Aggregation method, system, terminal and medium for multi-objective REDD project areas

Abstract

The present invention provides a method, system, terminal, and medium for aggregating multi-target REDD project areas, specifically relating to the field of forest carbon sequestration technology. This solution constructs a multidimensional weighted feature set for each coordinate point based on the collected coordinate points within several target REDD project areas and the geographic feature data of the target layer at each coordinate point, as well as preset spatial coordinate aggregation weights and preset geographic feature aggregation weights. A clustering algorithm is used to cluster all the multidimensional weighted feature sets to obtain grid point sets corresponding to the multidimensional weighted feature sets. A convex hull algorithm is used to calculate the convex hull of the coordinate points in each grid point set to obtain the convex hull set of the grid point sets. Based on the target REDD project areas corresponding to all convex hull sets, an aggregated region for all target REDD project areas is obtained. This solution can effectively improve the accuracy of REDD project area boundary identification, thereby facilitating the comprehensive management and assessment of dispersed areas.

Description

Technical Field

[0001] The present invention relates to the technical field of forest carbon sequestration, and in particular to a method, system, terminal and medium for aggregating multi-objective REDD project areas. Background Art

[0002] Currently, REDD (Reducing Emissions from Deforestation and Forest Degradation) projects aim to reduce carbon emissions by protecting and managing forest resources, thereby mitigating the impact of deforestation and forest degradation on greenhouse gas emissions. However, because REDD projects typically cover a wide geographical area and often involve a large number of fragmented areas, project management and monitoring become complex and difficult.

[0003] Existing technologies mainly manage and monitor data from a single forest area. However, due to the lack of comprehensive consideration of the correlation between data from different regions, it is impossible to manage and monitor data from multiple fragmented areas at the same time, resulting in relatively low management and monitoring efficiency of REDD projects as a whole. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention aims to provide a method, system, terminal and medium for aggregating multiple target REDD project areas, aiming to solve the problem of low efficiency in the prior art in simultaneously managing and monitoring data of multiple target REDD project areas.

[0005] To achieve the above objectives, the present invention provides a method for aggregating multi-objective REDD project areas, which mainly includes the following steps:

[0006] Collect the coordinates of several target REDD project areas and the geographic feature data of the target layer at each coordinate point;

[0007] Based on the geographic feature data of each coordinate point and the target layer at the corresponding coordinate point, and the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight, a multidimensional weighted feature set of each coordinate point is constructed;

[0008] Clustering all the multidimensional weighted feature sets using a clustering algorithm to obtain grid point sets corresponding to a number of the multidimensional weighted feature sets;

[0009] Using a convex hull algorithm, calculating the convex hull of each coordinate point in the grid point set to obtain a convex hull set of the grid point set;

[0010] Based on the target REDD project areas corresponding to all the convex hull sets, an aggregated area of ​​all the target REDD project areas is obtained.

[0011] Optionally, constructing a multidimensional weighted feature set for each coordinate point based on the geographic feature data of each coordinate point and the target layer at the corresponding coordinate point, and a preset spatial coordinate aggregation weight and a preset geographic feature aggregation weight includes:

[0012] Based on the geographic feature data of the target layer at each coordinate point, a set of geographic feature sets corresponding to each coordinate point is constructed;

[0013] Based on the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight, a multi-dimensional weighted feature set is constructed using the coordinate points and the corresponding geographic feature set.

[0014] Optionally, the multi-dimensional weighted feature set is constructed based on the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight, using the coordinate points and the corresponding geographic feature set, including:

[0015] Standardizing all the geographic feature sets in all the target REDD project areas to obtain a standardized geographic feature set corresponding to each coordinate point;

[0016] Based on the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight, a multi-dimensional weighted feature set is constructed using the coordinate points and the corresponding standardized geographic feature set.

[0017] Optionally, clustering all the multidimensional weighted feature sets using a clustering algorithm to obtain grid point sets corresponding to several multidimensional weighted feature sets includes:

[0018] Clustering the multidimensional weighted feature set using a clustering algorithm, and setting corresponding cluster labels for the multidimensional weighted feature set;

[0019] The multi-dimensional weighted feature set and the corresponding cluster labels are traversed to obtain a plurality of grid point sets corresponding to the multi-dimensional weighted feature set.

[0020] Optionally, clustering the multidimensional weighted feature set using a clustering algorithm includes:

[0021] If the preset spatial coordinate aggregation weight is greater than the preset geographic feature aggregation weight, a clustering algorithm matching the coordinate point is selected to cluster the multidimensional weighted feature set;

[0022] If the preset spatial coordinate aggregation weight is smaller than the preset geographic feature aggregation weight, a clustering algorithm matching the geographic feature data is selected to cluster the multi-dimensional weighted feature set.

[0023] Optionally, the using a convex hull algorithm to calculate the convex hull of each coordinate point in the grid point set to obtain the convex hull set of the grid point set includes:

[0024] Searching for a coordinate point in the grid point set that is closest to a preset direction, and adding the coordinate point in the grid point set that is closest to the preset direction to a vertex sequence of the convex hull;

[0025] The coordinate points in the grid point set except the coordinate point closest to the preset direction are traversed in sequence, and the coordinate points except the coordinate point closest to the preset direction are sorted according to the size of the polar angle to obtain the convex hull set of the grid point set.

[0026] A second aspect of the present invention provides a multi-objective REDD project area aggregation system, the system comprising:

[0027] The data collection module is used to collect the coordinates of several target REDD project areas and the geographic feature data of the target layer at each coordinate point;

[0028] A multidimensional weighted feature set construction module is used to construct a multidimensional weighted feature set for each coordinate point based on the geographic feature data of each coordinate point and the target layer at the corresponding coordinate point, as well as a preset spatial coordinate aggregation weight and a preset geographic feature aggregation weight;

[0029] A clustering module, configured to cluster all the multidimensional weighted feature sets using a clustering algorithm to obtain grid point sets corresponding to a plurality of the multidimensional weighted feature sets;

[0030] a convex hull set solving module, configured to use a convex hull algorithm to solve the convex hull of each coordinate point in the grid point set to obtain the convex hull set of the grid point set;

[0031] An aggregation module is configured to obtain an aggregation area of ​​all the target REDD project areas based on the target REDD project areas corresponding to all the convex hull sets.

[0032] Optionally, a clustering algorithm selection module is further included, wherein the clustering algorithm selection module is used to determine the clustering algorithm based on the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight.

[0033] If the preset spatial coordinate aggregation weight is greater than the preset geographic feature aggregation weight, a clustering algorithm matching the coordinate point is selected to cluster the multidimensional weighted feature set;

[0034] If the preset spatial coordinate aggregation weight is smaller than the preset geographic feature aggregation weight, a clustering algorithm matching the geographic feature data is selected to cluster the multi-dimensional weighted feature set.

[0035] A third aspect of the present invention provides an intelligent terminal comprising a memory, a processor, and a program for aggregating multi-objective REDD project areas stored in the memory and executable on the processor, wherein the program for aggregating multi-objective REDD project areas, when executed by the processor, implements any one of the steps of the multi-objective REDD project area aggregating method described above.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium storing a program for aggregating multi-objective REDD project areas. When the program is executed by a processor, the program implements any one of the steps of the multi-objective REDD project area aggregating method.

[0037] Compared with the existing technology, the beneficial effects of this solution are as follows:

[0038] The method of the present invention constructs a multidimensional weighted feature set for each coordinate point based on the coordinate points in a plurality of target REDD project areas and the geographic feature data of the target layer at each coordinate point, as well as preset spatial coordinate aggregation weights and preset geographic feature aggregation weights, so as to characterize various types of feature data at each coordinate point and the weight of each type of feature data; then, a clustering algorithm is used to cluster the multidimensional weighted feature sets corresponding to all coordinate points in all target REDD project areas to obtain grid point sets corresponding to the plurality of multidimensional weighted feature sets; and then a convex hull algorithm is used to obtain the convex hull set of the grid point sets, thereby obtaining the aggregation area of ​​all target REDD project areas.

[0039] This solution is based on geographic information systems and spatial analysis technology. By establishing a multi-dimensional weighted feature set based on the multi-dimensional feature data at each coordinate point in multiple target REDD project areas, the data of each dimension at each coordinate point can be accurately and quickly represented according to the weight. The use of clustering algorithms and convex hull algorithms can effectively improve the accuracy of REDD project area boundary recognition and reduce errors. The entire solution enables multiple scattered areas to be integrated into larger areas, which is conducive to the comprehensive management and evaluation of dispersed areas, and improves the accuracy and operability of REDD project area monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1is a flow chart of the aggregation method of the multi-objective REDD project area of ​​the present invention;

[0042] Figure 2 This is a schematic diagram of scattered target REDD project areas in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the region obtained by calculating the convex hull of the scattered target REDD project areas as a whole in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the regions obtained by calculating the convex hulls of the scattered target REDD project areas in an embodiment of the present invention;

[0045] Figure 5 This is a schematic diagram of regions obtained by clustering scattered target REDD project areas in different ways in an embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of the aggregation system structure of the multi-objective REDD project area of ​​the present invention;

[0047] Figure 7 It is a schematic diagram of the structure of the intelligent terminal of the present invention. DETAILED DESCRIPTION

[0048] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0049] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0050] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0051] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0052] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] To address the shortcomings of existing technologies, this invention provides a method for spatially aggregating fragmented REDD project areas. The core concept of this invention is to utilize geographic information systems and spatial analysis techniques to integrate fragmented areas through spatial aggregation, thereby enabling comprehensive management and assessment of REDD project areas. The technical effect of this invention is to improve the accuracy and operability of REDD project area monitoring.

[0055] Exemplary Methods

[0056] The embodiment of the present invention provides a method for aggregating multi-objective REDD project areas, which is deployed on electronic devices such as computers and servers. The application scenario is the comprehensive management and evaluation of fragmented multi-objective REDD project areas, and is aimed at the situation where the multi-objective REDD project areas are fragmented. This embodiment mainly discusses the spatial location data in geospatial data, but the types of data sources collected for the target REDD project areas include but are not limited to spatial location data, as well as various types of data related to REDD projects, such as grid data, categorical data, numerical data, density data, slope data in remote sensing data or / and geospatial data. Specifically, if Figure 1 As shown, the steps of the method in this embodiment include:

[0057] Step S100: Collecting coordinates of several target REDD project areas and geographic feature data of the target layer at each coordinate point.

[0058] Specifically, geospatial data or remote sensing data collected for REDD projects, for example, the corresponding feature types of the collected data include Value type, Type type, and Distance type. Among them, Value type refers to a layer whose point value data is a specific value, such as GFC, slope, altitude, GPP, etc. Each point of this type of raster data represents a specific value at this coordinate, usually a floating point number; Type type refers to a layer whose point value data is a specific category, such as surface cover category data, soil category data, etc. Each point of this type of raster data represents a certain category at this coordinate, usually an integer; Distance type refers to a layer whose point value is the distance to the nearest vector, such as a road distance layer, a distance layer to a certain surface cover type, etc.

[0059] Assume that the REDD project area is composed of N fragmented target REDD project areas R1, R2, ..., R N The overall target REDD project area of ​​a REDD project consists of N fragmented target REDD project areas. If the convex hull of all these fragmented areas is directly calculated as a whole, the convex polygon corresponding to the minimum enclosing area will be increased by a large amount of data from non-project areas, which can easily lead to significant errors when using this data to subsequently calculate REDD project carbon sinks. Therefore, this embodiment starts by improving the accuracy of the data source for the target REDD project area. By clustering each fragmented area and combining it with analysis indicators of actual projects, the accuracy of REDD project area boundary identification is maximized, thereby improving the accuracy of REDD project carbon sink calculations.

[0060] By traversing N fragmented target REDD project areas, we can find each fragmented target REDD project area R i (i=1,2…,N) m included in i A subset of coordinate points Utilize all fragmented targeted REDD project areas i All coordinate points in the corresponding coordinate point subset constitute the coordinate point set corresponding to the overall target REDD project area

[0061] This embodiment collects geospatial data or remote sensing data from REDD projects as data sources. In different REDD projects, different remote sensing data and geospatial data may be selected as data sources according to different geographical environments and data availability.

[0062] Step S200: constructing a multidimensional weighted feature set for each coordinate point based on the geographic feature data of each coordinate point and the target layer on the corresponding coordinate point, as well as the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight.

[0063] Specifically, based on the geographic feature data of the target layer at each coordinate point, a set of geographic feature sets corresponding to each coordinate point is constructed; based on the preset spatial coordinate aggregation weights and the preset geographic feature aggregation weights, the coordinate points and the corresponding geographic feature sets are used to construct a multidimensional weighted feature set.

[0064] Assumptions The slope, altitude, and surface coverage characteristics of the feature layer corresponding to the coordinate point are expressed as A set of feature sets corresponding to the coordinate point can be obtained By analogy, a set of feature sets corresponding to each coordinate point can be obtained.

[0065] Furthermore, based on the preset spatial coordinate aggregation weights and the preset geographic feature aggregation weights, a multidimensional weighted feature set is constructed using the coordinate points and the corresponding geographic feature sets, including: standardizing all geographic feature sets in all target REDD project areas to obtain a standardized geographic feature set corresponding to each coordinate point; based on the preset spatial coordinate aggregation weights and the preset geographic feature aggregation weights, a multidimensional weighted feature set is constructed using the coordinate points and the corresponding standardized geographic feature sets.

[0066] For example, the coordinate points and geographic features corresponding to all points in the raster map are subjected to Z-score normalization to convert data of different magnitudes into a unified Z-score for comparison. The aggregation weights of spatial coordinates and geographic features are set to W s and W G The spatial coordinate aggregation weight represents the importance of the result obtained by aggregating the (x, y) coordinates of the coordinate points, and the geographic feature aggregation weight represents the importance of the result obtained by aggregating the (x, y) coordinates of the coordinate points. i ) to aggregate the results, and calculate the weighted feature sets of spatial coordinates and geographic features respectively. As an example, we can get the weighted feature set Among them, x_s, y_s, S_s, E_s, and L_s are the values ​​after normalization of the corresponding features.

[0067] Step S300: clustering all multidimensional weighted feature sets using a clustering algorithm to obtain grid point sets corresponding to several multidimensional weighted feature sets.

[0068] Specifically, a clustering algorithm is used to cluster the multidimensional weighted feature set, and corresponding cluster labels are set for the multidimensional weighted feature set; the multidimensional weighted feature set and the corresponding cluster labels are traversed to obtain a plurality of grid point sets corresponding to the multidimensional weighted feature set. If the preset spatial coordinate aggregation weight is greater than the preset geographic feature aggregation weight, a clustering algorithm that matches the coordinate point is selected to cluster the multidimensional weighted feature set; if the preset spatial coordinate aggregation weight is less than the preset geographic feature aggregation weight, a clustering algorithm that matches the geographic feature data is selected to cluster the multidimensional weighted feature set. It can be seen that by setting the size of the spatial coordinate aggregation weight and the geographic feature aggregation weight, all grid points can be aggregated from different dimensions.

[0069] The steps of the clustering algorithm specifically include:

[0070] Initialize the cluster center and randomly select C points as the initial cluster center (C is the number of clusters set in advance); according to all the fragmented target REDD project areas R i The distance between the coordinate points in the cluster and the cluster centers is calculated, and each coordinate point is assigned to the cluster center closest to it, and a cluster label is added to each coordinate point; for each cluster, the mean of all coordinate points in the cluster is calculated, and the mean is used as the new cluster center to update the cluster center; the steps of assigning coordinate points to each cluster and updating the cluster center are repeated until the cluster centers remain basically unchanged or the preset maximum number of iterations is reached; according to the final distribution of cluster centers and coordinate points, all fragmented target REDD project areas R i The coordinate points in are divided into the corresponding clusters, and the coordinate point set P is traversed. sum The cluster label corresponding to each point in is obtained to obtain several subsets of coordinate points.

[0071] Assume that R i The region has a total of weighted feature samples, traversing all scattered regions R i After (i=1,2…,N), we get the total feature-weighted sample set. Set the cluster to C (here C=12), and use the KMeans algorithm to aggregate all feature-weighted samples into C clusters.

[0072] Furthermore, the clustering algorithm used can set different or equal weights for the characteristic data of multiple target types according to actual task requirements (e.g., the type of data in the target REDD project area to be processed), so as to flexibly select any clustering algorithm such as spatial location-based clustering algorithm, distance-based clustering algorithm, density-based clustering algorithm, graph-based clustering algorithm, partition-based clustering algorithm, hierarchy-based clustering algorithm, grid-based clustering algorithm, model-based clustering algorithm, etc.

[0073] The coordinate system corresponding to the coordinate point in this embodiment may be a geodetic coordinate system, or a two-dimensional coordinate system or a three-dimensional coordinate system constructed with any point in the target REDD project area as the origin.

[0074] Step S400: using a convex hull algorithm, calculating the convex hull of each coordinate point in the grid point set to obtain the convex hull set of the grid point set;

[0075] Specifically, the convex hull algorithm is used to find the intersection of the convex polygons of the coordinate points in each grid point set, that is, to solve the minimum enclosing convex polygon to obtain the convex hull set of the grid point set.

[0076] The steps of the convex hull algorithm specifically include:

[0077] Searching for the coordinate point closest to the preset direction in the grid point set, such as the leftmost point P0, and adding the coordinate point closest to the preset direction in the grid point set to the vertex sequence of the convex hull;

[0078] Traverse the remaining coordinate points P in the coordinate point subset in turn i , and sort the coordinate points except the coordinate point closest to the preset direction according to the size of the polar angle to obtain the convex hull set of the grid point set.

[0079] For example, in order to ensure that the edge of the convex hull extends counterclockwise, the following operation is performed for each coordinate point: If P i In P1 and P mi In the counterclockwise direction of the straight line, add it to the vertex sequence of the convex hull and set it as the new last point P mi If P i In P1 and P mi In the clockwise direction of the straight line formed by mi Delete from the vertex sequence of the convex hull and let P mi The previous point P mi-1 Become the new last point P mi Similarly, according to similar principles, corresponding operations can be performed on each coordinate point to extend the edge of the convex hull in the clockwise direction.

[0080] Step S500: Based on the target REDD project areas corresponding to all convex hull sets, obtaining the aggregated area of ​​all target REDD project areas.

[0081] Specifically, the areas corresponding to all convex hull sets are united to obtain the aggregated area of ​​all target REDD project areas, which is approximately considered to be the original fragmented project areas.

[0082] Furthermore, all fragmented target REDD project areas can be monitored and managed simultaneously based on the aggregated data of the target REDD project areas.

[0083] Furthermore, in the process of optimizing REDD projects, other related technologies, such as machine learning and artificial intelligence algorithms, can be combined to further optimize the boundary identification and management effects of REDD project areas.

[0084] For example, suppose the REDD project consists of 214 fragmented target REDD project areas, such as Figure 2 As shown in the figure, there are a total of P coordinate points in the set of coordinate points consisting of these scattered target REDD project areas. sum = 44097 coordinate points. If we directly calculate the convex hull of these scattered target REDD project areas, we can get the following Figure 3 The area shown will introduce data from non-project areas, which may lead to very large errors when using these data to calculate the carbon sink of REDD projects. Therefore, the KMeans algorithm is used to cluster these 214 scattered target REDD project areas. Assuming that the clusters C = 12, based on the spatial position distribution of the two-dimensional coordinate points (x, y), the coordinate point set P is traversed. sum The cluster label (1, 2, ..., 12) of each coordinate point in the cluster model is traversed according to the label result of each coordinate point, and the coordinate point set P is sum All coordinate points in are divided into 12 coordinate point subsets according to the numbers 1 to 12. Based on the principle of the above convex hull algorithm, the convex hull of the 12 coordinate point subsets of the REDD project are solved respectively to obtain 12 convex hull sets, such as Figure 4 As shown in , the area formed by these 12 convex hull sets is close to the original fragmented project area.

[0085] Similarly, assuming that the clusters C = 3 or 6, we can get the corresponding number of convex hull sets, such as Figure 5 As shown in the figure, by comparing the clustering effects of different clusters, it can be seen that when the cluster C = 12, the area formed by the convex hull set is closer to the original fragmented project area. The less non-project data introduced, the higher the accuracy of the subsequent measurement of REDD project carbon sinks. However, as the number of clusters increases, the amount of calculation will also increase, and the calculation efficiency will be relatively reduced. Therefore, in actual projects, the optimal number of clusters can be set based on the analysis and evaluation indicators of the project, taking into account factors such as data availability, calculation efficiency and accuracy, to facilitate the subsequent monitoring and management of REDD project carbon sinks.

[0086] In summary, the method of the present invention, based on geographic information systems and spatial analysis techniques, effectively improves the accuracy of REDD project area boundary identification and reduces boundary identification errors by utilizing specific remote sensing data and geospatial data through a convex hull algorithm. Based on a comprehensive consideration of the correlations between different regions, an appropriate aggregation algorithm is selected based on the data type to integrate scattered regional data, enabling the integration of scattered regions into larger areas. This enables comprehensive management and evaluation of dispersed regional data, improves the accuracy and operability of REDD project area monitoring and management, and significantly enhances monitoring and management. Furthermore, the number of clusters can be flexibly selected based on REDD project evaluation indicators to meet the needs of different projects, significantly improving the flexibility and adaptability of REDD project area data monitoring and management.

[0087] Exemplary Systems

[0088] like Figure 6 As shown, corresponding to the above-mentioned method for aggregating multi-objective REDD project areas, an embodiment of the present invention further provides a system for aggregating multi-objective REDD project areas. The system for aggregating multi-objective REDD project areas includes:

[0089] The data collection module is used to collect the coordinates of several target REDD project areas and the geographic feature data of the target layer at each coordinate point;

[0090] A multi-dimensional weighted feature set construction module is used to construct a multi-dimensional weighted feature set for each coordinate point based on the geographic feature data of each coordinate point and the target layer on the corresponding coordinate point, as well as the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight;

[0091] The clustering module is used to cluster all multidimensional weighted feature sets using a clustering algorithm to obtain grid point sets corresponding to several multidimensional weighted feature sets;

[0092] The convex hull set solving module is used to use the convex hull algorithm to find the convex hull of the coordinate points in each grid point set to obtain the convex hull set of the grid point set;

[0093] The aggregation module is used to obtain the aggregation area of ​​all target REDD project areas based on the target REDD project areas corresponding to all convex hull sets.

[0094] Optionally, a clustering algorithm selection module is further included, which is used to determine the clustering algorithm based on the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight.

[0095] If the preset spatial coordinate aggregation weight is greater than the preset geographic feature aggregation weight, a clustering algorithm matching the coordinate point is selected to cluster the multidimensional weighted feature set;

[0096] If the preset spatial coordinate aggregation weight is smaller than the preset geographic feature aggregation weight, a clustering algorithm matching the geographic feature data is selected to cluster the multidimensional weighted feature set.

[0097] Specifically, in this embodiment, the specific functions of the multi-objective REDD project area aggregation system can also refer to the corresponding description of the multi-objective REDD project area aggregation method, which will not be repeated here.

[0098] Based on the above embodiment, the present invention also provides an intelligent terminal, whose principle block diagram can be shown as follows: Figure 7 As shown. The above-mentioned smart terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor of the smart terminal is used to provide computing and control capabilities. The memory of the smart terminal includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a multi-objective REDD project area aggregation program. The internal memory provides an operating environment for the operating system and the multi-objective REDD project area aggregation program in the non-volatile storage medium. The network interface of the smart terminal is used to communicate with external terminals via a network connection. When the multi-objective REDD project area aggregation program is executed by the processor, the steps of any of the above-mentioned multi-objective REDD project area aggregation methods are implemented. The display screen of the smart terminal can be a liquid crystal display or an electronic ink display.

[0099] Those skilled in the art will understand that Figure 7 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention and does not constitute a limitation on the smart terminal to which the solution of the present invention is applied. The specific smart terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0100] In one embodiment, a smart terminal is provided. The smart terminal includes a memory, a processor, and a multi-objective REDD project area aggregation program stored in the memory and executable on the processor. When the multi-objective REDD project area aggregation program is executed by the processor, the steps of any one of the multi-objective REDD project area aggregation methods provided in the embodiments of the present invention are implemented.

[0101] An embodiment of the present invention further provides a computer-readable storage medium storing a program for aggregating multi-objective REDD project areas. When the program for aggregating multi-objective REDD project areas is executed by a processor, the steps of any one of the methods for aggregating multi-objective REDD project areas provided in the embodiments of the present invention are implemented.

[0102] It should be understood that the sequence numbers of the steps in the above embodiments do not imply a specific order of execution; the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0103] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0104] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0105] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0106] In the embodiments provided by the present invention, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units described above is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented.

[0107] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for aggregating multi-objective REDD project areas, characterized in that: The following steps are involved: Collect the coordinates of several target REDD project areas and the geographic feature data of the target layer at each coordinate point; Based on the geographic feature data of each coordinate point and the target layer at the corresponding coordinate point, and the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight, a multidimensional weighted feature set of each coordinate point is constructed; Clustering all the multidimensional weighted feature sets using a clustering algorithm to obtain grid point sets corresponding to a number of the multidimensional weighted feature sets; Using a convex hull algorithm, calculating the convex hull of each coordinate point in the grid point set to obtain a convex hull set of the grid point set; Obtaining an aggregated area of ​​all target REDD project areas based on the target REDD project areas corresponding to all the convex hull sets; The multi-dimensional weighted feature set of each coordinate point is constructed based on the geographic feature data of each coordinate point and the target layer at the corresponding coordinate point, and the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight, including: Based on the geographic feature data of the target layer at each coordinate point, a set of geographic feature sets corresponding to each coordinate point is constructed; Based on the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight, a multidimensional weighted feature set is constructed using the coordinate points and the corresponding geographic feature set; The clustering algorithm is used to cluster all the multidimensional weighted feature sets to obtain a plurality of grid point sets corresponding to the multidimensional weighted feature sets, including: Clustering the multidimensional weighted feature set using a clustering algorithm, and setting corresponding cluster labels for the multidimensional weighted feature set; Traversing the multidimensional weighted feature set and the corresponding cluster labels to obtain a plurality of grid point sets corresponding to the multidimensional weighted feature set; Clustering the multidimensional weighted feature set using a clustering algorithm, including: If the preset spatial coordinate aggregation weight is greater than the preset geographic feature aggregation weight, a clustering algorithm matching the coordinate point is selected to cluster the multidimensional weighted feature set; If the preset spatial coordinate aggregation weight is smaller than the preset geographic feature aggregation weight, a clustering algorithm matching the geographic feature data is selected to cluster the multi-dimensional weighted feature set.

2. The method for aggregating multi-objective REDD project areas according to claim 1, characterized in that: The method of constructing a multidimensional weighted feature set based on the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight by using the coordinate points and the corresponding geographic feature set includes: Standardizing all the geographic feature sets in all the target REDD project areas to obtain a standardized geographic feature set corresponding to each coordinate point; Based on the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight, a multi-dimensional weighted feature set is constructed using the coordinate points and the corresponding standardized geographic feature set.

3. The method for aggregating multi-objective REDD project areas according to claim 1, characterized in that: The method of using a convex hull algorithm to calculate the convex hull of each coordinate point in the grid point set to obtain a convex hull set of the grid point set includes: Searching for a coordinate point in the grid point set that is closest to a preset direction, and adding the coordinate point in the grid point set that is closest to the preset direction to a vertex sequence of the convex hull; The coordinate points in the grid point set except the coordinate point closest to the preset direction are traversed in sequence, and the coordinate points except the coordinate point closest to the preset direction are sorted according to the size of the polar angle to obtain the convex hull set of the grid point set.

4. Aggregate system of multi-objective REDD project areas, characterized by: The steps for implementing the method for aggregating a multi-objective REDD project area according to any one of claims 1 to 3, the system comprising: The data collection module is used to collect the coordinates of several target REDD project areas and the geographic feature data of the target layer at each coordinate point; A multidimensional weighted feature set construction module is used to construct a multidimensional weighted feature set for each coordinate point based on the geographic feature data of each coordinate point and the target layer at the corresponding coordinate point, as well as the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight; A clustering module, configured to cluster all the multidimensional weighted feature sets using a clustering algorithm to obtain grid point sets corresponding to a plurality of the multidimensional weighted feature sets; a convex hull set solving module, configured to use a convex hull algorithm to solve the convex hull of each coordinate point in the grid point set to obtain the convex hull set of the grid point set; An aggregation module is configured to obtain an aggregation area of ​​all the target REDD project areas based on the target REDD project areas corresponding to all the convex hull sets.

5. The aggregation system of multi-objective REDD project areas according to claim 4, characterized in that: It also includes a clustering algorithm selection module, which is used to determine the clustering algorithm based on the preset spatial coordinate aggregation weight and the preset geographic feature aggregation weight. If the preset spatial coordinate aggregation weight is greater than the preset geographic feature aggregation weight, a clustering algorithm matching the coordinate point is selected to cluster the multidimensional weighted feature set; If the preset spatial coordinate aggregation weight is smaller than the preset geographic feature aggregation weight, a clustering algorithm matching the geographic feature data is selected to cluster the multi-dimensional weighted feature set.

6. Intelligent terminal, characterized in that: The intelligent terminal includes a memory, a processor, and a multi-objective REDD project area aggregation program stored in the memory and executable on the processor. When the multi-objective REDD project area aggregation program is executed by the processor, the steps of the multi-objective REDD project area aggregation method according to any one of claims 1 to 3 are implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a multi-objective REDD project area aggregation program, which, when executed by a processor, implements the steps of the multi-objective REDD project area aggregation method according to any one of claims 1 to 3.

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