A natural resource data dynamic monitoring and tracing method and system

Abstract

The present invention relates to the field of data traceability, and specifically discloses a method and system for dynamic monitoring and traceability of natural resource data. The system establishes a multi-temporal database, automatically writes real-time data into corresponding partitions based on timestamps, and incrementally updates workflows. The system introduces data confidence weights into SQL filter statements for attribute traceability logic, and uses weighted logic to determine data legitimacy. A land class evolution state transition matrix is constructed to determine legal and illegal change paths, and path deduction is performed on missing attribute data to determine whether the change is illegal. Sudden land use type changes are detected based on changes in the NDVI index of remote sensing images of the target area. Based on attribute data and spatial relationships, a three-dimensional traceability database of attribute data, spatial evidence, and business documents is formed to dynamically monitor and trace natural resource data. The system addresses existing issues such as the inability to accurately trace the legal source of construction land due to misjudgments on land use and the difficulty in real-time data analysis and traceability.

Description

Technical Field

[0001] The present invention relates to the field of data tracing, and in particular to a method and system for dynamic monitoring and tracing of natural resource data. Background Art

[0002] Limited research exists on the traceability of natural resource data monitoring. Existing technologies integrate multi-temporal data through ArcGIS Model Builder, achieving traceability of construction land through a process of "data preparation, model construction, spatiotemporal analysis, and result output." In the specific traceability logic, this technology uses SQL statements to screen out construction land without legitimate sources and, combined with field calculations, traces back to the previous year's land classification status layer by layer. However, raw data often contains missing, erroneous, or incomplete attribute information. Therefore, this technology uses SQL statements for screening and judgment in the attribute traceability logic. If raw data contains missing, erroneous, or incomplete attribute information, such as incorrect land classification coding records or unclear ownership information, it can lead to misjudgments of illegal land use and make it impossible to accurately trace the legal source of construction land. Furthermore, natural resource data is subject to real-time dynamic changes. While existing technologies can process historical data through static ArcGIS Model Builder workflows, they struggle to rapidly access, analyze, and trace real-time data. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a method and system for dynamic monitoring and tracing of natural resource data, which effectively solves the existing problems of "misjudgment of land use and inability to accurately trace the legal source of construction land" and "difficulty in rapid access, analysis and tracing of data in real time".

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0005] The present invention provides a method for dynamic monitoring and tracing of natural resource data, comprising the following steps:

[0006] Standardize access to natural resource data to ensure consistency in data format and spatial reference;

[0007] Perform attribute integrity check and logical consistency check on data, and complete missing data, correct errors, and remove marks for abnormal data;

[0008] Establish a multi-temporal database. In this database, create a dynamic time partition index at three levels of granularity, automatically write real-time data to the corresponding partition based on the timestamp, and implement incremental update workflows.

[0009] Introducing data confidence weights into SQL filter statements for attribute tracing logic, using weighted logic to determine data legitimacy;

[0010] Construct a land type evolution state transition matrix to determine legal and illegal change paths, and perform path deduction on missing attribute data to determine whether it is an illegal change;

[0011] Obtain the NDVI index of the target area's remote sensing image, detect sudden changes in land use types based on changes in the NDVI index of the target area's remote sensing image, and form a three-dimensional traceability database of attribute data, spatial evidence, and business documents based on attribute data and spatial relationships;

[0012] This three-dimensional traceability database is used to dynamically monitor and trace natural resource data.

[0013] In another aspect, the present invention discloses an electronic device, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform one or more steps of the above method.

[0017] The present invention provides a method and system for dynamic monitoring and tracing of natural resource data, which has at least the following significant beneficial effects:

[0018] Data confidence weights are introduced into SQL filtering, creating a dual judgment mechanism combining "thresholds + logical combinations." Differentiated weights are assigned to key attributes such as land classification codes, ownership information, and approval document numbers. Combined with dynamic corrections to data source credibility, the overall confidence score reduces the false positive rate for single-condition predictions. Dynamic time partitioning is established based on "year-quarter-week," with real-time data automatically written to the corresponding partition by timestamp. Weekly partitions store high-frequency dynamic data, while quarterly / yearly partitions support rapid retrieval of historical data. Compared to traditional static models, this improves the efficiency of real-time data access and meets the needs of monitoring "real-time dynamic changes" in natural resources.

[0019] The three-dimensional traceability database is linked to form a cross-verification system of "attribute legitimacy → spatial authenticity → file consistency", solving the one-sided problem of traditional technology relying on single SQL screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of the natural resource data dynamic monitoring and tracing method of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] This application discloses a method for dynamic monitoring and tracing of natural resource data, such as Figure 1 The dynamic monitoring and tracing method of natural resource data includes the following steps: S100 standardizes the access of natural resource data to ensure the consistency of data format and spatial reference; S101 performs attribute integrity check and logical consistency check on the data; S102 performs missing completion, error correction, and mark removal processing on abnormal data; S103 establishes a multi-temporal database, and in the multi-temporal database, establishes a dynamic time partition index according to the three-level granularity, and automatically writes the real-time data into the corresponding partition according to the timestamp; S104 incremental update workflow; S105 introduces data confidence weights in the SQL filter statement for attribute tracing logic, and uses weighted logic to judge the legitimacy of the data to avoid single condition misjudgment; S106 constructs the land evolution state transfer matrix, determines the legal change path and the illegal path, and performs path deduction on the missing attribute data to determine whether it is an illegal change; S1071 obtains the NDVI index of the remote sensing image of the target area, and calculates the NDVI index of the remote sensing image of the target area according to the NDVI index of the remote sensing image of the target area. Index changes detect sudden changes in land use types; S1072 forms a three-dimensional traceability database of attribute data, spatial evidence, and business documents based on attribute data and spatial relationships; S108 uses the three-dimensional traceability database to dynamically monitor and trace natural resource data.

[0023] Specifically, S100 standardizes access to natural resource data to ensure consistency in data format and spatial reference; it automatically parses and extracts metadata items such as data source (such as "XX City Satellite Remote Sensing Monitoring System"), acquisition time, coordinate system parameters, etc., and refuses access to files that lack key metadata (such as no coordinate system information), and generates warning information (such as "the data file lacks spatial reference metadata and cannot be standardized").

[0024] Verify the existence of required fields (plot ID, land type code, change time) by comparing with the standard field table. Custom fields are automatically mapped to standard fields (such as "land use type" is mapped to "LAND_USE_CODE"). Mismatched fields are marked as yellow warnings, triggering the manual confirmation process.

[0025] For data in old formats, batch conversion to GPKG is carried out through the data conversion engine, and missing spatial reference information is simultaneously completed (such as extracting coordinate system parameters from the file name or data description); layer information is automatically extracted from CAD drawing data, and standard attribute values are assigned according to land classification coding rules (such as "layer name = 'construction land'" corresponds to code 07).

[0026] Real-time data from drones and satellite remote sensing is accessed through the OGC WFS interface, automatically parsing the timestamps and spatial ranges in the data, completing coordinate system conversion and format standardization, and generating GPKG files with spatial indexes.

[0027] Historical stock data is used to establish data subsets based on "administrative division + year", and the coordinate system and land type code are completed by spatially matching the latest cadastral data. Historical data that cannot be completed is marked as "historical non-standard data", and the land type evolution trend in adjacent years is combined with auxiliary judgment during tracing (if the land type has not changed for three consecutive years, the status of the previous year will be continued by default).

[0028] Specifically, S101 performs attribute integrity check and logical consistency check on the data. The data attribute integrity check includes:

[0029] Build a mandatory validation rule library to clearly define the standards that key data attributes must meet. Land classification codes must strictly comply with the GB / T 21010-2017 "Land Use Classification" standard to ensure standardization and accuracy. The ownership document number field cannot be left blank and must comply with administrative division coding rules to ensure the validity and traceability of ownership information.

[0030] Each feature class is meticulously inspected. Missing or incorrect fields are automatically identified and marked, such as highlighting incorrectly coded land-class areas in red, making it easier for staff to quickly locate problematic data.

[0031] Specific data logic consistency checks include:

[0032] Topological validation is used to verify the spatial relationships of land use patterns in the data. For example, this ensures that construction land does not overlap with basic farmland protection zones and that construction land does not appear within ecological protection redline areas, thereby maintaining the rationality and legality of the spatial layout.

[0033] Combined with attribute association rules, SQL statements are used to check whether the approval date corresponding to the approval document number is later than the land classification change date in the previous year. This cross-table logical check can detect logical conflicts between data and ensure logical consistency of data.

[0034] For data with time attributes, check whether its time sequence is reasonable. For example, the change time should be later than the collection time to avoid confusion in time logic.

[0035] Check whether the association between different attributes is reasonable.

[0036] For example, there should be a reasonable correlation between land type codes and attributes such as land area and ownership information. If there is an obvious logical contradiction, it should be marked and further verified.

[0037] Specifically, in S102, the abnormal data is supplemented with missing data, errors are corrected, and marks are removed. The supplementation of missing data for the abnormal data includes:

[0038] In the case of missing land class codes in historical data, the priority is to complete them by spatially matching the latest cadastral data. If this method is not possible, the land class evolution trend in adjacent years can be used to make judgments.

[0039] For example, if the land classification has not changed for three consecutive years, the land classification status of the previous year will be continued by default.

[0040] In the case of missing land classification codes in real-time data, they can be inferred based on the land classification conditions of surrounding plots and remote sensing image features.

[0041] For example, if the surrounding plots are all agricultural land, and the remote sensing image characteristics of the plot are consistent with agricultural land, it can be preliminarily determined to be agricultural land.

[0042] Missing ownership information is supplemented by spatially matching the ownership data of adjacent plots.

[0043] If the ownership certificate number is missing, it can be obtained by accessing the land registration system or relevant approval documents. If it cannot be obtained, the land parcel will be marked as pending ownership confirmation.

[0044] In the case of missing approval data such as approval document number and approval time, OCR recognition is performed on the relevant approval documents to extract key information.

[0045] Error correction for abnormal data includes:

[0046] When the land classification code is incorrect, first compare the land classifications of adjacent years for consistency. If the land classifications of adjacent years are consistent and do not match the current incorrect code, then correct the land classification code to be consistent with the adjacent year.

[0047] If the correct land classification code cannot be determined through the consistency of land classification in adjacent years, the data will be pushed to the manual review workbench, and the management personnel will make corrections based on relevant information (such as remote sensing images, field survey results, etc.).

[0048] If the ownership certificate number does not conform to the administrative division coding rules, it can be corrected by retrieving the administrative division coding table. If the owner information is incorrect, it can be corrected by retrieving the land registration file or contacting the owner.

[0049] For logical conflicts such as the approval time corresponding to the approval document number being later than the land classification change time of the previous year, corrections can be made by retrieving relevant approval documents and land classification change records in the correct time sequence.

[0050] For problems where the spatial relationship of land type patches is illogical, such as the overlap of construction land and basic farmland protection zones, the problem can be solved by re-calling the boundaries of the plots or modifying the land type codes.

[0051] Marking and removing abnormal data includes:

[0052] Abnormal data that cannot be corrected immediately, such as land classification codes or ownership information that cannot be completed due to missing historical data, are marked as "pending confirmation." In the subsequent tracing process, this data will not be used to determine illegal land use, and invalid data will be eliminated.

[0053] Specifically, in S103, a multi-temporal database is established. In the multi-temporal database, a dynamic time partition index is established at three levels of granularity, and real-time data is automatically written to the corresponding partition according to the timestamp. Specifically, the following steps are performed:

[0054] Set up year partitions: Based on the Gregorian calendar year, natural resource data is classified by year, such as independent partitions for 2023 and 2024. Each year partition stores all natural resource data for that year, including historical inventory data and real-time dynamic data. Historical year partition data older than 5 years is automatically archived to the offline storage cluster, while data from the past 5 years is retained on the online server to ensure efficient reading and writing of frequently accessed real-time data and recent data. When writing data, the data is automatically routed to the corresponding year partition based on the year field in the data timestamp, facilitating rapid spatiotemporal overlay analysis of data across years, such as comparing land class changes for the same plot in different years.

[0055] Set up quarterly partitions: Within each yearly partition, further subdivide it into four secondary partitions based on natural quarters: Q1 (January-March), Q2 (April-June), Q3 (July-September), and Q4 (October-December). This partitioning approach meets the needs of dynamic quarterly monitoring, such as for statistical analysis of quarterly land change survey data. Furthermore, a spatial index (such as an R-tree index) is established for each quarterly partition. This spatial index allows for rapid location of overlapping areas of newly added real-time remote sensing monitoring patches within the quarter, significantly improving the efficiency of spatiotemporal analysis and facilitating quarterly retrieval of areas with high rates of land-class change.

[0056] Set up weekly partitions: Use the natural week (Monday to Sunday) as the minimum time partition unit. For example, "2024W12" represents the 12th week of 2024. This partition is primarily used to store real-time dynamic data, such as drone inspection data and satellite remote sensing monitoring data. Real-time remote sensing imagery and sensor data are accurately written to the corresponding weekly partition based on timestamps. Weekly partition data older than two months is automatically merged into the corresponding quarterly partition to free up real-time storage resources. Weekly timestamps are retained for subsequent detailed tracing, such as tracking sudden land use type changes within a given week.

[0057] Specifically, the S104 incremental update workflow includes: for newly added data, such as the patch data obtained by drone aerial photography on the same day, the system matches it to the weekly partition through the timestamp, triggers the "three-level index synchronization mechanism" when the data is written, and updates the weekly partition index, quarterly partition index and yearly partition index in sequence to ensure the efficiency of data query. For changed data, such as approved construction land conversion information, the system automatically generates a historical version snapshot, records the status before and after the change in detail in the weekly partition, and marks the change time point in the quarterly and yearly partitions. At least 5 historical versions are retained for each patch, fully presenting the spatiotemporal evolution trajectory of the land type, facilitating subsequent data tracing and analysis.

[0058] It is understandable that in the dynamic monitoring and traceability of natural resource data, in order to solve the problem of misjudgment of illegal land use caused by missing or incorrect attribute information of the original data, this solution introduces data confidence weights in the SQL filter statement of the attribute tracing logic, and improves the traceability accuracy through weighted logical judgment. Specifically, for S105, data confidence weights are introduced in the SQL filter statement for the attribute tracing logic, and weighted logic is used to judge the legitimacy of the data to avoid misjudgment of single conditions. Specifically, it includes:

[0059] Differentiated weighting standards are formulated for key attributes that affect the judgment of land use legality (land classification code, ownership certificate number, approval document number, etc.):

[0060] For example, if the land classification code complies with the GB / T 21010-2017 standard, the ownership certificate number is complete and complies with the administrative division rules, and the approval document number can be verified in the official system, the corresponding attribute weight is set to 1, indicating that the data credibility is the highest.

[0061] For example, when ownership information is missing but the land classification code is correct, the ownership weight is set to 0.6; if the approval document number is missing but the land classification change conforms to the law of historical evolution, the approval weight is set to 0.7. By reducing the weight, the impact of a single condition on the legality judgment is weakened.

[0062] For example, when the land classification code conflicts with the remote sensing image features, or the ownership certificate document number format is incorrect, the corresponding attribute weight is directly set to 0, and the data is forcibly excluded as the basis for determining legal land use.

[0063] Further adjust the weights based on the data source:

[0064] For example, for data from the land change survey database of the Ministry of Natural Resources, the global weight is increased by an additional 0.1; for data collected in real time by drones and satellite remote sensing, the initial weight is reduced by 0.1 due to interpretation errors and will be restored after manual review; for attributes that are inferred and supplemented by land type evolution trends in adjacent years, the weight is set to 0.5 and marked with the "needs to be checked" logo.

[0065] For each data record, the confidence weight of each core attribute is automatically calculated.

[0066] During the attribute integrity check phase, if a land classification coding error is found, the system automatically resets its weight to 0 and recalculates the comprehensive score;

[0067] After the staff completes the review of the data in the "pending confirmation status", they manually adjust the corresponding attribute weight (for example, increase the weight from 0.6 to 1 after completing the ownership information) and simultaneously update the historical version snapshot;

[0068] Integrate confidence weights into SQL screening logic and use a "threshold + logic combination" approach to double-check data legitimacy:

[0069] For example, if the comprehensive confidence score is ≥0.8 and the weights of the core attributes of land classification code and approval document number are not less than 0.7, it is preliminarily determined to be legal land use; even if the comprehensive score meets the standard, if there is a logical contradiction of "land within the ecological protection red line → construction land" (weight is forced to be 0) and "approval time is earlier than land classification change time", it will be directly marked as illegal land use.

[0070] Specifically, S106 constructs a land type evolution state transfer matrix, determines legal change paths and illegal paths, performs path deduction on missing attribute data to determine whether it is an illegal change, constructs a land type evolution state transfer matrix, and determines legal change paths and illegal paths, specifically including:

[0071] The land types are divided into primary types (such as cultivated land, construction land, and forest land) and secondary types (such as paddy fields, urban residential land, and arbor forest land) as the basic state nodes of the matrix.

[0072] The matrix content includes the entire life cycle of natural resource data, including the initial land classification status, intermediate change process and final status, ensuring that the complete evolution trajectory of each plot from history to the present is recorded.

[0073] As land use policies change (such as adjustments to ecological protection red lines) or industry standards are updated, the matrix rules are quickly revised and expanded to maintain the timeliness of the judgment logic.

[0074] The legal change path determination rules include:

[0075] Clearly stipulate the land classification change paths that require approval, such as "agricultural land → construction land", and the approval time is later than the land classification change time.

[0076] For land use changes caused by natural factors (such as farmland becoming water area due to river diversion), in the absence of records of human intervention, changes in the NDVI index of historical remote sensing images and terrain data will be included in the legal change path.

[0077] All legal changes must form a complete chain of evidence, including approval documents, acceptance reports, and land classification change investigation records. The associated file storage paths must be marked in the matrix to ensure that they can be retrieved and verified at any time.

[0078] Illegal path determination rules include:

[0079] Land use changes that clearly violate policies and regulations, such as "land within the ecological protection red line → construction land" and "permanent basic farmland → construction land", are directly marked as illegal paths and can be judged without the need for additional evidence.

[0080] If there is a time logic error (such as the change time is earlier than the approval time) or spatial conflict (the changed land type has topological overlap with the adjacent plots) during the land classification change process, the illegal path determination will be automatically triggered and the conflict area will be highlighted.

[0081] For land type changes with missing attribute data, if no legal path can be matched in the status deduction in the two years before and after (such as a sudden change from "grassland" to "industrial land" with no approval record), it will be judged as an illegal change and included in the key inspection list.

[0082] Path deduction for missing attribute data to determine whether it is an illegal change includes:

[0083] Based on the land type status in adjacent years (such as "arable land" in the previous year and "commercial and service land" in the next year), all possible evolution paths are searched in the matrix.

[0084] Priority is given to matching paths with historical approval records and similar land parcel evolution cases. If a match is not possible, judgment is made based on the change characteristics of remote sensing images (such as whether there are construction traces).

[0085] When real-time data is accessed, land classification change data is scanned every hour. Once a change record that falls into an illegal path is found, an early warning is immediately triggered. The early warning information includes the coordinates of the plot, the land classification before and after the change, and the basis for the suspected illegal path.

[0086] Specifically, for S1071, the NDVI index of the remote sensing image of the target area is obtained, and the sudden change of land use type is detected according to the change of the NDVI index of the remote sensing image of the target area, including:

[0087] Connecting to multiple remote sensing satellites (such as the Gaofen series) and unmanned aerial vehicle (UAV) remote sensing platforms, remote sensing imagery data for the target area is acquired at regular intervals (e.g., every five days for satellite imagery and weekly for UAV imagery). Radiometric calibration and atmospheric correction algorithms are applied to different sensor types (optical and radar) to mitigate the effects of lighting, cloud cover, and other factors on image quality. Raw remote sensing imagery is cropped based on administrative boundaries and custom monitoring area boundaries. Using ground control points or high-precision digital elevation models (DEMs), the images are uniformly registered to the CGCS2000 coordinate system, ensuring consistent spatial position across time periods and minimizing registration errors to sub-pixel levels. Red and near-infrared (NIR) band data are extracted from the remote sensing images, and NDVI values are calculated pixel by pixel using the NDVI formula: (NIR - Red) / (NIR + Red), generating thematic NDVI images.

[0088] An NDVI time series was established for each monitoring plot, and the NDVI values of different periods were arranged in chronological order to form a continuous NDVI change curve, which intuitively reflected the dynamic change trend of vegetation cover over time.

[0089] Land use type mutation detection includes:

[0090] Abnormal NDVI change thresholds are set based on regional land use types and vegetation growth patterns. For example, for cultivated land, if the NDVI value in a certain area suddenly drops by more than 30% during the non-cultivated season, or fails to reach normal growth levels during the cultivated season (below 20% of the historical average for the same period), it will be marked as a land use type mutation area.

[0091] Smooth the NDVI time series and calculate the NDVI change slope within each sliding window. If the absolute value of the slope exceeds a set threshold (e.g., 0.2 / month) and persists for two window periods, it is considered a sudden change in land use type and a sudden change warning is generated.

[0092] For the detected mutation areas, high-resolution images, land use status data, and land approval documents of the same period are automatically retrieved and manually reviewed.

[0093] Specifically, for S1072, based on attribute data and spatial relationships, a three-dimensional traceability database of attribute data, spatial evidence, and business documents is formed; with the land parcel as the core unit, the attribute data such as land type code, ownership information, approval document number, change time, etc. that have been standardized, accessed and verified are fully recorded in chronological order.

[0094] Retain the confidence weight information of attribute data, mark the inferred or completed data with special identifiers (such as "needs to be checked"), and record the data correction history to ensure that the credibility of the data can be checked during the traceability process.

[0095] Remote sensing images (satellite, drone) of different periods and resolutions are matched with the spatial positions of the plots in chronological order to extract spatial features such as land class boundaries and terrain changes in the images.

[0096] For example, by comparing satellite images from 2022 and 2024, the expansion or contraction of land boundaries can be visually displayed.

[0097] Record the spatial conflict information between land type patches (such as the overlapping area between construction land and basic farmland), and mark the conflict coordinates and processing status (unprocessed, corrected) in the database.

[0098] After OCR recognition and classified storage of land use approval documents, land change investigation records, and acceptance report business documents, a two-way index of files, plots, and time is established in the database.

[0099] For example, by clicking on the change record of a certain plot of land in 2023, you can directly retrieve the corresponding construction land approval document.

[0100] Then, based on the three-level time partition index of the multi-temporal database, a "time-space-file" composite index is established for the three-dimensional database.

[0101] For example, the query "all land classification changes in a certain county in Q2 2024" can quickly locate the attribute data, remote sensing images and related files in the corresponding time partition.

[0102] Establish the logic of linking attribute data, spatial evidence, and business documents. Querying data in any dimension can trigger the linkage display of other dimensions. For example, selecting a sudden change area in remote sensing imagery of a certain plot of land will automatically display the attribute change records and related approval documents for that area.

[0103] Specifically, S108 utilizes the three-dimensional traceability database to dynamically monitor and trace natural resource data, including:

[0104] Conduct multi-dimensional real-time monitoring:

[0105] The attribute data in the database is scanned on a weekly basis to monitor changes such as land classification code changes, ownership information modifications, and approval document expiration. For example, when the land classification code of a plot of land is changed from "cultivated land (01)" to "construction land (07)", the system automatically extracts information such as the change time and operator, and marks it as an "attribute change event".

[0106] Based on spatial evidence from remote sensing images, computer vision technology is used to automatically identify spatial features such as changes in land boundaries and the addition or removal of buildings. For example, by comparing two consecutive satellite images, if the outline of a new building is detected in a certain area, an alert for spatial changes will be triggered immediately.

[0107] Real-time monitoring of the business document library. When new approval documents are uploaded or old documents are updated, the system automatically associates the corresponding land parcel attributes and spatial data. For example, if a new approval document indicates a change in land use, the system will also update its land classification code and spatial extent.

[0108] Conduct multi-dimensional cross-tracing and multi-dimensional cross-discrimination during the tracing process, including:

[0109] In practice, if the traceability starts from the current attributes of the target land parcel, the system will query the historical change records along the time axis in reverse order, and judge the legitimacy of each change step by combining the confidence weight. For example, if a parcel is currently "industrial land", the system will trace the entire process of its change from "agricultural land" and verify the match between the approval documents and the land class evolution state transition matrix.

[0110] Then, the system automatically triggers the verification of attribute changes using spatial features such as NDVI changes in multi-temporal remote sensing imagery, terrain data, and building evolution. For example, if a plot of land is declared "cultivated land reclamation," the system compares the NDVI values before and after reclamation with the vegetation cover in the imagery to determine whether the reclamation actually occurred.

[0111] Then, the system automatically triggers the process: using the approval document number and file number index, it retrieves related business documents (such as land transfer contracts and planning permits) and verifies the consistency between the document content and the attribute / spatial data. For example, if the attribute data indicates that a plot of land has been approved for construction use, the system automatically retrieves the corresponding approval document to verify key information such as the approved area and usage.

[0112] In implementation, if the traceability starts from spatial characteristics, the authenticity of attribute changes can be verified by using spatial characteristics such as NDVI index changes of multi-temporal remote sensing images, terrain data, building evolution, etc.

[0113] Then it is automatically triggered: the historical change records are queried from the current attributes of the target plot along the time axis in reverse, and the legitimacy of each change is judged based on the confidence weight.

[0114] Then it is automatically triggered: through the approval document number and file number index, the related business documents (such as land transfer contracts, planning permits) are retrieved to verify the consistency of the file content and attribute / spatial data.

[0115] During implementation, if the traceability starts from the business documents, the related business documents (such as land transfer contracts and planning permits) are retrieved through the approval document number and file number index to verify the consistency of the file content and attribute / spatial data.

[0116] Then it is automatically triggered: using spatial features such as NDVI index changes, terrain data, building evolution, etc. of multi-temporal remote sensing images to verify the authenticity of attribute changes.

[0117] Then it is automatically triggered: the historical change records are queried from the current attributes of the target plot along the time axis in reverse, and the legitimacy of each change is judged based on the confidence weight.

[0118] It is apparent that one or more steps of the method of the present invention can be implemented by a computer program. The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0119] Therefore, it can be understood that the present invention discloses an electronic device, including:

[0120] at least one processor; and

[0121] a memory communicatively connected to the at least one processor; wherein,

[0122] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform one or more steps of the above method.

[0123] This application also discloses a natural resource data dynamic monitoring and tracing system, which uses the above natural resource data dynamic monitoring and tracing method, including:

[0124] Access management module, used for standardized access to natural resource data;

[0125] The data verification module is used to perform attribute integrity verification and logical consistency verification on data, and to complete missing data, correct errors, and remove abnormal data;

[0126] A multi-temporal database with a dynamic time partition index established at three levels of granularity, which is used to automatically write real-time data to the corresponding partition based on the timestamp and incrementally update the workflow;

[0127] The data validity judgment module is used to introduce data confidence weights into SQL filter statements based on attribute tracing logic, and use weighted logic to judge data validity;

[0128] The state transition matrix management module is used to construct the land type evolution state transition matrix, determine the legal change path and the illegal path, and perform path deduction on missing attribute data to determine whether it is an illegal change;

[0129] Remote sensing management module, used to obtain the NDVI index of the remote sensing image of the target area and detect land use type mutations based on the changes in the NDVI index of the remote sensing image of the target area;

[0130] The three-dimensional traceability management module is used to form a three-dimensional traceability database of attribute data, spatial evidence, and business documents based on attribute data and spatial relationships, and is used to dynamically monitor and trace natural resource data using the three-dimensional traceability database.

[0131] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for dynamic monitoring and tracing of natural resource data, characterized in that: The following steps are involved: Standardized access to natural resource data; Perform attribute integrity and logical consistency checks on data, and complete missing data, correct errors, and remove abnormal data; Establish a multi-temporal database. In this database, create a dynamic time partition index at three levels of granularity, automatically write real-time data to the corresponding partition based on the timestamp, and implement incremental update workflows. Differentiated weighting standards are formulated for key attributes that affect the judgment of land use legality: corresponding attribute weights are set based on land classification codes, ownership information, and whether land classification codes conflict with remote sensing image features; The weights are then further modified based on the data source. For each data record, the confidence weights of each core attribute are automatically calculated, and the data's comprehensive confidence score is calculated. During the attribute integrity verification phase, the comprehensive score is recalculated based on the verification; or the corresponding attribute weights are reviewed and adjusted. The confidence weights are integrated into the SQL filtering logic, and a combination of thresholds and logic is used to dually judge the legitimacy of the data. Land corresponding to data with a comprehensive confidence score that meets the threshold and a core attribute weight that meets the threshold is preliminarily determined to be legal land. Land corresponding to data with logical contradictions is directly marked as illegal land. Land types are divided into primary and secondary types, which serve as the basic status nodes of the matrix. The matrix content includes the entire life cycle of natural resource data, including the initial land type status, intermediate change process and final status, ensuring that the complete evolution trajectory of each land parcel from history to the present is recorded. Clearly define the paths for land classification changes that require approval. For land classification changes caused by natural factors, in the absence of records of human intervention, changes in the NDVI index from historical remote sensing images and terrain data should be included in the legal change path. All legal changes must form a complete chain of evidence, including approval documents, acceptance reports, and land classification change investigation records. The associated file storage paths should be marked in the matrix to ensure that they can be retrieved and verified at any time. Land classification changes that clearly violate policies and regulations will be directly marked as illegal paths, without the need for additional evidence. If there are temporal logic errors or spatial conflicts during the land classification change process, illegal path determination will be automatically triggered, and the conflicting areas will be highlighted. For land classification changes with missing attribute data, if no legal path can be matched in the state deductions for the two years before and after, they will be determined to be illegal changes and included in the key inspection list. Obtain the NDVI index of the remote sensing image of the target area, detect the sudden change of land use type according to the change of the NDVI index of the remote sensing image of the target area, and form a three-dimensional traceability database of attribute data, spatial evidence, and business documents based on attribute data and spatial relationships; use this three-dimensional traceability database to dynamically monitor and trace natural resource data.

2. A natural resource data dynamic monitoring and tracing method according to claim 1, characterized in that: The data attribute integrity check includes: building a mandatory verification rule base to specify the standards that key attributes in the data must meet; conducting checks on each feature class; identifying and marking missing or erroneous fields; the data logical consistency check includes: using topology verification to verify the spatial relationship of land class patches in the data; based on attribute association rules, using SQL statements to check whether the approval time corresponding to the approval document number is later than the previous land class change time; for data with time attributes, checking whether its time sequence is reasonable; and checking whether the association relationship between different attributes is reasonable.

3. A natural resource data dynamic monitoring and tracing method according to claim 1, characterized in that: The incremental update workflow includes: for newly added data and daily data, matching to weekly partitions through timestamps, and updating weekly partition indexes, quarterly partition indexes, and yearly partition indexes in sequence when data is written; for changed data, automatically generating historical version snapshots, recording the status before and after the change in detail in the weekly partition, and marking the change time points in the quarterly and yearly partitions, retaining several historical versions for each map patch, and presenting the spatiotemporal evolution trajectory of land types.

4. A natural resource data dynamic monitoring and tracing method according to claim 1, characterized in that: Obtain the NDVI index from remote sensing images of the target area and detect sudden changes in land use types based on changes in the NDVI index of the target area. This includes: extracting red and near-infrared band data from remote sensing images to calculate and generate NDVI index thematic images; establishing an NDVI index time series for each monitored plot, arranging NDVI values from different periods in chronological order to form a continuous NDVI change curve to reflect the dynamic change trend of vegetation cover over time; Set the NDVI abnormal change threshold; when the NDVI value of a certain area does not meet the threshold, it is marked as a land use type mutation area; and / or The NDVI index time series is smoothed and the slope of the NDVI index change within each sliding window is calculated. If the absolute value of the slope exceeds the set threshold and the duration meets the threshold, it is determined to be a land use type mutation and a mutation warning information is generated. For the detected mutation area, high-resolution images, land use status data, and land approval documents of the same period are retrieved for manual review.

5. A natural resource data dynamic monitoring and tracing method according to claim 1, characterized in that: Based on attribute data and spatial relationships, a three-dimensional traceability database of attribute data, spatial evidence, and business documents is formed. This includes a three-level time partition index based on a multi-temporal database, establishing a composite index of "time-space-file". The association logic between attribute data, spatial evidence, and business documents is established, and queries on any dimension of data are configured to trigger the linked display of other dimensions.

6. A natural resource data dynamic monitoring and tracing method according to claim 1, characterized in that: Dynamic monitoring and tracing of natural resource data include: Scan attribute data in the database on a weekly basis to monitor changes in land classification codes, modifications to ownership information, and expiration of approval document numbers; Identify spatial features of land boundary changes, building additions or demolitions based on spatial evidence from remote sensing images; Real-time monitoring of the business document library. When new approval documents are uploaded or old documents are updated, the corresponding land parcel attributes and spatial data are automatically associated. as well as Conduct multi-dimensional cross-tracing and multi-dimensional cross-discrimination during the tracing process; the multi-dimensional cross-discrimination during the tracing process at least includes: If the traceability starts from the current attributes of the target plot, the legitimacy of each change is judged based on the confidence weight, and then the following actions are automatically triggered: using spatial features to verify the authenticity of the attribute change, and then automatically triggering: through indexing, retrieving related business files and verifying the consistency of the file content with the attributes and / or spatial data; If the traceability starts from spatial features, the authenticity of the attribute change is verified, and then the automatic trigger is: based on the current attributes of the target plot, the legitimacy of each change is judged according to the confidence weight, and then the automatic trigger is: through indexing, the related business files are retrieved and the consistency of the file content with the attributes and / or spatial data is verified; If the traceability starts from the business file, the related business files are retrieved through indexing, and the consistency between the file content and the attributes and / or spatial data is verified, then the automatic triggering is: using spatial features to verify the authenticity of the attribute changes, and then the automatic triggering is: judging the legality of each step of the change based on the current attributes of the target plot and the confidence weight.

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