A forest carbon sink intelligent monitoring system and method

Abstract

The present invention discloses an intelligent forest carbon sink monitoring system and method, which relates to the field of environmental monitoring technology. Through layered monitoring, gridded sensor layout and tree species and diameter class screening, real-time collection of tree diameter changes and biomass increments is achieved. Multi-head attention mechanism, residual connection and layer normalization technology are used to deeply fuse and predict the collected data, accurately calculate the carbon sink amount of each classification, region and the entire forest, and generate change trends and abnormal area marks. Based on the prediction results, the system dynamically adjusts the sensor sampling frequency and generates targeted early warning suggestions in combination with environmental variables. Finally, the monitoring results are remotely displayed through three-dimensional heat maps, tree species distribution maps and time series animations, providing scientific and intuitive decision-making support for forest carbon sink management and risk warning.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an intelligent forest carbon sink monitoring system and method thereof. Background Art

[0002] Forest carbon sink monitoring is an important part of ecological protection and carbon neutrality. Traditional methods rely on manual measurement or simplified models, and have problems such as low efficiency, limited coverage, and insufficient real-time data. With technological advancement, intelligent monitoring systems have gradually been applied to this field, improving the accuracy and operability of monitoring.

[0003] Chinese invention patent application CN104374882A discloses a wireless intelligent carbon sink monitoring system. This system utilizes multiple modules (such as an MCU minimum system module, sensor modules, and data storage modules) and a remote control host, leveraging a wireless sensor network to simultaneously monitor CO2 concentrations and environmental data. This system effectively improves the real-time nature of data acquisition. However, its design primarily relies on a fixed sensor network to collect atmospheric data. Due to the lack of direct monitoring of tree growth (such as diameter changes and biomass increments), it struggles to reflect the dynamic changes in forest carbon sinks. Furthermore, Chinese invention patent CN119152438B discloses a remote sensing forest carbon sink monitoring method and system. This system constructs a carbon storage prediction model based on ground survey data and vegetation indices, and uses machine learning to generate high-resolution carbon storage distribution maps. This addresses the limited adaptability of traditional models. However, this system, primarily based on remote sensing imagery and ground survey data, suffers from long data update cycles and insufficient real-time performance. Furthermore, it lacks dynamic monitoring of abnormal areas and the generation of management recommendations, limiting its application in real-time forest management.

[0004] The above design uses wireless sensor networks, remote sensing technology and machine learning methods to achieve real-time collection of data such as atmospheric environment and vegetation index and high-resolution prediction of regional carbon reserves, thereby improving the real-time and scalability of monitoring capabilities. However, there are still certain limitations, such as the lack of direct monitoring of the growth status of individual trees (such as diameter changes and biomass increments), which makes it difficult to fully reflect the dynamic changes of carbon sinks within the forest. At the same time, the data analysis model is relatively simple and lacks multi-dimensional data fusion, dynamic anomaly detection and intelligent early warning functions, which limits its application in real-time forest management and precise carbon sink regulation. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and to propose an intelligent forest carbon sink monitoring system and method thereof to solve the above-mentioned problems.

[0006] The object of the present invention is achieved through the following technical solutions: an intelligent forest carbon sink monitoring system, comprising a monitoring hierarchy division module, a classification sampling monitoring module, a tree species classification unit, a diameter class division unit, a sensor layout unit, a data calculation module, a large model analysis module, an intelligent monitoring module, and a data transmission and display module;

[0007] The monitoring layer division module is used to divide the forest carbon layer, sample plot or sample point and sample tree monitoring layers in sequence within the preset area;

[0008] The diameter-class division unit is used to classify trees of each species by their diameter at breast height, and select normal and representative trees from each diameter class as monitoring samples;

[0009] The sensor deployment unit is used to deploy monitoring points in a grid-like or representative manner within the forest sample plot, deploy high-precision sensors to collect data on tree growth rate and biomass change, and support the dynamic increase of monitoring point density based on abnormal preliminary data;

[0010] The data calculation module includes a sample tree carbon sink calculation unit, which is used to calculate the carbon sink of a single sample tree based on the collected sample tree data;

[0011] Large model analysis modules include:

[0012] The classification attention unit is used to assign independent attention heads to each tree species and diameter class through a multi-head attention mechanism. Each attention head extracts the carbon sink variation pattern based on the time series characteristics of the input data and calculates the carbon sink amount of each type of forest;

[0013] The global attention unit receives the output of the classification attention unit and uses the global attention mechanism to analyze the carbon sequestration synergy between different tree species and diameter classes, calculate the total carbon sequestration of the entire forest, and generate a carbon sequestration trend curve.

[0014] The regional attention unit is used to divide the forest into sub-regions based on site type, dominant tree species, and main stand factors based on geographic information system (GIS) data. Another set of multi-head attention mechanisms is used to focus on the carbon sink changes in each region and mark abnormal carbon sink areas.

[0015] A classification sampling monitoring module is used to collect carbon sequestration data of trees of different species and diameter classes through sensors deployed in the forest, and organize the data into multidimensional datasets based on tree species and diameter classes;

[0016] The tree species classification unit is used to classify trees into conifers, broad-leaved trees and specific tree species according to the growth characteristics and carbon sequestration capacity of tree species in the forest patch, and collect growth data and environmental variables of each tree species, including temperature, rainfall and soil moisture; the high-precision sensors in the sensor layout unit are used to monitor the rate of change of tree diameter in real time, and upload the data to the classification sampling monitoring module through the wireless communication module.

[0017] The comprehensive analysis unit is used to fuse the outputs of the classification attention unit, the global attention unit, and the regional attention unit, realize the interactive analysis of multi-head data through residual connection and layer normalization technology, output the monitoring and calculation results of the classification carbon sink, regional carbon sink, and total carbon sink, and train the model based on historical data and real-time data to support short-term and long-term carbon sink change prediction; the comprehensive analysis unit analyzes the contribution rate of each classification and regional carbon sink to the total carbon sink through the bidirectional information flow of the classification head, regional head, and global head of the multi-head attention mechanism, identifies key influencing factors, and generates GIS coordinate marks for abnormal areas.

[0018] An intelligent monitoring module, which dynamically adjusts sensor sampling frequency based on the prediction results of the large model analysis module, identifies carbon sink anomalies, and generates targeted early warning recommendations;

[0019] Intelligent monitoring modules include:

[0020] The sampling frequency adjustment unit is used to determine carbon sink anomalies based on the prediction results of the comprehensive analysis unit and increase the sampling frequency in abnormal areas and reduce the sampling frequency in stable areas. The early warning recommendation generation unit is used to analyze the causes of carbon sink anomalies in combination with environmental variables, including drought, pests and diseases, or insufficient soil nutrients, and generate specific recommendations.

[0021] The data calculation module also includes: a sample plot carbon sink estimation unit, which is used to calculate or estimate the carbon sink of a sample plot or sample point based on the carbon sink of a sample tree; a forest carbon layer calculation unit, which is used to combine sampling theory to perform statistical calculations on the carbon sinks of multiple sample plots to obtain the carbon sink of each forest carbon layer; a regional carbon sink statistics unit, which is used to summarize and count the carbon sinks of each forest carbon layer to calculate the total carbon sink of the entire region; the classified sampling monitoring module supports the adjustment of tree species classification and diameter class classification standards according to forest type and monitoring needs, and realizes the dynamic optimization layout of monitoring points through the sensor layout unit.

[0022] Data visualization unit, used to convert carbon sink data into three-dimensional heat maps, tree species distribution maps and time series animations, supporting users to interactively view them through remote terminals;

[0023] The data transmission and display module is used to transmit the classified carbon sink amount, regional carbon sink amount, total carbon sink amount and early warning recommendations to the remote terminal through the wireless network, and present them in a three-dimensional visual form; the early warning recommendation generation unit of the intelligent monitoring module can generate graded early warning recommendations based on the duration and impact range of carbon sink anomalies, and push them to the remote terminal through the data transmission and display module.

[0024] The large model analysis module is used to receive the multidimensional data set from the classification sampling monitoring module, analyze the carbon sink changes of different tree species, diameter classes and regions based on the multi-head attention mechanism, and predict the classified carbon sink amount, regional carbon sink amount and the total carbon sink amount of the entire forest; the data transmission and display module supports users to adjust the three-dimensional visualization perspective through the remote terminal, including zooming in on abnormal areas, switching tree species distribution views and replaying carbon sink change animations.

[0025] A method for intelligently monitoring forest carbon sinks comprises the following steps:

[0026] S1: After a comprehensive survey of trees within the sample plot, select healthy trees according to their diameter class and install high-precision sensors. Using a classified sampling monitoring module, collect carbon sequestration data for trees of different species and diameter classes in the forest and compile them into a multidimensional dataset.

[0027] S2: Input the multidimensional dataset into the large model analysis module, and use the multi-head attention mechanism to analyze and predict the carbon sink of the sample site, the carbon sink of the forest carbon layer, the regional carbon sink and the total carbon sink;

[0028] S3: The intelligent monitoring module adjusts the sampling frequency based on the prediction results, identifies anomalies and generates early warning suggestions;

[0029] S4: Use the data transmission and display module to transmit the prediction results and suggestions to the remote terminal in a three-dimensional visualization form.

[0030] Step S1 includes the following sub-steps: classifying trees according to their growth characteristics and carbon sequestration capacity through tree species classification units, and collecting growth data and environmental variables; grading trees according to their diameter at breast height through diameter class division units, and screening representative trees; arranging monitoring points in a grid or typical representative manner through sensor layout units, and collecting diameter change and biomass data.

[0031] Step S2 includes the following sub-steps: assigning multi-head attention to tree species and diameter classes through classification attention units, extracting time series features, and calculating classified carbon sinks; integrating classification results through global attention units, analyzing synergistic effects, and calculating total carbon sinks and change curves; dividing sub-regions based on GIS through regional attention units, paying attention to regional carbon sink changes, and marking abnormal areas; fusing multi-head data through comprehensive analysis units and outputting the final prediction results.

[0032] Step S3 includes the following sub-steps: adjusting the sampling frequency according to the prediction results through the sampling frequency adjustment unit; analyzing the cause of the abnormality and generating targeted suggestions through the early warning suggestion generation unit; converting the carbon sink data into a three-dimensional view through the data visualization unit and pushing it to the remote terminal.

[0033] The beneficial effects of the present invention are:

[0034] 1. Through the monitoring layer division module, the preset area is divided into forest carbon layer, sample plot and sample tree monitoring layers in sequence. Tree species classification units and diameter class division units are used to carefully classify and screen trees to ensure that the collected data is highly representative and accurate. High-precision sensors arranged in a grid or typical representative manner can collect real-time data on tree growth change rates and biomass increments, and dynamically increase the density of monitoring points when data anomalies occur, thereby ensuring comprehensive data coverage and high-precision collection.

[0035] 2. Utilizing the multi-head attention mechanism (including classification, global and regional attention units) in the large model analysis module, the collected multi-dimensional data is deeply integrated and time series features are extracted. This not only calculates the carbon sequestration of each tree species and diameter class, but also trains models based on historical data and real-time data to predict short-term and long-term carbon sequestration trends. Residual connection and layer normalization techniques are used to interactively analyze multi-head data, effectively identify key influencing factors, and output accurate classification, regional and overall forest carbon sequestration and trend curves. At the same time, the GIS coordinates of abnormal areas are calibrated to provide a scientific basis for subsequent risk warnings.

[0036] 3. Based on the signal prediction results, the intelligent monitoring module dynamically adjusts the sensor sampling frequency for different areas through the sampling frequency adjustment unit: the sampling frequency is automatically increased in abnormal areas to obtain higher-resolution data, while the sampling frequency is reduced in stable areas to achieve energy saving and resource optimization. The early warning suggestion generation unit combines environmental variables (such as temperature, rainfall, soil moisture, etc.) to analyze the cause of the anomaly and promptly generate specific early warning suggestions such as drought, pests and diseases, or insufficient soil nutrients, helping managers to take quick response measures.

[0037] 4. In addition to calculating the carbon sink for a single tree sample, the system also accurately estimates the carbon sink from local samples to the entire region through the sample plot carbon sink estimation unit, forest carbon layer calculation unit and regional carbon sink statistics unit, providing comprehensive carbon sink assessment data. The data transmission and display module displays these monitoring and prediction results in intuitive forms such as three-dimensional heat maps, tree species distribution maps and time series animations on the remote terminal. Users can zoom in on abnormal areas, switch views and replay historical data through the interactive interface to achieve real-time monitoring and intuitive judgment.

[0038] 5. The present invention organically integrates data collection, preliminary processing, intelligent analysis, dynamic regulation and three-dimensional visualization to form a full-process, automated forest carbon sink monitoring system, which greatly improves monitoring efficiency and system response speed. The accurate prediction results and real-time warnings not only provide forest managers with timely risk warnings, but also provide solid data support for the formulation of scientific forest carbon sink regulation strategies, thereby helping to ensure the healthy and stable development of forest ecosystems and promote the intelligent process of carbon sink management. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The system architecture of the present invention Figure 1 ;

[0040] Figure 2 The system architecture of the present invention Figure 2 ;

[0041] Figure 3 It is the workflow diagram of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0043] It is to be noted that the directions of "left", "right", "up", "down", "front", "back", "inside" and "outside" in the following schemes are all relative directions and are not listed here one by one.

[0044] Example 1

[0045] like Figures 1 to 3 As shown, this embodiment mainly realizes the basic data collection, hierarchical management and preliminary data processing functions of the forest carbon sink monitoring system, ensures the representativeness and real-time nature of the collected data, and provides an accurate data basis for subsequent intelligent analysis.

[0046] The monitoring layer division module divides the forest into layers based on the preset area range:

[0047] Forest carbon layer: First, the basic layer for forest carbon sink monitoring is divided within the entire region;

[0048] Sample plots or sampling points: Several sample plots or monitoring points are divided within the forest carbon layer according to geographical distribution and typical representativeness;

[0049] Sample tree monitoring level: further select representative individual trees in the sample plot as monitoring objects.

[0050] The sensor deployment unit deploys monitoring points in a grid-based typical representative layout within each forest plot. Each monitoring point is equipped with a high-precision sensor to collect real-time data on the growth rate and biomass changes of trees. The system supports dynamically increasing the density of monitoring points based on abnormalities in the preliminary collected data, thereby ensuring comprehensive and detailed data collection coverage.

[0051] The formula for dynamic layout of sensor grid is as follows:

[0052]

[0053] Classification sampling monitoring module: collects carbon sequestration data of trees of different species and diameters through sensors and organizes them into multidimensional data sets;

[0054] Species classification units: Using pre-defined growth characteristics and carbon sequestration capacity parameters, forest trees are classified into conifers, broadleaf trees, and specific species. Species growth data and environmental variables (such as temperature, rainfall, and soil moisture) are collected for each category.

[0055] The diameter-class division unit classifies and screens trees of each tree species according to their diameter at breast height, and selects trees with normal growth and representativeness as monitoring samples to ensure that the collected data can truly reflect the growth status and carbon sequestration changes of trees of each diameter class.

[0056] The representative sample scoring formula is as follows:

[0057]

[0058] The data calculation module includes a sample tree carbon sink calculation unit, which is used to calculate the carbon sink of each sample tree based on the collected sample tree data, which serves as the basic data for subsequent large-scale model analysis.

[0059] The large model analysis module uses a multi-head attention mechanism to achieve deep data processing, and it has three sub-units:

[0060] Classification attention unit: allocates independent attention heads to each tree species and diameter class, extracts carbon sink variation patterns based on the time series characteristics of the input data, and calculates the carbon sink amount of each type of forest;

[0061] Global attention unit: Receives the output of the classification attention unit, uses the global attention mechanism to analyze the synergistic effects between different tree species and diameter classes, calculates the total carbon sink of the entire forest, and generates a carbon sink change trend curve;

[0062] Regional attention unit: Combined with geographic information system (GIS) data, the forest is divided into several sub-regions according to site type, dominant tree species and main stand factors. Another set of multi-head attention mechanisms is used to focus on carbon sequestration changes in each region and mark possible abnormal areas.

[0063] The data transmission and display module transmits all calculation and analysis results (including classified carbon sinks, total carbon sinks and trend curves) to the remote terminal in real time via wireless network, and displays them in intuitive ways such as three-dimensional heat maps, tree species distribution maps and time series animations.

[0064] Working process

[0065] Monitoring level division

[0066] After the system is started, the monitoring layer division module divides the preset area into forest carbon layers, sample plots (or sample points) and sample tree monitoring layers in sequence, providing a hierarchical structure for subsequent data collection and management.

[0067] Tree species classification and environmental data collection

[0068] The tree species classification unit classifies all trees in the forest into conifers, broadleaf trees and specific species according to their growth characteristics and carbon sequestration capacity. Growth data and environmental variables (temperature, rainfall, soil moisture) are also collected for each category.

[0069] Diameter screening

[0070] The diameter class division units are graded according to the tree diameter at breast height, and sample trees with normal growth conditions and strong representativeness are screened out from each diameter class to provide accurate objects for carbon sink data collection.

[0071] Sensor deployment and data collection

[0072] The sensor deployment unit deploys monitoring points in a grid or typical representative manner within each sample plot. High-precision sensors are installed at each point. The sensors collect real-time data on the growth rate and biomass increment of the sample trees, and upload the data to the classified sampling monitoring module through the wireless communication module. When the preliminary data shows abnormalities, the system can dynamically increase the density of monitoring points to improve data collection accuracy.

[0073] Preliminary data processing

[0074] The classification sampling monitoring module organizes the collected data of different tree species and diameter classes into a multidimensional data set and transmits it to the data calculation module. The sample tree carbon sink calculation unit calculates the carbon sink amount of each sample tree based on the collected data to form preliminary data.

[0075] In-depth data analysis

[0076] The preliminary data enters the large model analysis module. First, the classification attention unit uses a multi-head attention mechanism to extract time series features for each tree species and diameter class, and calculates the carbon sink amount of each type.

[0077] Next, the global attention unit integrates various outputs, analyzes the synergistic effects between different trees, calculates the total carbon sink of the entire forest, and generates a carbon sink change trend curve;

[0078] At the same time, the regional focus unit combines GIS data to divide the forest into different sub-regions to further monitor carbon sink changes within the region and mark abnormal areas.

[0079] Data transmission and visualization

[0080] Ultimately, the data transmission and display module will transmit monitoring data such as classification, global and regional carbon sinks, and trend curves to the remote terminal in real time via the wireless network. Users can view three-dimensional heat maps, tree species distribution maps, and time series animations through the interactive interface to intuitively understand the changes in forest carbon sinks.

[0081] Through stratified monitoring (forest carbon layers, sample plots and sample trees) and strict screening of tree species and diameter classes, we ensure that the collected data are highly representative and reflect the actual carbon sequestration situation at all levels of the forest.

[0082] High-precision sensors and a representative grid layout are used to ensure real-time monitoring of forest growth dynamics; at the same time, the system supports dynamic adjustment of monitoring point density when data is abnormal, further improving the accuracy and coverage of data collection.

[0083] The multi-head attention mechanism is used to extract time series features, analyze synergistic effects, and divide regions. It can not only calculate the carbon sequestration of individual trees and the entire forest, but also generate trend curves and mark abnormal areas, providing a scientific basis for forest management and carbon sequestration regulation.

[0084] The three-dimensional heat map, tree species distribution map and time series animation realized through the data transmission and display module enable users to intuitively understand the forest carbon sink monitoring data and effectively support remote monitoring and decision-making.

[0085] The seamless connection between various functional modules realizes an integrated monitoring system from data collection, preliminary processing to in-depth analysis and visual display, improving the overall monitoring efficiency and management level.

[0086] Through the above implementation methods and working processes, Example 1 can provide an efficient, intelligent and accurate basic platform for forest carbon sink monitoring, realize full dynamic monitoring of forest carbon sink status, and provide solid data support for subsequent intelligent and management decision optimization.

[0087] Example 2

[0088] like Figures 1 to 3 As shown, this embodiment is described on the basis of embodiment 1, with the focus on realizing in-depth processing, prediction, anomaly detection and dynamic regulation of collected data through comprehensive analysis units, intelligent monitoring modules and data visualization upgrades, thereby greatly improving the intelligence level and monitoring accuracy of the system.

[0089] The comprehensive analysis unit receives the multidimensional data output by the large model analysis module in Example 1, including:

[0090] Classification attention unit output (for time series feature extraction and carbon sink calculation for each tree species and diameter class)

[0091] Global focus unit output (analyzing the synergistic effects between different tree species and diameter classes, calculating the total carbon sink of the entire forest, and generating a carbon sink trend curve)

[0092] Regional focus unit output (dividing sub-regions based on GIS data, monitoring regional carbon sink changes, and marking abnormal areas);

[0093] The formula for calculating multi-head attention weight is as follows:

[0094]

[0095] To achieve interactive analysis of multi-head data, the comprehensive analysis unit uses residual connection and layer normalization technology to fuse the data from each attention module. Through bidirectional information flow, the classification head, regional head and global head of the comprehensive analysis unit respectively analyze the contribution of each classification and regional carbon sink to the total carbon sink, identify key influencing factors, and generate GIS coordinate marks for abnormal areas.

[0096] The formula for multi-head output fusion and carbon sink prediction is as follows:

[0097]

[0098] Model training and prediction

[0099] Using historical data and real-time data, the comprehensive analysis unit trains the prediction model to support the prediction of short-term and long-term carbon sink changes, and outputs:

[0100] Carbon sinks in sample plots, carbon sinks in forest carbon layers, regional carbon sinks, and total carbon sinks; at the same time, they provide data support for subsequent monitoring and regulation.

[0101] Construction of intelligent monitoring module

[0102] Sampling frequency adjustment, the intelligent monitoring module is set according to the prediction results after information fusion:

[0103] Sampling frequency adjustment unit: Based on the prediction results of the comprehensive analysis unit, it determines whether there is carbon sink anomaly in the monitoring area. For abnormal areas detected, the sensor sampling frequency is automatically increased to obtain higher-resolution data; for stable areas, the sampling frequency is reduced to save system resources.

[0104] The formula for dynamic adjustment of sampling frequency is as follows:

[0105]

[0106] The early warning recommendation generation unit combines environmental variables (temperature, rainfall, soil moisture, etc.) to analyze the causes of carbon sink anomalies (such as drought, pests and diseases, and insufficient soil nutrients) and generates specific and targeted early warning recommendations.

[0107] The data calculation module is expanded beyond the original sample wood carbon sink calculation unit and adds:

[0108] Sample plot carbon sink estimation unit: The carbon sink of the entire sample plot or sample point is estimated based on the carbon sink of a single sample tree.

[0109] Forest carbon layer calculation unit: Combined with sampling theory, the carbon sequestration amounts of multiple sample plots are statistically calculated to obtain the carbon sequestration data of each forest carbon layer.

[0110] Regional carbon sink statistical unit: summarize the carbon sink amounts of each forest carbon layer and calculate the total carbon sink amount of the entire region.

[0111] In addition, the classification sampling monitoring module supports dynamic adjustment of tree species classification and diameter class division standards based on forest type and actual monitoring needs, and realizes dynamic optimization of monitoring point layout through sensor deployment units.

[0112] Calculation formula for early warning risk index:

[0113]

[0114] Data visualization and remote interaction

[0115] The data visualization unit converts the calculated classification, regional and total carbon sink amounts to generate:

[0116] 3D heat map; tree species distribution map; time series animation;

[0117] Data transmission and display module upgrade

[0118] The above data and warning suggestions are transmitted to the remote terminal via wireless network. Users can adjust the 3D visualization perspective through the interactive interface, including:

[0119] Zoom in on the abnormal area; switch the tree species distribution view; replay the carbon sink change animation;

[0120] Working process

[0121] Data fusion and prediction

[0122] In Example 1, the system has completed forest stratification, tree species and diameter classification, sensor deployment and data collection. The collected multi-dimensional data is sorted by the classification sampling monitoring module and then transmitted to the large model analysis module.

[0123] The comprehensive analysis unit receives the outputs from the classification attention, global attention and regional attention units, realizes data fusion through residual connection and layer normalization technology, outputs the prediction results of classification, region and total carbon sink, and analyzes the contribution of each part to the total carbon sink through bidirectional multi-head information flow, and generates GIS coordinates of abnormal areas at the same time.

[0124] Dynamic monitoring and adjustment

[0125] The intelligent monitoring module uses the prediction results of the comprehensive analysis unit to make dynamic judgments on the monitoring area.

[0126] The sampling frequency adjustment unit automatically increases the sensor sampling frequency in abnormal areas and reduces the sampling frequency in stable areas to ensure the accuracy of data collection and the rational use of resources.

[0127] At the same time, the early warning suggestion generation unit generates specific early warning suggestions based on environmental variable analysis, and provides managers with timely response plans for abnormal causes such as drought, pests and diseases, or insufficient soil nutrients.

[0128] Multidimensional Data Computing and Comprehensive Statistics

[0129] In the data calculation module, the newly added sample plot carbon sink estimation unit, forest carbon layer calculation unit and regional carbon sink statistics unit expand and count the collected data to ensure accurate estimation of carbon sinks from single tree sample data to the entire region.

[0130] Data display and interaction

[0131] Finally, all prediction results, calculated data and warning recommendations are displayed on the remote terminal in the form of three-dimensional heat maps, tree species distribution maps and time series animations through the data transmission and display module.

[0132] Users can use the interactive interface to zoom in on data, switch views, and replay carbon sink change animations, enabling intuitive monitoring and timely adjustments to forest carbon sink status.

[0133] By realizing multi-head attention mechanism data fusion through comprehensive analysis units and adopting residual connection and layer normalization technology, the accuracy and robustness of carbon sink data prediction are greatly improved, supporting reliable prediction of short-term and long-term change trends.

[0134] The intelligent monitoring module dynamically adjusts the sensor sampling frequency based on signal prediction, achieving high-density monitoring in abnormal areas and low-frequency sampling in stable areas, effectively improving system operating efficiency, reducing energy consumption, and ensuring data quality.

[0135] Through plot carbon sink estimation, forest carbon layer calculation and regional carbon sink statistical units, the data is expanded from sample data to regional carbon sink monitoring, providing a more comprehensive and detailed carbon sink assessment and providing a scientific basis for forest carbon sink management.

[0136] The data visualization unit and data transmission display module present complex carbon sink data in the form of three-dimensional heat maps, tree species distribution maps and time series animations, allowing users to intuitively identify abnormal areas and carbon sink change trends, facilitating remote monitoring and timely decision-making.

[0137] The early warning recommendation generation unit generates targeted early warnings based on environmental variables. It can not only detect anomalies in real time, but also provide cause analysis and response measures for problems such as drought, pests and diseases, and provide effective risk warnings and decision-making support for forest carbon sink managers.

[0138] In summary, based on Example 1, Example 2 realizes full-process intelligent monitoring from data collection to intelligent prediction, abnormal warning and dynamic adjustment through intelligent data comprehensive analysis, dynamic regulation and interactive three-dimensional visualization, which greatly improves the application effect and management level of the forest carbon sink monitoring system.

[0139] Example 3

[0140] like Figures 1 to 3 As shown, this embodiment proposes an intelligent monitoring method for forest carbon sinks based on Example 1 and Example 2, which realizes full-process intelligent monitoring and prediction of forest carbon sink status through data collection, in-depth analysis, dynamic regulation and three-dimensional visualization display.

[0141] Data collection and preliminary processing (step S1 and its substeps)

[0142] Full survey and sample selection: conduct a comprehensive survey of all trees in the pre-set sample plots, classify the trees according to their diameter at breast height (diameter class), use the diameter class division units to screen out normal and representative sample trees, select monitoring samples for each diameter class, and install high-precision sensors.

[0143] Tree species classification and environmental data collection

[0144] Using tree species classification units, the trees in the forest are divided into categories such as conifers, broad-leaved trees and specific species according to their growth characteristics and carbon sequestration capacity. At the same time, growth data and key environmental variables (such as temperature, rainfall, soil moisture, etc.) are collected for each tree.

[0145] Sensor grid deployment and data collection

[0146] Through the sensor deployment unit, monitoring points are arranged in a grid manner in each sample plot. High-precision sensors collect real-time data on the rate of change of tree diameter and biomass increment, and organize the collected results into a multidimensional data set as the basis for subsequent analysis.

[0147] In-depth data analysis and prediction (step S2 and its substeps)

[0148] Analysis of multi-head attention mechanism

[0149] The multidimensional dataset compiled in step S1 is input into the large model analysis module, which uses the following sub-units to work together:

[0150] Classification attention unit: assigns independent attention heads to different tree species and diameter classes, extracts time series features and calculates classified carbon sinks;

[0151] Global attention unit: Integrates the classification results and uses a global attention mechanism to analyze the synergistic effects between different tree species and diameter classes, calculates the total carbon sink of the entire forest, and generates a trend curve;

[0152] Regional attention unit: Based on GIS data, the forest is divided into several sub-regions, and another set of multi-head attention mechanisms is used to focus on the carbon sink changes in each sub-region and mark the areas with abnormal carbon sinks;

[0153] Comprehensive analysis unit: Through residual connection and layer normalization technology, the above multiple data are integrated to form a two-way information flow, and the final prediction results of classified carbon sinks, regional carbon sinks and total carbon sinks are output.

[0154] Dynamic Control and Early Warning (Step S3 and its Substeps)

[0155] The sampling frequency is dynamically adjusted. The sampling frequency adjustment unit in the intelligent monitoring module makes intelligent judgments on the monitoring area based on the prediction results after information fusion:

[0156] For areas where carbon sequestration anomalies are detected, the sensor sampling frequency is automatically increased to obtain higher-resolution data;

[0157] For areas with stable status, the sampling frequency is reduced to achieve resource optimization.

[0158] Abnormal analysis and early warning generation. The early warning suggestion generation unit combines real-time environmental variables to conduct in-depth analysis of the causes of detected carbon sink anomalies (such as drought, pests and diseases, or insufficient soil nutrients, etc.) and generate specific and targeted early warning suggestions. At the same time, the data visualization unit converts the collected carbon sink data into a three-dimensional view to provide an intuitive basis for early warning decisions.

[0159] Data transmission and interactive display (step S4)

[0160] 3D visualization and remote interaction: Using the data transmission and display module, the prediction results and early warning suggestions of the large model analysis and intelligent monitoring module are transmitted to the remote terminal via the wireless network. The display content includes:

[0161] 3D heat map; tree species distribution map; time series animation;

[0162] Users can perform interactive operations through remote terminals, such as zooming in on abnormal areas, switching between different tree species views, and replaying carbon sink change processes, facilitating real-time monitoring and decision-making.

[0163] Step S1: Data collection and processing

[0164] Conduct a full inspection within the sample plot, select trees with normal growth according to diameter class, and install high-precision sensors;

[0165] Collect growth data and environmental variables of various types of trees by tree species classification unit;

[0166] The sensor deployment unit is used to arrange monitoring points in a grid pattern, and the diameter change and biomass increment data are collected in real time, and organized into a multidimensional data set.

[0167] Step S2: Data input and depth prediction

[0168] Input the organized multidimensional data set into the large model analysis module;

[0169] The classification attention unit, global attention unit, and regional attention unit extract features of different dimensions respectively, and fuse multiple data through the comprehensive analysis unit. The residual connection and layer normalization technology are used to output the final classification carbon sink, regional carbon sink, and total carbon sink prediction results.

[0170] At the same time, the forest carbon sink change trend curve and GIS coordinate marks of abnormal areas are generated.

[0171] Step S3: Intelligent control and abnormal warning

[0172] The intelligent monitoring module dynamically adjusts the sampling frequency of the monitoring points through the sampling frequency adjustment unit based on the prediction results, achieving resource optimization of high-frequency monitoring of abnormal areas and low-frequency sampling of stable areas;

[0173] The early warning suggestion generation unit combines environmental data to analyze the causes of anomalies and generate targeted early warning suggestions on drought, pests and diseases, or soil nutrient deficiency;

[0174] The data visualization unit converts these carbon sink data and warning information into intuitive three-dimensional views.

[0175] Step S4 Data transmission and remote display

[0176] All prediction results, warning suggestions and three-dimensional visualization graphics are transmitted to the remote terminal via wireless network through the data transmission and display module;

[0177] Users can adjust the view through the terminal interface, such as zooming in on abnormal areas, switching tree species distribution maps, and playing back time series animations to obtain forest carbon sink monitoring and early warning information in a timely manner.

[0178] This method realizes full-process automated monitoring from full survey sampling, data collation, in-depth prediction to dynamic regulation, early warning prompts and remote interactive display, ensuring the scientific and systematic nature of forest carbon sink monitoring.

[0179] Through the multi-head attention mechanism and the residual connection and layer normalization technology of the comprehensive analysis unit, the carbon sequestration data of different tree species, diameter classes and regions are deeply integrated, which significantly improves the prediction accuracy and supports reliable prediction of short-term and long-term change trends.

[0180] The intelligent monitoring module can automatically adjust the sampling frequency according to the signal prediction results, realizing high-density monitoring of abnormal areas and low-frequency sampling of stable areas. It not only improves the accuracy of data collection, but also effectively reduces energy consumption and system resource consumption.

[0181] Through the display of three-dimensional heat maps, tree species distribution maps and time series animations, users can intuitively understand the carbon sequestration status and changing trends of various forest areas, making it easier to detect problems and take countermeasures in a timely manner, while also supporting remote monitoring and decision-making assistance.

[0182] The early warning recommendation generation unit combines real-time environmental variables to provide targeted early warnings, timely identify abnormal conditions such as drought, pests and diseases, provide forest managers with scientific risk warnings and decision-making basis, and ensure the healthy and stable development of forest ecosystems.

[0183] In summary, Example 3 upgrades the traditional data collection and monitoring method into a full-process, intelligent, and dynamically regulated forest carbon sink monitoring method through a series of systematic and automated operating steps, providing comprehensive, accurate, and efficient technical support for forest carbon sink management.

[0184] The above description is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be regarded as excluding other embodiments. Instead, it can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or technology or knowledge in related fields. The changes and modifications made by those skilled in the art do not depart from the spirit and scope of the present invention and should be within the scope of protection of the claims attached to the present invention.

Claims

1. An intelligent forest carbon sink monitoring system, characterized in that: It includes monitoring level division module, classification sampling monitoring module, tree species classification unit, diameter class division unit, sensor layout unit, data calculation module, large model analysis module, intelligent monitoring module, data transmission and display module; The monitoring layer division module is used to sequentially divide the forest carbon layer, sample plot or sample point and sample tree monitoring layers within a preset area; The classification sampling monitoring module is used to collect carbon sequestration data of trees of different species and diameter classes through sensors deployed in the forest, and organize the data into multidimensional data sets based on tree species and diameter classes; The diameter classification unit is used to classify the trees of each tree species in the sample plot according to the diameter at breast height, and select normal and representative trees from each diameter class as monitoring samples; The sensor deployment unit is used to deploy monitoring points in a typical sampling or grid manner within the forest plot, deploy high-precision sensors to collect data on the growth rate and biomass increment of trees, and support dynamic increase in the density of monitoring points based on abnormalities in preliminary data; The formula for dynamic layout of sensor grid is as follows: The data calculation module includes a sample tree carbon sink calculation unit, which is used to calculate the carbon sink of a single sample tree based on the collected sample tree data; The large model analysis module includes: The classification attention unit is used to assign independent attention heads to each tree species and diameter class through a multi-head attention mechanism. Each attention head extracts the carbon sink variation pattern based on the time series characteristics of the input data and calculates the carbon sink amount of each type of forest; The global attention unit receives the output of the classification attention unit, analyzes the carbon sequestration synergy between different tree species and diameter classes through the global attention and force mechanism, calculates the total carbon sequestration of the entire forest, and generates a carbon sequestration trend curve; The regional attention unit is used to divide the forest into sub-regions based on site type, dominant tree species, and main stand factors based on geographic information system (GIS) data. Another set of multi-head attention mechanisms is used to focus on the carbon sequestration changes in each region and mark areas with abnormal carbon sequestration. The formula for calculating multi-head attention weight is as follows: The formula for multi-head output fusion and carbon sink prediction is as follows: Where H is the number of attention heads.

2. The intelligent forest carbon sink monitoring system according to claim 1, characterized in that: The tree species classification unit is used to classify trees into conifers, broad-leaved trees and specific tree species according to the growth characteristics and carbon sequestration capacity of tree species in the forest patch, and collect growth data and environmental variables of each tree species, including temperature, rainfall and soil moisture; the high-precision sensors in the sensor layout unit are used to monitor the tree diameter change rate data in real time, and upload the data to the classification sampling monitoring module through the wireless communication module.

3. The intelligent forest carbon sink monitoring system according to claim 1, characterized in that: The comprehensive analysis unit is used to fuse the outputs of the classification attention unit, the global attention unit, and the regional attention unit. It uses residual connections and layer normalization technology to achieve interactive analysis of multi-head data, output monitoring and calculation results of classified carbon sinks, regional carbon sinks, and total carbon sinks, and train models based on historical data and real-time data to support short-term and long-term carbon sink change predictions. The comprehensive analysis unit analyzes the contribution rate of each classification and regional carbon sink to the total carbon sink through the bidirectional information flow of the classification head, regional head and global head of the multi-head attention mechanism, identifies key influencing factors, and generates GIS coordinate marks for abnormal areas.

4. The intelligent forest carbon sink monitoring system according to claim 3, characterized in that: The intelligent monitoring module is used to dynamically adjust the sensor sampling frequency based on the prediction results of the large model analysis module, identify carbon sink anomalies and generate targeted early warning suggestions; The intelligent monitoring module includes: The sampling frequency adjustment unit is used to determine carbon sink anomalies based on the prediction results of the comprehensive analysis unit and increase the sampling frequency in abnormal areas and reduce the sampling frequency in stable areas. The early warning recommendation generation unit is used to analyze the causes of carbon sink anomalies in combination with environmental variables, including drought, pests and diseases, or insufficient soil nutrients, and generate specific recommendations. The data calculation module also includes: a sample plot carbon sink estimation unit, which is used to calculate or estimate the carbon sink of the sample plot or sample point based on the carbon sink of the sample tree; a forest carbon layer calculation unit, which is used to combine sampling theory to perform statistical calculations on the carbon sinks of multiple sample plots to obtain the carbon sink of each forest carbon layer; a regional carbon sink statistics unit, which is used to summarize and count the carbon sinks of each forest carbon layer to calculate the total carbon sink of the entire region; the classified sampling monitoring module supports adjusting the standards for tree species classification and diameter class division according to forest type and monitoring needs, and realizes dynamic optimization layout of monitoring points through the sensor layout unit.

5. The intelligent forest carbon sink monitoring system according to claim 1, characterized in that: The monitoring system also includes a data visualization unit that converts carbon sink data into three-dimensional heat maps, tree species distribution maps, and time series animations, allowing users to interactively view them through remote terminals. The data transmission and display module is used to transmit the classified carbon sink amount, regional carbon sink amount, total carbon sink amount and early warning recommendations to the remote terminal via a wireless network, and present them in a three-dimensional visual form; the early warning recommendation generation unit of the intelligent monitoring module can generate graded early warning recommendations based on the duration and impact range of carbon sink anomalies, and push them to the remote terminal through the data transmission and display module.

6. The intelligent forest carbon sink monitoring system according to claim 1, characterized in that: The classification sampling monitoring module organizes the collected data of different tree species and diameter classes into a multidimensional data set and transmits it to the data calculation module. The sample tree carbon sink calculation unit calculates the carbon sink of each sample tree based on the collected data to form preliminary data. The preliminary data enters the large model analysis module. First, the classification attention unit uses the multi-head attention mechanism to extract time series features for each tree species and diameter class and calculates the carbon sink of each type. Then, the global attention unit integrates various outputs, analyzes the synergistic effects between different trees, calculates the total carbon sink of the entire forest, and generates a carbon sink change trend curve. The data transmission and display module supports users to adjust the three-dimensional visualization perspective through a remote terminal, including zooming in on abnormal areas, switching tree species distribution views, and replaying carbon sink change animations.

7. A method for intelligently monitoring forest carbon sinks, characterized by: The following steps are involved: S1: After a comprehensive survey of trees within the sample plot, select trees with normal growth according to their diameter class and install high-precision sensors. Using the classified sampling monitoring module, collect carbon sequestration data for trees of different species and diameter classes in the forest and compile them into a multidimensional dataset. S2: The classification sampling monitoring module organizes the collected data on different tree species and diameter classes into a multidimensional dataset and transmits it to the data calculation module. The sample tree carbon sink calculation unit calculates the carbon sink of each sample tree based on the collected data to form preliminary data. The preliminary data enters the large model analysis module, which uses the multi-head attention mechanism to analyze and predict the classified carbon sink, regional carbon sink, and total carbon sink. The large model analysis module includes: The classification attention unit is used to assign independent attention heads to each tree species and diameter class through a multi-head attention mechanism. Each attention head extracts the carbon sink variation pattern based on the time series characteristics of the input data and calculates the carbon sink amount of each type of forest; The global attention unit receives the output of the classification attention unit, analyzes the carbon sequestration synergy between different tree species and diameter classes through the global attention and force mechanism, calculates the total carbon sequestration of the entire forest, and generates a carbon sequestration trend curve; The regional attention unit is used to divide the forest into sub-regions based on site type, dominant tree species, and main stand factors based on geographic information system (GIS) data. Another set of multi-head attention mechanisms is used to focus on the carbon sequestration changes in each region and mark areas with abnormal carbon sequestration. The formula for calculating multi-head attention weight is as follows: The formula for multi-head output fusion and carbon sink prediction is as follows: S3: The intelligent monitoring module adjusts the sampling frequency based on the prediction results, identifies anomalies and generates early warning suggestions; The formula for dynamic adjustment of sampling frequency is as follows: S4: Use the data transmission and display module to transmit the prediction results and suggestions to the remote terminal in a three-dimensional visualization form.

8. The intelligent forest carbon sink monitoring method according to claim 7, characterized in that: The step S1 includes the following sub-steps: Through the tree species classification unit, trees are classified according to their growth characteristics and carbon sequestration capacity, and growth data and environmental variables are collected; through the diameter classification unit, trees are classified according to their breast diameter and representative trees are screened; through the sensor layout unit, monitoring points are arranged in a typical representative or grid manner to collect diameter change and biomass data.

9. The intelligent forest carbon sink monitoring method according to claim 7, characterized in that: The step S2 includes the following sub-steps: Through the classification attention unit, multi-head attention is assigned to tree species and diameter classes, time series features are extracted, and classified carbon sinks are calculated; through the global attention unit, classification results are integrated, synergistic effects are analyzed, and the total carbon sink and change curve are calculated; through the regional attention unit, sub-regions are divided based on GIS, regional carbon sink changes are paid attention to, and abnormal areas are marked; through the comprehensive analysis unit, multi-head data are integrated to output the final prediction results.

10. The intelligent forest carbon sink monitoring method according to claim 7, characterized in that: The step S3 includes the following sub-steps: The sampling frequency adjustment unit adjusts the sampling frequency according to the prediction results; the early warning suggestion generation unit analyzes the cause of the abnormality and generates targeted suggestions; the data visualization unit converts the carbon sink data into a three-dimensional view and pushes it to the remote terminal.

Images