Seedling cultivation system and method based on forestry

Abstract

The invention relates to the technical field of intelligent forestry, and discloses a seedling cultivation system and method based on forestry. The system comprises a distributed environment sensing module which is deployed in a forest seedling cultivation area and generates an environment data flow; the forest seedling growth situation prediction module accesses the environment data flow, analyzes the environment data flow through a neural network model, and outputs a growth environment expected value; the multi-mode control strategy module receives a growth environment expected value and real-time environment data, and generates an equipment control instruction set based on a preset strategy library; the execution effect feedback module acquires environment change data after the equipment control instruction set is executed, and compares the environment change data with a growth environment expected value to generate a deviation correction value; the control instruction optimization module performs iterative optimization on the equipment control instruction set by using the deviation correction value, and outputs an optimized control instruction set; and the environment adjustment execution module responds to the optimization control instruction set and drives the forest seedling cultivation environment adjustment equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent forestry, in particular to a forestry seedling cultivation system and method. BACKGROUND

[0002] Current forestry seedling cultivation environment regulation mainly adopts methods based on fixed thresholds or timing control. The existing technology mainly uses independent sensors for environmental parameter monitoring, lacking the ability of multi-parameter synchronous acquisition and correlation analysis. The environmental regulation strategy usually relies on manual experience setting and cannot be dynamically adjusted according to the actual growth needs of the seedlings. The information transmission between the modules of the control system lags behind, making it difficult to achieve real-time and accurate regulation. The control parameter adjustment lacks data support due to insufficient execution effect evaluation. The existing system needs to solve key technical problems such as synchronous acquisition of environmental data, growth demand prediction, control strategy optimization, and execution effect feedback.

[0003] Traditional forestry seedling cultivation systems have obvious deficiencies in environmental perception accuracy and intelligent regulation. The deployment of distributed sensor networks is imperfect, and there are problems of time and space asynchronization in environmental data acquisition. The growth prediction model is simple and cannot effectively learn the complex nonlinear relationship between environmental parameters and seedling growth. The control strategy library is updated slowly and cannot adapt to the regulation needs of different growth stages. The response characteristics of the execution mechanism are not matched, resulting in poor collaborative control effect of multiple devices. The feedback mechanism is not perfect, and the control deviation correction lacks a systematic method. The optimization process relies on manual intervention, making it difficult to achieve automatic iterative optimization of control parameters. The existing technology needs to establish a closed-loop intelligent regulation system from environmental perception to precise execution. SUMMARY

[0004] The present application relates to the technical field of intelligent forestry, in particular to a forestry seedling cultivation system and method.

[0005] To achieve the above-mentioned purpose, the present application provides a forestry seedling cultivation system, which comprises:

[0006] A distributed environmental perception module is arranged in the seedling cultivation area to synchronously capture temperature parameters, humidity parameters and light intensity parameters, and generate an environmental data stream;

[0007] A seedling growth trend prediction module is connected to the environmental data stream, analyzes the environmental data stream through a neural network model, and outputs an expected value of the growth environment;

[0008] A multi-modal control strategy module receives the expected value of the growth environment and real-time environmental data, and generates a set of device control instructions based on a pre-set strategy library;

[0009] An execution effect feedback module collects environmental change data after the equipment control instruction set is executed, compares the environmental change data with the expected value of the growth environment, and generates a deviation correction amount.

[0010] A control command optimization module, which uses the deviation correction amount to iteratively optimize the equipment control command set and outputs an optimized control command set;

[0011] An environmental regulation execution module, which responds to the optimized control instruction set, drives the environmental regulation equipment for forest seedling cultivation.

[0012] Preferably, the distributed environmental sensing module synchronously captures temperature parameters, humidity parameters, and light intensity parameters, and generates an environmental data stream, including:

[0013] Multiple temperature sensors, multiple humidity sensors, and multiple light sensors are deployed in the forest seedling cultivation area to form a sensor network;

[0014] The sensor network collects raw temperature data, raw humidity data, and raw illumination data at a preset sampling frequency.

[0015] The raw temperature data, raw humidity data, and raw illumination data are fused together to generate a time-series environmental data stream.

[0016] Preferably, the forest seedling growth status prediction module analyzes the environmental data stream through a neural network model and outputs expected growth environment values, including:

[0017] A neural network model for predicting the growth of forest tree seedlings is constructed, the neural network model comprising an input layer, a hidden layer, and an output layer;

[0018] The environmental data stream is input into the input layer of the neural network model, features are extracted through the hidden layer, and the output layer generates a growth status score for forest seedlings.

[0019] Based on the growth status score of the forest tree seedlings, a pre-set growth environment mapping table is queried to obtain the corresponding expected value of the growth environment.

[0020] Preferably, the multimodal control strategy module generates a device control instruction set based on a preset strategy library, including:

[0021] The pre-built policy library stores a variety of environmental control policies, each of which is associated with a range of environmental conditions.

[0022] The expected growth environment value and real-time environmental data are matched with the environmental condition range in the preset strategy library, and a matching environmental control strategy is selected.

[0023] A set of equipment control instructions is generated based on the selected environmental control strategy. The set of equipment control instructions includes temperature control instructions, humidity control instructions, and light control instructions.

[0024] Preferably, the execution effect feedback module collects environmental change data and compares the environmental change data with the expected value of the growth environment to generate a deviation correction amount, including:

[0025] After the equipment control command set is executed, the current temperature data, current humidity data and current light data of the seedling cultivation area are collected again through the distributed environmental sensing module;

[0026] The temperature deviation is obtained by calculating the difference between the current temperature data and the expected temperature value in the growth environment.

[0027] The humidity deviation is obtained by calculating the difference between the current humidity data and the expected humidity value in the growth environment.

[0028] The difference between the current light data and the expected light value in the growth environment is calculated to obtain the light deviation;

[0029] A comprehensive deviation correction is generated based on a weighted average algorithm considering temperature deviation, humidity deviation, and illumination deviation.

[0030] Preferably, the control command optimization module uses the deviation correction amount to iteratively optimize the device control command set, including:

[0031] The equipment control command set and the comprehensive deviation correction amount are input into the optimization algorithm model, which is a gradient descent algorithm.

[0032] The parameters in the device control command set are adjusted using the gradient descent algorithm to generate an intermediate control command set.

[0033] Perform a feasibility check on the intermediate control instruction set. If the check passes, output the optimized control instruction set.

[0034] Preferably, the environmental regulation execution module drives the forest tree seedling cultivation environmental regulation equipment, including:

[0035] Environmental control equipment includes heaters, humidifiers, and supplemental lighting;

[0036] The optimized control instruction set is parsed into heater control signal, humidifier control signal and supplementary light control signal;

[0037] Adjust the heater power according to the heater control signal, adjust the humidifier humidity output according to the humidifier control signal, and adjust the supplementary light intensity according to the supplementary light control signal.

[0038] Preferably, the neural network model training process in the forest seedling growth status prediction module includes:

[0039] Collect historical growth data of forest tree seedlings and corresponding environmental data to form a training dataset;

[0040] The neural network model is trained under supervision using the training dataset, and the model weights are adjusted using the backpropagation algorithm.

[0041] Verify the accuracy of the trained neural network model, and retrain it if the accuracy falls below a threshold.

[0042] Preferably, the preset strategy library update process in the multimodal control strategy module includes:

[0043] Monitor the growth performance indicators of forest tree seedlings, and trigger a strategy database update when the performance indicators are lower than expected;

[0044] Collect the latest successful environmental control cases and extract control strategy parameters;

[0045] Add the extracted control policy parameters to the preset policy library and remove expired policies.

[0046] Preferably, the present invention also includes an intelligent forestry seedling cultivation method, the method comprising all the modules and process flow of the above-mentioned forestry seedling cultivation system.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] The forest tree seedling growth status prediction module analyzes environmental data streams through a neural network model and outputs expected growth environment values. The neural network model employs a multilayer perceptron structure and trains network weight parameters using a backpropagation algorithm. The model's input layer receives time-series data on temperature, humidity, and light intensity; the hidden layers learn nonlinear combinations of environmental features; and the output layer generates corresponding expected environmental parameter values. The training process uses the correspondence between historical environmental data and optimal growth states as a supervisory signal, enabling the model to capture the complex mapping relationship between environmental parameters and growth status. The expected growth environment values ​​dynamically reflect the optimal environmental conditions for forest tree seedlings at different growth stages, providing a target basis for precise regulation.

[0049] The execution effect feedback module collects environmental change data after the equipment control command set is executed, comparing the actual environmental parameters with the expected values ​​of the growth environment. Environmental change data is acquired in real time through a distributed sensor network, recording changes in temperature, humidity, and light intensity after the implementation of control measures. The comparison process calculates the deviation between the measured and expected values ​​of each environmental parameter, quantifying the effectiveness of the control measures. The deviation calculation considers the importance and dimensional differences of the parameters, using a weighted average method to obtain the comprehensive deviation correction. The deviation correction accurately reflects the gap between the current environmental state and the ideal state, providing a quantitative indicator for optimizing the control strategy.

[0050] The control command optimization module iteratively optimizes the equipment control command set using deviation correction values. The optimization process employs a gradient descent algorithm, adjusting parameter settings in the control commands based on the deviation correction values. Each iteration updates parameters such as the execution intensity and duration of the control commands, gradually bringing the environmental control effect closer to the desired target. The optimization process sets convergence conditions; iteration stops when the deviation is less than a set threshold, and the optimized control command set is output. The environmental regulation execution module responds to the optimized control command set, driving environmental regulation equipment such as heating, humidification, and lighting to work collaboratively. The executing equipment precisely adjusts its output power and operating time according to the command requirements, achieving rapid and accurate environmental regulation. Through a closed-loop control system of sensing, prediction, control, feedback, and optimization, intelligent management of the seedling cultivation environment is achieved. Attached Figure Description

[0051] Figure 1 This is a schematic diagram illustrating the working principle of the forestry seedling cultivation system described in this invention.

[0052] Figure 2 A flowchart for generating environmental data streams for the distributed environment awareness module;

[0053] Figure 3 A flowchart for outputting the expected values ​​of the growth environment for the forest tree seedling growth status prediction module;

[0054] Figure 4 A comparison chart showing the effects of multimodal control strategies at different growth stages;

[0055] Figure 5 Trend chart of deviations in environmental parameters and comprehensive correction quantities for forest tree seedling cultivation. Detailed Implementation

[0056] 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.

[0057] Please see Figure 1 This invention provides a forestry seedling cultivation system and method. The system includes: a distributed environmental perception module, a forest seedling growth status prediction module, a multimodal control strategy module, an execution effect feedback module, a control command optimization module, and an environmental regulation execution module. Specific implementation methods are as follows:

[0058] A distributed environmental sensing module is deployed in the forest tree seedling cultivation area to synchronously capture temperature, humidity, and light intensity parameters and generate an environmental data stream. A forest tree seedling growth status prediction module receives the environmental data stream, analyzes it using a neural network model, and outputs the expected growth environment value. A multimodal control strategy module receives the expected growth environment value and real-time environmental data, and generates a set of equipment control instructions based on a pre-set strategy library. An execution effect feedback module collects environmental change data after the equipment control instructions are executed, compares the environmental change data with the expected growth environment value, and generates a deviation correction. A control instruction optimization module iteratively optimizes the equipment control instruction set using the deviation correction and outputs an optimized control instruction set. An environmental regulation execution module responds to the optimized control instruction set and drives the forest tree seedling cultivation environmental regulation equipment. The system's modules achieve closed-loop control through data and instruction streams. The forest tree seedling cultivation environmental regulation equipment includes heaters, humidifiers, and supplemental lighting; inter-module collaboration ensures dynamic adjustment of environmental parameters.

[0059] Example 1: See Figure 2 In practical implementation, the construction of the distributed environmental sensing module begins with the deployment of a sensor network in the forest seedling cultivation area. This area is divided into multiple logical grid units, each containing one temperature sensor, one humidity sensor, and one light sensor. The temperature sensor uses a digital DS18B20 temperature probe, whose measurement range covers the typical temperature range required for forest seedling growth. The humidity sensor employs a HIH-4000 series capacitive humidity sensing element, and the light sensor is a BH1750FVI digital light intensity sensor. These sensors form a star network topology via wired connections. The aggregation node communicates with the central processing unit via an RS-485 bus. In the wireless connection scheme, the sensor nodes are equipped with Zigbee communication modules, forming a self-organizing network. The physical installation locations of the sensor nodes are optimized: the temperature sensor maintains a fixed distance from the seedling canopy height, the humidity sensor avoids direct spraying areas, and the light sensor is installed facing upwards in an unobstructed location.

[0060] In practice, the preset sampling frequency is dynamically adjusted according to different physiological stages of forest seedling growth. During the seedling germination stage, the sampling frequency is set to once every five minutes; during the rapid growth stage, it is adjusted to once every ten minutes; and during the stable growth stage, it is reduced to once every thirty minutes. Sampling commands are uniformly issued by the central processing unit, and sensor nodes synchronously execute data acquisition operations. Raw temperature data, raw humidity data, and raw illumination data, along with timestamps and node numbers, are transmitted to the data buffer pool. The data buffer pool uses a circular queue structure to store the raw dataset within the latest acquisition period. Raw temperature data is stored as a 16-bit signed integer, raw humidity data is represented as a 12-bit analog-to-digital conversion value, and raw illumination data is stored as a 32-bit floating-point number in lux.

[0061] In its implementation, the data fusion process comprises three sequential stages. The first stage involves data cleaning, where an algorithm detects and marks outliers caused by transmission errors. Outlier identification is based on the statistical characteristics of the data within a sliding window, discarding data points exceeding three times the standard deviation for three consecutive sampling periods. The second stage involves missing data imputation, using a time-series prediction algorithm based on the ARIMA model to predict and interpolate data before and after the missing location. The third stage involves multi-sensor data fusion, employing a weighted average method. The weight coefficient of each sensor node is determined by its historical data reliability score, calculated through long-term operational stability testing. The resulting time-series environmental data stream includes fused temperature, humidity, and light intensity values, stored in CSV format with an attached integrity checksum.

[0062] In some embodiments, the sensor network employs a hybrid power supply mode. Sensor nodes located in well-lit areas are equipped with an energy system consisting of solar panels and lithium battery packs, while sensor nodes located in shaded areas are powered by a DC-DC regulated power supply converted from AC mains power. Each sensor node has a built-in power management unit that monitors the node's energy consumption status and triggers a power-saving mode when the battery is low. In power-saving mode, non-essential sensors enter a sleep state, and the communication module reduces its transmission power. The sensor nodes have an IP65 protection rating, effectively preventing irrigation water mist and dust intrusion. Temperature sensor probes are equipped with radiation shields to avoid direct sunlight causing inflated measurement values.

[0063] In some embodiments, the data fusion process incorporates a Kalman filter algorithm to improve data accuracy. The state variables of the Kalman filter are defined as the true values ​​of temperature, humidity, and light intensity, while the observed variables are the readings of each sensor. The process noise covariance matrix and the observation noise covariance matrix are obtained through calibration using historical data. The filter prediction step estimates the environmental state at the next moment based on a physical propagation model, and the update step corrects the estimation results by combining sensor observations. The environmental data stream after iterative filtering has higher smoothness and accuracy. The storage structure of the environmental data stream adopts a time-partitioned table format. Each data packet contains a millisecond-level timestamp, sensor network number, and checksum field. The data stream transmission employs a breakpoint resumption mechanism to ensure data integrity in the event of network anomalies.

[0064] Optionally, baseline calibration is performed during the sensor network deployment phase. Baseline calibration is conducted under standard environmental conditions. Temperature calibration uses a standard mercury thermometer as a reference, humidity calibration employs a saturated salt solution calibration method, and illumination calibration relies on a standard photometer. The readings of each sensor node are compared and analyzed with the standard instrument readings to calculate individual correction coefficients. These correction coefficients are stored in the node's non-volatile memory and are automatically applied to correct the raw data during data acquisition. During sensor network operation, periodic self-diagnostics are performed. The self-diagnostic program checks sensor response curves and communication quality. Abnormal nodes are automatically excluded from data fusion weight calculation and fault information is reported via the network management protocol.

[0065] Optionally, a data compression strategy is implemented during the generation of the environmental data stream. The lossless compression algorithm uses the LZW dictionary encoding method, while DPCM differential pulse code modulation is used for lossy compression scenarios. Compressed data packets are appended with frame header identifiers and cyclic redundancy check codes. The data stream transmission bandwidth is dynamically adjusted according to network conditions, prioritizing the integrity of temperature data transmission when bandwidth is limited. The environmental data stream access interface follows a RESTful architecture style, providing data query services based on the HTTP protocol. Clients can retrieve historical environmental data by specifying time and spatial ranges.

[0066] It is understandable that the timing synchronization mechanism of the distributed environmental sensing module adopts the IEEE 1588 precision clock synchronization protocol. The master clock node in the network periodically broadcasts synchronization messages, and the clock offset is calculated from the slave clock nodes to adjust the local timer. The synchronization accuracy reaches the microsecond level, ensuring that sensor data from different spatial locations are strictly comparable. The firmware program of the sensor node implements a lightweight embedded operating system, the task scheduler prioritizes data acquisition and transmission tasks, and the watchdog timer monitors the program's running status and triggers automatic restart in case of abnormalities.

[0067] It is understandable that the environmental data stream quality control system includes a real-time verification process. The verification algorithm constructs a physical constraint relationship model of temperature, humidity, and light intensity. When consecutive data combinations that violate physical laws appear in the data stream, an alarm is automatically triggered. The quality control log records the processing trajectory of each data point, including raw readings, cleaning markers, imputation values, and fusion weights. The log files are periodically archived for subsequent audit analysis. The operational status of the distributed environmental sensing module is displayed through a visual monitoring interface. The interface dynamically updates the location distribution, real-time readings, and health status of each sensor node, and supports interactive settings of sampling parameters and alarm thresholds by operators.

[0068] Example 2: See Figure 3 In its implementation, the neural network model for the forest seedling growth status prediction module adopts a hierarchical architecture. The number of nodes in the input layer is determined by the dimension of the environmental data stream, with each node corresponding to a time series point of an environmental parameter. The hidden layer contains three fully connected layers, and a Dropout mechanism is used between layers to prevent overfitting. The output layer uses a Sigmoid activation function to map the results to a range of 0 to 1, representing the seedling growth status score. The weights of the neural network model are initialized using the He normal distribution method, with the bias term initialized to zero. The number of neurons in the hidden layer is dynamically adjusted according to the size of the training dataset. The loss function is the mean squared error function, and the optimizer is configured as the Adam optimizer with an initial learning rate of 0.001. The inference process of the neural network model uses a sliding window approach to input the environmental data stream. Each time window contains temperature, humidity, and light data from 96 consecutive sampling points. The data is normalized before input to conform to a standard normal distribution.

[0069] In implementation, a pre-defined growth environment mapping table is stored in a relational database as a two-dimensional table. The row indexes correspond to the discretized intervals of seedling growth status scores, while the column indexes represent the types of environmental parameters. Each cell stores the optimal environmental parameter value for its corresponding score interval. The mapping table is constructed based on a large amount of historical planting experiment data. Clustering algorithms are used to group environmental parameters with similar growth effects, and equal-frequency binning is used to divide the score intervals to ensure each interval contains sufficient data samples. The query operation uses a binary search algorithm to locate the interval to which the current seedling growth status score belongs and retrieves the corresponding expected values ​​for temperature, humidity, and light to combine into the expected growth environment value. The expected growth environment value is output as a JSON data packet, containing a timestamp, score, and expected values ​​for each environmental parameter.

[0070] In practice, the collection period for historical forest tree seedling growth data during the neural network model training process covers the entire growing season. Environmental data and growth data are strictly aligned using timestamps. The growth data includes daily measurements of morphological indicators such as plant height, stem height, and leaf area index. The construction of the training dataset requires data preprocessing, including removing abnormal growth records, filling in missing measurements, and smoothing noisy data, ultimately forming sample-label paired data pairs. The supervised training phase employs a mini-batch gradient descent algorithm with a batch size of 32 samples. The number of training iterations is controlled using early stopping; training terminates when the validation set loss function fails to decrease for 10 consecutive cycles. The backpropagation algorithm calculates the gradient of the loss function with respect to the network weights, and gradient pruning techniques limit the gradient norm to within 1.0 to prevent gradient explosion.

[0071] In practice, the neural network model accuracy verification employs a k-fold cross-validation method. The training dataset is randomly divided into 10 mutually exclusive subsets, with 9 subsets used alternately as the training set and the remaining subset as the validation set. The accuracy metrics used are the coefficient of determination (R²) and the mean absolute error (MAE). The validation process records the evaluation metrics for each fold and calculates the mean and standard deviation. When the accuracy falls below a preset threshold of 0.85, a retraining process is triggered. During retraining, the training dataset is expanded, and network hyperparameters are adjusted, including the learning rate decay strategy, the number of hidden layer nodes, and the dropout ratio. The retrained neural network model must undergo a complete validation process before being deployed to the production environment. The model weight files are version-managed to support rapid rollback.

[0072] In some embodiments, residual connection structures are introduced into the hidden layers of the neural network model. The residual blocks contain two fully connected layers and cross-layer connections. This design alleviates the vanishing gradient problem in deep networks. A leaky linear rectified function is chosen as the activation function, with a negative slope parameter set to 0.01. A batch normalization layer is added after the output layer to accelerate model convergence. Training data augmentation techniques are applied to scenarios with limited datasets. Data augmentation methods include adding Gaussian noise, time-series warping, and random scaling, increasing the size of the augmented dataset by five times.

[0073] In some embodiments, the update mechanism of the pre-set forest growth environment mapping table adopts an online learning strategy. When new seedling growth data accumulates to a certain scale, an incremental update of the mapping table is triggered. The update algorithm is based on a weighted moving average method, with new data samples having higher weights. The update magnitude of each cell value in the mapping table is positively correlated with the data reliability score. The version control mechanism records the change history of the mapping table, supports manual rollback to any historical version, and the change log records in detail the time, data source, and modified content of each update.

[0074] Optionally, the neural network model is deployed using the TensorFlowServing architecture, with model files saved in SavedModel format and loaded onto the model server. The inference service provides online prediction via the gRPC interface; when the client calls the service, it passes in an environmental data stream sequence, and the service returns a seedling growth status score and confidence level. Service instances are deployed in a multi-replica mode to achieve load balancing, with each replica running in an independent Docker container. The container orchestration platform monitors the health status of the service instances and automatically restarts any abnormal instances.

[0075] Optionally, the training dataset is stored in a columnar storage format to improve read efficiency, environment data is stored in Parquet files, and growth data is stored in Avro files. The data access layer implements a caching mechanism, reserving frequently queried data blocks in an in-memory cache, with a cache invalidation strategy based on the Least Recently Used (LRU) algorithm. Training tasks are submitted to a distributed computing cluster for execution, with computing resources dynamically allocated; each training task is assigned dedicated GPU resources to accelerate matrix operations.

[0076] Understandably, the interpretative analysis of the neural network model employs a gradient-weighted activation mapping method to generate heatmaps showing the time periods in the input environmental data that significantly impact the prediction results. The interpretative analysis results assist forestry experts in validating the model's decision-making logic, and cases of abnormal decisions are submitted to experts for manual review. The model monitoring system continuously tracks prediction deviations, automatically triggering a model retraining process when the difference between the predicted and actual measured values ​​continues to widen.

[0077] It is understandable that the data interface between the forest seedling growth status prediction module and the distributed environmental perception module adopts an asynchronous message queue mode. The environmental data stream is segmented into message packets and sent to the message queue. The consumer process retrieves messages from the queue for batch prediction. The message queue implements a backpressure mechanism to prevent data backlog, automatically expanding the number of consumer instances when the processing speed is lower than the generation speed. Prediction results are written to the distributed database and simultaneously sent to the message topic of the multimodal control strategy module, achieving decoupling and asynchronous communication between modules.

[0078] Example 3: In specific implementation, the pre-set strategy library of the multimodal control strategy module adopts a relational database storage architecture. The pre-set strategy library contains three main data tables: a strategy definition table, an environmental condition mapping table, and an execution parameter table. The strategy definition table records the unique identifier, strategy name, and strategy type field of the environmental control strategy. The strategy type field distinguishes different control paradigms such as PID control, fuzzy logic control, and rule-based control. The environmental condition mapping table establishes the association between the strategy identifier and the environmental parameter range. The environmental parameter range is stored in the form of numerical intervals for the upper and lower bounds of temperature, humidity, and light intensity. The execution parameter table stores the specific control parameters for each environmental control strategy. The PID control strategy stores the proportional coefficient, integral time constant, and derivative time constant; the fuzzy logic control strategy stores the membership function definition and inference rule set; and the rule-based control strategy stores the antecedent and consequent expressions of the production rules.

[0079] In practical implementation, the matching process for environmental control strategies employs a multi-level matching algorithm. The first level of matching involves coarsely matching the expected values ​​of temperature, humidity, and light intensity for the desired growth environment against the environmental condition ranges in the pre-set strategy library. The coarse matching algorithm calculates the temperature range coverage, humidity range coverage, and light intensity range coverage for each environmental control strategy. The coverage calculation formula is as follows:

[0080]

[0081] in: , , These represent the matching degree of temperature, humidity, and light intensity, respectively. Represents temperature. Represents humidity. Represents light, It is an index symbol for environment parameter types. , , These represent the upper limits for temperature, humidity, and light intensity in the strategy, respectively. , , These represent the lower limits of temperature, humidity, and light intensity in the strategy, respectively. , , These represent the expected values ​​for temperature, humidity, and light intensity in the desired growth environment. The second level of matching combines real-time environmental data for fine-tuning, calculating the deviation between the real-time data and the desired growth environment. After weighted calculation of these deviations, the matching degrees at each level are adjusted, and finally, the environmental control strategy with the highest overall matching degree is selected as the chosen strategy.

[0082] In practical implementation, the generation of the equipment control instruction set adopts different code paths depending on the type of the selected environmental control strategy. For the PID control strategy, the equipment control instruction set calculates the control quantity through the PID control algorithm, and the calculation formula is based on the positional PID algorithm. The temperature control instruction is calculated with the expected temperature value as the setpoint and real-time temperature data as the feedback value. The humidity control instruction and the light control instruction use the same calculation logic. For the fuzzy logic control strategy, the equipment control instruction set is generated through a fuzzy inference system. The fuzzification interface converts precise environmental deviations into fuzzy linguistic variables, the fuzzy inference engine applies fuzzy rules for inference, and the defuzzification interface converts the fuzzy output into precise control instructions. For the rule-based control strategy, the equipment control instruction set is generated by matching production rules through a rule engine. The rule condition part matches the current environmental state, and the rule action part generates the corresponding control instructions.

[0083] In practice, the triggering condition for the pre-set strategy library update process is based on the continuous monitoring of forest tree seedling growth performance indicators, including daily growth, chlorophyll content, and root development index. The monitoring system periodically collects these growth indicator data and compares them with the expected growth curve. When the growth performance indicators for three consecutive monitoring cycles are lower than the expected threshold, the strategy library update process is automatically initiated. The expected threshold is dynamically adjusted according to the seedling type and growth stage, and the threshold settings differ for different tree species and seedling ages. After the update trigger signal is generated, the system records the current operating status of the environmental control strategy and the corresponding growth performance data, generating a strategy evaluation report.

[0084] In practice, the latest successful environmental control cases are collected through a distributed data acquisition network. The data sources include historical operating data from this system, operating data from similar systems, and publicly available research data. The selection criteria for successful cases are control instances where the growth effect index exceeds the expected value by more than 15%. Each successful case includes a complete environmental data sequence, a control command sequence, and the final growth effect data. Control strategy parameter extraction algorithms analyze the control logic in successful cases, identifying key control parameters and their value ranges. For PID control strategies, optimized values ​​of the proportional coefficient, integral time constant, and derivative time constant are extracted. For fuzzy logic control strategies, membership function parameters and rule weights are extracted. For rule-based control strategies, expressions for rule conditions and actions are extracted.

[0085] In practice, the update operation of the pre-configured policy library adopts a transactional update mechanism. The process of adding control policy parameters first creates a new policy record in a temporary area to verify conflicts between the new policy and existing policies. A conflict detection algorithm checks the overlap between the environmental conditions of the new policy and existing policies. When the overlap exceeds a set threshold, a policy optimization process is triggered, merging overlapping policies and recalculating control parameters. After a new policy is successfully added, the system initiates a policy effectiveness verification cycle, comparing the control effects of the new and old policies within the verification cycle. Expired policies are removed based on usage frequency and effectiveness score. Policies with effectiveness scores below the removal threshold are marked as expired, and a policy backup is automatically created before removal.

[0086] In some embodiments, the environmental control strategy matching process incorporates machine learning algorithms to optimize matching accuracy. The machine learning model is trained using environmental expectations, real-time environmental data, and historical strategy effects as features, and the strategy matching degree as a label. The matching model employs a gradient boosting decision tree algorithm, outputting the matching probability of each candidate strategy. The system selects the strategy with the highest matching probability as the chosen strategy. The model training data comes from historical successful matching records, and feature engineering includes statistical feature extraction and temporal feature construction of the environmental data.

[0087] In some embodiments, a safety verification step is added to the generation process of the device control command set. This safety verification checks whether the control command exceeds the physical limits of the executing device. Temperature control commands verify the maximum power limit of the heater, humidity control commands verify the maximum atomization amount of the humidifier, and illumination control commands verify the maximum brightness value of the supplementary lighting. When a control command exceeds the safe range, the system automatically scales the command value back to the safe range and records a warning message.

[0088] Optionally, the pre-built policy repository uses a version control mechanism. Each policy repository update generates a new version number, and version information records the update time, update content, and the operator. The policy rollback function supports quick restoration to any historical version, and the version comparison tool visually displays the differences between different versions of the policy repository. The policy repository access interface implements access control; policy modification operations require high-level authorization, while policy query operations are open to all authorized users.

[0089] Optionally, the environmental control strategy matching process supports a manual intervention mode. Expert users can adjust the weight parameters of the matching algorithm through a graphical interface to manually correct the automatic matching results. The intervention record saves detailed information about the matching degree calculation process before and after the adjustment, as well as the final strategy selection result, for subsequent algorithm optimization. The expert intervention interface provides a strategy effect comparison view, displaying the control effect prediction curves of different strategies side by side.

[0090] It is understandable that the data exchange between the multimodal control strategy module and the forest seedling growth status prediction module adopts an asynchronous message passing mechanism. The expected value of the growth environment is encapsulated in a standard message format and sent to the strategy matching queue. The message middleware ensures the reliability and order of data transmission. The consumer process retrieves messages from the queue for strategy matching and device control instruction set generation. The message processing results are written to the distributed database and simultaneously sent to the message topic of the execution effect feedback module.

[0091] It's understandable that the distributed deployment of the pre-built policy library adopts a master-slave replication architecture. The master policy library is responsible for policy update operations, while the slave policy library provides read-only query services. The master-slave synchronization mechanism ensures data consistency, and the slave policy library can continue to provide query services when a network partition occurs. The policy library's caching mechanism loads frequently accessed policy data into memory, improving the response speed of policy matching. The cache invalidation policy is based on policy update events; when the master policy library changes, the cached data in the slave policy library is automatically refreshed.

[0092] See Figure 4 This bar chart focuses on the multimodal control strategy module of the forest tree seedling cultivation system, visually presenting the control effectiveness scores of three strategies—PID control, fuzzy logic control, and rule-based control—across five growth stages (stages 1-5). The multimodal control strategy module, based on a pre-set strategy library, matches expected growth environment values ​​with real-time data to generate equipment control command sets. The chart clearly demonstrates the performance differences of different control strategies throughout the forest tree seedling growth cycle. Its value lies in quantifying the actual effectiveness of the multimodal control strategies, providing data for iterative updates to the strategy library, and intuitively reflecting the necessity of multimodal adaptation of control strategies at different growth stages in the forest tree seedling cultivation system. This facilitates closed-loop management from environmental perception to precise control, ultimately improving the accuracy of environmental regulation and growth quality in seedling cultivation.

[0093] Example 4: In specific implementation, the environmental change data acquisition operation of the execution effect feedback module establishes a collaborative working mechanism with the distributed environmental sensing module. After the equipment control command set is issued to the environmental regulation execution module and confirmed to be executed, the execution effect feedback module sends a data acquisition request to the distributed environmental sensing module. The data acquisition request includes acquisition time window parameters and sensor node selection parameters. The acquisition time window is set to a one-hour cycle after the control command is executed, and the sensor node selection covers representative monitoring points within the seedling cultivation area. After responding to the request, the distributed environmental sensing module starts a special acquisition task. The temperature sensor, humidity sensor, and light sensor synchronously acquire current temperature data, current humidity data, and current light data according to a predetermined sampling frequency. The acquired data is appended with a control cycle identifier and transmitted to the data processing unit of the execution effect feedback module.

[0094] In practice, the difference between the current temperature data and the expected temperature value in the growth environment is calculated using an absolute error algorithm; the difference between the current humidity data and the expected humidity value in the growth environment is calculated using a relative error algorithm; and the difference between the current light intensity data and the expected light intensity value in the growth environment is calculated using a logarithmic error algorithm. The formula for calculating temperature deviation is the arithmetic difference between the measured temperature value and the expected temperature value; the formula for calculating humidity deviation is the percentage difference between the measured humidity value and the expected humidity value; and the formula for calculating light intensity deviation is the logarithmic ratio difference between the measured light intensity value and the expected light intensity value. The deviation calculation results are stored in a structured deviation record table. Each record includes a timestamp, control cycle number, deviation type, and numerical information. The deviation record table serves as the input data source for generating the comprehensive deviation correction.

[0095] In practical implementation, the weighted average algorithm generates the comprehensive deviation correction amount based on the sensitivity analysis of the impact of environmental parameters on the growth of forest seedlings. The sensitivity analysis data comes from the statistical results of historical planting experiments. The weighting coefficient for temperature deviation is set to 0.5, the weighting coefficient for humidity deviation is set to 0.3, and the weighting coefficient for light deviation is set to 0.2. The weighting coefficients are normalized to ensure that the sum is 1. The weighted average calculation process first normalizes each type of deviation value to the 0-1 range, then multiplies it by the corresponding weighting coefficient, and finally sums them to obtain the comprehensive deviation correction amount. The value of the comprehensive deviation correction amount is between 0 and 1; the larger the value, the greater the deviation between the environmental control effect and the expected value. Referring to Table 1, the comprehensive deviation correction amount is stored in floating-point data format, with confidence index and validity flag.

[0096] Table 1: Deviation Weight Coefficient Allocation Table

[0097] Bias type Weight coefficient Normalization method Application condition Temperature bias 0.5 Min-max normalization Applicable throughout the year Humidity bias 0.3 Percentage normalization Humidity > 30% RH Illumination bias 0.2 Log normalization Illumination > 1000 lux

[0098] In practical implementation, the optimization algorithm model of the control command optimization module adopts the adaptive gradient descent algorithm. The learning rate of the gradient descent algorithm is dynamically adjusted according to the changing trend of the comprehensive deviation correction. The equipment control command set and the comprehensive deviation correction serve as the two input variables of the optimization algorithm. The equipment control command set is represented in the form of a multi-dimensional vector, with each dimension corresponding to a control parameter. The comprehensive deviation correction serves as the basis for calculating the loss function. During the iterative process of the gradient descent algorithm, the gradient of the loss function relative to the control parameters is calculated. The gradient direction indicates the steepest descent direction of the control parameter adjustment, and the gradient magnitude determines the step size of the parameter adjustment. Each iteration generates a set of intermediate control command sets, which serve as the starting point for the next iteration until the convergence condition is met.

[0099] In practical implementation, the feasibility verification of intermediate control instruction sets includes physical constraint checks and safety boundary verification. Physical constraint checks assess whether the control instructions conform to the physical characteristics of the executing equipment, while safety boundary verification ensures that the control instructions are within the equipment's permissible operating range. For example, the physical constraint check for heater control instructions verifies whether the power adjustment range is between zero and rated power; for humidifier control instructions, it checks whether the atomization amount is between zero and maximum atomization amount; and for supplementary lighting control instructions, it checks whether the brightness adjustment is between zero and maximum brightness. Safety boundary verification, referring to the technical parameter table provided by the equipment manufacturer, verifies whether the control instructions exceed the equipment's safe operating area. The safe operating area defines the parameter range for the long-term stable operation of the equipment. Feasibility verification results are indicated by flag bits. Intermediate control instruction sets that pass verification are sent to the next stage, while those that fail trigger a re-optimization process.

[0100] In practical implementation, the output interface of the optimized control instruction set adopts a standardized data exchange format. The data encapsulation includes three parts: an instruction header, an instruction body, and a checksum. The instruction header records the optimization algorithm version number, iteration count, and convergence flag; the instruction body stores the optimized control parameter values; and the checksum uses a cyclic redundancy check algorithm to ensure data transmission integrity. The optimized control instruction set is sent to the environmental adjustment execution module via a message queue. The message transmission mechanism ensures reliable delivery and sequential execution of instructions, while the message acknowledgment mechanism ensures that instructions are correctly received and processed. The version management of the optimized control instruction set records the input conditions and output results of each optimization, forming a complete optimization trajectory for subsequent analysis and auditing.

[0101] In some embodiments, the gradient descent algorithm incorporates a momentum term to accelerate the convergence process. The momentum coefficient is set to 0.9. The momentum term retains information about the historical gradient directions, reducing oscillations when the gradient direction changes. An adaptive learning rate adjustment strategy monitors the rate of descent of the loss function. When the rate of descent slows down, the learning rate is automatically reduced to improve convergence accuracy. When the rate of descent remains stable, the current learning rate is maintained to balance the convergence speed. Gradient clipping techniques limit the maximum gradient value to prevent gradient explosion. The gradient norm threshold is set to 1.0, and gradients exceeding the threshold are scaled proportionally.

[0102] In some embodiments, the feasibility verification process introduces a multi-level verification mechanism. The initial verification quickly checks for obvious out-of-bounds values ​​on the local processor; the intermediate verification performs device-specific checks on the edge server; and the advanced verification performs cross-device coordination verification on the cloud platform. This multi-level verification mechanism distributes the computing load, improving system response speed. Verification results are aggregated to a central decision-making unit for final adjudication. The verification rule base supports online updates, and verification rules are updated synchronously when device parameters change, ensuring consistency between verification results and the actual device state.

[0103] Optionally, the generation process of deviation correction values ​​supports a manual weight adjustment mode. Forestry experts can modify the default weight coefficients through the management interface, with adjustments based on factors including tree species characteristics, growth stage, and seasonal factors. Manual adjustment records save the weight values ​​before and after adjustment, the reasons for adjustment, and information on the personnel involved. The adjustment history is used to analyze the impact of weight coefficients on control effectiveness. The expert adjustment interface provides a weight coefficient recommendation function; the recommendation algorithm provides suggested values ​​based on historical optimization data under similar environmental conditions.

[0104] Optionally, the iteration termination conditions for the optimization algorithm model can be configured with multiple criteria. The absolute convergence criterion checks if the loss function value is less than a set threshold, while the relative convergence criterion checks if the improvement in the loss function after consecutive iterations is less than the tolerance. A maximum iteration limit prevents infinite loops. Termination condition parameters are set via a configuration file; different control scenarios can be configured with different combinations of termination conditions. During system runtime, the corresponding parameter group is selected based on the scenario identifier.

[0105] It is understandable that the data pipeline between the execution effect feedback module and the control command optimization module implements a flow control mechanism. The data producer controls the data transmission rate to match the consumer's processing capacity, and the backpressure mechanism automatically adjusts the data flow when the consumer's processing speed cannot keep up with the production speed. The data pipeline has persistent storage capabilities; data during transmission is temporarily stored in a persistent queue, allowing processing to resume from the breakpoint after system failure recovery. The data pipeline monitoring interface displays data flow rate, backlog, and processing latency metrics in real time, triggering alarms to notify operations and maintenance personnel in case of anomalies.

[0106] It is understandable that the optimization process of the control instruction optimization module records detailed logs, including the input parameters, gradient calculation process, intermediate results, and final output for each iteration. Log analysis tools support optimization process replay and parameter impact analysis, helping algorithm engineers debug and optimize algorithm parameters. Historical optimization data is periodically archived to a long-term storage system, with archived data stored in time-partitioned locations, supporting quick retrieval of historical optimization records by time range. Optimization performance evaluation reports are generated periodically, statistically analyzing the convergence performance and final control effect of the optimization algorithm, providing data support for algorithm improvement.

[0107] See Figure 5This line graph focuses on the execution effect feedback module and control command optimization module of the forest tree seedling cultivation system, visually presenting the dynamic changes of temperature deviation, humidity deviation, light deviation, and comprehensive deviation correction during the cultivation cycle. The value of this graph lies in providing core input for the control command optimization module: the changing trend of the comprehensive deviation correction directly guides the gradient descent algorithm to iteratively optimize the equipment control command set, ultimately achieving a precise closed-loop environmental control. The graph clearly shows the linkage relationship and comprehensive correction effect of various environmental parameter deviations, demonstrating the system's technical logic from environmental perception to deviation feedback to command optimization. It is crucial data support for ensuring the dynamic adaptation of the seedling cultivation environment to growth needs.

[0108] Example 5: In specific implementation, the hardware configuration of the environmental control execution module adopts an industrial-grade control architecture. The heater uses a three-phase electric heating tube array with a power adjustment range covering 0 to 20 kilowatts. The humidifier uses an ultrasonic atomizing plate group with a humidity output range of 30% to 95% relative humidity. The supplementary light is equipped with a full-spectrum LED module with a light intensity adjustment range of 0 to 50,000 lux. The optimized control instruction set is converted into control signals recognizable by the device through a protocol parsing engine. The heater control signal uses a pulse width modulation waveform with a carrier frequency set to 10 kHz and a duty cycle adjustment accuracy of 0.1%. The humidifier control signal uses an analog voltage output with a voltage range of 0 to 10 volts, corresponding to the linear adjustment of the atomizing plate vibration frequency. The supplementary light control signal uses a digital dimming protocol, transmitting brightness control data through a DMX512 interface, with each LED module's address code configured independently.

[0109] In practical implementation, heater power regulation is achieved through solid-state relays. The duty cycle of the pulse width modulation waveform is linearly related to the heating power, with 100% duty cycle corresponding to the rated power output. The power regulation circuit includes a zero-crossing detection unit and a phase control unit. The zero-crossing detection unit synchronizes with the zero-crossing point of the AC current, while the phase control unit delays the trigger angle to adjust the output power. The heating tube array group control strategy divides the heating tubes into multiple power levels, starting and stopping different combinations of heating tubes according to temperature control requirements. Power regulation accuracy is ensured by the number of groups and the switching frequency. The temperature feedback loop monitors the heater output effect in real time, automatically adjusting the pulse width modulation parameters when a temperature deviation persists.

[0110] In practical implementation, the humidifier's humidity output control is based on a PID algorithm, using an analog voltage signal to drive the piezoelectric ceramic atomizing plate. The voltage value is directly proportional to the atomization frequency. The humidity control loop includes a humidity sensor feedback circuit; when the monitored humidity reaches the set value, the output voltage is gradually reduced to prevent overshoot. The ultrasonic atomizing plate operates at a stable frequency of 1.7 MHz, and the atomization volume is precisely controlled by the voltage amplitude. The voltage-atomization volume calibration curve is stored in the controller's non-volatile memory. The humidifier's water tank is equipped with a water level sensor and an automatic water replenishment valve. When the water level falls below a threshold, a water replenishment operation is triggered. The water quality monitoring unit detects the water's conductivity to ensure atomization quality.

[0111] In practical implementation, the illumination intensity adjustment of the supplementary lighting adopts a constant current drive scheme, and a digital dimming protocol controls the output current of the LED driver, with a current adjustment range of zero to one thousand milliamps. Illumination intensity and drive current have a non-linear relationship, and a pre-stored calibration table compensates for changes in luminous efficacy under different currents. PWM dimming technology and analog dimming technology are used in combination; PWM dimming handles a wide range of brightness adjustment, while analog dimming achieves fine brightness adjustment. The spectral adjustment function is achieved by controlling the current ratio of different colored LED chips; the current ratio of red and blue LED chips is optimized according to the seedling growth stage.

[0112] In practice, the image acquisition system of the growth effect evaluation module is equipped with multiple industrial cameras. These cameras are mounted on a three-dimensional support frame in the seedling cultivation area, with the acquisition angle covering the seedling canopy and root system. The image acquisition cycle is set to a fixed time each day, with the acquisition trigger signal synchronized with the supplemental lighting system to avoid interference from ambient light. An automatic camera parameter adjustment mechanism sets exposure time, gain coefficient, and white balance parameters according to ambient lighting conditions to ensure consistent image quality. The acquired seedling growth images are compressed and encoded before being transmitted to the image processing server, while the original images are stored in a distributed storage system for subsequent analysis.

[0113] In its implementation, the image recognition algorithm is based on a convolutional neural network architecture. The network input layer receives normalized images of forest seedling growth, the hidden layers contain feature extraction convolutional layers and pooling layers, and the output layer generates seedling growth status parameters. The leaf area recognition branch calculates the pixel area of ​​the leaves and converts it to the actual area using calibration coefficients, while the stem recognition branch measures the pixel value of the stem diameter and converts it to physical dimensions. The growth status analysis algorithm calculates daily growth, chlorophyll index, and morphological indicators; these parameters are compared with standard growth curves to assess growth status. The training data for the image recognition model includes labeled images of multiple tree species at different growth stages, and data augmentation techniques enhance the model's generalization ability.

[0114] In practice, the assessment report generation module periodically integrates and analyzes the results. The report includes statistical tables of growth parameters, trend graphs, and early warnings for abnormal conditions. The growth environment expectation adjustment mechanism is automatically triggered based on the growth status score in the assessment report; when the score falls below a threshold, a recalibration process is initiated. The assessment report is visualized through a web interface, supporting filtering and comparative analysis by time range. Historical reports are archived and stored for traceability. The report generation cycle is linked to the seedling growth stage, with shorter reporting intervals during rapid growth periods and longer reporting intervals during maturity periods.

[0115] In some embodiments, the equipment status monitoring system of the environmental control execution module collects the operating parameters of the execution equipment in real time, monitors the surface temperature of the heater to prevent overheating damage, detects the impedance of the humidifier transducer in a timely manner to identify faults, and monitors the junction temperature of the supplementary lighting lamp to ensure the lifespan of the LED. Equipment operating data and control commands are recorded synchronously, and an equipment response characteristic model is established to optimize control accuracy. The early warning system sends alarm information when the equipment is abnormal, and maintenance personnel can view the equipment status and perform remote diagnostics through mobile terminals.

[0116] In some embodiments, the multi-camera synchronous acquisition system of the growth effect evaluation module adopts a hardware triggering scheme. The main controller sends a synchronization signal to simultaneously trigger the exposure of all cameras, achieving microsecond-level time synchronization accuracy. A 3D reconstruction algorithm, combined with multi-view images, generates a seedling point cloud model, achieving volume measurement accuracy superior to traditional 2D image analysis. The dynamic growth analysis function calculates the growth rate through continuous image sequences, and a time-series analysis algorithm identifies abnormal growth points, providing more timely feedback for environmental control. In specific implementations, the dynamic growth analysis function's process of calculating the growth rate through continuous image sequences begins with the acquisition of seedling growth images at multiple time points. Industrial cameras capture digital images of the canopy and root system of forest seedlings at fixed time intervals, and the image sequences are arranged chronologically to form a time-series dataset. The image recognition algorithm, based on a convolutional neural network architecture, extracts seedling morphological features from each time point image, including plant height pixel values, leaf area pixel values, and stem pixel values. After feature extraction, the pixel values ​​are converted into physical dimensions using calibration coefficients. Growth rate is calculated by comparing the changes in characteristic values ​​at adjacent time points. Dividing the change by the time interval yields the growth per unit time. Plant height growth rate is calculated by dividing the difference in plant height measurements across consecutive images by the acquisition time difference. Leaf area growth rate is obtained by dividing the change in leaf area by the time interval. Stem growth rate is calculated using the same method. Time alignment of consecutive image sequences is ensured by image acquisition timestamps, and time interval consistency is maintained by a timed triggering mechanism. Growth rate results are output in millimeters per day or square centimeters per day for subsequent growth trend analysis. In specific implementation, the time series analysis algorithm for identifying growth anomalies is based on historical data pattern analysis of forest tree seedling growth rates. The algorithm constructs a time series model of growth rates, and the model is trained using data from historical normal growth stages to learn rate change patterns. Anomaly detection is achieved by calculating the residual between the real-time growth rate and the model's predicted values. When the absolute value of the residual exceeds a dynamic threshold, it is marked as an anomaly. The dynamic threshold is adaptively adjusted according to the growth stage and forest tree seedling variety. A sliding window mechanism is applied to continuous monitoring. Statistical characteristics of the growth rate within the window, such as the mean and standard deviation, are used for anomaly determination. A mutation detection algorithm identifies points of abrupt change in rate values. Once an anomaly is confirmed, an early warning signal is triggered, and the time of the anomaly, the type of anomaly, and the degree of deviation are recorded for use by the assessment report generation module.

[0117] Optionally, the environmental control module supports a manual control mode, allowing authorized operators to directly set equipment parameters via a human-machine interface. Manual and automatic control commands are processed in parallel, with manual control taking priority. The operation log records detailed information on the time, parameters, and operator details of manual interventions; this data is used to analyze the optimization needs of the automatic control system. The equipment group control function allows the execution equipment to be divided into multiple zones for independent control, meeting the differentiated environmental requirements of different locations within the seedling cultivation area.

[0118] Optionally, the image quality detection algorithm in the growth effect evaluation module automatically assesses the analyzability of acquired images, with detection indicators including image sharpness, contrast compliance rate, and occlusion level. Images that fail to meet quality standards trigger an automatic re-acquisition process; if the number of re-acquisitions exceeds the limit, a manual intervention request is issued. The metadata recording system stores acquisition condition information for each image, including camera parameters, ambient light intensity, and shooting time. This information is used to correct for environmental influencing factors in the analysis results.

[0119] It is understandable that the data closed loop between the environmental regulation execution module and the growth effect evaluation module establishes a continuous optimization mechanism, with evaluation results fed back to adjust environmental control parameters, forming an adaptive control system. The control effect statistical analysis module calculates the correlation between the accuracy of environmental parameter regulation and the growth effect, identifying the most effective control strategy. The system's self-learning function optimizes control parameters based on historical data, continuously improving control accuracy and energy efficiency ratio as operating time accumulates.

[0120] Understandably, the multi-module collaborative working mechanism achieves loosely coupled communication through message middleware, with control commands and feedback data encapsulated in standard message formats for circulation between modules. The message persistence mechanism ensures no data loss after system fault recovery, and transaction processing ensures the atomicity and consistency of critical operations. The system's panoramic monitoring interface displays real-time equipment operating status, control effects, and growth data, supports remote configuration and fault diagnosis, and enables intelligent and precise management of the forest seedling cultivation environment.

[0121] 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.

[0122] 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. A forestry seedling cultivation system based on, characterized by, The system integrates: a distributed environment sensing module deployed in the forest seedling cultivation area for synchronously capturing temperature parameters, humidity parameters and light intensity parameters and generating an environment data stream; a forest seedling growth trend prediction module connected to the environment data stream, which analyzes the environment data stream through a neural network model and outputs a growth environment expectation value; a multi-modal control strategy module receiving the growth environment expectation value and real-time environment data and generating a device control instruction set based on a preset strategy library; an execution effect feedback module collecting environment change data after the execution of the device control instruction set and comparing the environment change data with the growth environment expectation value to generate a deviation correction amount; a control instruction optimization module iteratively optimizing the device control instruction set using the deviation correction amount and outputting an optimized control instruction set; an environment regulation execution module responding to the optimized control instruction set and driving forest seedling cultivation environment regulation equipment.

2. The forestry seedling cultivation system of claim 1, wherein, The distributed environment sensing module synchronously captures temperature parameters, humidity parameters and light intensity parameters and generates an environment data stream, including: arranging multiple temperature sensors, multiple humidity sensors and multiple light sensors in the forest seedling cultivation area to form a sensor network; collecting raw temperature data, raw humidity data and raw light data through the sensor network at a preset sampling frequency; performing data fusion processing on the raw temperature data, raw humidity data and raw light data to generate a time-series environment data stream.

3. The forestry seedling cultivation system of claim 2, wherein, The forest seedling growth trend prediction module analyzes the environment data stream through a neural network model and outputs a growth environment expectation value, including: constructing a neural network model for forest seedling growth prediction, which includes an input layer, a hidden layer and an output layer; inputting the environment data stream into the input layer of the neural network model, performing feature extraction through the hidden layer and generating a forest seedling growth trend score by the output layer; querying a preset growth environment mapping table according to the forest seedling growth trend score to obtain a corresponding growth environment expectation value.

4. The forestry seedling cultivation system of claim 3, wherein, The multi-modal control strategy module generates a device control instruction set based on a preset strategy library, including: the preset strategy library stores multiple environment control strategies, each of which is associated with an environment condition range; matching the growth environment expectation value and real-time environment data with the environment condition range in the preset strategy library to select a matched environment control strategy; generating a device control instruction set according to the selected environment control strategy, which includes temperature control instructions, humidity control instructions and light control instructions.

5. The forestry seedling cultivation system of claim 4, wherein, The execution effect feedback module collects environment change data and compares the environment change data with the growth environment expectation value to generate a deviation correction amount, including: after the execution of the device control instruction set, collecting current temperature data, current humidity data and current light data of the seedling cultivation area again through the distributed environment sensing module; Calculate the difference between the current temperature data and the temperature expected value in the growth environment expected value, and obtain the temperature deviation; Calculate the difference between the current humidity data and the humidity expected value in the growth environment expected value, and obtain the humidity deviation; Calculate the difference between the current light data and the light expected value in the growth environment expected value, and obtain the light deviation; Based on the temperature deviation, humidity deviation and light deviation, the comprehensive deviation correction amount is generated by weighted average algorithm.

6. The forestry seedling cultivation system of claim 5, wherein, The control instruction optimization module iteratively optimizes the device control instruction set using the deviation correction amount, including: Input the device control instruction set and the comprehensive deviation correction amount into the optimization algorithm model, and the optimization algorithm model is gradient descent algorithm; Adjust the parameters in the device control instruction set by gradient descent algorithm to generate intermediate control instruction set; The feasibility of the intermediate control instruction set is checked, and if the check is passed, the optimized control instruction set is output.

7. The forestry seedling cultivation system of claim 6, wherein, The environment adjustment execution module drives the forest seedling cultivation environment adjustment device, including: The environment adjustment device includes a heater, a humidifier and a light supplement lamp; The optimized control instruction set is parsed into a heater control signal, a humidifier control signal and a light supplement lamp control signal; According to the heater control signal, adjust the heater power, according to the humidifier control signal, adjust the humidifier humidity output, according to the light supplement lamp control signal, adjust the light intensity of the light supplement lamp.

8. The forestry seedling cultivation system of claim 1, wherein, The neural network model training process in the forest seedling growth trend prediction module includes: Collect forest seedling growth history data and corresponding environment data to form a training data set; Use the training data set to supervise the training of the neural network model, and adjust the model weight through the back propagation algorithm; Verify the accuracy of the trained neural network model, and retrain when the accuracy is lower than the threshold.

9. The forestry seedling cultivation system of claim 1, wherein, The preset strategy library updating process in the multi-modal control strategy module includes: Monitor the forest seedling growth effect index, and trigger the strategy library updating when the effect index is lower than the expected value; Collect the latest successful cases of environmental control, extract the control strategy parameters; Add the extracted control strategy parameters to the preset strategy library, and remove the expired strategies.

10. A method for cultivating forestry seedlings, characterized in that, All modules and method processes based on the forestry seedling cultivation system in any one of claims 1 to 9 are included.

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