Spatial-temporal data visualization index management and application method

Abstract

The invention discloses a spatio-temporal data visualization index management and application method, and belongs to the field of spatio-temporal data statistical analysis processing. Analyzing a time-space visualization service scene, and determining a statistical calculation rule of the time-space index data; a statistical calculation rule of spatio-temporal index data is configured through a visual interface, structured configuration is generated and stored in a standardized configuration table, an ETL process is automatically generated after the system analyzes the configuration to realize data statistics and circulation, and automatic calculation and timing calculation are supported; the method comprises the following steps: designing a hybrid storage architecture of a standardized index data statistical result, and adopting a relational database and data warehouse dual-mode storage strategy: storing original data and statistical results with small data volume by adopting a relational database; for mass data, the mass data are synchronized to a data warehouse through an ETL process, distributed calculation analysis is executed on a warehouse layer based on index configuration, and an analysis result is persistently stored in a column storage format; transparent access to a heterogeneous storage bottom layer is achieved by constructing a unified access interface layer.

Description

Technical Field

[0001] The present invention belongs to the technical field of spatiotemporal data statistical analysis and processing, and specifically relates to a spatiotemporal data visualization indicator management and application method. Background Art

[0002] Driven by digital transformation and smart city development, spatiotemporal big data has become a core element supporting smart cities, smart forestry and grassland, smart transportation, ecological environment, emergency management, and other fields. With the widespread adoption of technologies such as remote sensing satellites, the Internet of Things, and 5G, spatiotemporal data has experienced 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 "spatial-temporal intelligent decision-making."

[0003] As can be seen, spatiotemporal data visualization technology, as a core enabler for smart cities, smart forestry and grassland, geographic information analysis, and IoT monitoring, faces the challenge of explosive growth in multi-source, heterogeneous data (geographic information, business indicators, and real-time streaming data). Driven by decision-making scenarios like cockpits, industry demand is evolving from basic data visualization to intelligent spatiotemporal decision-making. This requires addressing three key technical bottlenecks: (1) Multi-source fusion: integrating multidimensional data such as geographic information, business indicators, and real-time streaming data to construct spatiotemporal correlation models; (2) Real-time computation: enabling second-level aggregation and analysis of billions of spatiotemporal data; and (3) Deep interaction: meeting the dynamic decision-making needs of spatiotemporal indicator data linked to geographic space.

[0004] Currently, the existing spatiotemporal data platforms have the following key problems: (1) Data silos and computational rigidity: Traditional systems rely on hard coding to implement indicator calculations, and new requirements require secondary development, which is inefficient; statistical refreshes need to be manually triggered after source data is updated, resulting in delayed data updates; (2) Single storage mode: Relational databases are difficult to handle high-concurrency queries on massive spatiotemporal data, while data warehouses lack transaction support, and the cost of integrating multi-source data is high; (3) Weak visualization configuration capabilities: Preset configurations cannot meet the needs of complex scenarios, and new requirements require coding development; map configuration is not supported, and it cannot be linked with visualization indicator data; (4) Limited spatial analysis capabilities: aggregation is not supported or the aggregation mode is single, the effect is poor, and it cannot meet business needs.

[0005] The core of these shortcomings lies in a fragmented technical architecture: computational logic is decoupled from business needs, resulting in a lack of flexibility; a single storage engine is incompatible with high-concurrency transactions and massive analysis; and visualization components are decoupled from spatial analysis, hindering in-depth decision-making. While existing solutions, such as those based on pre-computed data cubes, attempt to optimize query performance, they still cannot address the specific requirements of cockpit scenarios: the need to dynamically generate indicators based on administrative divisions and time dimensions; support real-time aggregate rendering of 100,000+ data points; and achieve real-time linkage between maps and indicators. Therefore, a method for managing and applying spatiotemporal data visualization indicators that integrates configurable indicator management, dual-mode intelligent storage, and multi-granularity spatial aggregation is urgently needed. Summary of the Invention

[0006] The present invention aims to solve the above-mentioned problems, make up for the deficiencies of the existing technology, and provide a method for managing and applying spatiotemporal data visualization indicators. The present invention is suitable for multi-source heterogeneous data application scenarios such as smart city management, smart forestry and grassland, geographic information analysis, Internet of Things monitoring, and business intelligence decision-making.

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

[0008] The present invention provides a method for managing and applying spatiotemporal data visualization indicators, comprising the following steps: S1. Analyze spatiotemporal visualization business scenarios and 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 implement data statistics and flow, and supports automatic and scheduled calculations. S3. Design a hybrid storage architecture for standardized indicator data statistical results, adopting a dual-mode storage strategy of relational databases and data warehouses. For small amounts of raw data and statistical results, a relational database is used to store them. For massive amounts of data, these are synchronized to the data warehouse through the ETL process. Distributed computing and analysis are performed at the warehouse level based on indicator configurations, and the analysis results are persistently stored in a columnar storage format. A unified access interface layer is built to achieve transparent access to the heterogeneous storage underlying layer. S4. Establish the association configuration between page rendering controls and indicator data, support multi-indicator merging and data format conversion, and provide a visual control library for users to drag and drop to generate interactive pages; S5. Configure the relationship between indicator data and map layers to achieve real-time linkage between indicator data changes or switching and map visualization; S6. For spatiotemporal point data, a dual-mode spatial aggregation method based on administrative division aggregation and location aggregation is adopted: a multi-level spatial index based on administrative divisions is used to achieve cross-level drill-down analysis; and location aggregation based on dynamic grid division is used to achieve cross-level statistical calculations.

[0009] Furthermore, the step S1 specifically includes: Based on administrative divisions and time dimensions, multi-source heterogeneous spatiotemporal data are analyzed to extract statistical calculation rules for spatiotemporal indicator data.

[0010] Furthermore, the statistical calculation rules of the spatiotemporal indicator data in step S2 include: Aggregation dimensions, calculation logic, filtering conditions, data units, and target statistical units at all levels. The system automatically converts data units based on unit configuration and supports column-to-row configuration to adapt to the source data structure.

[0011] Furthermore, the automatic switching logic of the dual-mode storage in step S3 is: The system adopts an intelligent dual-mode storage dynamic switching mechanism, automatically selecting the optimal storage engine based on a multi-dimensional assessment of data volume and change frequency: for business scenarios with data volumes less than tens of millions or high-frequency changes, relational database storage is preferred to meet transactional OLTP processing needs; for analysis scenarios with massive data volumes exceeding tens of millions, it is automatically routed to the data warehouse for OLAP analysis, achieving a response within seconds through columnar storage and distributed computing; this dual-mode storage dynamic switching mechanism ensures the optimal match between storage strategies and business scenarios by real-time monitoring of data characteristic indicators.

[0012] Furthermore, the step S4 specifically includes: The system uses a visual configuration table to dynamically bind page controls to indicator data, supports multi-indicator joint query and complex analysis, and provides a visual control library including map components, statistical charts, and timelines. After users place controls on the design canvas and associate them with data sources through drag-and-drop operations, the system automatically generates responsive HTML5+CSS3 front-end code, enabling zero-coding visual page construction.

[0013] Furthermore, the association between the configuration indicator data and the map layer in step S5 includes two aspects: On the one hand: map the indicator data changes to the color, size or multidimensional style attributes of the map layer, so that the spatial display can be updated in real time with the data; on the other hand: as the statistical indicator data switches, the map visualization data also switches accordingly.

[0014] Furthermore, the administrative region aggregation in step S6 is specifically as follows: A multi-level spatial index is constructed based on administrative divisions, and a spatial inclusion algorithm is used to determine the administrative region to which data points belong. Drill-down statistics at the provincial, municipal, county, and township levels are supported.

[0015] Furthermore, the position aggregation in step S6 is specifically as follows: Divide point data into multi-layer dynamic grids according to the scale range, calculate the grid size at each level, aggregate point features within the grid, and support cross-level drill-down statistics.

[0016] Furthermore, the spatiotemporal visualization business scenario of step S1 is a large-screen cockpit display in smart city management, smart forestry and grassland, geographic information analysis, or Internet of Things monitoring scenarios.

[0017] Furthermore, the frequency of the automatic calculation in step S2 is configured to be executed in real time, daily or monthly according to data update requirements.

[0018] Beneficial effects of the present invention: The core technical solutions of the present invention, such as the computing engine, dual-mode storage, and low-code visual orchestration for spatiotemporal data visualization indicator management, can solve the deficiencies of existing solutions in flexibility, performance, and ease of use, provide an adaptive integrated operating environment for spatiotemporal big data analysis, and be compatible with multi-source heterogeneous data access, automated computing scheduling, and a unified query interface, thereby realizing multi-source heterogeneous data integration and efficient query. In addition, the present invention meets the statistical needs of administrative divisions through political district aggregation, and realizes dynamic clustering of geographic coordinates through location aggregation. The two work together to support cross-scale spatiotemporal analysis, solving the problem of "spatial analysis capability limitations" and breaking through the limitations of traditional single aggregation. The spatiotemporal data visualization indicator management and application method provided by the present invention is suitable for multi-source heterogeneous data application scenarios such as smart city management, smart forestry and grassland, geographic information analysis, Internet of Things monitoring, and business intelligence decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 This is a flow chart of a spatiotemporal data visualization indicator management and application method of the present invention. DETAILED DESCRIPTION

[0021] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is 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 only used to explain the present invention and are not intended to limit the present invention.

[0022] like Figure 1 As shown, an embodiment of the present invention provides a method for managing and applying spatiotemporal data visualization indicators, including the following steps: S1. Analyze spatiotemporal visualization business scenarios and determine the statistical calculation rules for spatiotemporal indicator data; The step S1 specifically includes: Analyze multi-source heterogeneous spatiotemporal data based on administrative divisions and time dimensions, and extract statistical calculation rules for spatiotemporal indicator data; Among them, the spatiotemporal visualization business scenario is the cockpit large-screen display in smart city management, smart forestry and grassland, geographic information analysis or Internet of Things monitoring scenarios.

[0023] S2. Configure statistical calculation rules for spatiotemporal indicator data through a visual interface, generate structured configurations, and store them in standardized configuration tables. After the system parses the configurations, it automatically generates ETL processes to implement data statistics and flow, and supports automatic and scheduled calculations, achieving the technical effect of zero-code configuration. The ETL process refers to the automated process of extracting data from the source system, transforming it, and then loading it into the target storage. Its functions are as follows: Solve the "hard coding" defect: replace manual coding with configuration tables; Dynamic response to business: When adding new statistical indicators, only configuration adjustments are required instead of secondary development; Automatic unit conversion: Automatic conversion according to the "target statistical data units at all levels" configuration; The statistical calculation rules of the spatiotemporal indicator data in step S2 include: Aggregation dimensions, calculation logic, filtering conditions, data units, and target statistical units at all levels. The system automatically converts data units based on unit configuration and supports column-to-row configuration to adapt to the source data structure. The frequency of automatic calculation in step S2 is configured to be real-time, daily or monthly scheduling according to data update requirements; Step S2 realizes the data flow from the source data table to the target statistical table, and realizes the automatic conversion of data units to target statistical units at all levels according to the unit configuration; the column-to-row configuration can also be set according to the data situation to adapt to the diversified needs of the source data; the frequency of automatic calculation can be set according to the data situation to perform automatic calculation.

[0024] S3. Design a hybrid storage architecture for standardized indicator data statistical results, adopting a dual-mode storage strategy of relational databases and data warehouses. For small amounts of raw data and statistical results, a relational database is used to store them. For massive amounts of data, these are synchronized to the data warehouse through the ETL process. Distributed computing and analysis are performed at the warehouse level based on indicator configurations, and the analysis results are persistently stored in a columnar storage format. A unified access interface layer is built to achieve transparent access to the heterogeneous storage underlying layer. The automatic switching logic of the dual-mode storage in step S3 is: The system uses an intelligent dual-mode storage dynamic switching mechanism to automatically select the optimal storage engine based on a multi-dimensional assessment of data volume and change frequency. For business scenarios with data volumes less than 10 million or with high-frequency changes, relational database storage is preferred to meet transactional OLTP processing needs. For analysis scenarios with massive data volumes exceeding 10 million, data is automatically routed to the data warehouse for OLAP analysis, achieving sub-second response times through columnar storage and distributed computing. This dual-mode storage dynamic switching mechanism ensures the optimal match between storage strategies and business scenarios by monitoring data characteristic indicators in real time. OLAP analysis refers to online analytical processing, which supports fast multi-dimensional aggregate queries on massive amounts of data, such as total values by time and administrative divisions. 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 based on administrative requirements and time, and different scenarios and indicators are distinguished by indicator grouping and indicator names; at the same time, according to the application and data scale and requirements, statistical results with small data volumes can be stored in relational databases, and massive statistical results can be stored in data warehouses. Columnar storage is used to optimize OLAP performance, provide a unified access interface layer, shield the underlying storage differences, and realize automatic switching of dual-mode storage. Regardless of whether the query is for a small amount of data or billions of massive data, the response is in seconds.

[0025] S4. Establish the association configuration between page rendering controls and indicator data, support multi-indicator merging and data format conversion, and provide a visual control library for users to drag and drop to generate interactive pages; The step S4 specifically includes: The system dynamically binds page controls to indicator data through a visual configuration table, supports multi-indicator joint query and complex analysis, and provides a visual control library including map components, statistical charts, and timelines. After users drag and drop controls onto the design canvas and associate them with data sources, the system automatically generates responsive HTML5+CSS3 front-end code, enabling zero-coding visual page construction. Specifically, based on the storage of standard indicator results, a configuration table is added to establish the association between page indicator rendering controls and indicator data; this mechanism can realize the merging of multiple indicators, aggregated acquisition of indicator query requests, data format conversion and other features; and provides several sets of template libraries and visualization control libraries, such as: maps, various charts, single indicators, timelines, users can drag controls to the canvas area on the interface in a visual way, and bind data sources and style configurations. The system automatically generates responsive HTML5+CSS3 front-end code, thereby realizing user-defined interface and automatic rendering, achieving the technical effect of low-code visual orchestration.

[0026] S5. Configure the relationship between indicator data and map layers to achieve real-time linkage between indicator data changes or switching and map visualization; The relationship between the configuration indicator data and the map layer in step S5 is as follows: On the one hand: map the indicator data changes to the color, size or multidimensional style attributes of the map layer, so that the spatial display can be updated in real time with the data; on the other hand: as the statistical indicator data switches, the map visualization data also switches accordingly.

[0027] S6. For spatiotemporal point data, a dual-mode spatial aggregation method based on administrative division aggregation and location aggregation is adopted: multi-level spatial index based on administrative divisions enables cross-level drill-down analysis; location aggregation based on dynamic grid division enables cross-level statistical calculations; The administrative region aggregation is specifically: Construct a multi-level spatial index based on administrative divisions, use a spatial inclusion algorithm to determine the administrative region to which a data point belongs, and support drill-down statistics at the provincial, municipal, county, and township levels; The position aggregation is specifically as follows: Divide point data into multi-layer dynamic grids according to scale range, calculate the grid size of each layer, aggregate point features within the grid and support cross-layer drilling statistics; Specifically, a dual-mode aggregation method of political district aggregation and location aggregation is constructed for spatiotemporal point data; the political district aggregation: based on administrative division data, a multi-level spatial index structure is constructed to realize multi-level linkage aggregation at the provincial, municipal, county and township levels, and a spatial inclusion algorithm is used to determine the administrative area to which the data points belong, supporting cross-level statistical result drilling; the location aggregation: the point data of all elements are divided into multiple grid units, and divided into multiple layers according to the scale range, the grid size of each level is dynamically calculated, and the point elements in the grid are aggregated together at each level, supporting cross-level statistical result drilling.

[0028] In summary, the present invention constitutes the entire process of the spatiotemporal data visualization indicator management and application method through steps S1 to S6, which has the following technical advantages: (1) Scenario-driven design: By analyzing business scenarios and refining rules, indicator management can be accurately matched to actual needs such as the cockpit, thereby improving the pertinence of spatiotemporal data applications.

[0029] (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.

[0030] (3) Dual-mode storage: Intelligent switching between relational database and data warehouse, combined with columnar storage and unified SQL interface, to achieve query of billions of data in seconds, taking into account both transactional and analytical performance.

[0031] (4) Low-code visual orchestration: Drag-and-drop page construction and template library reuse, support for multi-indicator merging and style binding, business personnel can independently complete complex interface development.

[0032] (5) Data view linkage: Indicators are deeply associated with map layers, supporting color, size or multi-dimensional style attribute binding, and achieving real-time synchronization of data changes and spatial display.

[0033] (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.

[0034] It can be understood that the above specific description of the present invention is only used to illustrate 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 the present invention can still be modified or replaced by equivalents to achieve the same technical effects; as long as the use requirements are met, they are within the scope of protection of the present invention.

Claims

1. A method for managing and applying spatiotemporal data visualization indicators, characterized by: The following steps are involved: S1. Analyze spatiotemporal visualization business scenarios and 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 implement data statistics and flow, and supports automatic and scheduled calculations. S3. Design a hybrid storage architecture for standardized indicator data statistical results, using a dual-mode storage strategy of relational databases and data warehouses: A relational database is used to store small amounts of raw data and statistical results; Massive data is synchronized to the data warehouse through the ETL process, and distributed computing analysis is performed at the warehouse level based on indicator configuration. The analysis results are persistently stored in a columnar storage format. By building a unified access interface layer, transparent access to the heterogeneous storage layer is achieved. S4. Establish the association configuration between page rendering controls and indicator data, support multi-indicator merging and data format conversion, and provide a visual control library for users to drag and drop to generate interactive pages; S5. Configure the relationship between indicator data and map layers to achieve real-time linkage between indicator data changes or switching and map visualization; S6. For spatiotemporal point data, a dual-mode spatial aggregation method based on administrative division aggregation and location aggregation is adopted: a multi-level spatial index based on administrative divisions is used to achieve cross-level drill-down analysis; and location aggregation based on dynamic grid division is used to achieve cross-level statistical calculations.

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

3. The spatiotemporal data visualization indicator management and application method according to claim 2, characterized in that: The statistical calculation rules of the spatiotemporal indicator data in step S2 include: Aggregation dimensions, calculation logic, filtering conditions, data units, and target statistical units at all levels. The system automatically converts data units based on unit configuration and supports column-to-row configuration 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 of the dual-mode storage in step S3 is: The system adopts an intelligent dual-mode storage dynamic switching mechanism, automatically selecting the optimal storage engine based on a multi-dimensional assessment of data volume and change frequency: for business scenarios with data volumes less than tens of millions or high-frequency changes, relational database storage is preferred to meet transactional OLTP processing needs; for analysis scenarios with massive data volumes exceeding tens of millions, it is automatically routed to the data warehouse for OLAP analysis, achieving a response within seconds through columnar storage and distributed computing; this dual-mode storage dynamic switching mechanism ensures the optimal match between storage strategies and business scenarios by real-time monitoring of data characteristic indicators.

5. The spatiotemporal data visualization indicator management and application method according to claim 1, characterized in that: The step S4 specifically includes: The system uses a visual configuration table to dynamically bind page controls to indicator data, supports multi-indicator joint query and complex analysis, and provides a visual control library including map components, statistical charts, and timelines. After users place controls on the design canvas and associate them with data sources through drag-and-drop operations, the system automatically generates responsive HTML5+CSS3 front-end code, enabling zero-coding visual page construction.

6. The spatiotemporal data visualization indicator management and application method according to claim 1, characterized in that: The association between the configuration indicator data and the map layer in step S5 includes two aspects: On the one hand: map the indicator data changes to the color, size or multidimensional style attributes of the map layer, so that the spatial display can be updated in real time with the data; on the other hand: as the statistical indicator data switches, the map visualization data also switches accordingly.

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

8. The spatiotemporal data visualization indicator management and application method according to claim 1, characterized in that: The position aggregation in step S6 is specifically as follows: Divide point data into multi-layer dynamic grids according to the scale range, calculate the grid size at each level, aggregate point features within the grid, and support cross-level drill-down statistics.

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

10. The spatiotemporal data visualization indicator management and application method according to claim 3, characterized in that: The frequency of the automatic calculation in step S2 is configured to be executed in real time, daily or monthly according to the data update requirements.

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