FME-based adjacent pattern spot fusion method and system

Abstract

The invention discloses an FME-based adjacent pattern spot fusion method and system, and relates to the technical field of adjacent pattern spot fusion, and the method comprises the steps of data processing, parameter setting and rule configuration, adjacent pattern spot fusion execution, result verification and output and feedback and optimization, fusion standard and rule determination, index system unified evaluation, and spatial relationship optimization processing. Manual intervention is reduced through the automatic working process, and the processing efficiency is improved; the invention further provides an adjacent pattern spot fusion system based on FME, the adjacent pattern spot fusion system comprises a data preprocessing module, a data input module, a parameter setting and rule configuration module, a comprehensive correlation index calculation module, a pattern spot fusion module and a result verification and output module, and the adjacent pattern spot fusion method based on FME is executed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of adjacent patch fusion, and particularly relates to an adjacent patch fusion method and system based on FME. BACKGROUND

[0002] Adjacent patch fusion exhibits significant differences in different application fields, thereby putting forward different requirements for the selection of data processing methods and algorithms. For example, in land use research, patch fusion mainly focuses on the accurate division and quantity statistics of land types to ensure the rational allocation and effective use of land resources; in environmental monitoring, patch fusion emphasizes the spatial correlation and temporal variation between different environmental elements, aiming to improve the accuracy and timeliness of monitoring data to help decision-makers respond to environmental problems in a timely manner; and in urban planning, patch fusion focuses on integrating the spatial layout and functional zoning of urban elements to promote the sustainable development and optimal resource allocation of cities.

[0003] Currently, the fusion method of adjacent patches mainly relies on traditional spatial analysis and data processing techniques, such as buffer analysis, spatial connection and topological processing. These methods can achieve the fusion of adjacent patches in simple scenarios, but their processing efficiency and accuracy are obviously insufficient when facing complex index weights, irregular boundaries and diversified patch types. First, existing patch fusion methods usually require high human intervention, resulting in a tedious and time-consuming processing process, low automation, and difficulty in flexibly adapting to the needs of different application scenarios in a dynamically changing environment, limiting the efficiency and real-time performance of data processing. Second, the standards and rules of patch fusion differ significantly in different application scenarios, and traditional methods fail to systematically consider these differences, resulting in unclear fusion standards and rules, which leads to the inconsistency between the fusion results and the actual application needs. The quality standards, precision indicators and applicable rules required for fusion in different fields lack unified definitions, and the achievements of different projects are difficult to effectively compare and integrate, increasing the difficulty of data use. Third, the current patch fusion method does not establish a unified index system to evaluate the fusion effect. In the case of multiple indicators, there is a lack of effective weight weighting mechanism, and a unified index system, making it difficult to quantify and analyze the relative importance and influence of each element. Finally, the spatial relationship of adjacent patches is complex, with irregular boundaries and diverse morphologies. Existing spatial relationship processing methods cannot effectively capture the spatial interaction and influence between different elements, and the spatial relationship processing capability is insufficient, reducing the reliability and practicality of the fusion results.

[0004] In view of the above-mentioned deficiencies of the adjacent patch fusion method in terms of automation, standardization, index system and spatial relationship processing, the present application proposes an adjacent patch fusion method and system based on FME, which improves the efficiency, accuracy and adaptability of fusion to better meet the diverse needs of various fields. SUMMARY

[0005] To overcome the deficiencies of the prior art, the purpose of the present application is to provide a FME-based adjacent map patch fusion method and system, which improves the efficiency and accuracy of fusion and provides more reliable data support for geographic information applications.

[0006] In one aspect, the present application provides a FME-based adjacent map patch fusion method, S10, data preparation: according to the business needs of specific industries, collect data sets related to adjacent map patch fusion, and unify, clean and correct the collected data, prepare data for subsequent fusion, i.e. get the surface data of the to-be-fused map patch.

[0007] S20, parameter setting and rule configuration: based on the feature requirements of different map patches in specific business scenarios, set fusion parameters, then quantify key fusion parameters, calculate comprehensive correlation indexes and output.

[0008] The key fusion parameters include the area threshold of the fused map patch, the length of the common boundary line between the fused map patch and the adjacent map patch, whether there is a frame line cutting map patch, and the attribute correlation between the fused map patch and the surrounding map patches.

[0009] S30, execute adjacent map patch fusion: FME automatically applies the parameters and rules set and configured in S20 for fusion operation, and automatically identifies adjacent map patches; at this time, using the powerful stream processing capability of FME, the system can process multiple data sources in parallel, thereby significantly improving the overall processing efficiency, quickly responding to large-scale data fusion operations, and ensuring the efficiency and accuracy of the fusion process.

[0010] S40, result verification and output: multi-dimensional quality verification is performed on the fusion results to evaluate the accuracy and consistency of the fusion results to ensure that the final output meets the expected quality standards. After completing the verification, the system will generate standardized output results, which can be presented in the form of graphic files or database records for subsequent analysis and application.

[0011] S50, feedback and optimization: based on the evaluation of the fusion results and the expected target, collect user feedback, analyze the feedback information, and adjust the parameter setting and rule application if necessary.

[0012] In step S10, according to the business needs of specific industries, collect data sets related to adjacent map patch fusion, and according to the characteristics of different business scenarios, the specific data sets collected are as follows: If it is a reservoir resettlement land acquisition business, collect relevant resettlement land parceling archived results, including CAD files and Excel data results. The data should include the boundaries of the resettlement area, land use, land ownership information, etc., to ensure that the information of the resettlement land can be accurately reflected in the process of polygon fusion.

[0013] If it is a land use business, collect polygon data and attribute tables related to land use. This includes spatial distribution information of different land types such as arable land, forest land, grassland, etc., and their related attribute data such as land use type, land ownership, current use, etc., to reasonably consider the characteristics of various types of land during the fusion process.

[0014] If it is an environmental monitoring business, establish a polygon dataset related to environmental monitoring, focusing on collecting water quality monitoring points, air quality monitoring stations, and their monitoring index data. The integration of the spatial location, monitoring parameters, and historical data of these data will provide the necessary background information for polygon fusion, thereby improving the scientificity and practicality of the data.

[0015] If it is a city planning business, collect city planning related polygon data, including city land zoning, infrastructure layout, green space and public facilities information. At the same time, consider factors such as urban development trends, policies and regulations to ensure that the fused data can support urban development decisions.

[0016] Further, the step S10 of collecting data is unified in format, cleaned and corrected, specifically, the data format is unified, the data cleaning includes de-duplication, handling missing values and outliers, the data correction includes unifying coordinate system, ensuring accuracy and consistency, and finally the data quality is checked, to make full preparation for the subsequent fusion processing.

[0017] The step S20 sets the fusion parameters, when setting these parameters, researchers should first consult the suggestions and opinions of experts in the field to ensure that the set spatial parameters and attribute parameter indicators are reasonable and feasible. The feedback of experts will provide important support for formulating scientific fusion standards, thereby improving the effectiveness and accuracy of the fusion process.

[0018] The following is a specific analysis method and parameter setting for the four main business scenarios: Reservoir resettlement land acquisition: In this scenario, researchers need to focus on the area threshold of the fused polygon, which is recommended to be set at 3.33 square meters, equivalent to 0.01 mu, to ensure that polygons larger than or equal to this threshold can be marked for output, requiring users to complete the relevant attribute information; while small attributeless polygons less than the threshold can be effectively fused by adjacent polygons. The required attribute parameters include land ownership, land type, area, county, township, village group, etc.

[0019] Land use: For land use scenarios, researchers need to analyze the characteristics of different land types and set area thresholds based on specific business scenarios, such as setting 1 mu to exclude irrelevant small plots. If the adjacent polygons have the same land type, the correlation calculated by the attribute index is 1, and subsequent fusion of adjacent polygons can be performed. In addition, attribute correlation should be weighted based on the similarity of land types, and the calculated comprehensive index will be used as the basis for polygon fusion.

[0020] Environmental monitoring: In environmental monitoring, researchers need to focus on the spatial location and attribute information of monitoring points, and set a lower area threshold, such as 0.1 mu, to maintain attention to small monitoring areas. The distance requirement from the monitoring point should be based on the actual situation of the monitoring area to ensure effective integration of monitoring information. Attribute correlation should include environmental data such as water quality and air quality indicators to fully utilize monitoring information during fusion.

[0021] Urban planning: For urban planning, an appropriate area threshold should be set, such as 10 mu, to consider the main functional zoning of urban land. The setting of public boundary length should consider the connection of urban roads, green spaces, and public facilities to ensure reasonable layout of urban space. Attribute correlation should cover urban development indicators, planning use, and status matching to improve the scientific nature of the fusion process.

[0022] Further, the step S20 quantifies the key fusion parameters one by one, specifically, The polygons smaller or larger than or equal to the area threshold are merged into their adjacent polygons, and the selected polygons to be merged depend on the evaluation of the comprehensive correlation index, which is The calculation formula is: ; Where, represents spatial correlation, represents attribute correlation, and are weight coefficients, and satisfy By appropriately adjusting these two weights, researchers can balance the influence of spatial and attribute correlation in fusion decision-making according to specific business needs.

[0023] The spatial correlation is quantified by evaluating the public boundary length, center point distance, or whether it is a frame line cutting polygon. The public boundary length is the length of the common boundary between the merged polygon and the adjacent polygon, and the center point distance measures the distance between the center point of the merged polygon and the center point of the surrounding adjacent polygon.

[0024] Further, in the application scenario of reservoir resettlement land acquisition, the CAD introduces an index of "whether it is a frame line cutting polygon" in the case of frame line cutting in the result archiving. The index indicates whether a polygon is a frame line cutting polygon. If two adjacent polygons are frame line cutting polygons, the fusion of the two polygons is performed in the case of small polygon attribute loss in one of the two polygons, ; wherein, represents a frame line cutting polygon, represents a polygon that is not a frame line cutting polygon.

[0025] Further, the spatial correlation quantification is specifically as follows: If the common boundary length is used as the index, the spatial correlation is quantified using the following formula: ; wherein, is the common boundary line length of the fused polygon and the adjacent polygon, is the maximum common boundary line length of all adjacent polygons. If the distance is used as the index, the spatial correlation is quantified using the following formula: ; The formula ensures that the closer the distance is, the higher the value is, reflecting the strong spatial correlation between the two. Wherein, is the distance between the center point of the fused polygon and the center point of a certain adjacent polygon, is the distance between the center point of the fused polygon and the center point of the nearest adjacent polygon.

[0026] If a certain weight combination of the two is used: ; wherein, , is the corresponding spatial correlation weight value, , is the corresponding weight coefficient, and satisfies .

[0027] Further, the weight calculation of the common boundary length is specifically as follows: the maximum value in N values is obtained: ; The corresponding common boundary line length weight is: ; Wherein, A is a small area of the graph spot to be fused, N is the number of adjacent graph blocks, K is the adjacent graph spot of the graph spot A, and the value of K is from 1 to N, and the corresponding common boundary length is L K .

[0028] Further, the weight of the center point distance is calculated as follows: the minimum value in N values is obtained ; The distance weight of the corresponding center point is ; Wherein, A is a small area of the graph spot to be fused, N is the number of adjacent graph blocks, K is the adjacent graph spot of the graph spot A, and the value of K is from 1 to N, and the corresponding common boundary length is L .

[0029] Further, the attribute correlation covers the relationship between the fused graph spot and the surrounding graph spot in the aspects of land class, right ownership and economic value. Specifically, if the fusion is based on land class, if the land class of the fused graph spot and the surrounding adjacent graph spot is the same, the land class correlation is 1; if the land class is close, the land class correlation can be set as a value close to 1 to reflect the similarity between them. Similarly, when the fusion is based on right ownership, if the right ownership of the fused graph spot and the adjacent graph spot is the same, the right ownership correlation of the two graph spots is 1 to reflect the consistency of the right ownership.

[0030] When the attribute correlation considers the comprehensive influence of multiple attributes, the following formula is used for calculation, and if the attribute correlation is evaluated based on three attributes, the comprehensive attribute correlation evaluation is ; Wherein , and respectively represent the correlation values of the first, second and third attributes; , , are the weight coefficients of the corresponding attributes, and satisfy .

[0031] On the other hand, the present application provides a kind of adjacent graph spot fusion system based on FME, for carrying out the adjacent graph spot fusion method based on FME described, specifically including: Data preprocessing module: This module is responsible for receiving geospatial data from various sources, supporting formats including Shapefile, GDB, CAD, and Excel. The data preprocessing module fully prepares for subsequent adjacent polygon fusion. For different business needs, if the provided data is relatively raw, the module itself may be a system responsible for completing the preliminary processing and standardization of data; if there is already existing polygon data to be fused, the module is mainly used to standardize the corresponding field attributes to meet system requirements. For example, it can convert CAD partitioned data with annotations into GDB data containing attributes. In these converted data, some polygons may have attribute information, while others may not have complete attribute annotations due to CAD partitioning operations. According to business needs, these polygons need to be fused to achieve more accurate spatial data integration.

[0032] Data input module: This module is mainly responsible for selecting polygon data of the to-be-fused polygons processed by the data preprocessing module, providing basic data support for the polygon fusion process. According to specific business needs, this module can flexibly adapt to different data structures and attribute field naming standards. Due to differences in attribute names of polygon data in actual application scenarios, this module allows users to edit and adjust attribute fields to ensure that data input meets the needs of subsequent processing, thereby laying a solid foundation for polygon fusion.

[0033] Parameter setting and rule configuration module: This module allows users to flexibly set fusion parameters and rules according to specific application needs, including selected fusion standards, weight settings, and spatial relationship processing rules. Users can easily adjust parameters through a graphical interface to adapt to polygon fusion needs in different scenarios. In addition, this module allows users to save commonly used configuration templates for future quick access.

[0034] Comprehensive correlation index calculation module: This module is responsible for calculating the comprehensive correlation index between the fused small polygons and the surrounding adjacent large polygons. By analyzing factors such as spatial position, polygon attributes, length of fusion boundary, and whether it is a frame line cut polygon, the corresponding comprehensive correlation index is generated. This calculation process provides a scientific basis for subsequent polygon fusion, ensuring that the fusion results are more in line with actual business needs.

[0035] Figure spot fusion module: the module is one of the cores of the system, using the powerful processing capability of FME platform, through the set of fusion rules and indicators, realize the automatic fusion of adjacent figure spot. The module not only can deal with complex boundary and diversified figure spot type, but also will carry out figure spot fusion according to different rules and weights, ensure the accuracy and effectiveness of the fusion result. At the same time, the module will consider the weight requirement of complex indicators, to ensure that the optimal fusion result can be provided under various conditions.

[0036] Result verification and output module: the module is responsible for verifying the fusion result. By comparing with the original data, ensure that the fusion result meets the preset quality standard and application requirement. At the same time, the module supports outputting the final result in multiple formats for subsequent analysis, decision and visualization.

[0037] The beneficial effects of the present application are: 1. The present application formulates specific fusion standards and rules for different business scenarios, ensures the scientificity and consistency of the fusion process, so that the results of different projects can be effectively compared and integrated; a unified index system is established, combined with the weight weighting mechanism, so that the importance of different elements can be quantified; the optimized spatial relationship processing pays special attention to the spatial relationship between adjacent figure spots, and advanced algorithms are used to ensure that the fusion result truly reflects the actual business characteristics; the powerful data processing capability of FME platform is fully utilized, and an automatic workflow is designed, which reduces manual intervention, improves processing efficiency, so that the system can quickly adapt to the dynamically changing data environment, and significantly improves the real-time performance.

[0038] 2. The present application can adapt to the needs of different fields by establishing a standardized fusion process and flexible parameter configuration mechanism, which can ensure that the specific goals of each field are met while maintaining the consistency and efficiency of data processing, not only significantly improve the accuracy and precision of data processing, but also enhance the data interoperability between different fields, provide strong support for the comprehensive application of geographic information; at the same time, through specific rule definition, the problem of fusion of small and narrow figure spots generated by partial overlap of boundary lines and adjacent figure spots in the process of converting CAD line data to GDB surface data in the process of reservoir resettlement land acquisition, and the problem of attribute loss and spatial inconsistency caused by the cutting of CAD achievements due to the cutting of CAD achievements. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The data preparation flowchart for the embodiment 1 of the present application is shown in the figure; Figure 2 The parameter setting and rule configuration framework diagram for the embodiment 1 of the present application is shown in the figure; Figure 3 The system overall architecture flowchart for the embodiment 2 of the present application is shown in the figure; Figure 4Figure 2 shows a data preprocessing interface for the embodiment 2 of the present application; Figure 5 Figure 4 shows a system interface for the embodiment 2 of the present application based on FME adjacent polygon fusion system; Figure 6 Figure 5 shows a data preprocessing effect diagram for the embodiment 2 of the present application; Figure 7 Figure 6 shows an output diagram for the embodiment 2 of the present application after fusion of polygons in the adjacent polygon fusion system based on FME; Figure 8 Figure 7 shows a display diagram for the embodiment 2 of the present application after fusion of polygons.

[0040] English translation of the figures: Translation Parameter Values: translation parameter values; User Paraneters: user parameter values; Source DWG / DXF File(s): original DWG / DXF file; Source Microsoft Excel File(s): original Microsoft Excel file; Feature Types to Read: feature types to be read; File Geodatabase: file geodatabase; Save As User Paraneter Default Values: save as user parameter default values; Options: options; Presets: presets; Run: run; Cancel: cancel; spatial_weight: spatial correlation weight; boundary_weight: common boundary line length weight; distance_weight: center point distance weight; enable_frame_cut: enable frame cut; attribute weight: attribute correlation weight; attribute0l_name: attribute 01 name; attribute01_weight: attribute 01 weight. DETAILED DESCRIPTION

[0041] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions thereof are used to explain the present application, but are not intended to limit the present application.

[0042] Referring to Figures 1-2 A FME-based adjacent map patch fusion method, comprising the following steps: The present application provides a FME-based adjacent map patch fusion method, S10, data preparation: according to the business needs of specific industries, collect the data set related to adjacent map patch fusion, and unify the format, clean and correct the collected data, and prepare the data for subsequent fusion, that is, obtain the planar data of the to-be-fused map patch.

[0043] In step S10, according to the business needs of specific industries, collect the data set related to adjacent map patch fusion, according to the characteristics of different business scenarios, the specific collected data set is as follows: If it is a reservoir resettlement land acquisition business, collect the related resettlement land acquisition framing results, including CAD files and Excel data results. The data should contain the boundary, land type, land ownership information of the resettlement area, so as to ensure that each item of information of the resettlement land acquisition can be accurately reflected in the patch fusion process.

[0044] If it is a land use business, collect the patch data and attribute table related to land use. This includes the spatial distribution information of different land types such as arable land, forest land, grassland and their related attribute data, such as land use type, land ownership, and use status, so as to reasonably consider the characteristics of various types of land in the fusion process.

[0045] If it is an environmental monitoring business, establish a patch data set related to environmental monitoring, focusing on collecting water quality monitoring points, air quality monitoring stations and their monitoring index data. The integration of the spatial position, monitoring parameters and historical data of these data will provide necessary background information for patch fusion, thereby improving the scientificity and practicality of the data.

[0046] If it is a city planning business, collect the patch data related to city planning, including city land zoning, infrastructure layout, green space and public facilities information. At the same time, consider the factors such as city development trend, policies and regulations, to ensure that the fused data can support city development decision-making.

[0047] In step S10, the collected data is unified in format, cleaned and corrected. Specifically, the data format is unified, the data cleaning includes de-duplication, handling of missing values and outliers, the data correction includes unifying the coordinate system, ensuring accuracy and consistency, and finally the data quality is checked, fully preparing for the subsequent fusion processing.

[0048] S20, parameter setting and rule configuration: based on the analysis of the feature requirements of different map patches in specific business scenarios, the fusion parameters are set, then the key fusion parameters are quantified, the comprehensive correlation index is calculated and output. The key fusion parameters include the area threshold of the fused map patch, the length of the common boundary line between the fused map patch and the adjacent map patch, whether there is a frame line cutting map patch, and the attribute correlation between the fused map patch and the surrounding map patches.

[0049] In the step S20, the fusion parameters are set. When setting these parameters, researchers should first consult the suggestions and opinions of experts in the field to ensure that the set spatial parameter and attribute parameter indicators are reasonable and feasible. The feedback of experts will provide important support for formulating scientific fusion standards, thereby improving the effectiveness and accuracy of the fusion process.

[0050] The following is the specific analysis method and parameter setting for the four main business scenarios: Reservoir resettlement land acquisition: In this scenario, researchers need to focus on the area threshold of the fused map patch, which is recommended to be set to 3.33 square meters, equivalent to 0.01 mu, to ensure that map patches greater than or equal to the threshold can be marked for output, requiring users to complete the relevant attribute information; and small attributeless map patches less than the threshold can be effectively fused by adjacent map patches. The required attribute parameters include land ownership, land type, area, county, township, village group, etc.

[0051] Land use: For land use scenarios, researchers need to analyze the characteristics of different land types and set the area threshold according to specific business scenarios, such as setting it to 1 mu to exclude irrelevant small plots. If the land types of adjacent map patches are the same, the correlation calculated by the attribute index is 1, and subsequent fusion of adjacent map patches can be performed. In addition, attribute correlation should be calculated based on the similarity of land types, and the calculated comprehensive index will be used as the basis for map patch fusion.

[0052] Environmental monitoring: In environmental monitoring, researchers need to focus on the spatial location and attribute information of monitoring points and set a low area threshold, such as 0.1 mu, to maintain attention to small monitoring areas. The distance requirement from the monitoring point should be based on the actual situation of the monitoring area to ensure that monitoring information can be effectively integrated. Attribute correlation should include environmental data such as water quality and air quality indicators to make full use of monitoring information during the fusion process.

[0053] Urban planning: For urban planning, an appropriate area threshold should be set, such as 10 mu, to consider the main functional zoning of urban land. The setting of the common boundary length should consider the connection of urban roads, green spaces and public facilities to ensure the reasonable layout of urban space. Attribute correlation should cover urban development indicators, planning use and status matching to improve the scientific nature of the fusion process.

[0054] The step S20 quantifies the key fusion parameters one by one, specifically, The graphon that is less than or greater than the area threshold is merged into its adjacent graphon, and the graphon to be merged depends on the evaluation of the comprehensive correlation index, and the comprehensive correlation index The calculation formula is as follows: ; Among them, represents the spatial correlation, represents the attribute correlation, and are weight coefficients, and satisfy By properly adjusting the two weights, researchers can balance the influence of spatial and attribute correlation in fusion decision according to specific business needs.

[0055] The spatial correlation is quantified by evaluating the common boundary length, the center point distance, or whether it is a graph frame line cutting graphon. The common boundary length is the common boundary line length between the fused graphon and the adjacent graphon. The center point distance is the distance between the center point of the fused graphon and the center point of the surrounding adjacent graphon.

[0056] For the application scenario of reservoir resettlement land acquisition, the CAD is cut by the graph frame line in the result archiving, and the index of whether it is a graph frame line cutting graphon is introduced. The index of whether it is a graph frame line cutting graphon marks whether a graphon is a graph frame line cutting graphon. If two adjacent graphons are graph frame line cutting graphons, then in the case that one of them is a small graphon attribute loss, the fusion of the two graphons is performed, ; Among them, represents a graph frame line cutting graphon, represents a graph frame line cutting graphon.

[0057] The spatial correlation is quantified, specifically: If the common boundary length is used as an index, the spatial correlation is quantified using the following formula: ; Among them, is the common boundary line length of the fused graphon and the adjacent graphon, is the maximum common boundary line length of all adjacent graphons; If the distance is used as an index, the spatial correlation is quantified using the following formula: ; The formula ensures that the closer the distance, The higher the value, the stronger the spatial correlation between the two. Among them, is the distance between the center point of the fused patch and a certain adjacent patch center point, is the distance between the center point of the fused patch and the nearest adjacent patch center point.

[0058] If a certain combination of weights is used, then: ; Among them, , is the corresponding spatial correlation weight value, , is the corresponding weight coefficient, and satisfies .

[0059] The weight calculation of the common boundary length is specifically to obtain the maximum value in the N values: ; The corresponding common boundary line length weight is: ; Among them, A is a small area that needs to be fused, N is the number of adjacent patches, patch K is the adjacent patch of patch A, K is from 1 to N, and the corresponding common boundary line length is L K .

[0060] The weight calculation of the center point distance is to obtain the minimum value in the N values: ; The corresponding center point distance weight is: ; Among them, A is a small area that needs to be fused, N is the number of adjacent patches, patch K is the adjacent patch of patch A, K is from 1 to N, and the corresponding patch to patch A distance is .

[0061] The attribute correlation covers the relationship between the fused patch and the surrounding patches in terms of land class, right ownership and economic value. Specifically, if the fusion is based on land class, if the land class of the fused patch and the surrounding adjacent patches is the same, the land class correlation is 1; if the land class is close, the land class correlation can be set to a value close to 1 to reflect their similarity. Similarly, when performing right ownership-based fusion, if the right ownership of the fused patch and the adjacent patch is the same, the right ownership correlation of the two patches is 1, to reflect the consistency of the right ownership.

[0062] This embodiment shows the structure of attribute correlation calculation, including land class, utilization rate, location value and comprehensive attribute correlation evaluation method.

[0063] Land class correlation weight calculation, A is a small area that needs to be fused, N is the number of adjacent blocks, the land class of block A is land class X, and block K is an adjacent block of block A, where K is valued from 1 to N, and the corresponding land class is Y k , then the land class similarity is: ; Wherein, the land class similarity function takes value, a land class similarity matrix can be sorted out, and the similarity coefficient can be read from the matrix.

[0064] Utilization correlation weight calculation, A is a small area that needs to be fused, N is the number of adjacent blocks, the utilization rate of block A is X, and block K is an adjacent block of block A, where K is valued from 1 to N, and the corresponding land class is Y k , then the utilization rate similarity is: ; Wherein, the utilization rate function takes value, the utilization rate correlation can be set according to the relevant district data, if the utilization rates of two blocks are closer (the difference is smaller), the correlation is higher, and if the utilization rates are the same, the correlation is 1.

[0065] Location value correlation weight calculation, A is a small area that needs to be fused, N is the number of adjacent blocks, the location value coefficient of block A is X, and block K is an adjacent block of block A, where K is valued from 1 to N, and the corresponding land class is Y k , then the location value similarity is: ; Wherein, the location value coefficient function takes value, which can be calculated according to the distance from important settings (such as transportation hubs), such as close distance, value close to 1, and far distance, gradually reduce the location value correlation, if the location value coefficient is closer, the correlation is higher, and if the neighborhood value coefficient is the same, the correlation is 1.

[0066] The comprehensive attribute correlation is: .

[0067] Wherein, is the land class similarity value, same as ; is the utilization rate correlation value, same as ; is the location value correlation value, same as ; , , respectively, are the corresponding weight coefficients of the three in the comprehensive attribute correlation, which satisfy .

[0068] S30, adjacent patch fusion is performed: FME automatically applies the set and configured parameters and rules for fusion operation, and automatically identifies adjacent patches; at this time, by using the powerful stream processing capability of FME, the system can process multiple data sources in parallel, thereby significantly improving the overall processing efficiency, quickly responding to large-scale data fusion operation, and ensuring the efficiency and accuracy of the fusion process.

[0069] S40, result verification and output: multi-dimensional quality verification is performed on the fusion results to evaluate the accuracy and consistency of the fusion results, so as to ensure that the final output meets the expected quality standards. After completing the verification, the system will generate standardized output results, which can be presented in the form of graphic files or database records for subsequent analysis and application.

[0070] S50, feedback and optimization: based on the fusion results and expected targets, user feedback is collected, feedback information is analyzed, and parameter settings and rule applications are adjusted if necessary.

[0071] In the reservoir resettlement land acquisition project, the early CAD framing result data has been archived. In order to meet the needs of information system construction, this result needs to be converted into GDB spatial database format. In the early surveying process, the area threshold is set to 0.01 mu (equivalent to 3.34 square meters) based on CAD map area calculation. However, during the conversion of CAD line data to GDB surface data, it is found that due to the repeated measurement and overlapping of CAD boundary lines, small and narrow patches appear in the converted surface data. These small and narrow patches need to be merged into adjacent patches.

[0072] To solve this problem, the research team sets the boundary line length as the comprehensive correlation index for fusion, with a weight of 1, to ensure that the boundary line length is prioritized as the comprehensive index for adjacent patch fusion during the fusion process. At the same time, the project often loses attribute labels of small plots due to framing cutting during archiving. In this case, if the attribute of the converted patch is lost, it can be directly merged into its surrounding patches according to the "whether it is a frame line cutting patch" standard.

[0073] After optimization, for patches with an area greater than 3.34 square meters, if the attribute is lost, the relevant attribute information needs to be completed in the CAD map to ensure data integrity. For patches with an area less than 3.34 square meters, they can be fused according to the "whether it is a frame line cutting patch" standard without the need to complete the attribute information. This improves the efficiency and accuracy of adjacent patch fusion, and ensures that the final data output meets the actual needs and expected standards of users, providing reliable data support for subsequent reservoir resettlement land information management.

[0074] In the water resource monitoring project, the attributes of certain water body polygons do not fully reflect their relevance in water quality and quantity monitoring. Some small water bodies lack key water quality parameters and quantity information such as chemical oxygen demand, turbidity, and flow, water storage, etc., resulting in ineffective utilization of these polygons during data fusion.

[0075] To solve this problem, the research team updated the attribute relevance calculation. The specific steps are as follows: (1) Data collection and analysis: Researchers first collected relevant water quality and quantity monitoring data, and evaluated the importance of different water body polygons in the monitoring project. Through analysis of historical data, the correlation between water quality and quantity and each polygon was determined.

[0076] (2) Attribute relevance index design: Based on the analysis results, the research team designed new attribute relevance indexes, focusing on the weights of water quality parameters and quantity parameters. When synthesizing the attribute relevance index, water quality and quantity parameters are given different weight coefficients to highlight their influence on monitoring. For example, the weight of water quality parameters is set to 0.7, and the weight of water quantity parameters is set to 0.3.

[0077] (3) Adjustment of calculation formula: The updated attribute relevance calculation formula is: Attribute relevance = 0.7 × water quality index + 0.3 × water quantity index This formula is used to evaluate the comprehensive attribute relevance of each water body polygon, so that its monitoring needs can be more accurately reflected during polygon fusion.

[0078] (4) Optimization of fusion process: During polygon fusion, researchers evaluate according to the updated attribute relevance index. For polygons with high attribute relevance, fusion is prioritized to ensure that key water quality and quantity information is not lost. This improvement allows related water body polygons to better retain important monitoring data during the fusion process.

[0079] (5) Verification and feedback: After optimization, the research team verified the updated attribute relevance calculation results and found that the quality of polygon fusion improved significantly. At the same time, user feedback showed that the fused data better met the actual needs of water resource monitoring, improving the reliability and effectiveness of monitoring results.

[0080] This method of optimizing attribute relevance calculation makes the fusion process of water body polygons more scientific and reasonable, ensuring that the fusion results can better support water resource management and decision analysis.

[0081] Through these specific examples, researchers can effectively optimize parameter settings and rule adjustments, thereby improving the efficiency and accuracy of adjacent patch fusion, ensuring that the final output meets the actual needs and expected standards of users.

[0082] Embodiment 2 Referring to Figures 3-8 , Figure 3 The overall architecture of the FME-based adjacent patch fusion system is shown, including various functional modules and their mutual relationships. The FME-based adjacent patch fusion system specifically includes: Data preprocessing module: This module is responsible for receiving geographic spatial data from multiple sources, supporting formats including Shapefile, GDB, CAD, and Excel, etc. The data preprocessing module fully prepares for subsequent adjacent patch fusion. For different business needs, if the provided data is relatively raw, the module itself may be a system responsible for completing the preliminary processing and standardization of data; if there already exists face-shaped data to be fused, the module is mainly used to standardize the corresponding field attributes to meet system requirements. For example, it can convert CAD partitioned data with annotations into GDB data containing attributes. In these converted data, some patches may have attribute information, while others may not have complete attribute annotations manually added to the corresponding cut patches due to CAD partitioning operations. According to business needs, these patches need to be fused to achieve more accurate spatial data integration. Figure 4 The data preprocessing interface diagram shows the user interface layout of the system, intuitively presenting the interaction effect when the user operates.

[0083] Data input module: This module is mainly responsible for selecting face-shaped data of patches to be fused after processing by the data preprocessing module, providing basic data support for patch fusion process. According to specific business needs, this module can flexibly adapt to different data structures and naming specifications of attribute fields. Since the attribute names of patch data may differ in actual application scenarios, this module allows users to edit and adjust attribute fields to ensure that the data input meets the needs of subsequent processing, thereby laying a solid foundation for patch fusion.

[0084] Parameter setting and rule configuration module: This module allows users to flexibly set fusion parameters and rules according to specific application needs, including selected fusion standards, weight settings, and spatial relationship processing rules. Users can conveniently adjust parameters through a graphical interface to adapt to patch fusion needs in different scenarios. In addition, this module also allows users to save commonly used configuration templates for future quick call.

[0085] Comprehensive Correlation Index Calculation Module: This module calculates the comprehensive correlation index between the fused small patches and their adjacent larger patches. It generates the corresponding comprehensive correlation index by analyzing factors such as spatial position, patch attributes, the length of the fused boundary, and whether the patch is cut by the frame line. This calculation process provides a scientific basis for subsequent patch fusion, ensuring that the fusion results are more in line with actual business needs.

[0086] The patch fusion module is a core component of the system. Leveraging the powerful processing capabilities of the FME platform, it automatically fuses adjacent patches using predefined fusion rules and metrics. This module not only handles complex boundaries and diverse patch types, but also fuses patches based on different rules and weights, ensuring the accuracy and effectiveness of the fusion results. Furthermore, the module considers the weighting requirements of complex metrics to ensure optimal fusion results under various conditions. Figure 5 This is a schematic diagram of the adjacent patch fusion system interface based on FME.

[0087] Results Verification and Output Module: This module verifies the fusion results by comparing them with the original data to ensure they meet pre-defined quality standards and application requirements. This module also supports outputting the final results in various formats for subsequent analysis, decision-making, and visualization. Figure 6 This is a data preprocessing effect diagram, which shows the data examples before and after the fusion of adjacent spots, reflecting the differences between the spot data before and after the fusion. Figure 7 This is the output diagram after fusing the adjacent patches in the FME-based adjacent patch fusion system. Figure 8 This is a display diagram of the fused patches.

[0088] Although the present invention has been described in detail above using general descriptions and specific embodiments, the scope of protection of the present invention is not limited thereto. It will be apparent to those skilled in the art that modifications or improvements may be made based on the present invention. Therefore, such modifications or improvements that do not depart from the spirit of the present invention are intended to fall within the scope of protection claimed by the present invention.

Claims

1. A method for fusing adjacent patches based on FME, characterized in that: The following steps are involved: S10. Data preparation: Based on the business needs of specific industries, data sets related to the fusion of adjacent patches are collected, and the collected data are formatted, cleaned, and corrected to prepare the data for subsequent fusion, that is, to obtain the surface data of the patches to be fused; S20, parameter setting and rule configuration: Analyze the feature requirements of different patches based on specific business scenarios, set fusion parameters, quantify key fusion parameters, calculate and output comprehensive correlation indicators; The key fusion parameters include the area threshold of the fused patch, the length of the common boundary line between the fused patch and the adjacent patches, whether there is a frame line cutting the patch, and the attribute correlation between the fused patch and the surrounding patches; S30: Perform adjacent patch fusion: FME automatically applies the parameters and rules set and configured in S20 to perform the fusion operation and automatically identify adjacent patches. S40, Result Verification and Output: Perform multi-dimensional quality verification on the fusion results, evaluate the accuracy and consistency of the fusion results, and obtain standardized output results; S50, Feedback and Optimization: Evaluate based on the fusion results and expected goals, collect user feedback, analyze the feedback information, and adjust parameter settings and rule application when necessary.

2. The method for fusing adjacent patches based on FME according to claim 1, characterized in that: In step S10, data sets related to adjacent patch fusion are collected according to the business needs of specific industries, specifically: If it is a reservoir resettlement land acquisition project, relevant resettlement land acquisition parcel archiving results will be collected, including CAD files and Excel data results; the data includes the boundaries, land types, and land ownership information of the resettlement area; If it is a land use business, then the map data and attribute tables related to land use are collected, including the spatial distribution information of different land types and their attribute data. The data includes land use type, land ownership, and current use status; If it is an environmental monitoring business, a patch dataset related to environmental monitoring will be established to collect data on water quality monitoring points, air quality monitoring stations and their monitoring indicators; If it is an urban planning business, then the map data related to urban planning will be collected, including urban land zoning, infrastructure layout, green space and public facilities.

3. The method for fusing adjacent patches based on FME according to claim 1, characterized in that: In step S10, the collected data is formatted, cleaned, and corrected. Specifically, the data format is unified, data cleaning includes deduplication, processing of missing values ​​and outliers, data correction includes unifying the coordinate system, ensuring accuracy and consistency, and finally performing a data quality check.

4. The method for fusing adjacent patches based on FME according to claim 1, characterized in that: In step S20, the key fusion parameters are quantified one by one, specifically, The patches with an area less than or greater than the area threshold are merged into an adjacent patch. The selection of patches to be merged depends on the evaluation of the comprehensive correlation index. The calculation formula is: ； in, represents spatial correlation, Indicates attribute correlation, and is the weight coefficient and satisfies ; The spatial correlation is quantified by evaluating the common boundary length, center point distance, or whether the patch is cut by a frame line. The common boundary length is the length of the common boundary line between the fused patch and the adjacent patch. The center point distance is the distance between the midline point of the fused patch and the center points of the surrounding adjacent patches.

5. The method for fusing adjacent patches based on FME according to claim 4, characterized in that: Whether it is a frame line cutting spot, mark whether a spot is a frame line cutting spot, if two adjacent spots are spot line cutting spots, then if one of them is a small spot with lost attributes, the two spots will be merged, ； in, Indicates that the pattern line cuts the pattern. Indicates that it is not a pattern line cutting pattern.

6. The method for fusing adjacent patches based on FME according to claim 4, characterized in that: The spatial correlation quantification is specifically as follows: If the length of the common boundary is used as an indicator, the following formula is used to quantify spatial correlation: ； in, is the length of the common boundary line between the fused patch and the adjacent patch, is the maximum common boundary length of all adjacent patches; If distance is used as the metric, the following formula is used to quantify spatial correlation: ； in, is the distance between the center point of the fused patch and the center point of an adjacent patch, is the distance between the center point of the fused patch and the center point of the nearest adjacent patch; If a certain weighted combination of the two is adopted, then: ； in, 、 is the corresponding spatial correlation weight value, 、 is the corresponding weight coefficient, and satisfies .

7. The method for fusing adjacent patches based on FME according to claim 6, characterized in that: The weight calculation of the common boundary length is specifically to obtain the largest value among N values: ； The corresponding common boundary line length weight is: ； Among them, A is the small area that needs to be fused, N is the number of adjacent patches around it, and patch K is the adjacent patch of patch A, where the value of K ranges from 1 to N, and the corresponding common boundary line length is L K .

8. The method for fusing adjacent patches based on FME according to claim 6, characterized in that: The weight of the center point distance is calculated by obtaining the minimum value among N values: ； The distance weight of the corresponding center point is: ； Among them, A is the small area of ​​the patch that needs to be fused, N is the number of adjacent patches around it, and patch K is the adjacent patch of patch A. The value of K ranges from 1 to N, and the distance from the corresponding patch to patch A is .

9. The method for fusing adjacent patches based on FME according to claim 5, characterized in that: When the attribute relevance considers the combined influence of multiple attributes, the following formula is used for calculation. If the attribute relevance is evaluated based on three attributes, the comprehensive attribute relevance evaluation is: ； in 、 and Represent the correlation values ​​of the 1st, 2nd, and 3rd attributes respectively; 、 、 is the weight coefficient of the corresponding attribute, and satisfies .

10. An FME-based adjacent patch fusion system, characterized in that: Used to execute the FME-based adjacent patch fusion method according to any one of claims 1 to 9, specifically comprising: Data preprocessing module: This module receives geospatial data from various sources. Supported formats include Shapefile, GDB, CAD, and Excel. It unifies the format of the collected data, cleans and corrects it, and prepares the data for subsequent fusion. Data input module: This module selects the surface data of the patches to be fused after being processed by the data preprocessing module, and provides basic data support for the patch fusion process. According to specific business needs, this module adapts to different data structures and naming conventions of attribute fields, allowing users to edit and adjust attribute fields; Parameter setting and rule configuration module: This module sets fusion parameters and rules according to specific application requirements. Users adjust parameters through a graphical interface to adapt to the needs of patch fusion in different scenarios. This module also saves commonly used configuration templates for quick call; Comprehensive correlation index calculation module: This module calculates the comprehensive correlation index between the fused small spots and the surrounding large adjacent spots. By analyzing the spatial position, spot attributes, the length of the fusion boundary, and whether the spots are cut by the frame line, the corresponding comprehensive correlation index is generated; Image fusion module: This module is based on the FME platform and realizes the automatic fusion of adjacent image patches through the set fusion rules and indicators; Result verification and output module: This module verifies the fusion results by comparing them with the original data and outputs the final results in multiple formats, including graphic files and database records.

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