A method and system for monitoring rainfall redistribution characteristics in moso bamboo forests

Abstract

This invention relates to the field of forestry ecological monitoring and data processing, and discloses a monitoring method and system for rainfall redistribution characteristics in moso bamboo forests. It primarily addresses the problem of inaccurate quantitative assessment of the eco-hydrological characteristics of moso bamboo forests due to spatiotemporal misalignment of water quantity and quality data in traditional monitoring. Based on a unified rainfall trigger signal, water quantity and quality data from multiple forest stand comparison plots are collected simultaneously; unique parameters such as bamboo leaf clustering index and bamboo branch inclination angle are extracted to correct the canopy interception model and quantify water retention capacity indicators; machine learning models are used to predict nutrient output flux and non-point source pollution risk; and finally, a comprehensive evaluation index is generated. This invention achieves high-precision analysis of hydrological processes and can automatically generate forest management adjustment strategies, providing quantitative decision support for forestry ecological asset management.

Description

Technical Field

[0001] This invention relates to the field of forestry ecological monitoring and data processing, and discloses a method and system for monitoring the rainfall redistribution characteristics of moso bamboo forests. Background Technology

[0002] In the field of eco-hydrological monitoring and functional assessment of moso bamboo forests, existing technologies suffer from several key deficiencies that restrict their accuracy and practicality. Firstly, the underlying data acquisition stage generally suffers from spatiotemporal asynchrony between hydrological and water quantity data and water quality sample data. In conventional monitoring systems, water quantity sensors such as rain gauges, throughfall collectors, and trunk runoff flumes, along with automatic water quality samplers, typically operate independently based on their respective preset fixed time steps, lacking a global triggering and synchronous acquisition mechanism based on a unified extraforest rainfall event. This non-cooperative operation means that in sudden, short-duration rainfall events, the dynamic changes in water quantity and the migration characteristics of nutrient concentration cannot be accurately matched in time series, nor can they be strictly aligned in spatial scale. This results in significant spatiotemporal misalignment errors when subsequently calculating rainfall redistribution flux and non-point source pollution load, making it difficult to accurately reflect the transient coupling characteristics of hydrological processes in moso bamboo forests.

[0003] The core hydrological model suffers from severe limitations in its localization adaptation to the unique canopy structure of moso bamboo forests. Existing widely used canopy interception models are primarily designed for common coniferous and broadleaf forests, and their core parameters cannot effectively characterize the unique morphological features of moso bamboo forests, such as the dense clustering of bamboo leaves, the large inclination angle and smooth surface of bamboo branches, and the unique canopy mosaic structure formed when mixed with broadleaf trees. Because the model does not incorporate moso bamboo-specific parameters such as the "bamboo leaf clustering index," "bamboo branch inclination angle," and "mixed canopy mosaic coefficient," there are systematic biases in the calculation of the actual canopy storage capacity, free penetration coefficient, and final water interception volume. This leads to a distorted quantitative assessment of the true water conservation capacity of moso bamboo forests, significantly limiting the model's generalization performance in both pure moso bamboo forests and mixed bamboo-broadleaf forests.

[0004] Existing evaluation systems generally suffer from a disconnect in the "monitoring-assessment-decision" business logic. Most systems can only output abstract ecological health scores or static risk level reports based on monitoring data, lacking a mechanism to intelligently correlate evaluation results with specific, actionable physical interventions for forest management. Evaluation results cannot be directly transformed into substantive strategies to guide forestry production, leading to a disconnect between monitoring data and actual management needs, making it difficult to support closed-loop quantitative management and precise improvement of bamboo forest ecological assets.

[0005] In summary, existing technologies face significant technical bottlenecks in three key areas: the spatiotemporal synchronization of data acquisition, the adaptability of core hydrological models to the characteristics of moso bamboo, and the ability to transform evaluation results into management strategies. These bottlenecks severely restrict the high-precision monitoring of rainfall redistribution processes in moso bamboo forests and the scientific assessment and effective management of their eco-hydrological functions. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for monitoring rainfall redistribution characteristics in moso bamboo forests. This scheme achieves strict spatiotemporal alignment of dynamic water quantity changes and water quality nutrient migration data by constructing a hardware synchronization mechanism based on a unified rainfall event global trigger. Simultaneously, it establishes a moso bamboo forest canopy interception correction model that deeply integrates proprietary morphological parameters such as "bamboo leaf clustering index," "bamboo branch inclination angle," and "canopy mosaic degree." Furthermore, relying on a built-in strategy mapping library, it directly transforms eco-hydrological evaluation results into specific forest stand management physical actions. The core innovation of this invention lies in its data layer innovation, breaking the limitations of traditional independent sensor polling and achieving for the first time a monitoring method and system for rainfall redistribution in moso bamboo forests. High-precision spatiotemporal coupling of non-point source pollution fluxes at transient scales; algorithmic innovation, abandoning the general coniferous and broadleaf forest interception algorithm, completing the reconstruction of the hydrological mechanism model for the unique canopy morphology of moso bamboo, completely solving the problem of model generalization distortion, and greatly improving the quantitative accuracy of bamboo forest water conservation capacity; operational innovation, breaking down the decision-making barrier from abstract monitoring data to substantive forest management actions, and being able to automatically deduce and output physical intervention strategies such as "adjusting the ratio of bamboo and broadleaf mixed planting", "precise vector fertilization" or "tending and thinning intensity" based on evaluation indicators, truly constructing a modern bamboo forest ecological asset closed-loop management system integrating "precise monitoring, feature simulation and intelligent intervention".

[0007] The objective of this invention can be achieved through the following technical solutions: A method for monitoring rainfall redistribution characteristics in moso bamboo forests includes the following steps: S1: Receive a spatiotemporally coupled dataset from the underlying standardized monitoring network; wherein, the dataset is obtained by automatically and synchronously collecting hydrological and water quantity data and water quality sample data of the comparison plots of pure bamboo forests and mixed bamboo forests based on a unified extraforest rainfall event triggering signal; S2: Retrieve the stand structure characteristic data of the target forest stand, and extract the unique parameters of the moso bamboo forest. The unique parameters include at least the bamboo leaf clustering index, bamboo branch inclination angle and mixed canopy mosaic coefficient. Input the multi-stand spatiotemporal coupled dataset and the unique parameters into the preset canopy interception business model for calculation, and quantify and output the water interception and retention capacity index of the target forest stand in the rainfall redistribution process. S3: Extract rainfall feature data and forest stand structure feature data from the spatiotemporal coupling dataset as input features, input them into the pre-trained nutrient migration prediction model, calculate the predicted output flux values ​​of nitrogen, phosphorus and potassium nutrients in the target forest stand, and generate non-point source pollution risk assessment results for different forest management measures. S4: Based on the preset ecological resource evaluation index system, the water retention capacity index and the nutrient output flux prediction value are normalized and weighted to generate a comprehensive evaluation index of the ecological and hydrological function of moso bamboo forest. S5: Invoke the preset forestry management threshold library, which contains rigid mapping rules between ecological evaluation intervals and specific forestry physical intervention actions; use the system decision engine to perform a dual-parallel threshold comparison logic on the comprehensive evaluation index and the non-point source pollution risk assessment results respectively: When the comprehensive evaluation index is lower than the set safety threshold, physical structure control instructions for adjusting the spatial density and canopy structure of the target forest stand are automatically generated based on the mapping rules. When the non-point source pollution risk assessment results reach the preset high-risk threshold, an emergency command for pollution prevention and soil conservation is directly triggered to change the surface runoff pattern and block nutrient loss. By outputting and issuing intervention commands, the system achieves automated mapping and closed-loop control from eco-hydrological monitoring and perception to specific forest management physical actions; the intervention commands are physical structure regulation commands and pollution prevention and soil conservation emergency commands.

[0008] Preferably, the step of obtaining the spatiotemporal coupled dataset of the multi-forest stand in S1 specifically includes: Receive rainfall event trigger signals sent by external meteorological information systems; In response to the trigger signal, the underlying sensing network synchronously uploads rainfall data, penetration rainfall data, tree trunk runoff data, and corresponding water sample nutrient concentration analysis data based on a preset time step. Based on unique timestamps and plot identification codes, the rainfall data, throughfall rainfall data, trunk runoff data and water sample nutrient concentration analysis data are mapped to each other, and management assessment data tags are added to them to construct the spatiotemporal coupled dataset of the multi-stand forests with spatiotemporal matching and correlation characteristics; wherein, the data tags are used to structurally indicate the stand type of the data source, the mixing ratio gradient of moso bamboo and mixed tree species, and site condition parameters.

[0009] Preferably, the step of performing localized canopy interception feature analysis in S2 specifically includes: Extract the bamboo leaf clustering index, bamboo branch inclination angle, and mixed canopy mosaic coefficient of the target forest stand, and use the unique parameters to update the canopy storage capacity parameter and free penetration coefficient in the preset canopy interception business model; The extraforest rainfall, through rainfall, and trunk runoff from the spatiotemporal coupled dataset are input into the updated canopy interception operational model, and the actual canopy interception of the target forest stand is obtained based on the water balance algorithm. The ratio of the actual canopy interception amount to the concurrent rainfall outside the forest is used as the water interception and retention capacity index and stored in the eco-hydrological characteristic database.

[0010] Preferably, the step of performing non-point source pollution risk prediction based on machine learning in S3 specifically includes: Historical rainfall characteristics, forest stand structure characteristics, and corresponding non-point source pollution monitoring records from the management cycle were used as a training sample set. A prediction network was established using random forest or gradient boosting tree algorithms, with rainfall, rainfall intensity, stand density and mixed ratio as input nodes and nitrogen, phosphorus and potassium nutrient loss flux as output nodes. The real-time acquired feature data is input into the training converged prediction network to calculate the predicted values ​​of nitrogen, phosphorus, and potassium nutrient output fluxes of the target forest stand under the current rainfall scenario.

[0011] Preferably, the process of generating the non-point source pollution risk assessment result in S3 specifically includes: The calculated predicted values ​​of nitrogen, phosphorus, and potassium nutrient output fluxes were compared with the allowable threshold values ​​for nutrient loss set by the forestry ecological security standards. Using multi-condition branching logic, the deviation rate of each nutrient is calculated; Based on the weighted composite value of the exceedance rate, the non-point source pollution risk of the target forest stand is divided into four management assessment levels: low risk, medium risk, high risk, and extremely high risk, thus generating the non-point source pollution risk assessment result.

[0012] Preferably, the process of normalizing the water retention capacity index and the predicted nutrient output flux in step S4 specifically includes: An extreme value standardization algorithm is used to eliminate the differences in physical dimensions between different indicators, and the water retention capacity index is transformed into a positive water retention benefit score. The reverse mapping algorithm is used to convert the predicted nutrient output flux into a reverse nutrient loss penalty score. Both the water retention benefit score and the nutrient loss penalty score are mapped and converted to the standard scoring range of [0, 100].

[0013] Preferably, the process of generating the comprehensive evaluation index of the eco-hydrological function of the moso bamboo forest in step S4 specifically includes: A judgment matrix containing water conservation objectives, water purification objectives, and forestry economic objectives was constructed using the analytic hierarchy process. Calculate the largest eigenvalue and the corresponding eigenvector of the judgment matrix, and obtain the weight coefficient of each evaluation target through a consistency test; The water conservation benefit score and the nutrient loss penalty score are linearly weighted and summed according to the weighting coefficients to generate the comprehensive evaluation index of the moso bamboo forest eco-hydrological function, which is used to characterize the overall ecological asset value of the forest stand.

[0014] Preferably, the step of automatically generating intervention instructions in S5 specifically includes: The pre-constructed forestry management threshold library contains a mapping table between multiple evaluation index intervals and forestry physical intervention actions; When the comprehensive evaluation index is lower than the set safety threshold, the system matches the associated physical structure control instructions by looking up a table. The physical structure control instructions include: performing spatial tending and thinning at a preset intensity, replanting deep-rooted evergreen broad-leaved tree species, and transforming the target forest stand into a target proportion of heterogeneous multi-layered mixed forest. When the non-point source pollution risk assessment results reach the preset high-risk threshold, the system matches the associated pollution prevention and soil conservation emergency instructions by looking up a table. The pollution prevention and soil conservation emergency instructions include: freezing the fertilizer application operation in the target area, implementing the original ecological conservation of understory shrubs and grasses, and digging tiered intercepting ditches for water and fertilizer conservation along contour lines.

[0015] Preferably, the step of outputting and issuing intervention commands in S5 further includes: Generate a visual dashboard for the operation and management of ecological assets; The operation and management dashboard presents a quantitative comparison of the water retention capacity index and non-point source pollution risk level of different mixed gradient sample plots in the form of a multi-dimensional radar chart. The triggered physical structure control commands and pollution prevention and soil conservation emergency commands are pushed to the forestry management terminal interface in a structured text format containing specific afforestation construction parameters, thereby driving the corresponding physical afforestation operations and ecological risk early warning responses.

[0016] A monitoring system for rainfall redistribution characteristics in bamboo forests, comprising: The data receiving module is used to receive spatiotemporally coupled datasets from the underlying standardized monitoring network; wherein, the datasets are obtained by automatically and synchronously collecting hydrological and water quantity data and water quality and water sample data from comparative plots of pure bamboo forests and mixed bamboo forests based on a unified extraforest rainfall event triggering signal. The interception and analysis module is used to retrieve the stand structure feature data of the target forest stand, extract the unique parameters of the moso bamboo forest, and the unique parameters include at least the bamboo leaf clustering index, bamboo branch inclination angle and mixed canopy mosaic coefficient; input the multi-stand spatiotemporal coupled dataset and the unique parameters into the preset forest canopy interception business model for calculation, and quantify and output the water interception and retention capacity index of the target forest stand in the rainfall redistribution process; The prediction and assessment module is used to extract rainfall feature data and forest stand structure feature data from the spatiotemporal coupled dataset as input features, input them into a pre-trained nutrient migration prediction model, calculate the predicted output flux values ​​of nitrogen, phosphorus and potassium nutrients in the target forest stand, and generate non-point source pollution risk assessment results for different forest management measures. The comprehensive evaluation module is used to normalize and weight and aggregate the water retention capacity index and the nutrient output flux prediction value based on the preset ecological resource evaluation index system, so as to generate a comprehensive evaluation index of the ecological and hydrological function of moso bamboo forest. The strategy generation module is used to call a preset forestry management threshold library, which contains rigid mapping rules between ecological evaluation intervals and specific forestry physical intervention actions; the system decision engine uses a dual-parallel threshold comparison logic to perform the comprehensive evaluation index and the non-point source pollution risk assessment results respectively: When the comprehensive evaluation index is lower than the set safety threshold, physical structure control instructions for adjusting the spatial density and canopy structure of the target forest stand are automatically generated based on the mapping rules. When the non-point source pollution risk assessment results reach the preset high-risk threshold, an emergency command for pollution prevention and soil conservation is directly triggered to change the surface runoff pattern and block nutrient loss. By outputting and issuing intervention commands, the system achieves automated mapping and closed-loop control from eco-hydrological monitoring and perception to specific forest management physical actions; the intervention commands are physical structure regulation commands and pollution prevention and soil conservation emergency commands.

[0017] The beneficial effects of this invention are: This invention completely breaks through the limitations of traditional independent polling of each monitoring component by constructing a synchronization mechanism based on a unified global triggering of extraforest rainfall events. This mechanism ensures that the dynamic changes in water volume and the migration process of water nutrient concentrations are closely aligned in both time and spatial scales during sudden rainfall events, effectively eliminating truncation errors and misalignment lags caused by asynchronous acquisition. This greatly improves the accuracy and reliability of subsequent calculations of rainfall redistribution flux and non-point source pollution load.

[0018] This invention overcomes the parameter limitations of traditional coniferous and broadleaf forest interception assessment models. Specifically, it innovatively introduces unique plant physics parameters such as "bamboo leaf clustering index," "bamboo branch inclination angle," and "mixed canopy mosaic degree" to correct the model, tailored to the unique canopy morphology of moso bamboo forests. This method effectively solves the systematic biases that arise when general algorithms are directly applied to pure moso bamboo forests or mixed bamboo-broadleaf forests. It can more realistically reproduce the complex interception dynamics of the target bamboo forest, significantly improving the quantitative assessment accuracy of canopy storage capacity and actual water conservation capacity.

[0019] This invention establishes a mapping decision library between hydrological and ecological evaluation indicators and specific forest stand management at the top-level application level, changing the status quo of traditional systems that can only output abstract health scores or static early warning reports. Based on evaluation results at different gradients, the system can automatically deduce and output practical physical intervention strategies such as "adjusting the ratio of bamboo to broadleaf bamboo," "optimizing fixed-vector fertilization," or "implementing tending and thinning at specific intensities," truly providing reliable technical support for the precise quantitative enhancement and closed-loop management of modern moso bamboo forest ecological assets. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a method and system for monitoring rainfall redistribution characteristics in bamboo forests according to the present invention. Figure 2 This is a hardware synchronization architecture diagram of the present invention; Figure 3 The model correction logic diagram for this invention; Figure 4 This is a flowchart illustrating the decision transformation process of this invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] Example: Figure 1 - Figure 4 As shown, this invention provides a method and system for monitoring rainfall redistribution characteristics in bamboo forests. This solution achieves a closed-loop operation across the entire business chain, from "precise data perception" to "automatic forest management decision-making," through deep collaboration between the underlying IoT hardware synchronization mechanism, the mid-level hydrological parameter correction algorithm, and the top-level intelligent decision engine. Specifically, it includes the following steps S1 to S5: In this embodiment, step S1: Based on hardware-level interrupt-based distributed collaborative acquisition, a multi-forest spatiotemporal coupled dataset is obtained. The specific implementation method is as follows: In traditional forestry soil and water conservation and eco-hydrological monitoring systems, "water quantity sensors" that measure rainfall, canopy penetration rain, and trunk runoff, and "automatic water quality samplers" that collect water samples for analyzing nitrogen, phosphorus, and potassium concentrations, are typically independent and lack coordination. They rely on independent timers within the devices, operating using "asynchronous polling" or "fixed-interval wake-up" mechanisms. When faced with rapidly changing, short-duration, and extremely intense rainstorm events in nature, this mechanism exposes a "spatiotemporal misalignment" problem that can easily lead to serious errors. For example, the peak of trunk runoff caused by a rainstorm may occur at 14:15, carrying away a large amount of surface fertilizer at this time; however, the water quality sampler, according to its preset program, only starts pumping water at 14:30. The nutrient concentration of the water sample collected at this time has already been significantly diluted due to the decay of runoff. This "misaligned timestamp" will introduce spatiotemporally misaligned noise interference into the training samples subsequently input into the machine learning model, rendering the final non-point source pollution load assessment ineffective.

[0023] To ensure an absolute spatiotemporal mapping between physical water flow dynamics and chemical nutrient migration, this invention reconstructs the underlying triggering network, with the specific steps as follows: Deploying the trigger source and central control unit: A highly sensitive standard tipping bucket rain gauge is deployed in an open, unobstructed area outside the forest as the global "initial trigger source." This rain gauge is hardwired to the core "global synchronization controller" within the local area network via an industrial RS485 bus, employing a high-performance PLC or edge computing gateway. Deploying a multi-gradient contrast sensor array: In representative sample plots of different management types, such as 100% pure bamboo forest, 30% broadleaf mixed forest, and 50% broadleaf mixed forest, each plot measuring 20m × 20m, dense sensor arrays were deployed. The arrays included: 15 evenly distributed rainwater collection troughs, a flexible silicone trunk runoff ring and collection bucket specifically designed for bamboo culm diameter, a soil moisture sensor, and a programmable water quality sampling pump with constant temperature refrigeration function. Event-driven and interrupt-driven data acquisition: When the tipping bucket of the rain gauge outside the forest first flips, recording the effective rainfall threshold preset to 0.2mm, the rain gauge sends an electrical pulse to the global synchronization controller. Within milliseconds, the controller broadcasts a high-priority "hardware-level interrupt command" to all edge acquisition nodes within the sample plots via LoRaWAN low-power wide area network. Generating a spatiotemporally coupled dataset: Upon receiving an interrupt command, all sensors are forced to override their original sleep schedules and begin data acquisition precisely within the same second. For example, they are uniformly switched to high-frequency synchronous water volume acquisition every 10 minutes, simultaneously driving the water pump to extract 100ml of water sample. The central server uses a unified NTP timestamp to encapsulate the data acquired in the same batch in a "zipper-like" manner. Simultaneously, the system extracts the fixed attributes of the target sample plots by calling a GIS database. Finally, a "multi-forest stand spatiotemporally coupled dataset" is generated.

[0024] In this embodiment, step S2: extracting plant morphological features and performing localized canopy interception feature analysis and water balance calculation, specifically implemented as follows: Currently, the Gash analytical model is commonly used in scientific research and engineering to calculate canopy interception. However, this model is based on ordinary coniferous or broadleaf forests, and its core parameter relies on the "leaf area index," which assumes that leaves are uniformly distributed in space. However, moso bamboo exhibits three extreme morphological uniquenesses: First, moso bamboo leaves are not uniformly distributed but rather highly clustered at the top of the canopy, forming a typical "clustered" state and a dense "water-holding sponge layer"; second, the lateral branches of moso bamboo typically grow upwards at a very small acute angle, closely adhering to the smooth culm, and this "upward-pointing smooth branch" significantly alters the hydrodynamic path of water convergence towards the trunk; third, in mixed bamboo and broadleaf forests, the towering moso bamboo canopy and the low broadleaf canopy form a complex vertical nesting and overlap. Without correcting for these unique parameters, the calculated water interception capacity will be severely biased.

[0025] This invention proposes a mechanism for extracting morphological characteristic parameters of moso bamboo. The canopy is scanned using ground-based lidar, and a voxel density clustering algorithm is used to calculate the "bamboo leaf clustering index." Lateral photogrammetry and a skeleton extraction algorithm using UAVs are used to calculate the "bamboo branch inclination angle" between the main branches and the central culm. Finally, a hemispherical photogrammetry method is used to calculate the "mixed canopy mosaic coefficient" of canopies of different tree species in mixed forests.

[0026] After obtaining the above features, the system calls the canopy storage capacity update formula reconstructed in this invention to perform nonlinear correction on the basic capacity. The core formula is as follows: ,in, The updated actual canopy storage capacity of the target stand represents the maximum depth of precipitation that the canopy can absorb and suspend under the current unique structure of moso bamboo. The basic canopy storage capacity is a baseline empirical value retrieved from the basic database based on the local long-term climate zone characteristics. The leaf morphology characteristic weighting factor, calibrated through extensive artificial rainfall simulations, is used to measure the contribution of leaf clustering status to rainwater interception. This is the measured bamboo leaf clustering index. A higher value indicates a more significant phenomenon of dense clustering of bamboo leaves at the top of the canopy, and a stronger water retention capacity. This is a weighting factor for branch morphology characteristics, used to measure the proportion of contribution of branch angle to water retention time. The measured angle of inclination of the bamboo branch refers to the average spatial angle between the lateral branch and the main culm of the bamboo. The cosine value is used because the smaller the angle, the closer the cosine value is to 1, indicating that rainwater is more easily trapped and retained by the branches rather than dripping rapidly. This is the mosaic coefficient of mixed canopy, reflecting the secondary interception and amplification effect caused by the overlap of multiple canopies. A value of 1 is used for pure forests, while it usually fluctuates between 1.1 and 1.5 for mixed forests.

[0027] After the capacity update, the system needs to verify the absolute interception capacity of the canopy in a single real rainfall event. The system retrieves the spatiotemporal coupled dataset collected in step S1 and calculates the interception capacity using the following deduction formula based on the absolute forest hydrological "water balance principle": ,in, This refers to the actual canopy interception of a single rainfall event, which is the absolute amount of water that does not reach the ground surface and ultimately returns to the atmosphere through evaporation. This represents the total rainfall outside the forest monitored during the same period, measured by an initial trigger rain gauge in an open area. This refers to the total amount of throughfall monitored within the forest, which is the amount of water that passes through the gaps between branches and leaves or drips from the leaves onto the forest floor surface. The total trunk runoff monitored in the forest refers to the amount of water that flows gently down the bamboo culms or trunks to the base of the tree.

[0028] To generate standardized health indicators that can be compared across regions, the system converts the actual interception amount into a percentage: ,in, The water retention capacity index is a core macro-indicator characterizing positive ecological benefits. The higher the ratio, the stronger the canopy of the target bamboo forest's ability to play a water-holding buffering role, intercept rainwater, weaken the impact energy of raindrops, and delay surface runoff.

[0029] To verify the accuracy of the aforementioned localized correction model, the research team conducted a comparative verification in a subtropical mountainous area. During a torrential rain lasting 3 hours and totaling 45 mm, the actual interception rate, measured by manual water collection and weighing, was 19.2%. If the current meteorological data were substituted into the uncorrected traditional Gash model, the predicted result was only 12.1%. This is because the model blindly assumed a uniform distribution, severely underestimating the interception capacity of the clustered leaves of bamboo. However, when the localized correction model is applied to the present invention… and After applying the formula, the system output a predicted value of 18.7%, reducing the absolute error with the true value to 0.5 percentage points, and significantly improving the prediction accuracy.

[0030] In this embodiment, step S3: Based on a nonlinear machine learning mechanism, a dynamic risk assessment of non-point source pollution is implemented. The specific implementation method is as follows: Mechanism Deconstruction and Predictive Model Establishment: Due to the extreme expansion of underground rhizomes, moso bamboo forests often lead to the degradation of understory vegetation and soil exposure. Furthermore, bamboo farmers frequently apply large amounts of compound fertilizers in winter to promote the production of winter bamboo shoots. Once heavy rainfall causes surface erosion, it can easily trigger severe nitrogen, phosphorus, and potassium non-point source pollution. This pollution load exhibits a highly non-linear relationship with rainfall duration, slope, and prior soil moisture content, making traditional linear regression equations prone to failure.

[0031] Therefore, this invention introduces a "random forest" machine learning algorithm based on decision tree ensemble. The system extracts a massive spatiotemporally coupled dataset from the past three years, generated in step S1, as training samples. The independent variable matrix contains environmental and structural data, while the dependent variable label represents the actual loss of each nutrient. Through Bagging and random feature subset selection techniques, a highly robust nonlinear inference network is trained. During real-time monitoring, the latest meteorological data and stand static attributes are input into the network, which immediately calculates and outputs the predicted loss flux of a specific nutrient in parallel, with the specific nutrient denoted as an index. ,in They represent nitrogen, phosphorus, and potassium, respectively.

[0032] To transform the cold physical flux into a "risk warning" for forestry management, the system performs an ecological threshold comparison calculation: ,in, This represents the deviation rate for a specific single nutrient. The formula incorporates nonlinearity. The activation function logic ensures that the deviation rate is strictly zero when the predicted churn rate does not exceed the safety threshold; once the threshold is exceeded, the output is the multiple of the excess amount relative to the threshold. This refers to the real-time output of a random forest network to predict the loss flux of specific nutrients. The maximum permissible threshold for this specific nutrient, preset for water environment protection standards, is used by the system to perform a weighted risk summation because different nutrients have different destructive effects on receiving water bodies. ,in, The comprehensive risk score for non-point source pollution is a core macro-indicator characterizing the negative ecological costs. A higher score indicates a more severe risk of non-point source pollution caused by soil erosion in that forest stand. A hazard weighting factor pre-set for a specific nutrient, and satisfying the following conditions: The system is ultimately based on The mathematical interval into which the pollution occurs automatically labels the pollution status of the bamboo forest as one of four discrete management levels: "low, medium, high, and extremely high".

[0033] In this embodiment, step S4: eliminating dimensional barriers and generating a comprehensive evaluation index for the eco-hydrological function of moso bamboo forests, is specifically implemented as follows: Indicator Conflicts and Standardization Dimensionality Reduction: The system has two contradictory evaluation dimensions: the water retention capacity output from step S2. This is a positive indicator; the higher the better, and it correlates with the risk score output in step S3. This is a reverse indicator, and the smaller the better. In order to provide forest farm managers with an intuitive comprehensive evaluation reference, both must be dimensionless and aligned.

[0034] Perform a positive extreme value mapping for water retention capacity: ,in The score represents the normalized water conservation benefit, ranging from 0 to 100. and These are the absolute maximum and absolute minimum values ​​of the water retention capacity index for this area, recorded in the system's historical database.

[0035] Implement a reverse penalty mapping for pollution risk: ,in, This is the standardized and inverted nutrient loss penalty score, ranging from 0 to 100. After inverse calculation... A state of extreme initial risk will be severely punished and transformed into an extremely low score, thus forcibly unifying the evaluation direction—that is, the higher the score, the healthier the ecosystem. and These represent the maximum and minimum extreme values ​​of risk scores in historical records.

[0036] The system uses the analytic hierarchy process (AHP) to determine the matrix and then performs a weighted aggregation of the two standard scores: ,in, The final comprehensive eco-hydrological evaluation index ranges from 0 to 100. This is the most authoritative and intuitive single-value quantitative expression of the current ecological function of this bamboo forest. The global weighting coefficient for water conservation targets. The global weighting coefficients for water purification targets satisfy the following conditions: If the forest stand is located above a drinking water source protection area, then It will be given extremely high weight.

[0037] In this embodiment, step S5: driving rigid rule mapping, matching the forestry management threshold library and outputting a physical adjustment strategy, is specifically implemented as follows: Most existing technologies stop at step S4, outputting only an evaluation score. However, for grassroots forest rangers lacking an environmental science background, a score of "72" or "45" is meaningless in practice. The most significant and substantial advancement of this invention lies in the creation of a built-in "relational forestry management threshold library." This library rigidly binds abstract ecological evaluation ranges to specific, physical agricultural and forestry interventions, constructing a complete data and operational loop of "monitoring-evaluation-execution," and achieving automated mapping from state perception to physical intervention.

[0038] Automated strategy generation example: Physical structure control plan: When the cloud server calculates the response plan after three consecutive rainfalls on a certain plot of land... When a forest is in an unhealthy ecological state and its initial static label identifies it as a "high-density pure bamboo forest," the system's decision engine will determine through a logical chain that this is due to the collapse of the water retention system caused by a single structure and excessively tall bamboo. The system will immediately push a structured construction strategy to the management app: "Instruction: The water conservation function of the target area is degraded. It is required to carry out 20% intensity 'plum blossom' space thinning during this winter bamboo harvesting season, prioritizing the removal of diseased, weak, and old bamboo. After opening up forest gaps, deep-rooted evergreen broad-leaved tree species of the Fagaceae family will be replanted in the following spring. The plan is to forcibly transform the current pure forest into a mixed forest of different ages with a bamboo-to-broadleaf ratio of 7:3 within two growth cycles to restore the overlapping and mosaic degree of the canopy."

[0039] Pollution Prevention and Soil Conservation Emergency Plan: If, during a severe spring rainstorm, the system monitors the output of step S3... The red line was breached instantly, reaching the "extremely high risk" threshold. The system will block the overall score and directly trigger the highest priority red alert work order: "Alert: Soil and water erosion is out of control under current site conditions. Instructions: 1. Completely freeze all fertilizer application operations in the target area this year; 2. Strictly prohibit weeding under the forest canopy and enforce the original ecological conservation of understory shrubs and grasses to increase surface roughness; 3. After the rainy season, organize manpower to dig 40cm deep and 50cm wide stepped water-intercepting and fertilizer-conserving bamboo-joint ditches every 30 meters along the contour lines on the middle and lower sections of the hillside, using purely physical engineering to cut off nutrient runoff channels." Furthermore, to facilitate forestry managers' intuitive understanding of the overall situation, the system also generates a visualized ecological asset management dashboard. On the dashboard's terminal interface, the system utilizes front-end visualization technology to present, in the form of a multi-dimensional radar chart, the quantitative comparison results of water retention capacity indicators and non-point source pollution risk levels for multiple mixed-gradient comparison plots in real time. Simultaneously, the aforementioned triggered physical structure control commands and pollution prevention and soil conservation emergency commands are pushed to the terminal interface in a structured text format containing specific afforestation construction parameters, thereby efficiently driving corresponding physical afforestation operations and ecological risk early warning responses.

[0040] 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.

[0041] 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 method for monitoring rainfall redistribution characteristics in bamboo forests, characterized in that, Includes the following steps: S1: Receive a spatiotemporally coupled dataset from the underlying standardized monitoring network; wherein, the dataset is obtained by automatically and synchronously collecting hydrological and water quantity data and water quality sample data of the comparison plots of pure bamboo forests and mixed bamboo forests based on a unified extraforest rainfall event triggering signal; S2: Retrieve the stand structure characteristic data of the target forest stand, and extract the unique parameters of the moso bamboo forest. The unique parameters include at least the bamboo leaf clustering index, bamboo branch inclination angle and mixed canopy mosaic coefficient. Input the multi-stand spatiotemporal coupled dataset and the unique parameters into the preset canopy interception business model for calculation, and quantify and output the water interception and retention capacity index of the target forest stand in the rainfall redistribution process. S3: Extract rainfall feature data and forest stand structure feature data from the spatiotemporal coupling dataset as input features, input them into the pre-trained nutrient migration prediction model, calculate the predicted output flux values ​​of nitrogen, phosphorus and potassium nutrients in the target forest stand, and generate non-point source pollution risk assessment results for different forest management measures. S4: Based on the preset ecological resource evaluation index system, the water retention capacity index and the nutrient output flux prediction value are normalized and weighted to generate a comprehensive evaluation index of the ecological and hydrological function of moso bamboo forest. S5: Invoke the preset forestry management threshold library, which contains rigid mapping rules between ecological evaluation intervals and specific forestry physical intervention actions; use the system decision engine to perform a dual-parallel threshold comparison logic on the comprehensive evaluation index and the non-point source pollution risk assessment results respectively: When the comprehensive evaluation index is lower than the set safety threshold, physical structure control instructions for adjusting the spatial density and canopy structure of the target forest stand are automatically generated based on the mapping rules. When the non-point source pollution risk assessment results reach the preset high-risk threshold, an emergency command for pollution prevention and soil conservation is directly triggered to change the surface runoff pattern and block nutrient loss. By outputting and issuing intervention commands, the system achieves automated mapping and closed-loop control from eco-hydrological monitoring and perception to specific forest management physical actions; the intervention commands are physical structure regulation commands and pollution prevention and soil conservation emergency commands.

2. The method for monitoring rainfall redistribution characteristics in bamboo forests according to claim 1, characterized in that, The steps in S1 for obtaining the spatiotemporal coupled dataset of the multi-forest distribution specifically include: Receive rainfall event trigger signals sent by external meteorological information systems; In response to the trigger signal, the underlying sensing network synchronously uploads rainfall data, penetration rainfall data, tree trunk runoff data, and corresponding water sample nutrient concentration analysis data based on a preset time step. Based on unique timestamps and plot identification codes, the rainfall data, throughfall rainfall data, trunk runoff data and water sample nutrient concentration analysis data are mapped to each other, and management assessment data tags are added to them to construct the spatiotemporal coupled dataset of the multi-stand forests with spatiotemporal matching and correlation characteristics; wherein, the data tags are used to structurally indicate the stand type of the data source, the mixing ratio gradient of moso bamboo and mixed tree species, and site condition parameters.

3. The method for monitoring rainfall redistribution characteristics in bamboo forests according to claim 1, characterized in that, The step of performing localized canopy interception feature parsing in S2 specifically includes: Extract the bamboo leaf clustering index, bamboo branch inclination angle, and mixed canopy mosaic coefficient of the target forest stand, and use the unique parameters to update the canopy storage capacity parameter and free penetration coefficient in the preset canopy interception business model; The extraforest rainfall, through rainfall, and trunk runoff from the spatiotemporal coupled dataset are input into the updated canopy interception operational model, and the actual canopy interception of the target forest stand is obtained based on the water balance algorithm. The ratio of the actual canopy interception amount to the concurrent rainfall outside the forest is used as the water interception and retention capacity index and stored in the eco-hydrological characteristic database.

4. The method for monitoring rainfall redistribution characteristics in bamboo forests according to claim 1, characterized in that, The step of performing non-point source pollution risk prediction based on machine learning in S3 specifically includes: Historical rainfall characteristics, forest stand structure characteristics, and corresponding non-point source pollution monitoring records from the management cycle were used as a training sample set. A prediction network was established using random forest or gradient boosting tree algorithms, with rainfall, rainfall intensity, stand density and mixed ratio as input nodes and nitrogen, phosphorus and potassium nutrient loss flux as output nodes. The real-time acquired feature data is input into the training converged prediction network to calculate the predicted values ​​of nitrogen, phosphorus, and potassium nutrient output fluxes of the target forest stand under the current rainfall scenario.

5. The method for monitoring rainfall redistribution characteristics in bamboo forests according to claim 4, characterized in that, The process of generating the non-point source pollution risk assessment results in S3 specifically includes: The calculated predicted values ​​of nitrogen, phosphorus, and potassium nutrient output fluxes were compared with the allowable threshold values ​​for nutrient loss set by the forestry ecological security standards. Using multi-condition branching logic, the deviation rate of each nutrient is calculated; Based on the weighted composite value of the exceedance rate, the non-point source pollution risk of the target forest stand is divided into four management assessment levels: low risk, medium risk, high risk, and extremely high risk, thus generating the non-point source pollution risk assessment result.

6. The method for monitoring rainfall redistribution characteristics in bamboo forests according to claim 1, characterized in that, The process of normalizing the water retention capacity index and the predicted nutrient output flux in step S4 specifically includes: An extreme value standardization algorithm is used to eliminate the differences in physical dimensions between different indicators, and the water retention capacity index is transformed into a positive water retention benefit score. The reverse mapping algorithm is used to convert the predicted nutrient output flux into a reverse nutrient loss penalty score. Both the water retention benefit score and the nutrient loss penalty score are mapped and converted to the standard scoring range of [0, 100].

7. The method for monitoring rainfall redistribution characteristics in bamboo forests according to claim 6, characterized in that, The process of generating the comprehensive evaluation index of the eco-hydrological function of moso bamboo forest in S4 specifically includes: A judgment matrix containing water conservation objectives, water purification objectives, and forestry economic objectives was constructed using the analytic hierarchy process. Calculate the largest eigenvalue and the corresponding eigenvector of the judgment matrix, and obtain the weight coefficient of each evaluation target through a consistency test; The water conservation benefit score and the nutrient loss penalty score are linearly weighted and summed according to the weighting coefficients to generate the comprehensive evaluation index of the moso bamboo forest eco-hydrological function, which is used to characterize the overall ecological asset value of the forest stand.

8. The method for monitoring rainfall redistribution characteristics in bamboo forests according to claim 1, characterized in that, The step of automatically generating intervention instructions in S5 specifically includes: The pre-constructed forestry management threshold library contains a mapping table between multiple evaluation index intervals and forestry physical intervention actions; When the comprehensive evaluation index is lower than the set safety threshold, the system matches the associated physical structure control instructions by looking up a table. The physical structure control instructions include: performing spatial tending and thinning at a preset intensity, replanting deep-rooted evergreen broad-leaved tree species, and transforming the target forest stand into a target proportion of heterogeneous multi-layered mixed forest. When the non-point source pollution risk assessment results reach the preset high-risk threshold, the system matches the associated pollution prevention and soil conservation emergency instructions by looking up a table. The pollution prevention and soil conservation emergency instructions include: freezing the fertilizer application operation in the target area, implementing the original ecological conservation of understory shrubs and grasses, and digging tiered intercepting ditches for water and fertilizer conservation along contour lines.

9. A method for monitoring rainfall redistribution characteristics in bamboo forests according to claim 8, characterized in that, The step of outputting and issuing intervention commands in S5 also includes: Generate a visual dashboard for the operation and management of ecological assets; The operation and management dashboard presents a quantitative comparison of the water retention capacity index and non-point source pollution risk level of different mixed gradient sample plots in the form of a multi-dimensional radar chart. The triggered physical structure control commands and pollution prevention and soil conservation emergency commands are pushed to the forestry management terminal interface in a structured text format containing specific afforestation construction parameters, thereby driving the corresponding physical afforestation operations and ecological risk early warning responses.

10. A monitoring system for rainfall redistribution characteristics in bamboo forests, characterized in that, include: The data receiving module is used to receive spatiotemporally coupled datasets from the underlying standardized monitoring network; wherein, the datasets are obtained by automatically and synchronously collecting hydrological and water quantity data and water quality and water sample data from comparative plots of pure bamboo forests and mixed bamboo forests based on a unified extraforest rainfall event triggering signal. The interception and analysis module is used to retrieve the stand structure feature data of the target forest stand, extract the unique parameters of the moso bamboo forest, and the unique parameters include at least the bamboo leaf clustering index, bamboo branch inclination angle and mixed canopy mosaic coefficient; input the multi-stand spatiotemporal coupled dataset and the unique parameters into the preset forest canopy interception business model for calculation, and quantify and output the water interception and retention capacity index of the target forest stand in the rainfall redistribution process; The prediction and assessment module is used to extract rainfall feature data and forest stand structure feature data from the spatiotemporal coupled dataset as input features, input them into a pre-trained nutrient migration prediction model, calculate the predicted output flux values ​​of nitrogen, phosphorus and potassium nutrients in the target forest stand, and generate non-point source pollution risk assessment results for different forest management measures. The comprehensive evaluation module is used to normalize and weight and aggregate the water retention capacity index and the nutrient output flux prediction value based on the preset ecological resource evaluation index system, so as to generate a comprehensive evaluation index of the ecological and hydrological function of moso bamboo forest. The strategy generation module is used to call a preset forestry management threshold library, which contains rigid mapping rules between ecological evaluation intervals and specific forestry physical intervention actions; the system decision engine uses a dual-parallel threshold comparison logic to perform the comprehensive evaluation index and the non-point source pollution risk assessment results respectively: When the comprehensive evaluation index is lower than the set safety threshold, physical structure control instructions for adjusting the spatial density and canopy structure of the target forest stand are automatically generated based on the mapping rules. When the non-point source pollution risk assessment results reach the preset high-risk threshold, an emergency command for pollution prevention and soil conservation is directly triggered to change the surface runoff pattern and block nutrient loss. By outputting and issuing intervention commands, the system achieves automated mapping and closed-loop control from eco-hydrological monitoring and perception to specific forest management physical actions; the intervention commands are physical structure regulation commands and pollution prevention and soil conservation emergency commands.

Images