Forestry ecological environment real-time monitoring and management method based on big data

Abstract

The invention discloses a forestry ecological environment real-time monitoring and management method based on big data, and belongs to the technical field of forestry ecological environment management, and the method comprises the following steps: S1, multi-source heterogeneous data collection and sensor network deployment; s2, multi-modal data fusion and intelligent transmission: developing an adaptive communication protocol dynamic switching module, and automatically switching to a satellite communication link in a 4G / 5G network blind area; s3, mass data storage and distributed calculation: constructing a hybrid cloud storage architecture, and storing real-time monitoring data into a Redis cache queue; s4, intelligently extracting ecological environment indexes; s5, dynamically evaluating the ecological bearing capacity; s6, performing multi-level early warning and emergency response; s7, making a precise forestry management decision; and S8, carrying out system self-optimization and closed-loop management. According to the method, the air-space-ground integrated monitoring network is constructed, so that the comprehensiveness and the real-time performance of forestry ecological environment perception are remarkably improved; multi-source data of the unmanned aerial vehicle, the satellite remote sensing and the ground sensor are complementary.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry ecological environment management, and in particular to a real-time monitoring and management method of a forestry ecological environment based on big data. Background Art

[0002] Traditional forestry ecological and environmental monitoring relies primarily on manual inspections and decentralized sensors, resulting in limited monitoring coverage, delayed data updates, and fragmented information. The lack of multi-source data integration and intelligent analysis makes it difficult to grasp the dynamic changes in forest resources in real time, hindering early warning of risks such as pest and disease outbreaks, fire hazards, and ecological degradation. Furthermore, data silos exist across departments, and management decisions lack a unified scientific basis, making it difficult to achieve precise and dynamic ecological protection and resource allocation.

[0003] While remote sensing technology and IoT devices have been increasingly adopted for forestry monitoring in recent years, existing systems often focus on single-dimensional data collection, failing to effectively integrate multi-scale ecological factors such as meteorology, soil, and biology. Data processing still relies on static models and empirical judgments, lacking adaptive learning capabilities and struggling to cope with complex environmental changes. Furthermore, cross-regional and cross-departmental collaborative management mechanisms are not yet fully developed, resulting in inefficient emergency response and a disconnect between ecological restoration plans and actual needs. Therefore, we propose a big data-based approach to real-time monitoring and management of the forestry ecosystem to address this issue. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time monitoring and management method for forestry ecological environment based on big data to solve the problems raised in the above background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The real-time monitoring and management method of forestry ecological environment based on big data includes the following steps:

[0007] S1. Multi-source heterogeneous data acquisition and sensor network deployment: UAVs equipped with hyperspectral cameras, infrared thermal imagers, and gas sensors are combined with soil moisture probes, weather stations, and voiceprint collection equipment at fixed ground monitoring stations to build an integrated air-space-ground-integrated monitoring system.

[0008] S2. Multimodal Data Fusion and Intelligent Transmission: Develop an adaptive communication protocol dynamic switching module to automatically switch to satellite communication links in 4G / 5G network blind spots;

[0009] S3, Massive Data Storage and Distributed Computing: Build a hybrid cloud storage architecture and store real-time monitoring data in Redis cache queues;

[0010] S4. Intelligent extraction of ecological and environmental indicators: Using the improved YOLOv5 algorithm to identify pest and disease characteristics in drone images, combined with the random forest model to predict fire risk levels;

[0011] S5. Dynamic Assessment of Ecological Carrying Capacity: Establish an assessment system encompassing 32 indicators, including soil erosion coefficient, water conservation capacity, and carbon sink increment; develop a digital twin to simulate the ecological benefits of different afforestation plans;

[0012] S6. Multi-level warning and emergency response: Set three-level warning thresholds, and automatically trigger drone spraying instructions when the insect population density exceeds the economic threshold;

[0013] S7. Precision forestry management decision-making: Optimizing forest road construction plans based on space syntax analysis and solving the optimal solution for timber transportation routes using genetic algorithms;

[0014] S8. System self-optimization and closed-loop management: Design a reinforcement learning module to dynamically adjust the monitoring frequency and automatically increase frost damage monitoring points according to seasonal changes.

[0015] Preferably, the S1 comprises the following steps:

[0016] S101, Sensor Selection and Environmental Adaptability Testing: For the low-temperature environment of high mountains, a vibration-type humidity sensor that operates normally at -40°C was selected. An ultrasonic anemometer with an IP68 waterproof rating was deployed in humid areas. Equipment stability was verified through extreme laboratory simulation conditions.

[0017] S102, 3D monitoring network topology optimization: Use the ant colony algorithm to calculate the optimal cruising path of the UAV and combine the DEM digital elevation model to determine the height of the ground base station;

[0018] S103. Spatiotemporal alignment of multi-source data: Develop a BeiDou differential positioning module to correct the geographic coordinates of drone images, and use Kalman filtering to fuse LiDAR point clouds and satellite remote sensing data to achieve three-dimensional ecological parameter mapping with sub-meter accuracy.

[0019] Preferably, S2 comprises the following steps:

[0020] S201, Dynamic Channel Quality Assessment: Check the wireless signal strength every 15 minutes and automatically switch to the backup communication link when the transmission delay exceeds 200ms;

[0021] S202, Distributed Data Encryption Transmission: Use the SM4 national encryption algorithm to encrypt sensitive ecological data, and implement field-level permission control when sharing data across departments through a secure gateway;

[0022] S203. Transmission fault-tolerant mechanism construction: Design a three-copy storage strategy to automatically call the backup node to cache data when the primary link fails.

[0023] Preferably, S3 includes the following steps:

[0024] S301, Tiered storage of hot and cold data: Real-time monitoring data is retained in the in-memory database, and historical data is archived quarterly to the Blu-ray storage library;

[0025] S302, column storage optimization query: Delta encoding compression is used for continuous variables such as temperature and humidity, and an inverted index is constructed to accelerate range queries;

[0026] S303. Elastic scheduling of computing resources: Through Kubernetes container orchestration, computing nodes are automatically expanded during fire peak periods, and 50% of computing power is released for other businesses during normal periods.

[0027] Preferably, the S4 comprises the following steps:

[0028] S401, Multi-scale Feature Fusion: Pixel-level registration of high-resolution satellite imagery and drone infrared data to extract canopy temperature gradient features;

[0029] S402, Time Series Pattern Mining: Using CEEMDAN signal decomposition technology to separate climate trend terms and mutation terms, and identify the impact of extreme weather events on ecosystems;

[0030] S403, Mechanism-Data Hybrid Modeling: Combining the tree growth equation with the XGBoost algorithm, an ecological assessment model is constructed that includes 87 features such as photosynthetic efficiency and water stress.

[0031] Preferably, the S5 comprises the following steps:

[0032] S501. Dynamic adjustment of indicator weights: assign different weights to fire risk indicators according to seasonal changes, and increase the weight of landslide monitoring indicators in the rainy season;

[0033] S502, multi-scenario simulation: simulate carbon storage changes under different scenarios such as clear-cutting, selective felling, and afforestation, and generate a 10-year cycle prediction curve;

[0034] S503. Monetization of ecological value: Water conservation, carbon sequestration and other service functions are converted into annual ecosystem gross domestic product.

[0035] Preferably, the S6 comprises the following steps:

[0036] S601. Multi-dimensional early warning signal synthesis: superimpose weather forecasts, insect monitoring, and human activity data to generate a comprehensive risk index;

[0037] S602, hierarchical response strategy matching: Red alerts automatically initiate the fire reservoir pre-discharge program, and yellow alerts issue a forest operation ban;

[0038] S603. Dynamic generation of emergency plans: Based on the fire spread simulation results, a disposal plan including evacuation routes and equipment deployment is generated within 30 seconds.

[0039] Preferably, the S7 includes the following steps:

[0040] S701, Spatial Decision Support: Use GIS network analysis capabilities to complete emergency shelter accessibility analysis within 10 minutes;

[0041] S702, Intelligent Prescription Generation: After inputting the forest stand parameters, the system automatically recommends the type of fertilizer to be applied and the optimal time for drone application;

[0042] S703. Benefit-cost simulation: Establish an input-output model to compare the ecological and economic benefits of different tending measures over a 10-year cycle.

[0043] Preferably, the S8 comprises the following steps:

[0044] S801, System Performance Self-Diagnosis: Run Monte Carlo simulations daily to detect data link reliability and automatically repair abnormal sensor nodes;

[0045] S802, Iterative Optimization of Management Strategies: Compare the effectiveness of different parenting plans based on AB testing, and update the knowledge base with best practice cases monthly;

[0046] S803. Multi-party collaborative feedback mechanism: Open up data interfaces among forestry, environmental protection, emergency response and other departments to build a cross-regional ecological joint prevention and control system.

[0047] The beneficial effects of the present invention are:

[0048] 1. The present invention describes a real-time forestry ecological environment monitoring and management method based on big data. By building an integrated air-space-ground monitoring network, the method significantly enhances the comprehensiveness and real-time nature of forestry ecological environment perception. The complementary multi-source data from drones, satellite remote sensing, and ground sensors overcomes the limitations of traditional single monitoring methods and enables three-dimensional dynamic capture of vegetation growth, pests and diseases, fire conditions, and meteorological factors. The in-depth application of data fusion technology effectively eliminates the temporal and spatial differences between different monitoring sources, forming a continuous and complete ecological information map, providing a reliable foundation for subsequent analysis. This comprehensive, seamless monitoring system enables management departments to promptly grasp the status of forest resources, laying the data foundation for accurate decision-making.

[0049] 2. In the present invention, the real-time monitoring and management method for the forestry ecological environment based on big data promotes the shift from experience-driven to data-driven forestry management through an intelligent assessment model based on big data analysis. Through in-depth mining of multi-dimensional ecological indicators using machine learning algorithms, the system can automatically identify vegetation health hazards, predict disaster risk levels, and quantitatively evaluate the ecological benefits of different management measures. Digital twin technology simulates the combined effects of human activities and natural evolution, assisting in the formulation of scientific tending plans and resource allocation strategies. This intelligent decision-making support mechanism not only reduces the blind spots and lags of traditional manual inspections, but also achieves a balance between ecological protection and economic development through dynamic optimization of management strategies.

[0050] 3. In the present invention, the real-time monitoring and management method for the forestry ecological environment based on big data significantly enhances forestry risk prevention and control capabilities through a multi-level early warning and emergency response mechanism. The system uses real-time data stream analysis to capture the signs of abnormal events such as fires and insect pests in advance, generates graded early warning signals based on a historical case library, and mobilizes multiple forces such as drones and forest rangers for rapid response. The combination of a fire spread prediction model and an optimal path planning algorithm greatly compresses the time window for disaster response, while the intelligent pest and disease diagnosis module provides immediate prevention and control guidance to grassroots organizations. This "monitoring-early warning-handling" closed-loop model effectively reduces ecological losses caused by natural disasters and human damage, and ensures the safety of forest resources.

[0051] 4. In the present invention, the real-time monitoring and management method for the forestry ecological environment based on big data achieves efficient utilization of massive ecological data and knowledge accumulation through the application of distributed computing and knowledge graph technology. The hybrid cloud storage architecture takes into account the timeliness and economy of data processing, and the federated learning framework promotes cross-departmental collaborative analysis while protecting data privacy. Through continuous iterative optimization, the system continuously absorbs knowledge such as the characteristics of emerging species and experience in coping with extreme climates, forming a dynamically updated forestry smart brain. The accumulation and transformation of this data asset not only reduces the cost of repeated research, but also provides a scientific basis for long-term ecological restoration through the mining of historical patterns.

[0052] 5. In the present invention, the real-time monitoring and management method of the forestry ecological environment based on big data promotes systematic changes in the forestry management model through technological innovation; multi-source data fusion breaks down information barriers between departments, blockchain traceability ensures the credibility of ecological service value accounting, and the development of the carbon sink trading module provides quantitative support for green finance; while improving monitoring accuracy, the system pays more attention to ecological carrying capacity assessment and sustainable development path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the real-time monitoring and management method of the forestry ecological environment based on big data proposed in the present invention. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0055] Reference Figure 1 , a real-time monitoring and management method for forestry ecological environment based on big data, including the following steps:

[0056] S1. Multi-source heterogeneous data acquisition and sensor network deployment: UAVs equipped with hyperspectral cameras, infrared thermal imagers, and gas sensors are combined with soil moisture probes, weather stations, and voiceprint collection equipment at fixed ground monitoring stations to build an integrated air-space-ground monitoring system. LoRa / NB-IoT low-power communication modules are used to achieve signal coverage in mountainous areas, and edge computing nodes are used to perform preliminary screening and compression of raw data. Sensor deployment locations are optimized based on vegetation density, terrain slope, and animal migration paths, ensuring a monitoring density of 12 nodes per square kilometer in key areas.

[0057] S2. Multimodal Data Fusion and Intelligent Transmission: Develop an adaptive communication protocol dynamic switching module to automatically switch to satellite communication links in 4G / 5G network blind spots; use a federated learning framework to achieve distributed data encryption aggregation and record data flow trajectories through blockchain; build a data quality assessment model to remove duplicate data packets and correct abnormal fluctuations in real time to ensure the integrity and timeliness of transmitted data;

[0058] S3, Massive Data Storage and Distributed Computing: Build a hybrid cloud storage architecture, store real-time monitoring data in Redis cache queues, and archive historical data to Hadoop HDFS; design a time-space partitioned table structure, establishing dual indexes based on latitude and longitude grids and timestamps; deploy the Spark streaming computing engine to achieve sliding window analysis of tens of thousands of data points per second, supporting real-time training of subsequent machine learning models;

[0059] S4. Intelligent Extraction of Ecological and Environmental Indicators: Using an improved YOLOv5 algorithm to identify pest and disease characteristics in drone imagery, combined with a random forest model to predict fire risk levels; using an LSTM neural network to analyze the lag effect of soil moisture and meteorological factors, constructing a vegetation growth stress index; using a graph convolutional network to analyze species competition and generate a dynamic heat map of biodiversity;

[0060] S5. Dynamic Assessment of Ecological Carrying Capacity: Establish an assessment system encompassing 32 indicators, including soil erosion coefficient, water conservation capacity, and carbon sink increment; develop a digital twin to simulate the ecological benefits of different afforestation schemes, and identify key influencing factors through parameter sensitivity analysis; and combine land use change data to predict the evolution of ecological red line boundaries over the next 5-10 years.

[0061] S6. Multi-level early warning and emergency response: Set three-level early warning thresholds to automatically trigger drone spraying commands when insect population density exceeds the economic threshold; build a fire spread prediction model, output fire development probability maps, and plan the optimal firefighting path; use a voiceprint recognition system to monitor abnormal animal activity and link it to forest ranger terminals to send prevention recommendations;

[0062] S7. Precision forestry management decision-making: Optimize forest road construction plans based on space syntax analysis and use genetic algorithms to find the optimal solution for timber transportation routes; develop an intelligent pest and disease diagnosis app to provide forest farmers with targeted prevention and control plans through image recognition; establish a carbon sink trading data center to calculate forest carbon sequestration in real time and generate visual reports;

[0063] S8. System self-optimization and closed-loop management: Design a reinforcement learning module to dynamically adjust the monitoring frequency and automatically increase frost damage monitoring points according to seasonal changes; build a management effect feedback evaluation model to quantify and compare the ecological benefits of measures such as fertilization and thinning; regularly update the knowledge graph and incorporate new pest and disease characteristics and extreme weather response strategies into the decision support system.

[0064] In this embodiment, S1 includes the following steps:

[0065] S101, Sensor Selection and Environmental Adaptability Testing: For the low-temperature environment of high mountains, a vibration-type humidity sensor that operates normally at -40°C was selected. An ultrasonic anemometer with an IP68 waterproof rating was deployed in humid areas. Equipment stability was verified through extreme laboratory simulation conditions.

[0066] S102, 3D monitoring network topology optimization: Use the ant colony algorithm to calculate the optimal cruising path of the UAV and combine the DEM digital elevation model to determine the height of the ground base station;

[0067] S103. Spatiotemporal alignment of multi-source data: Develop a BeiDou differential positioning module to correct the geographic coordinates of drone images, and use Kalman filtering to fuse LiDAR point clouds and satellite remote sensing data to achieve three-dimensional ecological parameter mapping with sub-meter accuracy.

[0068] In this embodiment, S2 includes the following steps:

[0069] S201, Dynamic Channel Quality Assessment: Check the wireless signal strength every 15 minutes and automatically switch to the backup communication link when the transmission delay exceeds 200ms;

[0070] S202, Distributed Data Encryption Transmission: Use the SM4 national encryption algorithm to encrypt sensitive ecological data, and implement field-level permission control when sharing data across departments through a secure gateway;

[0071] S203. Transmission fault-tolerant mechanism construction: Design a three-copy storage strategy to automatically call the backup node to cache data when the primary link fails.

[0072] In this embodiment, S3 includes the following steps:

[0073] S301, Tiered storage of hot and cold data: Real-time monitoring data is retained in the in-memory database, and historical data is archived quarterly to the Blu-ray storage library;

[0074] S302, column storage optimization query: Delta encoding compression is used for continuous variables such as temperature and humidity, and an inverted index is constructed to accelerate range queries;

[0075] S303. Elastic scheduling of computing resources: Through Kubernetes container orchestration, computing nodes are automatically expanded during fire peak periods, and 50% of computing power is released for other businesses during normal periods.

[0076] In this embodiment, S4 includes the following steps:

[0077] S401, Multi-scale Feature Fusion: Pixel-level registration of high-resolution satellite imagery and drone infrared data to extract canopy temperature gradient features;

[0078] S402, Time Series Pattern Mining: Using CEEMDAN signal decomposition technology to separate climate trend terms and mutation terms, and identify the impact of extreme weather events on ecosystems;

[0079] S403, Mechanism-Data Hybrid Modeling: Combining the tree growth equation with the XGBoost algorithm, an ecological assessment model is constructed that includes 87 features such as photosynthetic efficiency and water stress.

[0080] In this embodiment, S5 includes the following steps:

[0081] S501. Dynamic adjustment of indicator weights: assign different weights to fire risk indicators according to seasonal changes, and increase the weight of landslide monitoring indicators in the rainy season;

[0082] S502, multi-scenario simulation: simulate carbon storage changes under different scenarios such as clear-cutting, selective felling, and afforestation, and generate a 10-year cycle prediction curve;

[0083] S503. Monetization of ecological value: Water conservation, carbon sequestration and other service functions are converted into annual ecosystem gross domestic product.

[0084] In this embodiment, S6 includes the following steps:

[0085] S601. Multi-dimensional early warning signal synthesis: superimpose weather forecasts, insect monitoring, and human activity data to generate a comprehensive risk index;

[0086] S602, hierarchical response strategy matching: Red alerts automatically initiate the fire reservoir pre-discharge program, and yellow alerts issue a forest operation ban;

[0087] S603. Dynamic generation of emergency plans: Based on the fire spread simulation results, a disposal plan including evacuation routes and equipment deployment is generated within 30 seconds.

[0088] In this embodiment, S7 includes the following steps:

[0089] S701, Spatial Decision Support: Use GIS network analysis capabilities to complete emergency shelter accessibility analysis within 10 minutes;

[0090] S702, Intelligent Prescription Generation: After inputting the forest stand parameters, the system automatically recommends the type of fertilizer to be applied and the optimal time for drone application;

[0091] S703. Benefit-cost simulation: Establish an input-output model to compare the ecological and economic benefits of different tending measures over a 10-year cycle.

[0092] In this embodiment, S8 includes the following steps:

[0093] S801, System Performance Self-Diagnosis: Run Monte Carlo simulations daily to detect data link reliability and automatically repair abnormal sensor nodes;

[0094] S802, Iterative Optimization of Management Strategies: Compare the effectiveness of different parenting plans based on AB testing, and update the knowledge base with best practice cases monthly;

[0095] S803. Multi-party collaborative feedback mechanism: Open up data interfaces among forestry, environmental protection, emergency response and other departments to build a cross-regional ecological joint prevention and control system.

[0096] In this embodiment, refined forestry ecological management is achieved by building a multi-dimensional perception network and intelligent analysis platform. First, an adaptive sensor cluster is deployed in the monitoring area, including hyperspectral imaging equipment carried by drones, ground-based fixed environmental monitoring stations, and animal behavior recognition terminals, and data interconnection and interoperability are achieved through low-power Internet of Things protocols. The system uses edge computing nodes to pre-process the raw data, and combines it with a federated learning framework to achieve secure cross-regional data aggregation, forming an ecological database covering meteorological, soil, biological and other elements. Based on a dynamic threshold algorithm and multimodal data fusion technology, the intelligent analysis engine extracts key indicators such as vegetation health index and disaster risk level in real time to provide support for management decisions.

[0097] During actual management, the system automatically triggers a tiered response mechanism based on monitoring data. When pest and disease indices exceed normal limits, a drone swarm is deployed to perform precision pesticide application and track unusual wildlife activity using voiceprint recognition technology. The fire risk warning module combines meteorological data and terrain features to simulate fire spread paths and plan emergency resource allocation. The management platform simultaneously optimizes forestry strategies, simulating the long-term ecological effects of different interventions through digital twin technology. It dynamically adjusts key monitoring areas and resource allocation, forming a closed-loop management system of "perception-decision-execution-feedback" to continuously improve the effectiveness of forestry ecological management.

[0098] The above is a detailed introduction to the real-time monitoring and management method of the forestry ecological environment based on big data provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above examples is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified in several ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A real-time monitoring and management method for forestry ecological environment based on big data, characterized in that: The following steps are involved: S1. Multi-source heterogeneous data acquisition and sensor network deployment: UAVs equipped with hyperspectral cameras, infrared thermal imagers, and gas sensors are combined with soil moisture probes, weather stations, and voiceprint collection equipment at fixed ground monitoring stations to build an integrated air-space-ground-integrated monitoring system. S2. Multimodal Data Fusion and Intelligent Transmission: Develop an adaptive communication protocol dynamic switching module to automatically switch to satellite communication links in 4G / 5G network blind spots; S3, Massive Data Storage and Distributed Computing: Build a hybrid cloud storage architecture and store real-time monitoring data in Redis cache queues; S4. Intelligent extraction of ecological and environmental indicators: Using the improved YOLOv5 algorithm to identify pest and disease characteristics in drone images, combined with the random forest model to predict fire risk levels; S5. Dynamic Assessment of Ecological Carrying Capacity: Establish an assessment system encompassing 32 indicators, including soil erosion coefficient, water conservation capacity, and carbon sink increment; develop a digital twin to simulate the ecological benefits of different afforestation plans; S6. Multi-level warning and emergency response: Set three-level warning thresholds, and automatically trigger drone spraying instructions when the insect population density exceeds the economic threshold; S7. Precision forestry management decision-making: Optimizing forest road construction plans based on space syntax analysis and solving the optimal solution for timber transportation routes using genetic algorithms; S8. System self-optimization and closed-loop management: Design a reinforcement learning module to dynamically adjust the monitoring frequency and automatically increase frost damage monitoring points according to seasonal changes.

2. The method for real-time monitoring and management of forestry ecological environment based on big data according to claim 1 is characterized in that: The S1 comprises the following steps: S101, Sensor Selection and Environmental Adaptability Testing: For the low-temperature environment of high mountains, a vibration-type humidity sensor that operates normally at -40°C was selected. An ultrasonic anemometer with an IP68 waterproof rating was deployed in humid areas. Equipment stability was verified through extreme laboratory simulation conditions. S102, 3D monitoring network topology optimization: Use the ant colony algorithm to calculate the optimal cruising path of the UAV and combine the DEM digital elevation model to determine the height of the ground base station; S103. Spatiotemporal alignment of multi-source data: Develop a BeiDou differential positioning module to correct the geographic coordinates of drone images, and use Kalman filtering to fuse LiDAR point clouds and satellite remote sensing data to achieve three-dimensional ecological parameter mapping with sub-meter accuracy.

3. The method for real-time monitoring and management of forestry ecological environment based on big data according to claim 1 is characterized in that: The S2 comprises the following steps: S201, Dynamic Channel Quality Assessment: Check the wireless signal strength every 15 minutes and automatically switch to the backup communication link when the transmission delay exceeds 200ms; S202, Distributed Data Encryption Transmission: Use the SM4 national encryption algorithm to encrypt sensitive ecological data, and implement field-level permission control when sharing data across departments through a secure gateway; S203. Transmission fault-tolerant mechanism construction: Design a three-copy storage strategy to automatically call the backup node to cache data when the primary link fails.

4. The method for real-time monitoring and management of forestry ecological environment based on big data according to claim 1 is characterized in that: The S3 comprises the following steps: S301, Tiered storage of hot and cold data: Real-time monitoring data is retained in the in-memory database, and historical data is archived quarterly to the Blu-ray storage library; S302, column storage optimization query: Delta encoding compression is used for continuous variables such as temperature and humidity, and an inverted index is constructed to accelerate range queries; S303. Elastic scheduling of computing resources: Through Kubernetes container orchestration, computing nodes are automatically expanded during fire peak periods, and 50% of computing power is released for other businesses during normal periods.

5. The method for real-time monitoring and management of forestry ecological environment based on big data according to claim 1 is characterized in that: The S4 comprises the following steps: S401, Multi-scale Feature Fusion: Pixel-level registration of high-resolution satellite imagery and drone infrared data to extract canopy temperature gradient characteristics; S402, Time Series Pattern Mining: Use CEEMDAN signal decomposition technology to separate climate trend terms and mutation terms, and identify the impact of extreme weather events on ecosystems; S403, Mechanism-Data Hybrid Modeling: Combining the tree growth equation with the XGBoost algorithm, an ecological assessment model is constructed that includes 87 features such as photosynthetic efficiency and water stress.

6. The method for real-time monitoring and management of forestry ecological environment based on big data according to claim 1 is characterized in that: The S5 comprises the following steps: S501. Dynamic adjustment of indicator weights: assign different weights to fire risk indicators according to seasonal changes, and increase the weight of landslide monitoring indicators in the rainy season; S502, multi-scenario simulation: simulate carbon storage changes under different scenarios such as clear-cutting, selective felling, and afforestation, and generate a 10-year cycle prediction curve; S503. Monetization of ecological value: Water conservation, carbon sequestration and other service functions are converted into annual ecosystem gross domestic product.

7. The method for real-time monitoring and management of forestry ecological environment based on big data according to claim 1 is characterized in that: The S6 comprises the following steps: S601. Multi-dimensional early warning signal synthesis: superimpose weather forecasts, insect monitoring, and human activity data to generate a comprehensive risk index; S602, hierarchical response strategy matching: Red alerts automatically initiate the fire reservoir pre-discharge program, and yellow alerts issue a forest operation ban; S603. Dynamic generation of emergency plans: Based on the fire spread simulation results, a disposal plan including evacuation routes and equipment deployment is generated within 30 seconds.

8. The method for real-time monitoring and management of forestry ecological environment based on big data according to claim 1 is characterized in that: The S7 comprises the following steps: S701, Spatial Decision Support: Use GIS network analysis capabilities to complete emergency shelter accessibility analysis within 10 minutes; S702, Intelligent Prescription Generation: After inputting the forest stand parameters, the system automatically recommends the type of fertilizer to be applied and the optimal time for drone application; S703. Benefit-cost simulation: Establish an input-output model to compare the ecological and economic benefits of different tending measures over a 10-year cycle.

9. The method for real-time monitoring and management of forestry ecological environment based on big data according to claim 1 is characterized in that: The S8 comprises the following steps: S801, System Performance Self-Diagnosis: Run Monte Carlo simulations daily to detect data link reliability and automatically repair abnormal sensor nodes; S802, Iterative Optimization of Management Strategies: Compare the effectiveness of different parenting plans based on AB testing, and update the knowledge base with best practices every month; S803. Multi-party collaborative feedback mechanism: Open up data interfaces among forestry, environmental protection, emergency response and other departments to build a cross-regional ecological joint prevention and control system.

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