3948 results about "Tea leaf" patented technology

Provided in the present invention are a creation method and apparatus for a tea leaf disease recognition model, and a device and a storage medium. The creation method comprises: acquiring a plurality of images of tea leaves, and disease and insect pest information corresponding to each image; generating a tea leaf disease recognition model, wherein the tea leaf disease recognition model includes a MobileNetV3 model, an ultra-lightweight attention model and a fully connected layer; and on the basis of the plurality of images of tea leaves and the disease and insect pest information corresponding to each image, training the tea leaf disease recognition model. The tea leaf disease recognition model can automatically recognize tea leaf diseases and insect pests.

The invention relates to the technical field of machine vision and optical detection, in particular to a tea color automatic evaluation method and system based on machine vision. The method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on the tea sample to obtain original data with spectrum and structural characteristics; separating mirror surface and diffuse reflection components on the surfaces of the tea leaves, and performing spectrum consistency correction by combining a radiation baseline; performing multiband spectrum reconstruction and normalization processing on the basis of the corrected diffuse reflection image, mapping to a CIE Lab color space, extracting pixel-level brightness, chromaticity and color distribution statistical indexes, and generating a color feature vector in combination with spatial weighting matrix optimization; a final score is calculated by constructing a quantitative evaluation and grade judgment model and integrating perception color separation, drying calibration color separation and a penalty term, and a grade result is output. According to the method, objective, accurate and automatic evaluation of the color of the tea leaves is realized.

The invention provides bacillus megaterium and application thereof in preparation of a nano-selenium organic fertilizer and improvement of the selenium content of tea leaves, and belongs to the technical field of microorganisms, the bacillus megaterium NT2 is preserved in the China Center for Type Culture Collection with the preservation number of CCTCC NO: M 20251806, and the bacillus megaterium NT2 is preserved in the China Center for Type Culture Collection with the preservation number of CCTCC NO: M 20251806. The bacillus megatherium NT2 has relatively strong resistance to reduction on selenium element, can survive in high-concentration inorganic selenium, can reduce the inorganic selenium into nano-selenium, and has relatively weak capability of enriching arsenic, cadmium and lead; the selenium source of the nano-selenium organic fertilizer can be prepared by mixed fermentation with high-selenium rock powder which is calcined and activated at high temperature, so that the content of heavy metals does not exceed the national standard while the selenium content of the tea leaves is increased. The bacillus megaterium provided by the invention not only can reduce the toxicity of inorganic selenium in high-selenium rock and convert the inorganic selenium into nano-selenium, but also can stably, safely and standardly produce selenium-rich tea leaves, the heavy metal content of the tea leaves does not exceed the standard, and the bacillus megaterium has a wide market prospect.

The invention discloses an intelligent tea withering control system based on multi-modal feature fusion and time sequence prediction. The system is composed of a multi-modal feature extraction module, a time sequence modeling module, a transfer learning module and an intelligent decision control module, a Transform attention mechanism is adopted to construct a cross-modal fusion framework, RGB images, hyperspectrum and time sequence information are fused, and accurate recognition and trend prediction of the withering state are achieved. According to the system, an adaptive attention fusion network is designed, optimal fusion of images, spectrums and grade information is realized through dynamic distribution of modal weights, and the recognition accuracy and stability are remarkably improved. The classification accuracy of 180 verification samples reaches 93.33%, and is improved by 15.93%-34.83% compared with that of a single-mode method. And cross-environment self-adaption is realized through fusion transfer learning, and the performance is improved by 6.7%-14.3%. The intelligent decision engine optimizes temperature and humidity parameters based on a multivariable coupling control theory, the control precision reaches + / -0.5 DEG C and + / -2.0%, and the response time is 2.5 seconds.

The invention discloses a tea review expert decision support method and system based on a knowledge graph, and relates to the technical field of tea quality evaluation, and the method comprises the steps: collecting tea sample data and expert review data, calculating an expert score consistency index, and constructing an expert decision knowledge graph; analyzing an expert score distribution rule to obtain a rule mapping weight, mapping the standardized features to a knowledge graph and generating a feature transmission path; correcting a transmission result according to the rule adaptability and the conflict degree to obtain a tea sample quality score and a feature analysis chart; and finally, converting the feature analysis graph into a process control rule sequence, determining an execution priority based on the quality score, and generating a process parameter adjustment scheme. According to the invention, the intelligent optimization of the tea production process is realized, and the stability and consistency of the tea quality are improved.

The invention discloses a visual detection method and system for tea impurities, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-detected tea image and a training image data set, and carrying out the preprocessing of image data in the training image data set; inputting a to-be-detected tea image into a trained tea impurity detection model to obtain a plurality of tea impurity detection results containing confidence coefficients of a to-be-detected target and a detection frame, the trained tea impurity detection model comprising a backbone network, a neck network and a plurality of detection heads, the backbone network comprises a ScharrStem module, a first SGCB improved module and a second SGCB improved module, and the neck network comprises a first SGCB improved module, a second SGCB improved module and a channel sensing spatial modulation module; and determining a target tea leaf impurity detection result according to the confidence coefficient and the detection frame in the plurality of tea leaf impurity detection results. The detection accuracy of the tea impurities can be improved.

The invention discloses a preparation method of a curcumin-loaded Pickering emulsion steady-state carrier and tea protein-chitosan composite nanoparticles used by the curcumin-loaded Pickering emulsion steady-state carrier, and belongs to the technical field of high-valued processing of food functional factors. The preparation method comprises the following steps: firstly, extracting tea protein from a tea leaf processing byproduct tea residue by adopting an alkali extraction and acid precipitation method, and secondly, constructing the tea protein-chitosan composite nanoparticles by adopting a pH induction method. And mixing the oil phase containing curcumin with the water phase, and emulsifying by using a high-speed shearing machine to prepare the Pickering emulsion. The method is simple to operate, the prepared Pickering emulsion stabilized by the tea protein-chitosan composite nanoparticles can improve the bioavailability of curcumin from 10.8% to 32.6%, meanwhile, high-value utilization of the tea protein is achieved, and the method has important significance in promoting development and application of functional food.

The application provides a tea garden identification method and device based on satellite images, which comprises the following steps: determining a evergreen vegetation area of a to-be-identified area based on Sentinel-2 image data and Landsat image data of the to-be-identified area; and identifying a tea garden area from the evergreen vegetation area through a decision tree model; wherein the decision tree model is constructed based on the following features and the classification threshold values corresponding to the features: a tea leaf phenology feature index, a terrain feature, and a spectral index determined by a separability index, wherein the SI is used to reflect the spectral reflectance separability of the tea garden and other evergreen vegetation; wherein the tea leaf phenology feature index is determined by an enhanced vegetation index in month N and a land surface water index in month M; the SI between the enhanced vegetation index in month N and the land surface water index in month M is the largest compared with the SI between the EVI in any other month and the LSWI in any other month, and N and M are integers greater than 0 and less than 13.

The invention relates to the technical field of temperature control, in particular to an automatic temperature regulation and control system and method for tea processing, and the system comprises a data collection module which is used for obtaining real-time environment parameters in a tea processing cabin, and the real-time environment parameters comprise an airflow speed change, a humidity change and a first temperature change; the difficult-to-control evaluation module is used for performing partition analysis on the tea processing cabin according to the real-time environment parameters to obtain a difficult-to-control degree evaluation value of at least one processing partition; and the first control module is used for performing temperature compensation regulation and control on the at least one processing partition according to the difficult control degree evaluation value to obtain at least one target temperature parameter, and the target temperature parameters are in one-to-one correspondence with the processing partitions. According to the method, the system can perform temperature compensation regulation and control according to different temperature control difficulty degrees through the difficulty degree evaluation value of each processing subarea, so that the temperature of each subarea is always maintained within the optimal processing range, and the processing efficiency is improved.

The invention provides a charcoal-loaded iron-based catalyst suitable for heterogeneous catalysis as well as a preparation method and application of the charcoal-loaded iron-based catalyst. The preparation method comprises the following steps: firstly, activating and modifying tea leaves by adopting a potassium hydroxide solution, and then carrying out heat-preservation pyrolysis in a carbon dioxide atmosphere to obtain tea-leaf-based biochar; uniformly mixing an iron source, a manganese source, a cerium source and a citric acid solution; adding tea-based biochar, uniformly mixing, and drying to obtain a precursor; and heating the precursor to 400-650 DEG C, and carrying out heat preservation and pyrolysis to obtain the biochar-loaded iron-based catalyst. The biochar-loaded iron-based catalyst has excellent degradation and removal effects and cycle stability on xylene.

The utility model relates to a crown cutter mechanism of an electric tea harvesting vehicle. The tree crown cutting mechanism is characterized in that the tree crown cutting mechanism is installed on a transverse support of the harvesting vehicle, the transverse support comprises single-lug bulb pieces, transverse support square steel and a vehicle body width adjusting mechanism, and the transverse support square steel is connected with a supporting plate arranged on the side face of a lower flange of a steering wheel support steering mechanism through the single-lug bulb pieces on the two sides; the vehicle body width adjusting mechanism is arranged on the transverse support square steel and divides the transverse support square steel into two sections of telescopic structures. When a first motor rotates, a first cylindrical cam mechanism is driven by a coupler to rotate, and a first grooved cam on an upper cutter driving rod and a first grooved cam on a lower cutter driving rod horizontally reciprocate along grooves of the cylindrical cams. The upper cutting knife driving rod and the lower cutting knife driving rod drive the upper cutting knife and the lower cutting knife to do opposite reciprocating motion to cut tea leaves; the structure is ingenious, the stability is high, and the harvesting operation efficiency can be greatly improved.

The invention discloses a tea making monitoring method and system based on a knowledge graph, and the method comprises the steps: integrating variety characteristics, processing technology parameters, equipment operation states and environmental factors into a structured data set in a unified format through a data fusion technology, and constructing a processing technology knowledge graph through a graph representation learning model; the method comprises the following steps: representing correlation between process links and parameters by nodes and edges, calculating a parameter deviation vector in real time, triggering cross-link influence propagation simulation, identifying a potential influence path, extracting an equipment related node sequence, analyzing an abnormal fluctuation mode, generating an abnormal index set, tracing the contribution degree of a preorder link parameter to abnormity, and generating a parameter adjustment vector; and finally, the optimized sequence output of the machining process parameters is realized. According to the invention, through fusion of the knowledge graph and the abnormity traceability, the problem of dynamic optimization of the processing parameters is solved, and the precise control capability of tea processing and the efficiency of equipment abnormity processing are significantly improved.

The invention discloses a method for detecting tea saponin in a tea extract, and relates to the technical field of chemical analysis and computer science, and the method comprises the following steps: eliminating high-frequency noise of tea extract data by using a Gaussian filtering algorithm, eliminating baseline drift by using a baseline correction algorithm, and generating a tea extract sample data stream; the method comprises the following steps: performing dynamic gradient separation through a chromatographic signal simulation algorithm on the basis of a tea extract sample data stream to generate a virtual chromatographic peak sequence, calculating a peak area through a sliding window integration algorithm on the basis of the virtual chromatographic peak sequence, and generating chromatographic signal data by using a wavelet transform threshold denoising algorithm, the chromatographic signal data are input into the deep convolutional neural model, the feature pyramid network is obtained through the multi-scale feature fusion algorithm, the feature information of the chromatographic peaks is extracted from different scales by constructing the deep convolutional neural model and combining the multi-scale feature fusion algorithm, and therefore the recognition precision of the feature peaks is improved.

The invention discloses a famous high-quality tea target detection method based on an improved RT-DETR model, and relates to the technical field of visual recognition. Comprising the following steps: obtaining tea photos under different conditions, and preprocessing the tea photos to obtain a training set; based on a feature enhancement module, an improved multi-scale feature pyramid network module, a feature fusion module, a partial convolution and CGLU module, an OmniKernel module and a frequency domain-space domain feature enhancement module, improving the RT-DETR model and establishing an improved RT-DETR model, and training the improved RT-DETR model by using a training set; the trained improved RT-DETR model can be used for identifying the tea leaves. According to the invention, through multi-module coordinated optimization, the model keeps light weight, and MAP50 reaches 0.87, which is improved by 3.3% compared with a reference model. Especially under a complex background, the omission ratio is reduced by about 40% in tea target identification.

The invention relates to the technical field of tea fermentation process data management, in particular to a tea fermentation process data management method and system, and the method comprises the following steps: collecting sensor data of a fermentation chamber environment and operation state data of fermentation chamber environment control equipment; calculating fermentation room environment control indexes; identifying the change trend of the fermentation chamber environment control indexes; dynamically adjusting the data relevance judgment range of the sensor according to the change trend of the environment control index of the fermentation chamber; calculating the correlation degree between the sensor data of the fermentation room environment in real time; comparing the correlation degree with the correlation judgment range of the adjusted sensor data; according to the comparison result and the change trend of the fermentation chamber environment control indexes, distinguishing the reasons of correlation degree deviation; and according to the distinguishing result, sending out differentiated alarms or recording. The accuracy and reliability of data management are improved.

The invention provides a dendrobium devonianum epiphytic tea tree cultivation method, which belongs to an epiphytic plant planting technology, and is characterized in that a dendrobium devonianum root system can be closely fused with a tea tree trunk through treatment of'minimal trauma + callus inducer ', and a structural foundation is laid for material exchange. The jasmonic acid methyl ester stress with a specific concentration is applied to the dendrobium nobile in the key phenological period, the dendrobium nobile is accurately induced to synthesize and secrete beneficial secondary metabolites, the theanine content in the tea can be remarkably increased, the proportion of ester type catechin can be reduced, and therefore the fresh degree of the tea is remarkably increased, and the astringent taste of the tea is remarkably reduced. The stress resistance of the dendrobium devonianum and the tea trees treated by the method is also enhanced, and the disease occurrence rate is reduced. While high-quality dendrobium nobile is harvested, the quality and economic benefits of the produced tea leaves are synchronously improved.

The invention discloses an automatic packaging machine for tea finished products, which relates to the technical field of automatic packaging equipment, and comprises a support, a feeding box, a discharging box and a packaging box, the screening net plate is arranged in the feeding box; the driving assembly is arranged on one side of the upper end of the support, when the driving assembly drives the feeding box to reciprocate up and down, the screening net plate in the feeding box shakes and screens finished tea leaves, and meanwhile, the air inlet assembly on the side portion of the feeding box continuously feeds air and provides air pressure thrust for the overturning assembly connected with the screening net plate; after the shaken and sieved tea leaves are shaken out, an exhaust assembly connected with an overturning assembly is triggered, and after exhaust is completed, the screening net plate is driven by the overturning assembly to reset; according to the equipment, integrated continuous operation of tea screening and packaging is achieved, the cleanliness and the high-quality rate of tea finished products are remarkably improved, the equipment structure is simplified, and the operation complexity and the maintenance cost are reduced.

The invention discloses a tea processing big data analysis and quality tracing system and method, and relates to the technical field of agricultural informatization management. The tea processing big data analysis and quality tracing system is constructed, full-process data collection, real-time monitoring and accurate tracing from fresh leaves to finished products are achieved, key parameter data in the processing process are collected in real time through a sensor, key factors influencing the tea quality are extracted through an advanced data analysis algorithm, and the quality of the tea is accurately traced. According to the method, the quality of tea leaves can be accurately identified, quality abnormal points are accurately positioned, the problems of incomplete data acquisition, lagged analysis and inaccurate tracing in a traditional method are solved, meanwhile, a unique tracing code is generated for each processing batch, a full-process data mapping table is constructed, dynamic updating and real-time query of quality information are realized, the quality management level of tea leaf processing is remarkably improved, and the method is suitable for large-scale popularization and application. The requirements of consumers on transparency and high quality are met.

The invention provides an extraction process and application of tea leaves, and particularly relates to the technical field of cosmetics. The extraction process of the tea leaves comprises the following steps: extracting raw dark green tea leaves with a color fixative, decolorizing with a clarifying agent, activated carbon and a clarifying agent in sequence, filtering, and finely filtering to obtain the tea leaf extract. The extraction process provided by the invention solves the problems of easy oxidation of active ingredients, many impurities, poor storage stability and the like in the traditional tea extraction process, can efficiently retain the active ingredients such as tea polyphenol and the like, and the extract is high in purity and good in stability and can be used in anti-aging and anti-inflammatory cosmetics. The tea leaf extract and the artemia extract have a remarkable synergistic effect in the anti-oxidation and anti-inflammatory aspects, can be used in anti-aging and anti-inflammatory cosmetics, and have a good application prospect.

The invention relates to the technical field of tea processing, in particular to an intelligent processing device for tea. The device comprises a frame body, a fermentation box and a spraying mechanism installed on the frame body, a bottom plate is arranged in the fermentation box, a fermentation cavity is formed by the bottom plate and the upper end of an inner cavity of the fermentation box, two vertical partition plates are arranged in the fermentation cavity, and the fermentation cavity is divided into a main fermentation cavity and two sub-cavities, and the bottoms of the main fermentation cavity and the sub-cavities are communicated. According to the tea leaf humidifying device, when the humidity sensor detects that the humidity of the tea leaves at the bottom of the main fermentation cavity is insufficient, the push plate is synchronously started again to push the tea leaves at the bottom to the branch cavities on the two sides, and targeted humidification is conducted in cooperation with the atomization spraying heads corresponding to the branch cavities in position; the tea leaves relative to the upper layer in the main fermentation cavity naturally fall into the vacant area of the lower layer, and along with the circulation of humidity detection and the action of the push plate, the tea leaves can complete the position exchange of the upper layer and the lower layer in the dynamic circulation of the main fermentation cavity and the sub-cavities.

The invention relates to the technical field of image processing, and discloses a tea maturity detection method and system, and the method comprises the steps: obtaining an original image of tea in a natural illumination imaging environment; generating a global illumination difference chart representing an ambient illumination state, and performing illumination compensation on the to-be-detected area of the tea in the original image to obtain a compensated image of the tea; performing gray scale range adjustment on the compensated image of the tea leaves to obtain an enhanced spectral image of the tea leaves; integrating the color and spectral characteristics of the specific spectral reflection characteristics of the tea leaves with different maturity degrees in the enhanced spectral image and the three-dimensional morphological characteristics reflecting the spreading degree and thickness of the tea leaves into the fusion image characteristics of the tea leaves; mapping the color spectrum emission characteristics and texture characteristics of the tea leaves in the enhanced spectrum image to a maturity discrimination knowledge base of the tea leaves to obtain a preliminary maturity result; performing confidence coefficient weighting on the preliminary maturity result to obtain a target maturity; according to the invention, the tea maturity detection efficiency can be improved.

The invention discloses a light-weight tea leaf tender shoot detection method and device for tea leaf picking, belongs to the technical field of image recognition and target detection, and aims to solve the problems of sensing and shielding of tender shoot targets for tea leaf picking. Comprising the steps of obtaining a to-be-detected image in a tea picking scene in real time; processing the to-be-detected image based on a pre-trained lightweight tender shoot detection model to obtain position information and a category identification result of the tender shoot small target; the training process of the lightweight tender shoot detection model comprises the following steps: acquiring an image data set containing a tea tender shoot target; performing data division on the image data set containing the tea tender shoot target to obtain a training set; and training a YOLO RGS detection model taking YOLOv8n as a basic network by using the training set to obtain a lightweight tea tender shoot detection model. The method can improve the perception level of tender shoot targets in a tea leaf picking scene, and provides efficient and stable visual support for intelligent equipment such as agricultural robots.

The invention relates to the technical field of image processing, and discloses a tea phenological period prediction method based on target detection, and the method comprises the steps: obtaining tea high-definition images in different phenological periods, marking the phenological periods of all bud leaves in the images, and generating tea phenological period image data; cutting the tea phenological period image data by adopting a constraint-based self-adaptive cutting method; performing data enhancement on the cut tea phenological period image data, and dividing the data into a training set and a test set according to a set proportion; inputting the training set into a YOLOv8 target detection model for training, and testing the trained YOLOv8 target detection model by using the test set to obtain a trained YOLOv8 target detection model; collecting a tea image of a research area, and inputting the tea image into the trained YOLOv8 target detection model to carry out bud leaf phenological period target detection so as to obtain a tea image detection result; according to a tea leaf image detection result, judging a waiting period stage of the whole variety or resource; according to the invention, accurate detection and determination of the phenological period of the tea leaves are realized.

The invention discloses a fresh tea leaf sorting method based on a frequency domain tree topology network, computer equipment and a storage medium. The method covers the complete process of image acquisition, preprocessing, frequency domain decomposition, deep modeling, map construction and classification. Firstly, image quality is improved through color normalization and edge enhancement, and frequency domain tree decomposition is carried out through wavelet transform and discrete cosine transform to extract multi-scale features. And then, fusing long and short range dependent modeling and a residual convolution module to realize multi-level feature representation, and constructing a tree topology attention path and a structure map for simulating a bud-leaf-vein relationship to enhance semantic understanding. A tree structure is adopted to perceive a classification function, and fine-grained classification of single bud, one bud and one leaf, one bud and two leaves and one bud and multiple leaves is achieved. In training, the robustness of the model is improved by combining cross entropy loss, data enhancement and a regularization strategy. The method is high in classification accuracy and good in stability on a plurality of tea image data sets, and the practical level of automatic fresh tea leaf sorting is effectively improved.

The invention discloses application of tea extracellular vesicles in preparation of a product for preventing and / or treating UVB-induced skin photoaging, and belongs to the technical field of application of the tea extracellular vesicles. The tea extracellular vesicles are derived from tea leaves and / or stem leaves and are prepared by a method of combining enzymolysis, multi-stage filtration and ultracentrifugation, and the particle size of the tea extracellular vesicles is mainly distributed in a range of 60-100nm. Experiments show that the tea extracellular vesicles can effectively reduce active oxygen accumulation and lipid peroxidation caused by UVB radiation, repair mitochondrial membrane potential and inhibit expression of matrix metalloproteinase and inflammatory factors, so that photoaging symptoms such as skin wrinkles, erythema and barrier function damage are relieved. The tea extracellular vesicle provided by the invention has the advantages of natural raw materials, good biocompatibility, small side effect and the like, can be used for preparing a plurality of external dosage forms such as gel, cream, spray and the like, and provides a safe and effective new natural product choice for prevention and treatment of skin photoaging.

The invention provides application of tea extracellular vesicles in preparation of drugs for relieving or treating allergic respiratory diseases, and belongs to the technical field of extracellular vesicles. The invention provides application of tea extracellular vesicles in preparation of drugs for relieving or treating allergic respiratory diseases. The tea extracellular vesicles are proved to be capable of remarkably improving related symptoms of allergic respiratory diseases including allergic rhinitis, have good anti-inflammatory and immunoregulation effects, and have the application potential of being used as natural active ingredients for preparing drugs or nasal delivery preparations for treating the allergic respiratory diseases. Compared with existing hormone drugs, the tea extracellular vesicles have the advantages of being natural in source, high in safety, low in side effect and the like; moreover, the extracellular vesicles have nanoscale particle sizes, good mucous membrane permeability and bioavailability, can improve the curative effect, and are suitable for long-term intervention.

The invention discloses a preparation method of CsPHT1; the invention discloses modification of selenium element absorbed by tea and application of the gene 3 to feed, and relates to the field of agricultural gene engineering. CsPHT1 (CsPHT1); the gene 3 is GenBank: OP219455.1, gene mutation of D144K and T150V is carried out on the gene 3, and the amino acid sequence after mutation is shown as SEQ ID No: 01; tea tree CsPHT1; 3, the cultivation of D144K and T150V gene mutation seedlings is improved by the gene. The new variety cultivated by the invention can improve the selenium content and the oxidation resistance of the selenized tea polysaccharide without being influenced by the change of pH 5-7; the selenium-supplementing feed is obtained by mixing the high-selenium tea leaves with the feed, and the requirements of the current high-end feed market are met.

The method comprises the following steps: S1, collecting unmanned aerial vehicle multispectral image data of a tea garden, and synchronously collecting measured values of chlorophyll and nitrogen content of tea leaves in a corresponding region; s2, preprocessing the multispectral image data, and constructing a training set and a test set; s3, an LSSCM-MobileTeNet detection model is constructed, the model takes MobileNetV3 as a reference framework, a multi-Stage Stem structure (Multi-Stage Stem) is introduced to carry out hierarchical feature extraction, and an original bottleneck layer is replaced with a lightweight spatial spectrum convolution module (LSSCM-Block); and S4, training the LSSCM-MobileTeNet detection model by using the constructed data set, processing the multispectral image of the area to be detected by using the trained model, and outputting predicted values of the chlorophyll content and the nitrogen content. The method has the advantages that RMSE and MAE are remarkably reduced, R2 is improved, and higher prediction precision is achieved; according to the method, the chlorophyll nitrogen content of the tea leaves can be rapidly detected on a large scale; the multi-stage Stem structure enhances the ability of the model to extract the hierarchical features of the multispectral image.

The invention relates to the technical field of tea processing, and discloses a tea processing auxiliary method and system based on intelligent decision making. The method comprises the following steps: acquiring a multi-source sensor data stream of tea processing equipment in real time, and identifying a critical time point of processing state switching; intercepting time window data by taking a critical point as a center, performing empirical mode decomposition on channel data of each sensor, extracting an intrinsic mode function component, calculating a sample entropy value, and constructing a multi-channel entropy value characteristic matrix; inputting the matrix into a graph attention network, learning a spatial dependency relationship between sensor channels, and outputting a graph embedding representation of a processing state; calculating an abnormal score and generating a processing quality deviation index; analyzing the influence of processing parameter adjustment on a quality index by adopting a gradient lifting decision tree model, and determining the contribution weight of a key sensor channel; high-weight sensor data are integrated, dynamic segmentation is carried out, timing sequence characteristics are fused by applying a gating circulation unit network, and an accurate processing control decision is output.

The invention discloses an intelligent monitoring and early warning warehouse for tea storage, and relates to the technical field of agricultural product storage monitoring and intelligent early warning. An acoustic respiration sensing array, a quantum dot gas mapping film and optical excitation imaging, long-wave infrared polarization insect trace detection, and an edge calculation and actuator cluster are integrated in the warehouse body; a limited scanning sector is triggered by acoustics first sense, a VOC / ammonia concentration heat map and a polarization disturbance heat map are obtained, graded early warning is output through time-space fusion consistency scoring, and low-energy-consumption zoning intervention is achieved through model prediction control cooperation dehumidification, air supply, cold plasma and a zoning air door. In cooperation with reference self-correction, channel health detection and fault-tolerant synchronization, a monitoring-decision-regulation-re-monitoring closed loop is formed, and mildew / insect pest early warning and traceable management are achieved under the condition that packaging is not damaged.