Authorization Chain-Based Data Governance and Application System and Method for Construction Land Approval, Supply and Use

Abstract

This invention discloses a data governance and application system and method for construction land approval, supply, and use based on an authorization chain, belonging to the field of construction land data management technology. This invention uses electronic supervision numbers to link approval data, supply data, and ownership change registration data to generate a data chain; it performs time-series and integrity checks on the data chain; analyzes abnormal features in the data chain and analyzes comprehensive problem indicators based on these abnormal features; it corrects the data in the problematic time-series chain; it analyzes the matching of approved and supplied land parcels and generates preliminary matching pairs based on the matching results; it analyzes the confidence index of each preliminary matching pair; it filters the preliminary matching pairs based on the confidence index; it classifies land parcels into idle risk warning levels based on a land idleness sensitivity index; and it divides land parcels into spatial clustering patterns using a local Moran's index, sending the division results to the administrator. This improves data reliability and reduces the risk of misjudgment.

Description

Technical Field

[0001] This invention relates to the field of construction land data management technology, specifically to a construction land approval, supply and use data governance and application system and method based on authorization chain. Background Technology

[0002] In the rapid process of urbanization, the approval and supervision of construction land is particularly important, as rational and efficient land management directly affects the quality and efficiency of urban development. However, due to historical reasons and imperfect information systems, the authenticity, accuracy, and completeness of construction land approval data are somewhat problematic.

[0003] Current methods are mostly limited to simple logical associations based on administrative document numbers or electronic supervision numbers, neglecting the actual correspondence between approved and supplied land parcels in terms of spatial location, time series, and geometric shape. This makes it impossible to accurately identify and handle complex scenarios such as one approval document corresponding to multiple supplied land parcels or multiple approval documents merging to supply one land parcel, resulting in discrepancies between subsequent analyses of "approved but not supplied" and "supplied but not used" and the actual situation. In addition, traditional analysis models often remain at the level of statistics and listing of single land parcels, failing to take a macro perspective of spatial agglomeration to identify regional risk distribution patterns and anomaly patterns in efficiency.

[0004] Therefore, this invention discloses a system and method for the governance and application of construction land approval, supply and use data based on authorization chain to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a system and method for the governance and application of construction land approval and supply data based on authorization chain, so as to solve the problems raised in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for the governance and application of construction land approval, supply, and use data based on an authorization chain, the method comprising the following steps:

[0007] S1: Collect land approval data, land supply data, and ownership change registration data; use the electronic supervision number to link the land approval data, land supply data, and ownership change registration data to generate a data chain; perform time sequence and integrity verification on the data chain;

[0008] S2: Analyze the abnormal features in the data chain, and analyze the comprehensive problem indicators based on the abnormal features; perform data correction on the problem time sequence chain according to the sorting order of the problem time sequence chain in the data chain;

[0009] S3: Analyze the matching of approved land parcels and supplied land parcels, generate preliminary matching pairs based on the matching results; analyze the confidence index of each preliminary matching pair; and filter the preliminary matching pairs based on the confidence index.

[0010] S4: Analyze and combine the time efficiency value, spatial efficiency value and ownership anomaly index of the land supply and supply relationship to calculate the land idling sensitivity index; classify the land parcels into idling risk warning levels based on the land idling sensitivity index; combine the local Moran index to classify the land parcels into spatial clustering patterns, and send the classification results to the administrator.

[0011] According to the above scheme, S1 includes the following:

[0012] S101: Collect land approval data, land supply data, and ownership change registration data; the land approval data includes the approval document number, electronic supervision number, approval time, approved area, land user, project name, and corresponding digital signature and timestamp; the land supply data includes the electronic supervision number, land supply time, land supply location, land supply area, land supply purpose, contract information, and corresponding digital signature and timestamp; the ownership change registration data includes the real estate unit number, registration time, right holder, right type, real estate area, electronic supervision number or contract information associated with the land supply data, and corresponding digital signature and timestamp; through the electronic supervision number, link the land approval data, land supply data, and ownership change registration data to generate a data chain;

[0013] S102: Extract the approval time of land allocation data, the land supply time of land supply data, and the registration time of ownership change registration data; record the data chain that satisfies the condition that the approval time of land allocation data is less than or equal to the land supply time of land supply data, and the land supply time of land supply data is less than or equal to the registration time of ownership change registration data, as a normal time-series chain; record other data chains as abnormal time-series chains; transform all spatial geometric coordinates in the data chain to a unified coordinate system through coordinate transformation and datum transformation algorithms;

[0014] S103: Analyze the completeness index of each field in the land approval data, land supply data, and ownership change registration data; the completeness index is equal to the ratio of the actual field length to the corresponding preset standard field length; analyze the completeness index of the data chain based on the completeness index of each field by weighted summation; and complete the missing data for data chains whose completeness index is less than the completeness index threshold.

[0015] For text or coded fields, optical character recognition technology is used to scan and extract information from associated electronic or paper documents to fill in missing fields; for spatial geometric fields, spatial interpolation technology is used to fit boundaries based on adjacent land parcel boundaries.

[0016] This invention defines a "normal time-series chain" and, based on the time logic corresponding to approval, land supply, and registration, achieves automated, batch-based initial screening of data chain quality, quickly identifying data with fundamental logical errors. Through coordinate unification processing, it solves the underlying technical obstacle of analyzing spatial data from different sources using a single map. By employing OCR and spatial interpolation techniques for missing data completion, it significantly improves data integrity and usability, reducing the workload of subsequent manual intervention.

[0017] According to the above scheme, S2 includes the following:

[0018] S201: Identify plot pairs with overlapping boundaries in the normal time-series chain using a spatial overlay method; mark these plot pairs with overlapping boundaries with a topological conflict identifier and assign a topological conflict level based on the overlapping area; analyze the area difference rate of each plot in the normal time-series chain, where the area difference rate is equal to the ratio of the absolute value of the difference between the supplied land area and the approved land area to the approved land area; analyze the coordinate offset value of each plot in the normal time-series chain, where the coordinate offset value is equal to the distance between the geometric center of the supplied land area and the geometric center of the approved land area; detect time anomalies according to a preset time rule base and assign a time anomaly Boolean value; the preset time rule base includes, but is not limited to, policy release time conflicts;

[0019] S202: Analyze the normal range of data corresponding to area difference rate and coordinate offset respectively; the upper limit of the normal range is equal to the mean plus the sensitivity interpolation; the lower limit of the normal range is equal to the mean minus the sensitivity interpolation; the sensitivity interpolation is equal to the product of the standard deviation and the sensitivity coefficient; mark the normal time series chains where the area difference rate or coordinate offset exceeds the normal range as problem time series chains; mark the normal time series chains with topological conflict identifiers and time anomaly Boolean values ​​of one as problem time series chains; normalize the coordinate offset values; generate a comprehensive problem index by weighted summation of topological conflict level, area difference rate, coordinate offset value, and time anomaly Boolean value; sort the problem time series chains according to the size of the comprehensive problem index;

[0020] S203: Based on the sorting order of the problem time-series chain, perform data correction on the problem time-series chain; extract the reference coordinates of the problem plots corresponding to the problem time-series chain, and perform spatial registration and visualization comparison with the extracted reference coordinates and remote sensing images; for problem plots with coordinate offsets or topological conflicts, use linear features on the remote sensing images as real-world references to perform spatial registration on the boundaries of the problem plots to generate a preliminary corrected version; the linear features include, but are not limited to, road red lines, permanent walls, and river boundaries; use UAVs to acquire 3D point cloud data and real-world 3D models of the problem plots, and analyze the planar position accuracy and elevation accuracy by overlaying and comparing the plot boundaries in the preliminary corrected version with the real-world 3D model; for plot boundaries that still have deviations, perform spatial registration on the plot boundaries in the preliminary corrected version based on the linear features on the real-world 3D model to generate a final corrected version.

[0021] This invention constructs a multi-dimensional diagnostic system encompassing topology, area, coordinates, and time, and quantifies, classifies, and integrates problems, providing a clear priority order for correction work. It employs dynamic data normal ranges, enabling problem identification standards to adapt to data characteristics in different regions and time periods, making it more scientific and flexible than fixed thresholds. It establishes a progressive correction process of "archival benchmark → preliminary correction of remote sensing images → fine correction of UAV-based 3D models." It utilizes various advanced technologies to improve the accuracy and reliability of data correction, ensuring consistency between the corrected data and the real world.

[0022] According to the above scheme, S3 includes the following:

[0023] S301: Allocate a time matching window for each land parcel data. The upper limit of the time matching window is equal to the approval time plus a preset time tolerance, and the lower limit of the time matching window is equal to the approval time minus the preset time tolerance. Select land parcels whose supply time falls within the time matching window of the land parcel and which are spatially intersecting or adjacent, forming a candidate matching set. For each land parcel and a candidate land parcel pair in the candidate matching set, analyze the land supply matching index (MPI). The specific calculation formula is as follows:

[0024] MPI=(I （a，b） / min(A a A b ))×[1-|T a -T b | / T max ]×C×S shape ;

[0025] Among them, I （a，b） A represents the spatial intersection area of ​​the allocated land parcel a and the candidate land parcel b; a and A bT represents the area of ​​the allocated land parcel a and the candidate land parcel b, respectively; a Indicates the approval time of land parcel a; T b Indicates the land supply time for candidate land parcel b; T max Indicates the preset time tolerance; C represents the ownership consistency coefficient, which is equal to the similarity between the name of the land parcel project and the name of the land transferee; S shape S represents the shape similarity factor; shape =1-|P a -P b | / max(P a P b ); where P a P represents the shape index of plot a in the land allocation; b The shape index represents candidate land parcel b; the shape index is equal to the perimeter-to-area ratio.

[0026] Iterate through all approved land parcels and their corresponding candidate land parcels, and calculate the approval-supply matching index. For each approved land parcel, retain the highest-ranking land parcels with an approval-supply matching index above a preset threshold as preliminary matching pairs, and generate a preliminary matching pair list.

[0027] S302: Integrate all approved land parcels and all supplied land parcels into a node set; establish directed edges between approved land parcel nodes and supplied land parcel nodes that have matching relationships in the preliminary matching pair list, with the direction from the approved land parcel node to the supplied land parcel node; the weight of the edge is the approval-supply matching degree index of the matching pair; construct an initial approval-supply relationship network graph based on the node set and the edge set.

[0028] The confidence index between two nodes is obtained by weighting and summing the matching index, shape similarity factor, time consistency score, and data integrity score; the time consistency score is equal to the percentile of the time difference between the land supply time and the approval time of the matching pair in the historical statistical distribution; the data integrity score is the percentage of data integrity of the matching pair.

[0029] Traverse all edges in the initial relationship graph and remove matching pairs with confidence indices below the confidence index threshold from the initial batch supply relationship network graph to generate the batch supply relationship network graph.

[0030] This invention elevates the analytical perspective from isolated matching pairs to a global relationship network by constructing a batch-supply relationship network diagram. This helps to understand the complex supply patterns between batch-supply and batch-supply land parcels, including phased supply and consolidated supply. Through confidence level screening, low-quality and unreliable matching relationships are effectively filtered out, ensuring the accuracy and credibility of the relationship data on which subsequent analysis depends and reducing the risk of misjudgment.

[0031] According to the above scheme, S4 includes the following:

[0032] S401: Based on the supply and approval relationship pairs in the supply and approval relationship network diagram, obtain the supply time and approval time, calculate the time difference between the two, subtract the standard approval cycle from the time difference to obtain the actual time delay; divide the actual time delay by the standard time threshold to obtain the standardized delay ratio; analyze the time efficiency value based on the standardized delay ratio; the time efficiency value is equal to the maximum value of the difference between one and the standardized delay ratio and zero.

[0033] Obtain the actual utilized area corresponding to the land supply area, and record the ratio of the actual utilized area to the total land supply area as the spatial efficiency value; count the number of land parcels with abnormal ownership status, and analyze the ratio of the number of land parcels with abnormal ownership status to the total number of land parcels supplied as the ownership abnormality index.

[0034] S402: Generate a land idling sensitivity index by weighted summation based on time efficiency value, spatial efficiency value, and ownership anomaly index; classify land parcels into idling risk warning levels based on the land idling sensitivity index; analyze the local Moran index of each land parcel based on the land idling sensitivity index; divide the land parcels into spatial clustering patterns based on the significance test of the local Moran index, and send the division results to the administrator.

[0035] The spatial clustering pattern includes high-high anomaly zones, low-low anomaly zones, high-low anomaly zones, and low-high anomaly zones. A high-high anomaly zone indicates a high-risk plot surrounded by other high-risk plots and is designated as a key risk control area. A low-low anomaly zone indicates a low-risk plot surrounded by other low-risk plots and is designated as an efficiency benchmark zone. A high-low anomaly zone indicates a high-risk plot surrounded by low-risk plots and is designated as a key case handling area. A low-high anomaly zone indicates a low-risk plot surrounded by high-risk plots and is designated as a risk diffusion early warning zone.

[0036] This invention uses a land idling sensitivity index to classify risks, helping managers quickly focus on high-risk plots and optimize resource allocation. By introducing a local Moran's index for spatial clustering analysis, it not only identifies individual high-risk plots but also reveals the spatial aggregation, diffusion, and anomaly patterns of risk. This guides managers from "plot-based governance" to "regional governance," enabling the development of more targeted, area-specific control strategies. For example, "high-high anomaly zones" can be prioritized for remediation, while "low-high anomaly zones" can be protected from risk spread.

[0037] Another aspect of this application provides a data governance and application system for construction land approval and supply based on an authorization chain. The system is applied to the above-mentioned data governance and application method for construction land approval and supply based on an authorization chain. The system includes a data acquisition and verification module, a data correction module, a matching and filtering module, and an early warning and classification module.

[0038] The data acquisition and verification module is used to collect land approval data, land supply data, and ownership change registration data; it associates the land approval data, land supply data, and ownership change registration data through the electronic supervision number to generate a data chain; and it performs time sequence and integrity verification on the data chain.

[0039] The data correction module is used to analyze abnormal features in the data chain, analyze comprehensive problem indicators based on abnormal features, and perform data correction on the problem time sequence chain according to the sorting order of the problem time sequence chain in the data chain.

[0040] The matching and filtering module is used to analyze the matching situation of land parcels approved for allocation and land parcels supplied for allocation, generate preliminary matching pairs based on the matching situation, analyze the confidence index of each preliminary matching pair, and filter the preliminary matching pairs based on the confidence index.

[0041] The early warning classification module is used to analyze and combine the time efficiency value, spatial efficiency value and ownership anomaly index of the supply and demand relationship to calculate the land idling sensitivity index; classify the land plots into idling risk early warning levels based on the land idling sensitivity index; and classify the land plots into spatial clustering patterns by combining the local Moran index, and send the classification results to the administrator.

[0042] According to the above scheme, the data acquisition and verification module includes a data acquisition unit and a data verification and completion unit;

[0043] The data acquisition unit is used to collect land approval data, land supply data, and ownership change registration data; and to link the land approval data, land supply data, and ownership change registration data through the electronic supervision number to generate a data chain;

[0044] The data verification and completion unit is used to split the normal time-series chain and the abnormal time-series chain according to the approval time of the land approval data, the land supply time of the land supply data, and the registration time of the ownership change registration data; analyze the completeness index of each field in the land approval data, land supply data, and ownership change registration data, and complete the missing data.

[0045] According to the above scheme, the data correction module includes an anomaly analysis unit, a comprehensive evaluation unit, and an anomaly correction unit;

[0046] The anomaly analysis unit is used to identify plot pairs with overlapping boundaries in the normal time series chain by using a spatial overlay method, analyze the area difference rate of each plot in the normal time series chain, analyze the coordinate offset value of each plot in the normal time series chain, detect time anomalies according to a preset time rule library, and assign time anomaly boolean values.

[0047] The comprehensive evaluation unit is used to analyze the normal range of data corresponding to area difference rate and coordinate offset, respectively; it marks the problem time sequence chain based on area difference rate, coordinate offset and topology conflict identifier; it generates a comprehensive problem index by weighted summation of topology conflict level, area difference rate, coordinate offset value and time anomaly Boolean value; and it sorts the problem time sequence chain according to the size of the comprehensive problem index.

[0048] The anomaly correction unit is used to extract the reference coordinates of the problem plots corresponding to the problem time-series chain, and to perform spatial registration and visualization comparison with the remote sensing image. For problem plots with coordinate offsets or topological conflicts, the linear features on the remote sensing image are used as real-world references to spatially register the boundaries of the problem plots to generate a preliminary correction version. UAVs are used to acquire 3D point cloud data and real-world 3D models of the problem plots. By overlaying and comparing the plot boundaries in the preliminary correction version with the real-world 3D model, the planar position accuracy and elevation accuracy are analyzed. For plot boundaries that still have deviations, the plot boundaries in the preliminary correction version are spatially registered with the linear features on the real-world 3D model to generate a final correction version.

[0049] According to the above scheme, the matching and filtering module includes a spatiotemporal matching unit and a confidence filtering unit;

[0050] The spatiotemporal matching unit is used to allocate a time matching window to each batch of land data, and filter the land supply plots to form a candidate matching set; for each batch of land plot and the candidate land supply plot pair in the candidate matching set, the batch-supply matching degree index is analyzed. For each batch of land plot, the batch-supply matching degree index of the highest and above the preset threshold is retained as a preliminary matching pair, and a preliminary matching pair list is generated.

[0051] The confidence filtering unit is used to construct an initial approval-supply relationship network graph based on the set of nodes and edges composed of approved land parcels and supplied land parcels; analyze the confidence index between nodes; and remove matching pairs with confidence indices lower than the confidence index threshold from the initial approval-supply relationship network graph to generate an approval-supply relationship network graph.

[0052] According to the above scheme, the early warning division module includes an idle feature analysis unit and a region division unit;

[0053] The idle feature analysis unit is used to analyze the standardized delay ratio, spatial efficiency value and ownership anomaly index of batch supply relationship pairs in the batch supply relationship network diagram.

[0054] The regional division unit generates a land idling sensitivity index by weighted summation based on time efficiency value, spatial efficiency value, and ownership anomaly index; the land parcels are classified into idling risk warning levels based on the land idling sensitivity index; the local Moran index of each land parcel is analyzed based on the land idling sensitivity index; the spatial clustering pattern of the land parcels is divided based on the significance test of the local Moran index, and the division results are sent to the administrator.

[0055] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention, by defining a "normal time-series chain," achieves automated, batch-based initial screening of data chain quality based on the time logic corresponding to approval, land supply, and registration, quickly identifying data with fundamental logical errors; through coordinate unification processing, it solves the underlying technical obstacle of analyzing spatial data from different sources using a single map; by employing OCR and spatial interpolation techniques for missing data completion, it significantly improves data integrity and usability, reducing the workload of subsequent manual intervention; by constructing a multi-dimensional diagnostic system covering topology, area, coordinates, and time, and by quantifying, classifying, and integrating problems, this invention provides a clear priority ranking for correction work; and by adopting dynamic... The normal range of data allows the problem identification standard to adapt to the data characteristics of different regions and periods, making it more scientific and flexible than fixed thresholds; it forms a progressive correction process; it utilizes various advanced technologies to improve the accuracy and reliability of data correction, ensuring the consistency between the remediated data and the real world; by constructing a supply-supplier relationship network diagram, this invention elevates the analytical perspective from isolated matching pairs to a global relationship network, helping to understand the complex supply patterns between approved and supplied land parcels, including batch supply and merged supply; through confidence screening, it effectively filters out low-quality and unreliable matching relationships, ensuring the accuracy and credibility of the relationship data relied upon for subsequent analysis and reducing the risk of misjudgment. This invention uses a land idling sensitivity index for risk classification, helping managers quickly focus on high-risk parcels and achieve optimal resource allocation. The introduction of the local Moran index for spatial clustering analysis not only identifies individual high-risk parcels but also discovers the spatial aggregation, diffusion, and abnormal patterns of risk, thereby guiding managers from "parcel governance" to "regional governance" and formulating more targeted regional control strategies. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0057] Figure 1 This is a flowchart illustrating the data governance and application method for construction land approval and supply based on the authorization chain of the present invention.

[0058] Figure 2This is a schematic diagram of the structure of the construction land approval, supply and use data governance and application system based on the authorization chain of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figure 1 This invention provides a technical solution: a method for the governance and application of construction land approval, supply, and use data based on an authorization chain, the method comprising the following steps:

[0061] S1: Collect land approval data, land supply data, and ownership change registration data; use the electronic supervision number to link the land approval data, land supply data, and ownership change registration data to generate a data chain; perform time sequence and integrity verification on the data chain;

[0062] S1 includes the following:

[0063] S101: Collect land approval data, land supply data, and ownership change registration data; land approval data includes approval document number, electronic supervision number, approval time, approved area, land user, project name, and corresponding digital signature and timestamp; land supply data includes electronic supervision number, land supply time, land supply location, land supply area, land supply purpose, contract information, and corresponding digital signature and timestamp; ownership change registration data includes real estate unit number, registration time, right holder, right type, real estate area, electronic supervision number or contract information associated with land supply data, and corresponding digital signature and timestamp; through the electronic supervision number, link the land approval data, land supply data, and ownership change registration data to generate a data chain;

[0064] Land approval data 1 includes approval document number PWFH001, electronic supervision number JGH001, approval time 20250101, land use name: a certain plot of land in a certain city, etc.

[0065] Land supply data 2 includes data such as electronic supervision number JGH001, land supply time 20250201, and the location of the land supply in a certain city and street.

[0066] The ownership change registration data 3 includes the real estate unit number BDCDYH001, the registration time 20250301, and the electronic supervision number JGH001;

[0067] The electronic supervision number of land approval data 1, land supply data 2, and ownership change registration data 3 is JGH001; land approval data 1, land supply data 2, and ownership change registration data 3 are linked and merged to generate a data chain: land approval data 1 - land supply data 2 - ownership change registration data 3;

[0068] S102: Extract the approval time of land allocation data, the land supply time of land supply data, and the registration time of ownership change registration data; record the data chain that satisfies the condition that the approval time of land allocation data is less than or equal to the land supply time of land supply data, and the land supply time of land supply data is less than or equal to the registration time of ownership change registration data, as a normal time-series chain; record other data chains as abnormal time-series chains; transform all spatial geometric coordinates in the data chain to a unified coordinate system through coordinate transformation and datum transformation algorithms;

[0069] S103: Analyze the completeness index of each field in land approval data, land supply data, and ownership change registration data; the completeness index is equal to the ratio of the actual field length to the corresponding preset standard field length; analyze the completeness index of the data chain based on the completeness index of each field by weighted summation; complete the missing data for data chains with completeness index less than the completeness index threshold; for text or coded fields, use optical character recognition technology to scan, identify, and extract information from associated electronic or paper archives to fill in the missing fields; for spatial geometric fields, use spatial interpolation technology to fit the boundary based on the boundary of adjacent land parcels.

[0070] S2: Analyze the abnormal features in the data chain, and analyze the comprehensive problem indicators based on the abnormal features; perform data correction on the problem time sequence chain according to the sorting order of the problem time sequence chain in the data chain;

[0071] S2 includes the following:

[0072] S201: Identify plot pairs with overlapping boundaries in the normal time-series chain using a spatial overlay method; mark these plot pairs with overlapping boundaries with a topological conflict identifier and assign a topological conflict level based on the overlapping area; analyze the area difference rate of each plot in the normal time-series chain, where the area difference rate is equal to the ratio of the absolute value of the difference between the supplied land area and the approved land area to the approved land area; analyze the coordinate offset value of each plot in the normal time-series chain, where the coordinate offset value is equal to the distance between the geometric center of the supplied land area and the geometric center of the approved land area; detect time anomalies according to a preset time rule library and assign a time anomaly Boolean value; the preset time rule library includes, but is not limited to, policy release time conflicts;

[0073] A city issues an environmental protection policy at time point A, prohibiting the approval of high-pollution projects in a specific area. If project B is located in the specific area mentioned in the environmental protection policy and its approval time is after time point A, assign a time anomaly Boolean value of 1 to project A.

[0074] S202: Analyze the normal range of data corresponding to area difference rate and coordinate offset respectively; the upper limit of the normal range is equal to the mean plus the sensitivity interpolation; the lower limit of the normal range is equal to the mean minus the sensitivity interpolation; the sensitivity interpolation is equal to the product of the standard deviation and the sensitivity coefficient; mark the normal time series chains where the area difference rate or coordinate offset exceeds the normal range as problem time series chains; mark the normal time series chains with topological conflict indicators and time anomaly Boolean values ​​of one as problem time series chains; normalize the coordinate offset values; generate a comprehensive problem index by weighted summation of topological conflict level, area difference rate, coordinate offset value, and time anomaly Boolean value; sort the problem time series chains according to the size of the comprehensive problem index;

[0075] S203: Based on the sorting order of the problem time-series chain, perform data correction on the problem time-series chain; extract the reference coordinates of the problem plots corresponding to the problem time-series chain, and perform spatial registration and visualization comparison with the extracted reference coordinates and remote sensing images; for problem plots with coordinate offsets or topological conflicts, use linear features on the remote sensing images as real-world references to perform spatial registration on the boundaries of the problem plots to generate a preliminary corrected version; linear features include, but are not limited to, road red lines, permanent walls, and river boundaries; use UAVs to acquire 3D point cloud data and real-world 3D models of the problem plots, and analyze the planar position accuracy and elevation accuracy by overlaying and comparing the plot boundaries in the preliminary corrected version with the real-world 3D model; for plot boundaries that still have deviations, perform spatial registration on the plot boundaries in the preliminary corrected version based on the linear features on the real-world 3D model to generate a final corrected version.

[0076] S3: Analyze the matching of approved land parcels and supplied land parcels, generate preliminary matching pairs based on the matching results; analyze the confidence index of each preliminary matching pair; and filter the preliminary matching pairs based on the confidence index.

[0077] S3 includes the following:

[0078] S301: Allocate a time matching window for each land parcel's data. The upper limit of the time matching window equals the approval time plus a preset time tolerance, and the lower limit equals the approval time minus the preset time tolerance. Select land parcels whose supply time falls within the time matching window and which are spatially intersecting or adjacent, forming a candidate matching set. For each land parcel and its pair with candidate land parcels in the candidate matching set, analyze the land supply matching index (MPI). The specific calculation formula is as follows:

[0079] MPI=(I （a，b） / min(A a A b ))×[1-|T a -T b | / T max ]×C×S shape ;

[0080] Among them, I （a，b） A represents the spatial intersection area of ​​the allocated land parcel a and the candidate land parcel b; a and A b T represents the area of ​​the allocated land parcel a and the candidate land parcel b, respectively; a Indicates the approval time of land parcel a; T b Indicates the land supply time for candidate land parcel b; T max Indicates the preset time tolerance; C represents the ownership consistency coefficient, which is equal to the similarity between the name of the land parcel project and the name of the land transferee; S shape S represents the shape similarity factor; shape =1-|P a -P b | / max(P a P b ); where P a P represents the shape index of plot a in the land allocation; b This represents the shape index of candidate land parcel b; the shape index is equal to the perimeter-to-area ratio.

[0081] Iterate through all approved land parcels and their corresponding candidate land parcels, and calculate the approval-supply matching index. For each approved land parcel, retain the highest-ranking land parcels with an approval-supply matching index above a preset threshold as preliminary matching pairs, and generate a preliminary matching pair list.

[0082] S302: Integrate all approved land parcels and all supplied land parcels into a node set; establish directed edges between approved land parcel nodes and supplied land parcel nodes that have matching relationships in the preliminary matching pair list, with the direction from the approved land parcel node to the supplied land parcel node; the weight of the edge is the approval-supply matching degree index of the matching pair; construct an initial approval-supply relationship network graph based on the node set and the edge set.

[0083] The confidence index between two nodes is calculated by weighting and summing the matching index, shape similarity factor, time consistency score, and data integrity score. The time consistency score is equal to the percentile of the time difference between the land supply time and the approval time of the matching pair in the historical statistical distribution. The data integrity score is the percentage of data integrity of the matching pair.

[0084] Traverse all edges in the initial relationship graph and remove matching pairs with confidence indices below the confidence index threshold from the initial batch supply relationship network graph to generate the batch supply relationship network graph.

[0085] S4: Analyze and combine the time efficiency value, spatial efficiency value and ownership anomaly index of the land supply and supply relationship to calculate the land idling sensitivity index; classify the land parcels into idling risk warning levels based on the land idling sensitivity index; combine the local Moran index to classify the land parcels into spatial clustering patterns, and send the classification results to the administrator.

[0086] S4 includes the following:

[0087] S401: Based on the supply and approval relationship pairs in the supply and approval relationship network diagram, obtain the supply time and approval time, calculate the time difference between the two, subtract the standard approval cycle from the time difference to obtain the actual time delay; divide the actual time delay by the standard time threshold to obtain the standardized delay ratio; analyze the time efficiency value based on the standardized delay ratio; the time efficiency value is equal to the maximum value of the difference between one and the standardized delay ratio and zero.

[0088] Obtain the actual utilized area corresponding to the land supply area, and record the ratio of the actual utilized area to the total land supply area as the spatial efficiency value; count the number of land parcels with abnormal ownership status, and analyze the ratio of the number of land parcels with abnormal ownership status to the total number of land parcels supplied as the ownership abnormality index.

[0089] S402: Generate a land idling sensitivity index by weighted summation based on time efficiency value, spatial efficiency value, and ownership anomaly indicators; classify land parcels into idling risk warning levels based on the land idling sensitivity index; analyze the local Moran index of each land parcel based on the land idling sensitivity index; divide the land parcels into spatial clustering patterns based on the significance test of the local Moran index, and send the division results to the administrator; the spatial clustering patterns include high-high anomaly zones, low-low anomaly zones, high-low anomaly zones, and low-high anomaly zones; high-high anomaly zones indicate that high-risk land parcels are surrounded by other high-risk land parcels and are marked as key risk prevention and control zones; low-low anomaly zones indicate that low-risk land parcels are surrounded by other low-risk land parcels and are marked as efficiency benchmark zones; high-low anomaly zones indicate that high-risk land parcels are surrounded by low-risk land parcels and are marked as key case handling zones; low-high anomaly zones indicate that low-risk land parcels are surrounded by high-risk land parcels and are marked as risk diffusion warning zones.

[0090] Example 1: In this example, there are plots A, B, C, and D. Plot A has a land idling sensitivity index of 0.85; its neighboring plots are plots B and D. Plot B has a land idling sensitivity index of 0.82, and its neighboring plots are plots A and D. Plot C has a land idling sensitivity index of 0.15 and is not adjacent to plots A, B, and D. Plot D has a land idling sensitivity index of 0.80, and its neighboring plots are plots A and B.

[0091] Therefore, the average land idling sensitivity index is (0.85+0.82+0.15+0.80) / 4=0.655;

[0092] Variance of Land Idle Sensitivity Index = [(0.85-0.655)² + (0.82-0.655)² + (0.15-0.655)² + (0.80-0.655)²] / 4 = (0.038+0.027+0.255+0.021) / 4 = 0.08525;

[0093] Binary adjacency weights are used, with adjacent elements being 1 and non-adjacent elements being 0.

[0094] Standardize the adjacency weights of A's neighboring plots; A's neighbors are B and D, a total of 2 neighbors; therefore, W ab =0.5, W ad =0.5; Calculated local Moran index = 0.354; Standardized Z-score of local Moran index ≈ 2.427;

[0095] In this embodiment, the critical value is ±1.96; therefore, the local Moran's index is statistically significant in this embodiment.

[0096] Plot A has a land idling sensitivity index greater than the mean, a neighbor weighted average greater than 0, and a local Moran index greater than 0; Plot A is classified as a high-high anomaly zone.

[0097] Please see Figure 2 The present invention provides a technical solution: a construction land approval and supply data governance and application system based on an authorization chain, which includes a data acquisition and verification module, a data correction module, a matching and filtering module, and an early warning and classification module;

[0098] The data acquisition and verification module is used to collect land approval data, land supply data, and ownership change registration data; it associates the land approval data, land supply data, and ownership change registration data through the electronic supervision number to generate a data chain; and it performs time sequence and integrity verification on the data chain.

[0099] The data correction module is used to analyze abnormal features in the data chain, analyze comprehensive problem indicators based on abnormal features, and perform data correction on the problem time sequence chain according to the sorting order of the problem time sequence chain in the data chain.

[0100] The matching and filtering module is used to analyze the matching situation of land parcels approved for allocation and land parcels supplied for allocation, generate preliminary matching pairs based on the matching situation, analyze the confidence index of each preliminary matching pair, and filter the preliminary matching pairs based on the confidence index.

[0101] The early warning classification module is used to analyze and combine the time efficiency value, spatial efficiency value and ownership anomaly index of the approval and supply relationship to calculate the land idling sensitivity index; classify the land plots into idling risk early warning levels based on the land idling sensitivity index; and classify the land plots into spatial clustering patterns by combining the local Moran index, and send the classification results to the administrator.

[0102] The data acquisition and verification module includes a data acquisition unit and a data verification and completion unit;

[0103] The data acquisition unit is used to collect land approval data, land supply data, and ownership change registration data; through the electronic supervision number, the land approval data, land supply data, and ownership change registration data are linked to generate a data chain;

[0104] The data verification and completion unit is used to split the normal time-series chain and the abnormal time-series chain based on the approval time of land approval data, the land supply time of land supply data, and the registration time of ownership change registration data; analyze the completeness index of each field in the land approval data, land supply data, and ownership change registration data, and complete the missing data.

[0105] The data correction module includes an anomaly analysis unit, a comprehensive evaluation unit, and an anomaly correction unit;

[0106] The anomaly analysis unit is used to identify plot pairs with overlapping boundaries in the normal time series chain by using the spatial overlay method, analyze the area difference rate of each plot in the normal time series chain, analyze the coordinate offset value of each plot in the normal time series chain, detect time anomalies according to the preset time rule library, and assign time anomaly boolean values.

[0107] The comprehensive evaluation unit is used to analyze the normal range of data corresponding to area difference rate and coordinate offset, respectively; it marks the problem time sequence chain based on area difference rate, coordinate offset and topology conflict identifier; it generates a comprehensive problem index by weighted summation of topology conflict level, area difference rate, coordinate offset value and time anomaly Boolean value; and it sorts the problem time sequence chain according to the size of the comprehensive problem index.

[0108] The anomaly correction unit extracts the reference coordinates of the problem plots corresponding to the problem time-series chain, and performs spatial registration and visualization comparison with the extracted reference coordinates and remote sensing images. For problem plots with coordinate offsets or topological conflicts, the linear features on the remote sensing images are used as real-world references to spatially register the boundaries of the problem plots and generate a preliminary corrected version. UAVs are used to acquire 3D point cloud data and real-world 3D models of the problem plots. By overlaying and comparing the plot boundaries in the preliminary corrected version with the real-world 3D model, the planar position accuracy and elevation accuracy are analyzed. For plot boundaries that still have deviations, the plot boundaries in the preliminary corrected version are spatially registered with the linear features on the real-world 3D model to generate a final corrected version.

[0109] The matching and filtering module includes a spatiotemporal matching unit and a confidence filtering unit;

[0110] The spatiotemporal matching unit is used to allocate a time matching window to each batch of land data and filter the land supply plots to form a candidate matching set. For each batch of land plot and the candidate land supply plot pair in the candidate matching set, the batch-supply matching degree index is analyzed. For each batch of land plot, the batch-supply matching degree index of the highest and above the preset threshold is retained as a preliminary matching pair, and a preliminary matching pair list is generated.

[0111] The confidence screening unit is used to construct an initial approval-supply relationship network graph based on the set of nodes and edges composed of approved land parcels and supplied land parcels; analyze the confidence index between nodes; and remove matching pairs with confidence indices lower than the confidence index threshold from the initial approval-supply relationship network graph to generate an approval-supply relationship network graph.

[0112] The early warning classification module includes an idle feature analysis unit and a region classification unit;

[0113] The idle feature analysis unit is used to analyze the standardized delay ratio, spatial efficiency value, and ownership anomaly index of batch supply relationship pairs in the batch supply relationship network diagram.

[0114] The regional division unit generates a land idling sensitivity index by weighted summation based on time efficiency value, spatial efficiency value, and ownership anomaly index; the land parcels are classified into idling risk warning levels based on the land idling sensitivity index; the local Moran index of each land parcel is analyzed based on the land idling sensitivity index; the spatial clustering pattern of the land parcels is divided based on the significance test of the local Moran index, and the division results are sent to the administrator.

[0115] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0116] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for the governance and application of construction land approval, supply, and use data based on an authorization chain, characterized in that: The method includes the following steps: S1: Collect land approval data, land supply data, and ownership change registration data; use the electronic supervision number to link the land approval data, land supply data, and ownership change registration data to generate a data chain; perform time sequence and integrity verification on the data chain; S2: Analyze the abnormal features in the data chain, and analyze the comprehensive problem indicators based on the abnormal features; perform data correction on the problem time sequence chain according to the sorting order of the problem time sequence chain in the data chain; S3: Analyze the matching of approved land parcels and supplied land parcels, generate preliminary matching pairs based on the matching results; analyze the confidence index of each preliminary matching pair; and filter the preliminary matching pairs based on the confidence index. S3 includes the following: S301: Allocate a time matching window for each land parcel data. The upper limit of the time matching window is equal to the approval time plus a preset time tolerance, and the lower limit of the time matching window is equal to the approval time minus the preset time tolerance. Select land parcels whose supply time falls within the time matching window of the land parcel and which are spatially intersecting or adjacent, forming a candidate matching set. For each land parcel and a candidate land parcel pair in the candidate matching set, analyze the land supply matching index (MPI). The specific calculation formula is as follows: MPI=(I （a，b） / min(A a ,A b ))×[1-|T a -T b | / T max ]×C×S shape ; Among them, I （a，b） A represents the spatial intersection area of ​​the allocated land parcel a and the candidate land parcel b; a and A b T represents the area of ​​the allocated land parcel a and the candidate land parcel b, respectively; a Indicates the approval time of land parcel a; T b Indicates the land supply time for candidate land parcel b; T max Indicates the preset time tolerance; C represents the ownership consistency coefficient, which is equal to the similarity between the name of the land parcel project and the name of the land transferee; S shape S represents the shape similarity factor; shape =1-|P a -P b | / max(P a P b ); where P a P represents the shape index of plot a in the land allocation; b The shape index represents candidate land parcel b; the shape index is equal to the perimeter-to-area ratio. Iterate through all approved land parcels and their corresponding candidate land parcels, and calculate the approval-supply matching index. For each approved land parcel, retain the highest-ranking land parcels with an approval-supply matching index above a preset threshold as preliminary matching pairs, and generate a preliminary matching pair list. S302: Integrate all approved land parcels and all supplied land parcels into a node set; establish directed edges between approved land parcel nodes and supplied land parcel nodes that have matching relationships in the preliminary matching pair list, with the direction from the approved land parcel node to the supplied land parcel node; the weight of the edge is the approval-supply matching degree index of the matching pair; construct an initial approval-supply relationship network graph based on the node set and the edge set. The confidence index between two nodes is obtained by weighting and summing the matching index, shape similarity factor, time consistency score, and data integrity score; the time consistency score is equal to the percentile of the time difference between the land supply time and the approval time of the matching pair in the historical statistical distribution; the data integrity score is the percentage of data integrity of the matching pair. Traverse all edges in the initial relationship graph and remove matching pairs with confidence indices below the confidence index threshold from the initial batch supply relationship network graph to generate the batch supply relationship network graph; S4: Analyze and combine the time efficiency value, spatial efficiency value and ownership anomaly index of the land supply and supply relationship to calculate the land idling sensitivity index; classify the land parcels into idling risk warning levels based on the land idling sensitivity index; combine the local Moran index to classify the land parcels into spatial clustering patterns, and send the classification results to the administrator.

2. The method for governance and application of construction land approval and supply data based on authorization chain according to claim 1, characterized in that: S1 includes the following: S101: Collect land approval data, land supply data, and ownership change registration data; the land approval data includes the approval document number, electronic supervision number, approval time, approved area, land user, project name, and corresponding digital signature and timestamp; the land supply data includes the electronic supervision number, land supply time, land supply location, land supply area, land supply purpose, contract information, and corresponding digital signature and timestamp; the ownership change registration data includes the real estate unit number, registration time, right holder, right type, real estate area, electronic supervision number or contract information associated with the land supply data, and corresponding digital signature and timestamp; through the electronic supervision number, link the land approval data, land supply data, and ownership change registration data to generate a data chain; S102: Extract the approval time of land allocation data, the land supply time of land supply data, and the registration time of ownership change registration data; record the data chain that satisfies the condition that the approval time of land allocation data is less than or equal to the land supply time of land supply data, and the land supply time of land supply data is less than or equal to the registration time of ownership change registration data, as a normal time-series chain; record other data chains as abnormal time-series chains; transform all spatial geometric coordinates in the data chain to a unified coordinate system through coordinate transformation and datum transformation algorithms; S103: Analyze the completeness index of each field in the land approval data, land supply data, and ownership change registration data; the completeness index is equal to the ratio of the actual field length to the corresponding preset standard field length; analyze the completeness index of the data chain based on the completeness index of each field by weighted summation; and complete the missing data for data chains whose completeness index is less than the completeness index threshold.

3. The method for governance and application of construction land approval and supply data based on authorization chain according to claim 2, characterized in that: S2 includes the following: S201: Identify plot pairs with overlapping boundaries in a normal temporal chain using a spatial overlay method, mark the plot pairs with overlapping boundaries with topological conflict indicators, and assign a topological conflict level based on the overlapping area. The analysis process includes: analyzing the area difference rate of each plot in the normal time-series chain, where the area difference rate is equal to the ratio of the absolute value of the difference between the supplied land area and the approved land area to the approved land area; analyzing the coordinate offset value of each plot in the normal time-series chain, where the coordinate offset value is equal to the distance between the geometric center of the supplied land area and the geometric center of the approved land area; detecting time anomalies according to a preset time rule base and assigning time anomaly Boolean values; the preset time rule base includes policy release time conflicts. S202: Analyze the normal range of data corresponding to area difference rate and coordinate offset respectively; the upper limit of the normal range is equal to the mean plus the sensitivity interpolation; the lower limit of the normal range is equal to the mean minus the sensitivity interpolation; the sensitivity interpolation is equal to the product of the standard deviation and the sensitivity coefficient; mark the normal time series chains where the area difference rate or coordinate offset exceeds the normal range as problem time series chains; mark the normal time series chains with topological conflict identifiers and time anomaly Boolean values ​​of one as problem time series chains; normalize the coordinate offset values; generate a comprehensive problem index by weighted summation of topological conflict level, area difference rate, coordinate offset value, and time anomaly Boolean value; sort the problem time series chains according to the size of the comprehensive problem index; S203: Based on the sorting order of the problem time-series chain, perform data correction on the problem time-series chain; extract the reference coordinates of the problem plots corresponding to the problem time-series chain, and perform spatial registration and visualization comparison with the extracted reference coordinates and remote sensing images; for problem plots with coordinate offsets or topological conflicts, use linear features on the remote sensing images as real-world references to perform spatial registration on the boundaries of the problem plots to generate a preliminary corrected version; the linear features include road red lines, permanent walls, and river boundaries; use UAVs to acquire 3D point cloud data and real-world 3D models of the problem plots, and analyze the planar position accuracy and elevation accuracy by overlaying and comparing the plot boundaries in the preliminary corrected version with the real-world 3D model; for plot boundaries that still have deviations, perform spatial registration on the plot boundaries in the preliminary corrected version based on the linear features on the real-world 3D model to generate a final corrected version.

4. The method for governance and application of construction land approval and supply data based on authorization chain according to claim 3, characterized in that: S4 includes the following: S401: Based on the supply and approval relationship pairs in the supply and approval relationship network diagram, obtain the supply time and approval time, calculate the time difference between the two, subtract the standard approval cycle from the time difference to obtain the actual time delay; divide the actual time delay by the standard time threshold to obtain the standardized delay ratio; analyze the time efficiency value based on the standardized delay ratio; the time efficiency value is equal to the maximum value of the difference between one and the standardized delay ratio and zero. Obtain the actual utilized area corresponding to the land supply area, and record the ratio of the actual utilized area to the total land supply area as the spatial efficiency value; count the number of land parcels with abnormal ownership status, and analyze the ratio of the number of land parcels with abnormal ownership status to the total number of land parcels supplied as the ownership abnormality index. S402: Generate a land idling sensitivity index by weighted summation based on time efficiency value, spatial efficiency value, and ownership anomaly index; classify land parcels into idling risk warning levels based on the land idling sensitivity index; analyze the local Moran index of each land parcel based on the land idling sensitivity index; divide the land parcels into spatial clustering patterns based on the significance test of the local Moran index, and send the division results to the administrator.

5. A construction land supply and approval data governance and application system based on an authorization chain, the system being used to implement the construction land supply and approval data governance and application method based on an authorization chain as described in any one of claims 1-4, characterized in that, The system includes a data acquisition and verification module, a data correction module, a matching and filtering module, and an early warning classification module; The data acquisition and verification module is used to collect land approval data, land supply data, and ownership change registration data; it associates the land approval data, land supply data, and ownership change registration data through the electronic supervision number to generate a data chain; and it performs time sequence and integrity verification on the data chain. The data correction module is used to analyze abnormal features in the data chain, analyze comprehensive problem indicators based on abnormal features, and perform data correction on the problem time sequence chain according to the sorting order of the problem time sequence chain in the data chain. The matching and filtering module is used to analyze the matching situation of land parcels approved for allocation and land parcels supplied for allocation, generate preliminary matching pairs based on the matching situation, analyze the confidence index of each preliminary matching pair, and filter the preliminary matching pairs based on the confidence index. The early warning classification module is used to analyze and combine the time efficiency value, spatial efficiency value and ownership anomaly index of the supply and demand relationship to calculate the land idling sensitivity index; classify the land plots into idling risk early warning levels based on the land idling sensitivity index; and classify the land plots into spatial clustering patterns by combining the local Moran index, and send the classification results to the administrator.

6. The construction land approval and supply data governance and application system based on authorization chain as described in claim 5, characterized in that: The data acquisition and verification module includes a data acquisition unit and a data verification and completion unit; The data acquisition unit is used to collect land approval data, land supply data, and ownership change registration data; and to link the land approval data, land supply data, and ownership change registration data through the electronic supervision number to generate a data chain; The data verification and completion unit is used to split the normal time-series chain and the abnormal time-series chain according to the approval time of the land approval data, the land supply time of the land supply data, and the registration time of the ownership change registration data; analyze the completeness index of each field in the land approval data, land supply data, and ownership change registration data, and complete the missing data.

7. The construction land approval and supply data governance and application system based on authorization chain as described in claim 5, characterized in that: The data correction module includes an anomaly analysis unit, a comprehensive evaluation unit, and an anomaly correction unit. The anomaly analysis unit is used to identify plot pairs with overlapping boundaries in the normal time series chain by using a spatial overlay method, analyze the area difference rate of each plot in the normal time series chain, analyze the coordinate offset value of each plot in the normal time series chain, detect time anomalies according to a preset time rule library, and assign time anomaly boolean values. The comprehensive evaluation unit is used to analyze the normal range of data corresponding to the area difference rate and coordinate offset, respectively; and marks the problem sequence chain based on the area difference rate, coordinate offset, and topological conflict identifier. The topological conflict level, area difference rate, coordinate offset value, and time anomaly Boolean value are weighted and summed to generate a comprehensive problem index; the problem time sequence chain is sorted according to the size of the comprehensive problem index. The anomaly correction unit is used to extract the reference coordinates of the problem plots corresponding to the problem time-series chain, and to perform spatial registration and visualization comparison with the remote sensing image. For problem plots with coordinate offsets or topological conflicts, the linear features on the remote sensing image are used as real-world references to spatially register the boundaries of the problem plots to generate a preliminary correction version. UAVs are used to acquire 3D point cloud data and real-world 3D models of the problem plots. By overlaying and comparing the plot boundaries in the preliminary correction version with the real-world 3D model, the planar position accuracy and elevation accuracy are analyzed. For plot boundaries that still have deviations, the plot boundaries in the preliminary correction version are spatially registered with the linear features on the real-world 3D model to generate a final correction version.

8. The construction land approval and supply data governance and application system based on authorization chain as described in claim 5, characterized in that: The matching and filtering module includes a spatiotemporal matching unit and a confidence filtering unit; The spatiotemporal matching unit is used to allocate a time matching window to each batch of land data, and filter the land supply plots to form a candidate matching set; for each batch of land plot and the candidate land supply plot pair in the candidate matching set, the batch-supply matching degree index is analyzed. For each batch of land plot, the batch-supply matching degree index of the highest and above the preset threshold is retained as a preliminary matching pair, and a preliminary matching pair list is generated. The confidence filtering unit is used to construct an initial approval-supply relationship network graph based on the set of nodes and edges composed of approved land parcels and supplied land parcels; analyze the confidence index between nodes; and remove matching pairs with confidence indices lower than the confidence index threshold from the initial approval-supply relationship network graph to generate an approval-supply relationship network graph.

9. The construction land approval and supply data governance and application system based on authorization chain as described in claim 5, characterized in that: The early warning division module includes an idle feature analysis unit and a region division unit; The idle feature analysis unit is used to analyze the standardized delay ratio, spatial efficiency value and ownership anomaly index of batch supply relationship pairs in the batch supply relationship network diagram. The regional division unit generates a land idling sensitivity index by weighted summation based on time efficiency value, spatial efficiency value, and ownership anomaly index; and classifies land parcels into idling risk warning levels based on the land idling sensitivity index. The local Moran index of each plot of land was analyzed based on the land idling sensitivity index; Based on the significance test of the local Moran index, the land parcels are divided into spatial clustering patterns, and the division results are sent to the administrator.

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