CN113434623B - Fusion method based on multi-source heterogeneous space planning data - Google Patents
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Abstract
A fusion method based on multi-source heterogeneous space planning data comprises the following steps: s1, GIS data processing and fusion; s2, processing and converting AutoCAD data; s3, hanging the place name and the address in a matching way; s4, batch processing, which can efficiently and rapidly read and process the territorial space planning data from different sources, eliminate data redundancy, achieve the purpose of data deduplication, and improve the data processing capability and efficiency to the maximum extent.
Description
Technical Field
The invention relates to the technical field of resource planning, in particular to a fusion method based on multi-source heterogeneous homeland space planning data for homeland resources.
Background
The territorial space planning is a scientific guide for guiding the national space development, is a strategic deployment for comprehensively planning various spatial plans and promoting 'multi-rule-in-one', is a core means for promoting governments at all levels to carry out space management and purpose control, and is an important measure for realizing ecological civilization construction. The homeland space planning data is the summary and integration of homeland planning result data and is an important embodiment of homeland planning informatization management, and through years of accumulation, the data has the characteristics of multiple sources, isomerism, multiple time spaces, multiple scales, different coordinate systems and the like, so that the problem of the multi-source data of the homeland planning is caused, for example, the data is repeated, and due to the regularity of time sequence data, the generated data has great redundancy.
Several problems currently exist are summarized as follows: 1. GIS data resources in the territorial space planning are rich and numerous, but territorial space planning results from various sources form systems, and territorial space information of various channels is perfect, the abundance is different, and positions and attributes conflict. 2. At present, the capacity, efficiency and data processing flow of a space data conversion processing system need systematization and perfection. 3. In the territorial space planning, there are many problems in the standardization angle of many place names, and although the names of administrative levels in towns or streets and above are basically standardized and unified, the matching accuracy and efficiency of various service data to the place name addresses in the territorial space planning construction are not high. 4. The territorial space planning has the difficulties of complex business, multi-source heterogeneous data integration, complex model rule flow, inconsistent threshold parameters, overlong index calculation time and the like.
The invention aims to realize lossless data sharing of data under different data sources, different storage formats, different time and space, different scales and different coordinate systems, realize platform type data management under an open unified standard, and develop a tool which can be operated simply and conveniently and realize batch, automatic and accurate correspondence of geographic text data and space coordinates, thereby improving the utilization efficiency of the data and improving the management level of homeland space planning data.
Disclosure of Invention
The invention provides a fusion method of multisource heterogeneous space planning data, which is mainly applied to the territorial space planning and aims at solving the problem that the territorial space planning data from different sources have differences in the aspects of format, coordinate reference, attribute structure and the like.
The method comprises the steps of firstly forming a native space planning master library on the basis of existing native space planning libraries, public POI data, basic mapping and other self-owned data.
Secondly, after the CAD, GIS and POI data of other sources are unified by coordinates, the data are compared with attribute information such as spatial position, place name and address and the like of a territorial space planning master library, and the territorial space planning data which is not in the master library is removed in duplication and is fused to form a territorial space planning fusion library which is regular in content and rich in information.
And finally, carrying out place address hanging based on the space constraint relationship, namely establishing an association relationship between each POI point and the corresponding place address, and improving the application approach of the homeland space planning library. Test results show that the method can realize efficient fusion of multi-source homeland space planning data, effectively solves the problems of high data cost, large workload, low efficiency and effectiveness and the like in homeland space planning data acquisition work after being articulated with a site address, and has important data support and technical support effects on improving the timeliness, the situation, the thematic abundance degree and the like of the data of a public service platform for homeland space planning.
A fusion method based on multi-source heterogeneous space planning data comprises the following steps: s1, GIS data processing and fusion; s2, processing and converting AutoCAD data; s3, hanging the place name and the address in a matching way; and S4, batch processing.
Further, the step S1 specifically includes: s11, cleaning, namely cleaning the acquired large amount of space planning data by using an ETL technology; s12, extracting codes, designing a word segmentation algorithm according to the characteristics of the resource information, and extracting the keywords of the homeland resource information elements and the corresponding codes; s13, preliminary fusion, namely, realizing multi-source data fusion and updating by adopting weighted multi-attribute similarity, on the basis of a preliminary fusion set, eliminating wrong corresponding objects found by corresponding homeland resource information by using a name feature attribute similarity method with a low threshold value and adopting a character string similarity algorithm by utilizing the high-similarity non-spatial data feature attributes of POI data, and finally finding out corresponding objects which can not be found by the homeland resource information by using the name feature attribute similarity method with a high threshold value and then updating the POI; and S14, capturing, fusing and updating the network data.
Further, the cleaning process of step S11 includes the following steps: s111, unifying data formats, converting the formats of the collected related data so as to operate the data subsequently, wherein related vectorization or space difference processing needs to be carried out on part of table and text data; s112, unifying coordinates and precision, wherein a 2000 national geodetic coordinate system (CGCS2000) and a 'Gauss-Kruger' projection are adopted, a 1985 national elevation standard is adopted for a land area part, a theoretical depth standard surface elevation standard is adopted for a sea area part, and on the other hand, grid units are determined according to the area size and the precision of the data which can be obtained, so that the consistency of the data precision in the same grid is ensured, and the grid sizes including terrain factors, precipitation factors and soil attribute factors are the same; s113, adding an attribute structure, carrying out data standardization input and output, constructing an integrated model based on a GIS, establishing a flow frame by constructing a multi-element system and an index system, standardizing, organizing and managing data collected by each channel, supporting dynamic loading and displaying of diagrams, documents, space vectors and raster data, and establishing and embedding a naming rule in result data so as to save results to finish corresponding standardization work; and S114, optimizing data quality, wherein firstly, the grid layers are guaranteed to have the same grid unit size and row and column number through sampling and masking, the data are cut to guarantee the consistency of the data space range, secondly, the design of a database table and fields is standardized, the dictionary table contains a main key to guarantee the consistency of field names and types, empty columns are avoided, and the work of deleting special characters, forming a standard data attribute structure, optimizing data quality and unifying attribute field names is respectively finished.
Further, the extracting the code of step S12 includes the following steps: s121, preprocessing data; s122, removing the duplicate of other POLs; s123, analyzing other sources and a parent library POL by a Levensiton algorithm; s124, assigning similarity and distance attributes of the text character strings; s125, judging whether other POLs need to be fused or not; and S126, updating the Oracle mother library and generating metadata. In the process of step S12, an index field is created to create an index for the KEY _ ID field, and meanwhile, a determination is made as to whether the database has the same name table.
Further, the performing POI update of step S13 includes the following steps: s131, analyzing and sorting data; s132, carrying out duplicate removal treatment; s133, giving weight; s134, building a POL code; and S135, forming a new mother library.
Further, the step S2 specifically includes: s21, finding a data frame; s22, copying the CAD files with the same data volume according to the data volume of the frame; s23, determining a conversion data range by using an AutoLisp code; and S24, generating an SCR file and executing corresponding operation.
Further, the step S3 specifically includes: s31, calculating a spatial relationship; s32, establishing a space constraint model; and S33, completing the matching verification.
Further, the step S4 specifically includes: s41, making a data processing template; s42, issuing the manufactured module to a data integration platform through a visual workflow editor of an ETL tool to form data processing service; and S43, updating and maintaining the template.
According to the fusion method based on the multi-source heterogeneous space planning data, a processing method aiming at a data source is adopted, so that the homeland space planning data from different sources can be efficiently and quickly read and processed; on the other hand, a data deduplication method based on dynamic time warping is adopted, and the method eliminates data redundancy and achieves the purpose of data deduplication by calculating the similarity between data.
Sometimes, in order to improve the data processing capacity and efficiency to the maximum extent, the invention develops and calls some external tools and methods, perfects the data processing flow, merges and fuses different source homeland space data to make the information quantity richer and more complete, and realizes the reuse and update of homeland space information.
The CAD data is an important component of homeland space planning, but because CAD data in an AutoCAD in a special format, such as OLE (object linking and embedding, OLE technology for short), may exist in data that needs to be converted, such special data may have been lost in the data reading process, which causes inconsistency between data results before and after conversion and the original data. The method for calling the AutoLisp codes by the spatial data conversion processing system completes the conversion operation of the CAD data, copies a plurality of data according to the data amount required to be converted, performs conversion operation on the copied data, automatically stores the converted data, deletes redundant data and ensures 100% accuracy of the data.
The method comprehensively utilizes 3 key technologies of fine place name address library construction, place name address characteristic word library construction and big data analysis based on user searching behaviors to improve the address matching degree and the matching precision under the place name address matching technology framework based on Chinese word segmentation.
The invention scientifically recognizes the natural law and the social and economic law of the different national soil space patterns through an intelligent data processing and analyzing tool, so that the functional regions of the national soil space are efficiently divided, and the key basic work of the national soil space planning is facilitated.
Drawings
FIG. 1 is a schematic diagram of an implementation of the present invention;
FIG. 2 is a diagram of a data processing system of the present invention;
FIG. 3 is a schematic flow chart of steps S1 and S2 according to the present invention;
FIG. 4 is a flowchart illustrating the extraction of the code at S12 in step S1 according to the present invention;
FIG. 5 is a flowchart illustrating the process of extracting the index field from the code S12 in step S1 according to the present invention;
FIG. 6 is a flowchart illustrating a POI updating process performed in step S13 according to the present invention;
FIG. 7 is a schematic flow chart of step S2 according to the present invention;
FIG. 8 is a schematic flow chart of step S3 according to the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described below with reference to the accompanying drawings and examples. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for fusing planning data based on multisource heterogeneous space, aiming at the problem that different sources of homeland space data have differences in the aspects of format, coordinate reference, attribute structure and the like, and the method is mainly applied to the homeland space planning data Accuracy, timeliness and availability.
On the basis, professional data, government public data (including fire fighting, weather, cultural relics and the like), image data (acquired by a network), open source data (such as Baidu, Goodle and the like) provided by a third party (such as a research institution) and AutoCAD data and the like generated in the production process are combined to be used as effective supplements.
The general graphic data in the data format adopts vector data supported by GIS software, the precision of the vector data is determined according to the work requirement and the menses attribute, and the general reference data adopts table data supported by Access or Excel software. The data of the national soil space planning master database is supplemented and perfected through comparison with the data of the master database; the internet POI data is combined, the internet data is captured and collected regularly, and the information of the territorial space planning master library is further improved through data comparison and fusion; and finally, the connection is carried out through the matching technology of the site address, so that a national soil space planning database with rich and uniform information is established, high-quality data finally output is stored in a national soil space information resource mother library, and a set of effective data comparison, fusion and updating mechanism is gradually formed, so that the national soil space planning database can accurately reflect the real national soil space planning condition in real time.
The implementation scheme is as shown in fig. 1, generally, the fineness and the accuracy of the data of the homeland space planning database are higher than those of the data from other parties such as a third party, the internet, a government internal network and the like, and the data of the other parties and the homeland space planning database are fused by the following basic rules:
the method comprises the steps of planning database repetition, reserving planning mother database data, and deleting other repeated data at the same spatial position;
and the planning of the mother database and the spatial positions of other data are contradictory, and the planning of the mother database is used as a spatial reference.
And thirdly, if the planning master database exists, the other database does not exist, and the planning master database data is reserved.
And a third party has the database, a planning master library does not exist, and database data of other parties are reserved.
The one-to-many and many-to-many relationship between the planning master library and the databases of other parties is solved.
In the invention, a data processing system diagram of the whole system is shown in fig. 2, which is also a data processing concept design of the whole invention, starting from a data source, data acquisition and preprocessing are carried out, then data storage is carried out, and then data analysis and mining and further data visualization processing are carried out. The method specifically comprises the following steps: s1, GIS data processing and fusion; s2, processing and converting AutoCAD data; s3, hanging the place name and the address in a matching way; and S4, batch processing.
The GIS technology is a technology for calculating, collecting, storing, analyzing and managing the spatial geographic distribution data of a formulated area under the support of a computer system, can realize effective classification and management of homeland spatial data, and realizes effective query and analysis of the spatial data through comprehensive connection of the characteristic data and the attribute characteristic data. Due to rich sources of homeland space data, the data formats are different, the data formats comprise AutoCAD, ESRIShape, Excel, ESRIGeodataase databases and the like, a uniform data representation method is not available, and the space elements can express GIS geometric data types and can also be stored in data files such as Excel, CSV and the like in a text coordinate mode. And with the development of data production technologies and data acquisition means such as POI, the sources of the territorial space planning data are more extensive and diversified.
The AutoCAD data mainly describes the spatial position and the geometric shape of a geographic entity, is expressed in the mode of points, lines and symbol blocks, has map information such as layers, colors and line types, comprises special format data such as OLE (object connection and embedding), pictures and tables, and needs to separate data according to a given picture frame to form a piece of AutoCAD result data, and an output file takes the characters at the upper left corner of the picture frame as a file name to output, so that the uniqueness of separation is ensured.
The invention relates to a method for compiling a base map of a traditional homeland space planning technology, which mainly adopts dwg format data in AutoCAD, generally draws according to a ratio of 1:1000 and has higher precision, a GIS generally draws according to a ratio of 1:10000 and is a unified technical base map.
Referring to fig. 3, the main flow diagrams of steps S1 and S2 mainly relate to the flow diagram of data processing, including data preprocessing, data processing data conversion and data checking, and the specific steps S1 and S2 respectively include: step S1: s11, cleaning, namely cleaning the acquired large amount of space planning data by using an ETL technology; s12, extracting codes, designing a word segmentation algorithm according to the characteristics of the resource information, and extracting the keywords of the homeland resource information elements and the corresponding codes; s13, performing preliminary fusion, namely fusing and updating multi-source data by adopting weighted multi-attribute similarity, on the basis of a preliminary fusion set (such as the direct distance of a POI), utilizing non-spatial data characteristic attributes of the POI data which are highly similar, adopting algorithms (such as Levenshteindstistance algorithm, Jarodistance algorithm and the like) which are similar in character strings, eliminating wrong corresponding objects found out by corresponding homeland resource information by using a name characteristic attribute similarity method with a low threshold value, finally finding out corresponding objects which can not be found out by the homeland resource information by using a name characteristic attribute similarity method with a high threshold value, and then updating the POI; and S14, capturing, fusing and updating the network data. Step S2: s21, finding a data frame; s22, copying the CAD files with the same data volume according to the data volume of the frame; s23, determining a conversion data range by using an AutoLisp code; and S24, generating an SCR file and executing corresponding operation.
The cleaning process of step S11 of the invention is to carry out cleaning process to a large amount of acquired homeland space planning data by using ETL technology, ETL is a data warehouse tool and mainly comprises the work of removing repeated data and missing information data, coordinate conversion and the like, and the cleaning process of step S11 comprises the following steps: s111, unifying data formats, converting the formats of the collected related data so as to operate the data subsequently, wherein related vectorization or space difference processing needs to be carried out on part of table and text data; s112, unifying coordinates and precision, wherein on one hand, a 2000 national geodetic coordinate system (CGCS2000) and a 'Gauss-Kruger' projection are adopted, a 1985 national elevation standard is adopted for a land area part, and a theoretical depth datum plane elevation standard is adopted for a sea area part, and on the other hand, grid units are determined according to the area size and the precision of the data which can be obtained, so that the consistency of the data precision in the same grid is ensured, and the grid sizes including terrain factors, precipitation factors and soil attribute factors are the same; s113, adding an attribute structure, mainly used for carrying out data standardized input and output, constructing an integrated model based on a GIS, establishing a flow frame by constructing a multi-element system and an index system, standardizing and organizing and managing data collected by each channel, supporting dynamic loading and displaying of charts, documents, space vectors and raster data, establishing result data and embedding a naming rule, and accordingly storing results to finish corresponding standardized work; s114, optimizing data quality, wherein firstly, the grid layers are guaranteed to have the same grid unit size and row and column number through sampling and masking, data are cut to guarantee the consistency of the data space range, secondly, the design of database tables and fields is standardized, a Dictionary table must contain a main key and generally ends with a Dictionary, the consistency of field names and types is guaranteed, the field name integer can be represented by containing Num, empty columns are avoided, and the work of deleting special characters, forming a standard data attribute structure, optimizing data quality, unifying attribute field names and the like is respectively finished. The data tags comprise ID, name, type, address, longitude and latitude coordinates, telephone, belonged city, belonged administrative district and the like.
With reference to fig. 4, in step S12, splitting the address field of the POI library to be fused according to the territorial resource elements in the territorial resource information model, comparing the split key territorial resource elements to confirm the correlation between the POI data, and using the correlation as the basis for POI data fusion, S12 includes the following steps: s121, preprocessing data; s122, removing the duplicate of other POLs; s123, analyzing other sources and a parent library POL by a Levensiton algorithm; s124, assigning similarity and distance attributes of the text character strings; s125, judging whether other POLs need to be fused or not; and S126, updating the Oracle mother library and generating metadata. Referring to fig. 5, in the process of step S12, the index field is created to improve the updating efficiency, so that the index is created for the KEY _ ID field, and meanwhile, the determination of whether the database has the same name table is added.
In the preliminary fusion of step S13 of the present invention, the POI update includes the following steps: s131, analyzing and sorting data; s132, carrying out duplicate removal treatment; s133, giving weight; s134, building a POL code; and S135, forming a new mother library.
S131, data analysis and arrangement, wherein the data analysis and arrangement are carried out by adopting discrimination of same-name ground objects, namely recognition of same-name POI points is carried out, the discrimination of the same-name POI points is carried out by adopting 3 indexes including third-party data similarity, internet data similarity and network POI data similarity, and reading and writing capacity and data model transformation of multi-source data are carried out by utilizing an ETL technology. Reading POI data of different sources; and constructing a data processing flow. For example, non-spatial data spatialization, data field combination and arrangement, the punctiform geometric elements of lines, surfaces and notes belong to data processing flows such as hooking, attribute processing and the like. And the data analysis and arrangement work can be rapidly finished by finding the result.
And S132, carrying out duplicate removal processing, and comparing the information in the POI mother library by adopting a fusion method based on space and attribute information. And according to the comparison result, adding, deleting and changing the POI mother library by using the data model.
S133, weighting is given, and in the judging process of the invention, weighting of 0.6, 0.2 and 0.2 is given respectively. For two POI objects, if the overall similarity is greater than a threshold value (set to 0.9 in the text), the POI points which are the same name are considered to be filtered and not updated, otherwise, the POI points are considered as new POI points.
And S134, building POL codes, and recoding the POI according to the unified coding rule to build a data model.
And S135, forming a new parent library, respectively generating three metadata tables of updating date, layer information and extended attributes according to the maximum serial number of the POI codes in the database, cleaning the result data, and writing the cleaned result data into the database to form the POI parent library. The specific flow is shown in fig. 6.
The invention fully utilizes various functions of GIS software, uses Python script to form a GIS software plug-in, the plug-in modifies parameters, processing flow and the like in a calculation model according to index change, area change and other factor change, quickly realizes weight assignment and threshold value setting, and introduces a vector calculation mode in the tracking process besides recording the updating result by a traditional grid calculation mode, thereby not only realizing high-precision calculation of the result, but also recording the influence of each element on the updating result and storing the influence into a final file, thus not only obtaining the comprehensive weight assignment of each result unit network, but also obtaining which key elements influencing the threshold value setting are, thereby realizing the tracking of the whole updating process.
And finally, performing step S14, capturing, fusing and updating the network data.
And after the multi-source data is subjected to preliminary fusion processing, the data quality is checked, open internet data such as government open data, high-grade data, Baidu data and the like of all levels are captured, converted into a standard POI data structure, and compared and fused with a POI master library. Researching a conversion method of a coordinate system of internet data, a WGS84 and a local coordinate system, ensuring that captured POI data and mother database data are unified on space coordinates, carrying out a partition color matching scheme to enhance information transmission and readability of results, carrying out database warehousing on the passed data, carrying out data management according to data types, and establishing a basic database data resource catalog.
The method is characterized in that the land resources have the characteristics of mass and heterogeneous structure, so that the method adopts a 'one-key' application tool for processing, utilizes a model builder of ArcGIS software to logically and organically integrate spatial data with different formats, sources, properties and characteristics with attribute data, realizes the operations of converting, adjusting, decomposing, combining and the like of all or part of the data to form a fully compatible seamless spatial data set, then carries out batch processing on various data, links the indexes by a building module for combined calculation according to the combination of each item of data related to indexes (such as elevation, gradient, topographic relief, precipitation, earthquake motion peak acceleration and the like) and the selection of threshold values, and finally establishes a GIS database.
As described above, the AutoCAD data processing and conversion of step S2 includes S21, finding a data frame; s22, copying the CAD files with the same data volume according to the data volume of the frame; s23, determining a conversion data range by using an AutoLisp code; and S24, generating an SCR file and executing corresponding operation. Referring to fig. 7, first, find the data frame, if the data frame is standard, then the data frame is placed in the CAD layer with unified standard, if the data is not standard, then the data frame can be found according to the spatial relationship decision. Secondly, copying CAD files with the same data volume according to the frame data volume, naming according to output names, knowing according to search results of data frames that the extracted frame data volume is actually the CAD data volume n needing to be separated, then, performing replacement operation by using source data, firstly copying n parts of original data, converting different data according to different frames, then, reading and writing names, element types and element numbers of a data set by using a reading module and a writing module of an ETL technology, and finally, transmitting an element type list to another ETL module for processing according to needs, wherein the reliability of the read and written data is ensured in the conversion operation process, the file name is the final result CAD file name, and the file content is consistent with the source data content. Again, the AutoLisp code is used to determine the transformation data range. The Autolisp language is an important tool for developing AutoCAD, is a product of organic combination of the Lisp language and Autocad, plays a great role in the program development process of the AutoCAD, two self-defined functions are designed by using the Autolisp language to realize repeated data conversion, namely a minimum outline function, the function performs repeated comparison work through a nine-point coordinate table of an entity peripheral frame, and the processing speed of a large-scale graphic program is improved; the other is a vertex coordinate table function, which transfers different parameters through reading of a vertex coordinate combination list and determines the range of processing and converting data. And finally, generating an SCR file and executing corresponding operation. SCR file (in AutoCAD, the SCR file is also called a script file, which is an ascii code text file that allows different AutoCAD commands to be combined and executed in a predetermined order).
The main flow of step S2 is as shown in fig. 7 below, in the present invention, the SCR file records the coordinate value and direction of the graphics block inserted by the GIS in the AutoCAD environment, the processed coordinate data is stored in the Excel, instead of in the AutoCAD, the data of GIS point, line and surface are converted to the two-dimensional image by using the strong function processing and computing function of the Excel, the coordinate value of the land block to be converted is generated, then the model structure in the GIS file is analyzed layer by layer, the coordinate information of the information database is indexed, converted to the SCR script command file compatible with the AutoCAD, and finally imported to the AutoCAD to complete the conversion of the land block.
The SCR file automatically processes entity primitives in different ranges on each CAD data, deletes repeated entities, enables the entities in the graph to have uniqueness and rationality, and lays a solid foundation for a basic terrain database.
S3, place name address matching and hanging, after GIS data cleaning and AutoCAD data conversion, the place name address matching and hanging work of the homeland space planning is needed, the place name address matching and positioning is a process of establishing a corresponding relation between a literal description address and a spatial geographic position coordinate of the literal description address, and the address data spatialization is realized by utilizing an address resolution function, and is an important process of the homeland space planning.
The invention utilizes the space constraint relation between the geographic code and the POI point-like ground object to carry out the matching and hanging of the place name and the address, compares and verifies the address analysis results of the Baidu, the God and the Tencent map on the basis of fully considering the logical relation between the geographic code and the reference ground object (comprising street data, cell data and building surface data), and selects a relatively accurate space drop point, thereby improving the accuracy of the address analysis function.
Step S3 specifically includes: s31, calculating a spatial relationship; s32, establishing a space constraint model; and S33, completing the matching verification.
With reference to the main flow diagram of step S3 in fig. 8, firstly, spatial relationship calculation is performed, and spatial relationship analysis between geographic objects in the GIS plays an important role in spatial data modeling, spatial query and analysis, formalized expression and reasoning, and the like.
The invention provides a space constraint address model considering the space relationship by taking the space relationship between the address elements as an entry point, the model is stored in a mode of combining structuralization and unstructured, Chinese addresses and space coordinates thereof expressed based on the space constraint model are stored through a structuralization hierarchical structure, the types of the address elements and the space constraint relationship between the address elements are expressed through the physical attributes of the unstructured address elements, and the two address models are associated through the unique codes of the addresses.
In order to improve the association efficiency and accuracy, a WebServiceAPI (Web service application program interface) provided by map service providers of a Baidu map, a Gauss map and an Tencent map can be used for configuration, the data interface is an HTTPS/HTTP protocol data interface, developers can use any client, server and development language to construct an HTTPS request as required according to WebService API specifications, obtain result data including city codes, areas where addresses are located, streets, doorplates, area codes, coordinate points, matching levels and the like, establish a reference entity library according to return information of various map service APIs and a selection principle of 'accuracy according to spatial information', and screen and help to check, calculate and improve the accuracy of the result of the spatial drop point of address resolution.
Finally, completing matching verification, if the matching data hooked by the place name address data is found to have larger access to the original address, performing cross verification based on distance comparison and confidence level, namely distance comparison, respectively calculating the distances among three falling points by using space falling points returned by three geocode interfaces of Baidu, Gaode and Tencent, and selecting a point pair with a closer distance as a candidate space falling point to remove obviously deviated data, thereby improving the accuracy of an address resolution result; the confidence coefficient is based on an address database and is the basis of data screening, data is matched by using a word segmentation algorithm and a confidence coefficient screening method based on normalized address coding, confidence coefficient scores of candidate space data obtained after distance comparison are compared, and a result with a higher confidence coefficient score is selected as the final effective space data to be matched and connected, so that automatic address matching and positioning are realized, and the matching accuracy is improved.
In order to improve the accuracy and the intelligence of the matching of the place name and the address, the invention applies the Chinese natural language processing technology which is an artificial intelligence medium-depth learning function, can reasonably infer the input intention of a user, record the behavior habit and the personal cognition of the user, and continuously add a word segmentation library to ensure that the word segmentation library is continuously perfected and specialized; and simultaneously associating results selected by the user from the matching candidate results with the search keywords, establishing a corresponding relation between the samples and the labels, generating deep learning sample data, continuously training and learning, and preferentially selecting the place name address associated with the deep learning sample data as a result entry when the same keywords are used for place name address retrieval next time. The address matching method through deep learning can correct errors in advance, and matching precision and efficiency are improved.
After the place name address matching hooks in step S3, POL fusion based on weighted multi-attribute similarity is also required.
And S4, performing batch processing, namely performing tasks such as address resolution, coordinate conversion and the like aiming at the related functions in batches, and covering contents such as editing and inputting, data comparison, fusion processing, parameter importing, address matching and the like. Step S4 specifically includes: and S41, making a data processing template, and mainly making the data processing of each stage in the POI processing flow step by step into a system template. The system comprises a data cleaning module, a POI mother library establishing module, a POI fusion module, a POI and address hanging module and a POI maintenance updating module.
And S42, distributing the manufactured module to a data integration platform through a visual workflow editor of the ETL tool to form data processing service. In the process, the ETL system selects the address data with the highest confidence coefficient from the address library as an automatic matching node according to a matching algorithm, and reads the geographic coordinate information of the automatic matching node for positioning. And when the highest confidence value is less than 0.95, simultaneously providing address data of the first three confidence values for selection, and for a large amount of address data needing to obtain the geographic coordinates, matching and writing the geographic coordinates one by a batch processing method and recording the matching confidence values of the geographic coordinates. And after the batch matching is finished, checking the data with the low reliability, and classifying the error data to the correct coordinate value.
And S43, updating and maintaining the template, downloading a working space from the data integration platform for maintenance through a visual workflow editor of the ETL tool, upgrading as required, and reissuing to the data integration platform.
The address updating maintenance is carried out when the confidence level is not high in the address matching process, and the method comprises the following steps: for the newly added space data, marking whether the newly added space data is a standard address or not and adding the newly added space data into an address library; for the changed space data, the changed space data is divided into a standard address and a common address, the former marks the corresponding standard address in the address base as the common address and adds the standard address, and the latter directly adds the standard address in the address base; for deleting spatial data, the data in the address library is marked as normal spatial data.
According to the fusion method based on the multi-source heterogeneous space planning data, a processing method aiming at a data source is adopted, so that the homeland space planning data from different sources can be efficiently and quickly read and processed; on the other hand, a data deduplication method based on dynamic time warping is adopted, and the method eliminates data redundancy and achieves the purpose of data deduplication by calculating the similarity between data.
Sometimes, in order to improve the data processing capacity and efficiency to the maximum extent, the invention develops and calls some external tools and methods, perfects the data processing flow, merges and fuses different source homeland space data to make the information quantity richer and more complete, and realizes the reuse and update of homeland space information.
The CAD data is an important component of homeland space planning, but because CAD data in an AutoCAD in a special format, such as OLE (object linking and embedding, OLE technology for short), may exist in data that needs to be converted, such special data may have been lost in the data reading process, which causes inconsistency between data results before and after conversion and the original data. The method for calling the AutoLisp codes by the spatial data conversion processing system completes the conversion operation of the CAD data, copies a plurality of data according to the data amount required to be converted, performs conversion operation on the copied data, automatically stores the converted data, deletes redundant data and ensures 100% accuracy of the data.
The method comprehensively utilizes 3 key technologies of fine place name address library construction, place name address characteristic word library construction and big data analysis based on user searching behaviors to improve the address matching degree and the matching precision under the place name address matching technology framework based on Chinese word segmentation.
The invention scientifically recognizes the natural law and the social and economic law of the different national soil space patterns through an intelligent data processing and analyzing tool, so that the functional regions of the national soil space are efficiently divided, and the key basic work of the national soil space planning is facilitated.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A fusion method based on multi-source heterogeneous space planning data is characterized by comprising the following steps:
s1, GIS data processing and fusion;
the step S1 specifically includes:
s11, cleaning, namely cleaning the acquired large amount of space planning data by using an ETL technology;
s12, extracting codes, designing a word segmentation algorithm according to the characteristics of the resource information, and extracting the keywords of the homeland resource information elements and the corresponding codes;
s13, preliminary fusion, namely, realizing multi-source data fusion and updating by adopting weighted multi-attribute similarity, on the basis of a preliminary fusion set, eliminating wrong corresponding objects found by corresponding homeland resource information by using a name feature attribute similarity method with a low threshold value and adopting a character string similarity algorithm by utilizing the high-similarity non-spatial data feature attributes of POI data, and finally finding out corresponding objects which can not be found by the homeland resource information by using the name feature attribute similarity method with a high threshold value and then updating the POI;
s14, capturing, fusing and updating network data;
s2, processing and converting AutoCAD data;
the step S2 specifically includes:
s21, finding a data frame;
s22, copying the CAD files with the same data volume according to the data volume of the frame;
s23, determining a conversion data range by using an AutoLisp code;
s24, generating an SCR file and executing corresponding operation;
s3, hanging the place name and the address in a matching way;
the step S3 specifically includes:
s31, calculating a spatial relationship; utilizing the self-adaptive quadtree to carry out spatial coding to calculate the grid topology, direction and distance relationship of spatial data, establishing the association between the spatial data and corresponding codes, and utilizing the inherent association relationship of the codes to carry out spatial relationship calculation;
s32, establishing a space constraint model;
s33, completing matching verification; respectively calculating the distances among the three falling points by utilizing the space falling points returned by the three geocoding interfaces, selecting a point pair with a closer distance as a candidate space falling point to eliminate obviously deviated data, and matching the data by utilizing a word segmentation algorithm and a confidence screening method;
s4, batch processing;
the step S4 specifically includes:
s41, making a data processing template;
s42, issuing the manufactured module to a data integration platform through a visual workflow editor of an ETL tool to form data processing service;
s43, updating and maintaining the template; and downloading the working space from the data integration platform for maintenance through a visual workflow editor of the ETL tool, upgrading as required, and reissuing to the data integration platform.
2. The fusion method based on multi-source heterogeneous space planning data according to claim 1, wherein the cleaning process of step S11 includes the following steps:
s111, unifying data formats, converting the formats of the collected related data so as to operate the data subsequently, wherein related vectorization or space difference processing needs to be carried out on part of table and text data;
s112, unifying coordinates and precision, wherein a 2000 national geodetic coordinate system (CGCS2000) and a 'Gauss-Kruger' projection are adopted, a 1985 national elevation standard is adopted for a land area part, a theoretical depth standard surface elevation standard is adopted for a sea area part, and on the other hand, grid units are determined according to the area size and the precision of the data which can be obtained, so that the consistency of the data precision in the same grid is ensured, and the grid sizes including terrain factors, precipitation factors and soil attribute factors are the same;
s113, adding an attribute structure, carrying out data standardization input and output, constructing an integrated model based on a GIS, establishing a flow frame by constructing a multi-element system and an index system, standardizing, organizing and managing data collected by each channel, supporting dynamic loading and displaying of diagrams, documents, space vectors and raster data, and establishing and embedding a naming rule in result data so as to save results to finish corresponding standardization work;
and S114, optimizing data quality, wherein firstly, the grid layers are guaranteed to have the same grid unit size and row and column number through sampling and masking, the data are cut to guarantee the consistency of the data space range, secondly, the design of a database table and fields is standardized, the dictionary table contains a main key to guarantee the consistency of field names and types, empty columns are avoided, and the work of deleting special characters, forming a standard data attribute structure, optimizing data quality and unifying attribute field names is respectively finished.
3. The fusion method based on multi-source heterogeneous space planning data according to claim 1, wherein the extracting the code of step S12 includes the following steps:
s121, preprocessing data;
s122, removing the duplicate of other POLs;
s123, analyzing other sources and a parent library POL by a Levensiton algorithm;
s124, assigning similarity and distance attributes of the text character strings;
s125, judging whether other POLs need to be fused or not;
s126, updating the Oracle master library and generating metadata;
in the process of step S12, an index field is created to create an index for the KEY _ ID field, and meanwhile, a determination is made as to whether the database has the same name table.
4. The multi-source heterogeneous space planning data-based fusion method according to claim 1, wherein the POI update of step S13 includes the following steps:
s131, analyzing and sorting data;
s132, carrying out duplicate removal treatment;
s133, giving weight;
s134, building a POL code;
and S135, forming a new mother library.
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