Black tea multi-vitamin flavor component extraction method based on quantum calculation optimization

Abstract

The invention discloses a black tea multi-dimensional flavor component extraction method based on quantum calculation optimization, which comprises the following steps: carrying out multi-dimensional component detection on black tea leaves by utilizing near infrared spectrum and Raman spectrum technologies, analyzing the distribution of flavor components in combination with a machine learning algorithm, screening out black tea raw materials meeting specific flavor requirements, and extracting the black tea raw materials according to the specific flavor requirements. A short pulse laser technology acts on black tea leaf cell walls, local microcracks are generated, release of flavor molecules is optimized, damage of high-temperature treatment to heat-sensitive components is avoided, in the extraction process, multiple variables are dynamically regulated and controlled based on a quantum optimization algorithm, pulse type extraction and a temperature dynamic control technology are combined, and the extraction efficiency is improved. Volatile and non-volatile flavor components in black tea are extracted by stages, extraction conditions are monitored in real time through a sensor, variables in the extraction process are always in an optimal state, the flavor components are separated by utilizing a nanoscale molecular screening technology, waste liquid generated in the extraction process is treated through an energy-saving optimization technology, and recyclable resources are recycled.

Description

Technical Field

[0001] The invention relates to the field of food science and engineering technology, and in particular to a method for extracting multidimensional flavor components of black tea based on quantum computing optimization. Background Art

[0002] In the black tea production process, the extraction and control of flavor components are key links that determine the quality and taste of black tea. Traditional black tea extraction methods usually optimize the extraction of flavor components by controlling a single or a small number of variables, such as temperature, time, tea amount, and solvent ratio. However, due to the wide variety of flavor components in black tea, the volatility, heat sensitivity, and extraction conditions of different components vary, making it difficult for traditional extraction methods to comprehensively and accurately control the extraction efficiency of each flavor component, and it is difficult to meet the modern consumer's demand for diversified and personalized black tea flavors.

[0003] There are multiple defects in the extraction process of black tea flavor components in the existing technology, which are mainly concentrated in the aspects of imprecise control of multi-dimensional variables, low extraction efficiency, easy degradation of flavor components and imperfect waste liquid treatment. Traditional black tea extraction technology can usually only control a few variables, such as temperature, time, solvent ratio, etc., and it is difficult to control multiple variables at the same time, which leads to unsatisfactory extraction effect of flavor components. There are many kinds of flavor components in black tea, and different components have different sensitivities to extraction conditions. It is difficult for the existing technology to comprehensively and accurately control each variable, which makes the extraction process lack precision and consistency. Since it is difficult to carry out personalized regulation for each flavor component, the final black tea flavor is often unstable, and it is difficult to ensure the consistency and diversity of product quality.

[0004] Traditional extraction methods also have limitations in extraction efficiency. High-temperature extraction is commonly used in existing technologies, but this method can easily damage volatile flavor components and heat-sensitive components. Some important flavor substances in black tea, such as aroma components, will quickly evaporate or degrade under high temperature conditions, resulting in insufficient aroma and flavor loss. Although high-temperature extraction can accelerate the extraction process, it often comes at the expense of the integrity of the flavor components and cannot meet the higher demands of modern consumers for black tea flavor and quality.

[0005] Traditional technologies lack real-time monitoring and dynamic control mechanisms during the extraction process, and it is difficult to make timely adjustments based on variable fluctuations that occur during the extraction process. Due to external factors such as environmental changes and raw material fluctuations, extraction conditions may vary between different batches, but existing extraction equipment is unable to achieve automatic feedback and adaptive adjustment of parameters such as temperature and solvent ratio, which can easily lead to inconsistencies in the flavor of different batches of black tea, thus affecting market competitiveness.

[0006] The existing technology has the problem of incomplete separation in the process of flavor component separation and concentration. Since the black tea extract contains a variety of molecules of different sizes and polarities, traditional separation methods often have difficulty in accurately distinguishing these flavor molecules, resulting in mutual interference between different flavor components. This incomplete separation makes the final extracted flavor components complex and messy, affecting the flavor layering and purity of black tea; waste liquid treatment is another key issue in the existing black tea extraction technology. A large amount of waste liquid is generated during the black tea extraction process, which usually contains solvents and some incompletely extracted organic components. The existing waste liquid treatment technology mostly relies on physical or chemical means to remove impurities, but the recovery efficiency is low, and it is difficult to effectively utilize the renewable resources in the waste liquid, which not only leads to waste of resources, but also increases the production cost of enterprises. In addition, the existing waste liquid treatment technology also has defects in environmental protection. Improperly treated waste liquid may cause pollution to the environment and is difficult to meet the current requirements for green production and sustainable development.

[0007] Therefore, how to provide a method for extracting multidimensional flavor components of black tea based on quantum computing optimization is an urgent problem that technicians in this field need to solve. Summary of the invention

[0008] One purpose of the present invention is to propose a method for extracting multi-dimensional flavor components of black tea based on quantum computing optimization. The present invention makes full use of advanced technologies such as quantum optimization algorithms, near-infrared spectroscopy and Raman spectroscopy technology, and short-pulse lasers, and describes in detail the entire process of black tea flavor components from raw material screening, flavor molecule release, dynamic extraction control to separation and concentration. The method has the advantages of high flavor extraction efficiency, high flavor component retention rate, real-time control of extraction conditions, and energy saving and environmental protection.

[0009] A method for extracting multidimensional flavor components of black tea based on quantum computing optimization according to an embodiment of the present invention comprises the following steps:

[0010] S1. Use near-infrared spectroscopy and Raman spectroscopy to perform multi-dimensional component detection on the flavor components in black tea leaves, analyze the distribution of flavor components through machine learning algorithms, and screen out black tea raw materials that meet specific flavor requirements;

[0011] S2. For the selected black tea raw materials, short pulse laser is used to act on the cell wall of black tea leaves through photothermal effect to generate local micro cracks, optimize the release of flavor molecules, and avoid the destruction of heat-sensitive components by high temperature treatment;

[0012] S3. Dynamically control multiple variables in the black tea extraction process based on quantum optimization algorithms, use parallel computing technology to determine the optimal combination of extraction parameters, and optimize the extraction efficiency and retention rate of flavor components;

[0013] S4. Based on the determined optimal extraction parameter combination, the volatile and non-volatile flavor components in black tea are extracted in stages through pulse extraction combined with temperature dynamic control technology, so that different flavor molecules can be extracted under optimal conditions;

[0014] S5. Use sensors to monitor the concentration of flavor components, temperature, and solvent status during the extraction process in real time. If abnormal fluctuations and deviations from the optimal range are detected during the extraction process, fine-tuning will be performed to keep the extraction conditions within the optimal range.

[0015] S6. After the extraction is completed, the flavor components in the extract are separated and concentrated using nanoscale molecular screening technology based on differences in molecular size and polarity, with the assistance of quantum computing, to avoid mutual interference between different flavor molecules;

[0016] S7. Through energy-saving optimization technology, the waste liquid generated during the extraction process is treated, the organic components in the waste liquid are recovered and converted into reusable resources, optimizing solvent and energy consumption and the environmental friendliness of the extraction process.

[0017] Optionally, the S1 specifically includes:

[0018] S11, placing the black tea sample in a near-infrared spectrometer, scanning the black tea sample using a near-infrared spectrum in a wavelength range of 780 nm to 2500 nm, obtaining its absorption spectrum data, and extracting spectral features related to volatile and non-volatile flavor components;

[0019] S12, placing the black tea sample in a Raman spectrometer, irradiating the sample with a laser beam having a wavelength of 785 nm, capturing the molecular vibration mode in the sample, obtaining a Raman scattering spectrum, and generating a chemical composition map of the black tea raw material;

[0020] S13, the spectral data obtained from the near-infrared spectrum and the Raman spectrum are fused to construct a joint spectral feature matrix M, and the spectral data is reduced in dimension and features are extracted through a convolutional neural network:

[0021]

[0022] Among them, m ij represents the characteristic value of the fused spectral data, n is the number of samples, and p is the spectral feature dimension;

[0023] The feature matrix M is reduced in dimension through the convolution operation of CNN, and the feature vector X is expressed as:

[0024] X = W*M+b;

[0025] Among them, W is the convolution kernel weight matrix, * represents the convolution operation, and b is the bias vector;

[0026] S14, input the extracted feature vector X into the support vector machine model for training, and establish a prediction model for the flavor component category:

[0027]

[0028] Among them, w is the weight vector, b is the bias term, and x i is the feature vector of the i-th sample, y i is the classification label, N is the number of samples;

[0029] The classification output Y of the model is classified by the kernel function:

[0030]

[0031] Among them, α i is the Lagrange multiplier, K(x i ,x) is the kernel function, and f(X) is the prediction result of the flavor component category.

[0032] S15. Based on the output result of the support vector machine model Y, black tea samples that meet the set flavor component requirements are automatically screened out.

[0033] Optionally, the S2 specifically includes:

[0034] S21. Using short pulse laser technology, irradiate the selected black tea raw materials with a laser beam having a wavelength ranging from 532nm to 1064nm, adjust the laser energy density and pulse frequency, generate local microcracks, release flavor molecules, and avoid damaging the heat-sensitive components in the black tea leaves;

[0035] S22. When the laser beam irradiates the cell wall of black tea leaves, the local temperature of the cell wall is raised to T1 through the photothermal effect, and the temperature change is controlled to form controllable microcracks:

[0036]

[0037] Where T(t) is the local temperature at time t, T0 is the initial temperature, E1 is the laser energy density, R is the reflectivity, ρ is the cell wall density, C p is the specific heat capacity, μ is the absorption coefficient of the material;

[0038] S23. Under the photothermal effect, the laser irradiation time t1 and the spot size d1 control the width and depth of the microcracks, so that the release rate of the flavor molecules reaches the optimal effect:

[0039] and

[0040] Among them, Wf is the crack width, D l is the crack depth, k1 and k2 are material constants, α, β, γ and δ are the adjustment coefficients of laser energy and time;

[0041] S24, by controlling the width and depth of the cracks, the release rate of volatile and non-volatile flavor molecules in the black tea leaves is adjusted, so that the flavor molecules can be efficiently released in the subsequent extraction process, and the formation of the cracks will not cause excessive damage to the cell structure, thereby maintaining the overall quality of the black tea leaves;

[0042] S25. Monitor the surface temperature of the black tea leaves in real time, and control the energy and action time of the laser to keep the temperature of the black tea leaves within a safe range, thereby preventing the degradation and loss of heat-sensitive flavor components due to excessive temperature.

[0043] Optionally, the S3 specifically includes:

[0044] S31. The key variables in the extraction process include temperature, extraction time, solvent ratio, tea amount, extraction pressure and ambient humidity. The key variables are quantified by quantum optimization algorithm.

[0045] S32. Quantum optimization algorithm uses the superposition and entanglement characteristics of quantum states. Quantum optimization algorithm processes variables V through parallel computing technology. e The goal is to maximize the extraction efficiency of flavor components E f and retention rate R f , while minimizing energy consumption E c and resource utilization U r :

[0046]

[0047] in, is the Lagrangian objective function, E f (V e ) and R f (V e ) are the objective functions of extraction efficiency and flavor component retention rate, E c (V e ) is energy consumption, U r (V e ) is the resource utilization rate, E c,target and U r,target are the preset energy consumption and resource usage target values, λ1 and λ2 are Lagrange multipliers;

[0048] S33, automatically adjusting the variable combination according to the nonlinear interaction between key variables in the extraction process, so that each variable in the extraction process is always in the optimal control state;

[0049] S34, using parallel computing technology to quickly calculate multiple variable combinations in the extraction process to determine the optimal extraction parameter combination V e (opt) to achieve the best extraction efficiency and flavor component retention rate.

[0050] Optionally, the S4 specifically includes:

[0051] S41. Optimal extraction parameter combination determined by quantum optimization algorithm for black tea extraction process V e (opt), the extraction of volatile and non-volatile components is carried out in stages during the extraction process, so that each temperature stage can maximize the extraction effect:

[0052]

[0053] Among them, R v (T l ) represents the rate of extraction of volatile components under low temperature conditions, R n (T h ) represents the rate of extraction of non-volatile components under high temperature conditions;

[0054] S42, under low temperature conditions, through the pulse time t l and pulse frequency f l Control the extraction of volatile components, adjust the pulse time and frequency to achieve the best release rate of volatile components:

[0055]

[0056] Among them, R v is the extraction rate of volatile components, k v is the volatile component extraction constant, T l is the temperature under low temperature conditions, t l is the pulse time, f l is the pulse frequency, α, β, γ are the adjustment parameters of the extraction efficiency;

[0057] S43, under high temperature conditions, by controlling the extraction temperature T h and pulse duration t h , extract non-volatile flavor components, optimize extraction temperature and pulse duration to maximize the extraction rate of non-volatile components:

[0058]

[0059] Among them, R n is the extraction rate of non-volatile components, k n is the non-volatile component extraction constant, T h is the temperature under high temperature conditions, t his the pulse extraction duration, δ,∈ is the adjustment parameter of the extraction efficiency;

[0060] S44. Real-time monitoring of extraction temperature and extraction conditions, dynamic adjustment of the extraction rate of volatile and non-volatile components through adaptive control of pulse time and frequency, control of conditions at different extraction stages, and continuous optimization of the entire extraction process.

[0061] Optionally, the S5 specifically includes:

[0062] S51. Real-time monitoring of key variables in the extraction process is performed through a sensor network. The collected variables include flavor component concentration, temperature, and solvent state. The sensor data is converted into quantifiable input variables V by a signal processing system. m ;

[0063] S52, the variable V collected in real time m With the set optimal interval V opt Compare and calculate the difference between the variable and the optimal range. If the variable is detected to deviate from the optimal range, it will be judged that the variable has abnormal fluctuation and generate a deviation value ΔV;

[0064] S53, based on the generated deviation value ΔV, when it exceeds a preset threshold, a fine-tuning mechanism is automatically triggered, and the fine-tuning mechanism adjusts the relevant extraction condition variables according to the size and direction of ΔV;

[0065] S54. After fine-tuning, continue to monitor the adjusted extraction conditions through sensors. If the adjustment effect fails to bring the variables completely back to the optimal range, the deviation will be automatically recalculated through the feedback loop and the regulation will be triggered again to keep all variables in the extraction process within the optimal range, thereby maximizing the extraction efficiency and the retention rate of flavor components.

[0066] Optionally, the S6 specifically includes:

[0067] S61. After the extraction process is completed, the flavor components in the extract are initially separated by nano-scale molecular sieves based on the molecular size d m and polarity p m Due to the differences in flavor, flavor molecules are classified by molecular size and polarity, generating several different molecular groups;

[0068] S62. Optimize the initially screened molecular groups through quantum computing. Quantum computing technology can quickly process the screening conditions of different molecular groups in parallel, optimize the screening efficiency and accuracy, and the screening parameter Q s Including molecular size, polarity and mesh size D s , dynamically adjust the screening conditions through the quantum optimization algorithm to make the separation efficiency of the molecular group E separation To achieve the best:

[0069]

[0070] S63, after the screening is completed, the selected flavor molecule group is concentrated by optimizing the volume of the extract and the concentration of the flavor molecules. The concentration process optimized by quantum computing is based on the change of the flavor molecule concentration C m Adjust the concentration conditions;

[0071] S64, Dynamically adjust the mesh aperture D through quantum optimized nano-scale screening technology s , based on the differences in molecular size and polarity, each group of flavor molecules will not interfere with other groups of molecules during the entire processing process. If possible interference is detected during the concentration process, the screening conditions will be automatically adjusted.

[0072] Optionally, the S7 specifically includes:

[0073] S71. After the extraction is completed, the waste liquid is classified according to its components. o and the residual solvent S r Preliminary treatment is carried out to remove large particle impurities in the waste liquid through physical filtration and sedimentation process to generate filtrate L containing recyclable organic components. f ;

[0074] S72, from the filtrate L f Recover organic components from wastewater by adjusting key variables in the wastewater treatment process, including temperature and residual solvent, and using an extraction process to separate the organic components from the wastewater;

[0075] S73. After the organic components are concentrated, they are further converted into reusable resources. The conversion step includes converting the organic components into materials and energy that can be used for reproduction. By optimizing the conversion conditions, the conversion efficiency R t Maximize and optimize resource consumption and waste;

[0076] S74. During the waste liquid treatment process, the energy consumption of the entire treatment process is optimized by adjusting the energy consumption and waste liquid treatment time. Energy saving optimization includes dynamically adjusting the temperature T according to the actual energy consumption during the waste liquid treatment process. o and time t p , in order to achieve the lowest energy consumption and the best processing effect;

[0077] S75. After the waste liquid is treated, the inorganic components in the waste liquid are further processed to minimize their emissions to the environment. The entire process of resource recovery optimizes energy consumption through energy conservation and achieves maximum utilization of resources in the waste liquid.

[0078] The beneficial effects of the present invention are:

[0079] The present invention uses a quantum optimization algorithm to accurately and dynamically control the multidimensional variables involved in the black tea extraction process, breaking through the limitations of traditional extraction technology in multivariable optimization. Parameters such as extraction temperature, time, and solvent ratio can be adjusted in real time according to actual conditions, thereby ensuring that the extraction efficiency and retention rate of flavor components are optimal. This not only improves the efficiency of the extraction process, but also ensures the stability and consistency of the black tea flavor.

[0080] The present invention uses short-pulse laser technology to finely process the cell walls of black tea leaves to generate local microcracks, which effectively improves the release efficiency of volatile and heat-sensitive flavor components, while avoiding the damage of high temperature to these sensitive components. Compared with traditional high-temperature extraction methods, this technology significantly reduces the degradation and loss of flavor substances, ensuring the pure flavor and rich taste levels of black tea. This method also has the advantage of precise control, so that the flavor of each batch of black tea remains consistent, greatly improving the quality and market competitiveness of black tea products.

[0081] The invention achieves efficient separation and concentration of flavor molecules through nano-scale molecular screening technology, avoids mutual interference between different flavor molecules, and ensures the purity of each type of flavor component in the extract. This innovation not only improves the accuracy of black tea flavor extraction, but also enhances the flavor layering of black tea products, allowing consumers to have a richer and purer taste experience; it also achieves a breakthrough in waste liquid treatment, recovering organic components in waste liquid through energy-saving optimization technology and converting them into reusable resources, which greatly improves resource utilization, reduces energy consumption in the production process, and achieves the goal of green environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] 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:

[0083] Figure 1 This is a flow chart of a method for extracting multidimensional flavor components of black tea based on quantum computing optimization proposed by the present invention;

[0084] Figure 2 A flowchart for dynamically controlling multidimensional variables in the black tea extraction process based on quantum optimization algorithm;

[0085] Figure 3 Schematic diagram of the process of generating local microcracks in the cell wall of black tea leaves using short-pulse laser technology. DETAILED DESCRIPTION

[0086] 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.

[0087] refer to Figure 1-3 , a method for extracting multidimensional flavor components of black tea based on quantum computing optimization, comprising the following steps:

[0088] S1. Use near-infrared spectroscopy and Raman spectroscopy to perform multi-dimensional component detection on the flavor components in black tea leaves, analyze the distribution of flavor components through machine learning algorithms, and screen out black tea raw materials that meet specific flavor requirements;

[0089] S2. For the selected black tea raw materials, short pulse laser is used to act on the cell wall of black tea leaves through photothermal effect to generate local micro cracks, optimize the release of flavor molecules, and avoid the destruction of heat-sensitive components by high temperature treatment;

[0090] S3. Dynamically control multiple variables in the black tea extraction process based on quantum optimization algorithms, use parallel computing technology to determine the optimal combination of extraction parameters, and optimize the extraction efficiency and retention rate of flavor components;

[0091] S4. Based on the determined optimal extraction parameter combination, the volatile and non-volatile flavor components in black tea are extracted in stages through pulse extraction combined with temperature dynamic control technology, so that different flavor molecules can be extracted under optimal conditions;

[0092] S5. Use sensors to monitor the concentration of flavor components, temperature, and solvent status during the extraction process in real time. If abnormal fluctuations and deviations from the optimal range are detected during the extraction process, fine-tuning will be performed to keep the extraction conditions within the optimal range.

[0093] S6. After the extraction is completed, the flavor components in the extract are separated and concentrated using nanoscale molecular screening technology based on differences in molecular size and polarity, with the assistance of quantum computing, to avoid mutual interference between different flavor molecules;

[0094] S7. Through energy-saving optimization technology, the waste liquid generated during the extraction process is treated, the organic components in the waste liquid are recovered and converted into reusable resources, optimizing solvent and energy consumption and the environmental friendliness of the extraction process.

[0095] In this implementation, S1 specifically includes:

[0096] S11, placing the black tea sample in a near-infrared spectrometer, scanning the black tea sample using a near-infrared spectrum in a wavelength range of 780 nm to 2500 nm, obtaining its absorption spectrum data, and extracting spectral features related to volatile and non-volatile flavor components;

[0097] S12, placing the black tea sample in a Raman spectrometer, irradiating the sample with a laser beam having a wavelength of 785 nm, capturing the molecular vibration mode in the sample, obtaining a Raman scattering spectrum, and generating a chemical composition map of the black tea raw material;

[0098] S13, the spectral data obtained from the near-infrared spectrum and the Raman spectrum are fused to construct a joint spectral feature matrix M, and the spectral data is reduced in dimension and features are extracted through a convolutional neural network:

[0099]

[0100] Among them, m ij represents the characteristic value of the fused spectral data, n is the number of samples, and p is the spectral feature dimension;

[0101] The feature matrix M is reduced in dimension through the convolution operation of CNN, and the feature vector X is expressed as:

[0102] X = W*M+b;

[0103] Among them, W is the convolution kernel weight matrix, * represents the convolution operation, and b is the bias vector;

[0104] S14, input the extracted feature vector X into the support vector machine model for training, and establish a prediction model for the flavor component category:

[0105]

[0106] Among them, w is the weight vector, b is the bias term, and x i is the feature vector of the i-th sample, y i is the classification label, N is the number of samples;

[0107] The classification output Y of the model is classified by the kernel function:

[0108]

[0109] Among them, α i is the Lagrange multiplier, K(x i ,x) is the kernel function, and f(X) is the prediction result of the flavor component category.

[0110] S15. Based on the output result of the support vector machine model Y, black tea samples that meet the set flavor component requirements are automatically screened out.

[0111] In this implementation, S2 specifically includes:

[0112] S21. Using short pulse laser technology, irradiate the selected black tea raw materials with a laser beam having a wavelength ranging from 532nm to 1064nm, adjust the laser energy density and pulse frequency, generate local microcracks, release flavor molecules, and avoid damaging the heat-sensitive components in the black tea leaves;

[0113] S22. When the laser beam irradiates the cell wall of black tea leaves, the local temperature of the cell wall is raised to T1 through the photothermal effect, and the temperature change is controlled to form controllable microcracks:

[0114]

[0115] Where T(t) is the local temperature at time t, T0 is the initial temperature, E1 is the laser energy density, R is the reflectivity, ρ is the cell wall density, C p is the specific heat capacity, μ is the absorption coefficient of the material;

[0116] S23. Under the photothermal effect, the laser irradiation time t1 and the spot size d1 control the width and depth of the microcracks, so that the release rate of the flavor molecules reaches the optimal effect:

[0117] and

[0118] Among them, W f is the crack width, D l is the crack depth, k1 and k2 are material constants, α, β, γ and δ are the adjustment coefficients of laser energy and time;

[0119] S24, by controlling the width and depth of the cracks, the release rate of volatile and non-volatile flavor molecules in the black tea leaves is adjusted, so that the flavor molecules can be efficiently released in the subsequent extraction process, and the formation of the cracks will not cause excessive damage to the cell structure, thereby maintaining the overall quality of the black tea leaves;

[0120] S25. Monitor the surface temperature of the black tea leaves in real time, and control the energy and action time of the laser to keep the temperature of the black tea leaves within a safe range, thereby preventing the degradation and loss of heat-sensitive flavor components due to excessive temperature.

[0121] In this implementation, S3 specifically includes:

[0122] S31. The key variables in the extraction process include temperature, extraction time, solvent ratio, tea amount, extraction pressure and ambient humidity. The key variables are quantified by quantum optimization algorithm.

[0123] S32. Quantum optimization algorithm uses the superposition and entanglement characteristics of quantum states. Quantum optimization algorithm processes variables V through parallel computing technology. eThe goal is to maximize the extraction efficiency of flavor components E f and retention rate R f , while minimizing energy consumption E c and resource utilization U r :

[0124]

[0125] in, is the Lagrangian objective function, E f (V e ) and R f (V e ) are the objective functions of extraction efficiency and flavor component retention rate, E c (V e ) is energy consumption, U r (V e ) is the resource utilization rate, E c,target and U r,target are the preset energy consumption and resource usage target values, λ1 and λ2 are Lagrange multipliers;

[0126] S33, automatically adjusting the variable combination according to the nonlinear interaction between key variables in the extraction process, so that each variable in the extraction process is always in the optimal control state;

[0127] S34, using parallel computing technology to quickly calculate multiple variable combinations in the extraction process to determine the optimal extraction parameter combination V e (opt) to achieve the best extraction efficiency and flavor component retention rate.

[0128] In this implementation manner, the S4 specifically includes:

[0129] S41. Optimal extraction parameter combination determined by quantum optimization algorithm for black tea extraction process V e (opt), the extraction of volatile and non-volatile components is carried out in stages during the extraction process, so that each temperature stage can maximize the extraction effect:

[0130]

[0131] Among them, R v (T l ) represents the rate of extraction of volatile components under low temperature conditions, R n (T h ) represents the rate of extraction of non-volatile components under high temperature conditions;

[0132] S42, under low temperature conditions, through the pulse time t l and pulse frequency f lControl the extraction of volatile components, adjust the pulse time and frequency to achieve the best release rate of volatile components:

[0133]

[0134] Among them, R v is the extraction rate of volatile components, k v is the volatile component extraction constant, T l is the temperature under low temperature conditions, t l is the pulse time, f l is the pulse frequency, α, β, γ are the adjustment parameters of the extraction efficiency;

[0135] S43, under high temperature conditions, by controlling the extraction temperature T h and pulse duration t h , extract non-volatile flavor components, optimize extraction temperature and pulse duration to maximize the extraction rate of non-volatile components:

[0136]

[0137] Among them, R n is the extraction rate of non-volatile components, k n is the non-volatile component extraction constant, T h is the temperature under high temperature conditions, t h is the pulse extraction duration, δ,∈ is the adjustment parameter of the extraction efficiency;

[0138] S44. Real-time monitoring of extraction temperature and extraction conditions, dynamic adjustment of the extraction rate of volatile and non-volatile components through adaptive control of pulse time and frequency, control of conditions at different extraction stages, and continuous optimization of the entire extraction process.

[0139] In this implementation manner, S5 specifically includes:

[0140] S51. Real-time monitoring of key variables in the extraction process is performed through a sensor network. The collected variables include flavor component concentration, temperature, and solvent state. The sensor data is converted into quantifiable input variables V by a signal processing system. m ;

[0141] S52, the variable V collected in real time m With the set optimal interval V opt Compare and calculate the difference between the variable and the optimal range. If the variable is detected to deviate from the optimal range, it will be judged that the variable has abnormal fluctuation and generate a deviation value ΔV;

[0142] S53, based on the generated deviation value ΔV, when it exceeds a preset threshold, a fine-tuning mechanism is automatically triggered, and the fine-tuning mechanism adjusts the relevant extraction condition variables according to the size and direction of ΔV;

[0143] S54. After fine-tuning, continue to monitor the adjusted extraction conditions through sensors. If the adjustment effect fails to bring the variables completely back to the optimal range, the deviation will be automatically recalculated through the feedback loop and the regulation will be triggered again to keep all variables in the extraction process within the optimal range, thereby maximizing the extraction efficiency and the retention rate of flavor components.

[0144] In this implementation manner, S6 specifically includes:

[0145] S61. After the extraction process is completed, the flavor components in the extract are initially separated by nano-scale molecular sieves based on the molecular size d m and polarity p m Due to the differences in flavor, flavor molecules are classified by molecular size and polarity, generating several different molecular groups;

[0146] S62. Optimize the initially screened molecular groups through quantum computing. Quantum computing technology can quickly process the screening conditions of different molecular groups in parallel, optimize the screening efficiency and accuracy, and the screening parameter Q s Including molecular size, polarity and mesh size D s , dynamically adjust the screening conditions through the quantum optimization algorithm to make the separation efficiency of the molecular group E separation To achieve the best:

[0147]

[0148] S63, after the screening is completed, the selected flavor molecule group is concentrated by optimizing the volume of the extract and the concentration of the flavor molecules. The concentration process optimized by quantum computing is based on the change of the flavor molecule concentration C m Adjust the concentration conditions;

[0149] S64, Dynamically adjust the mesh aperture D through quantum optimized nano-scale screening technology s , based on the differences in molecular size and polarity, each group of flavor molecules will not interfere with other groups of molecules during the entire processing process. If possible interference is detected during the concentration process, the screening conditions will be automatically adjusted.

[0150] In this implementation manner, the S7 specifically includes:

[0151] S71. After the extraction is completed, the waste liquid is classified according to its components. o and the residual solvent S rPreliminary treatment is carried out to remove large particle impurities in the waste liquid through physical filtration and sedimentation process to generate filtrate L containing recyclable organic components. f ;

[0152] S72, from the filtrate L f Recover organic components from wastewater by adjusting key variables in the wastewater treatment process, including temperature and residual solvent, and using an extraction process to separate the organic components from the wastewater;

[0153] S73. After the organic components are concentrated, they are further converted into reusable resources. The conversion step includes converting the organic components into materials and energy that can be used for reproduction. By optimizing the conversion conditions, the conversion efficiency R t Maximize and optimize resource consumption and waste;

[0154] S74. During the waste liquid treatment process, the energy consumption of the entire treatment process is optimized by adjusting the energy consumption and waste liquid treatment time. Energy saving optimization includes dynamically adjusting the temperature T according to the actual energy consumption during the waste liquid treatment process. o and time t p , in order to achieve the lowest energy consumption and the best processing effect;

[0155] S75. After the waste liquid is treated, the inorganic components in the waste liquid are further processed to minimize their emissions to the environment. The entire process of resource recovery optimizes energy consumption through energy conservation and achieves maximum utilization of resources in the waste liquid.

[0156] Embodiment 1:

[0157] In order to verify the feasibility of the present invention in implementation, the present invention was applied to a large-scale black tea production factory. The manufacturer has long been faced with problems such as unstable flavor in the black tea extraction process, low extraction efficiency, waste of resources and environmental pollution in the production process. In order to improve the flavor consistency of black tea, optimize the extraction efficiency, and reduce the consumption of energy and resources, the manufacturer decided to adopt the black tea multi-dimensional flavor component extraction method based on quantum computing optimization proposed in the present invention to improve the process.

[0158] During the black tea production process, the factory uses the near-infrared spectroscopy and Raman spectroscopy technology of the present invention to perform multi-dimensional flavor component detection on the raw black tea leaves. In the spectral detection equipment, black tea samples pass through the spectral analysis device at a feed rate of 500 kilograms per hour. The detection system analyzes the distribution of volatile and non-volatile flavor components in the tea leaves, and analyzes the composition differences of different batches of black tea leaves through machine learning algorithms to screen out raw tea leaves that meet specific flavor requirements. Within 30 days, the factory screened out more than 100 tons of black tea raw materials for subsequent extraction processes, successfully reducing the waste of raw materials that do not meet flavor standards and improving production consistency.

[0159] During the extraction process, the factory uses short-pulse laser technology to process the cell walls of black tea leaves. The short-pulse laser equipment is set to a wavelength of 532nm, a pulse frequency of 200 times per second, and a laser energy density of 2.5J / cm 2 . Through the photothermal effect of laser, local microcracks are formed on the cell walls of black tea leaves, promoting the release of flavor molecules. In the traditional high-temperature extraction method, tea leaves often need to be extracted at a temperature above 90°C for 30 minutes. However, this method easily leads to the loss of volatile flavor components and insufficient aroma. After the short-pulse laser treatment of the present invention, the temperature is controlled at about 75°C for extraction, the extraction time is shortened to 15 minutes, and the destruction of heat-sensitive components by high temperature is avoided. Experimental data show that compared with the traditional method, the content of volatile components of black tea extracted by this process is increased by about 20%, and the retention rate of non-volatile components is increased by about 15%.

[0160] In order to further optimize the extraction efficiency and the retention of flavor components, the factory introduced a quantum optimization algorithm in the extraction process, and achieved the optimal parameter combination by dynamically adjusting multiple extraction variables. The key variables in the extraction process include temperature, extraction time, solvent ratio, tea dosage and extraction pressure. The quantum optimization system uses parallel computing technology to analyze the nonlinear relationship between these variables in real time, and continuously adjusts the parameters during the extraction process of each batch to achieve the best extraction of flavor components. Experiments show that in the extraction process of 40 batches of black tea, the quantum optimization system realizes automatic adjustment of parameters such as extraction temperature and time, so that the extraction efficiency and flavor component retention rate of each batch of black tea are stabilized at the optimal state. The data show that after adopting quantum optimization technology, the extraction efficiency has increased by about 25%. At the same time, the flavor consistency of each batch has been significantly enhanced, and the coefficient of variation of flavor components has been reduced from 8% in traditional processes to 2%.

[0161] The entire extraction process is monitored in real time by sensors, which monitor the temperature, solvent status and flavor component concentration during extraction. The sensor system can detect any abnormal fluctuations in parameters through a feedback mechanism and make timely fine-tuning to ensure that the extraction conditions are always in the optimal range. For example, during a day's production process, the extraction temperature of a batch exceeded the set value due to a sudden increase in external temperature. The sensor detected the abnormality in just 5 seconds, and the system automatically adjusted the extraction temperature to restore it to the set range, thereby avoiding the loss of flavor components. During the entire 30-day production period, the sensor system monitored and adjusted the key parameters of the extraction process 62 times, ensuring the consistency of all batches of black tea.

[0162] After the extraction is completed, the factory uses nano-scale molecular screening technology to separate and concentrate the flavor components in the extract. Quantum computing technology is used to assist in the screening. The system automatically adjusts the mesh size according to the molecular size and polarity to ensure that different flavor components do not interfere with each other, and finally obtains a high-purity flavor extract. Experimental data shows that compared with traditional separation technology, nano-screening technology increases the purity of flavor components in the extract by 30%, the flavor levels are more distinct, and consumers' overall taste feedback on black tea is also significantly improved.

[0163] In the waste liquid treatment link, the present invention adopts energy-saving optimization technology to treat the waste liquid generated in the extraction process. The waste liquid is filtered and extracted to recover the organic components and convert them into reusable resources. During the treatment process, the system will automatically adjust the waste liquid treatment temperature and time to achieve maximum recovery of waste liquid resources with minimal energy consumption. During the experiment, the recovery rate of organic components in the waste liquid reached 85%, and the recovery rate of the solvent was also increased by 20%. Compared with traditional waste liquid treatment methods, the energy-saving optimization technology of the present invention reduces the factory's energy consumption by about 18% and waste liquid emissions by 12%, which meets the requirements of modern green and environmentally friendly production.

[0164] Table 1 Data table of black tea extraction process comparison

[0165]

[0166] It can be seen from the table data that compared with the traditional process, the present invention significantly improves multiple key indicators in the black tea extraction process. Specifically, while the extraction temperature is reduced to 75°C, the extraction time is shortened to 15 minutes, the extraction efficiency is increased by 25%, and the retention rates of flavor components are increased by 15% and 20%, respectively. In addition, the flavor consistency has been significantly improved, and the coefficient of variation has been reduced from 8% to 2%. In terms of waste liquid treatment, the recovery rate of organic components reached 85%, the solvent recovery rate increased by 20%, the energy consumption was reduced by 18%, and the waste liquid discharge was reduced by 12%, achieving significant energy-saving and environmental protection effects.

[0167] In summary, the present invention solves the problems of unstable flavor, low extraction efficiency, and easy degradation of flavor components in traditional black tea extraction by using quantum optimization algorithms, short pulse lasers, sensor monitoring, nano-scale molecular screening and other technologies in the black tea extraction process. Practical applications show that the present invention has significant advantages in improving the quality of black tea, extraction efficiency, energy conservation and environmental protection, and comprehensively improves the overall benefits of black tea production.

[0168] 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 method for extracting multidimensional flavor components of black tea based on quantum computing optimization, characterized in that: The steps include: S1. Use near-infrared spectroscopy and Raman spectroscopy to perform multi-dimensional component detection on the flavor components in black tea leaves, analyze the distribution of flavor components through machine learning algorithms, and screen out black tea raw materials that meet specific flavor requirements; S2. For the selected black tea raw materials, short pulse laser is used to act on the cell wall of black tea leaves through photothermal effect to generate local micro cracks, optimize the release of flavor molecules, and avoid the destruction of heat-sensitive components by high temperature treatment; S3. Dynamically control multiple variables in the black tea extraction process based on quantum optimization algorithms, use parallel computing technology to determine the optimal combination of extraction parameters, and optimize the extraction efficiency and retention rate of flavor components; S4. Based on the determined optimal extraction parameter combination, the volatile and non-volatile flavor components in black tea are extracted in stages through pulse extraction combined with temperature dynamic control technology, so that different flavor molecules can be extracted under optimal conditions; S5. Use sensors to monitor the concentration of flavor components, temperature, and solvent status during the extraction process in real time. If abnormal fluctuations and deviations from the optimal range are detected during the extraction process, fine-tuning will be performed to keep the extraction conditions within the optimal range. S6. After the extraction is completed, the flavor components in the extract are separated and concentrated using nanoscale molecular screening technology based on differences in molecular size and polarity, with the assistance of quantum computing, to avoid mutual interference between different flavor molecules; S7. Through energy-saving optimization technology, the waste liquid generated during the extraction process is treated, the organic components in the waste liquid are recovered and converted into reusable resources, optimizing solvent and energy consumption and the environmental friendliness of the extraction process.

2. The method for extracting multidimensional flavor components of black tea based on quantum computing optimization according to claim 1, characterized in that: The S1 specifically includes: S11, placing the black tea sample in a near-infrared spectrometer, scanning the black tea sample using a near-infrared spectrum in a wavelength range of 780 nm to 2500 nm, obtaining its absorption spectrum data, and extracting spectral features related to volatile and non-volatile flavor components; S12, placing the black tea sample in a Raman spectrometer, irradiating the sample with a laser beam having a wavelength of 785 nm, capturing the molecular vibration mode in the sample, obtaining a Raman scattering spectrum, and generating a chemical composition map of the black tea raw material; S13, the spectral data obtained from the near-infrared spectrum and the Raman spectrum are fused to construct a joint spectral feature matrix M, and the spectral data is reduced in dimension and features are extracted through a convolutional neural network: Among them, m ij represents the characteristic value of the fused spectral data, n is the number of samples, and p is the spectral feature dimension; The feature matrix M is reduced in dimension through the convolution operation of CNN, and the feature vector X is expressed as: X = W*M+b; Among them, W is the convolution kernel weight matrix, * represents the convolution operation, and b is the bias vector; S14, input the extracted feature vector X into the support vector machine model for training, and establish a prediction model for the flavor component category: Among them, w is the weight vector, b is the bias term, and x i is the feature vector of the i-th sample, y i is the classification label, N is the number of samples; The classification output Y of the model is classified by the kernel function: Among them, α i is the Lagrange multiplier, K(x i ,x) is the kernel function, and f(X) is the prediction result of the flavor component category. S15. Based on the output result of the support vector machine model Y, black tea samples that meet the set flavor component requirements are automatically screened out.

3. The method for extracting multidimensional flavor components of black tea based on quantum computing optimization according to claim 1, characterized in that: The S2 specifically includes: S21. Using short pulse laser technology, irradiate the selected black tea raw materials with a laser beam having a wavelength ranging from 532nm to 1064nm, adjust the laser energy density and pulse frequency, generate local microcracks, release flavor molecules, and avoid damaging the heat-sensitive components in the black tea leaves; S22, when the laser beam irradiates the cell wall of black tea leaves, the local temperature of the cell wall is raised to T by the photothermal effect. l , control the temperature change to form controllable micro cracks: Where T(t) is the local temperature at time t, T0 is the initial temperature, and E l is the laser energy density, R is the reflectivity, ρ is the cell wall density, C p is the specific heat capacity, μ is the absorption coefficient of the material; S23. Under the photothermal effect, the laser irradiation time t1 and the spot size d1 control the width and depth of the microcracks, so that the release rate of the flavor molecules reaches the optimal effect: and Among them, W f is the crack width, D l is the crack depth, k1 and k2 are material constants, α, β, γ and δ are the adjustment coefficients of laser energy and time; S24, by controlling the width and depth of the cracks, the release rate of volatile and non-volatile flavor molecules in the black tea leaves is adjusted, so that the flavor molecules can be efficiently released in the subsequent extraction process, and the formation of the cracks will not cause excessive damage to the cell structure, thereby maintaining the overall quality of the black tea leaves; S25. Monitor the surface temperature of the black tea leaves in real time, and control the energy and action time of the laser to keep the temperature of the black tea leaves within a safe range, thereby preventing the degradation and loss of heat-sensitive flavor components due to excessive temperature.

4. The method for extracting multidimensional flavor components of black tea based on quantum computing optimization according to claim 1, characterized in that: The S3 specifically includes: S31. The key variables in the extraction process include temperature, extraction time, solvent ratio, tea amount, extraction pressure and ambient humidity. The key variables are quantified by quantum optimization algorithm. S32. Quantum optimization algorithm uses the superposition and entanglement characteristics of quantum states. Quantum optimization algorithm processes variables V through parallel computing technology. e The goal is to maximize the extraction efficiency of flavor components E f and retention rate R f , while minimizing energy consumption E c and resource utilization U r : in, is the Lagrangian objective function, E f (V e ) and R f (V e ) are the objective functions of extraction efficiency and flavor component retention rate, E c (V e ) is energy consumption, U r (V e ) is the resource utilization rate, E c,target and U r,target are the preset energy consumption and resource usage target values, λ1 and λ2 are Lagrange multipliers; S33, automatically adjusting the variable combination according to the nonlinear interaction between key variables in the extraction process, so that each variable in the extraction process is always in the optimal control state; S34, using parallel computing technology to quickly calculate multiple variable combinations in the extraction process to determine the optimal extraction parameter combination V e (opt) to achieve the best extraction efficiency and flavor component retention rate.

5. The method for extracting multidimensional flavor components of black tea based on quantum computing optimization according to claim 1, characterized in that: The S4 specifically includes: S41. Optimal extraction parameter combination determined by quantum optimization algorithm for black tea extraction process V e (opt), the extraction of volatile and non-volatile components is carried out in stages during the extraction process, so that each temperature stage can maximize the extraction effect: Among them, R v (T l ) represents the rate of extraction of volatile components under low temperature conditions, R n (T h ) represents the rate of extraction of non-volatile components under high temperature conditions; S42, under low temperature conditions, through the pulse time t l and pulse frequency f l Control the extraction of volatile components, adjust the pulse time and frequency to achieve the best release rate of volatile components: Among them, R v is the extraction rate of volatile components, k v is the volatile component extraction constant, T l is the temperature under low temperature conditions, t l is the pulse time, f l is the pulse frequency, α, β, γ are the adjustment parameters of the extraction efficiency; S43, under high temperature conditions, by controlling the extraction temperature T h and pulse duration t h , extract non-volatile flavor components, optimize extraction temperature and pulse duration to maximize the extraction rate of non-volatile components: Among them, R n is the extraction rate of non-volatile components, k n is the non-volatile component extraction constant, T h is the temperature under high temperature conditions, t h is the pulse extraction duration, δ,∈ is the adjustment parameter of the extraction efficiency; S44. Real-time monitoring of extraction temperature and extraction conditions, dynamic adjustment of the extraction rate of volatile and non-volatile components through adaptive control of pulse time and frequency, control of conditions at different extraction stages, and continuous optimization of the entire extraction process.

6. The method for extracting multidimensional flavor components of black tea based on quantum computing optimization according to claim 1, characterized in that: The S5 specifically includes: S51. Real-time monitoring of key variables in the extraction process is performed through a sensor network. The collected variables include flavor component concentration, temperature, and solvent state. The sensor data is converted into quantifiable input variables V by a signal processing system. m ; S52, the variable V collected in real time m With the set optimal interval V opt Compare and calculate the difference between the variable and the optimal range. If the variable is detected to deviate from the optimal range, it will be judged that the variable has abnormal fluctuation and generate a deviation value ΔV; S53, based on the generated deviation value ΔV, when it exceeds a preset threshold, a fine-tuning mechanism is automatically triggered, and the fine-tuning mechanism adjusts the relevant extraction condition variables according to the size and direction of ΔV; S54. After fine-tuning, continue to monitor the adjusted extraction conditions through sensors. If the adjustment effect fails to bring the variables completely back to the optimal range, the deviation will be automatically recalculated through the feedback loop and the regulation will be triggered again to keep all variables in the extraction process within the optimal range, thereby maximizing the extraction efficiency and the retention rate of flavor components.

7. The method for extracting multidimensional flavor components of black tea based on quantum computing optimization according to claim 1, characterized in that: The S6 specifically includes: S61. After the extraction process is completed, the flavor components in the extract are initially separated by nano-scale molecular sieves based on the molecular size d m and polarity p m Due to the differences in flavor, flavor molecules are classified by molecular size and polarity, generating several different molecular groups; S62. Optimize the initially screened molecular groups through quantum computing. Quantum computing technology can quickly process the screening conditions of different molecular groups in parallel, optimize the screening efficiency and accuracy, and the screening parameter Q s Including molecular size, polarity and mesh size D s , dynamically adjust the screening conditions through the quantum optimization algorithm to make the separation efficiency of the molecular group E separation To achieve the best: S63, after the screening is completed, the selected flavor molecule group is concentrated by optimizing the volume of the extract and the concentration of the flavor molecules. The concentration process optimized by quantum computing is based on the change of the flavor molecule concentration C m Adjust the concentration conditions; S64, Dynamically adjust the mesh aperture D through quantum optimized nano-scale screening technology s , based on the differences in molecular size and polarity, each group of flavor molecules will not interfere with other groups of molecules during the entire processing process. If possible interference is detected during the concentration process, the screening conditions will be automatically adjusted.

8. The method for extracting multidimensional flavor components of black tea based on quantum computing optimization according to claim 1, characterized in that: The S7 specifically includes: S71. After the extraction is completed, the waste liquid is classified according to its components. o and the residual solvent S r Preliminary treatment is carried out to remove large particle impurities in the waste liquid through physical filtration and sedimentation process to generate filtrate L containing recyclable organic components. f ; S72, from the filtrate L f Recover organic components from wastewater by adjusting key variables in the wastewater treatment process, including temperature and residual solvent, and using an extraction process to separate the organic components from the wastewater; S73. After the organic components are concentrated, they are further converted into reusable resources. The conversion step includes converting the organic components into materials and energy that can be used for reproduction. By optimizing the conversion conditions, the conversion efficiency R t Maximize and optimize resource consumption and waste; S74. During the waste liquid treatment process, the energy consumption of the entire treatment process is optimized by adjusting the energy consumption and waste liquid treatment time. Energy saving optimization includes dynamically adjusting the temperature T according to the actual energy consumption during the waste liquid treatment process. o and time t p , in order to achieve the lowest energy consumption and the best processing effect; S75. After the waste liquid is treated, the inorganic components in the waste liquid are further processed to minimize their emissions to the environment. The entire process of resource recovery optimizes energy consumption through energy conservation and achieves maximum utilization of resources in the waste liquid.

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