Dynamic cadastral data updating and managing method based on space-time sequence

Abstract

A dynamic cadastral data updating and management method based on a space-time sequence specifically comprises the following steps: S1, collecting dynamic cadastral data, preprocessing the dynamic cadastral data, and outputting a standardized space-time sequence database; s2, performing dynamic modeling on the standardized space-time sequence database, adjusting granularity, and outputting an optimized space-time model; s3, performing space-time sequence comparison by utilizing the optimized space-time model, automatically identifying data change, and outputting a cadastre change incremental data set; s4, constructing a relation chain according to the change incremental data set, writing change data into a current potential library, and archiving historical data into a difference file library; s5, performing space-time consistency verification and efficiency optimization on the current potential library and the difference file library, and outputting a quality inspection passing library; and S6, performing service packaging on the quality inspection passing library, and deploying the quality inspection passing library to a cloud service platform. The method dynamically adapts to change features of different regions, reduces storage cost and update time consumption, improves verification efficiency, and provides dynamically updated spatial basic data support for smart city construction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cadastral data, in particular to a dynamic cadastral data updating and management method based on spatio-temporal sequences. BACKGROUND

[0002] Cadastral data is basic data recording the ownership, location, boundary, area, purpose and quality information of land and its attached objects, covering graphic data, attribute data and related document materials, and is the core information carrier of land management. In property management, cadastral data provides a statutory basis for real estate registration, clarifies the ownership and scope of land ownership and use rights, and safeguards transaction security; in urban and rural planning, cadastral data supports overall land use planning and construction land approval, and optimizes spatial resource allocation; in social governance, cadastral data provides data support for land expropriation compensation and ownership dispute mediation, and maintains social fairness; cadastral data is an important data basis for economic activities such as land taxation and real estate market supervision, and runs through the whole life cycle management of land.

[0003] In traditional cadastral data management, the data preprocessing stage lacks standardized processes, and different surveying and mapping teams use different coordinate systems and attribute coding rules, resulting in the need for a lot of effort to convert formats when integrating multi-source data, and even data conflicts. The database constructed under this mode is mostly static storage, and it is difficult to reflect the historical changes of the plot, and it is impossible to form a continuous spatio-temporal sequence. In terms of dynamic updating mechanism, the traditional scheme adopts a periodic batch updating mode, usually carrying out cadastral surveying and mapping quarterly or annually. Change identification relies on manual comparison of cadastral maps in different periods, and the boundary line offset and ownership change information are marked by the naked eye, which not only consumes time and effort, but also easily misses subtle changes. The change data and historical data lack associated management, and the original information is directly overwritten after modification, making it difficult to trace the complete chain of ownership change. In terms of data storage architecture, the traditional scheme mostly uses file servers or single-machine databases, and separates the storage of graphic data and attribute data. When querying the historical change records of a plot, it is necessary to search the map archive and attribute database at the same time, and there is no spatio-temporal indexing mechanism, and when the data volume reaches a certain amount, the query response time is significantly prolonged. SUMMARY

[0004] Therefore, the application aims to provide a dynamic cadastral data updating and management method based on space-time sequences, which is characterized by comprising a collection processing end, a model output end, a comparison and change end, a relationship construction end, a verification and optimization end, and a packaging and visualization end.

[0005] The application can be achieved by the following technical scheme: a dynamic cadastral data updating and management method based on space-time sequences, which is characterized by comprising a collection processing end, a model output end, a comparison and change end, a relationship construction end, a verification and optimization end, and a packaging and visualization end, and specifically comprising the following steps: S1, collecting dynamic cadastral data and outputting a standardized space-time sequence database after preprocessing; S2, dynamically modeling the standardized space-time sequence database and adjusting the granularity to output an optimized space-time model; S3, comparing the space-time sequences by using the optimized space-time model, automatically identifying data changes, and outputting a cadastral change incremental data set; S4, constructing a relationship chain according to the change incremental data set, writing the change data into a current situation database, and archiving historical data to a difference file database; S5, performing space-time consistency verification and efficiency optimization on the current situation database and the difference file database, and outputting a quality inspection passed database; S6, service packaging of the quality inspection passed database and deployment to a cloud service platform.

[0006] The preprocessing method is: integrating multi-source data of remote sensing images, real scene three-dimensional, and business approval records; after cleaning the multi-source data, converting all data to a unified coordinate system and timestamp format; taking a neighborhood or grid as the smallest management unit, associating graphic, attribute, and archive data to generate an initial database with version identification; assigning a unique version number to each update and marking the data effective time and source.

[0007] The dynamic modeling and granularity adjustment method is: dividing the space-time management unit according to the regional cadastral change frequency, dividing the high-frequency change area into small grid units, dividing the low-frequency area into large grid units, and setting different time update windows; selecting a historical reference version as the starting point of modeling, recording detailed change information for high-frequency grid, and updating low-frequency area in batches after combining multiple changes; generating a unique version identifier for each grid unit and change record, associating administrative division code, timestamp and data source; outputting an optimized model containing reference version, difference record and space-time index table, forming a storage framework that can dynamically adapt to change frequency.

[0008] The automatic identification of data changes is: spatiotemporal benchmark matching of real-time monitoring data with historical versions of the optimized space-time model; superimposed comparison of graphic contours and ownership attributes to automatically mark differences in new additions, demolitions or property transfers, generating a suspected change list; after reviewing the list, integrating the confirmed change items, packaging the change type, timestamp, associated historical version number and verification evidence by parcel unit, and outputting the incremental data set after logical inspection.

[0009] The change incremental data set constructs a relationship chain: extracting the parcel unit identifier, change type and timestamp from the incremental data set, identifying the associated historical parcel based on parcel ID or spatial location, and establishing a parent-child relationship mapping table; defining relationship levels according to change types and labeling associated attributes; checking the integrity of the relationship chain, manually reviewing or correcting conflicting data; binding the relationship chain with the version number of the change record, writing it into the present situation library and generating a historical version index table.

[0010] The spatiotemporal consistency verification method is: extracting the latest parcel graphics and attributes from the present situation library and synchronously retrieving the historical version data associated with the difference file library; performing multi-dimensional verification on the present situation library and the difference file library; when the verification is inconsistent, automatically correcting and marking a class of inconsistencies including attribute entry bias; triggering manual review for a second class of inconsistencies including graphic conflicts and ownership breaks, and tracing the source associated with the verification record; marking the data as having been inspected if the verification is passed, locking it as the present situation library version, and generating a correction work order if the verification is not passed, returning it to the previous level for reprocessing.

[0011] The multi-dimensional verification method is: graphic topology verification, superimposed comparison of adjacent parcel boundaries to check for overlapping, gaps or unclosed polygons in the present situation library; attribute logic verification, checking whether the data logic relationship before and after the change is accurate; spatiotemporal version verification, verifying whether the version of the difference file library has continuity.

[0012] The efficiency optimization mode is that a quick access index library is established for a recent history difference file, and data of high frequency query is preloaded; an upper limit of version tracing is set, and a request exceeding the limit is automatically truncated and prompted for a range; and a real-time load is automatically adjusted to update a frequency and a depth threshold of a cache index.

[0013] The service encapsulation mode is that a quality inspection pass library is divided into data modules according to business scenarios, and module input and output rules are defined; a standardized access interface is designed for each module, request parameters and response formats are set, and the access interface is associated with a real scene three-dimensional base map coordinate system; cadastral change data is fused with a real scene three-dimensional model, a dynamic track is rendered in layers, and an interactive control is configured; the service encapsulation mode is deployed to a cloud service platform, access permissions are set according to user roles; high frequency access interfaces are analyzed through logs, data loading strategies are optimized, and three-dimensional models and data versions are regularly updated.

[0014] In combination with all the technical solutions described above, the positive effects of the present application are as follows: 1. The present application improves the uniformity and traceability of cadastral data by standardizing the data. The multi-source data is converted to a unified coordinate system and timestamp, and the data is associated with graphic, attribute and archive data in units of blocks or grids. In combination with a unique version number and an effective time marker, the traditional data format confusion problem is solved, and the source tracing in the whole life cycle is realized, laying a high-quality data foundation for subsequent updating and checking.

[0015] 2. The present application improves the efficiency and adaptability of cadastral management through dynamic modeling and differentiated granularity adjustment mechanism. By dividing the grid units and time windows according to the change frequency, the high-frequency areas are recorded in detail and the low-frequency areas are updated in batches, which reduces the redundant storage while ensuring the data accuracy. The flexible storage framework can dynamically adapt to the change characteristics of different areas, reducing the storage cost and update time, and is suitable for high-frequency change areas such as urban-rural transition areas.

[0016] 3. The present application realizes the intelligentialization and continuity of cadastral updating through automatic change identification and relationship chain construction. Through space-time reference matching and superposition comparison, the newly added and removed difference items are automatically marked, and the complete change context is established in combination with the parent-child relationship mapping table, solving the problem of low efficiency and correlation fault in traditional manual comparison. The accurate binding of incremental data set and historical version makes the property change track traceable to any historical node, providing a complete evidence chain for property dispute handling and improving the verification efficiency.

[0017] 4. The present application guarantees the data quality and expands the application scenarios through space-time consistency checking and service encapsulation. Multi-dimensional checking reduces the data error rate through graphic topology, attribute logic checking, automatic correction and manual review mechanism. The combination of real scene three-dimensional visualization and cloud service interface enables cadastral data to quickly interface with government approval and real estate registration business systems, providing dynamic update of spatial basic data support for smart city construction.BRIEF DESCRIPTION OF DRAWINGS BRIEF DESCRIPTION OF DRAWINGS

[0018] The application will be further described with the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the application, and other embodiments can also be obtained by those of ordinary skill in the art without creative labor on the basis of the following drawings.

[0019] Figure 1 The method of the application implements a step flow chart. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the protection scope of the application.

[0021] Referring to Figure 1 As shown in the drawings, the application proposes a dynamic cadastral data updating and management method based on a space-time sequence, characterized by comprising a collection processing end, a model output end, a comparison and change end, a relationship construction end, a verification and optimization end, and a packaging and visualization end.

[0022] In a more specific application of the application, the pre-processing manner of the dynamic cadastral data is as follows: multi-source data is integrated, remote sensing images, real scene three-dimensional models, and electronic archives of real estate registration and planning approval business systems are synchronously collected, to form an original data set covering spatial forms and ownership information. The original data set is cleaned, repeated records are removed through an automatic script, format errors are corrected, and ambiguous or conflicting data is manually verified, to ensure the accuracy and integrity of the basic data and lay a reliable foundation for subsequent processing.

[0023] The cleaned data is standardized and converted, so that various types of data can be uniformly converted into a coordinate system and a standard timestamp format, and the coordinate deviation and time format confusion problems between different data sources can be eliminated. For non-standard data left over from history, the coordinate conversion software is used for batch processing and the conversion parameters are recorded for traceability, so that the space-time reference can be unified across data sources.

[0024] The units are managed and divided, the smallest management unit can be a neighborhood or a grid, the graphic, attribute, and archive data are associated, the three types of data are mapped through unique coding, and an initial database containing complete elements is generated. An incremental version number is automatically generated each time the data is updated, and the specific time point and the update channel when the data takes effect are synchronously marked, so that each version of data can be traced and verified, and a standardized space-time sequence database with clear time sequence and complete elements is formed.

[0025] The following methods can be used to dynamically model and adjust the granularity of the standardized spatio-temporal sequence database: statistical analysis of historical cadastral change data to calculate the change frequency in each region within a specific time window. According to the change frequency, set a frequency threshold to divide each region into high-frequency change regions and low-frequency change regions. At the same time, establish a dynamic monitoring mechanism for regional change frequency, and re-evaluate the regional classification regularly to ensure that the grid division strategy can adapt to changes in the actual change mode.

[0026] According to the frequency analysis results, fine-grained grid division strategy is adopted for high-frequency change regions, with grid size usually set to 50m x 50m or smaller, and time update window set to real-time or daily update; coarse-grained grid is used for low-frequency regions, with size of 500m x 500m or larger, and time update window set to weekly or monthly update. This differential processing not only ensures the data accuracy of high-frequency regions, but also avoids storage redundancy in low-frequency regions, achieving a balance between storage space and query efficiency.

[0027] A multi-dimensional version identification system based on timestamp and spatial location is established to generate a unique identifier for each grid cell in the format of administrative division code, grid ID, timestamp, and data source. A historical time point with relatively stable data is selected as the reference version for storage, and subsequent changes are stored in the form of difference records. High-frequency grids record detailed information of each change in real time, while low-frequency grids use a batch merging strategy to combine multiple small changes into one update record, reducing storage overhead.

[0028] A multi-level spatio-temporal index system is constructed, including spatial index, temporal index, and composite spatio-temporal index. For grid cells of different granularity, corresponding index strategies are designed, with fine-grained index used for high-frequency grids to support accurate queries, and aggregated index used for low-frequency grids to improve range query efficiency. At the same time, a metadata management table is established to record the granularity level, update frequency, and index type of each grid cell, providing decision basis for query optimizer to realize intelligent selection of query path and performance optimization.

[0029] Spatio-temporal sequence comparison is performed using the optimized spatio-temporal model to automatically identify data changes. The following methods can be used: spatio-temporal registration of real-time cadastral data obtained through real-time monitoring and historical reference version in the optimized spatio-temporal model through coordinate system normalization and time reference alignment to ensure data comparability. Spatial buffer analysis and time window matching techniques are used to accurately associate real-time data with historical versions of corresponding grid cells. For high-frequency change regions, the system automatically selects the nearest historical version as the comparison reference; for low-frequency regions, the latest batch update version is selected. A data quality pre-check mechanism is established to filter out abnormal data such as coordinate offset and attribute missing, providing a reliable data foundation for subsequent comparison and analysis.

[0030] After the difference is identified, the polygon overlay analysis, area change calculation and boundary displacement detection can be used to automatically identify the addition, reduction or boundary adjustment of cadastral scope; the key fields of ownership, land use and building area are compared one by one to identify the non-geometric changes of property transfer and use change; and the suspected change list containing the change type, influence range and confidence score is automatically generated through the rule engine.

[0031] The list is reviewed by establishing a multi-level change review system, combining automatic rule checking and manual review and confirmation. The system first performs logical consistency checking on the suspected change list to verify the rationality and completeness of the change, such as checking the relevance of adjacent land changes and the continuity of time sequence. For high-confidence change items, the system can directly pass through automatic review; for complex or low-confidence changes, they are pushed to the manual review queue. During the review process, the system provides auxiliary tools including historical image comparison, related document association and change trajectory tracking to help the reviewer make accurate judgments quickly.

[0032] The change items confirmed after review are standardized and packaged according to the land unit, and each change record contains change type code, accurate timestamp, associated historical version number, geometric change range, attribute change details and verification evidence link. The system uses incremental storage format to ensure data traceability and version consistency. Before output, the system performs final logical quality inspection, including data integrity verification, format specification checking and business rule compliance testing, to ensure that the output incremental data set meets the access requirements of subsequent application systems. At the same time, change statistics report and quality evaluation report are generated to provide decision support for data management and system optimization.

[0033] According to the change incremental data set, the relationship chain can be constructed by the following methods: extracting key identification information from the change incremental data set, directly matching through land ID and spatial position overlay analysis to identify the historical land records associated with the current change land. For land division, the system identifies a parent land corresponding to multiple child lands through spatial inclusion relationship; for land merger, it identifies multiple parent lands corresponding to a child land through spatial intersection analysis; for boundary adjustment, it identifies one-to-one evolution relationship through geometric similarity calculation. The system automatically generates a parent-child relationship mapping table to record the ID correspondence, association strength score and association type identification of the associated lands.

[0034] According to different change types, the system establishes a standardized relationship hierarchy system. New land markers have no parent land association; canceled land markers have no child land association; split changes establish a one-to-many parent-child link; merge changes establish a many-to-one parent-child link; adjustment changes establish a one-to-one evolution link. Each relationship type is configured with corresponding attribute annotation rules, such as inherited attributes, calculated attributes, and new attributes, to ensure the completeness of relationship chain information and the correctness of business logic.

[0035] Check the integrity of the relationship chain, manually review or correct conflicting data, and verify the relationship chain and change record version binding. Generate a globally unique version identifier, write the latest change data to the current state information of the land, and update the current state information of the land. At the same time, the historical data replaced is migrated to the difference file library, keeping the complete change track. The system automatically generates a historical version index table, recording the creation time of each version, the number of change items contained, the associated business batch, and the storage location meta-information.

[0036] Temporal and spatial consistency verification and efficiency optimization of the current library and difference file library can be achieved by: the system establishes a three-layer verification system to ensure the temporal and spatial consistency of the data. At the graphical topology verification level, the system uses computational geometry algorithms to comprehensively detect the land polygons in the current library, identifies overlapping areas between adjacent lands through polygon intersection analysis, detects gaps between land boundaries using buffer zone analysis, and verifies the geometric integrity of the polygons through closure algorithms. At the attribute logic verification level, the system constructs a business rule engine to verify the logical relationship between the data before and after the change, such as the rationality of area change, the integrity of ownership transfer, and the compliance of land use property change. Through pre-set business constraints, the system automatically identifies logical abnormalities. At the temporal and spatial version verification level, the system checks the continuity and integrity of the version chain in the difference file library, verifies whether the historical evolution track of each land has a broken link or repetition, and ensures the logical consistency of the time sequence.

[0037] A quick access index library is established for recent historical difference files, preloading high-frequency query data to ensure that the most commonly used data is always in memory, and updating the preloading strategy regularly to adapt to changes in query patterns. Set the upper limit of version traceability, when the user requests exceed the traceability range, the system automatically truncates the query and returns a friendly prompt message, and provides an alternative solution for data archive queries. Monitor the real-time load of the system, increase the cache update frequency and expand the traceability depth when the system load is low, provide better query experience; during high load periods, reduce the cache update frequency and narrow the traceability range to ensure system stability. Finally, output the quality inspection pass library.

[0038] The quality inspection is encapsulated by the library and deployed to the cloud service platform, and the quality inspection can be divided into data modules according to business scenarios, the module input and output rules are defined, a standardized access interface is designed for each module, data exchange can be uniformly carried out in the JSON format, a standard HTTP status code and an error processing mechanism are set. The interface design fully considers the association with the coordinate system of the real three-dimensional base map, supports automatic conversion of multiple coordinate systems, including WGS84, CGCS2000 and local coordinate system, and ensures accurate registration of cadastral data and three-dimensional scenes.

[0039] The cadastral change data and the real three-dimensional model are fused by adopting the WebGL technology stack, the cadastral data in different periods is displayed in layers according to a time axis through a layered rendering technology, and a user can view the historical evolution process of the land parcel through a time slider control.

[0040] The containerization deployment mode is adopted, the automatic deployment, expansion and contraction and fault recovery of the service are realized based on the Docker and Kubernetes technologies. A role-based access control mechanism is established, different permission levels are set according to user identities, an administrator has all data access and system configuration permissions; a business user can access cadastral data and basic analysis functions in a specific area; a public user can only view public statistical information and basic map services. A comprehensive log monitoring and analysis system is established, API calling frequency, response time and error rate indicators are collected in real time, high-frequency access interfaces and hot data areas are identified through big data analysis technology. Based on the analysis results, the data loading strategy is dynamically optimized, the high-frequency query data is pre-cached, the low-frequency data adopts a delayed loading mechanism, the latest cadastral change data and real three-dimensional models are synchronized regularly, the system response speed can be improved, and the timeliness and accuracy of service content can be ensured.

[0041] Secondly: the drawings of the disclosed embodiments only involve structures related to the disclosed embodiments, other structures can refer to general design, and the same embodiments and different embodiments of the present application can be combined with each other under the condition of no conflict; Finally: the above only describes preferred embodiments of the present application and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for dynamic cadastre data updating and management based on spatio-temporal sequences, characterized in that, It comprises a collection processing end, a model output end, a comparison and change end, a relationship construction end, a verification and optimization end, and a packaging and visualization end, and specifically comprises the following steps: S1. Collecting dynamic cadastral data, and outputting a standardized time-space sequence database after preprocessing; S2. Dynamically modeling the standardized time-space sequence database and adjusting the granularity, and outputting an optimized time-space model; S3. Comparing the time-space sequence by using the optimized time-space model, automatically identifying data changes, and outputting a cadastral change incremental dataset; S4. Constructing a relationship chain according to the change incremental dataset, writing the change data into a current situation library, and archiving the historical data into a difference file library; S5. Performing time-space consistency verification and efficiency optimization on the current situation library and the difference file library, and outputting a quality inspection passed library; S6. Servicizing the quality inspection passed library, and deploying it to a cloud service platform.

2. The method for updating and managing dynamic cadastral data based on spatiotemporal sequences as claimed in claim 1, characterized in that: The preprocessing manner is: integrating multi-source data of remote sensing images, real scene three-dimensional, and business approval records; after cleaning the multi-source data, converting all the data to a unified coordinate system and timestamp format; taking a neighborhood or a grid as the minimum management unit, associating graphic, attribute, and archive data, and generating an initial database with version identification; Each update is given a unique version number, and the data effective time and source are marked.

3. The method for dynamic cadastral data updating and management based on spatiotemporal sequences according to claim 1, characterized in that: The dynamic modeling and granularity adjustment manner is: dividing time-space management units according to the regional cadastral change frequency, dividing high-frequency change areas into small grid units, dividing low-frequency areas into large grid units, and setting different time update windows; Selecting a historical reference version as the modeling starting point, recording detailed change information for high-frequency grids, and updating low-frequency areas in batches after merging multiple changes; Generating a unique version identification for each grid unit and change record, associating administrative division code, timestamp, and data source; outputting an optimized model containing a reference version, difference records, and a time-space index table, and forming a storage framework that can dynamically adapt to change frequency.

4. The method for updating and managing dynamic cadastral data based on spatiotemporal sequences as claimed in claim 1, wherein: The automatic identification of data changes manner is: performing time-space reference matching between real-time monitoring data and historical versions of the optimized time-space model; superimposing and comparing graphic contours and ownership attributes, automatically marking the difference items of new additions, demolitions, or property transfers, and generating a suspected change list; after reviewing the list, integrating the confirmed change items, packaging the change type, timestamp, associated historical version number, and verification evidence according to the land unit, and outputting an incremental dataset after logical quality inspection.

5. The method for updating and managing dynamic cadastral data based on spatiotemporal sequences as claimed in claim 1, wherein: The manner of constructing a relationship chain for the change incremental dataset is: extracting the land unit identification, change type, and timestamp in the incremental dataset, identifying the associated historical land based on the land ID or spatial position, establishing a parent-child relationship mapping table; defining the relationship level according to the change type, and marking the associated attributes; Checking the integrity of the relationship chain, manually reviewing or correcting the conflict data; binding the relationship chain and the change record with version numbers, synchronously writing them into the current situation library and generating a historical version index table.

6. The method for updating and managing dynamic cadastral data based on spatiotemporal sequences as claimed in claim 1, wherein: The spatio-temporal consistency verification method is: extracting the latest land parcel graphics and attributes in the current situation database, synchronously calling the historical version data associated with the difference file database; multi-dimensional verification is performed on the current situation database and the difference file database; when the verification is inconsistent, a class of inconsistencies including attribute entry deviation is automatically corrected and marked; the second class of inconsistencies including graphic conflict and ownership fracture triggers manual review, and the associated verification records are traced; the data that pass the verification are marked as having been quality inspected, locked as the current situation database version, and the data that do not pass the verification generate a correction work order and are returned to the previous level for reprocessing.

7. The method for updating and managing dynamic cadastre data based on spatiotemporal sequences according to claim 6, characterized in that: The multi-dimensional verification method is: graphic topology verification, superimposed comparison of adjacent land parcel boundaries, checking whether there are overlaps, gaps or unclosed polygons in the current situation database; attribute logic verification, checking whether the data logic relationship before and after the change is accurate; spatio-temporal version verification, verifying whether the version of the difference file database has continuity.

8. The method for updating and managing dynamic cadastral data based on spatiotemporal sequences as claimed in claim 1, wherein: The efficiency optimization method is: establishing a quick access index database for recent historical difference files, preloading high-frequency query data; setting the upper limit of version tracing, automatically truncating and prompting the range for requests exceeding the limit; automatically adjusting the update frequency and backtracking depth threshold of the cache index according to the real-time load.

9. The method for updating and managing dynamic cadastral data based on spatiotemporal sequences as claimed in claim 1, wherein: The service encapsulation method is: dividing the quality inspection pass database into data modules according to business scenarios, defining module input and output rules; designing a standardized access interface for each module, setting request parameters and response formats, and associating with the real scene three-dimensional base map coordinate system; fuse cadastral change data and real scene three-dimensional model, hierarchical rendering dynamic trajectory and configuration of interactive controls; deploy to the cloud service platform, set access permissions according to user roles; through log analysis of high-frequency access interfaces, optimize data loading strategies, and update three-dimensional models and data versions regularly.

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