A method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data

Abstract

This invention discloses a method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data, including the following steps: S1, collecting data on three types of transportation infrastructure: highway, water transport, and aviation; S2, establishing a data dictionary and metadata standards based on specifications, and formulating a database partitioning strategy and interface specifications; S3, standardizing attribute data, satellite image data, and spatial vector data; S4, formulating a spatial data sharding strategy based on geographic regions, building a full-text index and spatial index system, and optimizing storage and query; S5, using MySQL, PostGIS, MongoDB, ElasticSearch, and K-V storage for collaborative management; S6, designing expansion mechanisms at the storage, retrieval, and computing layers; and S7, establishing a unified retrieval platform based on the database, integrating raster and vector data, and supporting spatial visualization and semantic retrieval. This invention enhances the ability to integrate, retrieve, and expand transportation infrastructure data, and is suitable for the construction of a comprehensive transportation information platform.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation infrastructure data management, and in particular to a method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data. Background Art

[0002] With the continuous acceleration of urbanization, the transportation infrastructure system is becoming increasingly complex, covering multiple modes of transportation such as roads, waterways, and aviation. To effectively support the planning, construction, operation, and management of urban transportation, it is necessary to efficiently and accurately collect, manage, and apply transportation infrastructure data. Traditional transportation infrastructure data management systems often use a single data source and a single data type storage method, usually based on relational databases such as Oracle or MySQL, and only store structured attribute data of the infrastructure. This approach was able to meet basic needs in the early days when the data volume was small and the data type was single. However, with the exponential growth of data scale and the continuous enrichment of data types, traditional management methods have exposed many limitations.

[0003] Current transportation infrastructure databases often face challenges such as heterogeneous data sources, inconsistent data standards, separate storage of spatial and attribute data, and inefficient retrieval. Transportation infrastructure encompasses not only traditional attribute table data but also extensive amounts of spatial vector data, satellite imagery, and dynamic traffic flow data. Existing systems often fail to achieve unified management of both structured and unstructured data, leading to severe data silos and difficulties in data sharing and linkage between different departments and subsystems. Furthermore, existing systems have limited spatial data processing capabilities, typically supporting only simple coordinate storage and static queries. They lack efficient spatial indexing mechanisms and dynamic scalability, making them difficult to adapt to the demands of large-scale, real-time transportation data management.

[0004] In terms of spatial data management, traditional transportation infrastructure databases commonly suffer from poor spatial retrieval performance and load imbalance caused by uneven spatial data distribution. Most systems utilize single-machine deployments when the amount of spatial data is small, and spatial retrieval relies on traditional B-tree or simple R-tree index structures. As the scale of spatial data increases, system retrieval latency increases significantly, and load balancing and scalability cannot meet business needs. Furthermore, the lack of effective sharding management and replica synchronization mechanisms for spatial data makes it difficult to ensure data consistency in high-concurrency environments, posing significant risks to the overall stability and availability of the system.

[0005] At the retrieval level, existing traffic data systems mostly rely on single relational database indexes or simple full-text search modules, lacking composite search mechanisms designed for the multi-source data characteristics of transportation infrastructure. Traditional methods are particularly incapable of efficient joint retrieval when dealing with mixed queries of spatial and textual data. Existing systems lack optimized designs for the collaborative work of full-text and spatial indexes, resulting in limited retrieval performance and an inability to support rapid queries under complex combinations of conditions. Furthermore, faced with the continued growth of data volumes, traditional retrieval systems lack scalability, and their replica expansion and sharding management mechanisms are simplistically designed. This concentrates query pressure on a small number of nodes, leading to unstable query response times and limited system throughput.

[0006] At the computing level, with the increasing complexity of traffic data processing, the single-server model for data query, retrieval, and computation is no longer sufficient. Existing transportation infrastructure database systems generally lack sharded cluster-based computing scheduling mechanisms, and their inter-node load balancing and task allocation strategies are poorly designed. This can easily lead to node computing bottlenecks in high-concurrency scenarios, resulting in overall performance degradation. This is particularly true for large-scale spatial queries, joint retrieval, and dynamic data updates, as traditional systems lack elastic scalability, and processing capacity struggles to scale linearly with data volume.

[0007] In terms of data standardization, current transportation infrastructure data management systems often lack unified data dictionaries and metadata standards. Data formats, field definitions, and value standards vary across departments and systems, leading to difficulties in data integration and poor interface interoperability, severely impacting data sharing and application efficiency. Furthermore, an incomplete metadata management system, fragmented dataset descriptions, and missing or inconsistently defined meta-attributes such as spatial reference systems, timestamps, and access permissions further exacerbate data isolation issues between systems.

[0008] Given the dynamic nature of transportation infrastructure, current systems lack adequate management and update mechanisms for dynamic data such as traffic flow, air traffic flow, and aviation business volume. Traditional systems often rely on periodic manual batch updates, lacking real-time dynamic updates and version control systems. This results in poor data timeliness and an inability to promptly reflect changes in traffic status, impacting the data's value in decision-making. Furthermore, the lack of flexible version control and change traceability mechanisms makes version conflicts and data consistency issues more likely to arise when simultaneously updating multi-source data.

[0009] At the retrieval application level, traditional transportation infrastructure data management systems typically only support simple keyword searches or basic spatial range queries. They lack semantic search mechanisms that combine multiple conditions, such as keywords, spatial ranges, timestamps, and attribute tags. This results in low search efficiency and poorly relevant results for complex queries. Existing systems struggle to meet the requirements for in-depth, fine-grained analysis of transportation data, particularly when comprehensive searches across data types and hierarchies are required. This severely restricts the effectiveness of applications such as transportation planning, intelligent traffic scheduling, and emergency response.

[0010] In summary, existing transportation infrastructure data management systems have significant deficiencies in multi-source data integration, spatial data sharding management, replica synchronization mechanisms, retrieval-layer index optimization, computational-layer elastic expansion, data standardization and unification, and dynamic data management. These deficiencies make them unable to adapt to the needs of building a large-scale, multi-type, and real-time integrated transportation infrastructure database. Therefore, a new method for constructing an integrated transportation infrastructure spatial database is urgently needed that can achieve multi-source heterogeneous data integration management, efficient spatial data sharding and retrieval, coordinated expansion of the three layers of storage, retrieval, and computation, standardized data governance, and dynamic data synchronization. This approach can effectively improve the management efficiency, retrieval performance, and system scalability of transportation infrastructure data, and support the development needs of future intelligent transportation systems.

[0011] Therefore, how to provide a method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0012] One purpose of the present invention is to propose a method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data. The present invention fully integrates relational database, document database and spatial database technologies, combines full-text indexing with spatial indexing systems, designs database sharding, master-copy synchronization, unified retrieval and elastic expansion mechanisms, and describes in detail a spatial database construction scheme for efficiently managing various transportation infrastructure data such as highways, water transport and aviation. The scheme has the advantages of high data integration, high retrieval efficiency, strong expansion capability and high degree of management standardization.

[0013] The method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data according to an embodiment of the present invention includes the following steps:

[0014] S1. Collect multi-source data on three types of transportation infrastructure: roads, water transport, and aviation;

[0015] S2. Establish data dictionaries and metadata standards for collected multi-source data, formulate database partitioning strategies and unified interface specifications based on industry standards;

[0016] S3, standardize the infrastructure attribute data, satellite image data and spatial vector data in multi-source data;

[0017] S4. Develop a spatial data sharding strategy based on geographic regions, build a full-text index and spatial index system, and optimize storage and query in a replicated cluster environment.

[0018] S5. Build a comprehensive transportation infrastructure spatial database, using MySQL, PostGIS, MongoDB, ElasticSearch, and KV storage for collaborative management;

[0019] S6. Design expansion mechanisms at the storage, retrieval, and computing layers to accommodate the growth of transportation infrastructure data and changes in retrieval requirements.

[0020] S7. Establish a unified retrieval platform based on the comprehensive transportation infrastructure spatial database, integrate raster image data and spatial vector data, and support spatial visualization and semantic retrieval.

[0021] Optionally, the multi-source data includes infrastructure attribute data, traffic flow data, navigation flow data, aviation business volume data, travel origin-destination distribution data, administrative division data, economic data, population data, satellite image data and spatial vector data.

[0022] Optionally, the standardization process includes:

[0023] Standardize fields of infrastructure attribute data;

[0024] Satellite image data is sliced ​​using a tile pyramid approach and metadata is managed using a combination of full-text indexing and the MongoDB document model.

[0025] Spatial vector data is expressed in a standardized format using GeoJSON and stored in the PostGIS spatial database.

[0026] Optionally, the replication cluster environment includes sharded storage of spatial data, full-text indexes, and metadata, configuring a master replica and at least one slave replica for each data shard, using a replica synchronization mechanism to maintain data consistency, performing load balancing queries based on replica sets, and having the slave replica take over the master replica role in the event of a node failure.

[0027] Optionally, the S2 specifically includes:

[0028] S21. Based on national standards and industry technical specifications in the field of geographic information, establish a data dictionary for the collected multi-source data. The data dictionary defines the data field name, field data type, field value range, and field unit type.

[0029] S22. Establish metadata standards based on multi-source data attribute information. The metadata standards include fields such as dataset name, data source, spatial reference system, data update time, and data access rights.

[0030] S23. Develop a database partitioning strategy to store infrastructure attribute data, traffic flow data, navigation flow data, air traffic volume data, travel origin-destination distribution data, administrative division data, economic data, and population data in a MySQL relational database; store satellite imagery data in a MongoDB document-based database; and store spatial vector data in a PostGIS spatial database.

[0031] S24. Develop unified interface specifications, which include data access interface, data retrieval interface and data exchange format types.

[0032] Optionally, the S3 specifically includes:

[0033] S31. Standardize the fields of infrastructure attribute data to unify data field names, field data types, field value ranges, and field unit types;

[0034] S32. Perform tile pyramid layered slicing processing on satellite image data:

[0035] ;

[0036] in, is the number of slice levels, is the original image spatial resolution, is the target tile resolution, Indicates rounding up operation;

[0037] S33. Use Geoserver to generate slice services for the sliced ​​satellite image data, establish full-text indexes with ElasticSearch, and manage the metadata of the image data based on the MongoDB document model.

[0038] S34. Spatial vector data is standardized in GeoJSON format and stored in the PostGIS spatial database. A spatial index based on the R-tree structure and an auxiliary index based on the hash structure are established for spatial vector elements to optimize spatial data retrieval performance.

[0039] Optionally, the S4 specifically includes:

[0040] S41. Divide spatial data based on geographical regions and formulate sharding strategies:

[0041] ;

[0042] in, Number the spatial data slices. and is the latitude and longitude coordinates of the spatial data, and are the minimum longitude and latitude of the spatial region, and Divide the intervals into spatial slices, is the number of slices in the longitude direction, Indicates a round-down operation;

[0043] S42. Within the spatial data shard, establish an R-tree-based spatial index for the spatial elements, record the minimum bounding rectangle parameters of the spatial objects, and use a hash-assisted index to accelerate attribute retrieval;

[0044] S43. Establish a full-text index for the metadata associated with the spatial data using the ElasticSearch indexing engine. The index fields include the dataset identifier, spatial range description, timestamp, and key information tags.

[0045] S44. In a replication cluster environment, spatial data shards and full-text index replicas are distributed and stored, and query optimization is performed based on the load status of replica nodes:

[0046] ;

[0047] in, For the The query load of replica nodes, For the The query request frequency of each node, For the The performance weight of each node, For the The number of replicas maintained by a node.

[0048] Optionally, the S5 specifically includes:

[0049] S51. Use a MySQL relational database to store structured data, including infrastructure attribute data, traffic flow data, air traffic flow data, aviation business volume data, travel origin-destination distribution data, administrative division data, economic data, and population data;

[0050] S52. Use PostGIS spatial database to manage spatial vector data and associated attributes, establish spatial index based on R-tree structure, and use auxiliary hash index to accelerate attribute retrieval;

[0051] S53. Use MongoDB document-based database to manage metadata, including dataset description information, spatial reference system parameters, data update time, and data access permission identifier;

[0052] S54. Use the ElasticSearch search engine to manage full-text index data, where the full-text index data includes a dataset identifier, a spatial range description, a time tag, and a description of key attributes;

[0053] S55. Use the KV storage system to manage frequently accessed cache data, including query result cache, index metadata cache, and replica synchronization status cache. Calculate the cache space requirements:

[0054] ;

[0055] in, is the total cache space required, For the The space occupied by a single item in the class cache, For the Class cache access frequency ratio, The total number of cache categories.

[0056] Optionally, the S6 specifically includes:

[0057] S61. The storage layer consists of MySQL, MongoDB, and PostGIS. A data expansion mechanism is designed in the storage layer, using partitioning and replica expansion to expand data capacity:

[0058] ;

[0059] in, is the total data capacity, is the number of partitions, Storage capacity for a single partition, is the replica redundancy ratio;

[0060] S62. The retrieval layer is composed of ElasticSearch. An index expansion mechanism is designed in the retrieval layer to expand the retrieval load based on the full-text index sharding and replica sharding strategies:

[0061] ;

[0062] in, is the number of index replicas, is the current query request rate, is the maximum query rate supported by a single replica, Indicates rounding up operation;

[0063] S63. The computing layer consists of a shard cluster. A task expansion mechanism is designed at the computing layer, using task load sharding and node concurrency expansion strategies:

[0064] ;

[0065] in, For the The load level of each computing node, For the The amount of tasks assigned to each node, For the The calculation performance coefficient of each node.

[0066] Optionally, the S7 specifically includes:

[0067] S71. Build a unified search platform based on the comprehensive transportation infrastructure spatial database, which accesses structured data, spatial data, metadata, and full-text index data;

[0068] S72, integrate raster image data and spatial vector data to build a multi-level spatial visualization model, supporting overlay rendering of spatial elements and raster layers;

[0069] S73. Establish a semantic retrieval mechanism based on full-text indexing, supporting combined queries based on keywords, spatial ranges, timestamps, and attribute tags;

[0070] S74. Based on the search results, dynamically load the spatial vector elements and the corresponding raster image layers for visualization and view update.

[0071] The beneficial effects of the present invention are:

[0072] This invention integrates multi-source data from three types of transportation infrastructure: highways, waterways, and aviation, establishes a unified data dictionary and metadata standard, and formulates standardized data access and management specifications. This addresses the existing system's issues of chaotic data formats, inconsistent standards, and difficulty sharing, enabling data integration and interconnection between transportation subsystems. By categorizing and storing different types of data and collaboratively managing them using a MySQL relational database, a PostGIS spatial database, a MongoDB document database, and the ElasticSearch full-text search engine, a hybrid storage architecture that integrates structured and unstructured data is constructed, significantly improving data storage flexibility and management efficiency.

[0073] This paper proposes a spatial data sharding strategy based on geographic region divisions, combines spatial indexing with full-text indexing to construct a multi-level retrieval system, and implements distributed storage and load balancing optimization of spatial data and index replicas in a replicated cluster environment. This addresses the performance bottlenecks and storage load imbalances inherent in existing technologies for spatial data retrieval, significantly improving query efficiency and system stability for large-scale spatial data. Furthermore, this paper incorporates elastic expansion mechanisms at the storage, retrieval, and computing layers, supporting dynamic expansion of data capacity, the number of replicas, and the number of computing nodes, ensuring the system maintains high availability and scalability in the face of massive data growth and peak access times.

[0074] In response to the dynamic nature of transportation infrastructure, this invention establishes a dynamic update mechanism and version control system for traffic flow, navigation flow, and aviation business volume data. This supports incremental data updates and historical version tracing, improving the timeliness and consistency of data management. Furthermore, through the construction of a unified search platform, this invention implements multi-conditional semantic search based on keywords, spatial range, timestamp, and attribute tags, integrating raster image data with spatial vector data for spatial visualization. This overcomes the single retrieval method and slow response of traditional systems, and enhances the comprehensive search and analysis capabilities of transportation infrastructure data.

[0075] In summary, the present invention systematically solves the shortcomings of existing transportation infrastructure databases in data standardization, storage and retrieval performance, system scalability and dynamic management capabilities through multi-source heterogeneous data fusion, standardized governance, spatial data sharding optimization, three-layer expansion mechanism and dynamic update system design, significantly improves the management efficiency, retrieval performance and application flexibility of comprehensive transportation infrastructure data, and provides strong data support for the construction and application of intelligent transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0077] Figure 1 This is a flow chart of the method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data proposed by the present invention;

[0078] Figure 2 This is a schematic diagram of the spatial data sharding strategy of the method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data proposed in the present invention;

[0079] Figure 3 This is a functional structure diagram of the unified retrieval platform for the method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data proposed in the present invention. DETAILED DESCRIPTION

[0080] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0081] refer to Figure 1-3 ,The method for constructing a comprehensive transportation infrastructure spatial database based on ,multi-source data includes the following steps:

[0082] S1. Collect multi-source data on three types of transportation infrastructure: roads, water transport, and aviation;

[0083] S2. Establish data dictionaries and metadata standards for collected multi-source data, formulate database partitioning strategies and unified interface specifications based on industry standards;

[0084] S3, standardize the infrastructure attribute data, satellite image data and spatial vector data in multi-source data;

[0085] S4. Develop a spatial data sharding strategy based on geographic regions, build a full-text index and spatial index system, and optimize storage and query in a replicated cluster environment.

[0086] S5. Build a comprehensive transportation infrastructure spatial database, using MySQL, PostGIS, MongoDB, ElasticSearch, and KV storage for collaborative management;

[0087] S6. Design expansion mechanisms at the storage, retrieval, and computing layers to accommodate the growth of transportation infrastructure data and changes in retrieval requirements.

[0088] S7. Establish a unified retrieval platform based on the comprehensive transportation infrastructure spatial database, integrate raster image data and spatial vector data, and support spatial visualization and semantic retrieval.

[0089] This invention builds a unified and integrated data foundation by collecting multi-source data of three types of transportation infrastructure: roads, water transport, and aviation. It realizes standardized access and comprehensive management of heterogeneous data between different transportation subsystems, significantly improving the integration of transportation infrastructure data and its ability to support the construction of intelligent transportation systems.

[0090] In this embodiment, the multi-source data includes infrastructure attribute data, traffic flow data, navigation flow data, aviation business volume data, travel origin-destination distribution data, administrative division data, economic data, population data, satellite image data and spatial vector data.

[0091] By clarifying the types and scope of multi-source data, the present invention covers all types of static and dynamic data required for the operation and management of transportation infrastructure, ensures the comprehensiveness and adaptability of the data in the integrated transportation database, and improves the integrity and practicality of the unified fusion of multi-source heterogeneous data.

[0092] In this embodiment, the standardization process includes:

[0093] Standardize fields of infrastructure attribute data;

[0094] Satellite image data is sliced ​​using a tile pyramid approach and metadata is managed using a combination of full-text indexing and the MongoDB document model.

[0095] Spatial vector data is expressed in a standardized format using GeoJSON and stored in the PostGIS spatial database.

[0096] The present invention eliminates data structure differences and description inconsistencies by standardizing infrastructure attribute data, satellite image data, and spatial vector data, thereby improving the compatibility, sharing, and sustainable management capabilities of multi-source data in a unified database environment.

[0097] In this embodiment, the replication cluster environment includes sharded storage of spatial data, full-text indexes, and metadata, configuring a master copy and at least one slave copy for each data shard, using a replica synchronization mechanism to maintain data consistency, performing load balancing queries based on replica sets, and having the slave copy take over the master copy role in the event of a node failure.

[0098] The present invention achieves high availability and load-balanced query by introducing spatial data and index replica shard storage and master-slave synchronization mechanism in a replication cluster environment, and enhances the stability, reliability and fault tolerance of the system in large-scale transportation spatial data management.

[0099] In this embodiment, S2 specifically includes:

[0100] S21. Based on national standards and industry technical specifications in the field of geographic information, establish a data dictionary for the collected multi-source data. The data dictionary defines the data field name, field data type, field value range, and field unit type.

[0101] S22. Establish metadata standards based on multi-source data attribute information. The metadata standards include fields such as dataset name, data source, spatial reference system, data update time, and data access rights.

[0102] S23. Develop a database partitioning strategy to store infrastructure attribute data, traffic flow data, navigation flow data, air traffic volume data, travel origin-destination distribution data, administrative division data, economic data, and population data in a MySQL relational database; store satellite imagery data in a MongoDB document-based database; and store spatial vector data in a PostGIS spatial database.

[0103] S24. Develop unified interface specifications, which include data access interface, data retrieval interface and data exchange format types.

[0104] This invention effectively standardizes the transportation infrastructure data management system by establishing a data dictionary and metadata standards, formulating database sub-strategies and unified interface specifications, improving data access consistency, system interoperability and multi-source data expansion flexibility, and supporting the standardization of transportation information systems.

[0105] In this embodiment, S3 specifically includes:

[0106] S31. Standardize the fields of infrastructure attribute data to unify data field names, field data types, field value ranges, and field unit types;

[0107] S32. Perform tile pyramid layered slicing processing on satellite image data:

[0108] ;

[0109] in, is the number of slice levels, is the original image spatial resolution, is the target tile resolution, Indicates rounding up operation;

[0110] S33. Use Geoserver to generate slice services for the sliced ​​satellite image data, establish full-text indexes with ElasticSearch, and manage the metadata of the image data based on the MongoDB document model.

[0111] S34. Spatial vector data is standardized in GeoJSON format and stored in the PostGIS spatial database. A spatial index based on the R-tree structure and an auxiliary index based on the hash structure are established for spatial vector elements to optimize spatial data retrieval performance.

[0112] The present invention systematically improves the retrieval efficiency, storage optimization and data access convenience of transportation infrastructure spatial data by standardizing attribute data fields, tiling satellite image data and constructing full-text indexes, and standardizing storage and index optimization of spatial vector data.

[0113] In this embodiment, the S4 specifically includes:

[0114] S41. Divide spatial data based on geographical regions and formulate sharding strategies:

[0115] ;

[0116] in, Number the spatial data slices. and is the latitude and longitude coordinates of the spatial data, and are the minimum longitude and latitude of the spatial region, and Divide the intervals into spatial slices, is the number of slices in the longitude direction, Indicates a round-down operation;

[0117] S42. Within the spatial data shard, establish an R-tree-based spatial index for the spatial elements, record the minimum bounding rectangle parameters of the spatial objects, and use a hash-assisted index to accelerate attribute retrieval;

[0118] S43. Establish a full-text index for the metadata associated with the spatial data using the ElasticSearch indexing engine. The index fields include the dataset identifier, spatial range description, timestamp, and key information tags.

[0119] S44. In a replication cluster environment, spatial data shards and full-text index replicas are distributed and stored, and query optimization is performed based on the load status of replica nodes:

[0120] ;

[0121] in, For the The query load of replica nodes, For the The query request frequency of each node, For the The performance weight of each node, For the The number of replicas maintained by a node.

[0122] This invention improves the retrieval performance, load balancing and system scalability of traffic spatial data in a distributed environment by designing a spatial data sharding strategy based on geographical area division and combining spatial indexing with full-text indexing to optimize query load, thus supporting high-concurrency and large-scale traffic data application scenarios.

[0123] In this embodiment, the S5 specifically includes:

[0124] S51. Use a MySQL relational database to store structured data, including infrastructure attribute data, traffic flow data, air traffic flow data, aviation business volume data, travel origin-destination distribution data, administrative division data, economic data, and population data;

[0125] S52. Use PostGIS spatial database to manage spatial vector data and associated attributes, establish spatial index based on R-tree structure, and use auxiliary hash index to accelerate attribute retrieval;

[0126] S53. Use MongoDB document-based database to manage metadata, including dataset description information, spatial reference system parameters, data update time, and data access permission identifier;

[0127] S54. Use the ElasticSearch search engine to manage full-text index data, where the full-text index data includes a dataset identifier, a spatial range description, a time tag, and a description of key attributes;

[0128] S55. Use the KV storage system to manage frequently accessed cache data, including query result cache, index metadata cache, and replica synchronization status cache. Calculate the cache space requirements:

[0129] ;

[0130] in, is the total cache space required, For the The space occupied by a single item in the class cache, For the Class cache access frequency ratio, The total number of cache categories.

[0131] The present invention classifies and manages different types of traffic data according to the characteristics of structured data, spatial data, metadata and index data, and uses multiple databases to collaboratively build a fusion storage system, which significantly improves the storage efficiency, query performance and system response speed of traffic infrastructure data.

[0132] In this embodiment, S6 specifically includes:

[0133] S61. The storage layer consists of MySQL, MongoDB, and PostGIS. A data expansion mechanism is designed in the storage layer, using partitioning and replica expansion to expand data capacity:

[0134] ;

[0135] in, is the total data capacity, is the number of partitions, Storage capacity for a single partition, is the replica redundancy ratio;

[0136] S62. The retrieval layer is composed of ElasticSearch. An index expansion mechanism is designed in the retrieval layer to expand the retrieval load based on the full-text index sharding and replica sharding strategies:

[0137] ;

[0138] in, is the number of index replicas, is the current query request rate, is the maximum query rate supported by a single replica, Indicates rounding up operation;

[0139] S63. The computing layer consists of a shard cluster. A task expansion mechanism is designed at the computing layer, using task load sharding and node concurrency expansion strategies:

[0140] ;

[0141] in, For the The load level of each computing node, For the The amount of tasks assigned to each node, For the The calculation performance coefficient of each node.

[0142] The present invention ensures the high availability and scalability of the transportation infrastructure database under the conditions of continuous growth in data volume and changes in access pressure by designing expansion mechanisms at the storage layer, retrieval layer and computing layer respectively, and adopting partition expansion, replica synchronization and distributed computing load balancing strategies.

[0143] In this embodiment, the S7 specifically includes:

[0144] S71. Build a unified search platform based on the comprehensive transportation infrastructure spatial database, which accesses structured data, spatial data, metadata, and full-text index data;

[0145] S72, integrate raster image data and spatial vector data to build a multi-level spatial visualization model, supporting overlay rendering of spatial elements and raster layers;

[0146] S73. Establish a semantic retrieval mechanism based on full-text indexing, supporting combined queries based on keywords, spatial ranges, timestamps, and attribute tags;

[0147] S74. Based on the search results, dynamically load the spatial vector elements and the corresponding raster image layers for visualization and view update.

[0148] The present invention integrates raster and vector data displays through a unified retrieval platform, establishes a semantic retrieval mechanism combining keywords, spatial range, timestamp and attribute tags, improves the intelligent retrieval capability and spatial visualization analysis capability of comprehensive traffic data, and enhances the application breadth and depth of the comprehensive traffic information system.

[0149] Example 1:

[0150] To verify the feasibility of the proposed method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data, the method was applied to the transportation informatization improvement project of the Anhui Provincial Integrated Transportation Management Center. The project aims to integrate provincial-wide transportation infrastructure data for highways, waterways, and aviation to build a unified, comprehensive transportation infrastructure database, enabling interconnection, visualization, and efficient retrieval of provincial transportation infrastructure resources.

[0151] In practical applications, traffic data from various business departments is first accessed through a data collection platform. Sources include road attribute tables and traffic flow records provided by the Highway Bureau, waterway cross-sections and port distribution data provided by the Water Transport Bureau, and airport facilities and flight network layouts provided by the Civil Aviation Administration. Data formats vary significantly, including structured tables, vector maps, satellite imagery, and various dynamic logs. The total initial data volume reached approximately 86TB.

[0152] To address the challenges of heterogeneous data sources and inconsistent formats, the project team established an industry-standard data dictionary and metadata specification based on the methods of this invention, unifying naming conventions, data types, and unit systems. For example, the "Route Code" and "Route Name" fields in the road attribute table were standardized into unified field names, with specified encoding lengths and formats. A unified spatial reference system information field, including EPSG codes and resolution, was established for satellite imagery data. This standardization significantly improved data compatibility, raising the field consistency rate from 61% to over 95%.

[0153] At the data storage level, according to the hybrid storage system proposed in this invention, different types of data are stored in appropriate databases. Structured data is managed using MySQL, storing tabular information such as traffic flow and infrastructure attributes. Spatial vector data is managed using PostGIS, and spatial indexes and auxiliary hash indexes are constructed for road network nodes, waterway networks, airport layouts, etc. Satellite image data is sliced ​​and processed by Geoserver in a tile pyramid manner, and metadata is stored in a MongoDB document database. At the same time, a full-text search index is established with the help of ElasticSearch. High-frequency query results and status caches are managed using Redis-like KV storage.

[0154] During the spatial data organization process, a spatial data sharding strategy was developed based on geographic regions. Spatial sharding was performed using city-level administrative districts as the basic unit, and then further refined into latitude and longitude grids. For example, the highway network originally contained 1.24 million road segment features. After spatial sharding, approximately 520 sublayers were generated, each less than 100MB in size. This ensures that queries can quickly locate the target area and significantly reduces system load. Testing has shown that a typical spatial query, such as "searching for the location of highway interchange nodes in northern Anhui," has reduced its time from 3.2 seconds to 0.48 seconds, an improvement of approximately 6.67 times.

[0155] In terms of data retrieval platform construction, a unified retrieval and visualization analysis platform is built based on the comprehensive transportation database. Users can quickly query information on facilities such as roads, waterways, and airports, as well as related business data, through multi-dimensional conditions such as keywords, spatial ranges, or timestamps. The platform supports real-time rendering of raster images and overlay display of spatial vector data, enabling dynamic visualization analysis of highway networks, port layouts, and route networks. Taking the "Spring Festival Transportation Situation Monitoring" special application launched in January 2025 as an example, the dispatching command center used the platform to retrieve traffic flow changes on major sections of highways across the province in the past 30 days. The average retrieval response time was less than 2 seconds, and the efficiency of data export and analysis increased by more than 5 times.

[0156] Regarding dynamic data updates, this invention effectively addresses the issue of spatiotemporal data consistency through an incremental update mechanism and version control system. Taking the December 2024 data update cycle as an example, the system accumulated approximately 180 million traffic flow records, approximately 32,000 port throughput update records, and approximately 270,000 dynamically accessed flight operation records. This incremental update mechanism increased new data storage space by approximately 8%, effectively avoiding the disk waste and reduced retrieval efficiency associated with full updates.

[0157] After six months of trial operation and evaluation, the comprehensive transportation infrastructure database platform achieved 98.2% data coverage, 97.5% spatial search accuracy, an average query response time of less than 0.5 seconds, and supported concurrent user access to more than five times the original system. Applications encompassed transportation department dispatch centers, urban transportation bureaus, emergency management departments, and key port operators, supporting traffic dispatch and emergency response during major events such as the 2025 Spring Festival, demonstrating excellent stability, scalability, and application value.

[0158] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data, characterized by: The steps include: S1. Collect multi-source data on three types of transportation infrastructure: roads, water transport, and aviation; S2. Establish data dictionaries and metadata standards for collected multi-source data, formulate database partitioning strategies and unified interface specifications based on industry standards; S3, standardize the infrastructure attribute data, satellite image data and spatial vector data in multi-source data; S4. Develop a spatial data sharding strategy based on geographic regions, build a full-text index and spatial index system, and optimize storage and query in a replicated cluster environment. S5. Build a comprehensive transportation infrastructure spatial database, using MySQL, PostGIS, MongoDB, ElasticSearch, and KV storage for collaborative management; S6. Design expansion mechanisms at the storage, retrieval, and computing layers to accommodate the growth of transportation infrastructure data and changes in retrieval requirements. S7. Establish a unified retrieval platform based on the comprehensive transportation infrastructure spatial database, integrating raster image data and spatial vector data to support spatial visualization and semantic retrieval; The S4 specifically includes: S41. Divide spatial data based on geographical regions and formulate sharding strategies: ； in, Number the spatial data slices. and is the latitude and longitude coordinates of spatial data, and are the minimum longitude and latitude of the spatial region, and Divide the intervals into spatial slices, is the number of slices in the longitude direction, Indicates a round-down operation; S42. Within the spatial data shard, establish an R-tree-based spatial index for the spatial elements, record the minimum bounding rectangle parameters of the spatial objects, and use a hash-assisted index to accelerate attribute retrieval; S43. Establish a full-text index for the metadata associated with the spatial data using the ElasticSearch indexing engine. The index fields include the dataset identifier, spatial range description, timestamp, and key information tags. S44. In a replication cluster environment, spatial data shards and full-text index replicas are distributed and stored, and query optimization is performed based on the load status of the replica nodes.

2. The method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data according to claim 1 is characterized in that: The multi-source data includes infrastructure attribute data, traffic flow data, navigation flow data, aviation business volume data, travel origin-destination distribution data, administrative division data, economic data, population data, waterway status data, port terminal data, route network data, satellite image data and spatial vector data.

3. The method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data according to claim 1 is characterized in that: The standardization process includes: Standardize fields of infrastructure attribute data; Satellite image data is sliced ​​using a tile pyramid approach and metadata is managed using a combination of full-text indexing and the MongoDB document model. Spatial vector data is expressed in a standardized format using GeoJSON and stored in the PostGIS spatial database.

4. The method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data according to claim 1 is characterized in that: The replication cluster environment includes sharding storage of spatial data, full-text indexes, and metadata, configuring a master copy and at least one slave copy for each data shard, using a replica synchronization mechanism to maintain data consistency, performing load balancing queries based on replica sets, and having the slave copy take over the master copy role when a node fails.

5. The method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data according to claim 1 is characterized in that: The S2 specifically includes: S21. Based on national standards and industry technical specifications in the field of geographic information, establish a data dictionary for the collected multi-source data. The data dictionary defines the data field name, field data type, field value range, and field unit type. S22. Establish metadata standards based on multi-source data attribute information. The metadata standards include fields such as dataset name, data source, spatial reference system, data update time, and data access rights. S23. Develop a database partitioning strategy to store infrastructure attribute data, traffic flow data, navigation flow data, air traffic volume data, travel origin-destination distribution data, administrative division data, economic data, and population data in a MySQL relational database; store satellite imagery data in a MongoDB document-based database; and store spatial vector data in a PostGIS spatial database. S24. Develop unified interface specifications, which include data access interface, data retrieval interface and data exchange format types.

6. The method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data according to claim 1 is characterized in that: The S3 specifically includes: S31. Standardize the fields of infrastructure attribute data to unify data field names, field data types, field value ranges, and field unit types; S32. Perform tile pyramid layered slicing processing on satellite image data: ； in, is the number of slice levels, is the original image spatial resolution, is the target tile resolution, Indicates rounding up operation; S33. Use Geoserver to generate slice services for the sliced ​​satellite image data, establish full-text indexes with ElasticSearch, and manage the metadata of the image data based on the MongoDB document model. S34. Spatial vector data is standardized in GeoJSON format and stored in the PostGIS spatial database. A spatial index based on the R-tree structure and an auxiliary index based on the hash structure are established for the spatial vector elements.

7. The method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data according to claim 1 is characterized in that: The S5 specifically includes: S51. Use a MySQL relational database to store structured data, including infrastructure attribute data, traffic flow data, navigation flow data, aviation business volume data, travel origin-destination distribution data, administrative division data, economic data, and population data. Waterway status data, port terminal data, and route network data are considered spatial data and are separately processed according to spatial data standardization. S52. Use PostGIS spatial database to manage spatial data, including spatial vector data and associated attributes. Establish spatial index based on R-tree structure, and use auxiliary hash index to accelerate attribute retrieval. S53. Use MongoDB document-based database to manage metadata, including dataset description information, spatial reference system parameters, data update time, and data access permission identifier; S54. Use the ElasticSearch search engine to manage full-text index data, where the full-text index data includes a dataset identifier, a spatial range description, a time tag, and a description of key attributes; S55. Use the KV storage system to manage frequently accessed cache data, including query result cache, index metadata cache, and replica synchronization status cache.

8. The method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data according to claim 1 is characterized in that: The S6 specifically includes: S61. The storage layer consists of MySQL, MongoDB, and PostGIS. A data expansion mechanism is designed in the storage layer, and partitioning and replica expansion methods are used to expand data capacity. S62. The retrieval layer is composed of ElasticSearch. An index expansion mechanism is designed in the retrieval layer to expand the retrieval load based on full-text index sharding and replica sharding strategies. S63. The computing layer is composed of shard clusters. A task expansion mechanism is designed at the computing layer, and a task load sharding and node concurrency expansion strategy is adopted.

9. The method for constructing a comprehensive transportation infrastructure spatial database based on multi-source data according to claim 1, characterized in that: The S7 specifically includes: S71. Build a unified search platform based on the comprehensive transportation infrastructure spatial database, which accesses structured data, spatial data, metadata, and full-text index data; S72, integrate raster image data and spatial vector data to build a multi-level spatial visualization model, supporting overlay rendering of spatial elements and raster layers; S73. Establish a semantic retrieval mechanism based on full-text indexing, supporting combined queries based on keywords, spatial ranges, timestamps, and attribute tags; S74. Based on the search results, dynamically load the spatial vector elements and the corresponding raster image layers for visualization and view update.

Images