Natural resource data management method and system

Abstract

The invention relates to the technical field of ecological environment management, in particular to a natural resource data management method and system.The method comprises the steps that firstly, an unmanned aerial vehicle is used for conducting multi-source remote sensing data collection on a target area, a soil sample is obtained synchronously through an airborne soil collection assembly, and soil pollution data is generated through detection; constructing a multi-dimensional resource database; then, remote sensing and soil data are analyzed, a pollution threshold value is set, illegal land use, ecological damage or excessive pollution behaviors are automatically recognized, and abnormal pattern spot sampling points are marked; aiming at the abnormal pattern spots, combining an ecological system service value index to calculate a restoration priority index, generating an ecological restoration plan containing vegetation restoration and pollution control parameters, and associating the ecological restoration plan with a database space; and finally, a cross-department sharing platform is established based on an OpenAPI interface. According to the invention, multi-source natural resource data can be efficiently collected, the defects of traditional manual survey are overcome, and the accuracy and timeliness of the data are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment management, and in particular to a natural resource data management method and system. Background Art

[0002] As global ecological and environmental challenges become increasingly prominent, the scientific management and sustainable use of natural resources have become a focus of attention worldwide. Natural resources encompass land, forests, water, minerals, and other areas. Their widespread distribution and frequent dynamic changes make accurate monitoring and efficient management crucial.

[0003] Traditional data collection in natural resource management has relied primarily on manual field surveys and limited satellite remote sensing monitoring. Manual surveys are inefficient, difficult to cover large areas, and susceptible to factors such as topography and climate, making it difficult to ensure data accuracy and timeliness.

[0004] To sum up, how to solve the problems in traditional natural resource data management, such as low efficiency of manual survey, difficulty in covering large areas, susceptibility to factors such as terrain and climate, and difficulty in ensuring data accuracy and timeliness, has become a difficult problem that needs to be solved urgently in this field. Therefore, it is necessary to propose a natural resource data management method and system. Summary of the Invention

[0005] To solve the above problems, the present invention provides a natural resource data management method and system for efficiently collecting multi-source natural resource data, overcoming the shortcomings of traditional manual surveys, and ensuring the accuracy and timeliness of the data.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows: A natural resource data management method comprises the following steps:

[0007] Step 1: Data collection: The target area is divided into several sampling points, and the optical camera, multispectral camera, thermal infrared camera, LiDAR and time-series remote sensing camera on the drone are used to collect multi-source remote sensing data from each sampling point in the target area; the soil collection component on the drone is also used to collect soil samples from each sampling point in the target area. After the collection is completed, the drone is retrieved and the soil samples in the soil collection component are tested to generate soil pollution data for each sampling point. The multi-source remote sensing data and soil pollution data of each sampling point are used to establish a resource database.

[0008] Step 2: Anomaly identification: Set a threshold for soil pollution data and analyze the multi-source remote sensing data and soil pollution data of each sampling point in the target area through data analysis technology. When the analysis of the multi-source remote sensing data and soil pollution data of each sampling point detects one or more behaviors of illegal land use, ecological damage, and soil pollution data exceeding the threshold, an abnormal patch sampling point is generated; otherwise, a normal patch sampling point is generated.

[0009] Step three: Establish an ecological restoration plan for the sampling points: Combine the abnormal patch sampling points with the ecosystem service value index to calculate the restoration priority index of the abnormal patch sampling points, generate an ecological restoration plan for the sampling points, and spatially associate the ecological restoration plan of the sampling points with the resource database to generate an associated resource database.

[0010] Step 4: Data storage and integration: The associated resource database is stored using a distributed storage method; structured data is stored in a relational database; unstructured image data and document data are stored in a non-relational database, and an ETL technology is used to build a data integration platform for the relational and non-relational databases.

[0011] Step 5: Data sharing: Use OpenAPI to provide a unified interface to build a cross-departmental and cross-field data sharing platform for the linked resource database.

[0012] The technical principles of the above scheme are as follows:

[0013] Soil samples were collected using a soil collection component to establish a resource database. A soil contamination threshold was then set, and data analysis techniques were used to identify anomalies and distinguish sampling points. Restoration priority indices were calculated based on ecosystem service value indicators, and ecological restoration plans were generated and spatially linked to the database. Distributed storage was used, with data structures stored in different databases and integrated using ETL technology. Finally, OpenAPI was used to build a cross-departmental and cross-domain data sharing platform.

[0014] The above scheme has the following beneficial effects:

[0015] 1. Traditional manual surveys are limited by terrain and climate. This invention uses drones equipped with multiple advanced cameras to collect multi-source remote sensing data, and uses soil collection components to obtain soil samples, thereby improving the efficiency and scope of data collection.

[0016] 2. The present invention uses data analysis technology to identify anomalies, and can promptly and accurately detect problems such as illegal land use, ecological damage, and excessive soil pollution, thereby promoting the protection of natural resources and the restoration of the ecological environment, and improving the scientific nature and effectiveness of natural resource management.

[0017] 3. The present invention adopts a distributed storage method to store the associated resource database, integrates different types of databases through ETL technology, and uses OpenAPI to build a data sharing platform across departments and fields, which promotes the circulation and collaboration of data among different departments and fields.

[0018] Furthermore, in step 1, the multi-source remote sensing data includes:

[0019] High-resolution optical images are used to identify land cover types and changes in construction land at sampling points.

[0020] Multispectral data are used to identify vegetation cover, chlorophyll content, and soil moisture at sampling points.

[0021] Thermal infrared data is used to identify industrial thermal pollution, abnormal surface temperature rise, and fire hazards at sampling points.

[0022] LiDAR point cloud data is used to identify terrain undulations, building heights, and soil erosion characteristics at sampling points.

[0023] Time series remote sensing data is used to obtain multi-temporal images of the same sampling point to identify the spread of pollution at the sampling point.

[0024] Beneficial effects: High-resolution optical images provide clear surface details; multispectral data analyzes vegetation and soil, measures coverage, etc., providing key data for ecological assessment, forestry and agricultural decision-making; thermal infrared data captures temperature anomalies, promptly detects industrial pollution and fire hazards, ensures ecological safety, and reduces disaster losses; LiDAR point cloud data constructs high-precision three-dimensional terrain models, assists water conservancy, geology, and urban planning, and provides a data basis for scientific decision-making; time-series remote sensing data compares multi-temporal images to intuitively show the pollution diffusion path, and assists in the evaluation of environmental pollution control and restoration effects.

[0025] Furthermore, in step one, the soil collection assembly includes a controller, a drive box, and a hollow cylindrical collection bin, both of which are fixedly connected to the bottom of the drone. A collection hole is formed in the side wall of the collection bin; a drive member is fixedly connected to the inner wall of the drive box, and the controller is used to control the rotation of the output shaft of the drive member. A support plate is fixedly connected to the inner wall of the drive box, and a rotating shaft is rotatably engaged with the support plate. A first connecting rod is fixedly connected to one end of the rotating shaft, and a gear is coaxially fixedly connected to the other end of the rotating shaft, and the gear is meshed with a rack. A "C"-shaped limit block is rotatably engaged with the rotating shaft, and the rack and the limit block are slidably engaged. A second connecting rod is hinged to one end of the rack, and the second connecting rod is fixedly connected to the output shaft of the drive member at its end away from the rack. A first slide groove in the shape of an inverted "C" is formed in one side wall of the support plate, and a second slide groove is formed on the first connecting rod. A slider is slidably engaged with the second slide groove, and the slider is also slidably engaged with the first slide groove. A third connecting rod is hinged on the side wall of the slider, and a rotating block is rotatably engaged on one side wall of the support plate. A third sliding groove is defined on the side wall of the rotating block away from the support plate, and the third connecting rod and the third sliding groove are slidably engaged. The end of the third connecting rod away from the slider is fixedly connected to a soil collecting barrel, which is connected to an air pipe on the side wall of the soil collecting barrel. A rebound airbag is also fixedly connected to one side wall of the support plate, located in the motion trajectory of the first connecting rod and connected to the air pipe.

[0026] Beneficial effects: The soil collection component can realize automatic and efficient soil collection, improve collection efficiency, reduce human interference, and ensure the integrity and accuracy of samples.

[0027] Furthermore, in step two, the ecological restoration plan includes vegetation restoration plans, soil and water management parameters, and soil restoration plans.

[0028] Beneficial effects: Covering vegetation restoration, water and soil management, and soil restoration programs, based on local ecological characteristics, working synergistically to improve the scientific nature and effectiveness of ecological restoration.

[0029] Furthermore, in step three, when the ecological restoration plan of the sampling point is spatially associated with the resource database, the geographic information system (GIS) technology is used to establish a spatial index of each abnormal map sampling point and the corresponding ecological restoration plan in the resource database based on the geographic coordinates of the sampling point.

[0030] Beneficial effects: Using GIS technology to establish a spatial index, closely linking the repair plan with the abnormal spots, making it easier for managers to query and analyze.

[0031] Furthermore, a natural resource data management system includes:

[0032] The data acquisition module is used to control the drone to fly to various sampling points in the target area, control the activation of the optical camera, multispectral camera, thermal infrared camera, LiDAR and time-series remote sensing camera on the drone to conduct multi-source remote sensing data collection, and is also used to control the soil collection component on the drone to complete soil sample collection and collect soil pollution data at each sampling point.

[0033] The data processing module is used to set soil pollution data thresholds, establish a resource database using multi-source remote sensing data and soil pollution data from each sampling point, and utilize data analysis techniques to analyze the multi-source remote sensing data and soil pollution data from each sampling point in the target area. If the analysis of the multi-source remote sensing data and soil pollution data from each sampling point detects one or more of illegal land use, ecological damage, and soil pollution exceeding the threshold, an abnormal patch sampling point is generated; otherwise, a normal patch sampling point is generated. The data processing module also combines the abnormal patch sampling points with ecosystem service value indicators to calculate restoration priority indexes for the abnormal patch sampling points, generate ecological restoration plans for the sampling points, spatially link the ecological restoration plans for the sampling points with the resource database, generate the linked resource database, and store the linked resource database using a distributed storage method. Structured data is stored in a relational database, while unstructured image data and document data are stored in a non-relational database. ETL technology is used to integrate the relational and non-relational databases into a data integration platform.

[0034] The data sharing module is used to build a cross-departmental and cross-domain data sharing platform by using the unified interface provided by OpenAPI to associate the resource database.

[0035] Beneficial Effects: The data acquisition module utilizes drones to efficiently collect multi-source data, overcoming the limitations of traditional manual surveys and providing high-quality data for subsequent analysis. The data processing module analyzes data, accurately identifies anomalies, generates remediation plans, and scientifically stores and integrates data, enhancing the scientific nature of management decisions. The data sharing module uses OpenAPI to build a shared platform, promoting collaboration across departments and fields, improving management efficiency, and supporting the sustainable use of natural resources.

[0036] Furthermore, the data acquisition module includes a wireless communication unit, which is used for wireless communication connection between the UAV and the ground control station.

[0037] Beneficial effects: The wireless communication unit monitors the flight and equipment status in real time, adjusts tasks promptly, transmits data quickly, and improves the timeliness and reliability of data collection.

[0038] Furthermore, the data processing module includes a data quality assessment unit, which is used to perform quality assessment on the multi-source remote sensing data and soil pollution data, and to remove outliers and noise data in the multi-source remote sensing data and soil pollution data.

[0039] Beneficial effects: The data quality assessment unit evaluates the quality of multi-source data, eliminates abnormal and noisy data, and improves the quality of database data.

[0040] Furthermore, in the data processing module, the relational database adopts a master-slave replication architecture, where the master database is responsible for data writing and the slave database is used for data reading.

[0041] Beneficial effects: The master database is responsible for writing, and the slave database shares the reading pressure, which improves query speed. In the event of a failure, the slave database can be switched to ensure the continuity of data storage and access.

[0042] Furthermore, the data sharing module includes a rights management unit, which is used to assign different data access rights according to users in different departments and fields.

[0043] Beneficial effects: Allocate data access rights based on departmental needs to ensure data security.

[0044] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a step diagram of the natural resource data management method of the present invention.

[0046] Figure 2 This is an axonometric diagram of the UAV in the natural resource data management method of the present invention.

[0047] Figure 3 This is a bottom view of the soil collection component in the natural resource data management method of the present invention.

[0048] Figure 4 for Figure 3 Cross-sectional view along the AA axis.

[0049] Figure 5 for Figure 3 Cross-sectional view along the BB direction.

[0050] Figure 6 This is a structural diagram of the natural resource data management system of the present invention.

[0051] The figure marks in the drawings of the specification include: 1. UAV; 2. Drive box; 3. Collection bin; 4. Collection hole; 5. Drive member; 6. Support plate; 7. First connecting rod; 8. Gear; 9. Rack; 10. Limit block; 11. Second connecting rod; 12. Slider; 13. Third connecting rod; 14. Rotating block; 15. Soil collecting barrel; 16. Rebound airbag. DETAILED DESCRIPTION

[0052] The following is further described in detail through specific implementation methods:

[0053] Example 1:

[0054] As attached Figure 1-Figure 5 A natural resource data management method includes the following steps:

[0055] Step 1: Data Collection: Control UAV 1 to fly over the target area and plan a flight path based on the pre-defined sampling points. Activate the optical camera, multispectral camera, thermal infrared camera, LiDAR, and time-series remote sensing camera onboard UAV 1.

[0056] Optical cameras capture surface images of each sampling point area, capturing changes in surface cover types and construction land use. Multispectral cameras simultaneously collect spectral data across a set wavelength range for subsequent analysis of vegetation cover, chlorophyll content, and soil moisture. Thermal infrared cameras monitor thermal radiation in the sampling area in real time, recording thermal signatures of industrial heat pollution, abnormal surface warming, and potential fire hazards. LiDAR devices emit laser beams to capture point cloud data on terrain undulations, building heights, and soil erosion characteristics at the sampling points. By analyzing and processing the laser reflection signals, a detailed three-dimensional terrain model is constructed. A time-series remote sensing camera captures the same sampling point multiple times at preset intervals, acquiring multi-temporal images for pollutant dispersion analysis. Simultaneously, the soil collection component on UAV 1 collects soil samples at each sampling point in the target area.

[0057] The structure of the soil collection component is as follows:

[0058] The soil collection assembly includes a controller, a drive box 2 and a hollow cylindrical collection bin 3. The drive box 2 and the collection bin 3 are both fixedly connected to the bottom of the drone 1 by screws.

[0059] A collecting hole 4 is opened on the side wall of the collecting bin 3 ; a driving member 5 is fixedly connected to the inner wall of the driving box 2 by screws, and the controller is used to control the rotation of the output shaft of the driving member 5 .

[0060] On the inner side wall of the drive box 2, a support plate 6 is fixedly connected by screws. A rotating shaft is rotatably fitted on the support plate 6. One end of the rotating shaft is fixedly connected with a first connecting rod 7 by screws, and the other end of the rotating shaft is coaxially fixedly connected with a gear 8 by screws. The gear 8 meshes with a rack 9.

[0061] A limiting block 10 in an "L" shape is rotatably fitted on the rotating shaft. The rack 9 and the limiting block 10 are slidably fitted.

[0062] One end of the rack 9 is hinged with a second connecting rod 11. The end of the second connecting rod 11 far from the rack 9 is fixedly connected with the output shaft of the driving member 5 by screws.

[0063] On one side wall of the support plate 6, a first chute in an inverted "C" shape is opened. A second chute is opened on the first connecting rod 7. A slider 12 is slidably fitted in the second chute, and the slider 12 is also slidably fitted with the first chute.

[0064] The side wall of the slider 12 is hinged with a third connecting rod 13. A rotating block 14 is rotatably fitted on one side wall of the support plate 6. A third chute is opened on the side wall of the rotating block 14 far from the support plate 6. The third connecting rod 13 and the third chute are slidably fitted.

[0065] The end of the third connecting rod 13 far from the slider 12 is fixedly connected with a soil sampling cylinder 15 by screws. An air pipe is connected to the side wall of the soil sampling cylinder 15. A rebound air bag 16 is also fixedly adhered to one side wall of the support plate 6. The rebound air bag 16 is located in the movement track of the first connecting rod 7, and the rebound air bag 16 is connected to the air pipe.

[0066] When the drone 1 flies to the soil surface at each sampling point, the controller in the soil sampling component starts the driving member 5 in the drive box 2 at the bottom of the drone 1. In this embodiment, the driving member 5 is a stepping motor. The output shaft of the stepping motor rotates, driving the second connecting rod 11 to move. Since the second connecting rod 11 is hinged with the rack 9 and the rack 9 meshes with the gear 8, the rack 9 makes a reciprocating linear motion under the constraint of the limiting block 10.

[0067] The reciprocating linear motion of the rack 9 drives the gear 8 to rotate reciprocally. The gear 8 drives the rotating shaft to rotate reciprocally, and then drives the first connecting rod 7 to rotate reciprocally around the rotating shaft. The slider 12 on the first connecting rod 7 slides in the first chute and the second chute, so as Figure 2 and Figure 4When the first link 7 rotates counterclockwise to the bottom of the first slide groove, the third link 13 can slide vertically downward on the third slide groove on the rotating block 14, thereby driving the soil barrel 15 to descend and insert the soil on the ground. At this time, the soil at the sampling point can enter the soil barrel 15. Subsequently, as the stepper motor output shaft continues to rotate, the first link 7 can rotate clockwise and drive the slider 12 to slide to the leftmost end of the lower part of the first slide groove. At this time, the third link 13 can slide vertically to the left on the third slide groove on the rotating block 14, so that the soil barrel 15 can extend into the collecting hole 4 on the collecting bin 3. At this time, the first link 7 can press the rebound airbag 16 on the side wall of the support plate 6. At this time, the gas in the rebound airbag 16 is squeezed into the soil barrel 15, and the gas pushes the soil sample in the soil barrel 15 into the collecting bin 3 for collection and storage. The stepper motor output shaft continues to rotate, and the soil barrel 15 repeats the above-mentioned collection action until a sufficient amount of soil sample is collected, and the drone 1 is controlled to return.

[0068] After the soil samples at all sampling points are collected, the soil samples in the collection bin 3 are tested. The heavy metal content and organic pollutants in the soil are analyzed using soil testing equipment to generate soil pollution data for each sampling point.

[0069] Step 2: Anomaly identification: Use data analysis technology to conduct in-depth analysis of the multi-source remote sensing data and soil pollution data of each sampling point in the resource database.

[0070] For multi-source remote sensing data, image recognition and spectral analysis are used to identify illegal expansion of construction land, vegetation damage, and abnormal thermal signatures. For soil pollution data, actual detection values ​​are compared with thresholds.

[0071] If a sampling point is detected to have one or more of the following: illegal land use, ecological damage (such as a significant decrease in vegetation cover, severe soil erosion), or soil pollution data exceeding a threshold, the sampling point will be marked as an abnormal sampling point; otherwise, it will be marked as a normal sampling point. At the same time, detailed information such as the type and degree of abnormality of the abnormal sampling point will be recorded.

[0072] Step three: Establish an ecological restoration plan for the sampling points: For each abnormal sampling point, collect ecosystem service value indicators, including biodiversity, carbon sequestration capacity, and water conservation capacity.

[0073] The ecosystem service value index is substituted into the pre-established restoration priority index calculation model to comprehensively evaluate the restoration priority of each abnormal map sampling point.

[0074] Based on the type of anomaly and the priority of restoration, a specific ecological restoration plan is selected or formulated from the preset ecological restoration plan library. If the anomaly is vegetation damage, a vegetation restoration plan is determined based on the local ecological environment and vegetation distribution, including the selection of suitable local tree species, planning of planting density, and formulation of maintenance measures. For water and soil management issues such as soil erosion, soil and water management parameters are calculated and determined, such as the height and slope of retaining walls, the range of slope consolidation for planting trees and grass, etc. For soil pollution anomalies, a soil restoration plan is formulated based on the type and degree of pollution. For example, when selecting the imported soil method in physical restoration, the source, replacement amount, and construction process of the imported soil are determined. If the plant restoration method of biological restoration is adopted, the selected hyperaccumulator plant species, planting area, and restoration period are clearly specified.

[0075] Using Geographic Information System (GIS) technology, we associate the geographic coordinates of each anomaly sampling point with relevant information about the corresponding ecological restoration plan, creating a spatial index within the resource database. This index allows us to visually display the distribution of anomaly sampling points and their corresponding ecological restoration plans on a map, facilitating subsequent management and query.

[0076] Step 4: Data Storage and Integration: Adopt a distributed storage architecture and deploy multiple storage nodes. Store structured data from the associated resource database, such as sampling point coordinates, soil pollution detection values, and quantitative parameters of ecological restoration plans, in a relational database such as MySQL or PostgreSQL. Leveraging its comprehensive data management capabilities, this facilitates data query, statistics, and analysis.

[0077] Unstructured image data (such as high-resolution images and thermal infrared images taken by optical cameras) and document data (such as soil testing reports and ecological restoration plan documents) are stored in non-relational databases such as MongoDB or Redis to adapt to their diverse data formats and flexible storage characteristics.

[0078] Develop data extraction, transformation, and loading programs using ETL (Extract Transform Load) technology. Extract relevant data from relational and non-relational databases, convert the format to a unified data standard, such as converting image data to JPEG or TIFF and document data to PDF, and then load it into the data integration platform to integrate and share different types of data.

[0079] Use OpenAPI (Open Application Programming Interface) technology to develop a unified associated resource database interface.

[0080] Step 5: Data Sharing: Build a cross-departmental and cross-domain data sharing platform and connect the linked resource database to the data sharing platform. Through the OpenAPI interface, other departments and fields (such as scientific research institutions and enterprises) can access and obtain relevant data in the resource database according to authorization, realizing the widespread sharing and efficient use of natural resource data, providing data support for multi-department collaborative decision-making, scientific research and innovation, and enterprise production activities.

[0081] Example 2:

[0082] As attached Figure 6 As shown, the difference from Example 1 is that a natural resource data management system mainly includes a data acquisition module, a data processing module and a data sharing module; wherein, the data acquisition module is mainly used for; the data processing module is mainly used for; the data sharing module is mainly used for.

[0083] The following is a detailed explanation of the functions of each module:

[0084] The data acquisition module is used to control the drone 1 to fly to each sampling point in the target area, control the activation of the optical camera, multispectral camera, thermal infrared camera, LiDAR and time-series remote sensing camera on the drone 1 to perform multi-source remote sensing data acquisition, and is also used to control the soil collection component on the drone 1 to complete soil sample collection and collect soil pollution data at each sampling point.

[0085] The data acquisition module includes a wireless communication unit, which is used for wireless communication connection between the UAV 1 and the ground control station.

[0086] The data processing module is used to set the threshold of soil pollution data, establish a resource database using the multi-source remote sensing data and soil pollution data of each sampling point, and use data analysis technology to parse the multi-source remote sensing data and soil pollution data of each sampling point in the target area. When the multi-source remote sensing data and soil pollution data of each sampling point are parsed and one or more behaviors of illegal land use, ecological damage and soil pollution data exceeding the threshold are detected, an abnormal map sampling point is generated; otherwise, a normal map sampling point is generated.

[0087] The data processing module is also used to combine the abnormal map sampling points with the ecosystem service value indicators to calculate the restoration priority index of the abnormal map sampling points, generate the ecological restoration plan of the sampling points, and spatially associate the ecological restoration plan of the sampling points with the resource database to generate the associated resource database, and store the associated resource database using a distributed storage method.

[0088] Among them, structured data is stored in relational databases; unstructured image data and document data are stored in non-relational databases, and ETL technology is used to build a data integration platform for relational databases and non-relational databases.

[0089] The data processing module includes a data quality assessment unit, which is used to perform quality assessment on multi-source remote sensing data and soil pollution data, and to remove outliers and noise data in the multi-source remote sensing data and soil pollution data.

[0090] In the data processing module, the relational database adopts a master-slave replication architecture, where the master database is responsible for data writing and the slave database is used for data reading.

[0091] The data sharing module is used to build a cross-departmental and cross-domain data sharing platform by using the unified interface provided by OpenAPI to associate the resource database.

[0092] The data sharing module includes a rights management unit, which is used to assign different data access permissions to users in different departments and fields.

[0093] Specifically, first, this embodiment uses mountainous areas as an example to carry out natural resource monitoring. The data acquisition module uses the wireless communication unit to control the drone 1 to take off from the ground control station and fly to each pre-divided sampling point. For example, at a sampling point in a valley area, the drone 1 activates the optical camera to capture clear surface images, showing the boundary between the farmland at the foot of the mountain and the surrounding woodland; at the same time, the multispectral camera is activated to analyze the vegetation coverage and determine whether the trees are growing healthily; the thermal infrared camera is turned on to detect whether there is abnormal surface temperature rise caused by illegal mining; the LiDAR equipment works to construct a high-precision three-dimensional model of the valley terrain; the time-series remote sensing camera records images of the sampling point at different time periods in preparation for subsequent monitoring of ecological changes. The data acquisition module uses the wireless communication unit to control the soil collection component on the drone 1 to complete soil sample collection and fly back to the ground station to collect soil pollution data at each sampling point.

[0094] After receiving multi-source remote sensing data and soil pollution data from each sampling point transmitted by the data acquisition module, the data processing module first sets thresholds for the soil pollution data, such as safety ranges for indicators like heavy metal content and pesticide residues. For a specific sampling point, data analysis techniques are used to analyze the multi-source remote sensing data and soil pollution data. If the thermal infrared data for that sampling point indicates a localized high temperature area, combined with optical imagery, it is suspected that illegal mining activity is occurring, and if the heavy metal content in the soil pollution data exceeds the threshold, the data processing module generates an abnormal sampling point. Next, based on ecosystem service value indicators, such as the area's importance to soil and water conservation and biodiversity maintenance, a restoration priority index is calculated for the abnormal sampling point. A vegetation restoration plan is then developed, including planting tree species suitable for the area and conducive to soil remediation. Soil and water management parameters are determined, including measures such as retaining walls and drainage channels. A soil restoration plan, such as bioremediation to reduce heavy metal content, is developed. Using geographic information system (GIS) technology, these ecological restoration plans are spatially linked to the resource database based on the geographic coordinates of the sampling points. When storing data, structured data such as soil pollution detection values ​​and sampling point coordinates is stored in a relational database. The master database is responsible for writing data, and the slave database is responsible for reading data, ensuring efficient and stable data storage. Unstructured image data and document data, such as various images captured by UAV 1 and related report documents, are stored in a non-relational database. ETL technology is used to build a data integration platform that integrates relational and non-relational databases, facilitating unified data management and access. Simultaneously, a data quality assessment unit evaluates the quality of multi-source remote sensing data and soil pollution data, eliminating outliers and noise data, such as removing erroneous image data caused by brief signal interference from UAV 1.

[0095] Finally, the data sharing module utilizes the unified interface provided by OpenAPI to build a cross-departmental and cross-domain data sharing platform. For example, environmental protection departments can use this platform to access soil pollution data in mountainous areas for environmental assessment and pollution control decision-making; forestry departments can view multi-source remote sensing data on vegetation coverage and tree growth to assist in forest resource management; and water conservancy departments can reference terrain models constructed using LiDAR and soil and water management parameters to plan water conservancy facility construction. The permission management unit assigns different data access rights to users from different departments and fields. Environmental protection departments have read and write access to soil pollution data, facilitating updates on pollution control progress; forestry departments only have read access to vegetation-related data, preventing data from being accidentally modified, ensuring data security and appropriate use, and promoting collaborative natural resource management efforts across departments.

[0096] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A natural resource data management method, characterized in that: The following steps are involved: Step 1, data collection: the target area is divided into several sampling points, and the optical camera, multispectral camera, thermal infrared camera, LiDAR and time series remote sensing camera on the drone (1) are used to collect multi-source remote sensing data from each sampling point in the target area; the soil collection component on the drone (1) is also used to collect soil samples from each sampling point in the target area. After the collection is completed, the drone (1) is retrieved, and the soil samples in the soil collection component are tested to generate soil pollution data for each sampling point, and a resource database is established using the multi-source remote sensing data and soil pollution data of each sampling point; Step 2: Anomaly Identification: Set a threshold for soil pollution data and analyze the multi-source remote sensing data and soil pollution data of each sampling point in the target area through data analysis technology. When the analysis of the multi-source remote sensing data and soil pollution data of each sampling point detects one or more behaviors of illegal land use, ecological damage, and soil pollution data exceeding the threshold, an abnormal patch sampling point is generated; otherwise, a normal patch sampling point is generated. Step 3: Establish an ecological restoration plan for the sampling points: Combine the abnormal spot sampling points with the ecosystem service value index to calculate the restoration priority index of the abnormal spot sampling points, generate an ecological restoration plan for the sampling points, and spatially associate the ecological restoration plan of the sampling points with the resource database to generate a linked resource database; Step 4: Data storage and integration: The linked resource database is stored using a distributed storage method. Structured data is stored in a relational database; unstructured image data and document data are stored in a non-relational database. ETL technology is used to build a data integration platform for the relational and non-relational databases. Step 5: Data sharing: Use OpenAPI to provide a unified interface to build a cross-departmental and cross-field data sharing platform for the linked resource database.

2. The natural resource data management method according to claim 1, characterized in that: In step 1, multi-source remote sensing data includes: High-resolution optical imagery to identify land cover types and changes in built-up land at sampling points; Multispectral data, used to identify vegetation cover, chlorophyll content, and soil moisture at sampling points; Thermal infrared data is used to identify industrial thermal pollution, abnormal surface temperature rise, and fire hazards at sampling points; LiDAR point cloud data is used to identify terrain relief, building height, and soil erosion characteristics at sampling points; Time series remote sensing data is used to obtain multi-temporal images of the same sampling point to identify the spread of pollution at the sampling point.

3. The natural resource data management method according to claim 2, characterized in that: In step 1, the soil collection assembly includes a controller, a drive box (2) and a hollow cylindrical collection bin (3), and the drive box (2) and the collection bin (3) are both fixedly connected to the bottom of the drone (1); A collecting hole (4) is provided on the side wall of the collecting bin (3); a driving member (5) is fixedly connected to the inner side wall of the driving box (2); and a controller is used to control the rotation of the output shaft of the driving member (5); A support plate (6) is fixedly connected to the inner wall of the drive box (2), a rotating shaft is rotatably fitted on the support plate (6), one end of the rotating shaft is fixedly connected to a first connecting rod (7), and the other end of the rotating shaft is coaxially fixedly connected to a gear (8), and the gear (8) is meshed with a rack (9); A limiting block (10) in the shape of "L" is rotationally fitted on the rotating shaft, and the rack (9) is slidably fitted with the limiting block (10); One end of the rack (9) is hinged with a second connecting rod (11), and the end of the second connecting rod (11) far from the rack (9) is fixedly connected to the output shaft of the driving member (5); A first chute in the shape of an inverted "C" is formed on one side wall of the support plate (6), a second chute is formed on the first connecting rod (7), a slider (12) is slidably fitted in the second chute, and the slider (12) is also slidably fitted with the first chute; A third connecting rod (13) is hinged on the side wall of the slider (12), a rotating block (14) is rotationally fitted on one side wall of the support plate (6), a third chute is formed on the side wall of the rotating block (14) far from the support plate (6), and the third connecting rod (13) is slidably fitted with the third chute; One end of the third connecting rod (13) far from the slider (12) is fixedly connected with a soil sampling cylinder (15), an air pipe is connected to the side wall of the soil sampling cylinder (15), a rebound air bag (16) is also fixedly connected to one side wall of the support plate (6), the rebound air bag (16) is located in the movement track of the first connecting rod (7), and the rebound air bag (16) is connected to the air pipe.

4. The natural resource data management method according to claim 3, characterized in that: In step two, the ecological restoration plan includes a vegetation restoration plan, soil and water treatment parameters, and a soil restoration plan.

5. The natural resource data management method according to claim 4, characterized in that: In step three, when spatially associating the ecological restoration plan of the sampling points with the resource database, using the geographic information system GIS technology, based on the geographic coordinates of the sampling points, a spatial index of each abnormal patch sampling point and the corresponding ecological restoration plan is established in the resource database.

6. A natural resource data management system, operating based on any one of the natural resource data management methods according to claims 1-5, characterized in that: Including: A data acquisition module, used to control the drone (1) to fly to each sampling point in the target area, control the start of the optical camera, multi-spectral camera, thermal infrared camera, LiDAR, and time-series remote sensing camera on the drone (1) to collect multi-source remote sensing data, and is also used to control the soil sampling component on the drone (1) to complete soil sample collection and collect the soil pollution data of each sampling point; A data processing module, used to set the threshold of the soil pollution data, establish a resource database using the multi-source remote sensing data and soil pollution data of each sampling point, and use data analysis technology to analyze the multi-source remote sensing data and soil pollution data of each sampling point in the target area. When detecting one or more behaviors such as illegal land use, ecological damage, and soil pollution data exceeding the threshold during the analysis of the multi-source remote sensing data and soil pollution data of each sampling point, generate abnormal patch sampling points, otherwise generate normal patch sampling points; The data processing module is also used to calculate the restoration priority index of the abnormal patch sampling points by combining the abnormal patch sampling points with the ecosystem service value index, generate the ecological restoration plan of the sampling points, spatially associate the ecological restoration plan of the sampling points with the resource database, generate the associated resource database, and store the associated resource database using the method of distributed storage; Among them, the structured data is stored in a relational database; the unstructured image data and document data are stored in a non-relational database, and a data integration platform is built between the relational database and the non-relational database through ETL technology; The data sharing module is used to build a cross-departmental and cross-domain data sharing platform by using the unified interface provided by OpenAPI to associate the resource database.

7. The natural resource data management system according to claim 6, characterized in that: The data acquisition module comprises a wireless communication unit, which is used for wireless communication connection between the UAV (1) and the ground control station.

8. The natural resource data management system according to claim 7, characterized in that: The data processing module includes a data quality assessment unit, which is used to perform quality assessment on multi-source remote sensing data and soil pollution data, and to remove outliers and noise data in the multi-source remote sensing data and soil pollution data.

9. The natural resource data management system according to claim 8, characterized in that: In the data processing module, the relational database adopts a master-slave replication architecture, where the master database is responsible for data writing and the slave database is used for data reading.

10. The natural resource data management system according to claim 9, characterized in that: The data sharing module includes a rights management unit, which is used to assign different data access permissions to users in different departments and fields.

Images