A method for collecting geographic information mapping data of real estate based on unmanned aerial vehicles (UAVs)

Abstract

This invention discloses a method for collecting geographic information mapping data for real estate based on unmanned aerial vehicles (UAVs), which relates to the field of digital processing technology. This invention generates collection task data by triggering data from events, and uses anchor points to identify the data and segment the collection task data to constrain the collection end, ensuring that the collected image data and boundary collection trajectory data are verifiable. The management end generates quality verification data through anchor point visibility verification, closure verification, and topological constraint verification, generates boundary element data, and then implements supplementary collection, dual-person or joint collection adjudication based on conflict comparison data, forming incremental update data and encapsulating evidence chain data, realizing low-cost, traceable and rapid updates of indoor and common area boundaries, serving real estate, fire protection and rental and sales businesses.

Description

Technical Field

[0001] This invention relates to the field of electronic digital processing technology, and in particular to a method for collecting real estate geographic information mapping data based on unmanned aerial vehicles (UAVs). Background Technology

[0002] Geographic information mapping data for real estate serves as the foundation for property records, rental and sales assessments, and fire safety verification. This is especially crucial in high-density residential buildings and mixed-use complexes, where interior unit boundaries, doorway locations, and common area boundaries frequently change due to renovation registration, rental / sales transactions, acceptance reviews, and inspection work orders. Current update methods primarily rely on professional on-site surveying or post-event centralized surveying, which suffers from long cycles, high costs, and untimely coverage. Relying solely on remote sensing and aerial surveying struggles to capture detailed information about interior and common areas, and lacks constraints and traceable evidence regarding the data collection process, leading to frequent boundary disputes, area calculation disputes, and a lack of evidence for fire safety verification. Furthermore, actual data collection is often carried out by property management, supervisors, or on-site personnel. Without task constraints, quality verification, and evidence chain encapsulation centered on anchor point data, even if collected imagery and boundary collection trajectory data are obtained, it's difficult to prove the credibility of the collected objects, scope, and results, hindering their use for incremental updates and cross-departmental sharing of geographic information databases.

[0003] Currently, Chinese invention patent application number 202310231199.8 discloses a method for collecting geographic information mapping data of real estate based on unmanned aerial vehicles (UAVs). The method includes: acquiring a point cloud dataset of real estate buildings; filtering feature points based on the Thiessen polygons corresponding to each data point in each acquisition map, the normal vectors of each data point in each acquisition map, and the normal vectors of key points in each acquisition map; obtaining feature descriptors based on the HOG operator corresponding to each feature point in each acquisition map, the gray value of each feature point, the Euclidean distance between each feature point and feature points in its preset neighborhood, and the structural similarity between the structure map corresponding to each feature point and the structure map corresponding to the feature points in its preset neighborhood; and matching the point cloud dataset of real estate buildings with a standard point cloud dataset based on the feature descriptors to determine the location information of the real estate building point cloud data.

[0004] The relevant technologies struggle to achieve verifiable, adjudicable, and traceable incremental updates of boundary-collected data triggered by non-professionals in indoor, invisible, and frequently changing scenarios, under privacy constraints. Summary of the Invention

[0005] The technical problem solved by this invention is that existing technologies are unable to achieve verifiable, adjudicable, and traceable incremental updates of boundary data collected by non-professionals under privacy constraints in indoor, invisible, and frequently changing scenarios.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A method for collecting real estate geographic information mapping data based on unmanned aerial vehicles (UAVs) includes the following steps: Step S1: Obtain event trigger data and real estate identification data, and combine them with privacy constraint data to generate collection task data; Step S2: Retrieve anchor point identification data from the anchor point database based on the real estate identification data, segment and arrange the data to be collected based on the anchor point identification data, output the segmented data to be collected and sent to the collection terminal. Step S3: The acquisition terminal performs guided acquisition according to the segmented acquisition task data, obtains the acquired image data and boundary acquisition trajectory data, and sends the acquired image data and boundary acquisition trajectory data back to the management terminal. Step S4: The management terminal performs anchor point visibility verification, closure verification, and topological constraint verification on the acquired image data and boundary acquisition trajectory data based on the anchor point identification data, outputs quality verification data, and generates boundary feature data from the acquired image data and boundary acquisition trajectory data when the quality verification data passes. Step S5: The management terminal performs a conflict comparison between the boundary feature data and the historical boundary feature data, outputs the conflict comparison data, performs a conflict resolution on the conflict comparison data to generate incremental update data, writes the incremental update data into the geographic information database and forms a chain of evidence data. In step S6, the management end generates output result data based on the incremental update data and the evidence chain data, and transmits the output result data to the business end.

[0007] Preferably, step S1 includes the following sub-steps: Step S101: Obtain event trigger data, which includes rental and sales registration trigger data, decoration reporting trigger data, acceptance review trigger data, and inspection work order trigger data; Step S102: Obtain real estate identification data, which includes building identification data, unit identification data, floor identification data, and room number identification data; Step S103: Obtain privacy constraint data and associate it with event trigger data and real estate identification data to generate collection task data. The collection task data includes collection scope data and collection purpose data.

[0008] Preferably, the logic of step S101 is as follows: Perform type parsing on event-triggered data; If the event trigger data is the decoration filing trigger data or the acceptance review trigger data, then the data collection target data will be set to boundary update; If the event trigger data is the inspection work order trigger data, then the data collection target will be set to the verification of common areas; If the event-triggered data is rental / sale registration-triggered data, then the data collection target will be set as boundary update and area verification, and the corresponding privacy constraint data will be written into the data collection task.

[0009] Preferably, step S2 includes the following sub-steps: Step S201: Retrieve anchor point identification data based on real estate identification data. The anchor point identification data includes anchor point ID and anchor point location description data. Step S202: The data acquisition task is segmented and arranged according to the anchor point location description data to generate segmented data acquisition task data. The segmented data acquisition task data includes the list of mandatory anchor points within the segment and the coverage requirement data within the segment. Step S203: Write the list of mandatory anchor points within the segment and the coverage requirement data within the segment into the segmented acquisition task data and send it to the acquisition terminal. The acquisition process of the acquisition terminal is constrained by the anchor point identification data.

[0010] Preferably, step S3 includes the following sub-steps: Step S301: The acquisition end identifies the anchor point ID corresponding to the list of mandatory anchor points in the segment at the beginning of each segment using the onboard camera component, generates anchor point identification record data and writes it into the boundary acquisition trajectory data. Step S302: The acquisition end performs guided acquisition along the coverage requirement data within the segment, continuously acquiring acquisition image data and boundary acquisition trajectory data; In step S303, the acquisition end performs bidirectional re-acquisition of trajectory data at the same boundary, generates re-acquisition consistency data, and sends it back to the management end. The re-acquisition consistency data serves as input data for quality verification.

[0011] Preferably, step S4 includes the following sub-steps: Step S401: Perform anchor point visibility verification on the acquired image data based on the anchor point identification data, output the anchor point visibility verification result and write it into the quality verification data. Step S402: Perform closure verification based on the boundary acquisition trajectory data. If the boundary acquisition trajectory data cannot be closed or has self-intersection, mark the closure verification result as failing and write it into the quality verification data. Step S403: Perform topology constraint verification on the boundary acquisition trajectory data based on the immutable structural constraint data corresponding to the real estate identification data. If the boundary acquisition trajectory data crosses the immutable structural constraint data, mark the topology constraint verification result as failing and write it into the quality verification data. Step S404: When all quality verification data are passed, boundary feature data is generated. The boundary feature data includes boundary line feature data and corner point feature data.

[0012] Preferably, the logic for generating the chain of evidence data in step S5 is as follows: The data collected from the task, anchor point identification data, collected image data, boundary collection trajectory data, quality verification data, and incremental update data are associated and encapsulated to output evidence chain data. The evidence chain data includes anchor point identification record data, re-collection consistency data, quality verification conclusion data, and incremental update basis data.

[0013] Preferably, the logic for conflict resolution is as follows: Determine the conflict type of the conflict comparison data; If the conflict type is indoor conflict, then generate supplementary data collection task data and require the anchor point ID corresponding to the list of mandatory anchor points within the supplementary collection segment and the boundary collection trajectory data corresponding to the conflict. If the conflict type is a shared part conflict, the data collection task will be upgraded to a two-person data collection task and the segmented data collection task will be regenerated. If the conflict type is an adjacent association conflict, then adjacent real estate identification data is generated based on the real estate identification data and the linkage collection task data is triggered, and incremental update data is updated based on the linkage collection task data.

[0014] Preferably, step S6 includes the following sub-steps: Step S601: Generate geographic information update result data based on incremental update data; Step S602: Generate evidence summary data based on the evidence chain data and associate it with the geographic information update result data, and output the output result data; Step S603: Transmit the output results data to the business terminals, which include the real estate archive business terminal, the fire safety verification business terminal, and the rental and sales appraisal business terminal.

[0015] Preferably, the processing logic for the privacy constraint data is as follows: Privacy constraint data is written into the collection scope data when generating data for the collection task. The acquisition end performs range cropping and occlusion processing on the acquired image data based on the acquisition range data, and outputs privacy-processed image data; The management system uses privacy-processed image data instead of collected image data to participate in the generation of quality verification data and evidence chain data.

[0016] The beneficial effects of this invention are as follows: This invention generates acquisition task data based on event-triggered data, and uses anchor points to identify the segmented acquisition task data to constrain the acquisition end, ensuring that the acquired image data and boundary acquisition trajectory data are verifiable. The management end generates quality verification data through anchor point visibility verification, closure verification, and topological constraint verification, generates boundary element data, and then implements supplementary acquisition, dual-person or joint acquisition adjudication based on conflict comparison data, forming incremental update data and encapsulating evidence chain data, realizing low-cost, traceable and rapid updates of indoor and common area boundaries, serving real estate, fire protection and rental and sales businesses. Attached Figure Description

[0017] Figure 1 The present invention provides a flowchart of a method for collecting real estate geographic information mapping data based on unmanned aerial vehicles (UAVs) according to an embodiment of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Example, refer to Figure 1 This embodiment is applied to real estate boundary update operations in high-density residential and office buildings. The management terminal is a geographic information update server with a built-in geographic information database and anchor point library; the data collection terminal is a drone-based data collection terminal, which includes the drone itself, onboard camera components, onboard positioning components, and communication components, possessing camera and network connectivity capabilities. The data collection terminal app can read segmented data collection tasks and transmit back collected image data and boundary data collection trajectory data. Anchor point identification data is stored in the form of anchor point IDs and anchor point location description data. The anchor point location description data is expressed in a text structure of building identification data, unit identification data, floor identification data, and location description. Anchor points are directly used as the constraint and audit basis for the data collection process. This embodiment includes the following steps: Step S1: Obtain event trigger data and real estate identification data, and combine them with privacy constraint data to generate collection task data.

[0020] Step S2: Retrieve anchor point identification data from the anchor point database based on the real estate identification data, segment and arrange the collected task data according to the anchor point identification data, output the segmented collected task data and send it to the collection terminal.

[0021] Step S3: The acquisition terminal performs guided acquisition according to the segmented acquisition task data, obtains the acquired image data and boundary acquisition trajectory data, and sends the acquired image data and boundary acquisition trajectory data back to the management terminal.

[0022] In step S4, the management terminal performs anchor point visibility verification, closure verification, and topological constraint verification on the acquired image data and boundary acquisition trajectory data based on the anchor point identification data, outputs quality verification data, and generates boundary feature data from the acquired image data and boundary acquisition trajectory data when the quality verification data passes.

[0023] In step S5, the management terminal performs a conflict comparison between the boundary feature data and the historical boundary feature data, outputs the conflict comparison data, performs a conflict resolution on the conflict comparison data to generate incremental update data, writes the incremental update data into the geographic information database, and forms a chain of evidence data.

[0024] In step S6, the management end generates output result data based on the incremental update data and the evidence chain data, and transmits the output result data to the business end.

[0025] This invention generates acquisition task data based on event-triggered data and uses anchor points to identify segmented acquisition task data to constrain the acquisition end, ensuring that the acquired image data and boundary acquisition trajectory data are verifiable. The management end generates quality verification data through anchor point visibility verification, closure verification, and topological constraint verification, generates boundary element data, and then implements supplementary acquisition, dual-person or joint acquisition adjudication based on conflict comparison data, forming incremental update data and encapsulating evidence chain data, realizing low-cost, traceable and rapid updates of indoor and common area boundaries, serving real estate, fire protection and rental and sales businesses.

[0026] Step S1 includes the following sub-steps: Step S101: Obtain event trigger data, which includes rental and sales registration trigger data, decoration reporting trigger data, acceptance review trigger data, and inspection work order trigger data.

[0027] Step S102: Obtain real estate identification data, which includes building identification data, unit identification data, floor identification data, and room number identification data.

[0028] Step S103: Obtain privacy constraint data and associate it with event trigger data and real estate identification data to generate collection task data. The collection task data includes collection scope data and collection purpose data.

[0029] The logic of step S101 is as follows: Perform type parsing on the event-triggered data.

[0030] If the event trigger data is the decoration filing trigger data or the acceptance review trigger data, then the data to be collected will be set as boundary update.

[0031] If the event trigger data is the inspection work order trigger data, then the data collection target will be set to the verification of common areas.

[0032] If the event-triggered data is rental / sale registration-triggered data, then the data collection target will be set as boundary update and area verification, and the corresponding privacy constraint data will be written into the data collection task.

[0033] The management system first acquires event trigger data, including rental and sales registration trigger data, decoration reporting trigger data, acceptance review trigger data, and inspection work order trigger data. Simultaneously, it acquires real estate identification data, including building identification data, unit identification data, floor identification data, and room number identification data.

[0034] Subsequently, the management system obtains privacy constraint data and binds it with event trigger data and real estate identification data to generate collection task data, which includes collection scope data and collection purpose data.

[0035] In this embodiment, the data collection range adopts a structured description of the allowed access area list, allowed shooting direction restrictions, and allowed image retention area. For example, in the rental and sales registration trigger data scenario, the data collection range can be limited to the area from the entrance door to the living room boundary, the public corridor doorway and door frame area, while prohibiting shooting in private areas such as bedrooms is written into the privacy constraint data.

[0036] The management system performs type analysis on event-triggered data. If the event-triggered data is decoration reporting or acceptance review, the data collection purpose is set to boundary update. If it is inspection work order trigger data, it is set to common area verification. If it is rental / sale registration trigger data, it is set to boundary update and area verification. The corresponding privacy constraint data is written into the data collection task data, thereby limiting the effective scope of subsequent image data collection at the source of the task and ensuring that subsequent processing will not result in broken links such as data being collected but unusable or used but not compliant.

[0037] Step S2 includes the following sub-steps: Step S201: Retrieve anchor point identification data based on real estate identification data. Anchor point identification data includes anchor point ID and anchor point location description data.

[0038] Step S202: Based on the anchor point location description data, the data to be collected is segmented and arranged to generate segmented data to collect data. The segmented data includes the list of anchor points to be scanned within the segment and the coverage requirements within the segment.

[0039] Step S203: Write the list of mandatory anchor points within the segment and the coverage requirement data within the segment into the segmented collection task data and send it to the collection terminal. The collection process at the collection terminal is constrained by the anchor point identification data.

[0040] The management system retrieves anchor point identification data from the anchor point database based on the real estate identification data. The anchor point identification data includes the anchor point ID and anchor point location description data. Subsequently, the management system segments and arranges the data collection task based on the anchor point location description data, generating segmented data collection task data. The segmented data collection task data includes a list of mandatory anchor points within the segment and coverage requirements within the segment.

[0041] In this embodiment, the list of mandatory anchor points within a segment must include at least the starting anchor point ID, the ending anchor point ID, and a set of mandatory intermediate anchor point IDs; the coverage requirement data within a segment must include at least the boundary type marker (indoor boundary, common area boundary, and doorway boundary), coverage direction requirements (clockwise, counterclockwise, and round trip), minimum coverage length requirements, and critical corner point requirements.

[0042] The management end writes the list of mandatory anchor points within a segment and the coverage requirement data within the segment into the segment collection task data and sends it to the collection end. This makes the collection process of the collection end constrained by the anchor point identification data from the beginning: if the collection end fails to identify the anchor point ID corresponding to the list of mandatory anchor points within the segment, the segment cannot enter the effective collection state. This avoids the problem of sending back a lot of data but being unable to prove the collection location and object from the process perspective.

[0043] Step S3 includes the following sub-steps: In step S301, the acquisition end uses the onboard camera component to identify the anchor point ID corresponding to the mandatory anchor point list data within each segment at the beginning of each segment, generates anchor point identification record data, and writes it into the boundary acquisition trajectory data.

[0044] Step S302: The acquisition end performs guided acquisition along the coverage requirement data within the segment, continuously acquiring acquisition image data and boundary acquisition trajectory data.

[0045] In step S303, the acquisition end performs bidirectional re-acquisition of trajectory data at the same boundary, generates re-acquisition consistency data, and sends it back to the management end. The re-acquisition consistency data serves as the input data for quality verification.

[0046] After receiving the segmented acquisition task data, the acquisition terminal identifies the anchor point ID corresponding to the mandatory anchor point list data within each segment at the beginning of each segment, generates anchor point identification record data, and writes it into the boundary acquisition trajectory data. In this embodiment, the anchor point identification record data includes at least the anchor point ID, identification timestamp, segment number, and acquisition terminal device identifier.

[0047] Subsequently, the acquisition end performs guided acquisition along the coverage requirement within the segment, continuously acquiring acquired image data and boundary acquisition trajectory data; wherein the boundary acquisition trajectory data is recorded in the form of a time series point set in this embodiment, and each point in the point set contains at least a timestamp, relative displacement increment, heading change, and association marker with the most recent anchor point identification record data, and the acquired image data is a video stream or keyframe sequence continuously acquired along the trajectory.

[0048] To improve verifiability, the acquisition terminal performs bidirectional re-acquisition of trajectory data at the same boundary: that is, after completing one acquisition according to the coverage direction of the data required within the segment, it acquires again along the same path in the opposite direction, and generates re-acquisition consistent data to be sent back to the management terminal.

[0049] In this embodiment, the consistency data from the re-collection is directly calculated by the acquisition end, and the conclusion field is given. The calculation logic is as follows: the trajectory data of the two boundary acquisitions are segmented into slices according to the anchor point ID sequence of the anchor point identification record data. In each slice, the path similarity of the two trajectories and the order of appearance of key corner points are compared. If the path similarity is low or the order of corner points is inconsistent, the slice is marked as inconsistent in re-collection, and the acquisition personnel are prompted to re-collect the slice before the data is transmitted back, thereby moving the quality problem to the site for resolution.

[0050] Step S4 includes the following sub-steps: Step S401: Perform anchor point visibility verification on the acquired image data based on the anchor point identification data, output the anchor point visibility verification result and write it into the quality verification data.

[0051] Step S402: Perform closure verification based on the boundary acquisition trajectory data. If the boundary acquisition trajectory data cannot be closed or has self-intersection, mark the closure verification result as failing and write it into the quality verification data.

[0052] Step S403: Perform topological constraint verification on the boundary acquisition trajectory data based on the immutable structural constraint data corresponding to the real estate identification data. If the boundary acquisition trajectory data crosses the immutable structural constraint data, mark the topological constraint verification result as failing and write it into the quality verification data.

[0053] Step S404: When all quality verification data are passed, generate boundary feature data, which includes boundary line feature data and corner point feature data.

[0054] After receiving the acquired image data and boundary acquisition trajectory data, the management terminal first performs anchor point visibility verification on the acquired image data based on the anchor point identification data and outputs the anchor point visibility verification result, which is then written into the quality verification data. For reproducibility, this embodiment employs a two-layer evidence logic for anchor point visibility verification. The first layer uses anchor point identification and record data as the main evidence, and checks whether all anchor point IDs in the list of mandatory anchor points within the segment appear and whether the time order is reasonable. The second layer uses acquired image data as supplementary evidence. Frames containing anchor point regions are extracted from keyframes, and the anchor point IDs are checked for readability or recognizability. If either layer of evidence is missing, the anchor point visibility check result is marked as failing. Subsequently, the management end performs a closure check based on the boundary acquired trajectory data: the trajectory point set is reconstructed into a boundary path according to segment numbering, and the start and end points are checked to see if they are within the allowed closure range, and whether the boundary path has self-intersections. If the closure cannot be closed or self-intersection exists, the closure check result will be marked as failing and written into the quality check data.

[0055] The management terminal performs topological constraint verification on the boundary acquisition trajectory data based on the immutable structural constraint data corresponding to the real estate identification data. In this embodiment, the immutable structural constraint data comes from the building basic archives and may include structural boundary expressions such as "shear wall boundary, pipe shaft boundary, stairwell boundary, elevator shaft boundary". The management terminal checks whether the boundary path crosses the above structural boundaries. If it does, the topological constraint verification result is marked as failing and written into the quality verification data.

[0056] When all quality verification data pass, the management end generates boundary element data, which includes boundary line element data and corner point element data. The generation logic is as follows: first, the boundary acquisition trajectory data is smoothed and redundant points are compressed; then, corner point element data is extracted where the path curvature changes significantly, and the corner point element data is connected in sequence to form boundary line element data. At the same time, each segment of boundary line element data is associated with its nearest anchor point ID to ensure that subsequent conflict comparison data and evidence chain data can be traced back to the anchor point constraint evidence.

[0057] The logic for generating the chain of evidence data in step S5 is as follows: The data collected from the task, anchor point identification data, collected image data, boundary acquisition trajectory data, quality verification data, and incremental update data are associated and encapsulated to output evidence chain data.

[0058] The evidence chain data includes anchor point identification record data, re-collection consistency data, quality verification conclusion data, and incremental update basis data.

[0059] The management terminal performs conflict comparison between the boundary feature data and the historical boundary feature data and outputs the conflict comparison data. In this embodiment, the conflict comparison data includes at least a list of boundary line feature difference segments, the length / area impact index of the difference segment, the segment number to which the difference segment belongs, and the anchor point ID associated with the difference segment.

[0060] The logic for obtaining the difference segment is as follows: overlap detection is performed on the boundary line feature data and historical boundary feature data under the same real estate identification data. If there is a non-overlapping segment or the deviation exceeds the allowable range, the segment is included in the difference segment list.

[0061] The management system performs conflict resolution on the conflict comparison data to generate incremental update data: First, the conflict comparison data is judged for conflict type. If the difference segment falls entirely within the household boundary and does not touch the boundary of common areas, it is judged as a household conflict; if the difference segment involves the boundary of common areas, it is judged as a common area conflict; if the difference segment is close to the household boundary and contradicts the historical boundary element data of adjacent households, it is judged as an adjacent association conflict.

[0062] For indoor conflicts, the management system prioritizes checking the anchor point visibility verification results, closure verification results, topology constraint verification results, and re-collection consistency data in the quality verification data. If all of these are passed and the re-collection consistency is consistent, the boundary element data is written into the incremental update data; otherwise, supplementary collection task data is generated and the anchor point ID corresponding to the mandatory anchor point list data in the supplementary collection segment and the boundary collection trajectory data corresponding to the conflict are required.

[0063] For conflicts involving shared areas, the management system will upgrade the data collection task to a dual-person data collection task and regenerate segmented data collection task data to reduce errors and disputes caused by single-person data collection.

[0064] For adjacent property conflicts, the management end generates adjacent property identification data based on the property identification data and triggers the linkage data collection task. After the linkage data collection is returned, incremental update data is generated in a unified manner to ensure the consistency of adjacent property boundary updates.

[0065] The logic of conflict resolution is as follows: Determine the conflict type from the conflict comparison data.

[0066] If the conflict type is indoor conflict, then generate supplementary data collection task data and require the anchor point ID corresponding to the list of mandatory anchor points within the supplementary collection segment and the boundary collection trajectory data corresponding to the conflict.

[0067] If the conflict type is a shared part conflict, the data collection task will be upgraded to a two-person data collection task and the segmented data collection task will be regenerated.

[0068] If the conflict type is an adjacent association conflict, adjacent real estate identification data is generated based on the real estate identification data, triggering the linkage collection task data, and incremental update data is updated based on the linkage collection task data. After the incremental update data is generated, the management terminal associates and encapsulates the collection task data, anchor point identification data, collected image data (or privacy-processed image data), boundary collection trajectory data, quality verification data, and incremental update data, and outputs evidence chain data; the evidence chain data includes anchor point identification record data, re-collection consistency data, quality verification conclusion data, and incremental update basis data.

[0069] The incremental update data in this embodiment includes at least the conflict comparison data summary, the adjudication type, whether supplementary collection was triggered, records of dual-person collection and joint collection, the final adopted boundary line element data version number, and an irreversible summary mark is generated for the entire evidence chain data and written into the geographic information database.

[0070] Step S6 includes the following sub-steps: Step S601: Generate geographic information update result data based on incremental update data.

[0071] Step S602: Generate evidence summary data based on the evidence chain data and associate it with the geographic information update result data, and output the output result data.

[0072] Step S603: Transmit the output results data to the business terminals, which include the property file business terminal, the fire safety verification business terminal, and the rental and sales appraisal business terminal.

[0073] The management system generates geographic information update result data based on incremental update data, and generates evidence summary data based on evidence chain data and associates it with geographic information update result data. The output result data includes at least geographic information update result data, evidence summary data, and real estate identification data.

[0074] The management end transmits the output data to the business end, which includes the property file business end, the fire safety verification business end, and the rental and sales appraisal business end. For example, the property file business end receives boundary line element data and corner point element data for updating the ownership file; the fire safety verification business end receives the updated results related to the boundaries of common areas for verifying the width of passageways and the location of obstruction risk points; and the rental and sales appraisal business end receives the boundary update results and evidence summary data to reduce area disputes.

[0075] The logic for processing privacy-constrained data is as follows: Privacy constraint data is written into the collection scope data when generating data for the collection task.

[0076] The acquisition end performs range cropping and occlusion processing on the acquired image data based on the acquisition range data, and outputs privacy-processed image data.

[0077] The management system uses privacy-processed image data instead of collected image data to participate in the generation of quality verification data and evidence chain data.

[0078] With the collection range data already written into the privacy constraint data, the collection end performs range cropping and occlusion processing on the collected image data based on the collection range data, and outputs privacy-processed image data; where range cropping is used to remove screen segments that are not in the list of allowed access areas, and occlusion processing is used to blur or occlude areas in the image that may involve privacy.

[0079] In subsequent anchor point visibility verification, the management system uses privacy-processed image data instead of collected image data to participate in the quality verification data generation. When encapsulating the evidence chain data, it records a consistency summary between the privacy-processed image data and the original collected image data, so that the output data meets privacy constraints and does not destroy the traceability link.

[0080] This invention generates collection task data through event-triggered data and real estate identification data, and arranges the collection task data into segmented collection task data based on anchor point identification data. This ensures that the collection terminal must identify and record the list of anchor points that must be scanned within the segment when collecting image data and boundary collection trajectory data. This guarantees that the collection object and collection location can be verified from the source, avoiding the problem of the collection data not being able to prove ownership in traditional methods.

[0081] Furthermore, the management end generates quality verification data through anchor point visibility verification, closure verification, and topological constraint verification. After the quality verification data passes, boundary feature data is generated, realizing the structured output of boundary line feature data and corner point feature data, avoiding error propagation and uninterpretability caused by relying solely on post-processing alignment.

[0082] The management system compares boundary element data with historical boundary element data to form conflict comparison data. Based on the conflict type, it executes conflict resolution for supplementary data collection tasks, dual-person data collection tasks, or joint data collection tasks, forming incremental update data. This enables an operable and verifiable update logic in typical dispute scenarios such as indoor conflicts, shared area conflicts, and adjacent related conflicts, reducing the cost of repeated site surveying and improving update timeliness.

[0083] This invention encapsulates the collected task data, anchor point identification data, collected image data, boundary collection trajectory data, quality verification data, and incremental update data into evidence chain data, and generates output result data which is transmitted to the real estate archive business terminal, fire protection verification business terminal, and rental and sales appraisal business terminal. This ensures that each geographic information update has traceable support from anchor point identification record data, recollection consistency data, and incremental update basis data, improving the credibility and compliance of cross-departmental use. At the same time, through privacy-constrained data-driven image cropping and occlusion processing, it ensures that the output result data can still be used for verification and auditing while meeting privacy requirements.

[0084] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention. All data acquisition actions in this application are carried out in compliance with the relevant data protection laws and policies of the country where the application is located and with the authorization granted by the owner of the corresponding device.

Claims

1. A method for collecting real estate geographic information mapping data based on a UAV, characterized in that, Includes the following steps: Step S1: Obtain event trigger data and real estate identification data, and combine them with privacy constraint data to generate collection task data; Step S2: Retrieve anchor point identification data from the anchor point database based on the real estate identification data, segment and arrange the data to be collected based on the anchor point identification data, output the segmented data to be collected and sent to the collection terminal. Step S3: The acquisition terminal performs guided acquisition according to the segmented acquisition task data, obtains the acquired image data and boundary acquisition trajectory data, and sends the acquired image data and boundary acquisition trajectory data back to the management terminal. Step S4: The management terminal performs anchor point visibility verification, closure verification, and topological constraint verification on the acquired image data and boundary acquisition trajectory data based on the anchor point identification data, outputs quality verification data, and generates boundary feature data from the acquired image data and boundary acquisition trajectory data when the quality verification data passes. Step S5: The management terminal performs a conflict comparison between the boundary feature data and the historical boundary feature data, outputs the conflict comparison data, performs a conflict resolution on the conflict comparison data to generate incremental update data, writes the incremental update data into the geographic information database and forms a chain of evidence data. In step S6, the management end generates output result data based on the incremental update data and the evidence chain data, and transmits the output result data to the business end.

2. The unmanned aerial vehicle based real estate geographic information mapping data collection method of claim 1, wherein, Step S1 includes the following sub-steps: Step S101: Obtain event trigger data, which includes rental and sales registration trigger data, decoration reporting trigger data, acceptance review trigger data, and inspection work order trigger data; Step S102: Obtain real estate identification data, which includes building identification data, unit identification data, floor identification data, and room number identification data; Step S103: Obtain privacy constraint data and associate it with event trigger data and real estate identification data to generate collection task data. The collection task data includes collection scope data and collection purpose data.

3. The unmanned aerial vehicle based real estate geographic information mapping data collection method of claim 2, wherein, The logic of step S101 is as follows: Perform type parsing on event-triggered data; If the event trigger data is the decoration filing trigger data or the acceptance review trigger data, then the data collection target data will be set to boundary update; If the event trigger data is the inspection work order trigger data, then the data collection target will be set to the verification of common areas; If the event-triggered data is rental / sale registration-triggered data, then the data collection target will be set as boundary update and area verification, and the corresponding privacy constraint data will be written into the data collection task.

4. The unmanned aerial vehicle based real estate geographic information mapping data collection method of claim 3, wherein, Step S2 includes the following sub-steps: Step S201: Retrieve anchor point identification data based on real estate identification data. The anchor point identification data includes anchor point ID and anchor point location description data. Step S202: The data acquisition task is segmented and arranged according to the anchor point location description data to generate segmented data acquisition task data. The segmented data acquisition task data includes the list of mandatory anchor points within the segment and the coverage requirement data within the segment. Step S203: Write the list of mandatory anchor points within the segment and the coverage requirement data within the segment into the segmented acquisition task data and send it to the acquisition terminal. The acquisition process of the acquisition terminal is constrained by the anchor point identification data.

5. The unmanned aerial vehicle based real estate geographic information mapping data collection method of claim 4, wherein, Step S3 includes the following sub-steps: Step S301: The acquisition end identifies the anchor point ID corresponding to the list of mandatory anchor points in the segment at the beginning of each segment using the onboard camera component, generates anchor point identification record data and writes it into the boundary acquisition trajectory data. Step S302: The acquisition end performs guided acquisition along the coverage requirement data within the segment, continuously acquiring acquisition image data and boundary acquisition trajectory data; In step S303, the acquisition end performs bidirectional re-acquisition of trajectory data at the same boundary, generates re-acquisition consistency data, and sends it back to the management end. The re-acquisition consistency data serves as input data for quality verification.

6. The unmanned aerial vehicle based real estate geographic information mapping data collection method of claim 5, wherein, Step S4 includes the following sub-steps: Step S401: Perform anchor point visibility verification on the acquired image data based on the anchor point identification data, output the anchor point visibility verification result and write it into the quality verification data. Step S402: Perform closure verification based on the boundary acquisition trajectory data. If the boundary acquisition trajectory data cannot be closed or has self-intersection, mark the closure verification result as failing and write it into the quality verification data. Step S403: Perform topology constraint verification on the boundary acquisition trajectory data based on the immutable structural constraint data corresponding to the real estate identification data. If the boundary acquisition trajectory data crosses the immutable structural constraint data, mark the topology constraint verification result as failing and write it into the quality verification data. Step S404: When all quality verification data are passed, boundary feature data is generated. The boundary feature data includes boundary line feature data and corner point feature data.

7. The unmanned aerial vehicle based real estate geographic information mapping data collection method of claim 6, wherein, The logic for generating the chain of evidence data in step S5 is as follows: The data collected from the task, anchor point identification data, collected image data, boundary collection trajectory data, quality verification data, and incremental update data are associated and encapsulated to output evidence chain data. The evidence chain data includes anchor point identification record data, re-collection consistency data, quality verification conclusion data, and incremental update basis data.

8. The unmanned aerial vehicle based real estate geographic information mapping data collection method of claim 7, wherein, The logic behind the conflict resolution is as follows: Determine the conflict type of the conflict comparison data; If the conflict type is indoor conflict, then generate supplementary data collection task data and require the anchor point ID corresponding to the list of mandatory anchor points within the supplementary collection segment and the boundary collection trajectory data corresponding to the conflict. If the conflict type is a shared part conflict, the data collection task will be upgraded to a two-person data collection task and the segmented data collection task will be regenerated. If the conflict type is an adjacent association conflict, then adjacent real estate identification data is generated based on the real estate identification data and the linkage collection task data is triggered, and incremental update data is updated based on the linkage collection task data.

9. The unmanned aerial vehicle based real estate geographic information mapping data collection method of claim 8, wherein, Step S6 includes the following sub-steps: Step S601: Generate geographic information update result data based on incremental update data; Step S602: Generate evidence summary data based on the evidence chain data and associate it with the geographic information update result data, and output the output result data; Step S603: Transmit the output results data to the business terminals, which include the real estate archive business terminal, the fire safety verification business terminal, and the rental and sales appraisal business terminal.

10. The unmanned aerial vehicle based real estate geographic information mapping data collection method of claim 9, wherein, The processing logic for the privacy constraint data is as follows: Privacy constraint data is written into the collection scope data when generating data for the collection task. The acquisition end performs range cropping and occlusion processing on the acquired image data based on the acquisition range data, and outputs privacy-processed image data; The management system uses privacy-processed image data instead of collected image data to participate in the generation of quality verification data and evidence chain data.

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