Spatiotemporal data visualization indicator management and application methods

Abstract

This invention discloses a method for managing and applying spatiotemporal data visualization indicators, belonging to the field of spatiotemporal data statistical analysis and processing. It involves analyzing spatiotemporal visualization business scenarios to determine statistical calculation rules for spatiotemporal indicator data; configuring these rules through a visual interface, generating structured configurations and storing them in a standardized configuration table; automatically generating an ETL process after system parsing the configuration to achieve data statistics and flow, supporting both automatic and scheduled calculations; designing a hybrid storage architecture for standardized indicator data statistical results, employing a dual-mode storage strategy of relational databases and data warehouses: using relational databases for storing small amounts of raw data and statistical results; and synchronizing massive amounts of data to the data warehouse via an ETL process, performing distributed computation analysis based on indicator configurations at the warehouse layer, and persistently storing the analysis results in a columnar storage format; and building a unified access interface layer to achieve transparent access to the heterogeneous storage underlying layer.

Description

Technical Field

[0001] This invention belongs to the field of spatiotemporal data statistical analysis and processing technology, specifically relating to a method for managing and applying spatiotemporal data visualization indicators. Background Technology

[0002] Driven by both digital transformation and smart city construction, spatiotemporal big data has become a core element supporting smart cities, smart forestry and grassland, smart transportation, ecological environment, and emergency management. With the widespread adoption of technologies such as remote sensing satellites, the Internet of Things, and 5G, spatiotemporal data is experiencing explosive growth. Its multi-source, heterogeneous, dynamic, real-time, and high-dimensional correlation characteristics place higher demands on data processing and analysis capabilities. Currently, the industry is evolving from "single data display" to "spatiotemporal intelligent decision-making."

[0003] Therefore, it is evident that spatiotemporal data visualization technology, as a core support for smart cities, smart forestry and grassland, geographic information analysis, and IoT monitoring, is facing the challenge of the explosive growth of multi-source heterogeneous data (geographic information, business indicators, and real-time streaming data). Driven by decision-making scenarios such as dashboards, industry demands are upgrading from basic data display to spatiotemporal intelligent decision-making, urgently requiring breakthroughs in three technical bottlenecks: (i) Multi-source fusion: Integrating multi-dimensional data such as geographic information, business indicators, and real-time streaming data to construct spatiotemporal correlation models; (ii) Real-time computing: Achieving second-level aggregation and analysis of hundreds of millions of spatiotemporal data; (iii) Deep interaction: Meeting the dynamic decision-making needs of spatiotemporal indicator data and geospatial linkage.

[0004] Currently, existing spatiotemporal data platforms have the following key problems: (1) Data silos and computational rigidity: Traditional systems rely on hard coding to calculate indicators, and new requirements require secondary development, which is inefficient; after the source data is updated, manual triggering of statistical refresh is required, resulting in data update delays; (2) Single storage mode: Relational databases are difficult to cope with high-concurrency queries of massive spatiotemporal data, while data warehouses lack transaction support, resulting in high costs for multi-source data integration; (3) Weak visualization configuration capabilities: Pre-set configurations cannot meet the needs of complex scenarios, and new requirements require coding development; Map configuration is not supported, and it is impossible to link with visualization indicator data; (4) Limited spatial analysis capabilities: It does not support aggregation or the aggregation mode is single, resulting in poor effects and failing to meet business needs.

[0005] The core of the aforementioned shortcomings lies in the fragmented technical architecture: computational logic is decoupled from business needs, resulting in a lack of flexibility; a single storage engine cannot accommodate high-concurrency transactions and massive analysis; and visualization components are decoupled from spatial analysis, hindering in-depth decision-making. Although existing solutions, such as pre-computed data cubes, attempt to optimize query performance, they still cannot solve the special requirements of the cockpit scenario: the need to dynamically generate indicators according to administrative divisions / time dimensions; the need to support real-time aggregation and rendering of 100,000+ data points; and the need to achieve real-time linkage between maps and indicators. Therefore, there is an urgent need for a spatiotemporal data visualization indicator management and application method that integrates configurable indicator management, dual-mode intelligent storage, and multi-granularity spatial aggregation. Summary of the Invention

[0006] This invention addresses the aforementioned problems and overcomes the shortcomings of existing technologies by providing a method for managing and applying spatiotemporal data visualization indicators. This invention is applicable to multi-source heterogeneous data application scenarios such as smart city management, smart forestry and grassland, geographic information analysis, IoT monitoring, and business intelligence decision-making.

[0007] To achieve the above objectives, the present invention adopts the following technical solution.

[0008] This invention provides a method for managing and applying spatiotemporal data visualization indicators, comprising the following steps:

[0009] S1. Spatiotemporal visualization business scenario analysis to determine the statistical calculation rules for spatiotemporal indicator data;

[0010] S2. Configure the statistical calculation rules for spatiotemporal indicator data through a visual interface, generate a structured configuration and store it in a standardized configuration table. After the system parses the configuration, it automatically generates an ETL process to realize data statistics and flow, and supports automatic calculation and scheduled calculation.

[0011] S3. Design a hybrid storage architecture for standardized indicator data statistical results, adopting a dual-mode storage strategy of relational database and data warehouse: relational database is used to store raw data and statistical results with small amounts of data; for massive data, it is synchronized to the data warehouse through ETL process, and distributed computing analysis is performed at the warehouse layer based on indicator configuration, and the analysis results are persistently stored in columnar storage format; by building a unified access interface layer, transparent access to the heterogeneous storage underlying layer is realized.

[0012] S4. Establish the association configuration between page rendering controls and indicator data, support the merging of multiple indicators and data format conversion, and provide a visual control library for users to drag and drop to generate interactive pages;

[0013] S5. Configure the association between indicator data and map layers to enable real-time linkage between changes or switches in indicator data and map visualization.

[0014] S6. For spatiotemporal point data, a dual-mode spatial aggregation method based on administrative region aggregation and location aggregation is adopted: multi-level spatial indexing based on administrative divisions enables cross-level drill-down analysis; location aggregation based on dynamic grid division enables cross-level statistical calculation.

[0015] Further, step S1 specifically includes:

[0016] Based on the analysis of multi-source heterogeneous spatiotemporal data using administrative divisions and time dimensions, statistical calculation rules for spatiotemporal indicator data are extracted.

[0017] Furthermore, the statistical calculation rules for the spatiotemporal index data in step S2 include:

[0018] The system automatically converts data units based on the unit configuration, including aggregation dimensions, calculation logic, filtering conditions, data units, and statistical units for each level of targets. It also supports column-to-row conversion to adapt to the source data structure.

[0019] Furthermore, the automatic switching logic for dual-mode storage in step S3 is as follows:

[0020] The system employs an intelligent dual-mode storage dynamic switching mechanism, automatically selecting the optimal storage engine based on a multi-dimensional evaluation of data volume and change frequency: for business scenarios with data volumes of less than tens of millions or high-frequency changes, relational database storage is prioritized to meet transactional OLTP processing requirements; for analysis scenarios involving massive amounts of data exceeding tens of millions, the system automatically routes to a data warehouse for OLAP analysis, achieving second-level response through columnar storage and distributed computing; this dual-mode storage dynamic switching mechanism ensures the best match between storage strategies and business scenarios by monitoring data characteristic indicators in real time.

[0021] Further, step S4 specifically includes:

[0022] The system uses a visual configuration table to dynamically bind page controls to indicator data, supports multi-indicator joint queries and composite analysis, and provides a visual control library including map components, statistical charts, and timelines. Users can drag and drop controls onto the design canvas and associate them with data sources, and the system will automatically generate responsive HTML5+CSS3 front-end code to achieve zero-code visual page construction.

[0023] Furthermore, the association between the configuration indicator data and the map layer in step S5 includes two aspects:

[0024] On the one hand, changes in indicator data are mapped to the color, size, or multi-dimensional style attributes of the map layer, enabling the spatial display to update in real time with the data; on the other hand, as statistical indicator data changes, the map visualization data also changes accordingly.

[0025] Furthermore, the administrative region aggregation in step S6 specifically involves:

[0026] A multi-level spatial index is constructed based on administrative divisions. A spatial inclusion algorithm is used to determine the administrative region to which data points belong, and drill-down statistics are supported at the provincial, municipal, county, and township levels.

[0027] Furthermore, the location aggregation in step S6 specifically involves:

[0028] The point data is divided into multiple dynamic grids according to the scale range, the size of each grid level is calculated, the point features within the grid are aggregated, and cross-level drill-down statistics are supported.

[0029] Furthermore, the spatiotemporal visualization business scenario in step S1 is a cockpit large screen display in smart city management, smart forestry and grassland, geographic information analysis, or Internet of Things monitoring scenarios.

[0030] Furthermore, the frequency automatically calculated in step S2 is configured to be scheduled and executed in real time, daily, or monthly according to data update requirements.

[0031] Beneficial effects of this invention:

[0032] The core technologies of this invention, including the spatiotemporal data visualization indicator management computing engine, dual-mode storage, and low-code visualization orchestration, address the shortcomings of existing solutions in terms of flexibility, performance, and ease of use. It provides an adaptive integrated operating environment for spatiotemporal big data analysis, compatible with multi-source heterogeneous data access, automated computation scheduling, and a unified query interface, enabling the integration and efficient querying of multi-source heterogeneous data. Furthermore, this invention satisfies the statistical needs of administrative divisions through administrative region aggregation and achieves dynamic clustering of geographic coordinates through location aggregation. These two technologies work together to support cross-scale spatiotemporal analysis, solving the problem of "limited spatial analysis capabilities" and breaking through the limitations of traditional single aggregation. The spatiotemporal data visualization indicator management and application method provided by this invention is applicable to multi-source heterogeneous data application scenarios such as smart city management, smart forestry and grassland, geographic information analysis, IoT monitoring, and business intelligence decision-making. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart of a spatiotemporal data visualization indicator management and application method according to the present invention. Detailed Implementation

[0035] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0036] like Figure 1 As shown in the figure, the spatiotemporal data visualization indicator management and application method provided by this invention includes the following steps:

[0037] S1. Spatiotemporal visualization business scenario analysis to determine the statistical calculation rules for spatiotemporal indicator data;

[0038] Step S1 specifically includes:

[0039] Based on the analysis of multi-source heterogeneous spatiotemporal data using administrative divisions and time dimensions, statistical calculation rules for spatiotemporal indicator data are extracted.

[0040] The aforementioned spatiotemporal visualization business scenario is a cockpit large screen display in smart city management, smart forestry and grassland, geographic information analysis, or Internet of Things monitoring scenarios.

[0041] S2. Statistical calculation rules for spatiotemporal indicator data are configured through a visual interface, generating structured configurations and storing them in a standardized configuration table. After the system parses the configuration, it automatically generates an ETL process to realize data statistics and flow, and supports automatic and scheduled calculations, achieving the technical effect of zero-code configuration.

[0042] The ETL process refers to the automated process of extracting data from the source system, transforming it, and loading it into the target storage. Its functions are as follows:

[0043] Addressing the shortcomings of "hard coding": Replacing manual coding with configuration tables;

[0044] Dynamic response to business needs: When adding new statistical indicators, only configuration adjustments are required, rather than secondary development.

[0045] Automatic unit conversion: Automatically converts units based on the configured "units of statistical data for each level of target".

[0046] The statistical calculation rules for the spatiotemporal index data in step S2 include:

[0047] The system automatically converts data units based on the unit configuration, including aggregation dimensions, calculation logic, filtering conditions, data units, and statistical units for each level of targets. It also supports column-to-row conversion to adapt to the source data structure.

[0048] The frequency automatically calculated in step S2 is configured to be executed in real time, daily or monthly according to data update requirements.

[0049] Step S2 enables the data flow from the source data table to the target statistical table, and automatically converts the data unit to the target statistical units at each level according to the unit configuration; it can also set the column to row configuration according to the data situation to adapt to the diverse needs of the source data; and it can set the frequency of automatic calculation according to the data situation to perform automatic calculation.

[0050] S3. Design a hybrid storage architecture for standardized indicator data statistical results, adopting a dual-mode storage strategy of relational database and data warehouse: relational database is used to store raw data and statistical results with small amounts of data; for massive data, it is synchronized to the data warehouse through ETL process, and distributed computing analysis is performed at the warehouse layer based on indicator configuration, and the analysis results are persistently stored in columnar storage format; by building a unified access interface layer, transparent access to the heterogeneous storage underlying layer is realized.

[0051] The automatic switching logic for dual-mode storage in step S3 is as follows:

[0052] The system employs an intelligent dual-mode storage dynamic switching mechanism, automatically selecting the optimal storage engine based on a multi-dimensional evaluation of data volume and change frequency: for business scenarios with data volumes of less than tens of millions or high-frequency changes, relational database storage is prioritized to meet transactional OLTP processing requirements; for analysis scenarios involving massive amounts of data exceeding tens of millions, the system automatically routes data to a data warehouse for OLAP analysis, achieving second-level response through columnar storage and distributed computing; this dual-mode storage dynamic switching mechanism ensures optimal matching between storage strategies and business scenarios by monitoring data characteristic indicators in real time.

[0053] Among them, OLAP analysis refers to online analysis and processing, which supports multi-dimensional and rapid aggregation queries of massive amounts of data, such as: total statistical value by time and administrative division;

[0054] Specifically, by designing a hybrid storage architecture for standardized indicator data statistical results and adopting a dual-mode storage strategy of relational databases and data warehouses, the configured indicators are automatically calculated and stored in a standard structure according to administrative requirements and time. Different scenarios and indicators are distinguished by indicator grouping and indicator names. At the same time, depending on the application, data scale and requirements, small statistical results can be stored in relational databases, while massive statistical results can be stored in data warehouses. Columnar storage is used to optimize OLAP performance, providing a unified access interface layer, shielding the differences in underlying storage and enabling automatic switching between dual-mode storage. Whether querying small amounts of data or hundreds of millions of data points, the response time is within seconds.

[0055] S4. Establish the association configuration between page rendering controls and indicator data, support the merging of multiple indicators and data format conversion, and provide a visual control library for users to drag and drop to generate interactive pages;

[0056] Step S4 specifically includes:

[0057] The system uses a visual configuration table to dynamically bind page controls and indicator data, supports multi-indicator joint queries and composite analysis, and provides a visual control library including map components, statistical charts, and time axes. Users can drag and drop controls onto the design canvas and associate them with data sources, and the system will automatically generate responsive HTML5+CSS3 front-end code to achieve zero-code visual page construction.

[0058] Specifically, in addition to storing standard indicator results, a configuration table is added to establish the association between page indicator rendering controls and indicator data. This mechanism enables features such as merging multiple indicators, aggregating indicator query requests, and data format conversion. Furthermore, it provides several template libraries and visualization control libraries, such as maps, various charts, single indicators, and timelines. Users can drag and drop controls onto the canvas area on the interface visually and bind data sources and style configurations. The system automatically generates responsive HTML5+CSS3 front-end code, thereby enabling user-customized interfaces and automatic rendering, achieving the technical effect of low-code visual orchestration.

[0059] S5. Configure the association between indicator data and map layers to enable real-time linkage between changes or switches in indicator data and map visualization.

[0060] The relationship between the configuration indicator data and the map layer in step S5 involves two aspects:

[0061] On the one hand, changes in indicator data are mapped to the color, size, or multi-dimensional style attributes of the map layer, enabling the spatial display to update in real time with the data; on the other hand, as statistical indicator data changes, the map visualization data also changes accordingly.

[0062] S6. For spatiotemporal point data, a dual-mode spatial aggregation method based on administrative division aggregation and location aggregation is adopted: multi-level spatial indexing based on administrative divisions enables cross-level drill-down analysis; location aggregation based on dynamic grid division enables cross-level statistical calculation.

[0063] Specifically, the aggregation of administrative regions refers to:

[0064] A multi-level spatial index is constructed based on administrative divisions. A spatial inclusion algorithm is used to determine the administrative region to which data points belong, and drill-down statistics are supported at the provincial, municipal, county, and township levels.

[0065] Specifically, the location aggregation refers to:

[0066] The point data is divided into multiple dynamic grids according to the scale range, the size of each grid level is calculated, the point features within the grid are aggregated, and cross-level drill-down statistics are supported.

[0067] Specifically, a dual-mode aggregation method of administrative region aggregation and location aggregation was constructed for spatiotemporal point data. The administrative region aggregation: based on administrative division data, a multi-level spatial index structure was constructed to realize multi-level linkage aggregation from the provincial level to the municipal level, the county level, and the township level. A spatial inclusion algorithm was used to determine the administrative region to which the data points belong, and cross-level statistical result drill-down was supported. The location aggregation: the point data of all elements was divided into multiple grid units and further divided into multiple levels according to the scale range. The grid size of each level was dynamically calculated, and the point elements within the grid were aggregated together at each level, supporting cross-level statistical result drill-down.

[0068] In summary, this invention, through steps S1 to S6, constitutes the entire process of a method for managing and applying spatiotemporal data visualization indicators, and has the following technical advantages:

[0069] (1) Scenario-driven design: By analyzing business scenarios and refining rules, the indicator management can be accurately matched with actual needs such as the cockpit, thereby improving the pertinence of spatiotemporal data applications.

[0070] (2) Zero-code configuration: Visually define statistical rules and automatically generate ETL processes, support dynamic unit conversion and calculation scheduling, and significantly reduce development costs and response cycles.

[0071] (3) Dual-mode storage: Intelligent switching between relational database and data warehouse, combined with columnar storage and unified SQL interface, to achieve second-level query of hundreds of millions of data, taking into account both transactional performance and analytical performance.

[0072] (4) Low-code visual orchestration: drag-and-drop page building and template library reuse, supports the merging of multiple indicators and style binding, and business personnel can independently complete the development of complex interfaces.

[0073] (5) Data view linkage: Indicators are deeply associated with map layers, and color, size or multi-dimensional style attribute binding is supported to realize real-time synchronization of data changes and spatial display.

[0074] (6) Multi-granularity spatial aggregation: The dual-mode compatibility of cross-level drilling of administrative divisions and dynamic grid aggregation solves the problem of cross-scale analysis of spatiotemporal point data.

[0075] It is understood that the above specific description of the present invention is only for illustrating the present invention and is not limited to the technical solutions described in the embodiments of the present invention. Those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention to achieve the same technical effect; as long as the use needs are met, they are all within the protection scope of the present invention.

Claims

1. A method for managing and applying spatiotemporal data visualization indicators, characterized by: Includes the following steps: S1. Spatiotemporal visualization business scenario analysis to determine the statistical calculation rules for spatiotemporal indicator data; S2. Configure the statistical calculation rules for spatiotemporal indicator data through a visual interface, generate a structured configuration and store it in a standardized configuration table. After the system parses the configuration, it automatically generates an ETL process to realize data statistics and flow, and supports automatic calculation and scheduled calculation. S3. Design a hybrid storage architecture for the statistical results of standardized indicator data, and adopt a dual-mode storage strategy of relational database and data warehouse: use relational database to store the raw data and statistical results with small data volume; For massive amounts of data, the data is synchronized to the data warehouse through an ETL process, and distributed computing and analysis are performed at the warehouse layer based on the metric configuration. The analysis results are persistently stored in columnar storage format. By building a unified access interface layer, transparent access to the heterogeneous underlying storage is achieved. S4. Establish the association configuration between page rendering controls and indicator data, support the merging of multiple indicators and data format conversion, and provide a visual control library for users to drag and drop to generate interactive pages; S5. Configure the association between indicator data and map layers to enable real-time linkage between changes or switches in indicator data and map visualization. S6. For spatiotemporal point data, a dual-mode spatial aggregation method based on administrative division aggregation and location aggregation is adopted: multi-level spatial indexing based on administrative divisions enables cross-level drill-down analysis; location aggregation based on dynamic grid division enables cross-level statistical calculation. Specifically, a dual-mode aggregation method of administrative region aggregation and location aggregation was constructed for spatiotemporal point data. The administrative region aggregation: based on administrative division data, a multi-level spatial index structure was constructed to realize multi-level linkage aggregation from the provincial level to the municipal level, the county level, and the township level. A spatial inclusion algorithm was used to determine the administrative region to which the data points belong, and cross-level statistical result drill-down was supported. The location aggregation: the point data of all elements was divided into multiple grid units and further divided into multiple levels according to the scale range. The grid size of each level was dynamically calculated, and the point elements within the grid were aggregated together at each level, supporting cross-level statistical result drill-down.

2. The spatiotemporal data visualization indicator management and application method according to claim 1, characterized in that: Step S1 specifically includes: Based on the analysis of multi-source heterogeneous spatiotemporal data using administrative divisions and time dimensions, statistical calculation rules for spatiotemporal indicator data are extracted.

3. The spatiotemporal data visualization indicator management and application method according to claim 2, characterized in that: The statistical calculation rules for the spatiotemporal index data in step S2 include: The system automatically converts data units based on the unit configuration, including aggregation dimensions, calculation logic, filtering conditions, data units, and statistical units for each level of targets. It also supports column-to-row conversion to adapt to the source data structure.

4. The spatiotemporal data visualization indicator management and application method according to claim 1, characterized in that: The automatic switching logic for dual-mode storage in step S3 is as follows: The system employs an intelligent dual-mode storage dynamic switching mechanism, automatically selecting the optimal storage engine based on a multi-dimensional evaluation of data volume and change frequency: for business scenarios with data volumes of less than tens of millions or high-frequency changes, relational database storage is prioritized to meet transactional OLTP processing requirements; for analysis scenarios involving massive amounts of data exceeding tens of millions, the system automatically routes to a data warehouse for OLAP analysis, achieving second-level response through columnar storage and distributed computing; this dual-mode storage dynamic switching mechanism ensures the best match between storage strategies and business scenarios by monitoring data characteristic indicators in real time.

5. The spatiotemporal data visualization indicator management and application method according to claim 1, characterized in that: Step S4 specifically includes: The system uses a visual configuration table to dynamically bind page controls to indicator data, supports multi-indicator joint queries and composite analysis, and provides a visual control library including map components, statistical charts, and timelines. Users can drag and drop controls onto the design canvas and associate them with data sources, and the system will automatically generate responsive HTML5+CSS3 front-end code to achieve zero-code visual page construction.

6. The method for managing and applying spatiotemporal data visualization indicators according to claim 1, characterized in that: The association between the configuration index data and the map layer in step S5 includes two aspects: On the one hand, changes in indicator data are mapped to the color, size, or multi-dimensional style attributes of the map layer, enabling the spatial display to update in real time with the data; on the other hand, as statistical indicator data changes, the map visualization data also changes accordingly.

7. The method for managing and applying spatiotemporal data visualization indicators according to claim 1, characterized in that: The administrative region aggregation in step S6 specifically involves: A multi-level spatial index is constructed based on national administrative divisions. A spatial inclusion algorithm is used to determine the administrative region to which data points belong, and drill-down statistics are supported at the provincial, municipal, county, and township levels.

8. The spatiotemporal data visualization indicator management and application method according to claim 1, characterized in that: The location aggregation in step S6 specifically involves: The point data is divided into multiple dynamic grids according to the scale range, the size of each grid level is calculated, the point features within the grid are aggregated, and cross-level drill-down statistics are supported.

9. The method for managing and applying spatiotemporal data visualization indicators according to claim 2, characterized in that: The spatiotemporal visualization business scenario in step S1 is a cockpit large screen display in smart city management, smart forestry and grassland, geographic information analysis, or Internet of Things monitoring scenarios.

10. The method for managing and applying spatiotemporal data visualization indicators according to claim 3, characterized in that: The frequency automatically calculated in step S2 is configured to be scheduled and executed in real time, daily, or monthly according to data update requirements.

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