Natural resource data dynamic monitoring and tracing method and system

Abstract

The invention relates to the field of data traceability, and particularly discloses a natural resource data dynamic monitoring traceability method and system, and the method comprises the steps: building a multi-tense database, automatically writing real-time data into a corresponding partition according to a timestamp, and carrying out the incremental updating of a workflow; data confidence coefficient weights are introduced into SQL screening statements according to attribute tracing logic, and data legality is judged through weighted logic; constructing a land class evolution state transition matrix, determining a legal change path and an illegal path, and carrying out path deduction on attribute data missing to judge whether the attribute data missing is illegal change or not; and detecting land type mutation according to NDVI index change of a target remote sensing image, forming a three-dimensional tracing database of attribute data, space evidence and business files according to the attribute data and the spatial relationship, and dynamically monitoring and tracing natural resource data. The problems that the legal source of the construction land cannot be accurately traced due to land judgment errors and real-time data analysis and traceability are difficult in the prior art are solved.

Description

Technical Field

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

[0002] There are few studies on the monitoring and tracing of existing natural resource data. The existing technology integrates multi-temporal data through the ArcGIS model builder, and realizes the traceability of construction land through the process of "data preparation, model construction, spatio-temporal analysis, and result output". In the specific traceability logic, this type of technology uses SQL statements to screen construction land without legal sources, and combines field calculations to trace the land type status of the previous year layer by layer. In applications, there are many cases where the original data has missing, incorrect, or incomplete attribute information. Therefore, in the attribute traceability logic, SQL statements are used for screening and judgment. If the original data has missing, incorrect, or incomplete attribute information, such as incorrect land type coding records and unclear ownership information, it will lead to misjudgment of illegal land use and inability to accurately trace the legal source of construction land. In addition, natural resource data has the characteristics of real-time dynamic changes. Although the existing technology can process historical data through the workflow of the static ArcGIS model builder, it is difficult to achieve rapid access, analysis, and traceability of real-time data. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides a method and system for dynamically monitoring and tracing natural resource data, effectively solving the problems of "misjudgment of land use and inability to accurately trace the legal source of construction land" and "difficulty in rapidly accessing, analyzing, and tracing data in real time" in the existing technology.

[0004] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for dynamically monitoring and tracing natural resource data according to the present invention includes the following steps: Standardize the access of natural resource data to ensure the consistency of data format and spatial reference; Perform attribute integrity verification and logical consistency verification on the data, and perform missing data completion, error correction, and marked deletion processing on abnormal data; Establish a multi-temporal database. In the multi-temporal database, establish a dynamic time partition index at the three-level granularity, automatically write real-time data into the corresponding partition according to the time stamp, and incrementally update the workflow; Introduce data confidence weights into the SQL screening statement for the attribute traceability logic, and use weighted logic to judge the legality of the data; Construct a land type evolution state transition matrix, determine legal change paths and illegal paths, and perform path deduction on missing attribute data to determine whether it is an illegal change; Obtain the NDVI index of the remote sensing image of the target area, detect the mutation of land use types 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 the attribute data and spatial relationship; Use this three-dimensional traceability database to trace the dynamic monitoring of natural resource data.

[0005] On the other hand, the present invention discloses an electronic device, including: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute one or more steps of the above method.

[0006] The present invention provides a method and system for dynamic monitoring and traceability of natural resource data, which at least has the following remarkable beneficial effects: Introduce data confidence weights in SQL filtering, and construct a dual decision-making mechanism of "threshold + logical combination". Set different weights for key attributes such as land type coding, ownership information, and approval document numbers, and dynamically correct them in combination with the credibility of data sources. The comprehensive confidence score reduces the misjudgment rate of single conditions. Establish a dynamic time partition of "year - quarter - week", and real-time data is automatically written into the corresponding partition according to the timestamp. The weekly partition stores high-frequency dynamic data, and the quarterly / yearly partitions support fast retrieval of historical data. Compared with the traditional static model, the access efficiency of real-time data is improved, meeting the monitoring requirements of the "real-time dynamic changes" of natural resources.

[0007] Link the three-dimensional traceability database to form a cross-verification system of "attribute legality → spatial authenticity → file consistency", solving the one-sidedness problem of traditional technologies relying on single SQL filtering. Brief Description of the Drawings

[0008] Figure 1 It is a flowchart of the method for dynamic monitoring and traceability of natural resource data of the present invention. Detailed Embodiments

[0009] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0010] This application discloses a method for dynamic monitoring and traceability of natural resource data, asFigure 1 The dynamic monitoring and traceability method for natural resource data includes the following steps: S100 Standardize the access of natural resource data to ensure the consistency of data format and spatial reference; S101 Check the integrity of attributes and logical consistency of the data; S102 Perform missing data completion, error correction, and marked deletion on abnormal data; S103 Establish a multi-temporal database. In the multi-temporal database, create a dynamic time partition index at three levels of granularity, and automatically write real-time data into the corresponding partition according to the time stamp; S104 Incrementally update the workflow; S105 Introduce data confidence weights into the SQL filtering statement for the attribute traceability logic, and use weighted logic to judge the legality of the data to avoid misjudgment due to a single condition; S106 Construct a land type evolution state transition matrix to determine legal change paths and illegal paths, and perform path deduction on missing attribute data to determine whether it is an illegal change; S1071 Obtain the NDVI index of the remote sensing image of the target area, and detect sudden changes in land use types based on the change of the NDVI index of the remote sensing image of the target area; S1072 Form a three-dimensional traceability database of attribute data, spatial evidence, and business documents based on the attribute data and spatial relationships; S108 Use the three-dimensional traceability database to perform dynamic monitoring and traceability of natural resource data.

[0011] Specifically, for S100 to standardize the access of natural resource data to ensure the consistency of data format and spatial reference; automatically parse and extract metadata items such as data source (e.g., "XX City Satellite Remote Sensing Monitoring System"), collection time, and coordinate system parameters. Files missing key metadata (such as no coordinate system information) are rejected for access, and warning messages are generated (e.g., "The data file lacks spatial reference metadata and cannot be standardized").

[0012] Check against the field standard table to verify the existence of required fields (patch ID, land type code, change time). Custom fields are automatically mapped to standard fields (e.g., "land use type" is mapped to "LAND_USE_CODE"). Fields that do not match are marked with a yellow warning to trigger the manual confirmation process.

[0013] For old-format data, batch convert it to GPKG through a data conversion engine, and synchronously complete the missing spatial reference information (such as extracting coordinate system parameters from the file name or data description); automatically extract layer information from CAD drawing data and assign standard attribute values according to the land type coding rules (e.g., "layer name = 'construction land'" corresponds to code 07).

[0014] Real-time data from drones and satellite remote sensing is accessed through the OGC WFS interface. Automatically parse the time stamp and spatial range in the data, complete coordinate system conversion and format standardization, and generate a GPKG file with a spatial index.

[0015] Historical stock data is used to establish data subsets according to "administrative division + year". First, the coordinate system and land use type codes are supplemented by matching the latest cadastral data through spatial location. Historical data that cannot be supplemented is marked as "historical non-standard data", and the trend of land use type evolution in adjacent years is used as an auxiliary judgment during tracing (for example, if the land use type has not changed for three consecutive years, it is defaulted to continue the previous year's status).

[0016] Specifically, for S101, attribute integrity verification and logical consistency verification are performed on the data. The attribute integrity verification of the data includes: Construct a mandatory verification rule library to clearly define the standards that key attributes in the data must meet. The land use type code must strictly comply with the "Classification of Current Land Use" standard of GB / T 21010 - 2017 to ensure the standardization and accuracy of the land use type code. The field of the document number of the right of ownership certificate cannot be blank and must comply with the administrative division coding rule to ensure the effectiveness and traceability of the right of ownership information.

[0017] Conduct a detailed inspection of each feature class. Automatically identify and mark missing or incorrect fields. For example, highlight the patches with incorrect land use type codes in red to facilitate the staff to quickly locate the problem data.

[0018] Specifically, the logical consistency verification of the data includes: Use topological verification to verify the spatial relationship of land use type patches in the data. Exemplarily, ensure that construction land does not overlap with the basic farmland protection area, and there should be no construction land within the ecological protection red line area, etc., to maintain the rationality and legality of the spatial layout.

[0019] Combined with the attribute association rule, use SQL statements to check whether the approval time corresponding to the approval document number is later than the land use type change time of the previous year. This cross-table logical verification can detect logical conflicts between data and ensure the logical consistency of the data.

[0020] For data with time attributes, check whether its time sequence is reasonable. Exemplarily, the change time should be later than the collection time to avoid the situation of chaotic time logic.

[0021] Check whether the association relationship between different attributes is reasonable.

[0022] Exemplarily, there should be a reasonable association between the land use type code and attributes such as land use area and right of ownership information. If there are obvious logical contradictions, they will be marked and further verified.

[0023] Specifically, for S102, missing data supplementation, error correction, and marked elimination are performed on abnormal data. The missing data supplementation for abnormal data includes: For the situation where the land type code is missing in historical data, it is prioritized to complete it by spatially matching with the latest cadastral data. If it cannot be completed in this way, it can be judged based on the evolution trend of land types in adjacent years.

[0024] Exemplarily, if the land type has not changed for three consecutive years, the land type status of the previous year is defaulted to continue.

[0025] For the situation where the land type code is missing in real-time data, it can be inferred based on the land types of surrounding plots and the characteristics of remote sensing images.

[0026] Exemplarily, if all surrounding plots are agricultural land and the characteristics of the remote sensing image of this plot match those of agricultural land, it can be preliminarily determined as agricultural land.

[0027] For the missing ownership information, it is completed by spatially matching the ownership data of adjacent plots.

[0028] For the situation where the ownership certificate file number is missing, it can be obtained by retrieving the land registration system or relevant approval documents. If it cannot be obtained, the plot will be marked as a state pending confirmation of ownership.

[0029] For the situation where approval data such as approval document numbers and approval times are missing, perform OCR recognition on relevant approval documents and extract key information.

[0030] The error correction of abnormal data includes: When the land type code is incorrect, first compare the consistency of land types in adjacent years. If the land types in adjacent years are consistent and do not match the current incorrect code, the land type code is corrected to be consistent with that of adjacent years.

[0031] If the correct land type code cannot be determined through the consistency of land types in adjacent years, the data will be pushed to the manual review workbench, and the management personnel will correct it according to relevant materials (such as remote sensing images, on-site investigation results, etc.).

[0032] If the ownership certificate file number does not conform to the administrative division coding rule, it can be corrected by retrieving the administrative division coding table. If the ownership person information is incorrect, it can be corrected by retrieving the land registration archives or communicating with the ownership person.

[0033] For logical conflict problems such as the approval time corresponding to the approval document number being later than the land type change time of the previous year, retrieve relevant approval documents and land type change records and correct them in the correct chronological order.

[0034] For problems where the spatial relationship of land type patches does not conform to logic, such as the overlap of construction land and the basic farmland protection area, it can be solved by retrieving the plot boundary again or modifying the land type code.

[0035] The marking and elimination processing of abnormal data includes: For abnormal data that cannot be corrected immediately, such as land use type codes or ownership information that cannot be supplemented due to missing historical data, it is marked as "pending confirmation status". During subsequent traceability processes, these data do not participate in the determination of illegal land use, and invalid data is excluded.

[0036] Specifically, for S103 to establish a multi-temporal database, in the multi-temporal database, a dynamic time partition index is established at a three-level granularity, and real-time data is automatically written into the corresponding partition according to the timestamp, specifically including: Set year partitions: Based on the Gregorian year, natural resource data is classified by year. For example, independent partitions are set for 2023, 2024, etc. Each year partition stores all natural resource data within that year, covering historical stock data and real-time dynamic data. Historical year partition data that is more than 5 years old is automatically archived to an offline storage cluster, while data from the past 5 years is retained on the online server to ensure efficient reading and writing of real-time data and recent data with high-frequency access. When writing data, the data is automatically routed to the corresponding year partition based on the year field in the data timestamp, facilitating rapid spatio-temporal overlay analysis of cross-year data, such as comparing land use type changes of the same plot in different years.

[0037] Set quarter partitions: Within each year partition, it is further subdivided into 4 secondary partitions according to natural quarters, namely Q1 (January - March), Q2 (April - June), Q3 (July - September), and Q4 (October - December). This partition method can meet the needs of quarterly dynamic monitoring, such as for the statistical work of quarterly land change survey data. At the same time, a spatial index (such as an R-tree index) is established for each quarter partition. For newly added real-time remote sensing monitoring patches within a quarter, the overlapping areas can be quickly located through the spatial index, greatly improving the efficiency of spatio-temporal analysis and facilitating users to retrieve high-frequency areas of land use type changes by quarter dimension.

[0038] Set week partitions: The natural week (Monday to Sunday) is used as the smallest time partition unit. For example, "2024W12" represents the 12th week of 2024. This partition is mainly used to store real-time dynamic data, such as drone inspection data, satellite remote sensing monitoring data, etc. Real-time access remote sensing images and sensor data are accurately written into the corresponding week partition according to the timestamp. For week partition data that is more than 2 months old, the system will automatically merge it into the corresponding quarter partition to release real-time storage resources, while retaining the week-level timestamp for subsequent fine-grained traceability requirements, such as tracking sudden land use type mutation events within a certain week.

[0039] Specifically, for the S104 incremental update workflow, it includes: for newly added data, such as the patch data obtained from drone aerial photography on the same day, the system matches it to the weekly partition through timestamps, and triggers the "three-level index synchronization mechanism" when writing the data, updating the weekly partition index, quarterly partition index, and annual partition index in sequence to ensure the efficiency of data query. For changed data, such as the construction land conversion information approved through the review, the system will automatically generate a historical version snapshot, record the states before and after the change in detail in the weekly partition, and mark the change time points in the quarterly and annual partitions at the same time, retain at least 5 historical versions for each patch, and fully present the spatio-temporal evolution trajectory of the land type, which is convenient for subsequent data traceability and analysis.

[0040] It can be understood that in the dynamic monitoring and traceability of natural resource data, 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 filtering statement of the attribute traceability logic, and improves the traceability accuracy through weighted logical judgment. Specifically, for S105, data confidence weights are introduced in the SQL filtering statement for the attribute traceability logic, and weighted logical judgment is used to determine the legality of the data to avoid misjudgment under a single condition. Specifically, it includes: For the key attributes (land type code, ownership certificate document number, approval document number, etc.) that affect the judgment of land use legality, differential weight standards are formulated: Exemplarily, if the land type code conforms to the GB / T 21010 - 2017 standard, the ownership certificate document number is complete and conforms to 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 the highest data credibility.

[0041] Exemplarily, when the ownership information is missing but the land type code is correct, the ownership weight is set to 0.6; if the approval document number is missing but the land type change conforms to the historical evolution law, the approval weight is set to 0.7, and the influence of a single condition on the legality judgment is weakened by reducing the weight.

[0042] Exemplarily, in the case where the land type code conflicts with the remote sensing image features or the format of the ownership certificate document number is incorrect, the corresponding attribute weight is directly set to 0, and this data is forcibly excluded as the basis for judging legal land use.

[0043] Further correct the weights according to the data source: Exemplarily, for the data from the land change survey database of the Ministry of Natural Resources, the global weight is additionally increased by 0.1; for the data collected in real time by drones and satellite remote sensing, due to the existence of interpretation errors, the initial weight is reduced by 0.1 and will be restored after manual review; for the attributes filled in by inferring the land type evolution trend of adjacent years, the weight is set to 0.5, and the "needs key verification" mark is marked.

[0044] For each data record, automatically calculate the confidence weights of each core attribute.

[0045] Exemplarily, for a certain plot of land data, the land type coding is correct (weight 1), the ownership information is missing (weight 0.6), and the approval document number is valid (weight 1). Then the comprehensive confidence score of this data = (1×land type weight + 0.6×ownership weight + 1×approval weight) ÷ 3 = 0.87.

[0046] In the attribute integrity verification link, if it is found that the land type coding is incorrect, the system automatically sets its weight to 0 and recalculates the comprehensive score; After the staff completes the review of the "to be confirmed status" data, manually adjust the corresponding attribute weights (such as increasing the weight from 0.6 to 1 after completing the ownership information), and synchronously update the historical version snapshot; Integrate the confidence weights into the SQL filtering logic, and use "threshold + logical combination" to double-judge the data legality: Exemplarily, for a comprehensive confidence score ≥ 0.8, and the core attribute weights of the land type coding and approval document number are not less than 0.7, it is initially determined as legally used land; even if the comprehensive score meets the standard, if there are logical contradictions such as "land within the ecological protection red line → construction land" (weight is forced to 0), "approval time is earlier than the land type change time", it is directly marked as illegally used land.

[0047] Specifically, for S106, construct a land type evolution state transition matrix, determine the legal change path and illegal path, and conduct path deduction for missing attribute data to determine whether it is an illegal change. Constructing the land type evolution state transition matrix and determining the legal change path and illegal path specifically include: Divide the land types into first-level categories (such as cultivated land, construction land, forest land) and second-level categories (such as paddy fields, urban residential land, arbor forest land) as the basic state nodes of the matrix.

[0048] The content of the matrix includes the full life cycle of natural resource data, including the initial land type state, intermediate change process, and final state, ensuring that the complete evolution trajectory of each plot of land from history to the present is recorded.

[0049] With the change of land use policies (such as the adjustment of the ecological protection red line) or the update of industry standards, quickly revise and expand the matrix rules to maintain the timeliness of the judgment logic.

[0050] The judgment rules for legal change paths include: Clearly stipulate the land type change paths that require approval, such as "agricultural land → construction land", and the approval time is later than the land type change time.

[0051] For land type changes caused by natural factors (such as cultivated land turning into water area due to river course diversion), in the absence of records of human intervention, it is included in the legal change path through the change of NDVI index in historical remote sensing images and topographic data as evidence.

[0052] All legal changes need to form a complete evidence chain, including approval documents, acceptance reports, and land type change investigation records, and mark the storage paths of associated documents in the matrix to ensure that they can be retrieved and verified at any time.

[0053] The rules for determining illegal paths include: Land type 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 determined without additional evidence.

[0054] If time logic errors (such as the change time being earlier than the approval time) or spatial conflicts (the changed land type overlapping topologically with adjacent plots) occur during the land type change process, the determination of illegal paths is automatically triggered, and the conflict area is highlighted.

[0055] For land type changes with missing attribute data, if no legal path can be matched in the state deduction of the previous and next two years (such as suddenly changing from "grassland" to "industrial land" without approval records), it is determined as an illegal change and included in the key verification list.

[0056] The path deduction for determining whether a land type change with missing attribute data is an illegal change includes: Based on the land type status of adjacent years (such as "cultivated land" in the previous year and "commercial service land" in the next year), search for all possible evolution paths in the matrix.

[0057] Give priority to matching paths with historical approval records and evolution cases of similar plots. If no match can be found, judge based on the change characteristics of remote sensing images (such as whether there are construction traces).

[0058] When real-time data is accessed, scan the land type change data once an hour. Once a change record falling into an illegal path is found, an alarm is immediately triggered. The alarm information includes the plot coordinates, land types before and after the change, and the basis for the suspected illegal path.

[0059] Specifically, for S1071, obtain the NDVI index of the remote sensing image of the target land, and detect sudden changes in land use types based on the change of the NDVI index of the remote sensing image of the target land, including: Dock multi-source remote sensing satellites (such as the high-resolution satellite series) and unmanned aerial vehicle (UAV) remote sensing platforms to obtain remote sensing image data of the target area at fixed time intervals (such as satellite images every 5 days and UAV images every week). For different sensor types (optical, radar), corresponding radiometric calibration and atmospheric correction algorithms are adopted to eliminate the influence of factors such as illumination and clouds on the image quality. Crop the original remote sensing images according to administrative division boundaries and custom monitoring area ranges; use ground control points or high-precision digital elevation models (DEMs) to register the images to the CGCS2000 coordinate system uniformly to ensure the consistency of the spatial positions of images in different periods, and control the registration error at the sub-pixel level. Extract the data of the red band (Red) and the near-infrared band (NIR) from the remote sensing images, and calculate the NDVI value for each pixel according to the NDVI calculation formula NDVI = (NIR - Red) / (NIR + Red) to generate an NDVI thematic image.

[0060] Establish an NDVI time series for each monitored plot, arrange the NDVI values in different periods in chronological order to form a continuous NDVI change curve, and intuitively reflect the dynamic change trend of vegetation cover over time.

[0061] Land use type mutation detection includes: Set the NDVI abnormal change threshold according to the regional land use type and vegetation growth law. Exemplarily, for cultivated land, if the NDVI value in a certain area suddenly drops by more than 30% during the non-cultivation season, or does not reach the normal growth level (below 20% of the historical average in the same period) during the cultivation season, it is marked as a land use type mutation area.

[0062] Smooth the NDVI time series and calculate the NDVI change slope within each sliding window. If the absolute value of the slope exceeds the set threshold (such as 0.2 / month) and lasts for 2 window periods, it is determined as a land use type mutation, and a mutation warning message is generated.

[0063] For the detected mutation areas, automatically retrieve the high-resolution images, current land use data, and land approval documents in the same period for manual review.

[0064] Specifically, for S1072, form a three-dimensional traceability database of attribute data, spatial evidence, and business documents based on attribute data and spatial relationships; take the plot as the core unit, and completely record the attribute data such as standardized access and verified land type codes, ownership information, approval document numbers, and change times in chronological order.

[0065] Retain the confidence weight information of the attribute data, mark special identifiers (such as "Needs Key Verification") for the data inferred or completed, and record the data correction history to ensure that the credibility of the data is verifiable during the tracing process.

[0066] Match remotely sensed images (satellite, drone) of different periods and resolutions with the plot spatial location in chronological order, and extract spatial features such as land use boundaries and terrain changes in the images.

[0067] Exemplarily, by comparing satellite images of 2022 and 2024, visually display the expansion or contraction of the plot boundaries.

[0068] Record the spatial conflict information between land use map patches (such as the overlapping area of construction land and basic farmland), and mark the conflict coordinates and processing status (unprocessed, corrected) in the database.

[0069] After performing OCR recognition and classified storage on land use approval documents, land change survey records, acceptance report business documents, establish a two-way index of files with plots and time in the database.

[0070] Exemplarily, by clicking on the change record of a certain plot in 2023, the corresponding construction land use approval document can be directly retrieved.

[0071] Then, based on the three-level time partition index of the multi-temporal database, establish a composite index of "Time - Space - File" for the three-dimensional database.

[0072] Exemplarily, querying "All land use changes within a certain county in Q2 of 2024" can quickly locate the attribute data, remotely sensed images, and associated files within the corresponding time partition.

[0073] Establish the association logic of attribute data, spatial evidence, and business documents. The query of any dimension data can trigger the linked display of other dimensions. For example, when selecting the mutation area of the remotely sensed image of a certain plot, the attribute change records and relevant approval documents of this area are automatically displayed.

[0074] Specifically, for S108, use this three-dimensional tracing database to dynamically monitor and trace natural resource data, including: Conduct multi-dimensional real-time monitoring: Scan the attribute data in the database on a weekly basis to monitor changes such as land use code changes, ownership information modifications, and approval document numbers becoming invalid. Exemplarily, when the land use code of a certain plot changes 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".

[0075] Based on the spatial evidence of remote sensing images, computer vision technology is used to automatically identify spatial features such as changes in plot boundaries, new construction / demolition of buildings, etc. For example, by comparing two adjacent satellite images, when a new building outline is detected in a certain area, a spatial change warning is immediately triggered.

[0076] The business document library is monitored in real time. When a new approval document is uploaded or an old document is updated, the attributes and spatial data of the corresponding plot are automatically associated. Exemplarily, if the new approval document shows a change in the use of a certain plot, the system will synchronously update its land type code and spatial scope.

[0077] Multi-dimensional cross-tracing is carried out, and multi-dimensional cross-discrimination is carried out during the tracing process, including: During implementation, if the tracing starts from the current attributes of the target plot and queries the historical change records along the time axis in reverse, the legitimacy of each change is judged by combining the confidence weight. Exemplarily, if a certain plot is currently "industrial land", the system traces the whole process of its change from "agricultural land" and verifies the matching degree between the approval documents and the land type evolution state transition matrix.

[0078] Then it is automatically triggered: Using spatial features such as the change of NDVI index, terrain data, and building evolution of multi-temporal remote sensing images to verify the authenticity of attribute changes. For example, if a plot declares "cultivated land reclamation", the system judges whether the reclamation actually occurred by comparing the NDVI values before and after reclamation and the vegetation coverage in the image.

[0079] Then it is automatically triggered: Through the approval document number and file number index, retrieve the associated business documents (such as land transfer contracts, planning permits), and verify the consistency between the document content and the attribute / spatial data. Exemplarily, if the attribute data shows that a certain plot has obtained a construction land approval, the system automatically retrieves the corresponding approval document and verifies the key information such as the approved area and use.

[0080] During implementation, if the tracing starts from spatial features, use spatial features such as the change of NDVI index, terrain data, and building evolution of multi-temporal remote sensing images to verify the authenticity of attribute changes.

[0081] Then it is automatically triggered: Query the historical change records along the time axis in reverse from the current attributes of the target plot, and judge the legitimacy of each change by combining the confidence weight.

[0082] Then it is automatically triggered: Through the approval document number and file number index, retrieve the associated business documents (such as land transfer contracts, planning permits), and verify the consistency between the document content and the attribute / spatial data.

[0083] In implementation, if the traceability starts from business documents, relevant business documents (such as land transfer contracts and planning permits) are retrieved through the approval document number and document number index, and the consistency between the document content and attributes / spatial data is verified.

[0084] Then it is automatically triggered: using spatial features such as the change of NDVI index, terrain data, and building evolution of multi-temporal remote sensing images to verify the authenticity of attribute changes.

[0085] Then it is automatically triggered: reverse query the historical change records along the time axis from the current attributes of the target plot, and judge the legality of each change in combination with the confidence weight.

[0086] Obviously, 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 the processors of general-purpose computers, special-purpose computers or other programmable data processing devices, so that when the computer programs are executed by the processors, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or entirely on a remote machine or server.

[0087] Therefore, it can be understood that the present invention discloses an electronic device, including: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute one or more steps of the above method.

[0088] The present application also discloses a natural resource data dynamic monitoring and traceability system, which uses the above natural resource data dynamic monitoring and traceability method, including: An access management module for standardizing the access of natural resource data; A data verification module for performing attribute integrity verification and logical consistency verification on the data, and performing missing complementation, error correction, and marked elimination processing on abnormal data; A multi-temporal database configured with a dynamic time partition index established at three levels of granularity for automatically writing real-time data into the corresponding partitions according to timestamps and incrementally updating the workflow; A data legality discrimination module for introducing data confidence weights into the SQL filtering statement for the attribute traceability logic and using weighted logic to judge the data legality; The state transition matrix management module is used to construct the state transition matrix of land type evolution, determine the legal change paths and illegal paths, and deduce the paths for the lack of attribute data to determine whether it is an illegal change; The remote sensing management module is used to obtain the NDVI index of the remote sensing image of the target area, and detect the mutation of land use types according to the change of the NDVI index of the remote sensing image of the target area; 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 trace the dynamic monitoring of natural resource data using the three-dimensional traceability database.

[0089] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for dynamically monitoring and tracing natural resource data, characterized in that, Including the following steps: Standardize the access of natural resource data; Perform attribute integrity verification and logical consistency verification on the data, and perform missing data filling, error correction, and marked elimination processing on abnormal data; Establish a multi-temporal database. In the multi-temporal database, establish a dynamic time partition index at the three-level granularity, automatically write real-time data into the corresponding partition according to the time stamp, and incrementally update the workflow; Introduce data confidence weights into the SQL filtering statement for the attribute traceability logic, and use weighted logic to judge the legality of the data; Construct a land type evolution state transition matrix, determine legal change paths and illegal paths, and perform path deduction on missing attribute data to determine whether it is an illegal change; Obtain the NDVI index of the remote sensing image of the target area, detect the mutation of land use types 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 the attribute data and spatial relationship; Use this three-dimensional traceability database to dynamically monitor and trace natural resource data.

2. The dynamic monitoring and traceability method for natural resource data according to claim 1, characterized in that, The attribute integrity verification of the data includes: constructing a mandatory verification rule library to stipulate the standards that the key attributes in the data must meet; checking each feature class; identifying and marking the missing or incorrect fields; the logical consistency verification of the data includes: using topological verification to verify the spatial relationship of the land type patches in the data; according to the attribute association rules, check whether the approval time corresponding to the approval document number is later than the previous land type change time through SQL statements; for data with time attributes, check whether its time sequence is reasonable; check whether the association relationship between different attributes is reasonable.

3. A method for dynamically monitoring and tracing natural resource data according to claim 1, characterized in that, Establish a multi-temporal database, specifically including: Set year partitions. Based on the Gregorian year, classify natural resource data by year. Each year partition stores all natural resource data within that year. Set quarter partitions. Within each year partition, further subdivide into 4 secondary partitions according to natural quarters, and establish a spatial index for each quarter partition. For newly added real-time remote sensing monitoring patches within a quarter, the overlapping area can be quickly located through the spatial index. Set week partitions. Using the natural week as the smallest time partition unit, the week partitions are used to store real-time dynamic data.

4. A method for dynamically monitoring and tracing natural resource data according to claim 1, characterized in that, The incremental update workflow includes: for newly added data, for the data of the current day, match it to the week partition through the time stamp, and update the week partition index, quarter partition index, and year partition index in sequence when the data is written; for changed data, automatically generate a historical version snapshot, record the status before and after the change in detail in the week partition, and at the same time mark the change time point in the quarter and year partitions, and retain several historical versions for each patch to present the spatio-temporal evolution trajectory of the land type.

5. A method for dynamically monitoring and tracing natural resource data according to claim 1, characterized in that, Introduce data confidence weights into the SQL filtering statement for attribute traceability logic, and use weighted logic to judge data legality, specifically including: formulating differential weight standards for key attributes affecting the judgment of land use legality: setting corresponding attribute weights according to land type codes, ownership information, and whether there are conflicts between land type codes and remote sensing image features; then further correcting the weights according to the data source. For each data record, automatically calculate the confidence weights of each core attribute, calculate the comprehensive confidence score of the data, and in the attribute integrity verification link, recalculate the comprehensive score according to the verification; or review and adjust the corresponding attribute weights; incorporate the confidence weights into the SQL filtering logic, and use the dual judgment of threshold and logical combination to judge data legality. For the land corresponding to the data whose comprehensive confidence score meets the threshold and the core attribute weights meet the threshold, it is initially determined as legal land, and for the land corresponding to the data with logical contradictions, it is directly marked as illegal land.

6. A method for dynamically monitoring and tracing natural resource data according to claim 1, characterized in that Constructing the land type evolution state transition matrix includes: classifying land types into first-level classes and second-level classes as the basic state nodes of the matrix; the matrix content includes the initial land type state, the intermediate change process, and the final state, recording the complete evolution trajectory of each plot from history to the present.

7. A method for dynamically monitoring and tracing natural resource data according to claim 1, characterized in that, Deducing the path of missing attribute data to determine whether it is an illegal change includes: Based on the land type states of adjacent years, search for all possible evolution paths in the land type evolution state transition matrix; Give priority to matching paths with historical approval records and similar plot evolution cases. If no match can be found, judge according to the change characteristics of remote sensing images.

8. A method for dynamically monitoring and tracing natural resource data according to claim 1, characterized in that Obtain the NDVI index of the remote sensing image of the target land, and detect the mutation of land use types according to the change of the NDVI index of the remote sensing image of the target land, including: extracting the data of the red light band and the near-infrared band from the remote sensing image, and calculating and generating the NDVI index thematic image; establishing an NDVI index time series for each monitoring plot, arranging the NDVI values of 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 Smooth the NDVI index time series, and calculate the change slope of the NDVI index within each sliding window; if the absolute value of the slope exceeds the set threshold and the duration meets the threshold, it is determined as a land use type mutation, and a mutation warning message is generated; for the detected mutation area, retrieve the high-resolution image, land use status data, and land approval documents of the same period, and wait for manual review.

9. A method for dynamically monitoring and tracing natural resource data according to claim 1, characterized in that, Form a three-dimensional traceability database of attribute data, spatial evidence, and business documents based on attribute data and spatial relationships, including a three-level time partition index based on a multi-temporal database, and establish a composite index of "time - space - file"; establish the association logic of attribute data, spatial evidence, and business documents, and configure the query of any dimension data to trigger the linked display of other dimensions.

10. A method for dynamically monitoring and tracing natural resource data according to claim 1, characterized in that, Dynamically monitor and trace natural resource data, including: Scan the attribute data in the database on a weekly basis to monitor changes in land type codes, modifications to ownership information, and changes in the invalidation of approval document numbers; Based on the spatial evidence of remote sensing images, identify changes in plot boundaries and spatial features of newly added or demolished buildings; Monitor the business document library in real time. When new approval documents are uploaded or old documents are updated, automatically associate the attributes and spatial data of the corresponding plots; And Conduct multi-dimensional cross-traceability and perform multi-dimensional cross-discrimination during the traceability process. The multi-dimensional cross-discrimination during the traceability process at least includes: If the traceability starts from the current attributes of the target plot, judge the legality of each change based on the confidence weight, and then automatically trigger: use spatial features to verify the authenticity of the attribute change, and then automatically trigger: through indexing, retrieve the associated business documents and verify the consistency of the document content with the attributes and / or spatial data; If the traceability starts from spatial features, verify the authenticity of the attribute change, and then automatically trigger: from the current attributes of the target plot, judge the legality of each change based on the confidence weight, and then automatically trigger: through indexing, retrieve the associated business documents and verify the consistency of the document content with the attributes and / or spatial data; If the traceability starts from business documents, through indexing, retrieve the associated business documents and verify the consistency of the document content with the attributes and / or spatial data, and then automatically trigger: use spatial features to verify the authenticity of the attribute change, and then automatically trigger: from the current attributes of the target plot, judge the legality of each change based on the confidence weight.

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