Rural house building remote sensing dynamic monitoring method and system and computer equipment

Abstract

The application relates to the technical field of remote sensing monitoring, and discloses a rural house building remote sensing dynamic monitoring method, a rural house building remote sensing dynamic monitoring system and computer equipment, wherein the method comprises the following steps: collecting homestead basic information investigation data, house and land integrated right confirmation registration and certificate issuing data, stock land occupation for house building graph spot data and national land space planning data to obtain rural house building basic data; performing change detection on remote sensing images of a target monitoring area to obtain new house building change detection results; comparing the new house building change detection results with approval information in the rural house building basic data to determine suspected illegal house building graph spots; issuing a mobile terminal application to patrol personnel for on-site verification to obtain illegal house building verification confirmation results; and performing visual display on the rural house building basic data, the suspected illegal house building graph spots and the illegal house building verification confirmation results in a preset cloud management platform, so that the accuracy of rural house building change detection is improved, and full-process digital management is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing monitoring, and particularly relates to a rural house construction remote sensing dynamic monitoring method and system and computer equipment. BACKGROUND

[0002] Traditional rural house construction supervision mainly relies on manual patrol and report verification, and has problems such as limited supervision coverage, late discovery of illegal construction, and low supervision efficiency. The existing supervision mode is difficult to realize real-time monitoring of house construction activities in vast rural areas, and often cannot discover illegal construction until a long time after the illegal construction occurs, missing the best disposal opportunity, resulting in the supervision dilemma of low illegal construction cost and high law enforcement cost. Another prominent problem facing current rural house construction supervision is that basic data is scattered and information island phenomenon is serious. This scattered data state causes the supervision department to be unable to comprehensively grasp the overall situation of rural house construction in the region, affecting the accuracy of illegal construction identification and the scientificity of supervision decision-making. SUMMARY

[0003] The present application provides a rural house construction remote sensing dynamic monitoring method, system and computer equipment, which improves the accuracy of rural house construction change detection and realizes full-process digital management.

[0004] In a first aspect, the present application provides a rural house construction remote sensing dynamic monitoring method, which comprises:

[0005] Summarizing the homestead basic information investigation data, the house and land integrated right confirmation registration and certificate issuance data, the stock land occupation for house construction polygon data and the national space planning data to obtain rural house construction basic data;

[0006] Based on the rural house construction basic data, change detection is performed on the remote sensing image of the target monitoring area to obtain new house construction change detection results;

[0007] According to the new house construction change detection results and the approval information in the rural house construction basic data, suspected illegal house construction polygons are determined;

[0008] The suspected illegal house construction polygons are issued to the patrol personnel through a mobile terminal application for on-site verification to obtain illegal house construction verification confirmation results;

[0009] In the preset cloud management platform, the rural house building basic data, the suspected illegal house building graph spot and the illegal house building verification confirmation result are visually displayed, wherein the geographical position distribution of the suspected illegal house building graph spot is subjected to spatial clustering analysis and monitoring frequency statistics to obtain illegal house building spatial activity track data; a spatial position correlation monitoring parameter adjustment mechanism is established based on the illegal house building spatial activity track data analysis of illegal house building occurrence law in different geographical positions, a full-coverage spatial repeated learning control law is obtained; the monitoring focus and monitoring frequency of different geographical regions are adaptively adjusted according to the full-coverage spatial repeated learning control law, and an adaptive spatial monitoring and control scheme is obtained; the adaptive spatial monitoring and control scheme is applied to the visual display updating process of the cloud management platform, and an adaptive optimized visual display result is obtained.

[0010] In combination with the first aspect, in a first implementation manner of the first aspect of the present application, the homogenization of the homestead basic information investigation data, the house and land integrated right confirmation registration and certificate issuing data, the stock land occupation by illegal house building graph spot data and the national space planning data is performed to obtain the rural house building basic data, including:

[0011] The spatial position coordinates, area boundary, ownership relationship and approval time in the homestead basic information investigation data are extracted to obtain homestead information records;

[0012] The building area, building height, building structure and use function in the house and land integrated right confirmation registration and certificate issuing data are extracted to obtain house right confirmation information records;

[0013] The illegal building position, illegal building type, illegal building area and disposal state in the stock land occupation by illegal house building graph spot data are extracted to obtain historical illegal building information records;

[0014] The homestead information records, the house right confirmation information records, the historical illegal building information records and the national space planning data are subjected to data homogenization to obtain the rural house building basic data.

[0015] In combination with the first aspect, in a second implementation manner of the first aspect of the present application, the remote sensing image of the target monitoring region is subjected to change detection based on the rural house building basic data to obtain new house building change detection results, including:

[0016] The remote sensing image of the target monitoring region is acquired, and the remote sensing image is corrected to obtain a quality corrected remote sensing image;

[0017] The quality corrected remote sensing image is subjected to geometric correction and spatial registration to obtain a spatial standardized remote sensing image, and the spatial standardized remote sensing image is subjected to time sequence arrangement according to the shooting time information to obtain a rural house building remote sensing image sequence;

[0018] Change detection is performed on the rural house remote sensing image sequence based on the rural house foundation data to obtain a new rural house change detection result.

[0019] In a third implementation form of the first aspect, the change detection is performed on the rural house remote sensing image sequence based on the rural house foundation data to obtain a new rural house change detection result, including:

[0020] Feature extraction is performed on the rural house remote sensing image sequence to obtain house recognition feature data containing rural building location, area, shape and spectral features;

[0021] Pixel-level difference calculation and target change analysis are performed on images of different times based on the house recognition feature data to obtain change region detection results of building addition, expansion and removal;

[0022] Spatial superposition and attribute matching analysis are performed on the change region detection results based on the rural house foundation data to screen out change regions that do not match existing building records, and a new rural house change detection result is obtained.

[0023] In a fourth implementation form of the first aspect, the spatial superposition and attribute matching analysis are performed on the change region detection results based on the rural house foundation data to screen out change regions that do not match existing building records, and a new rural house change detection result is obtained, including:

[0024] Change region spatial information containing location coordinates, area range and geometric shape is determined according to the change region detection results;

[0025] Spatial superposition analysis is performed on the change region spatial information and homestead information records in the rural house foundation data to identify suspected illegal location change regions;

[0026] Attribute matching retrieval is performed on the suspected illegal location change regions on house right information records and historical illegal building information records in the rural house foundation data to obtain unrecorded building change regions;

[0027] Building activity type discrimination and scale estimation are performed based on the unrecorded building change regions to obtain a new rural house change detection result.

[0028] In a fifth implementation form of the first aspect, the new rural house change detection result is compared with approval information in the rural house foundation data to determine suspected illegal house polygons, including:

[0029] extract building position coordinates, building area, construction time and building type from the new rural house change detection result and construct a new rural house feature information table;

[0030] According to the position coordinates in the new rural house feature information table, the homestead information record in the rural house foundation data is spatially queried and the approval state is retrieved, and a suspected plot of building without approval is identified;

[0031] The building area and position information in the new rural house feature information table are subjected to deviation analysis with the corresponding approved area and approved position in the rural house foundation data, and suspected plots of building small and building large and building A and building B with approved area are identified;

[0032] Based on the position coordinates in the new rural house feature information table, the land use property query and compliance determination are performed on the national space planning data in the rural house foundation data, the building position in the cultivated land protection area is identified, and then the suspected plot of building without approval, the suspected plot of building small and building large and building A and building B with approved area and the building position in the cultivated land are classified and summarized to obtain a suspected illegal house plot.

[0033] In combination with the first aspect, in a sixth implementation manner of the first aspect of the application, the suspected illegal house plot is issued to the patrol personnel through a mobile terminal application for on-site verification to obtain an illegal house verification confirmation result, which includes:

[0034] The suspected illegal house plot is subjected to position information extraction and task allocation to obtain a verification task list;

[0035] The verification task list is pushed to the corresponding patrol personnel through a mobile terminal application, and the mobile terminal application provides a mobile terminal verification work interface;

[0036] Based on the mobile terminal verification work interface, the patrol personnel is guided to arrive at the suspected illegal house site for on-site measurement and investigation to obtain on-site verification data records;

[0037] According to the on-site verification data records, the mobile terminal application is used for verification conclusion determination and result uploading to obtain the illegal house verification confirmation result.

[0038] In combination with the first aspect, in a seventh implementation manner of the first aspect of the application, in a preset cloud management platform, the rural house foundation data, the suspected illegal house plot and the illegal house verification confirmation result are visually displayed, which includes:

[0039] In a preset cloud management platform, the rural house foundation data is set as a bottom map layer, the suspected illegal house plot is set as a monitoring layer, and the illegal house verification confirmation result is set as a disposal layer to obtain a display layer.

[0040] Interface layout design and interactive function configuration are performed in the cloud management platform based on the display layer, a management interface is obtained, and the management interface is subjected to plot distribution rendering, disposal progress statistics and evaluation result display to obtain a visual display result.

[0041] In a second aspect, the present application provides a rural house remote sensing dynamic monitoring system, which comprises:

[0042] A data summarizing module is configured to summarize homestead basic information investigation data, house and land integrated right registration and certificate issuance data, stock land occupation for house building plot data and national space planning data to obtain rural house basic data.

[0043] A change detection module is configured to perform change detection on remote sensing images of a target monitoring area based on the rural house basic data to obtain new house change detection results.

[0044] A comparison module is configured to compare the new house change detection results with approval information in the rural house basic data to determine suspected illegal house plots.

[0045] A field verification module is configured to issue the suspected illegal house plots to patrol personnel for field verification through a mobile terminal application to obtain illegal house verification confirmation results.

[0046] A visual display module is configured to visually display the rural house basic data, the suspected illegal house plots and the illegal house verification confirmation results in a preset cloud management platform, wherein spatial clustering analysis and monitoring frequency statistics are performed on the geographic location distribution of the suspected illegal house plots to obtain illegal house spatial activity track data; a spatial position correlation monitoring parameter adjustment mechanism is established based on illegal house spatial activity track data analysis of illegal house occurrence regularity at different geographic locations to obtain full-coverage spatial repeated learning control regularity; adaptive adjustment is performed on the monitoring focus and monitoring frequency of different geographic regions according to the full-coverage spatial repeated learning control regularity to obtain an adaptive spatial monitoring control scheme; and the adaptive spatial monitoring control scheme is applied to the visual display update process of the cloud management platform to obtain an adaptively optimized visual display result.

[0047] In a third aspect, the present application provides a computer device, which comprises a memory and at least one processor, the memory storing instructions; and the at least one processor calling the instructions in the memory to enable the computer device to perform the above-mentioned rural house remote sensing dynamic monitoring method.

[0048] The technical scheme provided by the present application breaks the traditional information island state through unified collection and processing of homestead basic information investigation data, house and land integrated right registration and certificate issuing data, stock land occupation for house building plot data and national space planning data, and establishes a complete rural house building basic data bottom plate. Based on the data bottom plate, remote sensing image change detection is carried out, combined with multi-dimensional information such as historical building distribution, approval information and planning constraints, the accuracy of rural house building change detection is significantly improved. Through multiple comparison and analysis with approval information, the types of illegal house building such as unapproved construction, small approved construction, large approved construction, approved construction and land occupation can be accurately identified, and classified identification and accurate positioning are realized. A complete closed-loop management mechanism from suspected illegal discovery, mobile terminal issuing, field verification to result confirmation is constructed, and full-process digital management is realized. Through the one-map display function of the cloud management platform, the basic data, monitoring results and disposal progress are visualized and displayed, which provides a visual and convenient supervision tool for the management department, and effectively improves the intelligent level and management efficiency of rural house building supervision.

[0049] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the description, claims and drawings.

[0050] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 An embodiment schematic diagram of the rural house building remote sensing dynamic monitoring method in the embodiment of the present application is shown in the figure.

[0052] Figure 2 An embodiment schematic diagram of the rural house building remote sensing dynamic monitoring system in the embodiment of the present application is shown in the figure.

[0053] Figure 3 An embodiment schematic diagram of the computer device in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0054] In order to make the purposes, technical schemes and advantages of the embodiments of the present application more clear, the technical schemes of the present application will be described in detail below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0055] The terms "comprising" and "having" and any variations thereof used in the embodiments of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a list of steps or units is not limited to the listed steps or units, but can optionally further include other steps or units not listed or can optionally further include other steps or units inherent to such processes, methods, products, or apparatuses.

[0056] To facilitate the understanding of the embodiments, first, a rural house building remote sensing dynamic monitoring method disclosed by the embodiments of the present application is introduced in detail. As shown in the figure, Figure 1 The method comprises the following steps:

[0057] 101, the base information investigation data of homestead, the house and land integrated right registration and certificate issuing data, the stock land occupation for construction plot data and the land space planning data are summarized to obtain the rural house building basic data;

[0058] It can be understood that the execution subject of the present application can be a rural house building remote sensing dynamic monitoring system, and can also be a terminal or a server, and the specific place is not limited. The embodiments of the present application take the server as the execution subject for example.

[0059] Specifically, the key fields are extracted from various types of original data and structured and integrated, wherein when the base information investigation data of homestead is extracted, the spatial position coordinates, area boundary line, ownership relationship and approval time corresponding to each piece of homestead are obtained, and the standardized homestead information record is formed in a unified coding manner, so as to clearly show the distribution state, boundary contour and legal attribute of each piece of homestead in geographical space; four types of core indexes related to the house body are extracted from the house and land integrated right registration and certificate issuing data, including building area value, building height layer number, building structure type and house function classification, the house right information record is generated by binding these house indexes with the homestead code, and the integration of space mapping between homestead and house is realized; for the plot data of historical land occupation for construction, the historical violation construction information record is generated by extracting the recorded violation construction position coordinates, specific types (such as unapproved construction, small construction with large approval, etc.), violation construction area and historical disposal state (such as rectification, non-treatment, etc.). The above homestead information record, house right information record and historical violation construction information record are spatially overlapped and attribute-merged with the land space planning data, the land space planning data includes boundary information such as compliant construction area, restricted construction area and farmland protection red line, a unified data fusion engine is constructed to complete the integration of various types of spatial data and attribute data in the logical coordinate system, and the rural house building basic data is formed.

[0060] 102. Change detection is performed on the remote sensing images of the target monitoring area based on the rural housing construction basic data to obtain the new rural housing change detection result;

[0061] Specifically, a remote sensing data source with sufficient spatial resolution and time resolution is selected to obtain remote sensing images covering the target monitoring area. The remote sensing images contain image elements such as building outlines, ground materials, and spatial structures. Quality correction operations are performed on the original remote sensing images, including radiation correction to eliminate atmospheric scattering and solar angle effects, spectral correction to calibrate sensor response errors, and noise suppression, etc. to ensure the accuracy of the remote sensing images in spectral characteristics and brightness information, forming quality-corrected remote sensing images. Geometric correction is performed on the remote sensing images to eliminate geometric distortion caused by earth curvature, terrain undulation, or platform attitude. Through spatial registration technology, the remote sensing images and the rural housing construction basic data are aligned in the same coordinate system, realizing the alignment of the images and the basic data in two-dimensional or three-dimensional geographic space, and obtaining spatially standardized remote sensing images. According to the shooting time stamp information of each remote sensing image, the spatially standardized remote sensing images are sequentially arranged in chronological order to form a rural housing remote sensing image sequence with time logic. The rural housing construction basic data is used as the reference standard for change detection. Through change detection algorithms such as multi-temporal image difference analysis, change vector analysis, and time series convolution analysis, new changes in building areas are identified in the remote sensing image sequence. The historical buildings and compliant construction polygons registered in the basic database are excluded, and the newly added or significantly expanded building areas in the latest images compared with the historical images are highlighted to obtain the new rural housing change detection result.

[0062] 103. According to the new rural housing change detection result and the approval information in the rural housing construction basic data, the suspected illegal housing polygons are determined;

[0063] Specifically, the core information of all identified newly-built rural houses is extracted from the newly-built rural house change detection results, including the spatial position coordinates of the building, the actual building area, the detected construction time, and the preliminary determination of the building type (such as residential, auxiliary housing, or productive housing), and these elements are summarized in the form of a newly-built rural house element information table according to the standard field format. Taking the position coordinate field in the newly-built rural house element information table as the spatial query condition, the corresponding homestead information record is retrieved in the rural house building basic data, and the focus is on searching whether there is valid building approval information at this location, including whether there is a matching approval record, whether the approval time and the construction time are consistent, and whether the ownership relationship is consistent. When no corresponding approval item is retrieved or the construction time is earlier than the approval time, it is determined that the building has the possibility of "building before approval", and the corresponding plot is marked as a "suspected plot of building before approval". In order to identify the two types of irregular forms of "building larger than approved" and "building different from approved", the building area and center coordinates in the newly-built rural house element information table are used as the basis to query the approved area and approved location in the basic data, and the area overrun ratio and position offset distance are calculated through numerical deviation analysis algorithm. When the actual value of the area is higher than the approved area or the center point of the building deviates from the approved specified location by more than a preset threshold (such as 5 meters, 10 meters, etc.), it is respectively identified as a suspected case of "building larger than approved" or "building different from approved", and the plot is classified and marked. At the same time, the location of the newly-built rural house is used as the input condition to query the land use nature in the rural house building basic data of the national land space planning layer, especially the high-restriction land types such as the farmland protection red line area and the permanent basic farmland area. Through spatial overlay analysis, it is judged whether the building falls within the farmland protection area. If there is coverage or penetration, it is identified as a "random occupation of farmland" plot, and the illegal land use type and area of the "random occupation of farmland" plot in the land use planning map are recorded. The above-identified "building before approval" plot, "building larger than approved" plot, "building different from approved" plot, and "random occupation of farmland" plot are uniformly summarized and classified, and their attribute information is uniformly numbered and structured. The output is a suspected illegal house plot dataset.

[0064] 104. The suspected illegal house plot is issued to the patrol personnel through the mobile terminal application for on-site verification, and the illegal house verification confirmation result is obtained;

[0065] Specifically, each suspected illegal building polygon is standardized, and its spatial position information, polygon code, suspected illegal type, area index, and identification time are extracted as core fields. According to the geographical distribution and number density of the polygons, and the management area or work authority of the inspectors, an automatic task allocation algorithm is used to generate corresponding verification task lists for all polygons. The verification task list clearly marks the task number, polygon coordinates, recommended inspection order, on-site key items, and completion time limit. The verification task list is pushed to the corresponding responsible person through a dedicated mobile application platform. The mobile application has a built-in verification task management interface that supports task queries, navigation guidance, on-site photography, measurement input, and result submission functions. This ensures that each inspector can view the list of polygons to be verified and the basic attributes of each polygon on the platform. The one-key positioning function can directly link the polygon location to the map navigation interface to help the inspector efficiently reach the site via the shortest path. After arriving at the site, the mobile application provides a standardized verification operation interface to guide the inspector to perform measurement, investigation, and evidence collection tasks according to the set process, including building size measurement, structure photography, use property description, surrounding land use observation, and homeowner interview recording. All on-site collected data are automatically collected as on-site verification data records in the form of text and images. The inspector is guided to make a preliminary judgment on the polygon properties based on the observed and collected information, determine whether it is a true violation, and if it is, which type of violation it belongs to. The inspector fills in the verification conclusion and additional remarks through the mobile application and uploads all information to the cloud management platform to form the illegal building verification confirmation result.

[0066] 105. In the preset cloud management platform, the rural building basic data, suspected illegal building polygons, and illegal building verification confirmation results are visually displayed.

[0067] Specifically, the basic configuration of the layer architecture is completed in the cloud management platform. Standardized and organized basic data on rural housing construction is imported into the system as a base map layer. This base map layer includes records of homestead information, house ownership information, land use planning scope, and historical illegal construction information, ensuring the platform has a spatial benchmark. Identified suspected illegal construction patches are loaded as independent monitoring layers. These monitoring layers present all suspected building boundaries in vector form and bind core fields such as violation type, detection time, and changed area through patch attributes. Simultaneously, the results of illegal construction verification are imported as a disposal layer. In this layer, each confirmed illegal construction patch is marked with the verification conclusion, disposal status, disposal time, responsible person information, and on-site photos. Different disposal stages are distinguished by color coding, such as pending verification, verified, under rectification, and demolished. Based on this layer system, the cloud management platform's interface layout and interactive functions are designed. The interface design is modularized around usage logic, including a layer switching control panel, a patch attribute query window, a disposal progress statistics chart area, and an evaluation result display area. In terms of interactive functions, the system needs to support various operation methods such as click-to-click pop-ups for map features, attribute-linked queries, timeline scrolling, and map feature filtering and classification statistics to enhance users' ability to manipulate data and improve information perception efficiency. After the interface is officially deployed, it automatically performs map feature distribution rendering based on the loaded display layers. By setting parameters such as map feature color, boundary thickness, and transparency, it constructs a visual contrast relationship between layers, ensuring that the rural housing base map, monitoring map features, and treatment map features are clearly distributed in space without obscuring each other. At the same time, the platform calls the attribute data in the treatment layer in real time to automatically generate treatment progress statistics, including indicators such as the number of verified cases, the number of cases awaiting rectification, and the number of cases completed demolition, and embeds them into the display interface in the form of bar charts, pie charts, or dynamic charts. The verification conclusions of the verified map features are summarized and analyzed to generate statistics on the distribution of violation types, regional problem clustering trends, and treatment effectiveness evaluation reports, which are integrated into the management interface in the form of chart overlays, heat map displays, or list summaries, thus forming a visualized display of the entire process of rural housing monitoring and treatment.

[0068] In one specific embodiment, the process of performing step 101 may specifically include the following steps:

[0069] Extract spatial location coordinates, area boundaries, ownership relationships, and approval dates from the basic information survey data of homesteads to obtain homestead information records;

[0070] Extract the building area, building height, building structure, and usage function from the integrated real estate registration and certification data to obtain the housing ownership information record;

[0071] Extract the location, type, area, and status of illegal constructions from existing data on illegal construction on farmland to obtain historical records of illegal construction information;

[0072] The rural house building basic data is obtained by data aggregation of the homestead information record, the house right information record, the historical illegal building information record and the national space planning data.

[0073] Specifically, from the homestead base information survey data, the information items describing the spatial and administrative attributes of the homestead are extracted, and the spatial position coordinates are obtained to mark the geographic location of each homestead in the form of longitude and latitude. At the same time, the area boundary information is extracted, and the homestead boundary is expressed in the form of a closed polygon, and is formatted and coded according to the surveying and mapping technical specifications issued by the State Bureau, so that the boundary data has a unified vector format and spatial reference system. The ownership relationship field corresponding to the homestead is extracted synchronously, and the field information of the homestead owner or user identity, household registration, ID number, and household code is recorded. The approval time is used as a time tag for the legality of the homestead use, and the date of issue of the approval document and the approving authority are recorded. The spatial position, area boundary, ownership information, and time tag are combined to form a standard structure of homestead information record, and are indexed by a unique homestead number. From the house and land integrated right registration and certificate issuance data, the information fields that can reflect the actual construction status of the house are extracted, and are spatially associated with the corresponding homestead. The building area is the core field of house registration, which records the legal building area and total floor area, so as to calculate the area deviation with the remote sensing interpreted building outline; the building height is an important indicator to evaluate the scale of building structure, and its maximum vertical height is obtained by manual measurement or elevation data extraction, which is used to assist in judging the building category and over-limit behavior; the building structure information indicates the main building materials and load-bearing structure types, such as brick-concrete, steel, light steel, and wood structure, to support the feature mapping of remote sensing image texture recognition model; the building use function provides the basis for classified supervision, and clearly indicates whether the house is a self-owned house, an auxiliary house, a breeding facility, or a production purpose. The above fields are composed of house right information records according to field standards, supplemented by right certificate number, right time, and right person information, and are associated and matched with homestead information records through house and land spatial integration numbering. At the same time, the historical illegal construction information extracted from the illegal construction plot data of occupied cultivated land is extracted. The position coordinate information of each historical illegal construction plot is extracted, which is recorded in the form of center point or boundary surface, and is uniformly referenced to the coordinate system; when extracting the illegal construction type field, the behavior characteristics are classified and labeled, such as building before approval, building larger than approved, building type B instead of type A, or occupying cultivated land; the illegal construction area field records the actual occupied area and building area of the building corresponding to the plot, which is extracted by plane calculation method or building contour recognition method, and the calculation method identifier is retained; the disposal state field synchronously labels the processing progress of the illegal construction plot, such as having been demolished, having been rectified, being disposed, or not being processed, etc., forming a closed loop of historical problem data. After the historical illegal construction information record is formed, it is spatially or attributively associated with the homestead number. The homestead information record, house right information record, and historical illegal construction information record are taken as three types of core data units, which are fused with the national spatial planning data to form the rural house building basic data set.The land space planning data includes boundary information of construction land planning area, restricted development area, permanent basic farmland protection area, ecological red line area, and is loaded into the system in the form of GIS layer, and is subjected to spatial superposition analysis with the three types of records in the unified coordinate reference.

[0074] In a specific embodiment, the process of performing step 102 can specifically include the following steps:

[0075] Obtain remote sensing images of the target monitoring area, and correct the remote sensing images to obtain quality-corrected remote sensing images;

[0076] Perform geometric correction and spatial registration on the quality-corrected remote sensing images to obtain spatially standardized remote sensing images, and perform time sequence arrangement according to the shooting time information of the spatially standardized remote sensing images to obtain a sequence of rural house construction remote sensing images;

[0077] Based on the rural house construction basic data, the sequence of rural house construction remote sensing images is subjected to change detection to obtain a new rural house construction change detection result.

[0078] Specifically, the original remote sensing images covering the target monitoring area are extracted from a high-resolution remote sensing image acquisition system. The image sources are optical remote sensing satellites, aerial photography platforms, or unmanned aerial vehicle multi-spectral imaging devices. The original remote sensing images are processed for quality correction. Due to various interference factors such as radiation distortion, atmospheric interference, illumination differences, and photosensitive deviation, preprocessing operations including radiation correction, brightness normalization, atmospheric correction, and image enhancement are performed. Radiation correction is calculated through sensor calibration parameters and solar elevation angle correction formula to eliminate sensor response nonlinearity problems. Atmospheric correction uses algorithms based on physical models such as FLAASH or QUAC modules to subtract the influence of aerosol scattering. Image enhancement uses local contrast enhancement or edge sharpening operations based on the target scene to generate quality-corrected remote sensing images. The remote sensing images are processed for geometric correction and spatial registration to form spatially standardized remote sensing images that can be spatially overlaid and regionally aligned. In the geometric correction stage, high-precision terrain data (such as digital elevation model DEM) and control points (such as real estate reference points, road intersections, and bridge centers) are used for geometric distortion correction. Through a set of affine or polynomial transformation models, the images are reprojected into the standard map projection system and matched to a unified geographic coordinate system (such as WGS 84). Spatial registration is then performed to align different shooting phase remote sensing images at the pixel level, ensuring consistent spatial positions of the same building in different time images. The registration methods include SIFT algorithm based on feature point matching, gray optimization algorithm based on mutual information, or registration network based on deep learning. After registration, all remote sensing images have a unified geospatial reference, forming a spatially standardized remote sensing image set. Based on the shooting time label of each remote sensing image, all spatially standardized images are sorted in chronological order to form a rural house remote sensing image sequence with a clear time axis structure. The rural house remote sensing image sequence is labeled with metadata such as image shooting time, image source, resolution, coverage area, and quality level to construct a remote sensing time stack. Based on the rural house foundation data, the rural house remote sensing image sequence is subjected to change detection to dynamically identify new rural house targets. The change detection process introduces a spatial overlay mechanism and a feature extraction model to determine whether there is new building behavior by comparing the differences in ground targets between different time images. In each remote sensing image, a building recognition model is applied to extract the building area. The building recognition model uses a deep segmentation structure based on convolutional neural networks, such as U-Net, Mask R-CNN, or Transformer fusion architecture, and identifies buildings with typical geometric structures and spectral features through pre-trained samples to extract element information such as outline, area, boundary, and center position.The building extraction results in the same space unit in the time sequence are subjected to difference analysis, and whether new building area or building contour form appears obvious expansion between the previous phase and the current phase is compared. If the difference area does not exist in the previous phase, is clearly distinguishable in the current phase, and is not included in the existing building plot recorded in the rural house building basic data or the approval information, it is preliminarily determined as a new rural house. In order to improve the robustness of the change detection, a multi-dimensional threshold judgment mechanism is introduced, and a minimum area change threshold is set to filter non-substantial changes caused by image errors. At the same time, seasonal temporary structures or farmland shielding misjudgments are screened out by combining with the time span limit. The change detection module outputs the new rural house change detection result data set, including the spatial position coordinates, building area, change time, structure form, spectral attribute and other index fields of the new rural house.

[0079] Before the change detection of the remote sensing image sequence of the rural house building based on the rural house building basic data, the method further includes: performing image quality evaluation, time coverage continuity analysis and spatial coverage integrity calculation processing on the obtained multi-source remote sensing images of the target monitoring area, to obtain image evaluation parameters including image definition index, time sequence continuity and spatial coverage rate; establishing a rural house building monitoring remote sensing image multi-element optimization selection model based on the image evaluation parameters, setting the image definition index as a quality optimization target, the time sequence continuity as a time correlation optimization target, and the spatial coverage rate as a monitoring range optimization target, to obtain a three-element optimization objective function; performing constraint optimization calculation and weight distribution processing on the multi-source remote sensing images according to the three-element optimization objective function, solving the optimal image combination scheme by a genetic algorithm, and obtaining an optimized monitoring image set; performing image data slicing and redundant configuration processing on the optimized monitoring image set according to the rural house building monitoring requirements, distributing main and backup image data according to the importance of the monitoring area, and obtaining a distributed monitoring image data group; performing data integrity verification and quality consistency check processing on the distributed monitoring image data group, ensuring the reliability of the monitoring data through cross-validation and compensation reorganization, and obtaining a rural house building remote sensing image sequence.

[0080] In a specific embodiment, the step of performing change detection on the rural house building remote sensing image sequence based on the rural house building basic data can specifically include the following steps:

[0081] Performing feature extraction on the rural house building remote sensing image sequence to obtain house recognition feature data containing rural building position, area, shape and spectral features;

[0082] Performing pixel-level difference calculation and target change analysis on images of different times based on the house recognition feature data to obtain change area detection results of building addition, expansion and removal;

[0083] According to the rural house building foundation data, the change area detection result is subjected to spatial superposition and attribute matching analysis, the change area inconsistent with the existing building record is screened out, and the new rural house change detection result is obtained.

[0084] Specifically, for rural housing remote sensing image sequences, a deep learning algorithm is used to process each time-phase image one by one, and the housing recognition feature data including the spatial position, contour shape, actual area and spectral reflection characteristics of the building are extracted. To improve the recognition accuracy, a convolutional neural network model based on multi-scale feature fusion is used, such as the U-Net variant with an encoding-decoding structure or the Mask R-CNN improved structure combined with a spatial attention mechanism. The convolutional neural network model is preloaded with a large number of rural building samples in the training stage, and fully learns the texture structure, edge contour and spectral feature response in the remote sensing image, such as the reflection band of roof material, the shadow shape caused by height projection, the spatial pattern of building arrangement, etc. After the model identifies the building targets in each period of remote sensing image, a standardized building polygon set is formed, each polygon contains the position coordinates of the building (described in the form of center point or boundary polygon), the building area (converted from the pixel count within the boundary), the geometric shape of the building (such as aspect ratio, rectangularity, edge complexity, etc.), and the multi-band spectral feature vector of the building roof (selected from red, green, blue, near-infrared, etc. Multiple band combinations), the above information together constitutes the housing recognition feature data. Based on the time sequence, the housing recognition feature data is paired in a time-phase aligned manner, and the pixel-level difference calculation and building target change analysis are performed on the remote sensing images of different time. Pixel-level difference calculation requires direct difference of spectral reflection value, edge gradient and texture structure of the same position pixel after image registration, to identify the changed pixel points from non-building area to building area or from building area to non-building area; building target change analysis not only pays attention to pixel-level change, but also analyzes the spatial state transition from the whole building level, including the appearance of new polygons (representing new buildings), the expansion of original polygons (representing expansion behavior) or the disappearance of original polygons (representing demolition events). For such target-level analysis, the IoU (Intersection over Union) matching algorithm is used to establish a corresponding relationship between the building polygons in the previous and subsequent time phases. When a polygon does not exist in the previous time phase but appears in the subsequent time phase, it is marked as "newly added"; when the polygon boundary significantly expands in the two periods while maintaining positional consistency, it is marked as "expanded"; when the polygon is completely missing in the subsequent time phase and is not replaced by other polygons, it is determined as "demolished". All the newly added, expanded and demolished building polygons identified constitute the change area detection result, forming a time-layers clear and spatial boundary clear change target set. The change area detection result is spatially overlaid and attribute-matched with the rural housing basic data. The spatial overlay operation is performed to analyze the geometric intersection of each change polygon with the homestead information record, the housing right information record and the historical illegal building polygon record, to determine whether it is within the registered homestead, whether it has a housing right record or whether it belongs to the historical known illegal building boundary area.If the change plot is completely within the existing legal building plot and its shape and area do not exceed the set tolerance range of the known plot boundary, it is considered as existing building maintenance behavior and is excluded; if the change plot overlaps with the legal homestead boundary but the area is significantly expanded beyond the original approved value, and the building structure and use function parameters do not match the original record, it is further marked as an expansion type new house; if the change plot is completely located on an unregistered homestead or non-construction land, and there is no right record, no approval information, and no historical plot corresponding in the basic data, it is initially determined as a new rural house without approval. In the attribute matching process, the construction time label (determined by the remote sensing image shooting time) in the change plot is compared with the homestead approval time for time sequence consistency verification. If the construction time is earlier than the approval time or the two are completely mismatched, it is an important basis for distinguishing new rural houses. Combined with the national space planning layer, it is judged whether the change plot is located within the red line of cultivated land protection, restricted construction area or ecological control area. If it belongs to such prohibited building areas and has no legal land procedures, it is marked as illegal new building behavior of occupying cultivated land. After completing the spatial intersection analysis and attribute rule matching, the change plot that does not match the existing legal building record in the rural building foundation data is selected from the original change area detection result, and all registered, approved or disposed building targets are excluded. The plot set of newly appeared, area suddenly increased or attribute contradictory is retained to form the new rural house change detection result.

[0085] In a specific embodiment, the performing step performs spatial superposition and attribute matching analysis on the change area detection result according to the rural building foundation data, screens out the change area that does not match the existing building record, and obtains the new rural house change detection result. The process can specifically include the following steps:

[0086] According to the change area detection result, determine the change area spatial information including position coordinates, area range and geometric shape;

[0087] Superimpose and analyze the change area spatial information and the homestead information record in the rural building foundation data in space to identify the suspected illegal position change area;

[0088] According to the suspected illegal position change area, perform attribute matching retrieval on the house right information record and historical illegal building information record in the rural building foundation data to obtain the unrecorded building change area;

[0089] Based on the unrecorded building change area, perform building activity type discrimination and scale estimation to obtain the new rural house change detection result.

[0090] Specifically, the geometric element analysis is performed on the newly added, expanded or reconstructed building targets identified in the change area detection results to extract the core spatial parameters of each change polygon, including location coordinates, area range and geometric shape information. The location coordinates are represented by the geometric center point of the polygon or the boundary envelope box. The area range is calculated based on the pixel area within the polygon boundary and the actual area conversion according to the spatial resolution of the remote sensing image. The geometric shape information includes the complexity of the boundary structure, the length-width ratio of the contour, and the shape regularity. Through the above process, the change area detection results are converted into a set of structured spatial information of the change area. The spatial information of the change area is overlaid with the homestead information records in the rural housing foundation data to determine whether each change is within the approved construction range. The homestead information records are stored in the form of polygons, and the boundaries correspond to the legally approved range of the homestead. In the spatial overlay analysis process, the face-face intersection and point-in-polygon determination methods are used to geometrically compare the change polygons with the homestead boundaries to identify whether they are completely contained within a certain homestead or exceed the boundary and expand outward. If a change polygon is located in a non-homestead area or overlaps with a homestead boundary with a low overlap ratio (e.g., 60%), it is preliminarily determined as a suspected illegal location change area. The suspected illegal location change area is matched with the house right information records and historical illegal construction information records in the rural housing foundation data to determine whether the change area has been registered or listed as a past illegal polygon. The attribute matching retrieval is based on the polygon spatial number, spatial position overlap, building area range proximity, and right coding correspondence to make a comprehensive judgment. Especially when the center point or main boundary of a change polygon is located within a righted house polygon, and the area and shape are within the reasonable tolerance of the original registration information, it is considered as an extension or update of the registered polygon, and does not need to be recorded again. For those areas that cannot find corresponding records in the right information library and are not historical illegal polygons, they are marked as unrecorded building change areas. Based on the shape characteristics, area indicators and time nodes of the polygons, the type of building activities is intelligently identified, and the construction scale is estimated. The building activity type is determined by comparing the characteristic parameters of the polygon change with the historical sample model. If the polygon area is small, the shape is simple, and the change period is short, it is determined as a simple temporary building or auxiliary housing. If the polygon structure is regular, the area is within the permitted range of the homestead area, and the change trajectory follows the construction cycle, it is determined as a standard residential new house. If the polygon boundary is complex, the area significantly exceeds the homestead area, or it shows non-residential functional characteristics (such as abnormal roof reflection and location close to farmland), it is determined as illegal industrial construction, breeding farm or temporary processing point.On the basis of the preliminary judgment of the building activity type, the scale of the building area, the estimated value of the number of layers, and the building volume data are calculated. Among them, the area is directly calculated from the pixel area of the plot boundary, the height estimation is indirectly calculated by combining the image shadow length and the solar elevation angle, and the building volume is calculated by multiplying the area and the estimated height, which is used for classification management and hierarchical disposal. The new building plot data set formed after the space recognition, attribute removal, type judgment and scale calculation is summarized as the new rural house change detection result, and the location coordinates, change time, area index, type classification, compliance status and risk level are clearly marked in each record.

[0091] In a specific embodiment, the process of performing step 103 can specifically include the following steps:

[0092] Extract the building location coordinates, building area, construction time and building type from the new rural house change detection result and construct a new rural house element information table;

[0093] According to the location coordinates in the new rural house element information table, the homestead information record in the rural house building basic data is spatially queried and the approval state is searched to identify the suspected plot of unapproved construction;

[0094] The building area and location information in the new rural house element information table are analyzed for deviation with the corresponding approved area and approved location in the rural house building basic data to identify the suspected plot of small approved and large built and the suspected plot of A approved and B built;

[0095] Based on the location coordinates in the new rural house element information table, the land use property of the rural house building basic data is queried and the compliance is determined to identify the building location within the cultivated land protection area, and then the suspected plot of unapproved construction, the suspected plot of small approved and large built, the suspected plot of A approved and B built, and the building location occupying cultivated land are classified and summarized to obtain the suspected illegal house plot.

[0096] Specifically, the key fields of each building footprint in the new rural housing change detection results were extracted, including building location coordinates, building area, construction time, and building type. The building location coordinates were obtained by the geometric center point of the footprint or the minimum circumscribed rectangle calculation, and were projected into the system standard coordinate system. The building area was obtained by the pixel number of the footprint on the remote sensing image and the resolution conversion to the actual land area, with a quantitative error control boundary. The construction time was archived based on the remote sensing image shooting time, representing the time point when the building first appeared in the remote sensing sequence. The building type was determined as residential, auxiliary housing, or other building types according to the geometric shape, spectral characteristics, and context environment information of the change footprint, combined with the deep learning recognition model results. The above information was integrated into a standard field format to construct the new rural housing feature information table. Taking the building location coordinates in the new rural housing feature information table as the retrieval condition, the homestead information record was called in the rural housing foundation data for spatial query and approval status retrieval to identify the objects that were constructed but had no approval record. The spatial query process used the "point in polygon" or "face and face intersection" analysis method to position the new building footprint and the homestead boundary. When a footprint was completely outside a homestead or had no corresponding number in the homestead database, it was preliminarily determined that the building was constructed outside the permitted range. If the footprint was within the legal homestead boundary but could not find the approval information consistent with the construction time in the approval record attached to the homestead, or the approved building purpose was not consistent, it was considered as illegal construction, i.e., "construction without approval". Such footprints were logically classified and marked as suspected "construction without approval" footprints, and the information table was added with fields to identify their source as "missing approval record" or "inconsistent approval time sequence". Based on the identification of "construction without approval" footprints, the building area and location in the new rural housing feature information table were used for quantitative deviation analysis with the approved area and location of the corresponding homestead in the rural housing foundation data to identify "small approved large constructed" and "approved A constructed B" two types of over-limit construction behaviors. The identification of "small approved large constructed" relied on area deviation calculation, i.e., comparing the actual area of the new building footprint with the approved area registered in the approval document, and setting an area deviation threshold (e.g., 15%-20%) as the judgment basis. When the footprint area exceeded the approved area by the threshold or more, it was considered as over-scale construction, and was marked as a suspected "small approved large constructed" footprint. The identification of "approved A constructed B" focused on the judgment of location deviation. The spatial offset of the new building footprint center coordinates and the planned construction location in the approval record was calculated, and the Euclidean distance measurement or polygon overlap rate analysis was used. If the two deviated more than the set tolerance range (e.g., 10 meters) and the building outline overlap was less than the set standard (e.g., below 40%), it was determined that the construction location was inconsistent with the approved location, constituting "approved A constructed B" behavior, and the footprint was marked as a suspected "approved A constructed B" footprint. Area and location deviation could be judged simultaneously, and when both exceeded the limit, it should be classified as multiple violation types.The building position coordinates in the newly-built rural house element information table are matched with the land use property and the construction compliance of the rural house foundation data in the national space planning data to identify the building targets with land use type violation. The building plot position is spatially overlaid with the land use unit in the planning layer (such as urban construction land, farmland, garden land, forest land, water area, etc.) to identify whether the building is located in the non-construction purpose range, especially to check whether it is located in the national-level restricted construction area such as the farmland protection area, the permanent basic farmland control line, and the ecological protection red line coverage area. If the building spatial position is completely or partially located in the above red line range, whether it has an approval file or not, it is considered as a land use violation behavior, classified as a random occupation of farmland plot, and the information such as the type of illegal land use, the area of illegal land occupation, and the percentage of superposition range is recorded. The suspected plot, the small plot with large building, the plot with approved type A but built type B, and the building position of random occupation of farmland are classified and summarized to form a suspected illegal house plot dataset.

[0097] In a specific embodiment, the process of performing step 104 can specifically include the following steps:

[0098] The position information of the suspected illegal house plot is extracted and the task is assigned to obtain a check task list;

[0099] The check task list is pushed to the corresponding patrol personnel through the mobile terminal application, and the mobile terminal application provides a mobile terminal check work interface;

[0100] Based on the mobile terminal check work interface, the patrol personnel is guided to arrive at the suspected illegal house site to conduct field measurement and situation investigation to obtain field check data records;

[0101] According to the field check data records, the check conclusion is determined and the result is uploaded through the mobile terminal application to obtain the illegal house check confirmation result.

[0102] Specifically, the position information of the suspected illegal building plot is extracted and a task list is generated, wherein the position information of the plot includes the longitude and latitude values of the center coordinate point, the vector description of the boundary range, and the administrative division number to which the plot belongs. Based on the extraction of the plot position, the task weight calculation is performed in combination with the illegal type identified by the plot (such as building without approval, building larger than approved, occupying cultivated land, etc.), the plot area size, the discovery time, the priority level, the patrol history state and other information. Through the setting of task scheduling rules, the plot is automatically divided according to the responsibility jurisdiction, the function range of the patrol personnel and the available human resources, a structured inspection task list is generated, and each task in the inspection task list contains unique plot number, patrol object type, on-site operation suggestion, estimated time consumption and recommended verification time period and other auxiliary information. The inspection task list is issued in the form of a digital task package to the mobile terminal application of the corresponding patrol personnel, and the mobile terminal application provides a special mobile terminal verification work interface after receiving the task. The mobile terminal verification work interface integrates a task list module, a plot map navigation module, a task detail module and a data collection module. In the plot map navigation module, the plot center point coordinates are automatically called and the map service interface is linked to realize accurate marking and path navigation of the target plot, helping the patrol personnel to quickly arrive at the scene; the task detail module displays the illegal type of the current plot, the system determination basis, the spatial position, the approval information matching state and the historical verification record; the data collection module provides diversified data input methods such as photo shooting, voice recording, text note, form filling, etc. When the patrol personnel arrive at the suspected illegal building plot site under the guidance, they complete the on-site measurement and investigation according to the standard operation process provided by the mobile terminal verification work interface. The measurement content includes the length and width of the actual building boundary, the building height estimation, the structure material record, the on-site construction state, the surrounding land use situation, etc. The investigation content includes whether the building is inhabited, whether there is a construction permit posted, whether the on-site management personnel or the homeowner's statement is consistent with the system approval record, whether the public report information is verified, etc. All on-site verification data are directly filled in the mobile terminal and structured in the interface, such as the binding of the shooting time and the plot number for the on-site photos, the automatic transcription of the voice explanation into a text copy and the entry into the note column, the association of the map ranging track or the manual input field for the size measurement, the picture tag assisted selection for the material structure, etc., to improve the accuracy and convenience of the patrol information collection. After the verification is completed, the patrol personnel complete the verification conclusion determination in the mobile terminal application according to the collected data. The process is set as a standardized determination process, including four steps of “determining whether it is an actual building”, “determining whether it is consistent with the system plot”, “determining whether it is illegal construction” and “determining the illegal type attribution”. Each step is provided with an option judgment and a text description box to support the formation of conclusions in a combination of subjective judgment and objective evidence.After the integrity check of the verification conclusion, the user is guided to upload the data in one key, the illegal house construction verification confirmation result is formed, the illegal house construction verification confirmation result is logically bound with the original plot data, and is pushed into the cloud supervision platform database, at the same time, the task state update module is triggered, the plot state is switched from "to be verified" to "has been verified", and multiple fields such as verification result, verification time, verification personnel, field photo abstract, positioning track and the like are marked.

[0103] In a specific embodiment, the process of performing step 105 can specifically include the following steps:

[0104] In the preset cloud management platform, the rural house construction basic data is set as a bottom map layer, the suspected illegal house construction plot is set as a monitoring layer, and the illegal house construction verification confirmation result is set as a disposal layer, so as to obtain a display layer;

[0105] Based on the display layer, the interface layout design and the interactive function configuration are carried out in the cloud management platform, so as to obtain a management interface, and the plot distribution rendering, the disposal progress statistics and the evaluation result display are carried out on the management interface, so as to obtain a visual display result.

[0106] Specifically, various types of remote sensing analysis and management results are classified into layers, and layer roles are set according to data usage and management logic. The rural housing foundation data is used as the background geographic information for building the display interface and is loaded as the base map layer, which includes homestead boundaries, right-confirmed building outlines, historical illegal construction records, national space planning red lines, and land use classification boundaries. The base map layer is loaded in the form of vector layers and raster base maps to ensure clear outlines, excellent rendering performance, and support for zooming, querying, and other operations. The results of remote sensing change detection and comparison analysis, such as unapproved construction, small-scale construction, and illegal land occupation, are loaded as monitoring layers. The monitoring layers are presented in a surface patch form and include fields such as spatial range, identification time, initial judgment type, change area, judgment threshold, and patch code. The verification conclusion data formed after mobile inspection is loaded as the disposal layer, which records the completed verification patches and includes attributes such as field photos, verification conclusions, verification personnel, verification time, processing suggestions, and disposal progress. The patch state in the layer is distinguished by different colors according to the verification progress, such as yellow for "to be verified," red for "confirmed illegal," blue for "rectified," and green for "removed." This realizes the visualization of disposal conditions. The base map layer, monitoring layer, and disposal layer are loaded into the same spatial projection system, and the layer attribute structure, field naming, and coordinate reference are unified to form the display layer system. Based on the display layer, the interface layout design and interactive function configuration are performed in the cloud management platform to build a management interface for daily supervision, verification scheduling, and decision analysis. The interface layout takes the "map main view" as the core component, cooperates with the layer control area, attribute information pop-up area, statistical chart area, and tool operation bar to realize function collaboration. The map main view presents the spatial distribution state of various patches and supports layer superposition, patch selection, and dynamic rendering. The layer control area allows users to switch layer display states, adjust layer transparency, and set patch highlighting rules as needed. The attribute information pop-up area displays attribute fields when the user clicks on a patch, including patch source, identification parameters, patrol records, and disposal state. The statistical chart area displays visual indicators such as patch quantity, illegal type distribution, verification completion ratio, and rectification completion progress by region. The tool operation bar provides auxiliary functions such as patch filtering, time axis browsing, patch exporting, batch marking, and task issuing. The management interface elements are rendered for patch distribution, disposal progress statistics, and evaluation result display. The patch distribution rendering sets different visual performance rules for patches on the map, such as border color, fill transparency, patch boundary style, or dynamic flashing frequency, to improve patch identification and urgency. It also supports hierarchical rendering, which sets hierarchical rendering logic according to illegal level, area size, or discovery time, making important patches more prominent.The disposal progress statistics link automatically aggregates the field data in the disposal layer, classifies and counts the total number, the number of illegal polygons, the number of disposed polygons, the distribution of various illegal behaviors and the regional distribution trend according to the polygon state, and the statistical results are dynamically rendered in the interface chart area in the form of pie chart, column chart, line chart and the like, and the chart and the layer are supported to be linked and operated, and the user can click the chart to highlight the corresponding polygon. The evaluation result display link comprehensively analyzes the polygon verification confirmation result and the disposal effect data, displays indexes such as the illegal rate, the rectification rate and the repeated occurrence rate of each township, forms a disposal effect evaluation index, and supports exporting into a report, linking a warning module or generating a heat map.

[0107] In the embodiment, in the process of visual display in the preset cloud management platform, the process further includes: performing spatial clustering analysis and monitoring frequency statistical processing on the geographical position distribution of the suspected illegal house building polygons, identifying the non-periodic spatial distribution trajectory characteristics of the rural house building illegal behavior, and obtaining illegal house building spatial activity trajectory data; constructing an adaptive spatial repeated monitoring learning model based on the illegal house building spatial activity trajectory data, establishing a monitoring parameter adjustment mechanism of spatial position correlation by analyzing the occurrence law of illegal house building in different geographical positions, obtaining a full-coverage spatial repeated learning control law; adaptively adjusting the monitoring focus and monitoring frequency of different geographical regions according to the full-coverage spatial repeated learning control law, increasing the monitoring density and updating frequency for illegal high-incidence areas, and obtaining an adaptive spatial monitoring regulation scheme; applying the adaptive spatial monitoring regulation scheme to the visual display updating process of the cloud management platform, accurately correcting through a geographical position related seasonal and regional monitoring uncertainty compensation algorithm, obtaining high-precision rural house building illegal tracking display parameters; based on the high-precision rural house building illegal tracking display parameters, performing real-time adaptive updating and accurate positioning processing on the visual interface, realizing high-precision spatial tracking display of non-periodic rural house building illegal activities, and obtaining an adaptively optimized visual display result.

[0108] The above describes the rural house building remote sensing dynamic monitoring method in the embodiment of the application, and the following describes the rural house building remote sensing dynamic monitoring system in the embodiment of the application, please refer to Figure 2 An embodiment of the rural house building remote sensing dynamic monitoring system in the embodiment of the application includes:

[0109] The data aggregation module 201 is configured to aggregate the homestead basic information investigation data, the house and land integrated right confirmation registration and certificate issuance data, the stock land occupation by building polygons data and the national space planning data, and obtain rural house building basic data.

[0110] The change detection module 202 is configured to perform change detection on the remote sensing image of the target monitoring region based on the rural house building basic data, and obtain new rural house change detection results.

[0111] The comparison module 203 is used for comparing the newly-built rural house change detection result with the approval information in the rural house building basic data to determine a suspected illegal house building plot;

[0112] The field verification module 204 is used for issuing the suspected illegal house building plot to a patrol personnel through a mobile terminal application for field verification to obtain an illegal house building verification confirmation result.

[0113] The visual display module 205 is used for visually displaying the rural house building basic data, the suspected illegal house building plot and the illegal house building verification confirmation result in a preset cloud management platform.

[0114] Through the cooperation of the above-mentioned components, the application processes the homestead basic information investigation data, the house and land integrated right registration and certificate issuing data, the stock land occupation for house building plot data and the national space planning data, breaks the traditional information island state, establishes a complete rural house building basic data bottom plate, and performs change detection on the remote sensing image based on the rural house building basic data, can combine multi-dimensional information such as historical building distribution, approval information and planning constraints for comprehensive analysis, significantly improves the accuracy of rural house building change detection, and effectively reduces the false and missed report phenomenon. The application can accurately identify four main illegal house building types of unapproved construction, small approved construction, approved construction and land occupation through multiple comparison and analysis of the approval information, realizes the classification identification and accurate positioning of illegal behaviors, and improves the pertinence and effectiveness of supervision. The application constructs a complete closed-loop management mechanism from suspected illegal discovery, mobile terminal issuing, field verification to result confirmation, realizes the whole process digital management of discovery, verification, disposal and tracking, effectively improves the supervision efficiency and disposal timeliness. The application realizes the unified visual display of the basic data, the monitoring result, the disposal progress and other information through the one-map display function of the cloud management platform, provides an intuitive and convenient supervision tool for the management department, and effectively improves the scientific nature and management efficiency of the supervision decision.

[0115] The above Figure 2 The rural house remote sensing dynamic monitoring system in the embodiment of the application is described in detail from the perspective of modular functional entities, and the computer device in the embodiment of the application is described in detail from the perspective of hardware processing.

[0116] Figure 3is a structural schematic diagram of a computer device provided by an embodiment of the present application. The computer device 300 can have great differences due to different configurations or performances, and can include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, one or more storage media 330 (for example, one or more mass storage device ends) storing application programs 333 or data 332. The memory 320 and the storage medium 330 can be temporary storage or persistent storage. The programs stored in the storage medium 330 can include one or more modules (not shown in the figure), and each module can include a series of instruction operations in the computer device 300. Furthermore, the processor 310 can be configured to communicate with the storage medium 330, execute a series of instruction operations in the storage medium 330 on the computer device 300, so as to realize the steps of the above-mentioned rural house remote sensing dynamic monitoring method.

[0117] The computer device 300 can further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that, Figure 3 The computer device structure shown does not constitute a limitation on the computer device provided by the present application, and can include more or fewer components than shown, or combine certain components, or different component arrangements.

[0118] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-mentioned system, system and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0119] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0120] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the same; even though the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some of the technical features; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for remote sensing dynamic monitoring of rural housing construction, characterized in that, include: The basic data on rural housing construction is obtained by summarizing the basic information survey data of homesteads, the data on the integrated confirmation, registration and certification of housing and land rights, the data on existing illegal occupation of cultivated land for housing construction, and the data on land and space planning. Based on the aforementioned rural housing construction data, change detection is performed on remote sensing images of the target monitoring area to obtain the results of new rural housing construction change detection. Specifically, this includes: acquiring remote sensing images of the target monitoring area and correcting the images to obtain quality-corrected remote sensing images; performing geometric correction and spatial registration on the quality-corrected images to obtain spatially standardized remote sensing images; and arranging the spatially standardized remote sensing images chronologically according to their acquisition time information to obtain a rural housing construction remote sensing image sequence; extracting features from the rural housing construction remote sensing image sequence to obtain house identification feature data containing rural building location, area, shape, and spectral characteristics; and analyzing images from different times based on the house identification feature data. Pixel-level difference calculation and target change analysis are performed to obtain the detection results of changes in building additions, expansions, and demolitions. Based on the detection results, spatial information of the changed areas, including location coordinates, area range, and geometric shape, is determined. This spatial information is then spatially overlaid with homestead information records in the rural housing construction basic data to identify suspected illegal location changes. Based on these suspected illegal location changes, attribute matching is performed on house ownership information records and historical illegal construction information records in the rural housing construction basic data to obtain unrecorded building change areas. Finally, based on these unrecorded building change areas, building activity type discrimination and scale calculation are performed to obtain the detection results of changes in newly built rural houses. Based on the comparison between the newly constructed rural housing change detection results and the approval information in the rural housing basic data, suspected illegal housing construction plots are identified; The suspected illegal construction images were sent to the inspection personnel via a mobile application for on-site verification, and the results of the illegal construction verification were obtained. In a pre-defined cloud management platform, the basic data on rural housing construction, the suspected illegal construction plots, and the verification results of illegal construction are visualized. Specifically, spatial clustering analysis and monitoring frequency statistics are performed on the geographical distribution of the suspected illegal construction plots to obtain spatial activity trajectory data of illegal construction. Based on the analysis of the spatial activity trajectory data of illegal construction, a spatial location-related monitoring parameter adjustment mechanism is established to obtain a full-coverage spatial repetitive learning control law. According to the full-coverage spatial repetitive learning control law, the monitoring focus and frequency for different geographical areas are adaptively adjusted to obtain an adaptive spatial monitoring and control scheme. This adaptive spatial monitoring and control scheme is applied to the visualization display update process of the cloud management platform to obtain adaptively optimized visualization display results.

2. The method for remote sensing dynamic monitoring of rural housing construction according to claim 1, characterized in that, The data obtained by summarizing basic information survey data on homestead land, integrated land and housing ownership registration and certification data, existing data on illegally occupied farmland for housing construction, and land and space planning data are as follows: Extract spatial location coordinates, area boundaries, ownership relationships, and approval dates from the basic information survey data of homesteads to obtain homestead information records; Extract the building area, building height, building structure, and usage function from the integrated real estate registration and certification data to obtain the housing ownership information record; Extract the location, type, area, and status of illegal constructions from existing data on illegal construction on farmland to obtain historical records of illegal construction information; The homestead information records, house ownership confirmation information records, historical illegal construction information records, and land and space planning data are aggregated to obtain basic rural housing construction data.

3. The method for remote sensing dynamic monitoring of rural housing construction according to claim 1, characterized in that, The step of comparing the results of the newly built rural housing change detection with the approval information in the basic rural housing construction data to identify suspected illegal housing construction plots includes: Extract the building location coordinates, building area, construction time and building type from the newly built farmhouse change detection results and construct a new farmhouse element information table; Based on the location coordinates in the newly built rural housing element information table, spatial queries and approval status searches are performed on the homestead information records in the rural housing construction basic data to identify suspected unapproved construction plots. By performing a deviation analysis between the building area and location information in the newly built rural housing element information table and the corresponding approved area and approved location in the rural housing basic data, suspected plots of small-scale construction and large-scale construction and approved Class A construction and Class B construction are identified. Based on the location coordinates in the newly built rural housing element information table, the land use nature query and compliance determination are performed on the land spatial planning data in the basic rural housing construction data. The building locations located within the cultivated land protection area are identified. Then, the suspected unapproved construction plots, the suspected small-scale construction and large-scale construction and the suspected Class A construction and Class B construction plots, as well as the locations of buildings illegally occupying cultivated land, are classified and summarized to obtain suspected illegal housing construction plots.

4. The method for remote sensing dynamic monitoring of rural housing construction according to claim 1, characterized in that, The process of sending the suspected illegal construction images to inspection personnel via a mobile application for on-site verification, and obtaining confirmation results of illegal construction verification, includes: Location information was extracted and tasks were assigned to the suspected illegal construction sites to obtain a verification task list; The check task list is pushed to the corresponding patrol personnel through a mobile application, which provides a mobile check interface. Based on the mobile terminal verification interface, inspectors are guided to the suspected illegal construction site to conduct on-site measurements and investigations, and on-site verification data records are obtained. Based on the on-site inspection data records, the inspection conclusions are determined and the results are uploaded via a mobile application to obtain the verification confirmation results for illegal construction.

5. The method for remote sensing dynamic monitoring of rural housing construction according to claim 1, characterized in that, The aforementioned cloud management platform provides a visual display of the basic rural housing construction data, suspected illegal construction plots, and the results of illegal construction verification, including: In the preset cloud management platform, the basic data of rural housing construction is set as the base map layer, the suspected illegal housing construction patches are set as the monitoring layer, and the illegal housing construction verification and confirmation results are set as the disposal layer, thus obtaining the display layer; Based on the display layer, the interface layout is designed and the interactive functions are configured in the cloud management platform to obtain the management interface. The management interface is then rendered with patch distribution, and the progress of the disposal is statistically analyzed and the evaluation results are displayed to obtain the visual display results.

6. A remote sensing dynamic monitoring system for rural housing construction, characterized in that, For performing the rural housing construction remote sensing dynamic monitoring method as described in any one of claims 1-5, the rural housing construction remote sensing dynamic monitoring system comprises: The data aggregation module is used to aggregate basic information survey data on homesteads, data on integrated land and housing ownership registration and certification, data on existing illegal occupation of farmland for housing construction, and land and space planning data to obtain basic data on rural housing construction. The change detection module is used to perform change detection on the remote sensing image of the target monitoring area based on the rural housing construction basic data, and obtain the change detection results of newly built rural houses. The comparison module is used to compare the detection results of the changes in newly built farmhouses with the approval information in the basic data of rural housing construction to identify suspected illegal construction plots. The on-site verification module is used to send the suspected illegal building plots to the inspection personnel via a mobile application for on-site verification and to obtain the verification results of illegal building; The visualization module is used to visualize the basic data of rural housing construction, the suspected illegal construction patches, and the verification results of illegal construction in a preset cloud management platform. Specifically, it performs spatial clustering analysis and monitoring frequency statistics on the geographical distribution of suspected illegal construction patches to obtain spatial activity trajectory data of illegal construction. Based on the analysis of the spatial activity trajectory data of illegal construction, it establishes a monitoring parameter adjustment mechanism for spatial location correlation to obtain a full-coverage spatial repetitive learning control law. According to the full-coverage spatial repetitive learning control law, it adaptively adjusts the monitoring focus and frequency of different geographical areas to obtain an adaptive spatial monitoring and control scheme. The adaptive spatial monitoring and control scheme is applied to the visualization update process of the cloud management platform to obtain adaptively optimized visualization results.

7. A computer device, characterized in that, The computer device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the computer device to execute the rural housing construction remote sensing dynamic monitoring method as described in any one of claims 1-5.

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