Intelligent control system and method for automatic production of selenium-rich white tea

Abstract

The invention relates to the technical field of selenium-rich white tea production, and discloses an intelligent control system and method for automatic production of selenium-rich white tea. The system comprises a selenium-rich white tea growth environment regulation and control module, a tea growth stage recognition module, a selenium content self-adaptive supply module, a production equipment cooperative control module and a quality closed-loop feedback module. The growth environment regulation and control module collects tea garden multi-region environment parameters and establishes a dynamic monitoring set; the growth stage identification module divides growth stage identifiers in combination with the environment parameters, the bud and leaf morphological image sequence and the growth cycle time sequence data; the selenium content self-adaptive supply module generates a supply scheme and an equipment control instruction set according to the growth stage identifier and the soil selenium content data; the production equipment cooperative control module is linked with related equipment to execute a cooperative operation time sequence control process; and the quality closed-loop feedback module generates a correction amount according to the finished product detection data, and feeds the correction amount back to the growth environment regulation and control module for parameter iterative updating.

Description

Technical Field

[0001] This invention relates to the field of selenium-enriched white tea production technology, specifically to an intelligent control system and method for automated production of selenium-enriched white tea. Background Technology

[0002] Selenium-rich white tea is favored by the market due to its unique nutritional components, and its production process has relatively strict requirements on environmental conditions and selenium supplementation. In traditional tea garden management, the monitoring of soil selenium content mostly relies on manual sampling and testing. This method is not only time-consuming and labor-intensive, but also cannot reflect the differences in selenium distribution in different areas of the tea garden in real time, resulting in blind selenium supplementation. In terms of controlling the tea growing environment, the adjustment of parameters such as air temperature and humidity, and light intensity is mostly based on experience, lacking precise data support. Operations such as opening and closing shade nets and starting and stopping sprinklers often lag behind environmental changes, failing to provide suitable growing conditions for tea trees in a timely manner. The division of tea growth stages mainly relies on manual observation, which is greatly influenced by subjective factors and prone to judgment errors. Different growth stages have different selenium requirements; failure to accurately supplement selenium according to the growth stage will affect the quality and selenium content of the tea. The coordination between production equipment is poor. Equipment such as sprinkler irrigation, shading, and soil injection often operate independently, making it difficult to establish a unified work sequence and reducing production efficiency. Furthermore, the quality feedback mechanism for finished tea products is inadequate, making it impossible to adjust various parameters in the production process in a timely manner based on test results. This results in an open production process, making it difficult to guarantee the stability of product quality. These problems have hindered the development of automated and intelligent production of selenium-enriched white tea, and there is an urgent need for an intelligent control system that can integrate environmental monitoring, growth stage identification, selenium element replenishment, equipment collaborative control, and quality feedback. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent control system for the automated production of selenium-enriched white tea, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides an intelligent control system for the automated production of selenium-rich white tea, the system comprising: The selenium-enriched white tea growth environment control module is used to collect real-time data on the distribution of selenium content in tea garden soil, air temperature and humidity, and light intensity, and to establish a dynamic monitoring set of environmental parameters in multiple areas of the tea garden. The tea growth stage identification module receives the dynamic monitoring set of environmental parameters from multiple areas of the tea garden, and combines the tea tree bud and leaf morphology image sequence with growth cycle time data to identify the growth stages of tea: germination period, extension period and mature harvesting period. The selenium content adaptive replenishment module acquires the growth stage identifier and the current soil selenium content distribution data, and generates selenium nutrient solution replenishment schemes and sprinkler irrigation equipment control instruction sets for different growth stages. The production equipment collaborative control module parses the control instruction set of the sprinkler irrigation equipment, synchronously links the shade net control mechanism, the atomizing nozzle array and the soil injection device, and executes the multi-equipment collaborative operation timing control process. The quality closed-loop feedback module collects selenium concentration detection data and appearance characteristic data of the processed tea product, compares them with preset quality standard thresholds, generates selenium content compensation coefficient and environmental parameter correction amount, and feeds them back to the selenium-rich white tea growth environment control module for parameter iterative updates.

[0005] Preferably, the selenium-enriched white tea growth environment control module includes: The multi-source sensor fusion submodule deploys a soil selenium content sensor network, a weather station, and a spectral imager to periodically collect selenium concentration gradient data, microclimate parameter matrix, and leaf reflectance spectrum in the three-dimensional spatial coordinates of the tea garden. The environmental feature extraction submodule receives the selenium concentration gradient data, microclimate parameter matrix and leaf reflectance spectrum, removes abnormal fluctuation data points, calculates the mean soil selenium content, temperature and humidity change slope and light intensity distribution variance in each monitoring unit, and constructs a standardized environmental feature vector set. The dynamic weight allocation submodule, based on tea tree variety gene bank data and historical high-quality tea production records, assigns a first weight coefficient to the average soil selenium content in the standardized environmental feature vector set, a second weight coefficient to the slope of temperature and humidity changes, and a third weight coefficient to the variance of light intensity distribution, thereby generating a weighted environmental evaluation index set.

[0006] Preferably, the tea growth stage identification module includes: The morphological analysis submodule calls the machine vision unit to capture high-resolution images of tea buds and leaves, and extracts bud and leaf contour curvature feature points, leaf unfolding angle and new shoot length data. The growth sequence alignment submodule dynamically time-normalizes and matches the bud and leaf contour curvature feature points, leaf unfolding angle and new shoot length data with the standard growth curve library, and outputs the growth progress deviation index. The stage decision submodule maps the current growth stage identifier according to the standard growth curve library when the growth progress deviation index is lower than the progress tolerance threshold; when the growth progress deviation index exceeds the progress tolerance threshold, it triggers a manual review instruction and updates the standard growth curve library data.

[0007] Preferably, the selenium content adaptive replenishment module includes: The demand modeling submodule receives the growth stage identifier and the current soil selenium content distribution data, queries the selenium absorption rate comparison table, and calculates the difference between the theoretical selenium accumulation and the actual soil selenium content during the target harvest period. The replenishment strategy generation submodule inputs the difference between the theoretical selenium accumulation and the actual soil selenium content into the nutrient solution ratio decision tree and outputs the foliar spray concentration gradient, soil injection depth and single replenishment duration. The equipment instruction compilation submodule converts the foliar spray concentration gradient into atomizing nozzle pressure value, maps the soil injection depth into hydraulic cylinder stroke parameters, and compiles the single replenishment duration into equipment start-stop sequence code.

[0008] Preferably, the production equipment collaborative control module includes: The spatial coordinate mapping submodule parses the 3D model of the tea garden terrain and the topology of the equipment layout, converts the pressure value of the atomizing nozzle into the spatial coordinate control signal of the nozzle unit, and associates the stroke parameters of the hydraulic cylinder with the coordinates of the geographic information system. The timing synchronization submodule inserts an interlock delay time window into the equipment start-stop timing code based on the tea harvesting operation calendar and weather warning data to avoid conflicts between sprinkler irrigation operations and tea harvesting machinery operation. The abnormal fuse submodule monitors the water pressure fluctuation variance of the atomizing nozzle array and the flow mutation value of the soil injection device in real time. When the water pressure fluctuation variance exceeds the equipment safety threshold or the flow mutation value reaches the fault threshold, it sends an emergency shutdown command to all executing devices.

[0009] Preferably, the quality closed-loop feedback module includes: The non-destructive testing submodule uses an X-ray fluorescence spectrometer to scan finished tea samples and generate a heat map of selenium concentration distribution and a leaf integrity score. The quality correlation analysis submodule compares the spatial overlap between the selenium element concentration distribution heatmap and the preset quality standard threshold, calculates the concentration compliance coverage index, and correlates the leaf integrity score with the mechanical damage record. The parameter iteration submodule generates a selenium content compensation coefficient and transmits it to the selenium content adaptive replenishment module when the concentration compliance coverage index is lower than the preset qualification rate lower limit; when the leaf integrity score continuously decreases beyond the quality decay slope threshold, it generates an environmental parameter correction amount and transmits it to the selenium-rich white tea growth environment control module.

[0010] Preferably, the system further includes: The full-process traceability module is connected to the data output end of the quality closed-loop feedback module. It collects the selenium element concentration distribution heat map, equipment control instruction set and environmental feature vector set, generates a unique code for the production batch and binds it to the blockchain distributed ledger. When the full-process traceability module retrieves the blockchain distributed ledger of any batch of tea, it reassembles the selenium replenishment operation time point, environmental parameter fluctuation curve, and final selenium concentration detection results in chronological order.

[0011] Preferably, when the dynamic weight allocation submodule performs weight updates: Receive the optimal selenium concentration value from the historical high-quality tea production records transmitted by the quality closed-loop feedback module. When the optimal selenium concentration of the actual harvested tea leaves deviates from the target range, the first weighting coefficient of the average selenium content of the soil is increased. When the tea garden encounters extreme weather events, the second weighting coefficient of the slope of the temperature and humidity change is increased; When the finished tea product exhibits photo-oxidative damage characteristics, the third weighting coefficient of the variance of the light intensity distribution is increased.

[0012] Preferably, the present invention further includes an intelligent control method for automated production of selenium-enriched white tea, applied to the aforementioned intelligent control system for automated production of selenium-enriched white tea, the method comprising: Dynamic data on soil selenium content, canopy temperature and humidity gradient, and light intensity time series were simultaneously collected through a multi-dimensional sensor network deployed in the tea garden. By integrating the morphological characteristics of tea buds and leaves with time-series data of the growth cycle, the critical points for the transition of tea growth stages can be identified. Based on the difference between the target selenium accumulation and the real-time soil selenium content, a selenium nutrient solution replenishment parameter matrix is ​​dynamically generated. The selenium nutrient solution replenishment parameter matrix is ​​compiled into multi-device control instructions, and interlock delays for equipment collaborative operation are inserted; The spatial distribution and physical integrity of selenium in finished tea products are detected, and the resulting parameter compensation is fed back into the environmental control process. Bind all production process data to blockchain nodes to achieve cross-process traceability.

[0013] Preferably, the feeding back of the generated parameter compensation amount to the environmental control process specifically includes: When the selenium concentration at the edge of the finished tea leaves is lower than that in the center, the atomization coverage radius of the foliar spraying operation should be increased. When multiple batches of tea leaves suffer mechanical damage, the soil injection device must not be activated while the tea-picking machinery is in operation. When the selenium leaching loss rate exceeds the preset threshold during the rainy season, the baseline value of the selenium nutrient solution concentration for the next soil injection will be increased.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This system uses a selenium-enriched white tea growth environment control module to collect real-time data on the distribution of selenium content in the tea garden soil, air temperature and humidity, and light intensity. It constructs a dynamic monitoring set of environmental parameters for multiple regions, allowing for a comprehensive and timely understanding of the tea garden's environmental status, which facilitates subsequent work by each module based on accurate data. The tea growth stage identification module combines dynamic monitoring sets of environmental parameters, tea bud and leaf morphology image sequences, and growth cycle time series data to divide the tea growth stage into the germination period, extension period, and maturity harvesting period. This makes the judgment of tea growth stage more objective and accurate, and provides a suitable basis for operations such as selenium supplementation. The selenium content adaptive replenishment module generates selenium nutrient solution replenishment schemes and sprinkler irrigation equipment control instruction sets for different growth stages based on growth stage indicators and current soil selenium content distribution data. This achieves targeted and precise selenium replenishment and can be carried out according to the different growth needs of tea and the actual soil conditions. After the production equipment collaborative control module analyzes the control instruction set of the sprinkler irrigation equipment, it synchronously links the shade net control mechanism, the atomizing nozzle array, and the soil injection device to execute the multi-equipment collaborative operation sequence control process, which enhances the coordination between various production equipment, makes various operations orderly and connects, and improves the smoothness of the overall production operation. The quality closed-loop feedback module collects selenium concentration and appearance data of the finished tea product after processing, compares them with preset quality standard thresholds, generates selenium content compensation coefficient and environmental parameter correction amount, and feeds them back to the growth environment control module for parameter iterative updates, forming a complete quality control cycle. This allows various parameters in the production process to be continuously optimized according to the quality of the finished product, ensuring the continuity and stability of selenium-rich white tea production and helping to realize automated and intelligent production of selenium-rich white tea. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the working principle of the intelligent control system for automated production of selenium-enriched white tea as described in this invention. Figure 2 A flowchart illustrating the working principle of the selenium-enriched white tea growth environment control module. Figure 3 A flowchart illustrating the working principle of the tea growth stage identification module; Figure 4 This is a flowchart illustrating the working principle of the full-process traceability module. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 This invention provides an intelligent control system and method for automated production of selenium-enriched white tea. The system includes: a selenium-enriched white tea growth environment regulation module, a tea growth stage identification module, a selenium content adaptive replenishment module, a production equipment collaborative control module, and a quality closed-loop feedback module.

[0018] The selenium-enriched white tea growth environment control module collects real-time data on soil selenium content distribution, air temperature and humidity, and light intensity through a multi-source sensor network deployed in the tea garden, establishing a dynamic monitoring set of environmental parameters for multiple areas of the tea garden. The tea growth stage identification module receives this dynamic monitoring set and, combined with tea bud and leaf morphology image sequences and growth cycle time-series data, identifies the tea's germination, extension, and ripening / harvesting stages. The selenium content adaptive replenishment module generates selenium nutrient solution replenishment schemes and sprinkler irrigation equipment control command sets for different growth stages based on the growth stage identifiers and current soil selenium content distribution data. The production equipment collaborative control module parses the sprinkler irrigation equipment control command set, synchronously linking the shading net control mechanism, atomizing nozzle array, and soil injection device to execute a multi-equipment collaborative operation time-series control process. The quality closed-loop feedback module collects selenium concentration detection data and morphological characteristic data of the processed tea product, compares it with preset quality standard thresholds, generates a selenium content compensation coefficient and environmental parameter correction amount, and feeds this data back to the selenium-enriched white tea growth environment control module for iterative parameter updates.

[0019] Example 1: See Figure 2The system integrates a multi-source sensor fusion submodule, an environmental feature extraction submodule, and a dynamic weight allocation submodule. The soil selenium content sensor network within the multi-source sensor fusion submodule employs a combination of electrochemical sensors and X-ray fluorescence probes. The electrochemical sensors monitor real-time changes in the concentration of soluble selenium ions in the soil solution, while the X-ray fluorescence probes detect the total selenium content in soil solid particles. Sensor nodes are deployed in a 20m x 20m grid within the tea garden. Each node is equipped with a subsurface layered detection structure, enabling simultaneous acquisition of selenium concentration data at depths of 20cm, 35cm, and 50cm. The weather station equipment is mounted on a stainless steel bracket 1.5 meters above the tea canopy. Temperature and humidity sensors are protected by radiation shields to prevent measurement errors caused by direct sunlight. The anemometer is equipped with an automatic heating function to prevent the influence of dew condensation. The spectral response range of the photosynthetically active radiometer matches the sensitive wavelength range of tea tree photosynthesis, and the data acquisition interval is set to 15 minutes. The hyperspectral camera on the spectral imager is mounted on a track-mounted mobile platform. The platform moves at a constant speed of 0.2 m / s along the direction between the tea tree rows, and the vertical distance between the lens and the top of the canopy is kept at 1.2 m during the scanning process.

[0020] The data processing flow of the environmental feature extraction submodule includes three stages: signal preprocessing, outlier removal, and feature calculation. Soil selenium concentration gradient data is first processed by moving average filtering with a window width of 5 sampling periods to eliminate short-term measurement noise. Then, an outlier detection algorithm based on interquartile range (ICM) is used to remove data points deviating from the median by more than 1.5 times the ICM. Temperature and humidity data in the microclimate parameter matrix are decomposed using a time-series decomposition algorithm to separate seasonal trend, periodic, and residual terms, extracting the hourly temperature change rate as the feature slope. Light intensity data is used to generate a two-dimensional distribution map of the tea garden using a Kriging space interpolation algorithm, calculating the variance of light intensity within each monitoring unit. Leaf reflectance maps are processed by spectral normalization, extracting the reflectance value at 550 nm as an indicator of chlorophyll content, and the reflectance at 720 nm for assessing leaf moisture status. All feature parameters are aligned and integrated according to the monitoring unit number to construct a standardized environmental feature vector set containing indicators such as the mean soil selenium content, temperature and humidity change slope, and light intensity distribution variance.

[0021] The dynamic weight allocation submodule's weight calculation mechanism is deeply coupled with tea variety characteristics and historical environmental data. The tea variety gene bank stores selenium enrichment capacity classification data for different clonal varieties, categorized into high-enrichment, medium-enrichment, and low-enrichment types. The historical high-quality tea production record database stores selenium content testing reports for tea from each region by year, including weather conditions and agricultural operation logs at the time of harvest. The first weighting coefficient for the average soil selenium content is initially set at 0.5, increasing by 0.1 for high-enrichment varieties, remaining unchanged for medium-enrichment varieties, and decreasing by 0.1 for low-enrichment varieties. The second weighting coefficient for the slope of temperature and humidity changes is positively correlated with the frequency of extreme weather events in the same period over the past five years; frequency statistics are calculated using a sliding window algorithm with a window width of 30 days. The third weighting coefficient for the variance of light intensity distribution is dynamically adjusted based on the sensory evaluation results after tea processing; when descriptions of photo-oxidation defects such as "sun-dried flavor" appear in the evaluation records, the coefficient increases in increments of 0.05. All weighting coefficients are recalculated weekly, and the results are pushed to the environmental control decision-making system in real time via a message queue.

[0022] The hardware system of the multi-source sensor fusion submodule adopts a distributed architecture design, with each sensor node equipped with an independent edge computing unit. The edge computing unit incorporates a temperature compensation circuit to ensure the measurement stability of the electrochemical sensor under different soil temperatures. The excitation source of the X-ray fluorescence probe uses a miniature X-ray tube design, with the tube voltage programmable and adjustable within the range of 20kV to 50kV to adapt to the detection needs of different soil types. The meteorological station equipment integrates a LoRa wireless transmission module, and data transmission uses frequency hopping spread spectrum technology to enhance anti-interference capabilities. The orbital platform of the hyperspectral imaging system is equipped with a real-time dynamic positioning device, achieving a positioning accuracy of ±2 cm, ensuring the accuracy of the geographic coordinates of the scanned images.

[0023] The environmental feature extraction submodule employs a multi-threaded parallel computing architecture. The soil data processing thread runs on a dedicated DSP chip, achieving millisecond-level response times. The microclimate data analysis thread utilizes GPU-accelerated matrix operations, processing hourly data from 100 nodes in just 50 milliseconds. Light intensity interpolation calculations utilize the OpenCL framework, fully leveraging heterogeneous computing resources. All generated feature vectors are written to a time-series database, which employs a columnar storage structure to optimize query efficiency.

[0024] The decision-making logic of the dynamic weight allocation submodule is implemented through a rule engine. The rule base contains over 300 conditional statements, covering combinations of different varieties, seasons, and soil types. Fuzzy logic control is introduced during the weight coefficient adjustment process to prevent system oscillations caused by sudden parameter changes. A version log is generated after each weight update, containing the reason for the adjustment, the numerical changes, and an assessment of the expected impact. The system maintenance interface provides a visual traceability function for the weight coefficients, displaying coefficient change curves and related factors over any given time period.

[0025] The power supply system for the sensor network employs a combination of solar panels and supercapacitors. The tilt angle of the solar panels is precisely calculated based on the latitude of the tea plantation, and the supercapacitor bank can maintain normal operation for 7 days even during continuous cloudy or rainy weather. Each sensor node is equipped with a self-test function, periodically reporting battery voltage, signal strength, and sensor status. The network communication uses a mesh topology, automatically routing to a backup path in the event of a node failure.

[0026] The anomaly detection algorithm in the environmental feature extraction submodule employs adaptive thresholding. The initial threshold value is derived from historical data statistics and dynamically adjusted during runtime based on environmental changes. Quality control flags are introduced during feature calculation to mark potentially problematic data records for manual review. Standardization uses the Z-score algorithm, but a copy of the original data is retained for traceability.

[0027] The dynamic weight allocation submodule uses a RESTful API to interface with the tea garden management system. Weight coefficients undergo consistency verification before publication to ensure they match current environmental conditions. The system provides a weight impact simulation function to predict the potential impact of different coefficient settings on the final tea quality. All weight adjustment operations require dual authentication and record the operator's identity information.

[0028] The calibration and maintenance system for the multi-source sensor fusion submodule comprises three levels. Routine calibration is performed automatically by the nodes, with standard solution comparisons conducted weekly. Intermediate calibration is performed monthly, using standard substances to verify measurement accuracy on-site. Advanced calibration is implemented annually, with a metrology institution conducting performance certification of the entire system. Calibration data is automatically uploaded to a cloud platform to generate equipment health reports.

[0029] The environmental feature extraction submodule employs a multi-level caching strategy for its calculation results. Raw data is cached for 24 hours, feature data for 7 days, and aggregation analysis results for 30 days. Cache updates utilize a write-through strategy to ensure data consistency. All cached records are timestamped and versioned, allowing for rollback to historical states as needed.

[0030] Example 2: See Figure 3The project focuses on key technologies such as tea bud and leaf morphological feature extraction, growth sequence matching, and selenium supplementation strategy generation. The machine vision unit of the morphological analysis submodule uses a ring-shaped LED light source with a polarizer, maintaining a color temperature of 5600K to simulate natural lighting conditions. An industrial camera equipped with a telecentric lens achieves a spatial resolution of 0.05 mm / pixel at a working distance of 50 cm. During image acquisition, the inspection robot moves along the tea tree rows at a speed of 0.3 m / s, triggering three shots per tree to capture the morphological features of the terminal bud, middle shoots, and lower old leaves. The bud and leaf contour extraction algorithm employs an improved active contour model, with initial contour points set at the base of the bud and leaf. During iteration, gradient vector flow field and local texture feature constraints are combined. The leaf unfolding angle is calculated by fitting two straight lines from key points on the leaf margin to the base of the petiole, with an accuracy of 0.1 degrees. Shoot length measurement utilizes stereo vision principles, with an auxiliary laser rangefinder mounted on the camera bracket, and triangulation is used to eliminate perspective distortion.

[0031] The standard growth curve library of the growth sequence alignment submodule contains developmental models for twelve main varieties, each based on more than five years of fixed-point observation data. The dynamic time warping algorithm employs a multi-scale matching strategy, first coarsely aligning growth stages on a weekly timescale, then precisely calculating deviation indices on a daily scale. During the matching process, the sequence of bud and leaf contour curvature feature points uses dynamic programming to find the optimal bending path, with the cumulative path distance serving as a similarity measure. Phase correlation analysis is performed between leaf unfolding angle data and the standard curve, extracting the phase difference of the main frequency components as the time series offset. The rate of change in shoot length is calculated using first-order difference, and its ratio to the standard growth rate constitutes the growth vitality index. The progress tolerance threshold settings of the stage decision submodule vary by variety: the threshold is relaxed to ±10% during germination, tightened to ±5% during extension, and restored to ±7% during maturity and harvest. When the system detects that the deviation indices of multiple monitoring points simultaneously exceed the limit, it automatically raises the warning level and marks the boundary coordinates of the abnormal area. Manual review instructions are pushed through the tea garden management APP, accompanied by abnormal data comparison charts and suggested inspection items.

[0032] The selenium content adaptive replenishment module's demand modeling submodule incorporates a selenium absorption kinetics model. Model inputs include effective accumulated temperature at the current growth stage, soil pH, and estimated root biomass data. The theoretical selenium accumulation calculation incorporates a variety correction coefficient, determined through hydroponic experiments measuring the expression levels of selenium transport proteins in different varieties. Actual soil selenium content difference analysis uses Kriging interpolation to generate a two-dimensional distribution map, with the difference calculated to an accuracy of 0.1 mg / kg. The replenishment strategy generation submodule's nutrient solution ratio decision tree contains 27 decision nodes, covering combinations of different soil types, weather conditions, and equipment status. Foliar spray concentration gradients are set at three levels: a conservative concentration of 50 mg / L during germination, increasing to 80 mg / L during the extension stage, and decreasing to 30 mg / L two weeks before harvest. Soil injection depth is dynamically adjusted based on the distribution characteristics of the tea tree's taproot, set at 20 cm for one-year-old tea trees and 35 cm for tea trees three years and older. The duration of a single replenishment is negatively correlated with soil moisture content, shortening the operation time by 20% under drought conditions.

[0033] The hardware interface of the equipment instruction compilation submodule adopts the industrial Ethernet protocol, with a fixed transmission cycle of 100ms. The atomizing nozzle pressure value conversion table is stored in the programmable logic controller, with a pressure adjustment step of 0.05MPa. Soil compaction compensation is incorporated into the hydraulic cylinder stroke parameter mapping process, with the compensation coefficient derived from previous borehole resistance test data. The equipment start-up and stop sequence codes are encapsulated in Modbus TCP format, with each control cycle containing a timestamp, equipment group address, and operation code. The instruction queue management employs a priority scheduling algorithm, allowing emergency replenishment tasks to interrupt the regular operation sequence.

[0034] The image processing pipeline of the morphological analysis submodule is deployed on an edge computing device. The original image undergoes shadow correction, and homomorphic filtering is used to eliminate the effects of uneven illumination. During color space conversion, the lightness and chromaticity components of the LAB channels are preserved to enhance the contrast between young and old leaves. Curvature continuity constraints are incorporated into the energy function during contour extraction to avoid overfitting to jagged edges. Feature point coordinates are system-transformed to the tea garden geodetic coordinate system, facilitating spatial correlation analysis of data from multiple tea trees.

[0035] The standard curve update mechanism of the growth time-series alignment submodule adopts an incremental learning approach. After each manual review and confirmation, the system automatically extracts the growth data features of the reviewed area, and adds them to the standard curve library after double verification. The curve smoothing algorithm uses local weighted regression to maintain the original trend while incorporating new observation data. The deviation index calculation introduces the concept of confidence interval; when the deviation directions of multiple monitoring points are consistent, the tolerance threshold sensitivity is automatically reduced.

[0036] The decision-making submodule outputs its results in the form of a probability distribution. Each growth stage is identified by a probability percentage; when the probability difference between the primary and secondary stages is less than 15%, it is marked as a transition period. The decision results are published via the OPCUA protocol, and subscribing clients can obtain the complete decision-making data chain. The system retains the three most recent decision records, allowing manual viewing of historical trends.

[0037] The root biomass estimation in the demand modeling submodule employs multispectral imaging technology. A regression model is established between near-infrared reflectance and root development, with parameters updated weekly. Soil pH is measured using an ion-selective field-effect transistor array, with measurement points positioned within the tea tree drip line area. Effective accumulated temperature is calculated using a nonlinear cumulative model, with a base temperature set at 10℃ and an upper limit of 35℃.

[0038] The weather data for the replenishment strategy generation submodule is integrated into the meteorological bureau's short-term forecast system. Rainfall probability influencing factors are categorized into five levels, each corresponding to a different nutrient solution adsorption time correction coefficient. The decision tree training data includes historical selenium content monitoring records and agricultural operation logs, with node splitting criteria reassessed monthly. The strategy generation process displays the activation status of each decision node in real time, facilitating the traceability of decision logic.

[0039] The security verification mechanism of the device instruction compilation submodule includes triple verification. Instruction syntax checking uses a format description language to define rules; semantic checking compares the current device state with the instruction requirements; and logical checking verifies the timing rationality. All instructions must undergo simulated execution testing before being issued, and the test results are recorded in the audit log. The instruction retransmission mechanism uses an exponential backoff algorithm, with a maximum of five retries.

[0040] The morphological feature database is stored using a spatiotemporal index structure. Each bud and leaf image is associated with a snapshot of the acquisition time, spatial location, and environmental parameters. Data compression employs a combination of lossy and lossless compression strategies; key feature data uses the Zstandard algorithm for lossless compression, while background areas use JPEG2000 lossy compression. The query interface supports multi-dimensional filtering based on growth stage, variety type, and environmental conditions.

[0041] The real-time monitoring interface integrates 3D visualization functionality. The growth status of tea trees is represented by a color gradient, transitioning from dark green (normal) to red (abnormal). Selenium content distribution is displayed as overlaid contour lines, and replenishment plans are shown using dynamic path animations. All interface operation records are fully saved, supporting playback of system status at any point in time.

[0042] The anomaly handling process includes an automated tiered response mechanism. Level 1 anomalies trigger the device's self-test procedure; level 2 anomalies notify the area administrator; and level 3 anomalies activate the system-wide security protocol. All anomaly events generate standardized reports, including timestamps, device IDs, error codes, and suggested handling measures.

[0043] The data synchronization mechanism employs a hybrid clock strategy. Device control commands are synchronized using a hardware clock with microsecond-level accuracy. Growth data analysis uses a logical clock to mark the sequence of events, resolving time inconsistencies caused by network latency. Critical operations require double confirmation from the clock source before execution.

[0044] The system maintenance module supports remote diagnostics and configuration. The running status of any node can be viewed in real time via a secure tunnel, and new algorithm models or parameter configuration files can be uploaded. Firmware upgrades employ an A / B dual-partition design to ensure rapid rollback in case of upgrade failure. All maintenance operations require two-factor authentication, and operation logs are synchronized to the audit server.

[0045] Example 3: The implementation of the production equipment collaborative control module involves three core functional units: spatial coordinate mapping, timing synchronization, and abnormal circuit breaking. The tea garden digital elevation model loaded by the spatial coordinate mapping submodule is constructed using UAV aerial surveying, with a flight altitude set at 50 meters and an onboard LiDAR scanning point cloud density of 200 points / square meter. During model generation, ground control points are measured using RTK-GNSS, with horizontal accuracy controlled within ±3 cm and vertical accuracy within ±5 cm. The algorithm for generating the spatial coordinate control signal of the atomizing nozzles considers the influence of terrain slope; the nozzle elevation angle θ is automatically adjusted according to the terrain slope α at its location, using the following formula: ; Where θ0 is the standard spray angle on flat ground (default value 45°), and k is the variety characteristic correction coefficient (range 0.8-1.2). The slope angle is represented in degrees. The geographic coordinate transformation of the hydraulic cylinder stroke parameters uses a six-parameter affine transformation model to map the mechanical parameters from the equipment coordinate system to the unified coordinate system of the tea garden. The spatial resolution of the soil hardness raster data is 1 meter × 1 meter, and the data is obtained from measurements using a portable soil penetrator; the measurement depth is consistent with the requirements of the injection operation.

[0046] The water pressure fluctuation monitoring in the abnormal circuit breaker submodule uses a third-order Butterworth filter for preprocessing, with the cutoff frequency set to 1 / 5 of the sampling frequency. The calculation window for the water pressure fluctuation variance σ² includes 10 consecutive sampling points. When the threshold of 0.15 MPa² is exceeded, the system immediately initiates a two-stage response process: first, the upstream solenoid valve is closed; then, the residual pressure in the pipeline is vented; and finally, a fault diagnosis code is sent to the maintenance terminal. Soil injection flow monitoring uses a temperature-compensated electromagnetic flowmeter, and a flow change detection algorithm compares the current flow value. Compared to the moving average over the past 60 seconds Deviation: ; When ΔQ exceeds 20% three consecutive times, the hydraulic system pressure reducing valve is opened. Emergency stop commands are transmitted via a dual-channel system: a hard-wired loop and wireless communication. The hard-wired loop response time is <10ms, and the wireless communication uses frequency-hopping spread spectrum technology to enhance reliability. The stop command includes the faulty equipment ID, abnormal parameter values, and recommended maintenance measures, while simultaneously freezing the equipment's current status log for post-event analysis.

[0047] During the execution of the terrain compensation algorithm in the spatial coordinate mapping submodule, the quadtree index structure of the digital elevation model is queried in real time. The location calculation for each nozzle unit requires interpolation of the elevation values ​​of four surrounding terrain grid points, using bilinear interpolation. Slope calculation employs a 3×3 moving window method; the slope vector at the window's center point is obtained through the elevation difference between eight adjacent points. The geographic coordinate association for the hydraulic cylinder stroke uses a two-step verification mechanism: first, matching the nearest road network node in the GIS system, and then performing sub-meter level precise positioning using visual markers. Soil hardness data undergoes timeliness verification before injection operations; data older than 7 days requires on-site retesting for confirmation.

[0048] The timing synchronization submodule's device interlocking logic enables distributed collaborative control. Each executing device maintains a local job queue, with each task containing a 64-bit unique identifier, estimated execution time, and interlock flag. The central controller periodically broadcasts a timing synchronization signal; devices with a deviation exceeding 50ms automatically enter calibration mode. The path prediction results for tea-picking machinery are visualized as a probabilistic cloud map, with cloud map transparency indicating the collision risk level. During weather data fusion processing, radar echo intensity, ground rain gauge observations, and numerical weather prediction results are averaged using a Bayesian weighted average, with weighting coefficients automatically updated hourly.

[0049] The hardware architecture of the abnormal fuse submodule adopts a triple redundancy design. Two probes, one primary and one backup, are installed on each branch pipe for the water pressure sensor, and the data acquisition channels are physically isolated. The flow meter signal is transmitted dual-channel via 4-20mA analog and RS-485 digital signals, with analog readings given priority in case of abnormalities. The fault diagnosis knowledge base contains feature vectors for 17 typical fault modes, and the matching process uses a nearest neighbor classification algorithm. After receiving an alarm, the maintenance terminal automatically retrieves the equipment's 3D structural diagram and highlights suspected faulty components, while historical maintenance records are displayed sorted by relevance.

[0050] Control commands are issued using a priority marking system. Routine operation commands are marked in green, with a maximum allowable delay of 500ms; time-critical commands are marked in yellow, requiring delivery within 100ms; emergency stop commands are marked in red and transmitted via a dedicated communication channel. Command checksum calculations use the CRC-32 algorithm, and erroneous commands automatically trigger a retransmission mechanism, with retransmission intervals increasing according to the Fibonacci sequence. Equipment status feedback information is transmitted in a compressed format, with change data using delta encoding, and static parameters are fully reported every 30 minutes.

[0051] A version control mechanism is established for updating and maintaining the digital elevation model. For areas where terrain changes exceed 5 centimeters, a difference file is generated and synchronized to all control nodes via incremental updates. Model version compatibility checks are automatically performed upon system startup; if a mismatch occurs, an alarm is triggered and the operation permissions of the relevant equipment are locked. A terrain feature extraction tool can automatically identify key geomorphic elements such as ditches and field ridges, generating obstacle avoidance path suggestions for equipment.

[0052] The dynamic adjustment algorithm for the interlocked time window incorporates a learning mechanism. The system continuously records equipment spacing data during actual operations, gradually optimizing the empirical parameters v_max and d_safe. The dynamic model of the tea-picking machinery is updated monthly, including acceleration and deceleration characteristic curves under no-load and full-load conditions. A weather influence factor database records the impact coefficients of different precipitation intensities on operational efficiency, used to correct the length of the drying time window.

[0053] The circuit breaker mechanism's recovery process employs a gradual restart strategy. After a Level 1 fault is cleared, the equipment enters a low-power trial operation mode, continuously monitoring key parameters for 30 seconds until no abnormalities are detected before resuming normal operation. Level 2 faults require manual on-site confirmation and reset; the system automatically generates a maintenance report containing before-and-after fault comparison data. Level 3 faults trigger automatic equipment locking, requiring professional instrument testing and replacement of specified components before reactivation.

[0054] Spatial coordinate data is stored using a hierarchical index structure. Geodetic coordinates are stored as integer values ​​with centimeter precision to save storage space; local fine-tuning coordinates use floating-point numbers to record millimeter-level offsets. Coordinate transformation parameters are checked weekly, and a control point re-survey process is triggered when deviations exceed the tolerance limit. The terrain feature attribute table records auxiliary information such as soil type and water-holding capacity for each grid point, used for fine-tuning of operational parameters.

[0055] The timing synchronization signal is generated using a hybrid clock source. The master clock is a GPS-disciplined high-stability crystal oscillator, while the backup clock is synchronized using the local area network PTP protocol. Key timestamps use a dual-recording format of UTC and local time, and planned job adjustments are frozen during daylight saving time. The timeline reconstruction function of the historical job log can replay the equipment coordination status of any period, with adjustable playback speed and support for keyframe marking.

[0056] Example 4: Implementation of the Quality Closed-Loop Feedback Module. Dynamic optimization of tea production quality is achieved through three functional units: non-destructive testing, quality correlation analysis, and parameter iteration. The non-destructive testing submodule is equipped with an X-ray fluorescence spectrometer featuring a rhodium target X-ray tube. The tube voltage is set to 40kV, and the tube current to 1mA. The scanning platform is driven by a servo motor, with the movement step precisely controlled at 0.5mm. During testing, the tea sample is laid flat in a polytetrafluoroethylene sample tray, maintaining a uniform thickness of 3mm. The spectrometer detector collects 2000 energy spectrum data points per second, which are then Gaussian smoothed to generate a selenium concentration distribution heatmap. The heatmap has 16 color levels, gradually transitioning from deep blue (0mg / kg) to deep red (≥2.0mg / kg). Leaf integrity testing uses a 5-megapixel CMOS industrial camera with a ring LED cold light source, achieving a resolution of 50μm / pixel. The image processing algorithm first extracts the leaf region through Otsu thresholding, then uses morphological opening operations to eliminate interference from fine textures, and finally calculates the proportion of damaged edge pixels to the total outline of the leaf as the integrity score.

[0057] The quality correlation analysis submodule establishes preset quality standard thresholds, which are stored in a spatial database as polygonal vector layers. Each standard threshold region includes the minimum selenium concentration value, the maximum selenium concentration value, and ideal gradient curve parameters. During spatial overlay analysis, the measured heat map raster data is compared pixel-by-pixel with the standard vector layer to calculate the area ratio of compliant regions. Mechanical damage records are derived from historical data of the tea-picking machine's pressure sensors, with a data sampling frequency of 100Hz. The records include pressure values, application time, and robotic arm position coordinates. The correlation analysis employs a time window matching algorithm to extract feature vectors from the equipment operating parameters within 30 seconds before and after the damage event.

[0058] The parameter iteration submodule sets a minimum pass rate of 90%. When the concentration compliance coverage rate falls below this value, the compensation coefficient calculation considers three factors: the current difference magnitude, historical compensation effects, and future weather forecasts. The quality decay slope threshold is determined based on the change trend of integrity scores for three consecutive batches. Data storage for the scores includes metadata such as collection time, testing personnel, and environmental temperature and humidity. The generation of environmental parameter corrections distinguishes between two types: light-related damage and mechanical damage, with each type corresponding to a different adjustment strategy.

[0059] The sample preparation procedure for the non-destructive testing submodule specifies that 500g of finished tea leaves are randomly selected from each batch and reduced to the testing quantity using the quartering method. Before testing, the samples are equilibrated in a constant temperature and humidity chamber for 24 hours, with the temperature controlled at 25±1℃ and the relative humidity at 60±5%. The spectrometer needs to be calibrated daily with standard samples containing four concentration gradients (0.5mg / kg, 1.0mg / kg, 1.5mg / kg, and 2.0mg / kg). The camera system undergoes white balance calibration weekly, and color reproduction accuracy is verified using a standard color chart. The test report is automatically generated as a PDF document, including sample photographs, thermal pseudo-color images, and statistical tables of key indicators.

[0060] The spatial analysis algorithm of the quality correlation analysis submodule employs a pyramid hierarchical processing strategy. The original data layer maintains a resolution of 0.5mm, and during analysis, data from different precision layers are dynamically invoked based on the view zoom level. Mechanical damage correlation analysis establishes an event timeline, aligning damage characteristics with changes in equipment operating parameters. When visualizing the analysis results, a heatmap overlaid with a line chart is used to display the spatiotemporal correlation between selenium concentration distribution and equipment operating status.

[0061] The compensation coefficient calculation in the parameter iteration submodule uses a weighted moving average algorithm, with more recent compensation records receiving higher weights. Environmental parameter corrections are determined using a decision matrix, where rows represent damage types, columns represent environmental factors, and cells store the adjustment direction and magnitude. After each parameter adjustment, the system automatically creates a snapshot, recording the adjustment time, operator, and expected impact period. The effectiveness of parameter adjustments is evaluated using A / B testing, implementing different parameter settings in adjacent blocks and verifying the adjustment's effectiveness by comparing subsequent detection results.

[0062] The table below shows an example of the correlation analysis between selenium concentration detection data and equipment operating parameters in the quality closed-loop feedback module: Table 1 shows an example of the correlation analysis between selenium concentration detection data and equipment operating parameters in the quality closed-loop feedback module.

[0063] ; The quality control system for the nondestructive testing submodule includes a three-level calibration mechanism. Primary calibration is performed automatically by the equipment, including radiation intensity stability testing and image signal-to-noise ratio evaluation. Intermediate calibration is conducted daily, using standard samples of known concentrations to verify testing accuracy. Advanced calibration is implemented monthly, with the metrology department conducting performance certification of the entire system. Environmental monitoring data is recorded in real time, including influencing factors such as operating table vibration amplitude and environmental electromagnetic interference intensity.

[0064] The data preprocessing workflow of the quality correlation analysis submodule includes two steps: outlier filtering and time alignment. Outlier filtering employs a modified boxplot method, marking data exceeding 1.5 times the interquartile range as suspicious. Time alignment addresses clock synchronization issues between devices with different sampling frequencies, using cubic spline interpolation to match low-frequency data to the high-frequency time axis. The analysis results are stored with a confidence score, reflecting data quality and algorithm reliability.

[0065] The parameter iteration submodule's user interface provides parameter adjustment simulation functionality. Users can set different compensation coefficients and correction amounts, and the system predicts the changing trends of quality indicators for the next three production cycles. Simulation results are displayed in line graph format, with key turning points and risk warning information marked. Historical adjustment records support multi-dimensional filtering and searching, allowing for searches based on time range, operator, adjustment type, and other combined criteria.

[0066] The testing data management system adopts a distributed architecture. Raw testing data is stored in a high-speed time-series database, supporting millisecond-level timestamp retrieval. Analysis results are stored in a relational database with a complete indexing system. The data backup strategy combines incremental and full backups, with incremental backups performed daily at midnight and full backups performed weekly on Sundays. Data access permissions are granularly defined at the field level, and sensitive operations require dual authentication.

[0067] The quality trend analysis function enables multi-dimensional data drill-down. Users can drill down from the annual view to quarterly, monthly, and even specific batch inspection details. Trend charts support the addition of reference lines and annotation markers, facilitating comparisons with industry standards or historical best levels. Outliers are automatically marked with potential influencing factors, such as equipment maintenance records, abnormal weather events, and other related information.

[0068] The system integration interface adopts a standardized Web service protocol. The testing data service follows the HL7FHIR standard, and the device control interface adopts the OPCUA specification. Third-party systems must access the system through a security gateway, which implements traffic monitoring and access control. Interface call logs are fully recorded, including timestamps, caller identifiers, request parameters, and response status.

[0069] Example 5: Reference Figure 4The implementation of the end-to-end traceability module achieves transparent management of the tea production process through the integration of blockchain technology and multi-dimensional data. The system architecture adopts the Hyperledger Fabric consortium blockchain framework, consisting of a distributed network of five consensus nodes. Each node is deployed on an independent physical server, configured with an Intel Xeon Silver 4210 processor and 64GB of memory. The data acquisition end connects to the output interface of the quality closed-loop feedback module, receiving three core data types in real time: a heat map of selenium concentration distribution, equipment control command sets, and environmental feature vector sets. The heat map data is compressed and converted to GeoJSON format, retaining spatial coordinate information and the concentration value matrix. The control command set uses Protocol Buffers serialization protocol encoding to reduce storage space usage. The environmental feature vector set is organized in a columnar storage structure, with each feature field accompanied by data acquisition time and location labels.

[0070] The algorithm for generating a unique production batch code integrates three elements: time, spatial location, and equipment identification. The time component is accurate to the millisecond and recorded using the ISO8601 extended format. Spatial location is determined using a tea garden zoning system, dividing the entire planting area into 100m x 100m grid units. The equipment identifier consists of an equipment type code and a serial number, generated as a fixed-length hash value using the SHA-256 algorithm. Once generated, the code is immediately written to the blockchain's genesis block, and all subsequent related operation records are appended to this blockchain as transactions. The transaction data structure includes an operation type field, a timestamp field, and a detailed content field in JSON format. Each transaction is signed with a CA certificate and broadcast to all nodes on the network.

[0071] Communication between blockchain nodes uses the TLS 1.3 encryption protocol, and message propagation utilizes the gossip protocol for decentralized distribution. The smart contract logic is deployed in Docker containers and primarily includes data verification rules, access control policies, and query optimization algorithms. The data verification phase checks the continuity of time series data and the reasonableness of parameter ranges, rejecting write requests for obviously abnormal data. Access control is based on the RBAC model, defining three roles: grower, quality control officer, and administrator, each with different data viewing and operation permissions. The query optimization algorithm builds an inverted index for time-range queries and an R-tree index for spatial-range queries.

[0072] The dynamic weight allocation submodule's update mechanism maintains real-time synchronization with blockchain data. When the quality closed-loop feedback module transmits new historical high-quality tea production records, the system first verifies the data signature and integrity, then parses the optimal selenium concentration data. The deviation of the optimal selenium concentration of actual harvested tea from the target range is judged using a sliding window statistical method, with the window size being the most recent 20 production batches. Extreme weather event judgment is integrated with the meteorological bureau's warning signal coding system, identifying six types of events affecting tea tree growth, including heavy rain, high temperatures, and frost. Photo-oxidative damage characteristics are quantified using computer vision algorithms, analyzing the hue and saturation distribution of leaf images in the HSV color space.

[0073] The weighting coefficient adjustment process employs a gradual change strategy. The first weighting coefficient for the average soil selenium content is adjusted by no more than 0.1 each time to avoid drastic fluctuations in system parameters. The second weighting coefficient for the slope of temperature and humidity changes automatically increases its baseline sensitivity during the rainy season and reverts to its default value during the dry season. The third weighting coefficient for the variance of light intensity distribution is dynamically correlated with sunrise and sunset times, with its weighting influence appropriately reduced during periods of short sunshine duration. Each weighting update generates a version record, including the reason for the adjustment, the old value, the new value, and an assessment of the expected impact. These records are written to the blockchain as special transactions.

[0074] The traceability query function enables multi-dimensional data reconstruction. When a user selects a specific production batch, the system extracts all relevant transaction records from the blockchain and reconstructs the production process in chronological order. Key operational nodes, such as selenium replenishment time points, environmental control operations, and equipment maintenance events, are marked with different colors. The environmental parameter fluctuation curve is plotted using a smoothing algorithm to eliminate sensor noise interference while preserving trend characteristics. Finally, a correlation graph is established between the selenium concentration detection results and the operations at each stage, intuitively displaying the influencing factors and transmission paths.

[0075] The data security mechanism employs a layered protection design. The network layer deploys an intrusion detection system to monitor abnormal traffic in real time. The storage layer implements AES-256 encryption to protect static data. The access layer requires two-factor authentication, combining SMS verification codes and hardware keys. The audit layer records all data access and modification operations, with audit logs stored separately on tamper-proof storage devices. The system automatically generates a daily security status report, detecting potential vulnerabilities and abnormal behavior.

[0076] The system maintenance interface provides health monitoring capabilities for blockchain nodes. The dashboard displays real-time data on each node's CPU load, memory usage, and network throughput. Smart contract version management supports canary releases, allowing new contracts to be validated on the testnet before deployment to the production environment. The data migration tool can batch convert records from historical databases into blockchain transaction formats, maintaining data integrity and business continuity during the migration process.

[0077] Integration with external systems employs a standardized API gateway design. The supply chain system queries tea traceability information via REST API, with parameters including batch number, time range, and production region. The government regulatory platform uses a WebSocket interface to receive real-time monitoring data, with the data format conforming to the agricultural product quality and safety traceability system specifications. Mobile applications call a lightweight JSON API to obtain simplified traceability information, adapting to mobile network bandwidth limitations.

[0078] An anomaly handling process establishes a tiered response mechanism. Network partitioning anomalies trigger the RAFT algorithm to re-elect a master node. In cases of data inconsistency, the Byzantine fault tolerance protocol is activated to coordinate the states of all nodes. When a smart contract execution error occurs, the transaction is rolled back and the problematic contract version is marked. In the event of hardware failure, a backup node is automatically switched over; after the failed node is repaired, it rejoins the network through a data synchronization mechanism.

[0079] The user interface design emphasizes a balance between intuitive operation and information visualization. The timeline view supports zooming and panning, and key events display detailed information in floating windows. Environmental parameter curves can be overlaid to display comparative data from multiple monitoring points. Device operation logs are presented as flowcharts, annotating the execution time and status of each step. All visualization components support exporting to PDF reports or interactive HTML documents.

[0080] It should be noted that, in this document, relational 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 these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0081] Although embodiments of the invention 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 invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent control system for automated production of selenium-enriched white tea, characterized in that, The system includes: The selenium-enriched white tea growth environment control module is used to collect real-time data on the distribution of selenium content in tea garden soil, air temperature and humidity, and light intensity, and to establish a dynamic monitoring set of environmental parameters in multiple areas of the tea garden. The tea growth stage identification module receives the dynamic monitoring set of environmental parameters from multiple areas of the tea garden, and combines the tea tree bud and leaf morphology image sequence with growth cycle time data to identify the growth stages of tea: germination period, extension period and mature harvesting period. The selenium content adaptive replenishment module acquires the growth stage identifier and the current soil selenium content distribution data, and generates selenium nutrient solution replenishment schemes and sprinkler irrigation equipment control instruction sets for different growth stages. The production equipment collaborative control module parses the control instruction set of the sprinkler irrigation equipment, synchronously links the shade net control mechanism, the atomizing nozzle array and the soil injection device, and executes the multi-equipment collaborative operation timing control process. The quality closed-loop feedback module collects selenium concentration detection data and appearance characteristic data of the processed tea product, compares them with preset quality standard thresholds, generates selenium content compensation coefficient and environmental parameter correction amount, and feeds them back to the selenium-rich white tea growth environment control module for parameter iterative updates.

2. The intelligent control system for automated production of selenium-enriched white tea according to claim 1, characterized in that, The selenium-rich white tea growth environment control module includes: The multi-source sensor fusion submodule deploys a soil selenium content sensor network, a weather station, and a spectral imager to periodically collect selenium concentration gradient data, microclimate parameter matrix, and leaf reflectance spectrum in the three-dimensional spatial coordinates of the tea garden. The environmental feature extraction submodule receives the selenium concentration gradient data, microclimate parameter matrix and leaf reflectance spectrum, removes abnormal fluctuation data points, calculates the mean soil selenium content, temperature and humidity change slope and light intensity distribution variance in each monitoring unit, and constructs a standardized environmental feature vector set. The dynamic weight allocation submodule, based on tea tree variety gene bank data and historical high-quality tea production records, assigns a first weight coefficient to the average soil selenium content in the standardized environmental feature vector set, a second weight coefficient to the slope of temperature and humidity changes, and a third weight coefficient to the variance of light intensity distribution, thereby generating a weighted environmental evaluation index set.

3. The intelligent control system for automated production of selenium-enriched white tea according to claim 2, characterized in that, The tea growth stage identification module includes: The morphological analysis submodule calls the machine vision unit to capture high-resolution images of tea buds and leaves, and extracts bud and leaf contour curvature feature points, leaf unfolding angle and new shoot length data. The growth sequence alignment submodule dynamically time-normalizes and matches the bud and leaf contour curvature feature points, leaf unfolding angle and new shoot length data with the standard growth curve library, and outputs the growth progress deviation index. The stage decision submodule maps the current growth stage identifier according to the standard growth curve library when the growth progress deviation index is lower than the progress tolerance threshold; when the growth progress deviation index exceeds the progress tolerance threshold, it triggers a manual review instruction and updates the standard growth curve library data.

4. The intelligent control system for automated production of selenium-enriched white tea according to claim 3, characterized in that, The selenium content adaptive replenishment module includes: The demand modeling submodule receives the growth stage identifier and the current soil selenium content distribution data, queries the selenium absorption rate comparison table, and calculates the difference between the theoretical selenium accumulation and the actual soil selenium content during the target harvest period. The replenishment strategy generation submodule inputs the difference between the theoretical selenium accumulation and the actual soil selenium content into the nutrient solution ratio decision tree and outputs the foliar spray concentration gradient, soil injection depth and single replenishment duration. The equipment instruction compilation submodule converts the foliar spray concentration gradient into atomizing nozzle pressure value, maps the soil injection depth into hydraulic cylinder stroke parameters, and compiles the single replenishment duration into equipment start-stop sequence code.

5. The intelligent control system for automated production of selenium-enriched white tea according to claim 4, characterized in that, The production equipment collaborative control module includes: The spatial coordinate mapping submodule parses the 3D model of the tea garden terrain and the topology of the equipment layout, converts the pressure value of the atomizing nozzle into the spatial coordinate control signal of the nozzle unit, and associates the stroke parameters of the hydraulic cylinder with the coordinates of the geographic information system. The timing synchronization submodule inserts an interlock delay time window into the equipment start-stop timing code based on the tea harvesting operation calendar and weather warning data to avoid conflicts between sprinkler irrigation operations and tea harvesting machinery operation. The abnormal fuse submodule monitors the water pressure fluctuation variance of the atomizing nozzle array and the flow mutation value of the soil injection device in real time. When the water pressure fluctuation variance exceeds the equipment safety threshold or the flow mutation value reaches the fault threshold, it sends an emergency shutdown command to all executing devices.

6. The intelligent control system for automated production of selenium-enriched white tea according to claim 5, characterized in that, The quality closed-loop feedback module includes: The non-destructive testing submodule uses an X-ray fluorescence spectrometer to scan finished tea samples and generate a heat map of selenium concentration distribution and a leaf integrity score. The quality correlation analysis submodule compares the spatial overlap between the selenium element concentration distribution heatmap and the preset quality standard threshold, calculates the concentration compliance coverage index, and correlates the leaf integrity score with the mechanical damage record. The parameter iteration submodule generates a selenium content compensation coefficient and transmits it to the selenium content adaptive replenishment module when the concentration compliance coverage index is lower than the preset qualification rate lower limit; when the leaf integrity score continuously decreases beyond the quality decay slope threshold, it generates an environmental parameter correction amount and transmits it to the selenium-rich white tea growth environment control module.

7. The intelligent control system for automated production of selenium-enriched white tea according to claim 6, characterized in that, Also includes: The full-process traceability module is connected to the data output end of the quality closed-loop feedback module. It collects the selenium element concentration distribution heat map, equipment control instruction set and environmental feature vector set, generates a unique code for the production batch and binds it to the blockchain distributed ledger. When the full-process traceability module retrieves the blockchain distributed ledger of any batch of tea, it reassembles the selenium replenishment operation time point, environmental parameter fluctuation curve, and final selenium concentration detection results in chronological order.

8. The intelligent control system for automated production of selenium-enriched white tea according to claim 7, characterized in that, When the dynamic weight allocation submodule performs weight updates: Receive the optimal selenium concentration value from the historical high-quality tea production records transmitted by the quality closed-loop feedback module. When the optimal selenium concentration of the actual harvested tea leaves deviates from the target range, the first weighting coefficient of the average selenium content of the soil is increased. When the tea garden encounters extreme weather events, the second weighting coefficient of the slope of the temperature and humidity change is increased; When the finished tea product exhibits photo-oxidative damage characteristics, the third weighting coefficient of the variance of the light intensity distribution is increased.

9. An intelligent control method for automated production of selenium-enriched white tea, applied to the intelligent control system for automated production of selenium-enriched white tea as described in any one of claims 1-8, characterized in that, The method includes: Dynamic data on soil selenium content, canopy temperature and humidity gradient, and light intensity time series were simultaneously collected through a multi-dimensional sensor network deployed in the tea garden. By integrating the morphological characteristics of tea buds and leaves with time-series data of the growth cycle, the critical points for the transition of tea growth stages can be identified. Based on the difference between the target selenium accumulation and the real-time soil selenium content, a selenium nutrient solution replenishment parameter matrix is ​​dynamically generated. The selenium nutrient solution replenishment parameter matrix is ​​compiled into multi-device control instructions, and interlock delays for equipment collaborative operation are inserted; The spatial distribution and physical integrity of selenium in finished tea products are detected, and the resulting parameter compensation is fed back into the environmental control process. Bind all production process data to blockchain nodes to achieve cross-process traceability.

10. The method according to claim 9, characterized in that, The specific steps involved in feeding back the generated parameter compensation amount to the environmental control process are as follows: When the selenium concentration at the edge of the finished tea leaves is lower than that in the center, the atomization coverage radius of the foliar spraying operation should be increased. When multiple batches of tea leaves suffer mechanical damage, the soil injection device must not be activated while the tea-picking machinery is in operation. When the selenium leaching loss rate exceeds the preset threshold during the rainy season, the baseline value of the selenium nutrient solution concentration for the next soil injection will be increased.

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