Natural resource element investigation monitoring result visualization method and device based on entity

Abstract

The invention belongs to the technical field of natural resource survey monitoring and data visualization, and particularly relates to an entity-based natural resource element survey monitoring result visualization method and device. The method comprises the following steps: collecting natural resource element survey monitoring data of a to-be-monitored region; constructing an expression model containing an entity layer and a feature layer based on the natural resource element investigation monitoring data; and visualizing the expression model by adopting a web technology. Natural resource element survey monitoring data is divided into an entity layer and a feature layer according to multiple factors such as internal characteristics, attribute association and change rules; the entity view and the statistical view are constructed on the basis of the two levels, and the data are analyzed and expressed by utilizing a web-based visualization technology, so that the defects that a natural resource element expression method is single and the data change condition is difficult to track effectively in the prior art are overcome.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of natural resource investigation and monitoring and data visualization, and particularly relates to a natural resource element investigation and monitoring result visualization method and device based on entities. BACKGROUND

[0002] Natural resources refer to materials and energy that exist naturally in nature, have utilization value, and can directly or indirectly meet the needs of human production, life and development. They are an important part of the earth's ecological system and the material basis for the survival and development of human society; natural resource investigation and monitoring is a process of comprehensively investigating and dynamically monitoring the quantity, quality, distribution, utilization status and dynamic changes of land, water, forests, grasslands, mineral resources and other natural resources through systematic and scientific methods. Its importance is reflected in multiple aspects such as economy, ecology, society, technology and international cooperation; illegal occupation of cultivated land, illegal mining and ecological destruction directly threaten food security, ecological balance and sustainable use of resources. Natural resource investigation provides a data basis for fine management of natural resources, and natural resource monitoring provides technical support for testing the effectiveness of natural resource management.

[0003] Web-based visualization expression is a technology that uses html, css, javascrit, etc. to convert a large amount of complex data or abstract concepts into intuitive and easy-to-understand visual forms through graphics, charts, animations and other visual elements, helping users quickly understand information, discover patterns and make decisions, with characteristics such as cross-platform, real-time updating and strong interactivity.

[0004] Cesium is an open-source JavaScript library mainly used for three-dimensional geographic visualization and data analysis of spatial data. It realizes cross-platform and cross-browser three-dimensional earth and map rendering functions through WebGL technology, and is widely used in digital earth, smart city and other fields, with characteristics such as multi-view switching, dynamic data visualization and support for large-scale data rendering.

[0005] Due to the overallity, systematicness, data diversity, regional difference, law enforcement urgency and dynamic timeliness of natural resource element investigation and monitoring, there are prominent problems in the expression of natural resource investigation and monitoring results. The existing expression methods are relatively single, often limited to traditional charts, reports and other forms. This single expression mode makes it difficult to track the spatial and temporal changes of elements in each cycle. It is difficult to intuitively and effectively capture the dynamic evolution process of resource elements at different time nodes and spatial positions, and it is difficult to clearly present the change characteristics such as quantity increase and decrease, spatial displacement and other changes over time. At the same time, in the face of rich and diverse natural resource data, it is difficult to comprehensively analyze the data from multiple dimensions and multiple angles with single expression method. It is difficult to fully tap the rules and associations hidden behind the data, and it is more difficult to display the analysis results through intuitive and visual means, making it difficult for relevant decision makers and users to quickly and accurately obtain valuable information, and thus affecting the scientific judgment and reasonable decision of the natural resource situation. SUMMARY

[0006] To solve the problems existing in the prior art, the present application provides a natural resource element investigation and monitoring result visualization method and device based on entities, which aims to analyze and express data by using web-based visualization technology, and makes up for the defects of the prior art that the expression method of natural resource elements is single and it is difficult to effectively track the data changes.

[0007] To achieve the above-mentioned purpose, the present application provides the following solutions: A natural resource element investigation and monitoring result visualization method based on entities, the method comprising: collecting natural resource element investigation and monitoring data of the area to be monitored; constructing an expression model comprising an entity layer and a feature layer based on the natural resource element investigation and monitoring data; visualizing the expression model by using web technology.

[0008] Preferably, the method of constructing an expression model comprising an entity layer and a feature layer based on natural resource element investigation and monitoring data comprises: extracting entity data based on the natural resource element investigation and monitoring data to construct an entity layer, the entity layer being composed of natural resource element entities, limiting entities, changing entities and their respective attribute elements; extracting entity data with common features based on the location features and attribute features of the entity layer to construct a feature data set, statistically analyzing the feature data set to obtain statistical data, and constructing a feature layer based on the feature data set and the statistical data; constructing an expression model based on the entity layer and the feature layer.

[0009] Preferably, the method for visualizing the expression model by using web technology comprises: Taking the entity layer as the core, the entity view is constructed to display the natural resource element entity, the constraint entity and the change entity of the entity layer; The feature view is constructed based on the feature layer to express the entity data and the statistical data thereof.

[0010] Preferably, the entity view comprises a first map module, a function area module, an attribute module and a first chart module. The first map module is used for loading a three-dimensional image map base based on the cesium technology to realize the spatial-attribute-time multi-dimensional linkage display of the natural resource element entity, the constraint entity and the change entity. The function area module is used for providing operation support and covering historical data query, contour comparison and data export functions. The attribute module is used for basic information presentation and details of various attribute parameters and associated entity information of the map patch. The first chart module is used for intuitively presenting the historical change trend of the key attributes of the map patch through the visual chart.

[0011] Preferably, the feature view comprises a second map module, a second chart module and a statistical module. The second map module is used for constructing a layered geographic base map based on the cesium technology to intuitively present the natural resource data distribution through the color thematic map of the regional map patch. The second chart module is used for the spatio-temporal change analysis of the key attributes of the target region to realize data comparison and trend display in the form of a combination chart. The statistical module is used for presenting the quantitative information of the core indicators in the form of a digital dashboard.

[0012] The application further provides a natural resource element investigation and monitoring result visualization device based on an entity. The data acquisition module is used for acquiring the natural resource element investigation and monitoring data of the region to be monitored. The model construction module is used for constructing the expression model comprising the entity layer and the feature layer based on the natural resource element investigation and monitoring data. The visualization module is used for visualizing the expression model by using web technology.

[0013] Preferably, the model construction module comprises an entity layer unit, a feature layer unit and a construction unit. An entity layer unit is configured to extract entity data based on natural resource element survey monitoring data to construct an entity layer, and the entity layer is composed of natural resource element entities, restriction entities, change entities, and attribute elements of the entities; A feature layer unit is configured to extract entity data with common features based on location features and attribute features of the entity layer to construct a feature data set, perform statistical analysis on the feature data set to obtain statistical data, and construct a feature layer based on the feature data set and the statistical data. A construction unit is configured to construct an expression model based on the entity layer and the feature layer.

[0014] Preferably, the visualization module comprises an entity view unit and a feature view unit. The entity view unit is configured to take the entity layer as the core to construct an entity view to display natural resource element entities, restriction entities, and change entities of the entity layer. The feature view unit is configured to construct a feature view based on the feature layer to express entity data and statistical data thereof.

[0015] Preferably, the entity view comprises a first map module, a function area module, an attribute module, and a first chart module. The first map module is configured to load a three-dimensional image map base based on the cesium technology to realize spatial-attribute-temporal multi-dimensional linkage display of natural resource element entities, restriction entities, and change entities. The function area module is configured to provide operation support, covering historical data query, contour comparison, and data export functions. The attribute module is configured to present basic information, and details of various attribute parameters and associated entity information of a plot are listed. The first chart module is configured to intuitively present historical change trends of key attributes of a plot through a visual chart.

[0016] Preferably, the feature view comprises a second map module, a second chart module, and a statistical module. The second map module is configured to construct a layered geographic base map based on the cesium technology to intuitively present natural resource data distribution through a color thematic map of a regional plot. The second chart module is configured to perform spatio-temporal change analysis of key attributes of a target region, and a combination chart form is adopted to realize data comparison and trend display. The statistical module is configured to present quantitative information of core indicators in the form of a digital board.

[0017] Compared with the prior art, the present application has the following advantages: This invention provides a visualization method for the survey and monitoring results of natural resource elements based on entities. It divides the survey and monitoring data of natural resource elements into an entity layer and a feature layer according to various factors such as inherent characteristics, attribute relationships, and change patterns. Based on the above two layers, entity views and statistical views are constructed. Web-based visualization technology is used to analyze and express the data, which makes up for the shortcomings of existing technologies in expressing natural resource elements in a single way and in effectively tracking data changes. Attached Figure Description

[0018] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Fig. 1 This is a schematic diagram of the technical route for the visualization method of natural resource element survey and monitoring results based on entities, as described in an embodiment of the present invention. Fig. 2 This is a schematic diagram of the visualization method for the survey and monitoring results of natural resource elements based on entities, as described in an embodiment of the present invention. Fig. 3 This is a schematic diagram illustrating the expression model of an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] Example 1 This invention provides a method for visualizing the results of entity-based natural resource element surveys and monitoring, the method comprising: Collect survey and monitoring data on natural resource elements in the areas to be monitored; A representation model comprising an entity layer and a feature layer is constructed based on natural resource element survey and monitoring data. Web technology is used to visualize the expression model.

[0023] like Figs. 1-3 As shown, the specific implementation process of the present invention is as follows: The natural resource element survey monitoring data of the area to be monitored is collected, and an expression model containing an entity layer and a feature layer is constructed based on the natural resource element survey monitoring data, including: 1. Constructing an entity layer The obtained natural resource element survey monitoring data is classified according to the composition rules of the entity layer. The entity layer is composed of natural resource element entities, limiting entities, change entities, and their respective attributes, and is constructed in this way.

[0024] Among them, E denotes the entity layer data, N denotes the natural resource element entity set, R denotes the limiting entity set, C denotes the change entity set; E is the total number of entities, N denotes the number of natural resource element entities, R denotes the number of limiting entities, C| denotes the number of change entities.

[0025] (1) Natural resource element entity construction In this embodiment, an entity is defined as a geographic patch point, line or face contour data with a clear geographical boundary and its attributes, which also contains XY coordinates and elevation information.

[0026] Among them, N denotes the natural resource element entity set, denotes the i-th entity in the natural resource element entity set, denotes the location of the entity (such as coordinates, area, etc.), is the attribute set of the entity (the universal set of all possible attributes).

[0027] The geographical boundary is the clear range of the entity in the geographical space, which enables each entity to have a unique location identifier in the geographical coordinate system. Among them, the XY coordinates can accurately determine the position of the entity in the plane geographical space, and the elevation information further describes the height characteristics of the entity in the vertical direction, which is crucial for accurately expressing entities with three-dimensional characteristics such as mountains, hills, buildings, etc.

[0028] An attribute is defined as a collection of data closely related to a geographic entity, which can comprehensively and accurately describe various aspects of the entity's characteristics, status, and uses. For example, a mining entity may include attributes such as mineral type, mining method, and mining scale; an entity illegally occupying farmland may involve attributes such as the perpetrator, time of violation, and area of ​​violation; and a farmland entity may have attributes such as soil type, crops planted, and irrigation conditions.

[0029] (2) Construction of changing entities Natural resource elements are in a state of dynamic change over time. In order to accurately grasp these changes, it is necessary to extract historical data on changes in the spatial location and attributes of these entities over the years, and then construct a change layer.

[0030] Where C represents the set of changing entities, The first entity in the set of changing entities i One entity, Representing entities Location (such as coordinates, region, etc.) It is a physical entity The set of attributes (which is the complete set of all possible attributes). For geometric representation of entities In terms of changes in spatial location, we consider three entity types: points, lines, and surfaces.

[0031] 1) Point entity changes Emergence: At a certain point in time, a point entity that did not exist before appears in the data for the first time. Examples include the addition of an illegal mining site or an illegal land occupation site. Extraction methods can identify the coordinates of newly appearing points by comparing point entity datasets from different time points.

[0032] Disappearance: A point entity that originally existed no longer appears at a later point in time. For example, a historically abandoned mine being restored.

[0033] 2) Changes in line body Emergence: A new line entity appears in the data for the first time. For example, a newly built road, or a new river channel formed after a river has been rerouted.

[0034] Extension: The length of a line entity increases from its original length. Examples include the extension of a road or the lengthening of a river. The length of the line entity at different points in time is calculated, and the length values ​​of the later period and the earlier period are compared. If the length of the later period is greater than that of the earlier period, and the starting or ending point of the line entity has expanded outwards, then it is determined to be an extension.

[0035] Shortening: The length of a line entity is reduced. For example, a road is shortened due to reconstruction, or a river is shortened due to partial drying up.

[0036] Disappearance: The original line entity completely disappears at a later point in time. Examples include the lead wires of a demolished bridge or a dried-up and vanished river.

[0037] 3) Surface solid transformation Emergence: New polygon entities appear in the data for the first time. For example, newly established urban areas or newly formed lakes. Through spatial overlay analysis, the polygon dataset from a later period is compared with the polygon dataset from a previous period to identify the newly added polygon features in the later period.

[0038] Expansion: The area of ​​a surface entity increases compared to a previous time scale, and its boundaries expand outward. Examples include the expansion of cities and the increase in forest area. Calculate the area of ​​the surface entity at different times and compare the area values ​​of later and earlier periods. If the area of ​​the later period is larger than that of the earlier period, and the boundaries of the surface entity show a tendency to move outward, it is determined to be expansion.

[0039] Contraction: The area of ​​a solid entity decreases compared to a previous time scale, and its boundaries shrink inward. For example, demolition in parts of a city leads to a reduction in area, and a lake's area decreases due to a drop in water level.

[0040] Disappearance: The original surface entity completely disappears at a later point in time. Examples include a filled-in lake or a demolished building area.

[0041] Besides changes in spatial location, changes in entity attributes are also an important aspect of constructing the change layer. Entity attribute changes involve multiple aspects, such as changes in area, alterations in legality, and adjustments in usage. For example, a piece of farmland may be requisitioned for construction due to urban development needs, fundamentally changing its use; a mine may experience corresponding changes in its mining scale and methods due to improvements in mining technology or changes in resource reserves.

[0042] (3) Restrict entity construction in, R Indicates a restricted set of entities. Indicates the first element in the restricted entity set. i One entity, express The location of an entity (such as coordinates, region, etc.). It is a physical entity The set of attributes (is the complete set of all possible attributes).

[0043] In the field of natural resource management, data constructed based on the legal boundaries stipulated by policies and laws and regulations to ensure the rational use and effective protection of natural resources constitutes a restriction entity. This includes the scope of the restriction and its attribute information. Restriction layer data clarifies the boundaries and norms of natural resource utilization and serves as a crucial basis for natural resource management.

[0044] The restricted data includes, but is not limited to, boundaries of urban, agricultural, and ecological spaces, as well as the scope of mining and exploration rights. Urban spatial boundaries clearly define the scope and scale of urban construction and development. Ecological spatial boundaries delineate ecological protection red line areas to protect the integrity and stability of ecosystems. Natural resources within ecological spatial boundaries have important ecological functions, such as water conservation, soil and water conservation, and biodiversity protection. The scope of mining and exploration rights clearly defines the legal areas and authority for mineral resource development. Within the designated mining and exploration rights areas, mineral resource development enterprises can conduct mineral resource mining and exploration activities in accordance with relevant laws, regulations, and mining plans.

[0045] 2. Construct the feature layer Based on the location and attribute features of the entity layer, entity data with common features are extracted to construct a feature dataset. Statistical analysis is performed on the feature dataset to obtain statistical data. A feature layer is then constructed based on the feature dataset and statistical data.

[0046] The purpose of constructing a feature layer is to extract entity data with common characteristics from a large amount of entity data, enabling more targeted analysis and processing. The construction of the feature layer mainly includes two stages: feature dataset construction and statistical analysis.

[0047] (1) Construction of feature dataset Based on the location, key attributes, and other characteristics of the entity layer, entity data with common features are extracted to construct a feature layer.

[0048] 1) Location characteristics: Extract entity data from entity data that are located in the same region to construct a dataset. This can be done in two ways: first, from an administrative division perspective, by constructing a dataset from entity data located in the same province, city, or county; second, from a restricted area perspective, by constructing a dataset from entity data located within that same area. This type of dataset, divided by administrative division, is helpful for analyzing and comparing the status of regional natural resources.

[0049] in, This represents a dataset with the same locational features. This represents the i-th entity in a dataset with the same locational characteristics. Represents a set of natural resource element entities. express The location of an entity (such as coordinates, region, etc.), where D represents the target region.

[0050] 2) Attribute Features: Extract entity data with the same attribute value to construct a corresponding dataset. For example, extract entities whose legality is illegal to construct a dataset. This type of dataset, divided according to specific attribute values, helps to conduct in-depth research on entities with the same characteristics and discover their inherent patterns and features.

[0051] in, This represents a dataset with the same attribute characteristics. This represents the i-th entity in a dataset with the same attribute characteristics. Represents a set of natural resource element entities. and Representing entities The x and y attributes, and Represents the attribute value.

[0052] (2) Statistics and Analysis Statistical analysis of the constructed feature dataset is a crucial step in feature layer construction. By employing statistical methods and data analysis techniques to organize, calculate, and analyze the data in the feature dataset, valuable statistical data can be derived. This includes quantitative statistics (such as the number of entities at each administrative level, total area, etc.) and percentage statistics (such as the percentage of different types of entities, the distribution ratio of entities in different regions, etc.).

[0053] 1. Quantity statistics: Calculate the number of entities and the total area within a certain region.

[0054] in, Indicates that the position is satisfied simultaneously and attributes The number of entities. Indicates the first Individual entities. This indicates an indicator function (1 if the condition is true, 0 otherwise). Representing entities The location belongs to a range (such as a polygon, coordinate interval, etc.). Representing entities Attributes The conditions to be met (e.g.: , in Representing entities (area).

[0055] 2. Percentage statistics: Calculate the proportion of a certain type of entity within a certain administrative region to the total number of entities.

[0056] in, This indicates the percentage of entities of category C within region D. This represents the number of entities that simultaneously satisfy both the range D and category C. This indicates the total number of entities within the region.

[0057] Finally, an expression model is constructed based on the entity layer and the feature layer.

[0058] Furthermore, web technologies are used to visualize the representation model, including: With the entity layer as the core, an entity view is constructed to display the natural resource element entities, restricted entities, and changing entities in the entity layer; Feature views are constructed based on feature layers to express entity data and its statistical data.

[0059] Specifically, based on the entity layer and feature layer constructed from natural resource element survey and monitoring data, entity views and feature views are built to visualize these layers. The feature view aims to grasp the overall statistical characteristics and trends of entity data across different administrative divisions from a macro perspective; the entity view focuses on the micro level, conducting refined statistical analysis of individual geographic entities and clearly presenting their specific attributes and map details. The specific construction process of the entity layer and feature layer using entity views and feature views is as follows: 1. Entity View The entity layer consists of three parts: natural resource element entities, constraint entities, and change entities. These are displayed in the entity view, which is mainly divided into four modules: the first map module, the functional area module, the attribute module, and the first chart module. As the core module of the entity view, the first map module is based on Cesium technology to load a 3D image map base, enabling multi-dimensional interactive display of natural resource elements, constrained entities, and changing entities in terms of space, attributes, and time. Its design logic is divided into two main dimensions: static display of the entity layer and dynamic tracking of the change layer. Specifically, the entity layer uses red solid lines to outline the boundaries and transparent blank fills the surface. Clicking on it will trigger the linked attribute module to display information such as coordinates, area, and validity.

[0060] For the change layer data in the dynamic monitoring of natural resource elements, the system adopts time-series layered rendering, and realizes the intuitive presentation of the historical evolution process through differentiated boundary styles and color coding: the latest year's change data is the core focus layer, with the boundary outline of the map patch delineated by a 2px wide red solid line to ensure clear recognition in the three-dimensional terrain undulation; the area within the boundary is completely transparently filled to avoid obscuring the underlying image texture. Historical data from previous years is constructed with a visual metaphor of the time axis through dashed boundaries and warm and cool color gradients. The boundary uses a 1.5px wide dashed line to represent the historical state, clearly distinguishing it from the latest solid line boundary. The spacing between dashed line breakpoints is uniformly 4px to ensure display continuity when zooming; in terms of boundary line color, according to the data year from far to near, the dashed line color gradients from dark blue (RGB:0,0,139) to orange-red (RGB:255,69,0), forming a "cool-warm" time gradient; an intermediate color level (such as purple, red) is inserted every 5 years to enhance the readability of long-term time series data. Historical data is split into independent layers by year, displaying only the most recent 5 years of data by default. Users can dynamically load earlier years using a time slider. When multiple historical data periods overlap, the system automatically overlays them from most recent to oldest, with the transparency of the dashed lines for earlier years decreasing to 60% to avoid visual clutter. Attribute changes in the change layer are displayed through the first chart module, which mainly includes two types of charts: line charts and Sankey diagrams. Line charts primarily show the time-series changes of attribute data, focusing on the continuous changes of a single attribute over time. For example, the change in the area of ​​a map patch over years. Sankey diagrams mainly represent attribute transformations, quantifying the flow and distribution of attributes between different states, revealing transformation efficiency and path dependence. For example, the transformation between the original area of ​​a historical legacy mine and its restored and unrestored areas. The function area module provides operational support and covers core interactive functions such as historical data query, contour comparison, and data export.

[0061] The attribute module is used to present basic information, listing in detail the various attribute parameters of the map features and related entity information; The first chart module is used to visually present the historical trends of key attributes of a map feature through charts.

[0062] 2. Feature View The feature view adopts a multi-module collaborative display architecture, realizing a multi-dimensional visualization of natural resource elements through geospatial analysis and statistical chart linkage. The feature view consists of three core modules: a second map module, a second chart module, and a statistics module. These modules achieve dynamic linkage through data binding and interactive events.

[0063] As the view center, the second map module is responsible for spatial data visualization and interactive control. Based on Cesium technology, this module constructs a hierarchical geographical base map, and intuitively presents the distribution of natural resource data for administrative units at the provincial, municipal, and county levels through a thematic map of regional patches' colors. The second map module supports multi-level zooming and panning operations. When the user double-clicks on a specific administrative region, the system automatically triggers cross-module linkage: the target region is highlighted, and at the same time, the chart module and the statistics module are linked for display, achieving a dynamic analysis effect of "focusing on one area".

[0064] The second chart module is used for the spatio-temporal change analysis of key attributes in the target region, and adopts a combined chart form to achieve data comparison and trend display. According to the characteristics of different natural resource elements, stacked bar charts, line charts, pie charts, and dashboard charts are mainly used: (1) Stacked bar chart: Suitable for showing composite indicators with a compositional relationship, such as the proportion of land use types (cultivated land / forest land / construction land, etc.), the structure of water resource development and utilization (domestic water / industrial water / ecological water), the composition of mineral resource reserves (proven / mineable / reserved), etc. Through the stacking height of different color layers, the proportion of each subclass in the total amount and regional differences can be intuitively compared.

[0065] (2) Line chart: Suitable for showing the dynamic change law of time series data. Typical application scenarios include: the change of the area of historically abandoned mines repaired in recent years, the fluctuation trend of quarterly precipitation, and the change of the number of illegal mines. Through the fitting of a smooth curve and the annotation of a trend line, it can assist in identifying the periodic law and abnormal fluctuation points of the data.

[0066] (3) Pie chart: Used to present the proportion relationship of each classification element at a single time node, especially suitable for showing the resource structure characteristics. For example: the proportion of each illegal type or change situation of mines, the proportion distribution of coal / oil / natural gas / new energy in the energy consumption structure of a certain region, the area composition of cultivated land quality grades (excellent / good / medium / poor), the area proportion of wetland types (marsh / lake / river / artificial wetland), etc. Through the design of a 3D pie chart or a ring chart, the visual distinction of classification data can be enhanced, and dynamic proportion analysis can be achieved by combining with the label dragging function.

[0067] (4) Dashboard chart: By combining a dynamic dashboard and a threshold line, it realizes the monitoring of natural resource elements. For example, in the scenario of repairing historically abandoned mines, the proportion of the area and quantity of repaired and unrepaired mines can be intuitively displayed in real time through the dashboard.

[0068] The statistics module presents quantitative information on core indicators in the form of digital dashboards, including dynamic numerical cards (such as total regional natural resources and average annual change rate), comparative rankings (such as resource utilization efficiency rankings of counties and cities), and progress bars (such as the completion rate of ecological protection red line control targets). This module complements the chart module, providing both accurate numerical references and enhancing data interpretation efficiency through visual coding.

[0069] The system employs a three-dimensional analysis model of "map positioning - chart analysis - statistical verification" to present data from macro, micro, and comprehensive analysis perspectives. For example, in the scenario of farmland protection, users can quickly locate areas with severe farmland fragmentation through the map, compare the differences in farmland quality between this area and surrounding counties and cities using stacked bar charts, analyze the spatiotemporal trajectory of farmland reduction over the past five years using line charts, and finally provide decision support by combining the farmland occupation and compensation balance indicators from the statistical module.

[0070] In summary, this invention provides a visualization method for the survey and monitoring results of natural resource elements based on entities. It divides the survey and monitoring data of natural resource elements into entity layer and feature layer according to various factors such as inherent characteristics, attribute relationships, and change patterns. Based on the above two layers, entity view and feature view are constructed, and web-based visualization technology is used to analyze and express the data. This method overcomes the shortcomings of existing technologies, which have limited methods for expressing natural resource elements and are difficult to effectively track data changes.

[0071] Example 2 Based on the same inventive concept, the present invention also provides a visualization device for the results of surveys and monitoring of natural resource elements based on entities, used to implement the method described in the foregoing embodiments. The device includes: a data acquisition module, a model building module, and a visualization module. The data acquisition module is used to collect survey and monitoring data of natural resource elements in the area to be monitored. The model building module is used to construct an expression model containing an entity layer and a feature layer based on natural resource element survey and monitoring data. The visualization module is used to visualize the expression model using web technology.

[0072] Furthermore, the model building module includes: entity layer units, feature layer units, and building units; The entity layer unit is used to extract entity data based on natural resource element survey and monitoring data to construct the entity layer. The entity layer consists of natural resource element entities, restricted entities, change entities and their respective attribute elements. The feature layer unit is used to extract entity data with common features based on the location and attribute features of the entity layer to construct a feature dataset, perform statistical analysis on the feature dataset to obtain statistical data, and construct the feature layer based on the feature dataset and statistical data. Building units are used to construct representation models based on entity layers and feature layers.

[0073] Furthermore, the visualization module includes: an entity view unit and a feature view unit; The entity view unit is used to construct an entity view with the entity layer as the core to display the natural resource element entities, restricted entities, and changed entities of the entity layer; The feature view unit is used to construct a feature view based on the feature layer, which expresses entity data and its statistical data.

[0074] Furthermore, the entity view includes: a first map module, a function area module, an attribute module, and a first chart module; The first map module is used to load a 3D image map base based on Cesium technology to realize the spatial-attribute-temporal multi-dimensional linkage display of natural resource element entities, restricted entities and changing entities; The function area module provides operational support and covers functions such as historical data query, outline comparison, and data export. The attribute module is used to present basic information, listing in detail the various attribute parameters of the map features and related entity information; The first chart module is used to visually present the historical trends of key attributes of a map feature through charts.

[0075] Furthermore, the feature view includes: a second map module, a second chart module, and a statistics module; The second map module is used to construct a layered geographic base map based on Cesium technology, and to intuitively present the distribution of natural resource data through color thematic maps of regional map patches. The second chart module is used for spatiotemporal change analysis of key attributes in the target area, and uses combined charts to achieve data comparison and trend display. The statistics module is used to present quantitative information on core indicators in the form of digital dashboards.

[0076] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An entity-based natural resource factor investigation monitoring result visualization method, characterized in that, The method comprises: Collecting natural resource element investigation and monitoring data of a region to be monitored; Building an expression model comprising an entity layer and a feature layer based on the natural resource element investigation and monitoring data; Visualizing the expression model using web technology.

2. The method of claim 1, wherein, The method for building an expression model comprising an entity layer and a feature layer based on natural resource element investigation and monitoring data comprises: Building an entity layer based on entity data extracted from the natural resource element investigation and monitoring data, the entity layer comprising natural resource element entities, restriction entities, change entities, and their respective attribute elements; Building a feature layer based on entity layer position features and attribute features, extracting entity data with common features to build a feature data set, statistically analyzing the feature data set to obtain statistical data, and building the feature layer based on the feature data set and the statistical data; Building the expression model based on the entity layer and the feature layer.

3. The method of claim 2, wherein, The method for visualizing the expression model using web technology comprises: Taking the entity layer as the core, building an entity view to display the natural resource element entities, restriction entities, and change entities of the entity layer; Building a feature view based on the feature layer to express the entity data and statistical data.

4. The method of claim 3, wherein, The entity view comprises a first map module, a functional area module, an attribute module, and a first chart module; The first map module is configured to load a three-dimensional image map base based on cesium technology, and realize spatial-attribute-time multi-dimensional linkage display of the natural resource element entities, restriction entities, and change entities; The functional area module is configured to provide operation support, including historical data query, contour comparison, and data export functions; The attribute module is configured to present basic information, and lists various attribute parameters and associated entity information of the plot in detail; The first chart module is configured to intuitively present the historical change trend of the key attributes of the plot through a visual chart.

5. The method of claim 3, wherein, The feature view comprises a second map module, a second chart module, and a statistical module; The second map module is configured to build a layered geographic base map based on cesium technology, and intuitively present the natural resource data distribution through a color thematic map of the regional plot; The second chart module is configured to analyze the spatio-temporal change of the key attributes of the target region, and realize data comparison and trend display in the form of a combination chart; The statistical module is configured to present the quantitative information of the core indicators in the form of a digital dashboard.

6. An entity-based natural resource factor survey monitoring result visualization device, the device being used to implement the method of any one of claims 1-5, characterized in that, The device comprises a data collection module, a model building module, and a visualization module; The data collection module is configured to collect natural resource element investigation and monitoring data of a region to be monitored; The model building module is configured to build an expression model comprising an entity layer and a feature layer based on the natural resource element investigation and monitoring data; The visualization module is configured to visualize the expression model using web technology.

7. The apparatus of claim 6, wherein, The model building module comprises an entity layer unit, a feature layer unit, and a building unit; The entity layer unit is configured to build an entity layer based on entity data extracted from the natural resource element investigation and monitoring data, the entity layer comprising natural resource element entities, restriction entities, change entities, and their respective attribute elements; The feature layer unit is configured to build a feature layer based on entity layer position features and attribute features, extract entity data with common features to build a feature data set, statistically analyze the feature data set to obtain statistical data, and build the feature layer based on the feature data set and the statistical data. The feature layer unit is configured to extract entity data with common features based on position features and attribute features of the entity layer, construct a feature data set, perform statistical analysis on the feature data set to obtain statistical data, and construct a feature layer based on the feature data set and the statistical data. The construction unit is configured to construct an expression model based on the entity layer and the feature layer.

8. The apparatus of claim 6, wherein, The visualization module includes an entity view unit and a feature view unit. The entity view unit is configured to take the entity layer as a core to construct an entity view to display natural resource element entities, restriction entities, and change entities of the entity layer. The feature view unit is configured to construct a feature view based on the feature layer to express entity data and statistical data thereof.

9. The apparatus of claim 8, wherein, The entity view includes a first map module, a functional area module, an attribute module, and a first chart module. The first map module is configured to load a three-dimensional image map base based on cesium technology to realize spatial-attribute-time multi-dimensional linkage display of natural resource element entities, restriction entities, and change entities. The functional area module is configured to provide operation support, including historical data query, contour comparison, and data export functions. The attribute module is configured to present basic information, and lists various attribute parameters and associated entity information of a plot in detail. The first chart module is configured to intuitively present historical change trends of key attributes of a plot through a visual chart.

10. The apparatus of claim 8, wherein, The feature view includes a second map module, a second chart module, and a statistical module. The second map module is configured to construct a layered geographic base map based on cesium technology to intuitively present natural resource data distribution through a color thematic map of a regional plot. The second chart module is configured to analyze time-space changes of key attributes of a target region, and realizes data comparison and trend display in a combined chart form. The statistical module is configured to present quantitative information of core indicators in a digital board form.

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