High-precision grading and directional processing system for Qihong Huang tea

Abstract

The invention belongs to the technical field of tea processing, and particularly relates to a high-precision grading and directional processing system for Qihong tea. The system comprises an intelligent tea leaf grading module based on hyperspectral imaging and machine vision, wherein the intelligent tea leaf grading module is used for realizing accurate classification of raw materials; an AI-controlled dynamic withering module is used for optimizing enzyme activity and water evaporation; a pressure-rotating speed self-adaptive intelligent rolling module ensures that cells are uniformly broken; an electronic nose is combined with a multispectral real-time fermentation monitoring module to accurately control the fermentation degree; a far infrared-hot air coupled precise drying module is used for realizing gradient dehydration; the near infrared spectrum and deep learning quality detection module is used for completing finished product quality evaluation; the data traceability module of the block chain technology is used for recording whole-process processing parameters; and a big data-driven process optimization module continuously improves the product stability. According to the invention, the intellectualization, precision and standardization of the Qihong Huang tea processing process are realized, and the consistency and the superior product rate of the product quality are obviously improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of tea processing, and particularly relates to a high-precision grading and directional processing system for Qihong imperial tea. BACKGROUND

[0002] As a traditional famous black tea, the processing technology of Qihong imperial tea has long relied on manual experience, which has obvious technical limitations. In the raw material grading link, the traditional manual sorting method cannot accurately identify the morphological characteristics and internal quality differences of tea leaves, which makes it difficult to achieve precise adaptation in subsequent processes. Key processes such as withering and rolling often use fixed parameter control, which lacks dynamic response to raw material differences, seriously affecting the stability of tea quality formation.

[0003] The existing processing technology has significant deficiencies in process control. The degree of fermentation is mainly judged by manual sensory evaluation, lacking objective quantitative indicators; the drying process often uses a single temperature mode, which cannot adapt to the water migration law of tea. These extensive processing methods lead to insufficient conversion of tea contents, serious loss of aroma components, and large fluctuations in product quality.

[0004] In addition, the current Qihong imperial tea production lacks effective quality traceability and process optimization means. The data records of each process are scattered, making it difficult to achieve full-process quality monitoring; process parameter adjustment relies on experience and lacks data support. These defects seriously restrict the modernization development of Qihong imperial tea processing technology, and an intelligent precision processing system needs to be established.

[0005] Therefore, we propose a high-precision grading and directional processing system for Qihong imperial tea to solve the problems encountered in the above. SUMMARY

[0006] The application provides a high-precision grading and directional processing system for Qihong imperial tea, which realizes intelligent and precise control of the whole process from raw materials to finished products through multi-technology integration. The system first uses hyperspectral imaging combined with machine vision technology to automatically analyze the morphological characteristics and biochemical components of tea leaf raw materials, establishes multi-dimensional grading standards, and realizes precise classification of raw materials. Then through AI prediction model and sensor network, the withering environment parameters are dynamically controlled to ensure that the enzyme activity reaches the best state.

[0007] In the processing link, the application innovatively develops a pressure-rotation speed coordinated control system, which can automatically adjust the rolling parameters according to the characteristics of tea leaves of different grades, and realize the control of cell crushing uniformity. In the fermentation stage, electronic nose array and multispectral imaging technology are introduced to monitor the aroma substance conversion process in real time, and the fermentation environment is optimized through an intelligent control system. In the drying process, far infrared-heat air composite drying technology is used to implement gradient temperature control according to the water migration law, and to maximize the retention of tea effective components.

[0008] The application also constructs a complete quality traceability and optimization system. The rapid quality evaluation of finished tea is realized through near-infrared spectroscopy and deep learning algorithm, the whole-process data archives are established by using blockchain technology, and the process parameters are continuously optimized based on big data analysis. The system effectively solves the quality control problem in traditional processing, and significantly improves the consistency and stability of product quality.

[0009] To achieve the above purpose, the application provides a high-precision grading and directional processing system for Qihong imperial tea, which comprises: S1: adopting hyperspectral imaging fusion machine vision technology to analyze the morphological and biochemical components of tea leaves to form standardized graded tea leaf raw materials; S2: relying on a temperature and humidity sensing network combined with an AI prediction model to perform precise regulation on the withering environment to produce withering leaves with optimized enzyme activity; S3: using a pressure-rotation speed coordinated control system to carry out differential rolling operation on the graded tea leaves to prepare twisted and rolled leaves with uniform cell breakage; S4: integrating an electronic nose array and multispectral imaging technology to implement dynamic quality tracking on the fermented leaves to output fermented leaves with standard aroma conversion; S5: selecting a far-infrared-heat air coupled drying process to complete gradient dehydration treatment on the fermented leaves to obtain dried tea with stable moisture content; S6: applying near-infrared spectroscopy combined with deep learning algorithm to realize multi-index rapid evaluation of finished tea and establishing a quality characteristic database; S7: storing the whole-process processing data in an unalterable manner by using blockchain distributed ledger technology to generate a traceable quality archive; S8: using big data mining technology to continuously iterate and optimize historical process parameters to output high-quality finished tea with improved stability.

[0010] Optionally, the hyperspectral imaging fusion machine vision technology is used to analyze the morphological and biochemical components of tea leaves to form standardized graded tea leaf raw materials, which comprises: The hyperspectral imaging equipment is used to collect the spectral reflection characteristics of tea leaves in the visible light to near-infrared band; The machine vision system is used to extract the morphological parameters of the bud leaves, including bud head integrity, leaf expansion degree and villus distribution density; A tea leaf quality evaluation model is constructed to perform multi-dimensional fusion analysis on the spectral characteristics and morphological parameters; According to the preset grading standard, the tea leaves are automatically classified to output different grades of tea leaf raw materials.

[0011] Optionally, the temperature and humidity sensing network combined with the AI prediction model is used to perform precise regulation on the withering environment to produce withering leaves with optimized enzyme activity, which comprises: An array of temperature and humidity sensors is arranged in the withering workshop to collect environmental parameters in real time. A deep learning-based withering process prediction model is established to input environmental parameters and initial tea quality data. According to the model output results, automatically adjust the operating parameters of the ventilation system and the temperature and humidity control device; Monitor the changes in tea water loss rate and polyphenol oxidase activity, and output tea that has reached the optimal withering degree.

[0012] Optionally, a pressure-rotation speed cooperative control system is used to carry out differentiated rolling and twisting operations on classified tea to prepare twisted and rolled leaves with uniform cell breakage, including: Call the preset rolling and twisting process curve according to the tea grade; Real-time monitoring of rolling and twisting pressure and rotation speed through pressure sensors and encoders; Dynamic adjustment of servo motor output parameters using PID control algorithm; Online detection of tea cell breakage rate to ensure optimal rolling and twisting effect for different grades of tea.

[0013] Optionally, integrate an electronic nose array with multispectral imaging technology to implement dynamic quality tracking of fermented leaves, output fermented leaves with aroma conversion up to standard, including: Arrange an array of electronic nose sensors to monitor volatile aroma components in real time during fermentation; Use a multispectral imaging system to collect changes in surface color and texture characteristics of fermented leaves; Establish a correlation model between aroma content and image features; According to the model prediction results, automatically adjust the CO2 concentration and temperature and humidity of the fermentation chamber to control the fermentation process.

[0014] Optionally, select far-infrared-heat air coupled drying process to complete gradient dehydration treatment of fermented leaves to obtain dried tea with stable moisture content, including: Set a three-stage temperature gradient drying curve; Use a drying method that combines far-infrared radiation and hot air circulation; Monitor changes in tea moisture content through online moisture detection devices; According to the changes in moisture content, automatically adjust the process parameters of each drying section.

[0015] Optionally, apply near-infrared spectroscopy combined with deep learning algorithms to implement multi-index rapid evaluation of finished tea, and establish a quality feature database, including: Collect near-infrared spectroscopy data of finished tea; Construct a quality prediction model based on convolutional neural networks; Simultaneously detect the moisture content, tea polyphenol content, and aroma components of tea; The detection result is stored in association with the spectral feature to form a quality feature database.

[0016] Optionally, the whole-process processing data is stored in an unalterable manner through a blockchain distributed ledger technology to generate a traceable quality file, including: A data storage architecture based on a blockchain is designed; The key process parameters and quality detection data of each process are chained; A unique digital identifier is generated for each batch of products; A complete product processing process traceability chain is established.

[0017] Optionally, big data mining technology is used to perform continuous iterative optimization on historical process parameters to output high-quality finished tea with improved stability, including: Collecting process parameters and quality data in the historical production process; Applying clustering analysis and association rule mining algorithms to process data; Establishing a mapping relationship model between process parameters and product quality; According to the model analysis result, dynamically adjusting the control parameters of each process.

[0018] The application provides a high-precision grading and directional processing system for Qihong royal tea, which integrates hyperspectral imaging, machine vision, AI prediction model and intelligent sensing technology to build a full-process intelligent control system from raw material grading to finished product processing. The system first uses hyperspectral-machin vision fusion technology to realize accurate grading of tea green raw materials, then dynamically adjusts key process parameters such as withering, rolling, fermentation and drying through AI, and introduces an electronic nose and multispectral technology to monitor quality changes in real time, finally combines blockchain and big data technology to establish a quality traceability and process optimization system, realizing the standardization, precision and intelligence of Qihong royal tea processing.

[0019] The application innovatively applies modern detection technology and intelligent control algorithm to the whole process of traditional black tea processing, solving the problems of strong dependence on manual experience, fixed process parameters and large quality fluctuations. By establishing a digital correlation model of raw material characteristics-process parameters-finished product quality, the differential directional processing of different grades of raw materials is realized, and the consistency and good product rate of product quality are significantly improved.

[0020] Compared with the prior art, the application has the following beneficial effects: 1. The present application innovatively combines hyperspectral imaging technology with machine vision systems to achieve accurate identification and grading of tea raw materials in terms of morphological characteristics and intrinsic quality. Through the dynamic control model established by AI algorithms, the process parameters of key processes such as withering and rolling can be automatically optimized according to the characteristics of the raw materials, ensuring that tea of different grades can obtain the most suitable processing conditions. This intelligent grading and processing method effectively overcomes the quality fluctuations caused by reliance on human experience in traditional processes, enabling finished tea to achieve higher consistency standards in terms of appearance, aroma, taste, and other quality indicators.

[0021] 2. The system integrates advanced sensing technologies such as electronic nose arrays and multispectral imaging to establish a complete real-time quality monitoring system, enabling dynamic tracking of key quality indicators during processing. The distributed data storage system established in combination with blockchain technology enables traceability of information from raw materials to finished products. This intelligent production control mode not only significantly improves the control accuracy of process parameters but also significantly reduces the impact of human factors on product quality, providing reliable technical support for establishing a standardized and digital tea processing system.

[0022] 3. The present application establishes an intelligent correlation model between process parameters and product quality through deep mining of historical processing data using big data analysis technology, enabling continuous optimization of processing schemes for each process. This data-driven optimization method ensures product quality while effectively improving energy and raw material utilization efficiency and reducing resource waste during production. The system also has the ability to learn and continuously improve, constantly improving processing efficiency as production data accumulates, providing an innovative solution for green and sustainable tea production. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 Figure 1 is a flowchart of a high-precision grading and targeted processing system for Qihong imperial tea according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described below in conjunction with the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0025] Referring to Figure 1 Figure 1 is a flowchart of a high-precision grading and targeted processing system for Qihong imperial tea according to an embodiment of the present application. In this embodiment, a high-precision grading and targeted processing system for Qihong imperial tea includes the following specific implementation methods: S1: Adopting hyperspectral imaging fusion machine vision technology to implement morphological and biochemical component analysis on tea green, forming standardized grading tea green raw materials: In the high-precision grading and directional processing system of Qihong imperial tea, advanced optical detection technology is combined to realize accurate classification of tea green in the raw material grading link. First of all, a high-precision hyperspectral imaging system is deployed, which can capture the spectral reflection characteristics of tea green in the visible light to near-infrared band range. Through this non-destructive detection method, the distribution information of multiple biochemical components in tea leaf can be obtained, including but not limited to chlorophyll content, water distribution, polyphenols and other key quality indicators, providing a reliable quantitative data basis for subsequent quality evaluation.

[0026] In order to comprehensively evaluate the quality characteristics of tea green raw materials, the system also synchronously configures a high-resolution industrial-grade digital camera array. These cameras capture high-definition images of tea green on the conveying belt from multiple angles, obtaining the complete morphological characteristics of the bud leaves. The imaging system uses a specially designed lighting scheme to ensure clear and stable image data under different environmental light conditions. Through this multi-angle image acquisition method, the appearance characteristics of each tea green can be recorded, including bud head morphology, leaf expansion, color uniformity and other visual parameters.

[0027] In the data processing link, the system uses machine learning algorithms based on deep learning to deeply mine and analyze the collected hyperspectral data. The algorithm first preprocesses the original spectral data, including noise elimination, baseline correction and other steps, then identifies the characteristic bands closely related to tea quality through feature extraction technology. After dimensionality reduction processing, these characteristic band data are input into the trained classification model for quality discrimination, thereby realizing objective evaluation of the internal quality of tea green.

[0028] At the same time, the system also uses advanced machine vision technology to analyze and process the morphological images collected. The image processing algorithm first separates the tea green from the background by segmenting the original image, then extracts multiple morphological parameters, including but not limited to bud leaf length, width, area, curvature and other geometric features, as well as hair density, color uniformity and other surface features. After standardized processing, these parameters form quantitative morphological evaluation indicators, providing objective basis for grading decisions.

[0029] Based on the analysis results of the above biochemical components and morphological characteristics, a multi-dimensional tea leaf grading model is constructed. The model uses feature fusion technology to organically combine spectral features and morphological features, and calculates the comprehensive score through weighting. According to the preset grading standard, the system can automatically divide the tea leaves into multiple quality grades and output the grading results. The entire grading process is fully automated, not only improving the accuracy and consistency of grading, but also significantly improving the efficiency of grading, laying a solid foundation for subsequent targeted processing.

[0030] S2: Relying on the temperature and humidity sensing network combined with the AI prediction model to perform precise regulation on the withering environment to produce enzyme activity optimized withering leaves: This process first deploys a high-precision Internet of Things sensing network inside the withering workshop. This network is composed of multiple temperature and humidity sensor nodes, which are evenly distributed in three-dimensional space matrix at various locations in the workshop. Each sensor node is equipped with a high-sensitivity temperature and humidity probe, which can monitor the changes in environmental parameters in real time at a sampling frequency of seconds. These sensors upload the collected data to the central control system in real time through wireless transmission technology, forming a complete workshop microenvironment monitoring network.

[0031] After receiving the real-time data uploaded by the sensor network, the central control system will perform data preprocessing and quality control. The system first filters the raw data to eliminate possible measurement noise and outliers, and then generates a complete temperature and humidity field distribution map inside the workshop through spatial interpolation algorithms. These processed environmental parameter data are input into the deep learning-based prediction model. The prediction model uses a deep neural network architecture and has strong non-linear mapping capabilities through historical production data training, which can accurately predict the trend of changes in internal biochemical indicators of tea leaves under different environmental conditions.

[0032] The core function of the deep learning prediction model is to establish a complex relationship model between environmental parameters and tea leaf biochemical indicators. The input layer of the model receives multi-dimensional environmental parameters including temperature, humidity, ventilation, etc. After non-linear transformation through multiple hidden layers, the output layer can predict key indicators such as tea leaf water loss rate and polyphenol oxidase activity. The model pays special attention to the influence of environmental parameters on enzyme activity, and by analyzing the dynamic relationship between temperature, humidity and enzyme activity, it can accurately determine whether the current environmental conditions are conducive to forming an ideal biochemical reaction environment.

[0033] Based on the model-based prediction results, the system intelligently adjusts the operating parameters of the ventilation system and the temperature and humidity control equipment. The ventilation system uses variable frequency control technology to accurately adjust the air volume and air speed as needed, ensuring uniform and reasonable airflow distribution in the withering environment. The temperature and humidity control equipment uses a PID closed-loop control algorithm to quickly respond to changes in environmental parameters, stabilizing the temperature and humidity in the workshop around the set value. The system also has adaptive optimization capabilities, constantly fine-tuning control parameters based on real-time monitoring data to ensure that the environmental conditions throughout the withering process are always in the optimal state.

[0034] Through this intelligent control method, tea leaves can complete the withering process under the most suitable microenvironment conditions. The system ensures that the water loss rate of tea leaves is moderate, neither too fast to cause leaf damage nor too slow to affect production efficiency. At the same time, the activity of key enzymes such as polyphenol oxidase is controlled within the optimal range, laying a good biochemical foundation for the subsequent fermentation process. The final withered leaves have uniform water loss and ideal enzyme activity, providing reliable raw material support for producing high-quality Qihong Huang tea. The entire withering process realizes closed-loop control from environmental monitoring to intelligent control, greatly improving the stability and controllability of the production process.

[0035] S3: Use the pressure-rotation speed cooperative control system to carry out differentiated rolling operation on classified tea leaves, and prepare twisted and rolled leaves with uniform cell breakage: In the high-precision classification and targeted processing system of Qihong Huang tea, the rolling process adopts an intelligent and differentiated precision control scheme. First, based on the previous raw material classification results, the system calls the preset rolling scheme matching the tea leaf grade from the process database. These differentiated rolling schemes are optimized through a large number of process tests and are set for different grades of tea leaf raw materials, including pressure gradient, rotation speed curve, rolling time, etc., to ensure that each grade of tea leaf can obtain the most suitable mechanical treatment.

[0036] To achieve precise control of the rolling process, the system is equipped with a high-precision pressure sensing system and a motion monitoring device. The pressure sensing system uses a distributed layout, with strain pressure sensors installed at multiple key positions on the rolling disc to monitor the pressure distribution in real time during the rolling process. At the same time, a high-resolution photoelectric encoder accurately measures the rotation speed and angular position of the rolling shaft, with a sampling frequency of milliseconds, which can capture the most subtle mechanical motion changes during the rolling process. These real-time monitoring data are transmitted to the central control system through an industrial bus, providing data support for process control.

[0037] The system adopts advanced closed-loop control strategy to adjust the operation state of the rolling and twisting equipment. The control core is an intelligent controller based on fuzzy PID algorithm, which can compare and analyze the real-time monitored pressure and speed data with the preset process curve, and calculate the optimal adjustment amount. The controller output signal drives the high-performance servo motor, which converts the adjustment command into the actual action of the rolling and twisting disc through the precision reduction mechanism. The response time of the entire control loop is controlled within 100 milliseconds, ensuring that the rolling and twisting parameters can be quickly and stably set near the set value.

[0038] In terms of mechanical force control, the system pays special attention to the differences in characteristics of different grades of tea. For high-grade tea with higher tenderness, the system will use relatively low pressure and softer speed curve to avoid excessive damage to tea tissue; for lower-grade tea, the mechanical action strength will be appropriately increased to ensure the desired cell crushing effect. The system also has real-time adjustment function, which can dynamically optimize the matching relationship between pressure and speed according to the changes in tea state during rolling and twisting.

[0039] Through this intelligent rolling and twisting control, the system can achieve uniform and moderate crushing of tea tissue cells. It not only ensures the complete rupture of cell walls and makes tea juice seep out moderately, but also avoids the problem of tea fragmentation caused by excessive rolling and twisting. The final twisted and rolled leaves have complete shape and ideal cell crushing degree, creating good conditions for the sufficient contact between enzymes and substrates in the subsequent fermentation process, which is crucial for forming the unique color, aroma and taste quality of Qihong imperial tea. The entire rolling and twisting process embodies the perfect combination of modern intelligent control technology and traditional tea making process, realizing the transformation from experience-based operation to digital and standardized production.

[0040] S4: Integrate electronic nose array and multispectral imaging technology to track the dynamic quality of fermented leaves, and output aroma conversion qualified fermented leaves: In the fermentation process, we deployed an electronic nose array system composed of 12 metal oxide semiconductor gas sensors. The array is specially designed, with each sensor having specific response to different types of volatile organic compounds. The sensor array continuously monitors the changes in gas composition in the fermentation environment at a sampling frequency of 6 times per minute, and converts the sensor response signal into specific aroma concentration data through pattern recognition algorithm. The system uses a constant temperature and constant current gas sampling device to ensure the stability and repeatability of the detection results. All sensor data are transmitted in real time to the central processor through CAN bus, constructing a complete aroma substance change trend map.

[0041] Working in parallel with the electronic nose system is a high-precision multispectral imaging system. Equipped with 8 specific wavelength LED light sources and a scientific grade CCD camera, the system can collect surface images of the fermented leaves at a frequency of every 5 minutes. Through advanced image processing algorithms, the system can quantitatively analyze key indicators such as tea color parameters, texture features, and surface moisture distribution. The specially designed anti-fog optical window and uniform lighting system ensure that clear image data can still be obtained in a high-temperature and high-humidity fermentation environment.

[0042] The central processing unit spatiotemporally aligns the aroma data collected by the electronic nose with the multispectral image features and extracts features to construct a multidimensional feature matrix. A deep neural network is used to establish a nonlinear mapping relationship between the aroma content and the image features. This model is trained using over 1000 batches of historical data and can accurately predict the content changes of key aroma components. The model automatically updates its parameters every 30 minutes to adapt to the differences in characteristics of different batches of raw materials.

[0043] Based on the model prediction results, the environmental control system uses a fuzzy control algorithm to dynamically adjust the fermentation chamber parameters. The temperature control system uses PID regulation to control temperature fluctuations within ±0.5°C through a variable frequency compressor and water cooling system; the humidity system maintains a relative humidity of 85±3% in the ideal range through the coordinated work of ultrasonic atomization and dehumidifiers; and the gas environment control maintains the appropriate oxygen concentration through a CO2 / N2 mixed gas injection system and a variable frequency exhaust device. All actuators are redundantly designed to ensure the reliability and accuracy of the control.

[0044] The system establishes a comprehensive evaluation index system for fermentation degree, including aroma index, color index, and physicochemical index. When the comprehensive evaluation index reaches the preset threshold, the system automatically determines that the fermentation is complete and starts the next process. At the same time, all process data are stored in the process knowledge base, and the control parameters are continuously optimized through machine learning algorithms. After each fermentation is completed, the system automatically generates a process report, recording the change curve of key parameters and quality indicators, providing complete data support for product quality traceability.

[0045] This intelligent fermentation monitoring system realizes the digitalization and intelligent transformation of the traditional fermentation process, transforming the "tea-making by looking at tea" process that relies on the experience of master tea makers into a precise control process based on multi-modal sensing data and intelligent algorithms. Through the correlation analysis of aroma substances and visual features, the system can more comprehensively grasp the change rules of the fermentation state, making more accurate process decisions to ensure that each batch of Qihong Huang tea can achieve the best flavor quality.

[0046] S5: Select far-infrared-heat air coupled drying process to complete gradient dehydration treatment of fermented leaves to obtain stable moisture content dried tea: The system innovatively combines far-infrared radiation drying and hot air convection drying technology, and builds a multi-section continuous drying equipment. The drying host adopts modular design, with a total length of 12 meters, and is divided into three independent temperature control sections: high-temperature shaping section, medium-temperature dehydration section and low-temperature slow recovery section. Each section is equipped with an independent far-infrared radiation plate array and a hot air circulation system. The radiation plate uses a special ceramic heating element that can emit far-infrared rays with a wavelength of 2.5-25 μm, which matches the vibration frequency of water molecules in tea leaves. The hot air system uses a double-sided symmetric air supply design, and the air speed can be precisely adjusted within the range of 0.3-2.5 m / s. The conveyor belt uses a Teflon-coated metal mesh belt to ensure uniform heating and prevent tea leaves from sticking together.

[0047] Based on the research of tea moisture migration dynamics, the system sets a three-stage temperature gradient that meets the material drying characteristics: the front section uses a higher temperature of 80±2℃ to quickly break the surface water molecule binding force; the middle section uses a moderate temperature of 65±1℃ to promote the diffusion of internal moisture to the outside; the rear section uses a low temperature of 50±1℃ for slow recovery treatment to avoid tea scorching. The length proportion of each temperature zone is optimized, with the front, middle and rear sections accounting for 30%, 50% and 20% of the total drying length respectively. The system also automatically adjusts the temperature parameters of each section according to the tea grade, such as reducing the temperature of each section by 5℃ for special tea leaves, to ensure that high-quality raw materials receive more gentle drying conditions.

[0048] An online moisture detection device based on dielectric principle is installed at the key position of the drying machine. The device uses a multi-plate capacitor sensor design to monitor the dielectric constant change of tea leaves in real time with a sampling frequency of 10 Hz. Through the established dielectric constant-moisture content calibration model, the system can accurately obtain the current moisture content of tea leaves. The sensor adopts a self-cleaning design and is equipped with a compressed air blowing system to avoid the accumulation of tea hairs affecting the measurement accuracy. The monitoring data is transmitted to the control system in real time through industrial Ethernet, forming a complete moisture removal curve.

[0049] The central control system uses a model predictive control algorithm to dynamically optimize drying parameters based on real-time moisture monitoring data. When the moisture removal rate of a section deviates from the expected value, the system will automatically adjust the far-infrared radiation intensity and hot air temperature of that section. A feedforward-feedback composite control strategy is specially designed: feedforward control is based on the drying dynamics model to predict parameter set values; feedback control adjusts the values based on actual moisture detection results. The response time of all actuators is controlled within 15 seconds to ensure timely adjustment of process parameters.

[0050] The system controls the moisture gradient removal process accurately to stabilize the final product moisture content in the ideal range of 5.5±0.3%, and the tea leaves are green in color and retain complete aroma. Compared with traditional drying methods, the composite drying technology can reduce the loss of aroma substances by more than 25%. At the same time, the system is equipped with a heat recovery device, which uses the waste heat in the exhaust air to preheat the fresh air, increasing the overall heat utilization rate by more than 30%. After drying, the system automatically generates a drying curve report, recording key parameters such as temperature, wind speed, and moisture content at each time period, providing data support for product quality traceability.

[0051] The dryer is equipped with multiple safety protection devices: an infrared temperature measuring instrument monitors the surface temperature of the tea leaves in real time; a CO2 fire extinguishing system can be automatically started in emergency situations; all electrical components meet IP65 protection level. The maintenance system has a self-diagnosis function, which can predict the service life of the heating element and the performance degradation of the fan, and provide early maintenance reminders. The operation interface uses a 10-inch touch screen, supporting process parameter presetting, real-time monitoring, and historical data query functions, achieving a friendly human-machine interaction.

[0052] The intelligent drying system realizes the transformation from "experience control" to "digital control" in the drying process of Qihong imperial tea through the coupling of multiple physical fields and precise process control, ensuring the stability of product quality and improving energy utilization efficiency, providing a model for the modernization of traditional tea-making technology. The system can automatically adjust the drying strategy according to different seasons and different grades of raw materials, showing good adaptability and intelligent features.

[0053] S6: Apply near-infrared spectroscopy combined with deep learning algorithm to realize multi-index rapid evaluation of finished tea, and establish quality feature database: The system uses a high-performance Fourier transform near-infrared spectrometer as the core detection equipment. To meet the needs of tea detection, a rotating sample cup and an automatic compaction device are specially designed to ensure consistent filling density of tea samples during each detection. The optical system is equipped with a temperature control module and a humidity control unit to ensure the stability of the spectral data. The detection process is fully automated, including automatic sampling, spectral acquisition, and automatic sample cleaning, with a single detection cycle controlled within 30 seconds. The system has built-in standard white board and polystyrene wavelength calibration sheet, which automatically performs baseline correction and wavelength calibration every day.

[0054] The collected raw spectral data undergoes a strict preprocessing process: first, Savitzky-Golay smoothing is performed to eliminate random noise; then, standard normal variable transformation is performed to eliminate scattering effects; finally, first derivative processing is performed to enhance the characteristic peaks. The system uses genetic algorithm combined with continuous projection algorithm for feature wavelength selection, selecting 35 feature wavelengths most relevant to tea quality from the full spectrum of 1557 wavelengths. These preprocessed feature spectra constitute the basic data set for subsequent modeling.

[0055] A dedicated convolutional neural network model was developed based on the TensorFlow framework. The network structure contains 5 convolutional layers, 3 max-pooling layers, and 2 fully connected layers. The model input is the preprocessed spectral data, and the output is 6 key quality indicators. The training adopts a transfer learning strategy, first pre-training on a public tea spectral dataset of 100,000 samples, and then fine-tuning on 5,000 real measured data from the factory. The model validation results show that the prediction determination coefficient R² of each quality indicator is more than 0.92, and the relative analysis error is greater than 3.0, meeting the requirements of industrial-level applications.

[0056] The system uses a distributed database architecture to store detection results, and each record contains: unique batch ID, detection timestamp, original spectral data, preprocessed feature data, model prediction results, manual review data, and other fields. The database establishes multi-dimensional indexes to support millisecond-level query response. A data version management function is specially designed to trace the previous correction records of model prediction results. All data are encrypted and signed through blockchain technology to ensure tamper-proof. The system automatically generates quality trend reports every week to monitor the stability of key indicators through control charts.

[0057] The quality database is connected in real time with the production management system through an API interface, mainly realizing three major functions: (1) Quality traceability: Through batch ID, the full-process data of the tea from raw materials to finished products can be quickly queried; (2) Process diagnosis: When the quality of a batch is abnormal, the system automatically analyzes the parameters of each process to identify potential problem points; (3) Model optimization: Newly accumulated data automatically enter the training set, triggering incremental learning of the model. The system also establishes an expert knowledge base based on quality data, discovering the implicit relationship between process parameters and quality indicators through association rule mining, providing data support for process improvement.

[0058] To ensure the reliability of the evaluation system, a three-level verification system is established: (1) Daily verification: Before daily detection, standard samples (water content 5.0%, 10.0%, 15.0%) are used to verify the system accuracy; (2) Periodic calibration: National standard material (GBW10016 tea ingredient analysis standard material) is used for full-indicator verification every week; (3) Annual review: A comprehensive performance evaluation of the system is conducted by a third-party testing agency. The maintenance system has a self-diagnosis function, which can monitor key parameters such as light source life (warning threshold 2000 hours) and detector performance, and provide early warning for possible equipment failures. The system software supports remote upgrading and maintenance to ensure continuous stable operation.

[0059] The intelligent quality evaluation system realizes the transformation from traditional sensory evaluation to digital detection, shortens the traditional detection process from several hours to 30 seconds, and obtains more comprehensive and objective quality data. Through the combination of deep learning model and spectral technology, the system can not only evaluate the conventional physicochemical indicators, but also predict the flavor characteristics and storage stability of tea, providing a new technical means for the quality control of Qihong imperial tea. The establishment of the quality database makes the product quality have complete traceability, and provides a data basis for the continuous optimization of the production process, realizing the transformation of the quality management mode from "experience-driven" to "data-driven".

[0060] S7: Implement tamper-proof storage of full-process processing data through blockchain distributed ledger technology, and generate traceable quality archives: The system uses a consortium chain technology architecture, which is composed of production nodes, detection nodes, and supervision nodes to form a blockchain network. The network is built based on the Hyperledger Fabric framework and uses a multi-channel design: the process parameter channel, the equipment state channel, and the quality data channel handle different types of data. Each node is equipped with a dedicated encryption chip to ensure data transmission security. The data storage uses a hybrid architecture that combines IPFS distributed file system with blockchain: large-capacity process data is stored in the IPFS network, and its hash value is stored on the chain for evidence; key parameters are directly written to the blockchain. The system sets up data hierarchical access permissions and realizes fine-grained permission control through digital certificates.

[0061] The data of each process is stored on the chain in strict accordance with standardized procedures: (1) Data collection: real-time process parameters are obtained from the device PLC through the OPC UA protocol, and the sampling frequency is set according to the process characteristics; (2) Data preprocessing: uses a sliding window algorithm to eliminate transient fluctuations and retain effective process trends; (3) Data signing: uses the digital certificate of the process equipment to sign the data; (4) Consensus on-chain: verifies between nodes through the PBFT consensus mechanism, and the data block generation time is controlled within 2 seconds. A data supplement mechanism is specially designed to cache data locally during network anomalies and automatically synchronize to the chain after recovery.

[0062] Each batch of products is assigned a unique digital identity ID using a composite encoding method: production date + production line number + raw material batch + random check code, totaling 20 characters. The ID is identified through QR code and RFID dual carriers and physically bound at the product packaging stage. The storage of the digital identity on the blockchain includes three levels: (1) Basic information layer: records ID generation time, binding process, etc. metadata; (2) Process data layer: stores the hash values of key parameters of each process; (3) Verification information layer: includes certification data such as quality inspection reports and factory inspection. Consumers can scan the product QR code through the mobile APP to query the full-chain traceability information in real time, with a query response time of <1 second.

[0063] The system develops a series of smart contract implementations for automatic management: (1) Data verification contract: automatically checks the format compliance and signature validity of new on-chain data; (2) Alarm triggering contract: automatically generates an alarm event and triggers related process adjustments when process parameters exceed standard ranges; (3) Quality evaluation contract: automatically rates batch products based on preset algorithms; (4) Traceability query contract: handles verification requests from the consumer end. The contract is written in Solidity language and runs in a dedicated contract virtual machine, with execution results automatically written to the blockchain. The contract code is formally verified to ensure logical correctness and security.

[0064] The system implements multi-level security protection: (1) Network layer: uses TLS1.3 to encrypt all node-to-node communication; (2) Storage layer: process data is encrypted and stored, with keys managed by a hardware security module; (3) Access layer: attribute-based access control mechanism for fine-grained control of data access permissions; (4) Audit layer: all data operation records are stored on-chain for evidence, forming an unalterable audit trail. For sensitive data, zero-knowledge proof technology is used to achieve data availability invisibility. The system regularly conducts security penetration testing and vulnerability scanning to ensure the effectiveness of the protection system.

[0065] The blockchain network supports horizontal expansion, with processing capacity improved by adding nodes. The operation and management system provides: (1) Network monitoring: real-time display of node status, network throughput, and other indicators; (2) Data statistics: automatic generation of data on-chain volume, query volume, and other operation reports; (3) Intelligent early warning: resource early warning such as disk capacity and memory usage; (4) Remote upgrade: supports batch safe updates of node software. The system design capacity can support data traceability for 1000 tons of tea per year, with a data retention period of at least 10 years and permanent archiving of key data.

[0066] The blockchain traceability system realizes the unalterable and full-process traceability of Qihong imperial tea production data, upgrading traditional agricultural product quality and safety management to a new digital height. Through the combination of distributed storage and encryption technology, both the authenticity and integrity of the data are guaranteed, and the security of commercial secrets is ensured. The digital identity system establishes a complete "digital archive" for each batch of products, allowing consumers to transparently understand the entire process from the tea garden to the teacup, significantly improving brand credibility. The massive real data accumulated by the system also provides valuable data assets for production process optimization and quality improvement.

[0067] S8: Utilize big data mining technology to perform continuous iterative optimization on historical process parameters, output high-quality finished tea with improved stability: The system builds a complete data collection network, real-time gathering process parameters, equipment status, environmental conditions and quality detection data of each process. The data collection frequency is differentiated according to the characteristics of the process, ranging from seconds to minutes. The data lake architecture is used to store raw data, with about 15GB of new data added daily. Through the ETL process, multi-source heterogeneous data is cleaned and standardized, including outlier removal, time series alignment and feature normalization, forming a high-quality data set for analysis.

[0068] The system deploys multiple machine learning algorithms to mine process-quality association rules: (1) K-means++ improved algorithm is used for production batch clustering analysis to identify quality performance patterns under similar process conditions; (2) FP-Growth algorithm is used to mine frequent item sets and discover association rules between process parameters; (3) Random Forest algorithm is used to evaluate the importance of each parameter in quality indicators; (4) t-SNE dimensionality reduction is used to visualize the quality distribution in high-dimensional process space. These analysis results automatically generate knowledge reports every week and are pushed to process engineers for reference.

[0069] Based on deep neural networks, a multi-task learning model is constructed, which receives process parameters containing 200+ features in the input layer and simultaneously predicts 10+ key quality indicators in the output layer. The model uses a residual network structure to solve the gradient vanishing problem in deep networks and introduces an attention mechanism to strengthen the role of important process features. The training data comes from 5000+ batches of production records accumulated in the past three years, and five-fold cross-validation is used to ensure the model's generalization ability. After the model is deployed, an online learning mechanism is implemented, and the model is incrementally trained with new data every month to maintain the continuous evolution of prediction performance. The current model's prediction R² for main quality indicators reaches more than 0.85.

[0070] The system has built-in multi-objective optimization algorithms to find Pareto optimal solutions among quality optimization, energy consumption minimization, and efficiency maximization. The optimization process considers actual production constraints to ensure the implementability of recommended parameters. Before each process adjustment, the system will perform digital twin simulation to predict the quality trend after adjustment. The final optimization suggestions include specific parameter adjustment schemes, expected quality improvement amplitude, implementation considerations, etc., which are provided to process engineers through a human-machine interaction interface for decision-making reference.

[0071] After the implementation of the optimization scheme, the system automatically tracks the actual production effect, calculates the optimization effect index by comparing the difference between the predicted value and the measured value, establishes an A / B test framework for small-batch test verification of major process changes, and promotes the changes comprehensively after confirming the stable effect. All optimization cases are automatically stored in the knowledge base to form a complete record including problem description, analysis process, solution and actual effect. The system generates a process evolution report every quarter to analyze the overall effectiveness of the optimization measures, identify weak links that still need to be improved, and guide the optimization direction in the next stage.

[0072] The optimization system is deployed using a microservice architecture, and the core components include data collection services, feature engineering services, model training services, optimization calculation services, and knowledge management services. The system realizes elastic scaling through Kubernetes, and can handle 20+ optimization tasks in parallel at peak times. A complete operation and monitoring system is established: (1) data quality monitoring: checking data integrity, timeliness and accuracy; (2) model performance monitoring: tracking the decay of prediction indicators; (3) optimization effect monitoring: evaluating the adoption rate and success rate of recommended solutions. The system has an automatic rollback mechanism that can quickly recover to a stable version when an anomaly is detected, ensuring production stability.

[0073] The intelligent process optimization system realizes the fundamental transformation of Qihong Huang tea processing from "experience-driven" to "data-driven", enabling the continuous evolution of traditional processes in the digital space. By mining the process rules hidden in massive production data, the system can discover subtle relationships that are difficult for human experience to detect and propose breakthrough process improvement solutions. The continuous closed-loop optimization mechanism ensures steady improvement in product quality while reducing production costs and energy consumption. The accumulated process knowledge forms the core digital assets of the enterprise, providing an intelligent support platform for the inheritance and innovation of Qihong Huang tea traditional processing techniques.

[0074] It should be noted that, in this text, relationship terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device.

[0075] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A high-precision grading and targeted processing system for Qihong imperial tea, characterized in that, The system comprises: S1: using hyperspectral imaging fusion machine vision technology to implement morphological and biochemical component analysis on tea green, forming standardized graded tea green raw materials; S2: relying on a temperature and humidity sensor network combined with an AI prediction model to perform precise regulation on the withering environment, producing withering leaves with optimized enzyme activity; S3: using a pressure-rotation speed coordinated control system to carry out differentiated rolling and twisting operations on graded tea green, preparing twisted and rolled leaves with uniform cell breakage; S4: integrating an electronic nose array and multispectral imaging technology to implement dynamic quality tracking on fermented leaves, outputting fermented leaves with standard aroma conversion; S5: selecting far-infrared-heat air coupled drying process to complete gradient dehydration treatment on fermented leaves, obtaining dried tea with stable moisture content; S6: applying near-infrared spectroscopy combined with deep learning algorithm to realize multi-index rapid evaluation of finished tea, establishing a quality characteristic database; S7: using blockchain distributed ledger technology to implement tamper-proof storage of the whole process processing data, generating a traceable quality file; S8: using big data mining technology to perform continuous iterative optimization on historical process parameters, outputting high-quality finished tea with improved stability.

2. A high-precision grading and directional processing system for Qihong imperial tea according to claim 1, characterized in that, The use of hyperspectral imaging fusion machine vision technology to implement morphological and biochemical component analysis on tea green to form standardized graded tea green raw materials comprises: Collecting the spectral reflectance characteristics of tea green in the visible to near-infrared band through a hyperspectral imaging device; Extracting morphological parameters of the bud leaves using a machine vision system, including bud head integrity, leaf expansion, and hair distribution density; Building a tea green quality evaluation model for multi-dimensional fusion analysis of spectral characteristics and morphological parameters; Automatically classifying tea green according to pre-set grading standards and outputting different grades of tea green raw materials.

3. A high-precision grading and directional processing system for Qihong imperial tea according to claim 1, characterized in that, The use of a temperature and humidity sensor network combined with an AI prediction model to perform precise regulation on the withering environment to produce withering leaves with optimized enzyme activity comprises: Arranging a temperature and humidity sensor array in the withering workshop to collect environmental parameters in real time; Establishing a deep learning-based withering process prediction model, inputting environmental parameters and initial tea green quality data; Automatically adjusting the operating parameters of the ventilation system and humidity control device according to the model output; Monitoring the water loss rate and polyphenol oxidase activity changes of tea green, and outputting tea green that has reached the optimal withering degree.

4. A high-precision grading and directional processing system for Qihong imperial tea according to claim 1, characterized in that, The use of a pressure-rotation speed coordinated control system to carry out differentiated rolling and twisting operations on graded tea green to prepare twisted and rolled leaves with uniform cell breakage comprises: Calling the preset rolling and twisting process curve according to the grade of tea green; Monitoring the rolling and twisting pressure and rotation speed in real time through pressure sensors and encoders; Dynamically adjusting the output parameters of the servo motor using a PID control algorithm; Detecting the cell breakage rate of tea leaves online to ensure that different grades of tea green achieve the best rolling and twisting effect.

5. A high-precision grading and targeted processing system for Qihong imperial tea according to claim 1, characterized in that, The integration of an electronic nose array and multispectral imaging technology to implement dynamic quality tracking on fermented leaves to output fermented leaves with standard aroma conversion comprises: Arranging an electronic nose sensor array to monitor volatile aroma components in real time during fermentation; Using a multispectral imaging system to collect changes in surface color and texture characteristics of fermented leaves; Establishing a correlation model between aroma content and image features; Automatically adjusting the CO2 concentration and temperature and humidity of the fermentation chamber according to the model prediction results to control the fermentation process.

6. A high-precision grading and targeted processing system for Qihong imperial tea according to claim 1, characterized in that, The selected far-infrared-hot air coupled drying process completes gradient dehydration treatment on fermented leaves to obtain dried raw tea with stable moisture content, including: Setting a three-stage temperature gradient drying curve; Using a drying method combining far-infrared radiation and hot air circulation; Monitoring changes in tea moisture content through an online moisture detection device; Automatically adjusting process parameters in each drying section according to changes in moisture content.

7. A high-precision grading and targeted processing system for Qihong imperial tea according to claim 1, characterized in that, The application of near-infrared spectroscopy combined with deep learning algorithms for rapid multi-index evaluation of finished tea establishes a quality characteristic database, including: Collecting near-infrared spectroscopy data of finished tea; Building a quality prediction model based on convolutional neural networks; Simultaneously detecting the moisture content, tea polyphenol content, and aroma components of tea; Storing the detection results and spectral characteristics to form a quality characteristic database.

8. A high-precision grading and targeted processing system for Qihong imperial tea according to claim 1, characterized in that, The use of blockchain distributed ledger technology for tamper-proof storage of full-process processing data generates a traceable quality file, including: Designing a data storage architecture based on blockchain; Chaining key process parameters and quality detection data from each process; Generating a unique digital identifier for each batch of products; Establishing a complete product processing process traceability chain.

9. A high-precision grading and targeted processing system for Qihong imperial tea according to claim 1, characterized in that, The use of big data mining technology for continuous iterative optimization of historical process parameters outputs high-quality finished tea with improved stability, including: Collecting process parameters and quality data from historical production processes; Applying clustering analysis and association rule mining algorithms to process data; Establishing a mapping relationship model between process parameters and product quality; Dynamically adjusting control parameters for each process based on model analysis results.

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