Space-time big data visualization system for natural resource asset accounting

Abstract

The invention discloses a space-time big data visualization system for natural resource asset accounting. The space-time big data visualization system comprises a data acquisition and preprocessing module, a natural resource asset accounting module, a space-time big data storage and management module and a visualization engine module. According to the natural resource asset accounting space-time big data visualization system constructed by the invention, the dynamic, continuous and refined natural resource asset accounting is realized by deeply fusing multi-source heterogeneous space-time big data, and the timeliness and accuracy of the accounting are remarkably improved. According to the multi-level, multi-dimensional and interactive visualization function provided by the system, abstract accounting data can be converted into a visual space-time distribution diagram, a dynamic change diagram, a statistical chart and the like, so that a manager can clearly master the space-time pattern, the evolution trend and the value distribution of resource assets, and the information cognition efficiency and the decision support level are greatly improved.

Description

Technical Field

[0001] This application relates to the field of natural asset management technology, specifically to a spatiotemporal big data visualization system for natural resource asset accounting. Background Technology

[0002] The scientific, accurate, and dynamic accounting and evaluation of natural resource assets has become a key requirement for national macro-decision-making and refined management.

[0003] Most existing technologies rely on static reports and periodic survey data. For example, Chinese invention patent application number 201810465418.8 discloses a natural resource asset accounting platform, which adopts a technical solution of a data import module, a data management module, an accounting formula storage module, a data accounting module, an accounting result management module, and an accounting result output module. Although it can perform real-time, detailed, and efficient natural resource asset accounting and effectively promote the implementation of the natural resource asset balance sheet compilation plan, the use of static reports leads to data update lag. Consequently, it cannot reflect the stock, flow, quality, and spatiotemporal changes of natural resource assets (such as forest stock, water resources, arable land quality, and mineral resource reserves) in real time or near real time. Furthermore, it cannot provide users with an intuitive and concise display of natural resource assets during implementation, making it difficult for users to quickly grasp the status of resource assets.

[0004] Therefore, there is an urgent need for a spatiotemporal big data visualization system for natural resource asset accounting. Summary of the Invention

[0005] To address this, this application provides a spatiotemporal big data visualization system for natural resource asset accounting, in order to solve the problems of data update lag and the inability to intuitively understand natural resource assets in existing technologies.

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] The spatiotemporal big data visualization system for natural resource asset accounting includes a data acquisition and preprocessing module, a natural resource asset accounting module, a spatiotemporal big data storage and management module, and a visualization engine module.

[0008] The data acquisition and preprocessing module is used to automatically access and collect multi-source heterogeneous data of natural resources; the data acquisition and preprocessing module is also used to clean, convert formats, spatially register, align time series and verify the quality of the collected data to generate a standardized spatiotemporal dataset;

[0009] The natural resource asset accounting module is used to construct and run an accounting model based on the standardized spatiotemporal dataset and according to a preset natural resource asset classification system and accounting rule base. The accounting model calculates the physical quantity, flow, quality grade and value of the selected natural resource asset type within a specific spatiotemporal range. The accounting rule base includes physical quantity accounting methods, value assessment methods and corresponding parameter libraries for various types of resources.

[0010] The spatiotemporal big data storage and management module is used to store and manage standardized spatiotemporal datasets, accounting models, intermediate data generated during the accounting process, and final accounting result data; the accounting result data includes at least resource type identifiers, spatial location information, timestamps, physical quantity indicators, value indicators, and their corresponding data;

[0011] The visualization engine module is used to receive the calculation result data or intermediate data, as well as the data from the data acquisition and preprocessing module, and to convert the calculation data into graphic symbols; the visualization engine module can generate visualization views.

[0012] Preferably, the multi-source heterogeneous data includes at least: periodic or real-time remote sensing image data, Internet of Things data monitored by ground sensor networks, data reported by manual field surveys, basic geospatial data, socio-economic statistical data, and historical natural resource ledger data.

[0013] Preferably, the remote sensing image data is collected through satellite remote sensing platforms and UAV remote sensing platforms, the Internet of Things data is collected through various environmental monitoring sensors deployed at natural resource sites, and the reported data is collected online through mobile terminal APP or Web-based reporting system. The basic data includes administrative division boundaries, digital elevation models, land use / cover data, and basic surveying and mapping results, and the statistical data comes from the resource value assessment data released by the government statistical department.

[0014] Preferably, the visualization view includes: a heat map or hierarchical rendering map of the spatial distribution of resource assets based on an electronic map, a dynamic trend map or animation reflecting the changes in the quantity or value of resource assets over time, statistical charts showing the comparison of resource assets in different regions or types, and interactive charts for multidimensional attribute correlation analysis.

[0015] Preferably, the accounting model includes a physical quantity accounting sub-model, a quality assessment sub-model, and a value accounting sub-model:

[0016] Physical quantity accounting sub-model: Based on remote sensing interpretation, ground monitoring and survey data, it uses methods such as area measurement, biomass inversion and reserve estimation to calculate the physical quantity of a specific natural resource type within a selected spatiotemporal range;

[0017] Quality assessment sub-model: Combining remote sensing spectral characteristics, ground monitoring indicators and expert knowledge base, it quantifies or classifies the quality level or status of natural resources;

[0018] Value Quantity Calculation Sub-model: Based on the value assessment methods and corresponding parameters set in the accounting rule base, the physical quantity calculation results and quality assessment results are transformed into the monetized value of natural resource assets.

[0019] Preferably, the evaluation method includes the market approach, income approach, cost approach, substitution cost approach, and shadow engineering approach, and the parameters include the unit price of resource products, discount rate, and unit service function value.

[0020] Preferably, it also includes a report generation and export module, which is used to generate a structured natural resource asset accounting and analysis report based on the template set by the user and the selected accounting result range, and can export the report as a PDF, Word or image file.

[0021] Preferably, the user interface module provides a graphical user interface to receive user input and transmits user interaction instructions to the visualization engine module and / or the natural resource asset accounting module to trigger corresponding data query, accounting execution, or view update operations.

[0022] Compared with the prior art, this application has at least the following beneficial effects:

[0023] This invention breaks through the limitations of traditional static accounting by dynamically capturing data. The system accesses multi-source spatiotemporal data streams such as satellite remote sensing and Internet of Things monitoring in real time, driving the accounting model to automatically update the stock and value indicators of natural resource assets on an hourly / daily basis, realizing the progress from "periodic census" to real-time response.

[0024] Through the visualization engine module, on the one hand, it can intuitively present the spatial clustering characteristics and gradient heterogeneity of resource assets based on geocoding calculation results through heat maps, iso-region rendering, and other methods; on the other hand, it can dynamically trace the evolution of resources through timeline animation (and also through charts), thereby making it easier for users to grasp the status of natural asset resources. Attached Figure Description

[0025] Figure 1 This is a module diagram of the spatiotemporal big data visualization system for natural resource asset accounting in this application. Detailed Implementation

[0026] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] like Figure 1As shown, this application discloses a spatiotemporal big data visualization system for natural resource asset accounting, including a data acquisition and preprocessing module, a natural resource asset accounting module, a spatiotemporal big data storage and management module, a visualization engine module, and a user interaction interface module.

[0028] The data acquisition and preprocessing module is used to automatically access and collect multi-source heterogeneous data of natural resources. The multi-source heterogeneous data includes at least: periodic or real-time remote sensing image data, Internet of Things data monitored by ground sensor network, data reported by manual field surveys, basic geospatial data, socio-economic statistical data, and historical natural resource ledger data. The data acquisition and preprocessing module is also used to clean, convert, spatially register, align time series, and verify the quality of the collected data to generate a standardized spatiotemporal dataset.

[0029] The natural resource asset accounting module is used to construct and run an accounting model based on the standardized spatiotemporal dataset and according to a preset natural resource asset classification system and accounting rule base. The accounting model calculates the physical quantity, flow, quality grade, and value of a selected natural resource asset within a specific spatiotemporal range. The accounting rule base includes physical quantity accounting methods, value assessment methods, and corresponding parameter libraries for various resources (such as forests, grasslands, wetlands, arable land, water bodies, minerals, and energy), supporting the dynamic configuration and updating of the accounting model.

[0030] The spatiotemporal big data storage and management module adopts distributed spatiotemporal database technology to store and manage preprocessed standardized spatiotemporal datasets, accounting model configuration information, intermediate data generated during the accounting process, and final accounting result data. The accounting result data includes at least resource type identifiers, spatial location information, timestamps, physical quantity indicators, value indicators, and their corresponding data. This module provides efficient data indexing, query retrieval, and spatiotemporal analysis service interfaces.

[0031] The visualization engine module is configured to receive calculation result data or intermediate data from the spatiotemporal big data storage and management module, as well as basic geospatial data; based on a preset visualization rule base, it transforms abstract calculation data into graphic symbols; the visualization engine module supports the generation of various visualization views, including: heat maps or hierarchical rendering maps of the spatial distribution of resource assets based on electronic maps, dynamic trend maps or animations reflecting the changes in the quantity or value of resource assets over time, statistical charts showing the comparison of resource assets in different regions or types, and interactive charts for multi-dimensional attribute correlation analysis;

[0032] The user interface module provides a graphical user interface and receives user input. The user input includes: selecting the target natural resource type, setting the spatiotemporal range (including geographical region and time interval), specifying accounting indicators (physical quantity or value), selecting the visualization view type, zooming / panning the map, dragging the time axis, and filtering / drilling down chart elements. The user interaction operation instructions are then transmitted to the visualization engine module and / or the natural resource asset accounting module to trigger corresponding data queries, accounting execution, or view updates, thereby enabling dynamic interaction between the user and the visualization results.

[0033] When using this system, asset accounting can be performed on forest resources, woodland resources, and water resources. The data acquisition and preprocessing module can collect data comprehensively, thus ensuring the accuracy of the accounting. The natural resource asset accounting module is the core module of this system. Through its use, natural resources can be accounted for and the accounting results can be output. The spatiotemporal big data storage and management module can manage the accounting process and results. The visualization engine module allows users to easily view the accounting data. The user interface module serves as a human-computer interaction window, providing graphical control functions such as map operation, parameter configuration, and view switching. It responds to user commands in real time and triggers data queries, accounting, or view updates, realizing dynamic interactive decision support.

[0034] The real-time remote sensing image data accessed by the data acquisition and preprocessing module mainly comes from satellite remote sensing platforms and UAV remote sensing platforms. The Internet of Things data comes from various environmental monitoring sensors deployed at natural resource sites (such as water quality sensors, soil moisture sensors, forest camera monitoring equipment, and weather stations). Data reported by manual field surveys is collected online through mobile terminal APP or web-based reporting systems. Geospatial basic data includes administrative division boundaries, digital elevation models, land use / cover data, and basic surveying and mapping results. Socioeconomic statistical data comes from data related to resource value assessment released by government statistical departments (such as forest product prices, land prices, water prices, and mineral product price indices).

[0035] The accounting model constructed by the natural resource asset accounting module specifically includes:

[0036] Physical quantity accounting sub-model: Based on remote sensing interpretation, ground monitoring and survey data, it uses techniques such as area measurement, biomass inversion and reserve estimation to calculate the physical quantity of specific natural resource types (such as forest stock volume in cubic meters, water resources in cubic meters, cultivated land area in hectares, and mineral resource reserves in tons) within a selected spatiotemporal range.

[0037] Quality assessment sub-model: Combining remote sensing spectral characteristics, ground monitoring indicators (such as water quality parameters, soil nutrient content, vegetation coverage index) and expert knowledge base, the quality level or status of natural resources is quantitatively assessed or classified.

[0038] Value Quantity Calculation Sub-model: Based on the value assessment methods (such as market approach, income approach, cost approach, substitution cost approach, shadow project approach) and corresponding parameters (such as unit price of resource products, discount rate, unit service function value) set in the accounting rule base, the physical quantity calculation results and quality assessment results are transformed into the monetized value of natural resource assets.

[0039] The accounting rule base allows administrators to dynamically maintain it through the user interface module, including adding, modifying, or deleting accounting methods, evaluation formulas, parameter values, and applicable conditions for specific resource types.

[0040] The visualization engine module generates a spatial distribution visualization view of resource assets based on electronic maps. It supports rendering on the geographic base map using color gradient, symbol size, density clustering or isotropic region method based on the spatial location information in the accounting results and the selected indicator values. It intuitively displays the spatial aggregation, diffusion, gradient distribution characteristics and spatial heterogeneity of natural resource assets.

[0041] The visualization engine module generates dynamic visualization views that reflect the changes of resource assets over time. It supports the continuous display of the temporal changes in the physical quantity or value of specific natural resource assets within a selected geographical area through timeline animations, sequence frames, and charts, clearly presenting dynamic trends such as growth, decline, fluctuation, and migration.

[0042] It also includes a report generation and export module, which automatically summarizes data and calls up visualization views based on user-defined templates and selected accounting result ranges (spatial and temporal ranges, resource types, indicators) to generate structured natural resource asset accounting and analysis reports. It supports exporting to PDF, Word, or image formats. Users can organize the exported files to generate reports for use in various scenarios.

[0043] The technical features of the above embodiments can be combined in any way (as long as there is no contradiction in the combination of these technical features). For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; these embodiments not explicitly written should also be considered to be within the scope of this specification.

Claims

1. A spatiotemporal big data visualization system for natural resource asset accounting, characterized in that, It includes a data acquisition and preprocessing module, a natural resource asset accounting module, a spatiotemporal big data storage and management module, and a visualization engine module; The data acquisition and preprocessing module is used to automatically access and collect multi-source heterogeneous data of natural resources; the data acquisition and preprocessing module is also used to clean, convert formats, spatially register, align time series and verify the quality of the collected data to generate a standardized spatiotemporal dataset; The natural resource asset accounting module is used to construct and run an accounting model based on the standardized spatiotemporal dataset and according to a preset natural resource asset classification system and accounting rule base. The accounting model calculates the physical quantity, flow, quality grade and value of the selected natural resource asset type within a specific spatiotemporal range. The accounting rule base includes physical quantity accounting methods, value assessment methods and corresponding parameter libraries for various types of resources. The spatiotemporal big data storage and management module is used to store and manage standardized spatiotemporal datasets, accounting models, intermediate data generated during the accounting process, and final accounting result data; the accounting result data includes at least resource type identifiers, spatial location information, timestamps, physical quantity indicators, value indicators, and their corresponding data; The visualization engine module is used to receive the accounting result data or intermediate data, as well as the data from the data acquisition and preprocessing module, and to convert the accounting data into graphic symbols. The visualization engine module can generate visual views.

2. The spatiotemporal big data visualization system for natural resource asset accounting according to claim 1, characterized in that, The multi-source heterogeneous data includes at least: periodic or real-time remote sensing image data, Internet of Things data monitored by ground sensor networks, data reported from manual field surveys, basic geospatial data, socio-economic statistical data, and historical natural resource ledger data.

3. The spatiotemporal big data visualization system for natural resource asset accounting according to claim 2, characterized in that, The remote sensing image data is collected through satellite remote sensing platforms and UAV remote sensing platforms, while IoT data is collected through various environmental monitoring sensors deployed at natural resource sites. The reported data is collected online through mobile terminal APP or web-based reporting systems. The basic data includes administrative division boundaries, digital elevation models, land use / cover data, and basic surveying and mapping results. The statistical data comes from resource value assessment data released by government statistical departments.

4. The spatiotemporal big data visualization system for natural resource asset accounting according to claim 1, characterized in that, The visualization views include: heat maps or hierarchical rendering maps of the spatial distribution of resource assets based on electronic maps, dynamic trend maps or animations reflecting the changes in the quantity or value of resource assets over time, statistical charts showing comparisons of resource assets in different regions or types, and interactive charts for multidimensional attribute correlation analysis.

5. The spatiotemporal big data visualization system for natural resource asset accounting according to claim 3, characterized in that, The accounting model includes a physical quantity accounting sub-model, a quality assessment sub-model, and a value accounting sub-model: Physical quantity accounting sub-model: Based on remote sensing interpretation, ground monitoring and survey data, it uses methods such as area measurement, biomass inversion and reserve estimation to calculate the physical quantity of a specific natural resource type within a selected spatiotemporal range; Quality assessment sub-model: Combining remote sensing spectral characteristics, ground monitoring indicators and expert knowledge base, it quantifies or classifies the quality level or status of natural resources; Value Quantity Calculation Sub-model: Based on the value assessment methods and corresponding parameters set in the accounting rule base, the physical quantity calculation results and quality assessment results are transformed into the monetized value of natural resource assets.

6. The spatiotemporal big data visualization system for natural resource asset accounting according to claim 5, characterized in that, The evaluation methods include the market approach, income approach, cost approach, substitution cost approach, and shadow engineering approach. The parameters include the unit price of resource products, discount rate, and unit service function value.

7. The spatiotemporal big data visualization system for natural resource asset accounting according to claim 1, characterized in that, It also includes a report generation and export module, which generates structured natural resource asset accounting and analysis reports based on user-defined templates and selected accounting result ranges, and can export PDF, Word, or image files.

8. The spatiotemporal big data visualization system for natural resource asset accounting according to claim 1, characterized in that, It also includes a user interface module, which provides a graphical user interface to receive user input and transmits user interaction instructions to the visualization engine module and / or the natural resource asset accounting module to trigger corresponding data query, accounting execution, or view update operations.

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