Mangrove forest restoration area carbon sequestration data monitoring system

Abstract

The invention provides a mangrove forest restoration area carbon sink quantity data monitoring system, and relates to the technical field of ecological carbon sink metering, and the system comprises an acquisition module which is used for collecting a carbon exchange original data set of an atmosphere-vegetation-soil interface based on a multi-type sensor network arranged in an intertidal wetland of a mangrove forest restoration area; the data generation module is used for carrying out space-time coordinate matching based on the original data set and generating a multi-dimensional carbon flux fusion data set of geographic position and time joint indexing; and the identification module is used for calculating a local space-time dispersion and a global correlation threshold value based on the fused data set, and identifying and marking abnormal data points. According to the invention, real-time and multi-dimensional monitoring of the carbon sink amount of the mangrove forest restoration area is realized, the monitoring efficiency and the data reliability are improved, and support is provided for assessment of restoration and management strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological carbon sink measurement, and particularly to a carbon sink data monitoring system for a mangrove restoration area. Background Art

[0002] Traditional monitoring mostly relies on sensors at single fixed positions to collect data, making it difficult to cover the diverse ecological environments of mangroves. For example, in the intertidal zone of the restoration area, carbon dioxide concentration sensors are only set in a few areas. When the tide rises, some sensors are submerged by seawater, resulting in interrupted data collection and the inability to obtain carbon exchange information during the submerged period. Moreover, traditional sensors lack the ability to adapt to environmental changes. In high-salt and high-humidity environments, the performance of sensors deteriorates, the data accuracy decreases, and there are significant deviations between the collected soil carbon flux data and the actual situation.

[0003] In addition, when traditional technologies analyze data, they use preset fixed standards and processing procedures, making it difficult to keep up with the dynamic change rhythm of the mangrove ecosystem. During the typhoon season, the vegetation structure of mangroves is damaged and the carbon sink capacity changes. However, the traditional monitoring system processes data according to fixed processing standards and procedures, without timely adjusting the data processing strategy according to the damaged situation of the vegetation, resulting in the calculated carbon sink amount not matching the actual situation. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a carbon sink data monitoring system for a mangrove restoration area, which realizes dynamic monitoring and spatial correction of the carbon sink amount in the mangrove restoration area through multi-type sensor fusion and adaptive grid division.

[0005] To solve the above technical problem, the technical solution of the present invention is as follows:

[0006] In a first aspect, a carbon sink data monitoring system for a mangrove restoration area includes:

[0007] A collection module, configured to collect an original data set of carbon exchange at the atmosphere-vegetation-soil interface based on a multi-type sensor network deployed in the intertidal wetland of the mangrove restoration area;

[0008] A data generation module, configured to generate a multi-dimensional carbon flux fusion data set with a joint index of geographical location and time based on the original data set through spatio-temporal coordinate matching;

[0009] An identification module, configured to calculate the local spatio-temporal dispersion and the global correlation threshold based on the fusion data set, and identify and mark abnormal data points;

[0010] The grid module is used to determine four boundary detection points within the restoration area based on abnormal data points, form an irregular polygon monitoring area that changes with ecological processes, and perform non-uniform grid cutting on the polygon area to generate a set of adaptive grid cells covering the monitoring area;

[0011] The index generation module is used to calculate the topographic deformation rate, environmental factor gradient change rate, and hydrological inundation frequency of each cell within the tidal cycle for the set of grid cells, and generate spatial correction quantification indices;

[0012] The compensation and correction module is used to extract sensor data within the grid cells, and perform carbon flux value compensation in combination with the spatial correction quantification indices to obtain a corrected carbon sink quantity dataset;

[0013] The comprehensive evaluation module is used to calculate the carbon sink quantity stability index, spatial coverage contribution index, and ecological process characterization index of each node within the evaluation cycle based on the corrected carbon sink quantity dataset, fuse the three indices into a comprehensive evaluation parameter, and determine the prominent nodes as the final layout points for carbon sink quantity monitoring according to the distribution characteristics of the comprehensive evaluation parameter.

[0014] Furthermore, perform spatio-temporal coordinate matching based on the original dataset to generate a multi-dimensional carbon flux fusion dataset with a joint index of geographical location and time, including:

[0015] Perform timestamp synchronization processing on the original dataset collected by multi-type sensor networks to obtain a time-synchronized dataset with a unified time reference for all sensor data;

[0016] Based on the time-synchronized dataset, map each data point to a unified spatial coordinate system of the preset restoration area according to the geographical coordinates of the deployment location to generate a mapped dataset with a unified spatio-temporal reference;

[0017] Use the mapped dataset, with the geographical location coordinates and the synchronized timestamp as the joint primary key, to associate and integrate the carbon exchange-related parameters from different types of sensors at the same geographical location and the same timestamp to form a preliminary associated and integrated dataset;

[0018] For the spatio-temporal point data missing in the preliminary associated and integrated dataset due to sensor failures, terrain occlusion, and communication interruptions, based on the unified spatial coordinate system and the synchronized timestamp, use the valid data of adjacent sensor nodes to obtain a complete dataset after filling in the missing values;

[0019] Organize the complete dataset into a data matrix with geographical location coordinates and timestamp as the joint index, including multi-dimensional carbon fluxes and related environmental parameters, to generate a multi-dimensional carbon flux fusion dataset.

[0020] Further, based on the fused dataset, calculate the local spatio-temporal dispersion and the global correlation threshold, and identify and mark abnormal data points, including:

[0021] Based on the multi-dimensional carbon flux fused dataset, construct a spatio-temporal sliding window centered on each sensor node, and calculate the standard deviation of the data points within the window to obtain the local spatio-temporal dispersion dataset of each node under the corresponding spatio-temporal window;

[0022] Based on the multi-dimensional carbon flux fused dataset, calculate the covariance matrix of the data of all sensor nodes, and calculate the global correlation threshold based on the covariance matrix to obtain the global correlation threshold characterizing the overall correlation of the sensor network data in the entire restoration area;

[0023] Utilize the local spatio-temporal dispersion dataset and the global correlation threshold, and generate an abnormal determination boundary by comparing the deviation degree of the local spatio-temporal dispersion of each sensor node with the global correlation threshold;

[0024] Based on the abnormal determination boundary, judge whether the data points of each sensor node exceed the boundary. For the node data points that exceed the current boundary, record the abnormal status and mark them as abnormal data points.

[0025] Further, according to the abnormal data points, determine four boundary detection points in the restoration area to form an irregular polygon monitoring area that changes with the ecological process, and perform non-uniform grid cutting on the polygon area to generate an adaptive grid cell set covering the monitoring area, including:

[0026] Based on the abnormal data points, calculate the kernel density estimate value of the abnormal data points in the geospatial of the mangrove restoration area to generate an abnormal point spatial kernel density distribution field covering the entire restoration area;

[0027] Based on the abnormal point spatial kernel density distribution field, calculate the gradient vector field of the density field in space, and identify four spatial position points in the gradient vector field, determine the four points as boundary detection points, and connect the four points based on the spatial coordinates of the four boundary detection points to form a convex hull polygon, that is, a dynamic irregular polygon monitoring area;

[0028] Based on the abnormal point spatial kernel density distribution field, calculate the abnormal point density values of each sub-region inside the convex hull polygon monitoring area. At the same time, based on the water level gauge and salinity gauge data in the multi-dimensional carbon flux fused dataset, extract the data reflecting the internal hydrological connectivity characteristics of the monitoring area;

[0029] Based on the outlier density values and hydrological connectivity characteristic data of each sub-region within the polygon, set fine grid sizes in the sub-regions where outliers are relatively concentrated and in the key channel regions with significant hydrological connectivity; set sparse grid sizes in the sub-regions where outliers are sparsely distributed and in the regions with gentle hydrological connectivity, so as to construct a spatial parameter mapping of a non-uniform grid division scheme adapted to spatial heterogeneity.

[0030] Based on the convex hull polygon monitoring region boundary and the spatial parameter mapping of the non-uniform grid division scheme, perform non-uniform grid cutting on the polygon monitoring region to generate an adaptive grid cell set covering the entire dynamic polygon monitoring region.

[0031] Furthermore, for the grid cell set, calculate the topographic deformation rate, environmental factor gradient change rate, and hydrological inundation frequency of each cell within the tidal cycle to generate spatial correction quantization indicators, including:

[0032] Obtain the data recorded by the elevation sensor, salinity sensor, temperature sensor, and water level sensor deployed on the grid cells in the mangrove restoration area within the tidal cycle.

[0033] Based on the elevation sensor data, calculate the surface settlement amount of each grid cell within a single tidal cycle, and based on the surface settlement amount and the corresponding tidal cycle time, calculate the topographic deformation rate of each grid cell; based on the gradient monitoring values recorded by the salinity sensor and the temperature sensor, calculate the environmental factor gradient change rate of each grid cell; based on the data recorded by the water level sensor, count the daily average inundation frequency of each grid cell and calculate the hydrological inundation frequency value of each grid cell.

[0034] Normalize the topographic deformation rate, environmental factor gradient change rate, and hydrological inundation frequency values to obtain the corresponding normalized topographic deformation rate parameters, environmental factor change rate parameters, and hydrological inundation frequency parameters.

[0035] Perform weighted fusion calculation on the topographic deformation rate parameters, environmental factor change rate parameters, and hydrological inundation frequency parameters to obtain the fusion parameter value of each grid cell, and form a correction coefficient matrix representing the spatial heterogeneity of the entire mangrove restoration area with the fusion parameter values, that is, the spatial correction quantization indicator.

[0036] Furthermore, extract the sensor data within the grid cells and perform carbon flux value compensation in combination with the spatial correction quantization indicators to obtain a corrected carbon sink amount data set, including:

[0037] For each adaptive grid cell, extract the original carbon flux data recorded by the carbon flux sensor nodes deployed within the cell during the monitoring period.

[0038] Read the quantization index of spatial correction, that is, extract the correction coefficient corresponding to the current processing grid cell from the correction coefficient matrix, and calculate according to the correction coefficient using the topographic deformation rate, the gradient change rate of environmental factors, and the hydrological inundation frequency parameter to obtain the carbon flux compensation value of the grid cell;

[0039] Combine the original carbon flux data with the carbon flux compensation value to generate the corrected carbon sink amount data of the grid cell. At the same time, fuse all the corrected carbon sink amount data to form a corrected carbon sink amount data set covering the irregular polygon monitoring area of the entire mangrove restoration area.

[0040] Furthermore, based on the corrected carbon sink amount data set, calculate the carbon sink amount stability index, the spatial coverage contribution degree index, and the ecological process characterization index of each node during the evaluation period. Fuse the three indexes into a comprehensive evaluation parameter, and determine the prominent nodes as the final layout points for carbon sink amount monitoring according to the distribution characteristics of the comprehensive evaluation parameter, including:

[0041] Based on the corrected carbon sink amount data set, calculate the ratio of the standard deviation to the mean of the carbon sink amount data of each sensor node during the preset evaluation period to generate a carbon sink amount stability index characterizing the data fluctuation degree of the node;

[0042] Based on the spatial distribution of the adaptive grid cell set and the node positions, calculate the ratio of the total area of the grid cells associated with each node to the total area of the entire irregular polygon monitoring area to generate a spatial coverage contribution degree index characterizing the spatial representativeness of the node;

[0043] Based on the hydrological inundation frequency, salinity gradient, and temperature data in the multi-dimensional carbon flux fusion data set, calculate the absolute value between the monitoring data of each node and the key ecological process parameters to generate an ecological process characterization index characterizing the ability of the node to reflect ecological processes;

[0044] Normalize the carbon sink amount stability index, the spatial coverage contribution degree index, and the ecological process characterization index of each node, and perform weighted summation with preset weights to generate the comprehensive evaluation parameter of each node;

[0045] According to the numerical distribution of the comprehensive evaluation parameters of all nodes, determine the nodes with the comprehensive evaluation parameter value greater than the preset threshold as the final layout points for carbon sink amount monitoring.

[0046] In a second aspect, a computing device includes:

[0047] One or more processors;

[0048] A storage device for storing one or more programs, which when executed by the one or more processors cause the one or more processors to implement the system described above.

[0049] In a third aspect, a computer-readable storage medium stores a program which, when executed by a processor, implements the described system.

[0050] The above solution of the present invention has at least the following beneficial effects:

[0051] Based on multi-type sensor networks deployed in intertidal wetlands (covering carbon dioxide concentration, temperature and humidity, and soil carbon flux sensors), three-dimensional data collection of the carbon exchange process at the atmosphere-vegetation-soil interface is realized, breaking through the coverage blind spots of traditional single-point monitoring and completely capturing the carbon flux dynamics in complex environments with tidal cycles and terrain changes. Through timestamp synchronization, spatial coordinate system mapping, and missing value filling, multi-source heterogeneous sensor data is integrated into a multi-dimensional carbon flux fusion dataset that is spatio-temporally aligned, solving the problems of data time misalignment and spatial discreteness in traditional monitoring. Based on the identification of abnormal data using local spatio-temporal discreteness and global correlation thresholds, noise data caused by sensor failures and environmental interference factors are effectively filtered out; combined with spatial correction quantization indicators such as terrain deformation rate, environmental factor gradient change rate, and hydrological inundation frequency, adaptive compensation is performed on the carbon flux data to improve the accuracy of carbon sink calculation and reduce the interference of complex habitats on monitoring results. According to the distribution of abnormal data points, a dynamic irregular polygon monitoring area is constructed, and through non-uniform grid cutting technology, fine grids are set in areas with dense abnormal points and hydrological key channels, and sparse grids are used in sparse areas to achieve intelligent allocation of monitoring resources, conforming to the spatial heterogeneity characteristics of the mangrove ecosystem and improving monitoring efficiency and pertinence. The generated corrected carbon sink dataset and comprehensive evaluation parameters can reflect the spatio-temporal evolution law of the carbon sink capacity of the mangrove restoration area in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a schematic diagram of a carbon sink data monitoring system for a mangrove restoration area provided by an embodiment of the present invention.

[0053] Figure 2 is a schematic flow diagram of calculating local spatio-temporal discreteness and global correlation thresholds based on a fusion dataset of a carbon sink data monitoring system for a mangrove restoration area provided by an embodiment of the present invention, identifying and marking abnormal data points. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0055] AsFigure 1 As shown in Figure 1 , an embodiment of the present invention provides a carbon sink data monitoring system for a mangrove restoration area, including:

[0056] A collection module, configured to collect an original data set of carbon exchange at the air-vegetation-soil interface based on a multi-type sensor network deployed in the intertidal wetland of the mangrove restoration area. Specifically, the sensor network includes carbon dioxide concentration sensors, temperature and humidity sensors, and soil carbon flux sensors that are spatially distributed at intervals.

[0057] A data generation module, configured to perform spatio-temporal coordinate matching based on the original data set to generate a multi-dimensional carbon flux fusion data set with a joint index of geographical location and time.

[0058] An identification module, configured to calculate the local spatio-temporal dispersion and the global correlation threshold based on the fusion data set, and identify and mark abnormal data points.

[0059] A gridification module, configured to determine four boundary detection points in the restoration area according to the abnormal data points, form an irregular polygon monitoring area that changes with the ecological process, and perform non-uniform grid cutting on the polygon area to generate a set of adaptive grid cells covering the monitoring area.

[0060] An index generation module, configured to calculate the terrain deformation rate, the environmental factor gradient change rate, and the hydrological inundation frequency of each cell within the tidal cycle for the set of grid cells, and generate a spatial correction quantization index.

[0061] A compensation and correction module, configured to extract the sensor data within the grid cell, and perform carbon flux value compensation in combination with the spatial correction quantization index to obtain a corrected carbon sink data set.

[0062] A comprehensive evaluation module, configured to calculate the carbon sink stability index, the spatial coverage contribution index, and the ecological process characterization index of each node within the evaluation period based on the corrected carbon sink data set, fuse the three indexes into a comprehensive evaluation parameter, and determine the prominent nodes as the final layout points for carbon sink monitoring according to the distribution characteristics of the comprehensive evaluation parameter.

[0063] In the embodiments of the present invention, by deploying multi-type sensor networks in the intertidal wetland of the mangrove restoration area, comprehensive and dynamic acquisition of carbon exchange data at the atmosphere-vegetation-soil interface is achieved. The multi-type sensors (carbon dioxide concentration sensors, temperature and humidity sensors, and soil carbon flux sensors) work together to synchronously obtain multi-dimensional data such as gas concentration, environmental temperature and humidity, and soil carbon flux, completely covering the key links of the carbon cycle in the mangrove ecosystem. Compared with the traditional single-point or single-type sensor acquisition method, the one-sidedness of data acquisition is avoided. For example, the key information of carbon release caused by soil microbial activities will not be missed due to only monitoring the atmospheric carbon dioxide concentration. At the same time, the distributed deployment of the sensor network can adapt to the complex and changeable environment of the intertidal zone. Whether it is the area flooded by seawater during high tide or the tidal flat area after low tide, data can be stably collected, reducing data loss caused by environmental interference.

[0064] Perform spatio-temporal coordinate matching on the original data set to generate a multi-dimensional carbon flux fusion data set, effectively solving the spatio-temporal inconsistency problem of multi-source data. Through timestamp synchronization processing, data collected by different types of sensors with different acquisition frequencies are unified to the same time reference to avoid data chaos caused by time dislocation; using geographic coordinate mapping, the data of each sensor is accurately mapped to the unified spatial coordinate system of the restoration area to ensure the accuracy of the spatial position of the data. Calculate the local spatio-temporal dispersion and global correlation threshold based on the fusion data set to achieve accurate identification and marking of abnormal data points.

[0065] Dynamically determine the monitoring area according to the abnormal data points and perform non-uniform grid cutting to improve the pertinence and efficiency of monitoring. By calculating the kernel density estimate value of the abnormal data points, it is possible to determine the areas where the carbon flux changes actively or the data fluctuates greatly in the mangrove restoration area. Based on this, an irregular polygon monitoring area is delimited, making the monitoring range closely fit the actual ecological change hotspots and avoiding the blindness of traditional fixed-area monitoring. The non-uniform grid cutting strategy sets fine grids in areas where abnormal points are concentrated and ecological changes are complex (such as the confluence of tidal creeks and areas with severely damaged vegetation) according to the density of abnormal points and the characteristics of hydrological connectivity in the area to ensure that subtle carbon flux changes can be captured; sparse grids are used in areas with stable data to reduce data redundancy.

[0066] In a preferred embodiment of the present invention, spatio-temporal coordinate matching is performed based on the original data set to generate a multi-dimensional carbon flux fusion data set with a joint index of geographical location and time, which may include:

[0067] Perform timestamp synchronization processing on the original data set collected by the multi-type sensor network to obtain a time-synchronized data set in which all sensor data has a unified time reference;

[0068] Based on the time-synchronized dataset, each data point is mapped to the unified spatial coordinate system of the preset repair area according to the geographical coordinates of the deployment location, generating a mapped dataset with a unified spatio-temporal reference.

[0069] Using the mapped dataset, with the geographical location coordinates and the synchronized timestamp as the combined primary key, the carbon exchange-related parameters from different types of sensors at the same geographical location and the same timestamp are associated and integrated to form a preliminary associated and integrated dataset.

[0070] For the spatio-temporal point data missing in the preliminary associated and integrated dataset due to sensor failures, terrain occlusion, and communication interruptions, based on the unified spatial coordinate system and the synchronized timestamp, the effective data of adjacent sensor nodes are used to obtain a complete dataset after filling in the missing values.

[0071] The complete dataset is organized into a data matrix with geographical location coordinates and timestamp as the combined index, including multi-dimensional carbon fluxes and related environmental parameters, generating a multi-dimensional carbon flux fusion dataset.

[0072] In the embodiment of the present invention, the timestamp information corresponding to each data point is extracted from the raw dataset collected by the multi-type sensor network. These timestamps may vary due to the clock accuracy of the sensors themselves and network transmission delays. For example, the timestamps of the same moment recorded by different sensors may differ by several seconds or even several minutes. A high-precision time source is selected as the unified time reference, such as using the time signal of the Global Positioning System (GPS), whose time accuracy can reach the nanosecond level. The original timestamp of each sensor data is compared with the reference time benchmark to calculate the time deviation. Suppose the original timestamp of sensor A is , and the reference time benchmark is , then the time deviation . According to the calculated time deviation, the timestamp of each sensor data is corrected, and the corrected timestamp , In this way, the timestamps of all sensor data are unified to the reference time base, thus obtaining a time-synchronized dataset. Specify the type of spatial coordinate system used in the repair area, such as the common geographic coordinate system (represented by longitude and latitude) or the projected coordinate system (such as the UTM projected coordinate system). Set the origin, axis directions, and measurement unit parameters of this coordinate system as the target coordinate system for data mapping. From the deployment records of the sensor network, obtain the actual geographic coordinates of each sensor. If the original coordinates of the sensor are geographic coordinates represented by longitude and latitude, and the target spatial coordinate system is a projected coordinate system, coordinate conversion is required. Use the geographic coordinate conversion formula to convert the original geographic coordinates of the sensor data points into the unified spatial coordinate system of the preset repair area. Taking the conversion from the geographic coordinate system (longitude and latitude) to the UTM projected coordinate system as an example, it includes the conversion from geodetic coordinates to space rectangular coordinates and then to UTM projected plane coordinates. Earth ellipsoid parameters are involved, and through trigonometric functions and geometric operations, the original longitude and latitude coordinates are converted into coordinates in the target spatial coordinate system, thus generating a mapped dataset with a unified spatio-temporal reference.

[0073] From the mapped dataset, extract the geographical location coordinates of each data point (such as the , coordinates in the projected coordinate system) and the synchronized timestamp, and use them as the composite primary key. Traverse the entire mapped dataset, associate the data according to the composite primary key. For data points with the same geographical location coordinates and timestamp, determine the types of sensors they belong to, and find all the carbon exchange related parameter data from different types of sensors at the same geographical location and the same timestamp. Integrate the carbon exchange related parameter data of different types of sensors that are associated to form a data record containing multi-dimensional information. During the integration process, fill each parameter data into the corresponding position according to the preset data format and field order, thus forming a preliminary associated and integrated dataset. Traverse the preliminary associated and integrated dataset, and determine whether the spatio-temporal point data is missing according to whether there are null values or invalid values in the data record. Missing data can be identified through set flags and data ranges. For example, when the carbon dioxide concentration value is -9999, it is determined that this data is a missing value. Based on the unified spatial coordinate system, calculate the spatial distance between each sensor node with missing data and other sensor nodes. The Euclidean distance formula can be used. For the coordinates of two sensor nodes in a two-dimensional space, select several sensor nodes with closer distances as adjacent nodes. Usually, the distance threshold can be set according to the actual situation, and the nodes with distances less than the threshold are selected as adjacent nodes.

[0074] Based on the valid data of adjacent sensor nodes, appropriate interpolation methods are used to fill in the missing values. For example, the inverse distance weighted interpolation method (IDW) is used. This method calculates the missing value by taking the weighted average of the valid data of adjacent nodes according to the distance between the adjacent nodes and the node with missing data. Assume that the adjacent node has a valid data value of , and the distance from the node with missing data is , then the missing value , where is the number of adjacent nodes, is the distance weight parameter, usually taken as 2. In this way, the missing values in the preliminary associated and integrated dataset are filled to obtain a complete dataset. Based on the complete dataset, the dimension and structure of the data matrix are determined. Using geographical location coordinates and timestamps as the joint index, the multi-dimensional carbon flux and related environmental parameters are used as the elements of the matrix. For example, the rows of the data matrix can represent different geographical location coordinates, and the columns can represent different timestamps. Each element in the matrix corresponds to the multi-dimensional carbon flux and related environmental parameter data at that geographical location and timestamp. Each data record in the complete dataset is accurately filled into the corresponding position of the data matrix according to its geographical location coordinates and timestamps to ensure the integrity and accuracy of the data, thereby generating a multi-dimensional carbon flux fusion dataset with geographical location coordinates and timestamps as the joint index and including multi-dimensional carbon flux and related environmental parameters.

[0075] Through timestamp synchronization processing and spatial coordinate system mapping, the differences in time and space of multi-type sensor data are eliminated, making all data have a unified spatio-temporal reference. This effectively avoids data errors and chaos caused by spatio-temporal inconsistencies and improves the accuracy and consistency of the data. Using geographical location coordinates and timestamps as the joint primary key for data association and integration, the carbon exchange related parameters from different types of sensors are organically combined together. This integration method breaks the boundaries of sensor types, enabling the originally scattered data to form a dataset with internal connections, which can more comprehensively reflect the relationship between carbon flux and environmental factors and is convenient for multi-dimensional comprehensive analysis. For the spatio-temporal point data missing due to various reasons, the valid data of adjacent sensor nodes are used to fill in the missing values, ensuring the integrity of the dataset. The complete data can more accurately reflect the actual carbon flux change situation, avoid analysis biases caused by data missing, and improve the usability of the data and the reliability of the analysis results. Organizing the complete dataset into a data matrix with geographical location coordinates and timestamps as the joint index to form a multi-dimensional carbon flux fusion dataset, this structured data organization method is convenient for data storage, management, and query. The multi-dimensional carbon flux fusion dataset contains rich carbon flux and related environmental parameter information, providing the possibility for more in-depth data analysis and research.

[0076] In a preferred embodiment of the present invention, based on the fused dataset, calculating the local spatio-temporal dispersion and the global correlation threshold, and identifying and marking abnormal data points may include:

[0077] Based on the multi-dimensional carbon flux fused dataset, construct a spatio-temporal sliding window centered on each sensor node, and calculate the standard deviation of the data points within the window to obtain the local spatio-temporal dispersion dataset of each node under the corresponding spatio-temporal window;

[0078] Based on the multi-dimensional carbon flux fused dataset, calculate the covariance matrix of all sensor node data, and calculate the global correlation threshold based on the covariance matrix to obtain the global correlation threshold characterizing the overall correlation of the sensor network data in the entire repair area;

[0079] Using the local spatio-temporal dispersion dataset and the global correlation threshold, generate an abnormal determination boundary by comparing the deviation degree between the local spatio-temporal dispersion of each sensor node and the global correlation threshold;

[0080] Based on the abnormal determination boundary, determine whether the data points of each sensor node exceed the boundary, and for the node data points that exceed the current boundary, record the abnormal state and mark them as abnormal data points.

[0081] In the embodiment of the present invention, set the size of the spatio-temporal sliding window for each sensor node, including the length of the time dimension and the range of the space dimension. For example, the time dimension can be set to be centered on the current time point, with time steps before and after ( determined according to the actual data acquisition frequency and research requirements. For example, if the data is collected once a minute, it can be set =5, that is, the window covers a 10-minute time period); the space dimension is centered on the sensor node, and a circular area with a radius of is set ( determined according to the deployment density of the sensor network. For example, in a relatively dense area, set =50 meters). Traverse the multi-dimensional carbon flux fused dataset, and for each sensor node, filter out the data points that fall within the window according to the set spatio-temporal window parameters. These data points contain the multi-dimensional carbon flux and environmental parameter data related to the sensor node within a specific spatio-temporal range. For the data points within each spatio-temporal sliding window, extract the data features of each dimension respectively, such as parameters such as carbon flux concentration, temperature, and humidity. Suppose there are data points in a certain window, and each data point contains dimensional features, which can be expressed as a data matrix , where represents the th data point's th dimensional feature value. For each dimension , calculate its mean value, and based on the mean value, calculate the variance of each dimension Take the square root of the variance of each dimension to obtain the standard deviation. Process the standard deviations of each dimension comprehensively (such as taking the average value or weighted average value, and the weights can be set according to the importance of each dimension) to obtain the local spatio-temporal dispersion value of the sensor node under the corresponding spatio-temporal window. Repeat this process to calculate the local spatio-temporal dispersion for each sensor node under its corresponding spatio-temporal window, and finally form a local spatio-temporal dispersion data set.

[0082] Extract all the data of all sensor nodes from the multi-dimensional carbon flux fusion data set, and construct a large data matrix containing all nodes, all time points, and all dimension data , where is the number of sensor nodes, is the number of time points, is the number of data dimensions, represents the th spatial node, represents the th time point. Standardize the data matrix . For each dimension , calculate its mean value and standard deviation. Based on the standardized data, calculate the covariance matrix , where represents the covariance between the th dimension and the th dimension. Analyze the calculated covariance matrix. Methods such as principal component analysis (PCA) can be used to extract the main features and determine the key information of the covariance matrix. According to the eigenvalue distribution of the covariance matrix, the actual characteristics of the data, or the research requirements, set the calculation method of the global correlation threshold. For example, a certain percentile (such as the 95th percentile) of the eigenvalues of the covariance matrix can be selected as the global correlation threshold. Traverse the local spatio-temporal dispersion data set. For the local spatio-temporal dispersion value of each sensor node, calculate its deviation degree from the global correlation threshold. The deviation degree can be calculated by ratio or by difference. According to the distribution of the deviation degree, combined with the actual data characteristics and research requirements, determine the parameters of the abnormal judgment boundary. For example, it can be set that when the deviation degree is greater than a certain multiple (such as 2 times) of the global correlation threshold, or the deviation degree exceeds a preset absolute difference (such as 5), the data point is considered abnormal. Through these parameter settings, generate an abnormal judgment boundary for judging whether a data point is abnormal. Traverse the multi-dimensional carbon flux fusion data set again. For each data point of each sensor node, judge whether the data point exceeds the boundary according to the local spatio-temporal dispersion of its affiliated node and the abnormal judgment boundary. For the node data points that exceed the current abnormal judgment boundary, record their abnormal status, which can be done by adding a marker field and marking them as abnormal data points.

[0083] By calculating the local spatio-temporal discreteness and the global correlation threshold, abnormal data points are identified and marked, effectively excluding the error data caused by sensor failures and environmental interference factors. This makes the retained data more capable of truly reflecting the actual carbon flux changes and environmental conditions. The existence of abnormal data will seriously affect the accuracy of data analysis results. After marking and removing abnormal data points, the spatio-temporal variation analysis of carbon flux and the research on the correlation between environmental factors and carbon flux will be based on more accurate data, and the conclusions and laws obtained will be more scientific and reliable, avoiding the wrong analysis results and misleading conclusions caused by abnormal data. The process of identifying abnormal data points can timely detect the problematic nodes in the sensor network. For example, sensors with frequent data anomalies may be faulty. This helps the operation and maintenance personnel quickly locate the faulty nodes, perform repairs or replacements in a timely manner, optimize the operation status of the sensor network, improve the efficiency and stability of the entire monitoring system, and ensure the continuous and accurate monitoring of the carbon flux and environmental information in the restoration area. During the data storage and analysis process, dealing with abnormal data consumes additional computing resources and storage capacity. By marking abnormal data points and performing targeted processing (such as removing or correcting), the amount of invalid data processing can be reduced, the data storage cost and computing resource consumption can be lowered, the data processing efficiency can be improved, and the data processing process can be made more efficient and economical. The abnormal data point identification technology ensures the quality of the multi-dimensional carbon flux fusion data set, providing reliable data support for decision-making processes such as carbon emission management and the formulation of ecological restoration plans.

[0084] In a preferred embodiment of the present invention, according to the abnormal data points, four boundary detection points are determined in the restoration area to form an irregular polygon monitoring area that changes with the ecological process, and the polygon area is subjected to non-uniform grid cutting to generate an adaptive grid cell set covering the monitoring area, which may include:

[0085] Based on the abnormal data points, calculate the kernel density estimation value of the abnormal data points in the geographical space of the mangrove restoration area to generate an abnormal point spatial kernel density distribution field covering the entire restoration area;

[0086] Based on the abnormal point spatial kernel density distribution field, calculate the gradient vector field of the density field in space, identify four spatial position points in the gradient vector field, determine the four points as boundary detection points, and based on the spatial coordinates of the four boundary detection points, connect the four points to form a convex hull polygon, that is, a dynamic irregular polygon monitoring area;

[0087] Based on the abnormal point spatial kernel density distribution field, calculate the abnormal point density values of each sub-region inside the convex hull polygon monitoring area. At the same time, based on the water level gauge and salinity gauge data in the multi-dimensional carbon flux fusion data set, extract the data reflecting the internal hydrological connectivity characteristics of the monitoring area;

[0088] Based on the outlier density values and hydrological connectivity characteristic data of each sub-region within the polygon, set fine grid sizes in the sub-regions where outliers are relatively concentrated and in the key channel regions with relatively significant hydrological connectivity; set sparse grid sizes in the sub-regions where outliers are sparsely distributed and in the regions with gentle hydrological connectivity, so as to construct a spatial parameter mapping of a non-uniform grid division scheme adapted to spatial heterogeneity.

[0089] Based on the convex hull polygon monitoring area boundary and the spatial parameter mapping of the non-uniform grid division scheme, perform non-uniform grid cutting on the polygon monitoring area to generate a set of adaptive grid cells covering the entire dynamic polygon monitoring area.

[0090] In the embodiment of the present invention, the geographical space of the mangrove restoration area is discretized and divided into regular small regions (such as grids), and each small region has a clear geographical coordinate range. For each outlier data point, a neighborhood range with a certain radius is selected around it (this radius can be set according to actual situations and experience, such as comprehensively determined based on factors such as the scale of the mangrove restoration area and the density of data distribution). Then, a kernel function (common kernel functions include Gaussian kernel function, uniform kernel function, etc.) is used to calculate the contribution value of this outlier data point to each small region within the neighborhood. Taking the Gaussian kernel function as an example, the closer a small region is to the outlier data point, the greater the influence and the higher the contribution value; the farther away, the contribution value decreases exponentially. Specifically, during calculation, according to the distance between the center of the small region and the outlier data point, substitute it into the Gaussian kernel function formula to calculate the contribution value. Perform the above operations on all outlier data points, and accumulate the contribution values obtained by each small region from all outlier data points to obtain the kernel density estimate value of this small region. Traverse all small regions in the entire mangrove restoration area. After calculating the kernel density estimate values of all small regions, an outlier spatial kernel density distribution field covering the entire restoration area is generated, and this distribution field intuitively shows the spatial distribution density of outlier data points within the restoration area.

[0091] For each small region in the spatial kernel density distribution field of outliers, calculate its density change rate in three spatial dimensions (assumed to be the X, Y, and Z directions, and mainly consider the X and Y directions in the two-dimensional geographical space), that is, the gradient. Taking the X direction as an example, by calculating the difference between the kernel density estimation values of this small region and the adjacent small region in the X direction, and then dividing it by the distance between them (the grid side length), the density change rate in the X direction is obtained; similarly, calculate the density change rate in the Y direction. In this way, each small region obtains a two-dimensional gradient vector, and the gradient vectors of all small regions together constitute the gradient vector field of the density field in space. In the gradient vector field, find the positions where the gradient changes relatively violently and is representative. The positions with larger gradient modulus values (i.e., the length of the gradient vector) can be screened out by setting a threshold. From these candidate points, select four points that can cover the boundary of the outlier concentration area as boundary detection points as much as possible. The selection principle can be: the four points are as scattered as possible in spatial distribution and can roughly outline the contour of the outlier concentration area. After determining the spatial coordinates of the four boundary detection points, use a convex hull algorithm (such as Graham scan method, Andrew monotone chain method, etc.) to connect these four points to form a convex hull polygon. This convex hull polygon is the dynamic irregular polygon monitoring area, which can tightly enclose the outlier concentration area. As the ecological process changes and the outlier distribution changes, its shape and scope will also be adjusted accordingly.

[0092] Further divide the convex hull polygon monitoring area into multiple sub-regions (it can be divided according to certain rules, such as dividing it into smaller grids of equal area). For each sub-region, count the number of outlier data points falling into this sub-region, and then divide it by the area of the sub-region to obtain the outlier density value of this sub-region. Extract the data and real-time data of the water level gauge and salinity gauge at each monitoring point in the monitoring area from the multi-dimensional carbon flux fusion dataset. Analyze the change trend of the water level data over time, such as the rising and falling amplitude and frequency of the water level; analyze the spatial distribution differences and temporal variation laws of the salinity data. Through these data analyses, determine the correlation degree of the water level and salinity between different positions in the monitoring area, so as to extract the data reflecting the internal hydrological connectivity characteristics of the monitoring area. For example, if the water level change trends of two positions are highly consistent and the salinity difference is small, it indicates that the hydrological connectivity between these two positions is good; otherwise, the connectivity is poor.

[0093] Taking into account the outlier density values and hydrological connectivity characteristic data of each sub-region, for sub-regions with relatively high outlier density values, it means that the ecological process changes in this area may be relatively complex and require more refined monitoring. Therefore, a smaller grid size is set to more accurately capture the changes in outlier data points. For key channel areas with significant hydrological connectivity, due to frequent exchanges of water flow, substances, etc., which have a greater impact on the ecosystem, a smaller grid size is also set to strengthen the monitoring of this area. On the contrary, for sub-regions with relatively sparse outlier distributions, the ecological process changes are relatively stable and do not require overly refined monitoring. Therefore, a larger grid size is set to reduce the unnecessary amount of monitoring data. For areas with gentle hydrological connectivity, the exchanges of water flow and substances are relatively less, and the impact on the ecosystem is smaller, so a larger grid size is also set. Based on the spatial parameter mapping of the convex hull polygon monitoring area boundary and the non-uniform grid division scheme, the polygon monitoring area is cut into non-uniform grids to generate a set of adaptive grid cells covering the entire dynamic polygon monitoring area. The cutting range is determined according to the boundary of the convex hull polygon monitoring area. According to the spatial parameter mapping of the non-uniform grid division scheme, the cutting is carried out in different sub-regions according to the set grid size. Starting from the boundary of the monitoring area, the grid division is gradually carried out inward according to the set grid size to ensure that each sub-region is divided into grid cells according to the corresponding size. As the cutting progresses, all the generated grid cells are integrated, and finally a set of adaptive grid cells covering the entire dynamic polygon monitoring area is formed. The sizes and distributions of these grid cells are optimized according to the outlier distribution and hydrological connectivity characteristics in the monitoring area, and can better meet the monitoring requirements.

[0094] By means of kernel density estimation and gradient vector field analysis based on abnormal data points, the boundary of the monitoring area is accurately determined, enabling the monitoring area to closely fit the area where ecological processes change actively. At the same time, non-uniform grid division is carried out according to the density of abnormal points and the characteristics of hydrological connectivity. Fine grids are set in key areas, which can capture the changes of ecological data more meticulously. Compared with uniform grid division, it improves the monitoring accuracy and helps to understand the ecological status of the mangrove restoration area more accurately. Sparse grids are set in areas where abnormal points are sparsely distributed and hydrological connectivity is gentle, avoiding over-monitoring in unnecessary areas and reducing the waste of monitoring data volume and computing resources. This adaptive grid division method enables monitoring resources to be more reasonably allocated to key areas, improves the resource utilization efficiency, and reduces the monitoring cost. The generated dynamic irregular polygon monitoring area and the set of adaptive grid cells can be adjusted with the changes of ecological processes. When the distribution of abnormal data points changes, the shape and scope of the monitoring area will change accordingly; when characteristics such as hydrological connectivity in the ecosystem change, the grid division will also be adjusted accordingly, always maintaining effective monitoring of the ecological status of the mangrove restoration area and providing timely and accurate data support for ecological restoration and management. A variety of ecological factors such as the distribution of abnormal points and hydrological connectivity are comprehensively considered, and the mangrove restoration area is analyzed and monitored from multiple perspectives.

[0095] In a preferred embodiment of the present invention, for the set of grid cells, calculate the terrain deformation rate, the environmental factor gradient change rate, and the hydrological inundation frequency within the tidal cycle for each cell to generate a spatial correction quantization index, which may include:

[0096] Obtain the data recorded by the elevation sensor, salinity sensor, temperature sensor, and water level sensor deployed on the grid cells in the mangrove restoration area within the tidal cycle;

[0097] Based on the elevation sensor data, calculate the surface settlement amount of each grid cell within a single tidal cycle, and based on the surface settlement amount and the corresponding tidal cycle time, calculate the terrain deformation rate of each grid cell; based on the gradient monitoring values recorded by the salinity sensor and temperature sensor, calculate the environmental factor gradient change rate of each grid cell; based on the data recorded by the water level sensor, count the daily average inundation frequency of each grid cell and calculate the hydrological inundation frequency value of each grid cell;

[0098] Normalize the terrain deformation rate, the environmental factor gradient change rate, and the hydrological inundation frequency value to obtain the corresponding normalized terrain deformation rate parameter, environmental factor change rate parameter, and hydrological inundation frequency parameter;

[0099] Perform weighted fusion calculations on the terrain deformation rate parameter, the environmental factor change rate parameter, and the hydrological inundation frequency parameter to obtain the fusion parameter value for each grid cell, and form a correction coefficient matrix representing the spatial heterogeneity of the entire mangrove restoration area with the fusion parameter values, that is, a quantitative index for spatial correction.

[0100] In the embodiments of the present invention, clarify the set of adaptive grid cells already divided in the mangrove restoration area, and each grid cell has a unique identifier and spatial location. Check the installation locations and operating status of the elevation sensors, salinity sensors, temperature sensors, and water level sensors deployed on each grid cell to ensure that the sensors are working properly and the data acquisition function is enabled. Determine a complete tidal cycle time range, which can usually be determined according to the local tidal rules (such as obtained through ocean tide tables, data statistics, etc.), generally being a semi-diurnal tide (about 12 hours and 25 minutes) or a diurnal tide (about 24 hours and 50 minutes). During the selected tidal cycle, use the data acquisition system to record the data generated by each sensor in real time. For each grid cell, store the data collected by the corresponding sensor at certain time intervals (such as every minute, every 5 minutes, etc., which can be set according to the sensor performance and monitoring requirements) during the entire tidal cycle to form the tidal cycle data sequence of each sensor for each grid cell.

[0101] Calculate the terrain deformation rate:

[0102] For the elevation sensor data sequence of each grid cell, select the elevation value at the start moment of the tidal cycle as the initial elevation and the elevation value at the end moment of the tidal cycle as the end elevation. Subtract the end elevation from the initial elevation to obtain the surface settlement amount of the grid cell during a single tidal cycle (if the result is negative, it means the surface is uplifted). Given the total duration of the tidal cycle (in units of hours or minutes), divide the surface settlement amount by the tidal cycle duration to obtain the terrain deformation value per unit time for each grid cell, that is, the terrain deformation rate (unit such as centimeters per hour).

[0103] Calculate the environmental factor gradient change rate:

[0104] Salinity sensors and temperature sensors usually arrange multiple monitoring points at different positions within the grid cell to obtain gradient monitoring values. For salinity data, calculate the salinity difference between adjacent monitoring points and then divide it by the spatial distance between the two points to obtain the gradient change value of salinity in space. Similarly, calculate the gradient change value of temperature between adjacent monitoring points. Perform comprehensive processing on the gradient change values of salinity and temperature. For example, a weighted average method (the weights can be set according to research needs or the importance of salinity and temperature to the mangrove ecosystem) can be used to combine the gradient change values of the two into one value to obtain the environmental factor gradient change rate for each grid cell.

[0105] Calculate the hydrological inundation frequency value:

[0106] Analyze the data recorded by the water level sensor within the tidal cycle. Set a water level threshold. When the water level is higher than this threshold, it is considered that the grid cell is in a flooded state. Count the total duration during which the water level in the grid cell is higher than the threshold throughout the tidal cycle. Divide the total duration by the duration of the tidal cycle to obtain the inundation ratio of the grid cell within this tidal cycle. Assume there are multiple tidal cycles in a day. Add up the inundation ratios of each tidal cycle to get the daily average inundation frequency of each grid cell, which is used as the hydrological inundation frequency value.

[0107] Collect the data of the terrain deformation rate, the gradient change rate of environmental factors, and the hydrological inundation frequency value for all grid cells, and find the maximum and minimum values of these three types of data. For the terrain deformation rate data, for the terrain deformation rate of each grid cell, use the normalization formula (current terrain deformation rate - minimum terrain deformation rate) / (maximum terrain deformation rate - minimum terrain deformation rate) to calculate the normalized terrain deformation rate parameter, and map its numerical range to the interval [0, 1]. Similarly, for the gradient change rate of environmental factors and the hydrological inundation frequency value, use the same normalization formula for calculation respectively to obtain the corresponding normalized environmental factor change rate parameter and hydrological inundation frequency parameter, so that these three types of data are on the same numerical scale for subsequent processing. According to the requirements of mangrove ecological research and restoration management, set weights for the terrain deformation rate parameter, the environmental factor change rate parameter, and the hydrological inundation frequency parameter respectively (for example, if it is considered that terrain deformation has a greater impact on mangrove ecology, a higher weight can be given). For each grid cell, multiply its normalized terrain deformation rate parameter by the corresponding weight, add the environmental factor change rate parameter multiplied by its weight, and then add the hydrological inundation frequency parameter multiplied by its weight. Obtain the fusion parameter value of this grid cell through weighted summation. Arrange the fusion parameter values of all grid cells in the order of the spatial positions of the grid cells to form a matrix. Each element in this matrix corresponds to the fusion parameter value of a grid cell, and this matrix constitutes the correction coefficient matrix representing the spatial heterogeneity of the entire mangrove restoration area, that is, the quantitative index of spatial correction.

[0108] By separately calculating the topographic deformation rate, the gradient change rate of environmental factors, and the hydrological inundation frequency, and performing weighted fusion, the ecological changes in the mangrove restoration area during the tidal cycle are comprehensively and accurately quantified from multiple dimensions. Compared with single-index monitoring, it can more precisely reflect the dynamic process of the ecosystem, providing rich and accurate data support for in-depth research on mangrove ecology. The generated spatially corrected quantification index is presented in matrix form, intuitively reflecting the spatial heterogeneity among different grid cells in the entire mangrove restoration area. Managers and researchers can quickly locate areas with significant ecological changes through this matrix and understand the comprehensive change characteristics of ecological factors in each area, which helps to formulate targeted ecological restoration and management strategies. Normalizing each ecological index eliminates the differences in dimension and numerical range between different indexes, making the data of different grid cells and different ecological factors comparable. On this basis, weighted fusion calculation is carried out, further enhancing the comprehensiveness and scientific nature of the data, improving the quality and application value of the monitoring data. This method collects data and calculates indexes based on the tidal cycle, and can reflect the dynamic characteristics of the mangrove ecosystem changing with the tides in real time. The generated spatially corrected quantification index can be used to correct subsequent monitoring data, improve the accuracy of the monitoring data, provide strong support for the dynamic assessment, ecological early warning, and scientific decision-making of the mangrove restoration project, and promote the sustainable development of the mangrove ecosystem.

[0109] In a preferred embodiment of the present invention, sensor data within a grid cell is extracted, and carbon flux value compensation is performed in combination with the spatially corrected quantification index to obtain a corrected carbon sink amount data set, which may include:

[0110] For each adaptive grid cell, the original carbon flux data recorded by the carbon flux sensor nodes deployed within the cell during the monitoring period is extracted;

[0111] The spatially corrected quantification index is read, that is, the correction coefficient corresponding to the currently processed grid cell is extracted from the correction coefficient matrix, and based on the correction coefficient, calculations are performed using the topographic deformation rate, the gradient change rate of environmental factors, and the hydrological inundation frequency parameters to obtain the carbon flux compensation value of the grid cell;

[0112] The original carbon flux data is combined with the carbon flux compensation value to generate the corrected carbon sink amount data of the grid cell. At the same time, all the corrected carbon sink amount data is fused to form a corrected carbon sink amount data set covering the irregular polygon monitoring area of the entire mangrove restoration area.

[0113] In the embodiments of the present invention, the set of adaptive grid cells already divided in the mangrove restoration area is identified, and each grid cell has a unique number and accurate spatial location information. For each grid cell, determine the number and specific locations of the carbon flux sensor nodes deployed within the cell. These sensor nodes are usually distributed within the grid cell according to certain layout rules to ensure that the carbon flux situation in this area can be monitored more comprehensively. Set the monitoring period, which can be determined according to the research purpose and actual needs. For example, it can be one day, one week, one month, etc. During the determined monitoring period, the carbon flux sensor nodes will collect carbon flux data at a preset sampling frequency (such as once per minute, once per hour, etc.) and store the data locally or transmit it to the data center. From the data storage location or the data center, through the data query and extraction program, for each grid cell, extract the data collected by all the carbon flux sensor nodes within the cell during the entire monitoring period. Organize the extracted data in the order of the sensor node numbers and the collection time to form the original carbon flux data sequence for each grid cell.

[0114] Find the calibration coefficient matrix for the storage space calibration quantization index. This matrix is generated based on the previous calculation steps, based on parameters such as the terrain deformation rate, environmental factor gradient change rate, and hydrological inundation frequency parameter of each grid cell. Each element in the matrix corresponds to the comprehensive calibration coefficient of a grid cell. According to the number or spatial location information of the grid cell currently being processed, accurately find the corresponding element in the calibration coefficient matrix, which is the calibration coefficient of this grid cell. It is known that each grid cell has corresponding terrain deformation rate parameters, environmental factor change rate parameters, and hydrological inundation frequency parameters, which are the normalized data obtained during the previous calculation of the spatial calibration quantization index. Determine the calculation method for the carbon flux compensation value. This method is usually a calculation formula that comprehensively considers the above three parameters and the calibration coefficient. For example, a linear weighting method can be used. Multiply the terrain deformation rate parameter by a weight related to the impact of the terrain on the carbon flux, multiply the environmental factor change rate parameter by the corresponding environmental factor weight, multiply the hydrological inundation frequency parameter by the hydrological related weight, then add the results of these three products, and finally multiply by the calibration coefficient of this grid cell to obtain the carbon flux compensation value of this grid cell.

[0115] For each grid cell, each data point in the original carbon flux data sequence during the monitoring period is added to (subtracted if the compensation value is negative) the calculated carbon flux compensation value. In this way, the original carbon flux data is corrected to obtain the corrected carbon sink amount data sequence for each grid cell. After processing all grid cells in sequence, the corrected carbon sink amount data of all grid cells are arranged and integrated according to the spatial position order of the grid cells. These data can be stored in a data file or a database table, such that each data record corresponds to the corrected carbon sink amount data of a grid cell, and the storage order of the data is consistent with the spatial distribution order of the grid cells within the irregular polygon monitoring area of the mangrove restoration area. The finally formed data set is the corrected carbon sink amount data set covering the entire irregular polygon monitoring area of the mangrove restoration area, and this data set can more accurately reflect the actual carbon sink situation of the mangrove restoration area.

[0116] The spatial correction quantization index generated by combining multiple factors such as the terrain deformation rate, the environmental factor gradient change rate, and the hydrological inundation frequency is used to compensate and correct the original carbon flux data, fully considering the influence of various environmental factors on carbon flux in the mangrove ecosystem. Compared with only using the original carbon flux data, the corrected carbon sink amount data set can more truly and accurately reflect the actual carbon sink capacity of the mangroves, reduce the data deviation caused by environmental factor interference, and provide a reliable data basis for carbon sink research. By closely associating the carbon flux data with spatial heterogeneity, the corrected carbon sink amount data contains more ecological information. Researchers can intuitively understand the influence of terrain changes, environmental gradient differences, and hydrological conditions on carbon sink in different regions from the data, which helps to deeply explore the internal relationship between the carbon cycle and other ecological processes in the mangrove ecosystem and provides strong data support for revealing the mangrove carbon sink mechanism. The accurate corrected carbon sink amount data set can provide a more scientific basis for the assessment of the mangrove ecosystem. Managers can, based on this data set, more accurately evaluate the effectiveness of the mangrove restoration project, judge the changing trend of the carbon sink capacity, and thus formulate more reasonable ecological protection and restoration strategies. For example, for areas with a low carbon sink amount, the influencing factors can be analyzed in key aspects, and targeted measures can be taken to improve the ecological environment, enhance the carbon sink function of the mangroves, and promote the sustainable development of the ecosystem. The unified data correction method enables better comparability of the carbon sink amount data of different grid cells and different monitoring periods.

[0117] In a preferred embodiment of the present invention, based on the corrected carbon sink amount data set, the carbon sink amount stability index, the spatial coverage contribution degree index, and the ecological process characterization index of each node during the evaluation period are calculated, the three indexes are fused into a comprehensive evaluation parameter, and according to the distribution characteristics of the comprehensive evaluation parameter, the prominent nodes are determined as the final layout points for carbon sink amount monitoring, which may include:

[0118] Based on the corrected carbon sequestration data set, the ratio of the standard deviation to the mean of the carbon sequestration data of each sensor node within the preset evaluation period is calculated to generate a carbon sequestration stability index that characterizes the degree of fluctuation of the node data.

[0119] Based on the spatial distribution of the adaptive grid cell set and the node position, the ratio of the sum of the areas of the grid cells associated with each node to the total area of the entire irregular polygon monitoring area is calculated to generate a spatial coverage contribution index that represents the spatial representativeness of the node.

[0120] Based on the hydrological inundation frequency, salinity gradient, and temperature data in the multidimensional carbon flux fusion dataset, the absolute value between each node monitoring data and key ecological process parameters is calculated to generate an ecological process representation index that characterizes the node's ability to reflect ecological processes.

[0121] The carbon sequestration stability index, spatial coverage contribution index and ecological process representation index of each node are normalized and assigned preset weights for weighted summation to generate comprehensive evaluation parameters for each node;

[0122] According to the numerical distribution of comprehensive evaluation parameters of all nodes, the nodes with comprehensive evaluation parameter values greater than the preset threshold are determined as the final layout points for carbon sink monitoring.

[0123] In an embodiment of the present invention, a corrected carbon sequestration dataset is determined, comprising corrected carbon sequestration data for all adaptive grid cells in a mangrove restoration area, each grid cell being associated with a sensor node deployed therein. A preset evaluation period is determined, which can be set based on research needs and actual monitoring conditions, such as a month or a quarter. For each sensor node, all carbon sequestration data recorded by the node during the evaluation period is extracted from the corrected carbon sequestration dataset to form a carbon sequestration data sequence for the node. The mean of the data sequence is calculated, all carbon sequestration data in the data sequence are summed and then divided by the number of data to obtain the average carbon sequestration value for the node during the evaluation period. The standard deviation of the data sequence is calculated by first calculating the difference between each data point and the mean, squaring these differences, averaging these squared values, and finally taking the square root of the average to obtain the standard deviation. The standard deviation reflects the degree of data dispersion; a larger standard deviation indicates more severe data fluctuations. The calculated standard deviation is divided by the mean, and the resulting ratio is the carbon sequestration stability indicator for the sensor node. The smaller the value of this indicator, the lower the fluctuation of the carbon sequestration data of the node during the assessment period and the better the data stability; conversely, the data fluctuates greatly and the stability is poor.

[0124] Determine the spatial distribution of the set of adaptive grid cells. Each grid cell has precise boundary coordinates. The area of each grid cell can be calculated through Geographic Information System (GIS) technology. At the same time, determine the grid cell where each sensor node is located. A node may be associated with multiple grid cells. For each sensor node, count the areas of the grid cells it is associated with, add up the areas of these grid cells one by one to obtain the total area of the grid cells associated with the node. Calculate the total area of the entire irregular polygon monitoring area. The GIS technology can be used to process the polygon boundary and calculate the geographical space area it covers. Divide the total area of the grid cells associated with each node by the total area of the entire irregular polygon monitoring area, and then multiply by 100%. The resulting percentage value is the spatial coverage contribution degree index of the node. The higher this index, the larger the spatial range represented by the node and the stronger its spatial representativeness in the monitoring area.

[0125] Extract the hydrological inundation frequency, salinity gradient, and temperature data from the multi-dimensional carbon flux fusion dataset. These data record the ecological environment information at different locations and times in the monitoring area. At the same time, obtain the corresponding ecological data recorded by each sensor node during the evaluation period. For the hydrological inundation frequency, calculate the difference between the hydrological inundation frequency data monitored by each node and the average hydrological inundation frequency of the entire monitoring area, and take the absolute value of this difference. This absolute value reflects the deviation degree of the hydrological inundation frequency monitored by the node from the overall average situation. The smaller the deviation degree, the closer the reflection of the node to the hydrological inundation process is to the overall level. For the salinity gradient, calculate the difference value between the salinity data monitored by the node and the salinity gradient trend in the surrounding area, and also take the absolute value. This absolute value reflects the characterization ability of the salinity data monitored by the node for the regional salinity change process. The smaller the value, the higher the degree of coincidence between the node monitoring data and the regional salinity gradient change. For the temperature data, calculate the absolute value of the difference between the temperature data monitored by the node and the temperature change trend of the entire monitoring area. This absolute value reflects the degree of reflection of the node on the temperature ecological process. The smaller the value, the more representative the temperature data monitored by the node is of the regional temperature change. After dimensionless processing of the above three absolute values and then comprehensive calculation, for example, first divide the absolute values of the hydrological inundation frequency, salinity gradient, and temperature data by the maximum values of their respective indicators to convert them into dimensionless relative deviation values, and then add up these standardized values. The resulting sum is the ecological process characterization degree index of the node; the smaller the value of this index, the higher the degree of coincidence between the node monitoring data and the key ecological process parameters and the stronger the ability to reflect the ecological process.

[0126] Collect the data of the carbon sink amount stability index, spatial coverage contribution index, and ecological process characterization index of all sensor nodes, and find the maximum and minimum values of these three types of index data respectively. For the carbon sink amount stability index, for the index value of each node, use the normalization formula (current index value - minimum value of this index) / (maximum value of this index - minimum value of this index) to calculate the normalized carbon sink amount stability parameter, and map its numerical range to the interval [0, 1]. Similarly, perform normalization processing on the spatial coverage contribution index and the ecological process characterization index respectively to obtain the corresponding normalized spatial coverage contribution parameter and ecological process characterization parameter. According to the focus of mangrove carbon sink monitoring research and the importance of each index to carbon sink monitoring, set preset weights for the normalized carbon sink amount stability parameter, spatial coverage contribution parameter, and ecological process characterization parameter respectively (for example, if it is considered that the carbon sink amount stability has the greatest impact on the monitoring results, a higher weight can be assigned). For each node, multiply its normalized carbon sink amount stability parameter by the corresponding weight, add the spatial coverage contribution parameter multiplied by its weight, and then add the ecological process characterization parameter multiplied by its weight, and obtain the comprehensive evaluation parameter of this node through weighted summation. The comprehensive evaluation parameter comprehensively considers the performance of the node in terms of data stability, spatial representativeness, and ecological process reflection ability, etc.

[0127] Analyze the numerical values of the comprehensive evaluation parameters of all sensor nodes, and observe their distribution range and central tendency. According to research experience and actual monitoring requirements, set a reasonable preset threshold. Compare the comprehensive evaluation parameter value of each node with the preset threshold one by one, and select the nodes whose comprehensive evaluation parameter values are greater than the preset threshold. These selected nodes have outstanding comprehensive performance in terms of carbon sink amount stability, spatial coverage contribution, and ecological process characterization, and are determined as the final layout points for carbon sink monitoring.

[0128] By calculating multiple indicators such as the stability of carbon sink volume, the contribution degree of spatial coverage, and the representation degree of ecological processes, the performance of sensor nodes is comprehensively evaluated, avoiding the blindness of the layout of monitoring nodes. The final layout points determined can cover the mangrove restoration area more scientifically and reasonably, ensuring that the monitoring data has broad spatial representativeness and ecological relevance, and improving the overall rationality and effectiveness of the layout of monitoring nodes. The stability index of carbon sink volume screens out nodes with small data fluctuations, ensuring the reliability of monitoring data; the representation index of ecological processes ensures that the nodes can accurately reflect key ecological processes, enhancing the explanatory ability of data for ecological phenomena. The data collected at the final layout points determined after comprehensive evaluation has higher quality and stronger credibility, providing more accurate data support for mangrove carbon sink research. Determining the final layout points based on comprehensive evaluation parameters avoids deploying too many nodes in unnecessary positions, reducing data redundancy and resource waste. Concentrating limited monitoring resources on key nodes, while ensuring the monitoring effect, reduces the monitoring cost, improves the monitoring efficiency, and makes the monitoring work more targeted and efficient.

[0129] An embodiment of the present invention also provides a computing device, including: a processor and a memory storing a computer program. When the computer program is run by the processor, it executes the system as described above. All implementation manners in the above system embodiment are applicable to this embodiment and can achieve the same technical effects.

[0130] An embodiment of the present invention also provides a computer-readable storage medium storing instructions. When the instructions are run on a computer, the computer is made to execute the system as described above. All implementation manners in the above system embodiment are applicable to this embodiment and can achieve the same technical effects.

[0131] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A carbon sink data monitoring system for a mangrove restoration area, characterized in that, Including: A collection module, which is used to collect the original data set of carbon exchange at the atmosphere-vegetation-soil interface based on a multi-type sensor network deployed in the intertidal wetland of the mangrove restoration area; A data generation module, which is used to perform spatio-temporal coordinate matching based on the original data set to generate a multi-dimensional carbon flux fusion data set with a combined index of geographical location and time; An identification module, which is used to calculate the local spatio-temporal dispersion and the global correlation threshold based on the fusion data set, and identify and mark abnormal data points; A gridification module, which is used to determine four boundary detection points in the restoration area according to the abnormal data points, form an irregular polygon monitoring area that changes with the ecological process, and perform non-uniform grid cutting on the polygon area to generate a set of adaptive grid cells covering the monitoring area; An index generation module, which is used to calculate the terrain deformation rate, the environmental factor gradient change rate, and the hydrological inundation frequency of each cell during the tidal cycle for the set of grid cells, and generate a spatial correction quantization index; A compensation and correction module, which is used to extract the sensor data in the grid cell, and perform carbon flux value compensation in combination with the spatial correction quantization index to obtain a corrected carbon sink amount data set; A comprehensive evaluation module, which is used to calculate the carbon sink amount stability index, the spatial coverage contribution index, and the ecological process characterization index of each node during the evaluation period based on the corrected carbon sink amount data set, fuse the three indexes into a comprehensive evaluation parameter, and determine the prominent nodes as the final layout points for carbon sink amount monitoring according to the distribution characteristics of the comprehensive evaluation parameter.

2. The mangrove restoration area carbon sink data monitoring system according to claim 1, characterized in that The sensor network includes carbon dioxide concentration sensors, temperature and humidity sensors, and soil carbon flux sensors that are distributed at spatial intervals.

3. The mangrove restoration area carbon sink data monitoring system according to claim 2, characterized in that, Performing spatio-temporal coordinate matching based on the original data set to generate a multi-dimensional carbon flux fusion data set with a combined index of geographical location and time, including: Performing timestamp synchronization processing on the original data set collected by the multi-type sensor network to obtain a time-synchronized data set in which all sensor data has a unified time reference; Based on the time-synchronized data set, mapping each data point to a preset unified spatial coordinate system of the restoration area according to the geographical coordinates of the deployment location to generate a mapped data set with a unified spatio-temporal reference; Using the mapped data set, using the geographical location coordinates and the synchronized timestamp as the combined primary key, associating and integrating the carbon exchange related parameters from different types of sensors at the same geographical location and the same timestamp to form a preliminary associated and integrated data set; For the spatio-temporal point data missing in the preliminary associated and integrated data set due to sensor failure, terrain occlusion, and communication interruption, based on the unified spatial coordinate system and the synchronized timestamp, using the valid data of adjacent sensor nodes to obtain a complete data set after filling in the missing values; Organizing the complete data set into a data matrix with geographical location coordinates and timestamp as the combined index, including multi-dimensional carbon flux and related environmental parameters, to generate a multi-dimensional carbon flux fusion data set.

4. The mangrove restoration area carbon sink data monitoring system according to claim 3, characterized in that, Calculating the local spatio-temporal dispersion and the global correlation threshold based on the fusion data set, and identifying and marking abnormal data points, including: Based on the multi-dimensional carbon flux fusion dataset, a spatio-temporal sliding window is constructed centered on each sensor node, and the standard deviation of the data points within the window is calculated to obtain the local spatio-temporal dispersion dataset of each node under the corresponding spatio-temporal window; Based on the multi-dimensional carbon flux fusion dataset, the covariance matrix of all sensor node data is calculated, and the global correlation threshold is calculated based on the covariance matrix to obtain the global correlation threshold characterizing the overall relevance of the sensor network data in the entire restoration area; Using the local spatio-temporal dispersion dataset and the global correlation threshold, an anomaly determination boundary is generated by comparing the deviation degree of the local spatio-temporal dispersion of each sensor node with the global correlation threshold; Based on the anomaly determination boundary, it is judged whether the data points of each sensor node exceed the boundary. For the node data points that exceed the current boundary, the anomaly status is recorded and marked as anomaly data points.

5. The mangrove restoration area carbon sink data monitoring system according to claim 4, wherein According to the anomaly data points, four boundary detection points are determined in the restoration area to form an irregular polygon monitoring area that changes with the ecological process, and the polygon area is non-uniformly grid-cut to generate an adaptive grid cell set covering the monitoring area, including: Based on the anomaly data points, the kernel density estimation value of the anomaly data points in the geographical space of the mangrove restoration area is calculated to generate an anomaly point spatial kernel density distribution field covering the entire restoration area; Based on the anomaly point spatial kernel density distribution field, the gradient vector field of the density field in space is calculated, and four spatial position points in the gradient vector field are identified. The four points are determined as boundary detection points, and based on the spatial coordinates of the four boundary detection points, the four points are connected to form a convex hull polygon, that is, a dynamic irregular polygon monitoring area; Based on the anomaly point spatial kernel density distribution field, the anomaly point density values of each sub-region inside the convex hull polygon monitoring area are calculated. At the same time, based on the data of the water level gauge and salinity gauge in the multi-dimensional carbon flux fusion dataset, the data reflecting the hydrological connectivity characteristics inside the monitoring area are extracted; Based on the anomaly point density values of each sub-region inside the polygon and the hydrological connectivity characteristic data, a fine grid size is set in the sub-regions where the anomaly points are relatively concentrated and the key channel areas with significant hydrological connectivity; a sparse grid size is set in the sub-regions where the anomaly points are sparsely distributed and the areas with gentle hydrological connectivity, so as to construct a spatial parameter mapping of a non-uniform grid division scheme adapted to spatial heterogeneity; Based on the boundary of the convex hull polygon monitoring area and the spatial parameter mapping of the non-uniform grid division scheme, the polygon monitoring area is non-uniformly grid-cut to generate an adaptive grid cell set covering the entire dynamic polygon monitoring area.

6. The mangrove restoration area carbon sink data monitoring system according to claim 5, wherein For the grid cell set, the terrain deformation rate, environmental factor gradient change rate and hydrological inundation frequency of each cell within the tidal cycle are calculated to generate spatial correction quantization indexes, including: Obtain the data recorded by the elevation sensor, salinity sensor, temperature sensor and water level sensor deployed on the grid cells in the mangrove restoration area within the tidal cycle; Based on the elevation sensor data, calculate the surface settlement amount of each grid cell within a single tidal cycle, and based on the surface settlement amount and the corresponding tidal cycle time, calculate the topographic deformation rate of each grid cell; based on the gradient monitoring values recorded by the salinity sensor and the temperature sensor, calculate the environmental factor gradient change rate of each grid cell; based on the data recorded by the water level sensor, count the daily average inundation frequency of each grid cell, and calculate the hydrological inundation frequency value of each grid cell; Normalize the topographic deformation rate, environmental factor gradient change rate, and hydrological inundation frequency value to obtain the corresponding normalized topographic deformation rate parameter, environmental factor change rate parameter, and hydrological inundation frequency parameter; Perform weighted fusion calculation on the topographic deformation rate parameter, environmental factor change rate parameter, and hydrological inundation frequency parameter to obtain the fusion parameter value of each grid cell, and form a correction coefficient matrix representing the spatial heterogeneity of the entire mangrove restoration area with the fusion parameter value, that is, the quantitative index of spatial correction.

7. The mangrove restoration area carbon sink data monitoring system according to claim 6, wherein Extract the sensor data within the grid cell, and combine it with the spatial correction quantitative index to perform carbon flux value compensation to obtain the corrected carbon sink amount data set, including: For each adaptive grid cell, extract the original carbon flux data recorded by the carbon flux sensor nodes deployed within the cell during the monitoring period; Read the quantitative index of spatial correction, that is, extract the correction coefficient corresponding to the currently processed grid cell from the correction coefficient matrix, and calculate according to the correction coefficient using the topographic deformation rate, environmental factor gradient change rate, and hydrological inundation frequency parameter to obtain the carbon flux compensation value of the grid cell; Combine the original carbon flux data with the carbon flux compensation value to generate the corrected carbon sink amount data of the grid cell. At the same time, fuse all the corrected carbon sink amount data to form a corrected carbon sink amount data set covering the irregular polygon monitoring area of the entire mangrove restoration area.

8. The mangrove restoration area carbon sink data monitoring system according to claim 7, characterized in that Based on the corrected carbon sink amount data set, calculate the carbon sink amount stability index, spatial coverage contribution index, and ecological process representation index of each node during the evaluation period, fuse the three indexes into a comprehensive evaluation parameter, and determine the prominent nodes as the final layout points for carbon sink amount monitoring according to the distribution characteristics of the comprehensive evaluation parameter, including: Based on the corrected carbon sink amount data set, calculate the ratio of the standard deviation to the mean of the carbon sink amount data of each sensor node within the preset evaluation period to generate the carbon sink amount stability index representing the degree of data fluctuation of the node; Based on the spatial distribution of the adaptive grid cell set and the node positions, calculate the proportion of the total area of the grid cells associated with each node in the total area of the entire irregular polygon monitoring area to generate the spatial coverage contribution index representing the spatial representativeness of the node; Based on the hydrological inundation frequency, salinity gradient, and temperature data in the multi-dimensional carbon flux fusion data set, calculate the absolute value between the monitoring data of each node and the key ecological process parameters to generate the ecological process representation index representing the ability of the node to reflect ecological processes; Normalize the carbon sink volume stability index, spatial coverage contribution index, and ecological process characterization index for each node, and assign preset weights for weighted summation to generate the comprehensive evaluation parameters for each node; According to the numerical distribution of the comprehensive evaluation parameters of all nodes, determine the nodes whose comprehensive evaluation parameter values are greater than the preset threshold, and determine them as the final layout points for carbon sink volume monitoring.

9. A computing device, characterized in that, Including: One or more processors; A storage device for storing one or more programs, which when executed by the one or more processors cause the one or more processors to implement the system according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, A program is stored in the computer-readable storage medium, and when the program is executed by a processor, the system according to any one of claims 1 to 8 is implemented.

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