CN-122019655-A - Automatic conversion method for land contract management right data format

CN122019655ACN 122019655 ACN122019655 ACN 122019655ACN-122019655-A

Abstract

The invention discloses an automatic conversion method of a land contract management right data format, which relates to the technical field of geographic information, and comprises the following steps of comparing and analyzing a source database standard and a target database standard to construct a multidimensional mapping relation model; and constructing an automatic conversion model by taking the GIS platform as a core according to the multidimensional mapping relation model, performing performance tuning by the model through a distributed computing architecture, a staged hash association algorithm, a signature space mapping deduplication mechanism, an incremental migration mechanism, a coordinate batch generation algorithm and a storage optimization strategy, performing data inspection on data converted by the automatic conversion model, and returning an error report if the inspection is not passed. The invention can realize the high-efficiency automatic conversion of the database format, ensure the integrity and the availability of the data in the cross-platform application and provide powerful support for the smooth development of the real estate registration work.

Inventors

ZHANG JINSHENG
XIANG JUNHUA
ZHAO YONG
ZHOU YANG
WANG LINGRONG
JIANG TAO
FENG JINGYI
YAN JIE
ZHANG YU
LUO LEI
LU JINGHONG
ZHANG YI

Assignees

贵州省第二测绘院

Dates

Publication Date: 20260512
Application Date: 20260416

Claims (10)

1. The automatic conversion method for the land contract management right data format is characterized by comprising the following steps of: S1, comparing and analyzing a layer structure, field definition, value domain coding and space topology constraint in a source database standard and a target database standard, and constructing a multidimensional mapping relation model by establishing a corresponding table of a source layer and a target layer, a mapping table of a source field and a target field, a value domain code conversion table and a space topology rule corresponding table; S2, taking a GIS platform as a core, taking a layer corresponding table, a field mapping table, a code conversion table and a space topology rule corresponding table in the multidimensional mapping relation model as input parameters, and connecting a data extraction tool, a field calculation tool, a space connection tool and a Python script in series through a modeling tool provided by the GIS platform to construct an automatic conversion model capable of automatically executing batch extraction, conversion and reconstruction of space and attribute data according to the mapping relation, wherein the automatic conversion model performs performance optimization through a distributed calculation architecture, a staged hash association algorithm, a signature space mapping deduplication mechanism, an incremental migration mechanism, a coordinate batch generation algorithm and a storage optimization strategy; and S3, checking the data integrity, the logic consistency, the space topology correctness and the standard compliance of the data converted by the automatic conversion model, and if the data does not pass the checking, returning an error report.
2. The method according to claim 1, wherein said step S1 comprises the steps of: S1.1, analyzing the sources of attribute information one by taking a layer of a target database standard as a reference, and establishing a one-to-many or many-to-one mapping relation with the source database standard; s1.2, respectively establishing field mapping rules and value domain mapping rules according to field value domain dissimilarity and generation logic on the basis of layer mapping, wherein the field mapping rules are defined as direct mapping, direct assignment, derivative calculation and irrelevant neglect, the value domain mapping rules are defined as code conversion types and are used for processing the condition that the same semantic field in a source standard and a target standard adopts different code values and forming a code conversion table; and S1.3, defining a spatial data topological relation conversion rule from a source database standard to a target database standard.
3. The method according to claim 2, wherein in step S2, the staged hash association algorithm specifically comprises the steps of: constructing a hash index for the small table participating in the association based on the association key; Broadcasting the hash index to all computing nodes; And carrying out local hash matching on the data on each computing node.
4. A method according to claim 3, wherein in step S2, the signature space mapping deduplication mechanism specifically comprises the steps of: mapping the field combination of the record to be deduplicated into an m-dimensional signature vector through a plurality of independent hash functions; partitioning the m-dimensional signature vector space by adopting a random hyperplane to enable similar records to fall into the same partition or adjacent partitions; And calculating a signature vector for the new record, positioning the affiliated partition, calculating vector similarity only with the historical records in the partition and the adjacent partition, and judging whether the operation is repeated or not according to a preset threshold value.
5. The method according to claim 4, wherein in the step S2, the coordinate batch generation algorithm specifically includes the steps of: ordering the religion data by using Morton codes, and adaptively determining a space slicing boundary by minimizing an objective function comprising a load balancing term and a space compactness term; in each fragment, utilizing a geometric object iterator of the GIS platform to extract the break point coordinates of the land in batches; and establishing a topology multiplexing cache for the boundary line shared by adjacent land, so as to avoid repeated generation.
6. The method according to claim 5, wherein in the step S2, the distributed computing architecture is composed of a master node and a computing node, the master node is responsible for task slicing and scheduling, the computing node is responsible for executing data processing tasks in the slices, the master node monitors loads of the nodes and dynamically distributes tasks, and finally the master node merges the results of the nodes and rebuilds indexes.
7. The method according to claim 6, wherein the step S3 specifically comprises the steps of: s3.1, checking field integrity, value range compliance and logic consistency of the converted data by using data quality checking software; S3.2, verifying the geometric and topological relation of the converted space data by using a topological inspection tool of the GIS platform; And S3.3, positioning a problem source and generating an error report according to errors found by the data quality inspection software and the topology inspection.
8. The method according to claim 7, wherein in the step S2, the storage optimization strategy includes uniformly storing all intermediate data and result data in a file geographic database format, and reducing disk input/output overhead by using binary storage, space and attribute indexes.
9. The method according to claim 8, wherein in the step S2, the incremental migration mechanism includes marking a data change timestamp in the source database, reading a configured time point through the Python script, extracting only change data with a timestamp greater than the time point for conversion, and merging with existing data in the target database.
10. The method of claim 9, wherein the distributed computing architecture is provided with a fault tolerant mechanism, and when any computing node fails, the master node reassigns its incomplete tasks to other computing nodes.

Description

Automatic conversion method for land contract management right data format Technical Field The invention relates to the technical field of geographic information, in particular to an automatic conversion method for a land contract management right data format. Background With the comprehensive promotion of a unified real estate registration system in China, rural land contractual operation right confirmation registration data accumulated by agricultural rural departments need to be integrally transferred to a real estate registration platform so as to realize unified management of natural resources. However, the agricultural rural department database complies with the rural land contract right-of-business registration database specification, while the real estate registration database complies with the real estate registration database standard, and the two have significant differences in field structure, data type, coding rules, spatial topological relationship and the like. Currently, the conversion of agricultural entitlement data to real estate standards relies primarily on manual processing or single software tool conversion. The manual processing is extremely low in efficiency, human errors are easy to introduce, the requirement of large-scale data conversion is difficult to meet, a single software tool is limited by functions, collaborative conversion of space data and attribute data cannot be processed simultaneously, particularly when the county-level agricultural weight data base is faced with huge amounts of data of hundreds of thousands of land parcels and tens of millions of boundary points, the conversion process can take weeks or even months, and the accuracy and the integrity of conversion results are difficult to guarantee. In addition, the conventional method lacks an effective verification mechanism for data quality, so that the converted data is often returned due to the fact that the data does not meet the target standard requirement, and the advancing efficiency of real estate registration work is seriously affected. Therefore, there is a need for an efficient, accurate, automated method for converting a data format of a data stream of a large amount of complex space data, so as to solve the above-mentioned technical problems. Disclosure of Invention The invention aims to solve the technical problem of providing an automatic conversion method for a land contract management right data format aiming at the defects of the prior art. In order to achieve the above purpose, the invention adopts the following technical scheme: an automatic conversion method for land contract management right data format comprises the following steps: S1, comparing and analyzing a layer structure, field definition, value domain coding and space topology constraint in a source database standard and a target database standard, and constructing a multidimensional mapping relation model by establishing a corresponding table of a source layer and a target layer, a mapping table of a source field and a target field, a value domain code conversion table and a space topology rule corresponding table; S2, taking a GIS platform as a core, taking a layer corresponding table, a field mapping table, a code conversion table and a space topology rule corresponding table in the multidimensional mapping relation model as input parameters, and connecting a data extraction tool, a field calculation tool, a space connection tool and a Python script in series through a modeling tool provided by the GIS platform to construct an automatic conversion model capable of automatically executing batch extraction, conversion and reconstruction of space and attribute data according to the mapping relation, wherein the automatic conversion model performs performance optimization through a distributed calculation architecture, a staged hash association algorithm, a signature space mapping deduplication mechanism, an incremental migration mechanism, a coordinate batch generation algorithm and a storage optimization strategy; and S3, checking the data integrity, the logic consistency, the space topology correctness and the standard compliance of the data converted by the automatic conversion model, and if the data does not pass the checking, returning an error report. Further, the step S1 specifically includes the following steps: S1.1, analyzing the sources of attribute information one by taking a layer of a target database standard as a reference, and establishing a one-to-many or many-to-one mapping relation with the source database standard; s1.2, respectively establishing field mapping rules and value domain mapping rules according to field value domain dissimilarity and generation logic on the basis of layer mapping, wherein the field mapping rules are defined as direct mapping, direct assignment, derivative calculation and irrelevant neglect, the value domain mapping rules are defined as code conversion types and are used for processing