor and adjust the temperature and pressure of the black tea soaking liquid in real time. DETAILED DESCRIPTION

[0083] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0084] refer to Figure 1-2 A processing technology of natural nectar black tea comprises the following steps:

[0085] S1. Select cleaned and screened black tea raw materials, simulate the behavior trajectories of tea polyphenols and other active molecules during the black tea soaking process through a molecular dynamics simulation system, and determine the soaking temperature and time parameters;

[0086] S2. During the black tea soaking process, the thermodynamic state of the soaking liquid is monitored in real time, and the temperature and pressure are dynamically adjusted by applying a non-equilibrium thermodynamic inversion control system;

[0087] S3, cooling the soaked black tea soup to a target temperature under specified conditions, and mixing it with the fruit honey that has been vacuum-low-temperature concentrated in an optimized volume ratio, so that the tea soup and the fruit honey are evenly integrated at the molecular level;

[0088] S4. During the mixing process, a non-equilibrium thermodynamic inversion control system is used to monitor and adjust the thermodynamic parameters of the mixed liquid in real time, and the probability fluctuation theory is used to analyze the small fluctuations in the mixed liquid to automatically adjust the stirring speed and pressure;

[0089] S5, pasteurizing the mixed fruit honey black tea, optimizing the sterilization temperature and time to ensure microbial safety and maintain the active ingredients and flavors in the black tea and fruit honey;

[0090] S6. The sterilized fruit honey black tea is filled in a sterile environment and sealed by automatic filling equipment to prevent the product from being contaminated;

[0091] S7. After filling, the fruit honey black tea is rapidly cooled and tested for pH value, sugar content and microbiological indexes to ensure that it meets product quality standards;

[0092] S8. Store the qualified fruit honey black tea in a constant temperature environment of 6℃ to 8℃, and monitor the storage conditions through Internet of Things technology to maintain the stability of the product's flavor and nutritional ingredients.

[0093] In this implementation, S1 includes the following steps:

[0094] S11, selecting black tea raw materials that have been physically cleaned, and screening them through a standard sieve to ensure that the particle size of the tea leaves is uniform and meets the requirements of the soaking process;

[0095] S12. Establish a simulation system based on molecular dynamics, construct molecular models of active ingredients of black tea such as tea polyphenols and caffeine, and initialize simulation parameters, including temperature, pressure, and solution concentration, to truly simulate the dynamic behavior of molecules during the soaking process;

[0096] S13. In the simulation system, the molecular dynamics method is used to simulate the behavior trajectories of tea polyphenols and other active molecules during the soaking process of black tea, and the molecular diffusion rate and interaction under different temperature and pressure conditions are evaluated;

[0097] S14, determining the optimal temperature and time parameters for soaking black tea based on the simulation results;

[0098] S15, applying the determined optimal soaking temperature and time parameters to the actual black tea soaking process, and monitoring the state of the soaking liquid through a temperature sensor and a pressure sensor;

[0099] S16. During the entire soaking process, continuously monitor the tea polyphenols concentration in the black tea soaking liquid.

[0100] In this implementation, S2 includes the following steps:

[0101] S21. Use temperature sensors and pressure sensors to monitor the temperature T(t) and pressure P(t) of the soaking liquid in real time during the soaking process of black tea. The sampling frequency of the sensor is 1000 times per second to capture small fluctuations in temperature and pressure. The concentration of tea polyphenols C(t) in the soaking liquid is monitored online by an analytical instrument using ultrafast laser spectroscopy technology. The real-time data of temperature, pressure and tea polyphenol concentration are collected and stored.

[0102] S22. Construction of non-equilibrium thermodynamic inversion model:

[0103] S221. Establish a non-equilibrium thermodynamic inversion model. The model is based on the extended entropy generation equation and combines the changes in the microscopic state function to describe the evolution of the thermodynamic state of the system:

[0104]

[0105] Among them, σ ( t) is the entropy generation rate at time t, which describes the thermodynamic behavior of the system in a non-equilibrium state; It represents the heat rate per unit time of the system in time t; It represents the power of the system per unit time in time t; T(t) and P(t) represent the real-time temperature and pressure respectively; is the correction factor for the entropy generation term, taking into account the internal material flow J ( t) and energy density E ( t) gradient effect; It is the divergence of material flow, which indicates the change in the distribution of material in the system; is the divergence of energy density, reflecting the spatial variation of energy distribution in the system;

[0106] S222. According to the extended entropy generation equation, calculate the sensitivity of entropy change to the system temperature and pressure response, and generate the control factor λ T (t) and λ P (t):

[0107]

[0108] Among them, λ T (t) is the temperature control factor, which describes the response of entropy change to temperature change; λ P (t) is the pressure regulation factor, which describes the response of entropy change to pressure change; and are the spatial gradients of temperature and pressure, respectively, indicating the distribution changes of temperature and pressure in the system;

[0109] S223, based on control factor λ T ( t), and automatically adjust the temperature T(t) of the immersion liquid using the nonlinear control equation:

[0110]

[0111] Where T0 is the initial temperature; λ T( t') is the temperature control factor at time t'; is the second-order derivative of temperature, indicating the acceleration of temperature change; α T is the third-order nonlinear control coefficient, describing the nonlinear effect of temperature change;

[0112] S224, according to λ P (t), the pressure P(t) of the immersion liquid is dynamically adjusted, and the coupling term is combined to reflect the mutual influence of temperature and pressure:

[0113]

[0114] Where P0 is the initial pressure; λ P( t') is the pressure control factor at time t'; is the rate of change of pressure over time; TP is the coupling coefficient between temperature and pressure, describing the nonlinear interaction between the two;

[0115] S23, feedback control is performed through real-time monitoring of temperature T(t), pressure P(t) and tea polyphenol concentration C(t) data, and the control factor λ is updated every second T ( t) and λ P The value of (t);

[0116] S24, based on the feedback control data, an adaptive control strategy is used to fine-tune the temperature and pressure control formulas;

[0117] S25. A nonlinear multivariable control model is introduced to describe the complex dynamic relationship between temperature and pressure through the following equation:

[0118]

[0119] Among them, γ is the nonlinear control coefficient, which describes the coupling relationship between temperature and pressure; δ is the second-order term coefficient of nonlinear control, which describes the high-order effect;

[0120] S26. Continuously collect data from each batch of soaking processes, extract features from the soaking process data using a convolutional neural network, and identify key variables that affect the extraction efficiency of tea polyphenols; use a recurrent neural network to model inter-batch data and optimize the soaking conditions for subsequent batches;

[0121] S27, perform data analysis and automatically optimize the soaking conditions for subsequent batches to ensure that the quality of each batch of products is consistent and optimal:

[0122]

[0123] Where N is the total number of batches; C k (t) represents the tea polyphenols concentration of the kth batch; ΔT k (t) and ΔP k (t) represent the deviation of the temperature and pressure of the kth batch from the optimal state; β is the control parameter to adjust the balance between different batches;

[0124] S28. Monitor the long-term stability of the soaking process, especially during long-term operation, by detecting progressive changes that affect extraction efficiency and product quality.

[0125] In this implementation, S3 includes the following steps:

[0126] S31, introducing the soaked black tea soup into the cooling system, setting the target temperature T(t); the temperature change during the cooling process is controlled by the optimized cooling curve;

[0127] S32. During the cooling process, the temperature T(t) of the tea soup is monitored in real time using a temperature sensor, and the cooling parameters are dynamically adjusted through the feedback mechanism of the control system;

[0128] S33, preliminarily filtering and purifying the fruit honey, and then placing it in a vacuum low-temperature concentration device; setting the vacuum degree and concentration temperature, and the conditions in the concentration process are predicted and adjusted by the aforementioned optimization model;

[0129] S34, during the concentration process, the vacuum degree P(t) and temperature T(t) are monitored in real time, and the vacuum degree and temperature are dynamically adjusted based on the feedback data;

[0130] S35, determining the optimal mixing ratio of tea soup and concentrated fruit nectar based on a molecular dynamics simulation system; the mixing process is performed by an automated device to uniformly mix the tea soup and the fruit nectar according to the optimal ratio;

[0131] S36. During the mixing process, a high-speed shear mixer is used to process the tea soup and the fruit nectar so that they are fully integrated at the molecular level; the physical properties of the mixed liquid, such as viscosity and density, are monitored in real time, and the mixing conditions are adjusted through an intelligent control system;

[0132] S37. Use online sensors to monitor the key physical and chemical properties of the mixed liquid, collect real-time data, and fine-tune the mixing speed, time and other parameters based on the real-time data to maintain the stability and uniformity of the mixed liquid; use intelligent optimization algorithms to continuously optimize the mixing process;

[0133] S38. Conduct comprehensive quality testing on the mixed fruit nectar black tea, including pH value, sugar content, tea polyphenol content and microbial indicators; the test results must meet the predetermined quality standards, otherwise the production parameters will be automatically adjusted for remedial measures. Qualified fruit nectar black tea will be placed in a sterile environment for filling and storage; monitor storage conditions to maintain product stability and safety.

[0134] In this implementation, S4 includes the following steps:

[0135] S41. During the mixing process, use an integrated sensor network to monitor key thermodynamic parameters of the mixed liquid in real time, including temperature T(t), pressure P(t), and energy density E(t) in the mixed liquid; generate real-time monitoring data;

[0136] S42. Based on real-time monitoring data, the thermodynamic control factor α is calculated using the non-equilibrium thermodynamic inversion model. T (t) and α P (t) to dynamically adjust the temperature and pressure of the mixed liquid:

[0137]

[0138] Among them, ΔS(t) is the entropy change at time t, describing the change of energy state during the mixing process; and are the spatial gradients of temperature and pressure; η and ζ are the control coefficients, which are related to the material flow J(t) and energy distribution E(t), respectively, and characterize the influence of these effects on the overall thermodynamic state.

[0139] S43. Use sensor networks to capture microscopic fluctuations in the mixed liquid in real time during the mixing process, mainly monitoring local density fluctuations, temperature micro-oscillations and microscopic pressure fluctuations caused by changes in intermolecular forces; perform spectrum analysis and time domain analysis on real-time data to extract fluctuation characteristics;

[0140] S44. Based on the captured microscopic fluctuation data, the probabilistic fluctuation theory is applied to construct a dynamic fluctuation model; the dynamic fluctuation model is based on the time-dependent fluctuation dissipation theorem to describe the fluctuation evolution process in the mixed liquid:

[0141]

[0142] Among them, ψ(x,t) is the wave function at position x and time t, describing the probability distribution of the fluctuation; ψ0 is the initial wave function; ΔE(t') is the energy deviation of the system at time t'; σ(t') is the standard deviation at time t', indicating the intensity of the fluctuation; λ(t') is the viscous dissipation coefficient at time t', indicating the speed of the fluctuation decay; is the density gradient, which describes the density change when the wave propagates;

[0143] S45. Analyze the evolution of the wave function ψ(x, t) to evaluate the impact of local fluctuations in the mixture on the overall fluid stability, especially the impact of the wave propagation speed and amplitude on the mixing uniformity. By calculating the vorticity Ω in the mixture ( t) to quantify the impact of fluctuations:

[0144]

[0145] Where v(x,t) is the velocity field of the mixed liquid at position x and time t; Ω(t) is the vorticity, which describes the fluid rotation effect caused by the wave;

[0146] S46. According to the results of the wave model, the stirring speed ω(t) during the mixing process is fine-tuned:

[0147]

[0148] Among them, Δω ( t) is the stirring speed adjustment at time t; γ is the control coefficient, which indicates the sensitivity of the system to the fluctuation response; Ω(t) is the vorticity caused by the fluctuation, which reflects the rotation effect of the fluid; α T (t) and α P (t is the temperature and pressure thermodynamic control factor at time t; and Respectively represent the rate of change of temperature and pressure with time; is the time derivative of the wave function, describing the evolution speed of the fluctuation;

[0149] S47. During the mixing process, the uniformity of the mixed liquid is maintained by adjusting the pressure P(t) to prevent uneven mixing caused by local pressure fluctuations; the pressure regulation is based on a non-equilibrium thermodynamic model and probability fluctuation analysis to dynamically optimize the pressure changes during the control process;

[0150] S48, through the feedback loop, continuously monitor and record thermodynamic parameters, stirring speed, pressure and physical properties of the mixed liquid; according to the real-time feedback data, adaptively adjust the control strategy to ensure fluid stability and uniformity during the mixing process;

[0151] S49. The system accumulates mixing process data over a long period of time and analyzes the changing trends of thermodynamic parameters and fluid stability during the mixing process. It uses historical data for pattern recognition and optimizes future mixing conditions. After the mixing process is completed, the system conducts a quality assessment of the mixed liquid and optimizes the mixing strategy for the next batch based on the quality assessment results.

[0152] Embodiment 1:

[0153] In the production base of a large tea beverage manufacturer, the company plans to launch a new high-end natural fruit honey black tea beverage. In the previous production process, the company has been facing problems such as unstable product quality, large flavor differences between batches, and low active ingredient extraction rate. These problems have led to the product's low competitiveness in the market and unable to meet consumers' demand for high-quality tea beverages. In order to solve these problems, the company decided to adopt the processing technology of natural fruit honey black tea provided by the present invention, in order to improve the overall production efficiency and product quality while maintaining the consistency of product flavor and nutritional ingredients.

[0154] During the production process, the company first selects high-quality black tea raw materials from high mountain tea gardens, cleans and screens them. In order to ensure that the particle size of the tea is uniform and suitable for the subsequent soaking process, the company uses precision screening equipment to classify the tea by particle size. This step ensures the uniformity of the tea during the soaking process.

[0155] Next, the R&D team used molecular dynamics simulation technology to conduct in-depth simulation research on the main active ingredients in black tea, such as tea polyphenols and caffeine. The simulation results show that the extraction efficiency of tea polyphenols is best at a temperature of 82°C, while the 90°C temperature used in traditional processes not only easily causes excessive oxidation of active ingredients, but also affects the flavor of black tea. Therefore, the team decided to optimize the soaking temperature to 82°C and set the soaking time to 12 minutes. This optimization process not only improves the extraction rate of tea polyphenols, but also ensures the retention of the taste and nutrients of black tea.

[0156] In actual production, the team applied a non-equilibrium thermodynamic inversion control system to monitor and dynamically adjust the temperature and pressure during the black tea soaking process in real time. Through temperature sensors and pressure sensors, the system collects data 1,000 times per second, and monitors the concentration of tea polyphenols in real time through ultrafast laser spectroscopy technology. Under optimized conditions, the extraction efficiency of tea polyphenols was increased to 95%, far higher than the 75% of traditional processes. In addition, through the automatic adjustment function of the system, the temperature and pressure fluctuations between product batches were greatly reduced, and the extraction consistency of tea polyphenols was significantly improved.

[0157] After the soaking is completed, the black tea soup is quickly cooled to 5℃ to facilitate mixing with the fruit honey. After preliminary filtration and purification, the fruit honey is placed in a vacuum low-temperature concentration device for processing. The set vacuum degree is 0.1 Pa and the concentration temperature is 35℃. Under this condition, the loss rate of vitamin C in the fruit honey is reduced to 5%, while the loss rate is as high as 15% under the traditional high-temperature concentration method. At the same time, the flavor substances of the fruit honey are better retained, enhancing the taste of the final product.

[0158] The mixing of tea soup and fruit nectar is a key step in the entire process. In order to ensure the full integration of the two at the molecular level, the company introduced a high-speed shear mixer and used the probability fluctuation theory to analyze the tiny fluctuations in the mixture in real time. The system monitors the viscosity and density of the mixture in real time through sensors, and automatically adjusts the stirring speed and pressure when fluctuations are detected. Data shows that after applying this method, the uniformity of the mixture has increased by 30%, and the flavor consistency of the product has also been significantly improved.

[0159] The mixed fruit honey black tea is pasteurized to ensure microbial safety. The sterilization process is carried out under the optimized conditions of 75°C and 30 seconds. Compared with the traditional process, this method ensures the microbial safety of the product while retaining the active ingredients of black tea and fruit honey. The sterilized fruit honey black tea is filled in a sterile environment, and the storage conditions are monitored in real time through the Internet of Things technology to ensure the stability of the product throughout its shelf life. The test results show that the pH value of the product is stable between 4.0 and 4.2, and the sugar content is between 10 and 12°Brix, which fully meets the company's high quality standards.

[0160] After multiple batches of production, the company conducted a detailed data analysis of the effect of the new process. The results showed that the quality consistency of the product was greatly improved, the batch-to-batch fluctuation of tea polyphenols content was reduced by 50%, and the flavor and nutritional content of the fruit nectar remained at a high level. The shelf life of the product was extended from the original 6 months to 9 months, significantly improving the market competitiveness.

[0161] Table 1 Data table

[0162]

[0163] It can be seen from the data and application cases that the new process of the present invention is superior to the traditional process in many key performance indicators. It not only significantly improves the flavor and nutritional content of the product, but also greatly improves the production efficiency and the stability of product quality, bringing significant economic benefits to the enterprise.

[0164] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A processing technology for natural nectar black tea, characterized in that: The following steps are involved: S1. Select cleaned and screened black tea raw materials, simulate the behavior trajectories of tea polyphenols and other active molecules during the black tea soaking process through a molecular dynamics simulation system, and determine the soaking temperature and time parameters; S2. During the black tea soaking process, the thermodynamic state of the soaking liquid is monitored in real time, and the temperature and pressure are dynamically adjusted by applying a non-equilibrium thermodynamic inversion control system; S3, cooling the soaked black tea soup to a target temperature under specified conditions, and mixing it with the fruit honey that has been vacuum-low-temperature concentrated in an optimized volume ratio, so that the tea soup and the fruit honey are evenly integrated at the molecular level; S4. During the mixing process, a non-equilibrium thermodynamic inversion control system is used to monitor and adjust the thermodynamic parameters of the mixed liquid in real time, and the probability fluctuation theory is used to analyze the small fluctuations in the mixed liquid to automatically adjust the stirring speed and pressure; S5, pasteurizing the mixed fruit honey black tea, optimizing the sterilization temperature and time to ensure microbial safety and maintain the active ingredients and flavors in the black tea and fruit honey; S6. The sterilized fruit honey black tea is filled in a sterile environment and sealed by automatic filling equipment to prevent the product from being contaminated; S7. After filling, the fruit honey black tea is rapidly cooled and tested for pH value, sugar content and microbiological indexes to ensure that it meets product quality standards; S8. Store the qualified fruit honey black tea in a constant temperature environment of 6℃ to 8℃, and monitor the storage conditions through Internet of Things technology to maintain the stability of the product's flavor and nutritional ingredients.

2. The processing technology of a natural nectar black tea according to claim 1, characterized in that: The S1 comprises the following steps: S11, selecting black tea raw materials that have been physically cleaned, and screening them through a standard sieve to ensure that the particle size of the tea leaves is uniform and meets the requirements of the soaking process; S12. Establish a simulation system based on molecular dynamics, construct molecular models of active ingredients of black tea such as tea polyphenols and caffeine, and initialize simulation parameters, including temperature, pressure, and solution concentration, to truly simulate the dynamic behavior of molecules during the soaking process; S13. In the simulation system, the molecular dynamics method is used to simulate the behavior trajectories of tea polyphenols and other active molecules during the soaking process of black tea, and the molecular diffusion rate and interaction under different temperature and pressure conditions are evaluated; S14, determining the optimal temperature and time parameters for soaking black tea based on the simulation results; S15, applying the determined optimal soaking temperature and time parameters to the actual black tea soaking process, and monitoring the state of the soaking liquid through a temperature sensor and a pressure sensor; S16. During the entire soaking process, continuously monitor the tea polyphenols concentration in the black tea soaking liquid.

3. The processing technology of a natural nectar black tea according to claim 1, characterized in that: The S2 comprises the following steps: S21. Use temperature sensors and pressure sensors to monitor the temperature T(t) and pressure P(t) of the soaking liquid in real time during the soaking process of black tea. The sampling frequency of the sensor is 1000 times per second to capture small fluctuations in temperature and pressure. The concentration of tea polyphenols C(t) in the soaking liquid is monitored online by an analytical instrument using ultrafast laser spectroscopy technology. The real-time data of temperature, pressure and tea polyphenol concentration are collected and stored. S22. Construction of non-equilibrium thermodynamic inversion model: S221. Establish a non-equilibrium thermodynamic inversion model. The model is based on the extended entropy generation equation and combines the changes in the microscopic state function to describe the evolution of the thermodynamic state of the system: Among them, σ(t) is the entropy generation rate at time t, which describes the thermodynamic behavior of the system in a non-equilibrium state; It represents the heat rate per unit time of the system in time t; It represents the power of the system per unit time in time t; T(t) and P(t) represent the real-time temperature and pressure respectively; is the correction factor for the entropy generation term, taking into account the internal material flow J ( t) and the gradient effect of energy density E(t); It is the divergence of material flow, which indicates the change in the distribution of material in the system; is the divergence of energy density, reflecting the spatial variation of energy distribution in the system; S222. According to the extended entropy generation equation, calculate the sensitivity of entropy change to the system temperature and pressure response, and generate the control factor λ T (t) and λ P (t): Among them, λ T (t) is the temperature control factor, which describes the response of entropy change to temperature change; λ P (t) is the pressure regulation factor, which describes the response of entropy change to pressure change; and are the spatial gradients of temperature and pressure, respectively, indicating the distribution changes of temperature and pressure in the system; S223, based on control factor λ T ( t), and automatically adjust the temperature T(t) of the immersion liquid using the nonlinear control equation: Where T0 is the initial temperature; λ T( t') is the temperature control factor at time t'; is the second-order derivative of temperature, indicating the acceleration of temperature change; α T is the third-order nonlinear control coefficient, describing the nonlinear effect of temperature change; S224, according to λ P (t), the pressure P(t) of the immersion liquid is dynamically adjusted, and the coupling term is combined to reflect the mutual influence of temperature and pressure: Where P0 is the initial pressure; λ P( t') is the pressure control factor at time t'; is the rate of change of pressure over time; TP is the coupling coefficient between temperature and pressure, describing the nonlinear interaction between the two; S23, feedback control is performed through real-time monitoring of temperature T(t), pressure P(t) and tea polyphenol concentration C(t) data, and the control factor λ is updated every second T (t) and λ P The value of (t); S24, based on the feedback control data, an adaptive control strategy is used to fine-tune the temperature and pressure control formulas; S25. A nonlinear multivariable control model is introduced to describe the complex dynamic relationship between temperature and pressure through the following equation: Among them, γ is the nonlinear control coefficient, which describes the coupling relationship between temperature and pressure; δ is the second-order term coefficient of nonlinear control, which describes the high-order effect; S26. Continuously collect data from each batch of soaking processes, extract features from the soaking process data using a convolutional neural network, and identify key variables that affect the extraction efficiency of tea polyphenols; use a recurrent neural network to model inter-batch data and optimize the soaking conditions for subsequent batches; S27, perform data analysis and automatically optimize the soaking conditions for subsequent batches to ensure that the quality of each batch of products is consistent and optimal: Where N is the total number of batches; C k (t) represents the tea polyphenols concentration of the kth batch; ΔT k (t) and ΔP k ( t) represent the deviation of the temperature and pressure of the kth batch from the optimal state; β is the control parameter to adjust the balance between different batches; S28. Monitor the long-term stability of the soaking process, especially during long-term operation, by detecting progressive changes that affect extraction efficiency and product quality.

4. The processing technology of a natural nectar black tea according to claim 1, characterized in that: The S3 comprises the following steps: S31, introducing the soaked black tea soup into the cooling system, setting the target temperature T(t); the temperature change during the cooling process is controlled by the optimized cooling curve; S32. During the cooling process, the temperature T(t) of the tea soup is monitored in real time using a temperature sensor, and the cooling parameters are dynamically adjusted through the feedback mechanism of the control system; S33, preliminarily filtering and purifying the fruit honey, and then placing it in a vacuum low-temperature concentration device; setting the vacuum degree and concentration temperature, and the conditions in the concentration process are predicted and adjusted by the aforementioned optimization model; S34, during the concentration process, the vacuum degree P(t) and temperature T9t) are monitored in real time, and the vacuum degree and temperature are dynamically adjusted based on the feedback data; S35, determining the optimal mixing ratio of tea soup and concentrated fruit nectar based on a molecular dynamics simulation system; the mixing process is performed by an automated device to uniformly mix the tea soup and the fruit nectar according to the optimal ratio; S36. During the mixing process, a high-speed shear mixer is used to process the tea soup and the fruit nectar so that they are fully integrated at the molecular level; the physical properties of the mixed liquid, such as viscosity and density, are monitored in real time, and the mixing conditions are adjusted through an intelligent control system; S37. Use online sensors to monitor the key physical and chemical properties of the mixed liquid, collect real-time data, and fine-tune the mixing speed, time and other parameters based on the real-time data to maintain the stability and uniformity of the mixed liquid; use intelligent optimization algorithms to continuously optimize the mixing process; S38. Conduct comprehensive quality testing on the mixed fruit nectar black tea, including pH value, sugar content, tea polyphenol content and microbial indicators; the test results must meet the predetermined quality standards, otherwise the production parameters will be automatically adjusted for remedial measures. Qualified fruit nectar black tea will be placed in a sterile environment for filling and storage; monitor storage conditions to maintain product stability and safety.

5. The processing technology of natural nectar black tea according to claim 1, characterized in that: The S4 comprises the following steps: S41. During the mixing process, use an integrated sensor network to monitor key thermodynamic parameters of the mixed liquid in real time, including temperature T(t), pressure P(t), and energy density E(t) in the mixed liquid; generate real-time monitoring data; S42. Based on real-time monitoring data, the thermodynamic control factor α is calculated using the non-equilibrium thermodynamic inversion model. T (t) and α P (t) to dynamically adjust the temperature and pressure of the mixed liquid: Among them, ΔS(t) is the entropy change at time t, describing the change of energy state during the mixing process; and are the spatial gradients of temperature and pressure; η and ζ are the control coefficients, which are related to the material flow J(t) and energy distribution E(t), respectively, and characterize the influence of these effects on the overall thermodynamic state. S43. Use sensor networks to capture microscopic fluctuations in the mixed liquid in real time during the mixing process, mainly monitoring local density fluctuations, temperature micro-oscillations and microscopic pressure fluctuations caused by changes in intermolecular forces; perform spectrum analysis and time domain analysis on real-time data to extract fluctuation characteristics; S44. Based on the captured microscopic fluctuation data, the probabilistic fluctuation theory is applied to construct a dynamic fluctuation model; the dynamic fluctuation model is based on the time-dependent fluctuation dissipation theorem to describe the fluctuation evolution process in the mixed liquid: Among them, ψ(x,t) is the wave function at position x and time t, describing the probability distribution of the fluctuation; ψ0 is the initial wave function; ΔE(t') is the energy deviation of the system at time t'; σ(t') is the standard deviation at time t', indicating the intensity of the fluctuation; λ(t') is the viscous dissipation coefficient at time t', indicating the speed of the fluctuation decay; is the density gradient, which describes the density change when the wave propagates; S45. Analyze the evolution of the wave function ψ(x, t) to evaluate the impact of local fluctuations in the mixture on the overall fluid stability, especially the impact of the wave propagation speed and amplitude on the mixing uniformity. Quantify the impact of the fluctuations by calculating the vorticity Ω(t) in the mixture: Where v(x,t) is the velocity field of the mixed liquid at position x and time t; Ω(t) is the vorticity, which describes the fluid rotation effect caused by the wave; S46. According to the results of the wave model, the stirring speed ω(t) during the mixing process is fine-tuned: Where Δω(t) is the stirring speed adjustment at time t; γ is the control coefficient, which indicates the sensitivity of the system to fluctuation response; Ω ( t ) is the vorticity caused by the fluctuation, reflecting the rotation effect of the fluid; α T( t ) and α P( t) are the temperature and pressure thermodynamic control factors at time t; and Respectively represent the rate of change of temperature and pressure with time; is the time derivative of the wave function, describing the evolution speed of the fluctuation; S47. During the mixing process, the uniformity of the mixed liquid is maintained by adjusting the pressure P(t) to prevent uneven mixing caused by local pressure fluctuations; the pressure regulation is based on a non-equilibrium thermodynamic model and probability fluctuation analysis to dynamically optimize the pressure changes during the control process; S48, through the feedback loop, continuously monitor and record thermodynamic parameters, stirring speed, pressure and physical properties of the mixed liquid; according to the real-time feedback data, adaptively adjust the control strategy to ensure fluid stability and uniformity during the mixing process; S49. The system accumulates mixing process data over a long period of time and analyzes the changing trends of thermodynamic parameters and fluid stability during the mixing process. It uses historical data for pattern recognition and optimizes future mixing conditions. After the mixing process is completed, the system conducts a quality assessment of the mixed liquid and optimizes the mixing strategy for the next batch based on the quality assessment results.

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