A wetland resource data fusion method based on typical ecological environment element information

Abstract

The present invention provides a method for fusion of wetland resource data based on typical ecological environment element information, including: obtaining multi-source wetland resource data and performing data standardization processing, constructing a data resource pool of wetland resources, the data resource pool including a number of standard wetland information; extracting element information of the typical ecological environment of wetland resources in the data resource pool based on the several standard wetland information, the element information including natural elements and artificial elements; constructing matching determiners of typical ecological environment elements respectively according to the expression form of element information; setting the matching priority of element information, and performing multi-level entity matching on standard wetland information according to the matching priority; performing information fusion processing of standard wetland information based on the matching relationship between standard wetland information, and obtaining a wetland resource database. The present invention forms a complete and accurate spatial data set of wetland resources, the data has high precision, good timeliness and rich attributes, and can meet actual needs.

Description

Technical Field

[0001] The present invention relates to the technical field of data fusion, and in particular to a wetland resource data fusion method based on typical ecological environment element information. Background Art

[0002] Wetland resource spatial data is a digital carrier that truly reflects the quantity, quality and spatial distribution of wetland resources. It is the data basis for the protection, utilization and management of wetland resources. The existing wetland resource spatial data include the results of the first national water conservancy census, the second wetland resource survey, and the third national land survey. The above three types of survey results all contain wetland resource spatial data, but there are large differences between the data. The main reason is that there are differences in wetland connotations, survey standards, survey accuracy, timeliness, etc., and they cannot truly reflect the current status of wetland resources.

[0003] The wetland connotation of the second wetland resource survey results is consistent with the actual wetland management connotation, but the survey accuracy is insufficient, the survey timeliness is poor, and there is a big difference with the current wetland status; the wetland survey standards and survey accuracy in the third national land survey results are higher, and the timeliness is better, but the wetland connotation is inconsistent with the actual wetland management connotation, and only includes wetlands in a narrow sense, which cannot meet the needs of compiling wetland resource bulletins, wetland classification, protection and restoration, etc.; the existing wetland resource spatial data cannot truly reflect the current status of wetland resources, and cannot simultaneously meet the wetland resource management and application needs with complete wetland connotation, high survey standards, high survey accuracy, and good timeliness.

[0004] Therefore, there is an urgent need for a wetland resource data fusion method that can fuse multi-source wetland data to improve data accuracy and meet timeliness requirements. Summary of the invention

[0005] Based on this, it is necessary to provide a wetland resource data fusion method based on typical ecological environmental element information to address the above technical issues.

[0006] A method for fusion of wetland resource data based on typical ecological environment element information, comprising the following steps: acquiring multi-source wetland resource data and performing data standardization processing to construct a data resource pool of wetland resources, wherein the data resource pool includes a number of standard wetland information; extracting element information of a typical ecological environment of wetland resources from the data resource pool based on the number of standard wetland information, wherein the element information includes natural elements and man-made elements; constructing matching determiners of typical ecological environment elements respectively according to the expression form of the element information, comprising: determining the expression form of the element information, wherein the expression form includes vector information and attribute information; constructing a spatial matching determiner of typical ecological environment elements according to the vector information; constructing an attribute matching determiner of typical ecological environment elements according to the attribute information; wherein the matching modes of the spatial matching determiner include point-surface matching, line-surface matching and surface-surface matching;

[0007] The point-to-surface matching includes: assuming that the point element is A=(x a ,y a ), the surface element is B = {(x1,y1),(x2,y2)...(x N ,y N )}, a closed surface is formed by N sample points, then the centroid coordinates are:

[0008]

[0009] The centroid distance between point feature A and surface feature B is:

[0010]

[0011] Where N is the number of sample points, (x i ,y i ) is the coordinate of the i-th sample point, with the centroid Centroid as the endpoint, and the distance from point A to the boundary point of the surface element is Dis B-centroid , the formula for determining whether point and surface features match is:

[0012]

[0013] In the formula, γ A is the spatial error of the point element, γ B is the spatial error of the surface element;

[0014] The line-surface matching includes: assuming that the linear element is L C ={(x1,y1),(x2,y2),(x3,y3)...(x M ,y M )}, consisting of M points, with a spatial error of γ C, the surface element set D = {D1, D2, D3...D K ,} is composed of K discrete surface vectors, and the spatial error is γ D ; Use the Dilation function and Erosion function to perform morphological closing operations, merge the surface element set D into a single surface element D′ in batches, and then convert the surface element D′ into a linear element L in batches. D , for the linear element L C and L D Resampling is performed to make the number of sample points consistent and the sample points evenly distributed. The linear features after resampling are:

[0015]

[0016] Among them, P is the number of sample points after resampling, and the length of the line segment between adjacent sample points is fixed. The linear feature L is calculated. C With L D Distance:

[0017] Dis=max||(x Ci ,y Ci )-(x Di ,y Di )||,i∈{1,2,3...P}

[0018]

[0019] Among them, ||·|| represents the Euclidean distance, Dis represents the distance between line vectors;

[0020] The surface matching includes: using the spot overlapping matching method, assuming that the surface element L E and L F The areas are S E and S F , use the Intersect function to perform spatial superposition in batches, and get the overlapping area as L Z , the overlapping area is S Z , the formula for determining whether a polygon element matches is:

[0021]

[0022] Where ε1 is the first threshold;

[0023] The matching methods of the attribute matching determiner include template matching, text similarity matching and numerical matching; setting the matching priority of the element information, and performing multi-level entity matching on the standard wetland information according to the matching priority; based on the matching relationship between the standard wetland information, performing information fusion processing of the standard wetland information to obtain a wetland resource database.

[0024] In one of the embodiments, the data standardization process includes spatialization, coordinate conversion and format unification.

[0025] In one embodiment, the natural elements include the scope of the ecosystem, the scope of the water area, the scope of the vegetation, the type of natural landscape, the number of plant species, the wetland area, the vegetation area and the water area; the man-made elements include the wetland name, location, development and utilization, protection type and ownership.

[0026] In one embodiment, the template matching includes: mapping the attribute text in the standard wetland information to the attribute standard matching template; replacing the original attribute text with the standard text, and determining whether the attributes of the standard wetland information are consistent. If they are consistent, the matching is successful, otherwise the matching fails; the text similarity matching includes: extracting the attribute information in the standard wetland information, which are character string one and character string two respectively; segmenting the character string one and character string two and eliminating common words, and counting the word frequency to construct a sparse vector, each dimension corresponds to a word, and constructs a word vector α={α1,α2,…α N} and β={β1,β2,β3…β N}, where α i , β i are the word frequencies of the words respectively; the cosine similarity of the sparse vector is calculated, and the formula is:

[0027]

[0028] Among them, ||α|| and ||β|| represent the modulus of vectors α and β, α·β represents the dot product of vectors α and β, cos(θ) represents cosine similarity, and the formula for determining whether the text matches is:

[0029]

[0030] Wherein, ε2 is the second threshold value; the numerical matching is performed by matching the number of wetland plant species in the standard wetland information, and a successful match is considered if the numerical values ​​are of the same order of magnitude.

[0031] In one of the embodiments, the matching priority of the element information is set, and the multi-level entity matching of the standard wetland information is performed according to the matching priority, including: setting the matching priority of the element information according to the importance of the element information, and the priority includes primary, secondary and tertiary levels; selecting objects in the complete wetland resource ecosystem in the data resource pool as indexes, and selecting objects whose spatial accuracy and timeliness meet preset standards as spatial bases; combining the spatial matching determiner and the attribute matching determiner to match the element information with the first matching priority, if the match is successful, it is determined to be the same wetland resource entity, and the information is associated through coding and hanging; if any element information If the match fails, it will be judged as a failure and included in the supplementary survey data set; the spatial matching determiner and the attribute matching determiner are combined to match the element information with a matching priority of level two. If the match is successful, the corresponding standard wetland information is judged to be consistent, and the standard wetland information with higher accuracy is retained; if any match fails, real-time corrections are made based on the correct standard wetland information; the spatial matching determiner and the attribute matching determiner are combined to match the element information with a matching priority of level three. If the match is successful, the corresponding standard wetland information is judged to be consistent, and the standard wetland information with higher accuracy is retained; if any match fails, the standard wetland information is marked as pending for confirmation for supplementary survey.

[0032] In one of the embodiments, based on the matching relationship between the standard wetland information, information fusion processing of the standard wetland information is performed to obtain a wetland resource database, including: according to the matching relationship between the standard wetland information, the standard wetland information with the highest spatial accuracy and the best timeliness is selected as the spatial base, and combined with remote sensing images, geometric information fusion of patch cutting and entity merging is performed to assign the same entity code to multiple patches of the same wetland resource object; according to the entity code, attribute fusion operation is performed on the multi-source attribute information of the standard wetland information, and the fused multi-source attribute information is integrated into the same spatial data to obtain a wetland resource fusion data set; the wetland resource fusion data set is combined with the supplementary survey data set to obtain a fused data set; the patches in the fused data set are corrected through topology check and quality check; unique identification codes are compiled for the patches in the corrected fused data set to obtain a wetland resource database.

[0033] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: by acquiring multi-source wetland resource data and performing data standardization, a number of standard wetland information is obtained, and a data resource pool for wetland resources is constructed; based on a number of standard wetland information, element information of a typical ecological environment of wetland resources is extracted from the data resource pool, and the element information includes natural elements and man-made elements; according to the expression form of the element information, matching determiners of typical ecological environment elements are respectively constructed; the matching priority of the element information is set, and multi-level entity matching of the standard wetland information is performed according to the matching priority, so as to realize the fusion and enhancement of geometric and attribute information between different data, and obtain wetland resource spatial data with higher accuracy and more comprehensive attribute information; based on the matching relationship between standard wetland information, information fusion processing of standard wetland information is performed to obtain a wetland resource database, and the fused data has high accuracy, good timeliness and rich attributes, forming a complete and accurate wetland resource spatial data set, which greatly reduces the repetitive workload of wetland surveys, saves a lot of manpower and financial resources, and facilitates wetland resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of a wetland resource data fusion method based on typical ecological environment element information in one embodiment;

[0035] Figure 2 A flowchart of building a data resource pool in an embodiment. DETAILED DESCRIPTION

[0036] Before describing the specific embodiments of the present invention, the overall concept of the present invention is described as follows:

[0037] The present invention is mainly developed based on the wetland resource data fusion process of the ecological environment. Currently, the timeliness of wetland resource data is poor and the accuracy of wetland data cannot be guaranteed, which cannot meet actual needs.

[0038] Therefore, the present invention proposes a wetland resource data fusion method based on typical ecological environment element information, by acquiring multi-source wetland resource data and performing data standardization, a number of standard wetland information is obtained, and a data resource pool of wetland resources is constructed; based on the number of standard wetland information, element information of the typical ecological environment of wetland resources is extracted in the data resource pool, and the element information includes natural elements and artificial elements; according to the expression form of the element information, matching determiners of typical ecological environment elements are respectively constructed; the matching priority of the element information is set, and multi-level entity matching of the standard wetland information is performed according to the matching priority, which can realize the fusion and enhancement of geometric and attribute information between different data, and obtain wetland resource spatial data with higher accuracy and more comprehensive attribute information; based on the matching relationship between standard wetland information, information fusion processing of standard wetland information is performed to obtain a wetland resource database, and the fused data has high accuracy, good timeliness and rich attributes, forming a complete and accurate wetland resource spatial data set, which greatly reduces the repetitive workload of wetland surveys, saves a lot of manpower and financial resources, and facilitates wetland resource management.

[0039] After introducing the overall concept of the present invention, in order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail by specific implementation methods in combination with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] In one embodiment, Figure 1 As shown, a wetland resource data fusion method based on typical ecological environment element information is provided, including the following steps:

[0041] Step S101, obtain multi-source wetland resource data and perform data standardization processing to build a data resource pool of wetland resources, which includes a number of standard wetland information.

[0042] Specifically, wetland resource data from multiple management departments such as natural resources, forestry, and water conservancy are collected and summarized. Typical data include the results of the third national land survey and annual changes, the results of the second wetland resources survey, the results of forest and grassland wetland surveys and monitoring, etc. In order to facilitate subsequent data processing and aggregation, it is necessary to standardize multi-source data, including spatialization, coordinate conversion, format unification, etc., to obtain multi-source standard wetland information and build a data resource pool for wetland resources.

[0043] like Figure 2As shown in the figure, when constructing a data resource pool, wetland resource data is obtained through multiple data sources. The wetland resource data includes point, line, surface spatial data, visualization platform display data, spreadsheets, electronic documents, pictures and other data forms. By performing one or more processing such as coordinate conversion, data capture, format recognition, spatialization, information extraction, and picture recognition on the above data, standard wetland information is finally obtained and used to construct a data resource pool for wetland resources. The data resource pool includes: spatial distribution information of wetland resources, monitoring information of wetland ecological sample sites, protection scope of wetland resources, and special information on wetland resources.

[0044] Step S102, based on some standard wetland information, extract element information of typical ecological environment of wetland resources from the data resource pool, the element information includes natural elements and artificial elements.

[0045] Specifically, the element information of wetland resources involves multiple schemes, which together constitute the wetland ecosystem and determine the structure, function and biodiversity of the wetland. Specifically, it includes hydrological conditions, soil characteristics, climatic factors, biological composition, topography and human factors and other levels. In order to facilitate quantitative processing and comparison, this application is based on the obtained information of several standard wetland resources, and extracts the natural elements and human elements of the typical ecological environment of wetland resources in the data resource pool to facilitate subsequent data processing.

[0046] Among them, natural elements include ecosystem scope, water area, vegetation scope, natural landscape type, number of plant species, wetland area, vegetation area and water area; human elements include wetland name, location, development and utilization, protection type and ownership.

[0047] Step S103, constructing matching determiners for typical ecological environment elements respectively according to the expression forms of element information.

[0048] Specifically, since the element information of standard wetland information exists in different forms, such as spatial vector form and attribute information form, as shown in Table 1, a matching determiner for typical ecological environmental elements can be constructed according to the form of expression of the element information to facilitate subsequent entity matching of multi-source data.

[0049] Table 1. Relevant information on matching of typical ecological environment elements

[0050]

[0051] Among them, step S103 includes: determining the expression form of element information, which includes vector information and attribute information; constructing a spatial matching judgement of typical ecological environment elements according to the vector information; and constructing an attribute judgement of typical ecological environment elements according to the attribute information.

[0052] Specifically, the expression forms of feature information include spatial vectors and attribute information. Spatial vectors include shp and gdb files in the form of points, lines and surfaces, and attribute information includes dbf, excel, word and other format files in the form of strings and double-precision numbers. Therefore, the selected feature information can be standardized according to different expression forms to carry out feature matching and comparative analysis; among them, according to the vector information in the feature information, a spatial matching judge of a typical ecological environment is constructed, and according to the attribute information in the feature information, an attribute matching judge of a typical ecological environment is constructed, thereby achieving matching of feature information in different expression forms.

[0053] The matching methods of the spatial matching determiner include point-surface matching, line-surface matching and surface-surface matching; wherein, the point-surface matching includes: assuming that the point element is A=(x a ,y a ), the surface element is B = {(x1,y1),(x2,y2)...(x N ,y N )}, a closed surface is formed by N sample points, then the centroid coordinates are:

[0054]

[0055] The centroid distance between point feature A and surface feature B is:

[0056]

[0057] Where N is the number of sample points, (x i ,y i ) is the coordinate of the i-th sample point, with the centroid Centroid as the endpoint, and the distance from point A to the boundary point of the surface element is Dis B-centroid , the formula for determining whether point and surface features match is:

[0058]

[0059] In the formula, γ A is the spatial error of the point element, γ B is the spatial error of the surface element;

[0060] Line-surface matching includes: Assuming the linear feature is L C ={(x1,y1),(x2,y2),(x3,y3)...(x M ,y M )}, consisting of M points, with a spatial error of γ C , the surface element set D = {D1, D2, D3...D K,} is composed of K discrete surface vectors, and the spatial error is γ D ; Use the Dilation function and Erosion function to perform morphological closing operations, merge the surface element set D into a single surface element D′ in batches, and then convert the surface element D′ into a linear element L in batches. D , for the linear element L C and L C Resampling is performed to make the number of sample points consistent and the sample points evenly distributed. The linear features after resampling are:

[0061]

[0062] Among them, P is the number of sample points after resampling, and the length of the line segment between adjacent sample points is fixed. The linear feature L is calculated. C With L D Distance:

[0063] Dis=max||(x Ci ,y Ci )-(x Di ,y Di )||,i∈{1,2,3...P}

[0064]

[0065] Among them, ||·|| represents the Euclidean distance, Dis represents the distance between line vectors;

[0066] The surface matching includes: using the overlapping matching method, assuming that the surface element L E and L F The areas are S E and S F , use the Intersect function to perform spatial superposition in batches, and get the overlapping area as L Z , the overlapping area is S Z , the formula for determining whether a polygon element matches is:

[0067]

[0068] Where ε1 is the first threshold.

[0069] Specifically, since standard wetland information from multiple sources has differences in scale, accuracy, geometry, etc., it can be divided into three spatial matching methods: point-to-surface matching, line-to-surface matching, and surface-to-surface matching according to the geometric form of the matching object.

[0070] Among them, the methods for matching point and surface elements include judging inclusion, centroid distance, etc., but due to the influence of data accuracy, there may be a situation where the points and surfaces do not intersect and miss the match. Therefore, this application adopts an improved centroid distance matching method, through which it can be determined whether the point element and the surface element are the same object, and entity matching can also be achieved for the same object that is not contained in the space.

[0071] Line and surface feature matching methods include line-surface intersection method, ray intersection method, geometric topology matching, etc. In wetland resource data, continuous linear features are rivers, ditches, etc., but they are blocked by bridges and trees, and surface rivers, ditches, etc. are interrupted. This application adopts a method that combines morphological centerline Frechet distance matching, builds a Python environment and calls the Arcpy toolkit, uses the Dilation expansion function and Erosion corrosion function to perform morphological closing operations, and batch-automatically merges surface feature sets into single linear features, converting the line and surface feature matching problem into a line-line matching problem, and matching line and surface features based on the distance between the current vectors.

[0072] The main methods of face-to-face matching include: overlap, shape factor, position similarity, etc. In wetland resource data, face elements are types such as reservoirs and ponds. In the actual situation of multi-source wetland resource data fusion, the matching method of patch overlap is better. This application adopts the patch overlap matching method, builds a Python environment and calls the Intersect function in the Arcpy toolkit to achieve batch automated spatial superposition, obtain the overlapping area, and use it to determine whether the face elements match.

[0073] Among them, the matching methods of the attribute matching determiner include: template matching, text similarity matching and numerical matching; among them, template matching includes: mapping the attribute text in the standard wetland information to the attribute standard matching template; using the standard text to replace the original attribute text, and determining whether the attributes of the standard wetland information are consistent. If they are consistent, the matching is successful, otherwise the matching fails;

[0074] Among them, text similarity matching includes: extracting attribute information from standard wetland information, which are string one and string two respectively; segmenting string one and string two and removing common words, and counting word frequencies to construct sparse vectors, each dimension corresponds to a word, and constructing word vectors α={α1,α2,…α N} and β={β1,β2,β3…β N}, where α i , β i etc. are the word frequencies of the words respectively; the cosine similarity of the sparse vector is calculated using the formula:

[0075]

[0076] Among them, ||α|| and ||β|| represent the modulus of vectors α and β, α·β represents the dot product of vectors α and β, cos(θ) represents cosine similarity, and the formula for determining whether the text matches is:

[0077]

[0078] Where ε2 is the second threshold; numerical matching is performed by matching the number of wetland plant species in the standard wetland information, and the matching is successful if the numerical values ​​are of the same order of magnitude.

[0079] Specifically, there are three ways of matching using the attribute matching judger: template matching, text similarity matching, and value matching.

[0080] Among them, template matching is aimed at enumerable attribute information such as natural landscape, protection type, ownership and development and utilization, combined with standard noun expressions in national or industry standards and specifications, to develop attribute matching standard templates, as shown in Table 2. The attribute text in the multi-source wetland resource data is mapped to the attribute standard matching template, and the standard text is used to replace the original attribute text to determine whether the attributes of the multi-source data are consistent. If they are consistent, the matching is successful, otherwise the matching fails.

[0081] Table 2 Attribute matching standard template (partial)

[0082] Natural landscape types Protection Type Ownership Development and Utilization Reservoir water surface National Parks State ownership Plantation River surface Nature Reserve State-owned use rights Animal Husbandry Lake surface Wetland Park Collective ownership Travel and Leisure Pond water surface Wetland Nature Reserve Collective use rights Water source ... ... ...

[0083] According to the classification of land survey work stipulated in the "Technical Regulations for the Third National Land Survey" (TD / T 1055 2019), it is used as the standard description of the natural landscape of wetland resources; according to the "Guiding Opinions on Establishing a Natural Protection Area System with National Parks as the Main Body" issued by the State Council, the natural protection area system is used as the standard description of the type of wetland resource protection; according to the "Land Administration Law of the People's Republic of China", land ownership is divided into two categories: state-owned and collective, and divided into ownership and use rights according to actual use. According to the classification of wetland utilization methods in the "National Wetland Resources Survey Technical Regulations (Trial)", it is used as the standard description of wetland resource development and utilization.

[0084] Text similarity matching is for non-enumerable attribute information such as wetland names and addresses. Text matching can be performed according to the process of word segmentation, elimination of common words, vector construction, and cosine similarity calculation. The attribute information in the standard wetland information is extracted, which are strings A and B respectively. The open source HanLP is used for word segmentation and elimination of common words. Common words include high-frequency words such as wetlands, parks, lakes, and reservoirs. The word frequency is counted to construct a sparse vector. Each dimension corresponds to a word, and its value is the number of times the word appears. The cosine similarity of the coefficient vector is calculated as the text similarity measurement criterion and used for text matching.

[0085] Numerical matching is used for numerical attribute information such as plant species and wetland area. Since wetland area, water area and vegetation area are determined by spatial range and reflected in spatial matching, they are no longer used as the main matching information. The main matching is the number of wetland plant species in multi-source standard wetland information. If the two values ​​are of the same order of magnitude, the match is considered successful.

[0086] Step S104, setting the matching priority of the element information, and performing multi-level entity matching on the standard wetland information according to the matching priority.

[0087] Specifically, the matching priority is determined according to the importance of the element information. The ecosystem scope and natural landscape are the most typical manifestations of the wetland resource ecosystem, water areas and vegetation are important elements for maintaining wetland functions, and wetland names and locations are important characteristics artificially given to wetlands. Multi-level entity matching is performed on the standard wetland information in turn according to the determined matching priority.

[0088] Among them, step S104 includes: setting the matching priority of the element information according to the importance of the element information, and the priority includes primary, secondary and tertiary levels; selecting objects in the complete wetland resource ecosystem in the data resource pool as indexes, and selecting objects whose spatial accuracy and timeliness meet the preset standards as spatial bases; combining the spatial matching determiner and the attribute matching determiner to match the element information with the first matching priority, if the match is successful, it is determined to be the same wetland resource entity, and the information is associated through coding and hanging; if any element information fails to match, it is determined to be a failure and included in the supplementary survey data set. ; Combine the spatial matching determiner and the attribute matching determiner to match the feature information with the second level matching priority. If the match is successful, the corresponding standard wetland information is determined to be consistent, and the standard wetland information with higher accuracy is retained; if any match fails, real-time corrections are made based on the correct standard wetland information; Combine the spatial matching determiner and the attribute matching determiner to match the feature information with the third level matching priority. If the match is successful, the corresponding standard wetland information is determined to be consistent, and the standard wetland information with higher accuracy is retained; if any match fails, the standard wetland information is marked as pending for supplementary investigation.

[0089] Specifically, the matching priority of element information is shown in Table 3. When performing entity matching, objects that can fully describe the wetland resource ecosystem in multi-source data (such as the results of the second wetland resource survey, with complete wetland ecosystems such as reservoirs and rivers as the objects) are selected as indexes, and objects with the highest spatial accuracy and the best timeliness (such as the results of the third national land survey and change survey, with the highest spatial accuracy and updated in real time every year) are selected as spatial backgrounds. Element information with missing data does not participate in matching. Multi-level entity matching is divided into three levels, including initial matching, re-matching and information verification.

[0090] Table 3. Information matching priority of typical ecological environment elements

[0091]

[0092]

[0093] When performing the initial matching, the spatial and attribute matching judgers are used to match the feature information with priority 1 in the multi-source wetland resource data. If all the matches are successful, they are determined to be the same wetland resource entity, and the association is achieved through coding, so as to facilitate the fusion of multi-source data according to the associated coding; if any of the matches fail, it is determined to be a judgment failure and included in the supplementary survey data set.

[0094] When rematching, the spatial and attribute matching judgers are used to match the feature information with priority 2 in the multi-source wetland resource data. If all matches are successful, it is determined that the wetland resource information is consistent, and the feature information with higher accuracy is selected; if any match fails, it is necessary to combine remote sensing images and thematic data to select the correct standard wetland information for real-time correction.

[0095] When verifying information, the spatial and attribute matching determiners are used to match the feature information with priority 3 in the multi-source wetland resource data. If all matches are successful, it is determined that the wetland resource information is consistent, and the feature information with higher accuracy is selected; if any match fails, the feature information is marked as pending confirmation, so that a supplementary investigation can be carried out in the future based on the pending confirmation mark combined with thematic materials or field verification.

[0096] For wetland resource patches that failed to match, records will be kept and supplementary surveys can be conducted through in-house remote sensing interpretation and field survey verification to obtain supplementary survey data sets.

[0097] Step S105, based on the matching relationship between the standard wetland information, information fusion processing of the standard wetland information is performed to obtain a wetland resource database.

[0098] Specifically, information fusion processing is carried out based on the matching relationship between multi-source standard wetland information. By fusing multi-source data of standard wetland information, more accurate and complete wetland information is obtained to form a wetland resource database, which can provide real-time and continuous data for wetland protection and management.

[0099] Among them, step S105 includes: according to the matching relationship between the standard wetland information, the standard wetland information with the highest spatial accuracy and the best timeliness is selected as the spatial base, and the geometric information fusion of patch cutting and entity merging is performed in combination with remote sensing images, and the same entity code is assigned to multiple patches of the same wetland resource object; according to the entity code, the attribute fusion operation is performed on the multi-source attribute information of the standard wetland information, and the fused multi-source attribute information is integrated into the same spatial data to obtain a wetland resource fusion data set; the wetland resource fusion data set is combined with the supplementary survey data set to obtain a fused data set; the patches in the fused data set are corrected through topological inspection and quality inspection; and a unique identification code is compiled for the patches in the corrected fused data set to obtain a wetland resource database.

[0100] Specifically, according to the matching relationship between multi-source wetland data, information fusion processing including geometric information fusion and attribute information fusion is carried out. When performing geometric information fusion, the data with the highest spatial accuracy and the best timeliness (such as the results of the third national land survey and change survey, which have the highest spatial accuracy and are updated in real time every year) are used as the spatial base, combined with high-definition remote sensing images, geometric information fusion such as spot cutting and entity merging is carried out, and multiple spots of the same wetland resource object are given the same entity code.

[0101] When performing attribute information fusion, the multi-source attribute information is subjected to field replacement, recalculation, field merging and other operations, and the fused attribute information is integrated into the same data space. Through geometric information fusion and attribute information fusion, a relatively accurate and complete wetland resource fusion dataset is obtained.

[0102] Physically merge the wetland resource fusion dataset and the supplementary survey dataset, and unify the coordinate system, storage format, attribute fields and other contents; conduct topological checks and correct erroneous patches such as self-intersection and overlap; conduct quality checks and correct patches that do not meet the spatial and attribute accuracy standards; compile unique identification codes for wetland resource patches and summarize them to form a complete wetland resource database.

[0103] In this embodiment, by acquiring multi-source wetland resource data and performing data standardization, a number of standard wetland information is obtained, and a data resource pool for wetland resources is constructed; based on the number of standard wetland information, element information of a typical ecological environment of wetland resources is extracted from the data resource pool, and the element information includes natural elements and man-made elements; according to the expression form of the element information, matching determiners of typical ecological environment elements are respectively constructed; the matching priority of the element information is set, and multi-level entity matching of the standard wetland information is performed according to the matching priority, so as to realize the fusion and enhancement of geometric and attribute information between different data, and obtain wetland resource spatial data with higher accuracy and more comprehensive attribute information; based on the matching relationship between the standard wetland information, the information fusion processing of the standard wetland information is performed to obtain a wetland resource database, and the fused data has high accuracy, good timeliness and rich attributes, forming a relatively complete and accurate wetland resource spatial data set, which greatly reduces the repetitive workload of wetland surveys, saves a lot of manpower and financial resources, and facilitates wetland resource management.

[0104] Obviously, those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a computer storage medium (ROM / RAM, magnetic disk, optical disk) and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Therefore, the present invention is not limited to any specific combination of hardware and software.

[0105] The above contents are further detailed descriptions of the present invention in combination with specific implementation methods, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A wetland resource data fusion method based on typical ecological environment element information, characterized in that: The following steps are involved: Acquire multi-source wetland resource data and perform data standardization processing to build a wetland resource data pool, wherein the data resource pool includes a number of standard wetland information; Based on the plurality of standard wetland information, extracting element information of a typical ecological environment of wetland resources in the data resource pool, wherein the element information includes natural elements and human elements; According to the expression form of the element information, respectively constructing a matching determiner of a typical ecological environment element, including: determining the expression form of the element information, the expression form including vector information and attribute information; constructing a spatial matching determiner of a typical ecological environment element according to the vector information; constructing an attribute matching determiner of a typical ecological environment element according to the attribute information; wherein the matching methods of the spatial matching determiner include point-surface matching, line-surface matching, and surface-surface matching; The point-surface matching includes: Point element is A=(x a ,y a ), the surface element is B = {(x1,y1),(x2,y2)...(x N ,y N )}, a closed surface is formed by N sample points, then the centroid coordinates are: The centroid distance between point feature A and surface feature B is: Where N is the number of sample points, (x i ,y i ) is the coordinate of the i-th sample point, with the centroid Centroid as the endpoint, and the distance from point A to the boundary point of the surface element is Dis B-centroid , the formula for determining whether point and surface features match is: In the formula, γ A is the spatial error of the point element, γ B is the spatial error of the surface element; The line-surface matching includes: Linear feature is L C ={(x1,y1),(x2,y2),(x3,y3)...(x M ,y M )}, consisting of M points, with a spatial error of γ C , the surface element set D = {D1, D2, D3...D K ,} is composed of K discrete surface vectors, and the spatial error is γ D ; The Dilation function and Erosion function are used to perform morphological closing operations, and the surface element set D is merged into a single surface element D′ in batches, and then the surface element D′ is converted into a linear element L in batches. D , for the linear element L C and L D Resampling is performed to make the number of sample points consistent and the sample points evenly distributed. The linear features after resampling are: Among them, P is the number of sample points after resampling, and the length of the line segment between adjacent sample points is fixed. The linear feature L is calculated. C With L D Distance: Dis=max||(x Ci ,y Ci )-(x Di ,y Di )||,i∈{1,2,3...P} Among them, ||·|| represents the Euclidean distance, Dis represents the distance between line vectors; The face-to-face matching includes: Using the patch overlapping matching method, the surface element L E and L F The areas are S E and S F , use the Intersect function to perform spatial superposition in batches, and get the overlapping area as L Z , the overlapping area is S Z , the formula for determining whether a polygon element matches is: Where ε1 is the first threshold; The matching methods of the attribute matching determiner include template matching, text similarity matching and value matching; Setting a matching priority of element information, and performing multi-level entity matching on the standard wetland information according to the matching priority; Based on the matching relationship between the standard wetland information, information fusion processing of the standard wetland information is performed to obtain a wetland resource database.

2. The wetland resource data fusion method based on typical ecological environment element information according to claim 1 is characterized in that: The data standardization process includes spatialization, coordinate conversion and format unification.

3. The wetland resource data fusion method based on typical ecological environment element information according to claim 1 is characterized in that: The natural elements include the scope of the ecosystem, the scope of the water area, the scope of the vegetation, the type of natural landscape, the number of plant species, the wetland area, the vegetation area and the water area; the man-made elements include the wetland name, location, development and utilization, protection type and ownership.

4. The wetland resource data fusion method based on typical ecological environment element information according to claim 1 is characterized in that: The template matching includes: Mapping the attribute text in the standard wetland information to the attribute standard matching template; The original attribute text is replaced by the standard text to determine whether the attributes of the standard wetland information are consistent. If they are consistent, the match is successful, otherwise the match fails; The text similarity matching includes: Extracting attribute information from the standard wetland information, which are character string one and character string two; The string 1 and string 2 are segmented and common words are removed, and the word frequency is counted to construct a sparse vector. Each dimension corresponds to a word, and the word vector α={α1,α2,…α N } and β={β1,β2,β3…β N }, where α i , β i are the frequency of words respectively; The cosine similarity of the sparse vector is calculated using the formula: Among them, ||α|| and ||β|| represent the modulus of vectors α and β, α·β represents the dot product of vectors α and β, cos(θ) represents cosine similarity, and the formula for determining whether the text matches is: Where ε2 is the second threshold; The numerical matching is performed by matching the number of wetland plant species in the standard wetland information, and a successful match is considered to be successful if the numerical values ​​are of the same order of magnitude.

5. The wetland resource data fusion method based on typical ecological environment element information according to claim 1 is characterized in that: The setting of the matching priority of the element information and performing multi-level entity matching on the standard wetland information according to the matching priority include: Setting a matching priority of the element information according to the importance of the element information, wherein the priority includes primary, secondary and tertiary levels; Selecting objects in the complete wetland resource ecosystem in the data resource pool as indexes, and selecting objects whose spatial accuracy and timeliness meet preset standards as spatial bases; Combined with the spatial matching determiner and the attribute matching determiner, the element information with the first-level matching priority is matched. If the match is successful, it is determined to be the same wetland resource entity, and the information is associated through coding and hanging; if any element information fails to match, it is determined to be a failure and included in the supplementary survey data set; Combined with the spatial matching determiner and the attribute matching determiner, the element information with the second-level matching priority is matched. If the match is successful, the corresponding standard wetland information is determined to be consistent, and the standard wetland information with higher accuracy is retained; if any match fails, it is corrected in real time according to the correct standard wetland information; Combined with the spatial matching determiner and the attribute matching determiner, the feature information with the third matching priority is matched. If the match is successful, the corresponding standard wetland information is judged to be consistent, and the standard wetland information with higher accuracy is retained; if any match fails, the standard wetland information is marked as pending for confirmation for additional investigation.

6. The wetland resource data fusion method based on typical ecological environment element information according to claim 5 is characterized in that: The information fusion processing of the standard wetland information is performed based on the matching relationship between the standard wetland information to obtain a wetland resource database, including: According to the matching relationship between the standard wetland information, the standard wetland information with the highest spatial accuracy and the best timeliness is selected as the spatial base, and combined with remote sensing images, geometric information fusion of image patch cutting and entity merging is performed to assign the same entity code to multiple images of the same wetland resource object; According to the entity coding, an attribute fusion operation is performed on the multi-source attribute information of the standard wetland information, and the fused multi-source attribute information is integrated into the same spatial data to obtain a wetland resource fusion data set; Combining the wetland resource fusion dataset with the supplementary survey dataset to obtain a fusion dataset; Correcting the spots in the fused data set by topology checking and quality checking; Unique identification codes are compiled for the patches in the corrected fused data set to obtain a wetland resource database.

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