A forest natural resources big data cloud platform and query method thereof

Abstract

The present invention discloses a forest area natural resources big data cloud platform and a query method thereof. The big data cloud platform is established based on a forest area model and publishes a map analysis service tool and a layer tool set to a client; the forest area model is formed by superimposing, cutting, buffering, and analyzing and processing the original data for line of sight and viewshed; the map analysis service tool includes vector map service publishing and image map service publishing. The forest area natural resources big data cloud platform and natural resource data tool set established by the present invention realize the centralized display and unified management of the data of the entire forest area, and the function of intelligently analyzing various spatial information in combination with actual work needs, providing scientific support for leadership decision-making.

Description

Technical Field

[0001] The present invention belongs to the technical field of geographic information systems and relates to a forest natural resources big data cloud platform and a query method thereof. Background Art

[0002] As the largest terrestrial ecosystem on Earth, forests play a vital role in maintaining ecological balance and are the basic conditions for human survival and development. Therefore, they are crucial for forest resource management, forest resource surveys, engineering planning and design, and mapping. For a long time, forestry resource management has faced typical characteristics such as complex data, diverse types, and massive data. In addition, the user levels and needs are diverse, making it difficult to organize data and unable to conveniently and quickly present it to relevant department personnel for use. At the same time, it is impossible to perform relevant intelligent analysis and other operations on forestry resource data, making it difficult for government departments to make scientific decisions.

[0003] With the rapid development of computer technology, image processing technology, aerospace remote sensing, database management, network communication and other technologies, the traditional forest resource management methods have been broken. At present, most systems have realized the use of geographic information systems as the basic platform, and have established a comprehensive management system based on spatial graphic information, statistical data information, etc., which can meet some business needs to a certain extent. However, there are still some shortcomings: the visualization data of the current forestry resource management system relies on software customization and update, which has high update costs, poor flexibility and timeliness; lacks comprehensive intelligent decision support analysis functions; the query of forestry resource data in three-dimensional mode is relatively single; it is difficult to conduct dynamic monitoring of multi-temporal remote sensing images and compare changes in different years. Summary of the invention

[0004] The technical problem solved by the present invention is to provide a forest area natural resources big data cloud platform and a query method thereof, establish a forest area natural resources big data cloud platform and a natural resources data tool set, and realize centralized display and unified management of data from the entire forest area.

[0005] The present invention is achieved through the following technical solutions:

[0006] A forest area natural resources big data cloud platform, which is established based on a forest area model and publishes map analysis service tools and layer tool sets to clients;

[0007] The forest area model is based on the original data to be analyzed, including a forest land map data, two-category small class data, and DEM elevation data, and is formed by superimposing, cutting, buffering, and analyzing and processing the line of sight and viewshed of the original data;

[0008] The map analysis service tool includes vector map service publishing and image map service publishing, wherein the vector map service publishing is used to provide data for browsing, querying and analysis on a web interface, and the image map service publishing is used to provide data for browsing on a web interface;

[0009] The layer toolset is connected to the publishing tool and can edit the layer list freely. The defined layer list is consistent with that defined in the cloud platform and then saved synchronously to the web-side cloud platform.

[0010] The client can directly access the server through the URL, make a display or analysis request, and present the content fed back by the server in the browser window.

[0011] The big data cloud platform also releases a three-dimensional analysis tool to the client, which processes the road data to break up all intersecting road graphic data so that all graphics do not intersect, and adds a string type field Name in the road network layer to store road name information; adds a floating point field Length to store road length information;

[0012] Still in the road network layer file, select New Network Dataset to create a network dataset, set the name of the network dataset, turns, connectivity, elevation modeling, network dataset impedance, travel mode and navigation settings, and then create and build the network dataset.

[0013] The big data cloud platform also releases the nearest facility analysis tool to the client, which stores the longitude and latitude coordinates of the event point, selects the recorded event or directly clicks on the screen map to select the point, and sets the analysis radius and analysis content;

[0014] Use these point features and event point objects to construct the closest facility parameters, and then create and execute the closest facility analysis task in conjunction with the route analysis service.

[0015] After the task is executed, the event point and the nearest facility point are loaded into the map, and the corresponding path will be highlighted.

[0016] The big data cloud platform also releases an overlay analysis tool to the client, which performs overlay analysis in three ways: clipping, intersection and inclusion on line graphics, surface graphics and custom vector files, so as to obtain the related small class attribute information.

[0017] A query method for a forest natural resources big data cloud platform includes the following operations:

[0018] 1) Create a model: Use the model tool Model to create a model. When creating a model, based on the original data such as the forest land map data, the second-class small class data, and the DEM elevation data to be analyzed, insert the ArcGIS clipping, line of sight, and viewshed tools through the insert tool, use the variable connection tool to connect all the tools and set the corresponding parameters, and overlay, clip, buffer, and analyze and process the line of sight and viewshed of the original data to form a model and verify it;

[0019] 2) Publish the service: Use the Results and Geoprocessing Options tools under the Geoprocessing menu, select the model to run and generate the calculation results in Results; use the Geoprocessing Service tool to analyze the calculation results, enter the service name and the input description of each sub-item under the model name, and then publish the service;

[0020] 3) Perform the following intelligent analysis in a 3D scene:

[0021] Network analysis: Process the original road data to break up all intersecting road graph data so that all graphs do not intersect, and add a string type field Name to the road network layer to store road name information; add a floating point field Length to store road length information;

[0022] Select the road network layer file in ArcCatlog, select New Network Dataset to create a network dataset, set the name, turns, connectivity, elevation modeling, network dataset impedance, travel mode, and navigation settings of the network dataset, and then create and build the network dataset;

[0023] Recent Facility Analysis:

[0024] Store the longitude and latitude coordinates of event points, select recorded events or directly click on the screen map to select points, and set the analysis radius and analysis content;

[0025] Construct the closest facility parameters with these point features and event point objects; then create and execute the closest facility analysis task in conjunction with the route analysis service;

[0026] After the task is executed, the event point and the nearest facility point are loaded into the map, and the corresponding path will be highlighted;

[0027] Overlay analysis: Perform overlay analysis on line graphics, surface graphics and custom vector files in three ways: clipping, intersection and inclusion, so as to obtain the related small class attribute information.

[0028] Compared with the prior art, the present invention has the following beneficial technical effects:

[0029] The forest area natural resource big data cloud platform provided by the present invention realizes the functions of querying, managing, and updating forestry resource information, and provides multi-channel data integration and visualization means for forestry management personnel, breaking the traditional forest resource data management mode; the present invention organizes and displays basic geographic data, natural resource data, DEM, forestry resource data, fire prevention facilities, etc. on the cloud platform, realizes the conversion of traditional paper maps into three-dimensional electronic maps, and more intuitively displays the distribution characteristics of forest resources;

[0030] In order to facilitate the subsequent updating of various resource data on the cloud platform, the present invention provides a set of publishing tools for uploading various types of data to the cloud platform, so as to achieve real-time updating of data and reduce maintenance costs; the present invention can also perform spatial query, attribute query, combination query, I-key query, etc. on various types of forestry resources in two-dimensional and three-dimensional environments, and render the query result spots on the image map, generate small class attribute data statistics tables and corresponding summary statistics such as area and storage, and export statistical tables, which can be used for forest felling analysis, requisition and occupation of forest land, disaster damage analysis, etc.

[0031] The present invention can effectively integrate various types of forestry resource data at the regional level, and can realize the integrated analysis and basic data mining functions of forestry resource data, meeting the use needs of different types of users for forestry data; and can establish a connection with the cloud platform through the publishing tool, so that users can update the data that the cloud platform needs to display in real time according to work needs. At the same time, using three-dimensional rendering technology, the forest resource data is displayed vividly and intuitively, supporting the ecological construction of the forest area and providing strong data support for applications such as forestry governance and ecological civilization construction.

[0032] The platform of the present invention can perform intelligent analysis in three-dimensional scenes. Combined with forest settlements, management and protection points, fire prevention facilities, roads and multiple periods of Class II small class data, it can quickly obtain the fire spread range, settlements, management and protection points, fire prevention facilities distribution around the disaster point, as well as the nearest water intake point, the nearest fire-fighting team, the best rescue route, disaster loss assessment, etc. through buffer zone analysis, nearest facility analysis, shortest path analysis, overlay analysis, viewshed analysis, line of sight analysis, dispatch command, isolation belt, event mapping, etc., to provide a scientific basis for rapid and standardized decision-making.

[0033] The present invention is based on multi-period remote sensing images of forest areas in different years, small class data or a map of forest land and other data, and according to the temporal GIS technology, dynamically manages the forest resource data, studies the changes of historical data from a macro perspective, conducts historical retrospection and comparative analysis of images and small classes, thereby providing technical support for forest area management and planning personnel in making predictions and decision-making analyses; at the same time, it realizes dynamic change detection of two-phase forest images, understands the changing process and laws of forest resources, automatically generates change patches through change detection and superimposes them on the images; and compares and analyzes the two-phase and two-category small classes, generates change vector maps and heat maps, extracts the change area and attributes of the small classes, and can more efficiently derive the dynamic changes of forest resources, providing support for forest resource management policy research, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Implemented a flowchart for the map publishing tool;

[0035] Figure 2 This is a schematic diagram of the analysis results of the nearest facilities;

[0036] Figure 3 This is a schematic diagram of forest resources results superimposed on the map interface;

[0037] Figure 4 Implement a flow chart for two-phase image comparison;

[0038] Figure 5 Implement a flow chart for the comparison of the second type of small classes;

[0039] Figure 6 Overlay analysis implementation flow chart;

[0040] Figure 7 Implement a flow chart for nearest facility analysis. DETAILED DESCRIPTION

[0041] The present invention is further described in detail below in conjunction with the embodiments, which are intended to explain the present invention rather than to limit it.

[0042] According to the limitations of current forest natural resource management, the present invention establishes a forest natural resource big data cloud platform based on B / S and WebGIS technology. The big data cloud platform is established based on the forest model and publishes map analysis service tools and layer tool sets to the client.

[0043] The forest area model is based on the original data to be analyzed, including a forest land map data, two-category small class data, and DEM elevation data, and is formed by superimposing, cutting, buffering, and analyzing and processing the line of sight and viewshed of the original data;

[0044] The map analysis service tool includes vector map service publishing and image map service publishing, wherein the vector map service publishing is used to provide data for browsing, querying and analysis on a web interface, and the image map service publishing is used to provide data for browsing on a web interface;

[0045] The layer toolset is connected to the publishing tool and can edit the layer list freely. The defined layer list is consistent with that defined in the cloud platform and then saved synchronously to the web-side cloud platform.

[0046] The client can directly access the server through the URL, make a display or analysis request, and present the content fed back by the server in the browser window.

[0047] The present invention provides decision-making support for forest resource management policy research, etc., and supports the service needs of departments at all levels. At the same time, it uses high-resolution remote sensing images, and adopts C / S mode, postgres database, ArcGIS Server spatial data service and other technologies for the second-class small classes, forest land map, fire prevention facilities and other data in the forest area for many years. A highly flexible and scalable map publishing toolset is developed, and users can update the data that needs to be displayed on the cloud platform in real time according to work needs.

[0048] A query method for a forest natural resources big data cloud platform includes the following operations:

[0049] 1) Create a model: Use the model tool Model to create a model. When creating a model, based on the original data such as the forest land map data, the second-class small class data, and the DEM elevation data to be analyzed, insert the ArcGIS clipping, line of sight, and viewshed tools through the insert tool, use the variable connection tool to connect all the tools and set the corresponding parameters, and overlay, clip, buffer, and analyze and process the line of sight and viewshed of the original data to form a model and verify it;

[0050] 2) Publish the service: Use the Results and Geoprocessing Options tools under the Geoprocessing menu, select the model to run and generate the calculation results in Results; use the Geoprocessing Service tool to analyze the calculation results, enter the service name and the input description of each sub-item under the model name, and then publish the service;

[0051] 3) Perform the following intelligent analysis in a 3D scene:

[0052] Network analysis: Process the original road data to break up all intersecting road graph data so that all graphs do not intersect, and add a string type field Name to the road network layer to store road name information; add a floating point field Length to store road length information;

[0053] Select the road network layer file in ArcCatlog, choose New Network Dataset to create a network dataset, set the name, turns, connectivity, elevation modeling, network dataset impedance, travel mode, and navigation settings of the network dataset, and then create and build the network dataset.

[0054] The following is a detailed description of each part.

[0055] 1. Forest resource management construction and data update

[0056] 1.1 Create the model

[0057] (1) Using the model tool Model in ArcGIS to create a model, the model is created based on the original data such as the forest land map data, the second-class small class data, and the DEM elevation data to be analyzed. The model is formed by overlaying, cutting, buffering, and analyzing the line of sight and viewshed of the original data;

[0058] (2) Insert ArcGIS's clipping, line of sight, and viewshed tools in the model editing window through the insert tool, and set the input variables, output variables, and parameters for the inserted tools; for example, in the process of creating a line of sight analysis model, it is necessary to set the input variables to the observation point and target point that need to be analyzed for line of sight, set the output variable to the line of sight between the two points, and set the color green for the simultaneous range and red for the non-line of sight range to create the model;

[0059] (3) After all tools and variables are set, use the variable connection tool to connect all tools and set the corresponding parameters;

[0060] (4) Add data and set parameters including query statements, and click Verify Service to verify the service model. For example, in the line of sight analysis model, it is necessary to add the DEM elevation data of the area and input the observation points and target points to verify the model to see whether the correct results can be output.

[0061] 1.2 Publishing Services

[0062] Release of GP Services

[0063] (1) In ArcGIS, use the Geoprocessing menu to select Results and Geoprocessing Options tools, double-click the created model to run it, and generate the calculation results in Results;

[0064] (2) Use the Geoprocessing Service tool to analyze the calculation results, enter the service name and the input description of each sub-item under the model name, and then publish the service after analyzing the service and finding no problems.

[0065] (3) Model release is mainly used to provide map analysis service tools for intelligent analysis functions such as overlay analysis, line of sight analysis, viewshed analysis, and nearest facility analysis on a forest map and DEM elevation data.

[0066] Publishing map services

[0067] The publishing of map services mainly includes vector map service publishing and image map service publishing. The publishing of vector map services is to set the color and annotation of the vector layer, and then share it as a service and set the relevant service name and other attribute parameters. Finally, after analysis and no errors are reported, it is determined that the publishing is successful. It is usually used to provide data for browsing, querying and analysis on the web interface; Image map service publishing is to publish the image as a service, and the service type is set to Image Service, the caching stage sets the slice plan, the slice format selects MIEXD, compression 75, data format COMPAT, DPI96, which is usually used to provide data for browsing on the web interface.

[0068] 1.3 Map Publishing Toolset

[0069] The layer toolset is mainly used to manage the loading and display of layer data in the cloud platform web interface. The specific usage of the toolset is as follows:

[0070] (1) Connect to the publishing tool. In the layer classification definition, the layer list can be freely edited, and the defined layer list is consistent with that defined in the cloud platform;

[0071] (2) In the absence of any layer definition, you can enter the node number and node name, add a layer name, and define multiple levels of nodes. Taking basic geographic data as an example, after the user sets it in the tool set, the parent node and child node will be saved by number. The node with a single digit is the highest-level parent node, the first digit of the second-level child node is the parent node number, and the last digit is the current child node number. The n-level child node can determine its own parent nodes at all levels and its position in each parent node through the first n digits.

[0072] (3) After defining the layer name, you need to define the layer type, which includes FeatureLayer, MapImageLayer, and GroupLayer. When determining which layer type the layer is, you need to check the code documentation to determine the corresponding layer type;

[0073] (4) Set the attribute information of the layer according to the requirements, such as field information, service information, setting scale, labeling, rendering information, etc. Set the corresponding layer node attributes in the tool set, and the attributes will be saved to the corresponding fields of the database through the desktop tool;

[0074] (5) After logging in, the system obtains the json file containing the node number and attribute data through the data interface. By parsing the json file, the data of each node is rendered in a tree structure in the system interface, so that the cloud platform can achieve real-time update of the layer.

[0075] For example, to update the forest map data, first publish the forest map service of the latest year through the map service, then add the layer node of the latest year in the toolset, set the layer name and the map service, field information, annotation, rendering information, etc. associated with the layer, and after the application is saved, the web-based cloud platform will automatically load and display the newly added forest map layer data of the latest year.

[0076] See also Figure 1 The map publishing tool implementation flowchart shown includes the following steps:

[0077] (1) Log in and connect to the publishing tool for the user;

[0078] (2) After connecting to the publishing tool, users can enter the layer classification definition page and freely edit the layer list in the cloud platform. The layers defined in this interface are consistent with those in the cloud platform. In the absence of any layer definition, users can enter the node number and node name to add a layer, and can also add sub-nodes;

[0079] (3) After adding the layer name, you need to define the layer type, which mainly includes FeatureLayer, MapImageLayer, and GroupLayer. Users need to set the corresponding type for the layer according to the code documentation;

[0080] (4) Set the corresponding properties for the layer, such as: MapImageLayer: fill in the basic information in the classification description. If there is listMode in the code, select hide-children for the list mode. If not, no need to fill in. The contents of the list mode include: Show to display all sublayers, Hide to hide in the layer list (basically not used), Hide_children to hide all sublayer nodes and control the display and closing of sublayers; FeatureLayer needs to set relevant information, such as: field information, service information, set scale, annotation, rendering information, etc.

[0081] (5) After all layer information is set, it can be updated synchronously in the cloud platform, providing a convenient technical means for updating the layer.

[0082] 2. Intelligent analysis in three-dimensional scenes

[0083] 2.1 Network Analysis Services

[0084] (1) Data preprocessing: Add a road network layer in ArcMap, enable the Network Analyst module in the extension tool, open the Advanced Editing toolbar and the topology toolbar to process the original road data obtained from the customer, break up all intersecting road graphic data so that all graphics do not intersect, and add a string type field Name to the road network layer to store road name information; add a floating point field Length to store road length information;

[0085] (2) Create a network dataset: In ArcCatlog, select the road network layer file, choose New NetworkDataset to create a network dataset, set the name of the network dataset, turns, connectivity, elevation modeling, network dataset impedance, travel mode, and navigation settings, and then create and build the network dataset.

[0086] 2.2 Analysis of nearest facilities

[0087] Based on the call to the published network analysis service, the network analysis layer of the nearest facility point is first required, the RouteTask is instantiated and the completion and failure events are registered, the parameters of the nearest facility point analysis are set, and then the nearest facility point analysis is performed, the analysis results are obtained, and the analysis failure is handled. Users can manually enter the latitude and longitude coordinates of the event point directly through the client, select the recorded event, or directly click on the screen map to select the point, and set the analysis radius (the default is 5km) and the analysis content (fire prevention facilities, settlements, maintenance stations, etc.). According to the queryFeatures method in the FeatureLayer object, all point features can be queried, and these point features and event point objects are used to construct the nearest facility parameters (incidents and facilities); then, the closest facility analysis task (ClosestFacilityTask) is created and executed in combination with the path analysis service;

[0088] When the task request is successful, the client can receive the return result and load the event point and the nearest facility point into the map. The corresponding path will also be highlighted. For example, the nearest facility analysis can be used to analyze the location of the nearest water reservoir, fire inspection station, material storage depot, maintenance station, etc. near the current fire site, and the nearest road can be displayed based on the road layer data. The result is shown in the figure below. Figure 2 shown.

[0089] 2.3 Overlay analysis

[0090] It mainly realizes the overlay analysis of three methods: clipping, intersection and inclusion of line graphics, surface graphics and custom vector files, so as to obtain the related small class attribute information (such as the number of small classes, area and accumulation, etc.).

[0091] Add vector graphics to the map through spatial drawing or external import, set the corresponding analysis method, the client converts the vector graphics into wkt strings, and transmits the analysis method set by the user and the name of the layer database table to be superimposed to the server. Then, according to different SQL statements (i.e., the language for database operations, such as select*from table name where range), the PostgreSQL database is operated, and the commonly used functions in PostGIS, such as ST_GeomFromText(), ST_Within(geometry A,geometry B), ST_Intersects(geometry A,geometry B), etc., are used to complete the operation and storage of spatial data;

[0092] Then the wkt string of the operation result is returned to the front end and converted into a Polygon (i.e., a surface graphic in a graphic geometric object), a Graphic (i.e., a graphic, including geometry, symbol, attributes, and info template, where geometry is point, polyline, polygon, etc.) is created and rendered to the map, and at the same time, a small class attribute table for overlay analysis is generated in combination with the Layui framework (i.e., a front-end UI framework that follows native HTML / CSS / JS). For example, to analyze the forest resources overlaid within the current map outline range, draw a surface graphic on the map interface, and click Analyze to analyze the corresponding results. The result is shown in the figure below. Figure 3 shown.

[0093] Specific examples are given below.

[0094] See also Figure 4 The two-phase image comparison implementation flow chart shown in the figure includes the following steps:

[0095] (1) The user selects a region through the client, and selects the previous image and the current image to be compared, sets the minimum filter area and the maximum filter area, enters the task name, and confirms the detection. The detection task is submitted to the task management. If the task name is duplicated, it will prompt to reset the task name;

[0096] (2) The detection task is stored in the task table of the database, and for the unfinished tasks, the task status is displayed as "processing";

[0097] (3) In the back-end processing, the parameters are passed in through the front-end, the slice images within the detection range are downloaded, and they are stitched together;

[0098] (4) performing change detection on the spliced ​​front and back images using a corresponding detection algorithm;

[0099] (5) The geometry, wkt string and changed area obtained by change detection are stored in the database change detection table, and the status of the task in its task table is updated to "completed";

[0100] (6) The front end retrieves the detection results in the database change detection table through the task name, displays the geometry graphics on the image, and generates a table containing the changed area.

[0101] See also Figure 5 The two-type small class comparison implementation flow chart shown in the figure includes the following steps:

[0102] (1) In the publishing tool, the user sets the layer path of the previous small class and the current small class, and also sets the path of the comparison result layer of the second type of small class;

[0103] (2) Step 2 is the detection process, which involves performing the intersection operation in the superposition analysis on the previous small classes and the current small classes, and the result obtained is the changed small class element;

[0104] (3) Setting a minimum value for the change in the area of ​​a small class, removing the elements whose area is smaller than the limit value from the change result obtained in step 2, and ignoring the small classes whose area change is too small; steps 2 and 3 are both background operations, and the user can directly operate step 4;

[0105] (4) In step 4, the generated small class change vector diagram is published as a map service through ArcGIS Server;

[0106] (5) The front end retrieves the service of publishing the small class change vector layer and renders it into the image, so that the changed small classes can be directly viewed.

[0107] See also Figure 6 The overlay analysis implementation flow chart shown in the figure includes the following steps:

[0108] (1) Users can choose spatial drawing or external import on the client to add vector graphics to the map. Spatial drawing supports line buffer and surface graphics, and external import supports compressed files in shp format.

[0109] (2) There are three analysis methods for overlay analysis: clipping, intersection, and inclusion. Users can choose the corresponding analysis method according to their needs;

[0110] (3) The front end converts the vector graphics on the map into wkt strings, and transmits the analysis method and the name of the database table to be superimposed to the server;

[0111] (4) The backend uses SQL statements and commonly used functions in PostGIS to operate spatial data. For example, when clipping, ST_Union, ST_SymDifference, ST_Intersection, and ST_AsText functions need to be used; when intersecting, ST_Intersection and ST_AsText functions need to be used; when performing inclusion operations, ST_Within and ST_AsText functions need to be used;

[0112] (5) The results of the overlay analysis are returned to the front end and rendered into the map, and the attribute table is generated in combination with the Layui framework.

[0113] See also Figure 7The nearest facility analysis process diagram shown in the figure includes the following operations:

[0114] (1) The client can select an event point in three ways: pressing the mouse on the screen to select a point, entering the longitude and latitude coordinates of the event point on the client, or selecting an event point that has been entered into the event module;

[0115] (2) The user sets the analysis radius and analysis content through the client. The default radius is 5 km. The analysis content includes: fire prevention facilities, residential areas and management stations. They can be combined arbitrarily for analysis.

[0116] (3) Use the queryFeatures method provided by the FeatureLayer object in the facility layer to query the facility point features and construct the nearest facility analysis parameters together with the event point object;

[0117] (4) Create and execute the nearest facility analysis task through the published network analysis service;

[0118] (5) After the task is completed, the execution result is returned to the front end, and its facility points and event points are rendered. The corresponding paths are also highlighted. At the same time, a statistical table is generated to count the name of the nearest facility point and the distance between the event point and the nearest facility point.

[0119] The forest area natural resources big data cloud platform and natural resources data tool set established by the present invention realize the centralized display and unified management of data from the entire forest area, and can perform intelligent analysis of various spatial information in combination with actual work needs, thus providing scientific support for leadership decision-making.

[0120] The above embodiments are preferred examples for implementing the present invention, and the present invention is not limited to the above embodiments. Any non-essential additions and substitutions made by those skilled in the art based on the technical features of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A forest natural resources big data cloud platform, characterized in that: The big data cloud platform is built based on the forest area model and publishes map analysis service tools and layer tool sets to clients; The forest area model is based on the original data to be analyzed, including a forest land map data, two-category small class data, and DEM elevation data, and is formed by superimposing, cutting, buffering, and analyzing and processing the line of sight and viewshed of the original data; The map analysis service tool includes vector map service publishing and image map service publishing, wherein the vector map service publishing is used to provide data for browsing, querying and analysis on a web interface, and the image map service publishing is used to provide data for browsing on a web interface; The layer toolset is connected to the publishing tool and can edit the layer list freely. The defined layer list is consistent with that defined in the cloud platform and then saved synchronously to the web-side cloud platform. The client can directly access the server through the URL, make a display or analysis request, and present the content fed back by the server in the browser window; The big data cloud platform also releases a three-dimensional analysis tool to the client, which processes the road data to break up all intersecting road graph data so that all graphs do not intersect, and adds a string type field Name in the road network layer to store road name information; Add a floating point field Length to store road length information; In the road network layer file, select New Network Dataset to create a network dataset, set the name, turns, connectivity, elevation modeling, network dataset impedance, travel mode and navigation settings of the network dataset, and then create and build the network dataset; The big data cloud platform also releases the nearest facility analysis tool to the client, which stores the longitude and latitude coordinates of the event point, selects the recorded event or directly clicks on the screen map to select the point, and sets the analysis radius and analysis content; Use these point features and event point objects to construct the closest facility parameters, and then create and execute the closest facility analysis task in conjunction with the route analysis service. After the task is executed, the event point and the nearest facility point are loaded into the map, and the corresponding path will be highlighted; The big data cloud platform also releases an overlay analysis tool to the client, which performs overlay analysis in three ways: clipping, intersection and inclusion on line graphics, surface graphics and custom vector files, so as to obtain the related small class attribute information.

2. A query method for a forest natural resources big data cloud platform, characterized in that: The following operations are included: 1) Create a model: Use the model tool Model to create a model. When creating a model, based on the original data including the forest land map data to be analyzed, the second-class small class data, and the DEM elevation data, insert the ArcGIS clipping, line of sight, and viewshed tools through the insert tool, use the variable connection tool to connect all the tools and set the corresponding parameters, and overlay, clip, buffer, and analyze and process the line of sight and viewshed of the original data to form a model and verify it; 2) Publish the service: Use the Results and Geoprocessing Options tools under the Geoprocessing menu, select the model to run and generate the calculation results in Results; use the Geoprocessing Service tool to analyze the calculation results, enter the service name and the input description of each sub-item under the model name, and then publish the service; 3) Perform the following intelligent analysis in a 3D scene: Network analysis: Process the original road data to break up all intersecting road graph data so that all graphs do not intersect, and add a string type field Name to the road network layer to store road name information; Add a floating point field Length to store road length information; Select the road network layer file in ArcCatlog, select New Network Dataset to create a network dataset, set the name, turns, connectivity, elevation modeling, network dataset impedance, travel mode, and navigation settings of the network dataset, and then create and build the network dataset; Closest facility analysis: store the latitude and longitude coordinates of the event point, select the recorded event or directly click on the screen map to select the point, and set the analysis radius and analysis content; Construct the nearest facility parameters with these point features and event point objects; Then combine the route analysis service to create the nearest facility analysis task and execute it; After the task is executed, the event point and the nearest facility point are loaded into the map, and the corresponding path will be highlighted; Overlay analysis: perform overlay analysis of line graphics, surface graphics and custom vector files in three ways: clipping, intersection and inclusion, so as to obtain the related small class attribute information; The overlay analysis includes the following operations: Vector graphics are added to the map through spatial drawing or external import, and the corresponding analysis method is set; the client converts the vector graphics into wkt strings, and transmits the analysis method set by the user and the database table name of the layer to be superimposed to the server, and then operates the PostgreSQL database according to different SQL statements, and uses the commonly used functions in PostGIS to complete the operation and storage of spatial data; Then the wkt string of the operation result is returned to the front end and converted into a Polygon, a Graphic is created and rendered into the map, and at the same time, the Layui framework is combined to generate a small class attribute table for overlay analysis.

3. The query method of the forest natural resources big data cloud platform according to claim 2, characterized in that: Map publishing tools include the following operations: (1) Log in and connect to the publishing tool for the client user; (2) Enter the layer classification definition page to freely edit the layer list in the cloud platform and add sub-nodes; (3) Define the layer type; then set the corresponding type for the layer according to the code documentation; (4) Set corresponding properties for the layer; (5) After setting all the layer information, update it synchronously on the cloud platform.

4. The query method of the forest natural resources big data cloud platform according to claim 2, characterized in that: It also includes two imaging comparisons: (1) The user selects a region through the client, and selects the previous image and the current image to be compared, sets the minimum filter area and the maximum filter area, enters the task name, and confirms the detection. The detection task is submitted to the task management. If the task name is the same, it will prompt to reset the task name; (2) The detection task is stored in the task table in the database, and for tasks that have not been completed, their task status is displayed as "processing"; (3) For back-end processing, the parameters are passed in through the front-end, the slice images within the detection range are downloaded, and they are stitched together; (4) Perform change detection on the stitched front and back images using a corresponding detection algorithm; (5) The geometry, wkt string and changed area obtained by change detection are stored in the database change detection table, and the status of the task in its task table is updated to "completed"; (6) The front end retrieves the detection results in the database change detection table through the task name, displays the geometry graphics on the image, and generates a table containing the changed area.

5. The query method of the forest natural resources big data cloud platform according to claim 2, characterized in that: It also includes two types of small class comparison: (1) In the publishing tool, users can set the layer paths of the previous small classes and the current small classes, and also set the path of the comparison result layer of the second type of small classes; (2) Perform the intersection operation in the superposition analysis on the previous small classes and the current small classes, and the result is the changed small class elements; (3) Setting a minimum value for the change in the area of ​​a small class, removing the elements whose area is smaller than the limit value from the change result obtained in step (2), and ignoring the small classes whose area change is too small; (4) Publishing the generated small class change vector map as a map service through ArcGIS Server; (5) The front end retrieves the service that publishes the small class change vector layer and renders it into the image, so that the changed small classes can be directly viewed.

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