Intelligent management method and system for linkage of real estate surveying and mapping data and electronic certificate

Abstract

The invention relates to the field of digital real estate intelligent computing, and provides a real estate surveying and mapping data and electronic license linkage intelligent management method and system, and the method comprises the steps: obtaining a historical electronic license data package and a current surveying and mapping point cloud data stream, and constructing a license semantic expectation graph and a surveying and mapping entity observation graph; generating an initial probability incidence matrix based on the two types of maps, constructing a composite energy function containing boundary violation potential energy and surveying and mapping deformation strain energy, and generating an optimal flexible mapping matrix and a global minimum deformation energy value through Sinkhorn iteration; calculating a local residual stress index, and generating a compliance classification label, a difference attribution thermodynamic diagram and a compliance diagnosis report; generating a dynamic confidence score according to the global minimum deformation energy value and instantiating verifiable state nodes; and triggering a service flow control instruction in combination with the compliance classification label. According to the method, the elastic registration solution of the heterogeneous atlas and the composite energy function is fused, and the intelligent closed loop of the difference attribution delivery and the dynamic confidence service flow is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent computing for digital real estate, and in particular to an intelligent management method and system for linking real estate surveying data with electronic certificates. Background Technology

[0002] With the widespread application of high-precision 3D mapping technology in fields such as real estate registration and land space governance, intelligent recognition and linkage between the flexible topological semantics of historical electronic certificates and the rigid geometric data of current point clouds have become key technologies for ensuring the credibility of property rights data. However, how to effectively handle semantic and geometric heterogeneity issues and avoid confusion and misjudgment caused by reasonable measurement errors and substantial boundary violations by traditional rigid registration algorithms has become a key technical challenge that real estate intelligent management systems urgently need to solve.

[0003] Chinese patent application CN119557664A discloses a system for identifying and processing anomalies in real estate registration data. The system includes a feature extraction unit, a vector transformation unit, a data acquisition unit, a data transmission unit, a data storage unit, a data processing unit, an error verification unit, a data correction unit, and a terminal monitoring unit. The data acquisition unit is used to collect information data generated during the anomaly identification process of real estate registration data in real time, belonging to the field of real estate data anomaly identification technology. The system utilizes complex algorithms to comprehensively evaluate the data anomaly index, ensuring alarm accuracy, reducing false alarms and missed alarms, and effectively improving the efficiency of identifying anomalies in real estate registration data. Simultaneously, through multi-dimensional feature extraction and vector transformation, combined with real-time data acquisition and analysis, it can automatically detect anomalies in identity information and photo features, promptly triggering alarms and guiding subsequent verification and correction, significantly reducing the risk of human error and data tampering.

[0004] However, current technology still faces many challenges. When dealing with the boundary and ownership determination of old residential areas, such as historical land parcels like those from housing reform projects, historical electronic certificates often use natural language to describe flexible topological semantics such as "east to the wall" and "south to the passage," while current surveying data comes from high-precision 3D point clouds, which are rigid geometric data. There is a fundamental semantic and geometric heterogeneity gap between the two. In this case, if traditional rigid registration algorithms are used, they will be affected by nonlinear deformation factors such as slight crustal changes and natural foundation settlement, making it difficult for the system to distinguish between reasonable measurement errors and substantial violations involving changes in ownership. If the system fails to make flexible judgments based on the logic of maintaining constant relative positional relationships and instead forcibly performs coordinate overlay, it will lead to problems such as misjudgment of infringement or incorrect boundary point reporting at the data level. This will cause compliant surveying data to be incorrectly locked, preventing the triggering of automatic release processes, thus relying on time-consuming manual review for intervention, missing valuable data update opportunities, and affecting the efficiency and ultimate credibility of real estate data flow. Summary of the Invention

[0005] To achieve the above objectives, this invention provides an intelligent management method that links real estate surveying data with electronic certificates. The specific technical solution is as follows:

[0006] The system acquires historical electronic certificate data packages and current survey point cloud data streams. It uses a spatial semantic constraint encoder to parse the spatial constraints of the historical electronic certificate data packages to construct a certificate semantic expectation map. Simultaneously, it uses a 3D semantic segmentation network to extract the physical entity features of the current survey point cloud data streams to construct a survey entity observation map.

[0007] Based on the semantic expectation graph of the certificate and the observation graph of the surveyed entity, the semantic nodes and physical nodes are projected onto the latent feature space to generate an initial probability correlation matrix. A composite energy function containing the boundary violation potential energy and the surveyed deformation strain energy is constructed in memory. The Sinkhorn iterative algorithm is executed on the composite energy function to generate the optimal flexible mapping matrix and the global minimum deformation energy value.

[0008] Based on the optimal flexible mapping matrix, the semantic expectation map of the certificate, and the observation map of the surveyed entity, the local residual stress index is calculated and compared to generate a compliance classification label. Based on the local residual stress index and the current surveyed point cloud data stream, dual-channel data processing is performed to generate a difference attribution heatmap and a compliance diagnosis report.

[0009] Perform nonlinear mapping calculations on the global minimum deformation energy value to generate dynamic confidence scores and instantiate verifiable state nodes; automatically trigger hierarchical business flow control instructions based on the compliance classification labels in the compliance diagnostic report and preset conditional control logic.

[0010] Furthermore, the method for constructing the semantic expectation graph of the certificate includes performing optical character recognition on historical electronic certificate data packets to generate a text sequence to be processed, instantiating the text sequence to be processed into a set of semantic nodes using a spatial semantic constraint encoder, constructing a set of directed edges connecting the set of semantic nodes based on a dependency parsing algorithm, calculating the elastic stiffness coefficient by fusing static context components and dynamic context components, generating a spatial constraint vector by combining the semantic orientation vector and the expected topological distance value, and constructing a semantic expectation graph of the certificate containing a set of semantic nodes, a set of directed edges, and a set of stiffness attributes encapsulated by the elastic stiffness coefficient;

[0011] The method for constructing the survey entity observation map includes: preprocessing the current survey point cloud data stream; calling a three-dimensional semantic segmentation network to cluster coordinate points to generate a set of physical nodes; using principal component analysis algorithm to solve the geometric feature tensor containing the component centroid coordinate vector, component surface normal vector, and minimum bounding box volume; calculating the physical spatial correlation degree based on the adjacency relationship between physical nodes to determine the set of physical spatial correlation edges; and constructing a survey entity observation map containing the set of physical nodes, the set of physical spatial correlation edges, and the geometric feature tensor.

[0012] Furthermore, the spatial constraint vector is constructed by constructing an ordered triplet containing a semantic orientation vector, a desired topological distance value, and an elastic stiffness coefficient for the directed edges connecting the source semantic node to the target semantic node in the semantic node set.

[0013] The semantic orientation vector is obtained by extracting the orientation descriptors from the text sequence to be processed, and mapping the orientation descriptors into unit vectors in the Cartesian coordinate system according to a preset orientation mapping table.

[0014] The desired topological distance value is obtained by parsing the length value in the text sequence to be processed.

[0015] The elastic stiffness coefficient is calculated as follows: based on the deterministic score of the location descriptor retrieved from a pre-set legal dictionary, the deterministic score is calculated using a normalization function and multiplied by a preset lexical factor weight coefficient to generate a static semantic component; a spatial semantic constraint encoder is used to extract the normalized attention value of each word in the text sequence to be processed, and the contextual uncertainty entropy is calculated based on the Shannon information entropy formula; using the logarithm of the context window length of the text sequence to be processed as a benchmark, the contextual uncertainty entropy is subjected to inverse normalization processing to obtain a contextual deterministic operator, and multiplied by a preset contextual factor weight coefficient to generate a dynamic contextual component; the static semantic component and the dynamic contextual component are added together to obtain the elastic stiffness coefficient.

[0016] Furthermore, the method for calculating the physical spatial correlation degree includes:

[0017] Call the spatial indexing algorithm for the first The physical node and the first The minimum bounding box volume of each physical node is used to perform intersection detection and neighborhood search to generate a binary physical adjacency discriminant operator.

[0018] In response to the physical adjacency discrimination operator being true, the first... The physical node and the first The component centroid coordinate vectors of each physical node are obtained, and Euclidean distance difference calculation is performed to obtain the absolute spatial distance value;

[0019] The absolute spatial distance value and a preset numerical stability constant are added together, and the result of the addition is counted inversely to generate the physical spatial correlation degree.

[0020] Furthermore, the method for generating the optimal flexible mapping matrix and the global minimum deformation energy value includes:

[0021] The graph attention network is invoked to perform parallel feature encoding on the semantic expectation graph of the certificate and the observation graph of the surveying entity. The heterogeneous semantic nodes and physical nodes are projected onto a unified latent feature space to generate semantic embedding vector sequences and geometric embedding vector sequences respectively. The matching confidence is calculated based on the semantic embedding vector sequences and geometric embedding vector sequences to generate an initial probability association matrix.

[0022] Based on the initial probability correlation matrix, spatial constraint vector, and geometric feature tensor, a composite energy function containing the boundary violation potential energy and the measured deformation strain energy is constructed in memory.

[0023] Starting with the initial probability correlation matrix as the iteration starting point, the Sinkhorn iterative algorithm is used to perform gradient descent and bidirectional normalization constraint updates on the composite energy function until the system energy converges, outputting the optimal flexible mapping matrix and the global minimum deformation energy value; based on the comparison result between the global minimum deformation energy value and the boundary legality tolerance threshold, the judgment conclusion on the boundary state of the real estate unit is output.

[0024] Further, the steps for constructing the boundary violation potential energy are as follows: extracting semantic orientation vectors from spatial constraint vectors and extracting component surface normal vectors from geometric feature tensors; performing spatial transformation operations on the component surface normal vectors using the mapping matrix to be solved to generate transformed physical normal vectors; calculating the vector similarity between the semantic orientation vectors and the transformed physical normal vectors to generate topological orientation deviation values; and using the elastic stiffness coefficient in the spatial constraint vectors as an exponential adjustment factor to perform nonlinear weighting on the topological orientation deviation values ​​to generate the boundary violation potential energy.

[0025] The mapping matrix to be solved is a double random matrix or an affine transformation matrix, which represents the mapping relationship between the physical node set in the survey point cloud and the certificate semantic node space.

[0026] The steps for constructing the measured deformation strain energy are as follows: extracting the component centroid coordinate vector from the geometric feature tensor to construct the physical node coordinate matrix, and generating the semantic node reference coordinate matrix based on the semantic expectation graph using a graph embedding algorithm; using the Frobenius norm to calculate the overall displacement of the physical node coordinate matrix relative to the semantic node reference coordinate matrix under the action of the mapping matrix to be solved, thereby generating the measured deformation strain energy.

[0027] Furthermore, the method for generating the difference attribution heatmap and compliance diagnostic report includes:

[0028] Based on the optimal flexible mapping matrix and the semantic expectation graph of the certificate, the local residual stress index of the physical nodes in the survey entity observation map is calculated. The local residual stress index and elastic stiffness coefficient are compared and analyzed by the differential attribution classifier. The physical nodes are divided into different compliance states and a node stress dataset containing compliance classification labels of "compliance passed", "violation warning" and "questionable pending investigation" is output.

[0029] Based on the node stress dataset and the current survey point cloud data stream, dual-channel data processing is performed. In the visualization channel, a nonlinear transfer function is constructed to map the local residual stress index into a color rendering vector. Combined with the current survey point cloud data stream, a difference attribution heatmap is generated. In the readable channel, a slot filling algorithm is executed based on compliance classification labels to generate a compliance diagnostic report.

[0030] Furthermore, the method for dividing the compliance classification labels includes:

[0031] If the maximum posterior matching probability of a physical node is less than the preset confidence level, the physical node is determined to be a topological outlier and is assigned a "questionable" label; the posterior matching probability is extracted from the columns and rows of the optimal flexible mapping matrix.

[0032] If the maximum posterior matching probability of a physical node is greater than or equal to the preset confidence lower limit, the following comparison is performed: if the local residual stress index is less than the preset stress melting threshold, or if the local residual stress index is greater than or equal to the stress melting threshold and the elastic stiffness coefficient is less than the preset stiffness tolerance limit, then the physical node is determined to be in an elastic tolerance state and is given a "compliance passed" label.

[0033] If the local residual stress exponent is greater than or equal to the stress melting threshold and the elastic stiffness coefficient is greater than or equal to the stiffness tolerance limit, the physical node is determined to be in a hard constraint conflict state and is given a "violation warning" label.

[0034] Furthermore, the triggering method for the hierarchical business flow control instruction includes parsing the compliance classification label of the compliance diagnostic report, judging according to the preset condition control logic, if the compliance classification label is "compliance passed", then the business flow control instruction is "automatic release" is triggered, and if the compliance classification label is "violation warning" or "questionable and pending investigation", then the business flow control instruction is "circuit breaker warning".

[0035] An intelligent management system linking real estate surveying and mapping data with electronic certificates is used to implement the aforementioned intelligent management method linking real estate surveying and mapping data with electronic certificates. The system includes a heterogeneous map construction module, a flexible registration solution module, a difference attribution delivery module, and a business flow control module.

[0036] The heterogeneous map construction module is used to acquire historical electronic certificate data packages and current survey point cloud data streams, and to parse the spatial constraints of historical electronic certificate data packages through a spatial semantic constraint encoder to construct a certificate semantic expectation map. Simultaneously, a three-dimensional semantic segmentation network is used to extract the physical entity features of the current survey point cloud data stream to construct a survey entity observation map.

[0037] The elastic registration solution module: based on the certificate semantic expectation map and the surveyed entity observation map, projects the semantic nodes and physical nodes to the latent feature space to generate an initial probability correlation matrix, constructs a composite energy function in memory that includes the boundary violation potential energy and the surveyed deformation strain energy, and executes the Sinkhorn iterative algorithm on the composite energy function to generate the optimal flexible mapping matrix and the global minimum deformation energy value.

[0038] The difference attribution delivery module: based on the optimal flexible mapping matrix, the certificate semantic expectation map and the survey entity observation map, calculates the local residual stress index and compares and analyzes it to generate a compliance classification label. Based on the local residual stress index and the current survey point cloud data stream, it performs dual-channel data processing to generate a difference attribution heatmap and a compliance diagnosis report.

[0039] The business flow control module is used to perform nonlinear mapping calculations on the global minimum deformation energy value to generate dynamic confidence scores and instantiate verifiable state nodes; and to automatically trigger hierarchical business flow control instructions based on the compliance classification labels of the compliance diagnosis report and preset conditional control logic.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] This invention uses a dual-track parallel data structuring processing mechanism to transform the flexible topological semantics of historical certificates into a certificate semantic expectation graph with stiffness attributes, and to abstract the rigid geometric data of current surveying into a surveying entity observation graph with situational characteristics. This solves the problem of data source dimension incommensurability in existing technologies, which cannot directly measure the similarity between natural language descriptions and discrete point clouds.

[0042] This invention constructs a topological-geometric elastic coupling function in memory that includes the potential energy of boundary violations and the energy of measured deformation strain, and uses the Sinkhorn iterative algorithm to find the minimum deformation energy value. This avoids the nonlinear deformation problem caused by the forced overlay of traditional coordinates, and enables the differentiation between reasonable measurement errors or natural displacements and substantial boundary violations when coordinates are offset.

[0043] This invention calculates the local residual stress index of physical nodes and introduces the elastic stiffness coefficient as a decision adjustment factor to construct a difference attribution classifier that automatically maps surveyed entities to different compliance states. The calculation results are transformed into a visualized difference attribution heatmap and a structured compliance diagnosis report, enabling intuitive differentiation and digital evidence of reasonable measurement errors and substantial boundary violations. This solves the problem of misjudgment of infringement caused by the lack of an interpretable elastic decision mechanism in traditional rigid registration algorithms.

[0044] This invention generates a dynamic confidence score by performing a nonlinear mapping on the global minimum deformation energy value, and encapsulates this score and the compliance diagnosis conclusion into an immutable and verifiable state node embedded in the real estate data version chain DAG. Combined with compliance classification tags, it automatically triggers hierarchical business flow control instructions to achieve automatic release for reasonable measurement errors and circuit breaker warnings for substantial boundary violations, ensuring the traceability of the decision-making process and solving the problem of traditional technologies lacking an interpretable and flexible judgment mechanism. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating the principle of the intelligent management method for linking real estate surveying data with electronic certificates according to the present invention.

[0047] Figure 2 This is a functional module diagram of the intelligent management system for linking real estate surveying data and electronic certificates according to the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1

[0050] Please see Figure 1 As shown, this embodiment provides an intelligent management method for linking real estate surveying data with electronic certificates, including:

[0051] Step S1000: Obtain historical electronic certificate data package and current mapping point cloud data stream Historical electronic certificate data packets are parsed using a spatial semantic constraint encoder. Spatial constraints to construct the semantic expectation graph of the certificate Simultaneously, a 3D semantic segmentation network is used to extract the current mapping point cloud data stream. The physical entity characteristics are used to construct a mapping entity observation map. .

[0052] Specifically, this step aims to address the incommensurability of data source dimensions between the flexible text descriptions of historical electronic certificates and the rigid discrete point clouds of current mapping. It utilizes a dual-track parallel data structuring mechanism, combined with natural language processing algorithms, to integrate historical electronic certificate data packages. The flexible topological semantics in the model are transformed into a logical network that can be traversed by a computer, namely, the certificate semantic expectation graph. Simultaneously, the current mapping point cloud data stream is processed using a 3D deep learning algorithm. The rigid geometric data in the model is abstracted into an entity network with semantic labels, i.e., a mapping entity observation map. This step enables the system to distinguish between reasonable measurement errors and substantial address violations, providing a unified mathematical space and isomorphic basis for subsequent flexible registration.

[0053] Further, step S1000 includes:

[0054] Step S1100: Process historical electronic certificate data packets. Perform optical character recognition to generate a text sequence to be processed, and use a spatial semantic constraint encoder to instantiate the text sequence into a set of semantic nodes. The set of semantic nodes is constructed based on the dependency parsing algorithm. The set of directed edges The elastic stiffness coefficient is calculated by fusing static and dynamic context components. Combined with semantic orientation vector and expected topological distance value Generate spatial constraint vectors Construct a set containing semantic nodes Set of directed edges and the elastic stiffness coefficient Encapsulated stiffness property set Semantic expectation graph of certificates .

[0055] Specifically, this step aims to utilize natural language processing algorithms to process historical electronic certificate data packets. The unstructured and ambiguous legal text descriptions are transformed into a directed topological network with weighted parameters that can be traversed by a computer, namely, the semantic expectation graph of certificates. This process maps qualitative natural language location descriptions into quantitative spatial constraint vectors. This approach eliminates the dimensional differences between text data and subsequent surveying and mapping geometric data at the data structure level, establishes a basis for isomorphic mapping between heterogeneous data, and solves the technical bottleneck that existing technologies cannot directly measure the similarity between text descriptions and geometric figures.

[0056] In the specific implementation process, the obtained historical electronic certificate data packets are... Optical character recognition (OCR) converts image data into a machine-encodeable character stream. A pre-defined set of regular expressions is then loaded to denoise the character stream. Text fragments containing directional descriptors such as "east to," "south border," "west boundary," and "north" are traversed and filtered to generate a text sequence to be processed. Subsequently, a spatial semantic constraint encoder embedded in the geospatial ontology feature library is invoked to perform structured parsing of the text sequence. This parsing process specifically involves index matching of text fragments in the text sequence with preset boundary terms in the geospatial ontology feature library. When a match is found, the corresponding text fragment is marked as an entity object and instantiated as a set of semantic nodes in the graph data structure. Simultaneously, based on dependency parsing algorithms, the syntactic distance and logical subordination of each entity object in the text sequence to be processed are calculated. Locative prepositions and numerical quantifiers connecting two entity objects are identified, and these are converted into edge data with directional and weight attributes. A pointer index from the source node to the target node is established, thereby generating the set of semantic nodes connected to the target node. The set of directed edges corresponding to each element in the set. This completes the isomorphic mapping from unstructured text streams to graph data structures containing node and edge indices.

[0057] Based on this, in order to achieve a quantitative representation of the strength of boundary constraints, this step constructs a certificate semantic expectation graph. And define it as a triplet structure. It contains a set of semantic nodes. Set of directed edges and stiffness property set For connection source semantic nodes. Pointing to the target semantic node any directed edge A unique spatial constraint vector is generated through multi-dimensional feature fusion calculation. The spatial constraint vector It is an ordered triple containing feature components of three dimensions: direction, distance, and stiffness, i.e., a semantic orientation vector. Expected topological distance value and elastic stiffness coefficient .in, The ordered tuple constructor is used to characterize the spatial constraint vector. It is an overall data structure encapsulated by three internally listed feature components.

[0058] The semantic orientation vector The orientation descriptor is obtained by mapping the extracted orientation descriptor to a unit vector in a three-dimensional Cartesian coordinate system. Its value is determined by a preset orientation mapping table, for example, "due east" corresponds to... , used to quantify the theoretical geometric orientation of the boundary edges.

[0059] The desired topological distance value It is a scalar value obtained based on text parsing. Its value is either the length value explicitly stated in the text or the default value of topological adjacency preset by the system, and is used to define the theoretical expected length of the boundary edge.

[0060] The elastic stiffness coefficient It is a dimensionless scalar between 0 and 1, used to quantize the spatial constraint vector. The tolerance for geometric deformation allowed in subsequent steps. This elastic stiffness coefficient. The specific execution logic is as follows: First, calculate the static semantic components. Obtain the deterministic score of the current location descriptor by searching a pre-built legal dictionary. The deterministic score is transformed into a standardized lexical strength value through a normalization function. This lexical strength value is then multiplied by a preset lexical factor weight coefficient to generate a static semantic component. Secondly, the dynamic contextual component is calculated. When parsing the current elastic stiffness coefficient using a spatial semantic constraint encoder, the normalized attention value assigned to each word in the text sequence to be processed represents the attention weight distribution for the current semantic understanding. The contextual uncertainty entropy is calculated based on the information entropy formula defined by Shannon's information theory. This contextual uncertainty entropy is then reverse-normalized using the logarithm of the context window length of the text sequence to obtain a contextual deterministic operator reflecting contextual clarity, i.e., a deterministic probability value. This deterministic probability value is multiplied by a preset context factor weight coefficient to generate a dynamic contextual component. The reverse normalization process is a data processing technique designed to counteract and eliminate the potential dilution effect of the context window length on information entropy, remapping the contextual uncertainty entropy to a deterministic probability value independent of text length.

[0061] Finally, the static semantic components and the dynamic contextual components are summed to generate a unique elastic stiffness coefficient. and all directed edges Corresponding elastic stiffness coefficient The assembly is stored as a set of stiffness properties. This process ensures that the system not only makes judgments based on the definition of the words themselves, but also dynamically corrects them based on whether the words are ambiguous in a specific context, thereby achieving a digital representation of the strength of the spatial constraints implicit in unstructured text.

[0062] Step S1200: Process the current mapping point cloud data stream. Preprocessing is performed by calling a 3D semantic segmentation network to cluster the coordinate points to generate a set of physical nodes. Principal component analysis algorithm is used to solve for the centroid coordinate vectors of the components. Component surface normal vector and minimum bounding box volume geometric feature tensor Calculate the physical spatial correlation degree based on the adjacency relationship between physical nodes. To determine the set of physical space-related edges Construct a set of physical nodes Physical space associated edge set and geometric feature tensor Observation map of the surveyed entity .

[0063] Specifically, this step aims to stream the current mapping point cloud data. The massive, unordered discrete coordinate points are transformed into a set of entity objects with clear geometric semantics, realizing the abstraction of rigid geometric data into graph nodes containing features such as orientation and volume, and constructing a semantic expectation graph of the certificate generated in step S1100. Observation map of surveying entities that are completely isomorphic in data structure This provides a computable physical reference for subsequent steps.

[0064] In the specific implementation process, the current mapping point cloud data stream collected by the lidar is received. The data is then downsampled and denoised to generate standardized current mapping point cloud data. Subsequently, this data is input into a pre-built voxel-based 3D semantic segmentation network, such as an improved PointNet++ or SparseConvNet. This semantic segmentation network performs convolution operations on the input current mapping point cloud data through multi-scale feature extraction layers, clustering and classifying discrete coordinate points into concrete sets of physical components, such as physical walls, roadbeds, building facades, and isolated vegetation.

[0065] For each cluster, the generated physical components undergo geometric feature abstraction, which is then mapped to a mapping entity observation map. The physical nodes in the data are then compiled into a physical node set. To enable physical nodes to have features that can be mathematically compared with text descriptions, Principal Component Analysis (PCA) is used to calculate the feature vector of each physical component, constructing a geometric feature tensor. The geometric feature tensor It is a multidimensional feature array structure stored in system memory, used as a mapping entity observation map. The data carrier of physical node attributes, including component centroid coordinate vectors. Component surface normal vector and minimum bounding box volume The geometric feature tensor The specific calculation logic is as follows: Iterate through all discrete point data belonging to the current physical component, and use an algorithm of summation and division by the total number of points to calculate the component's centroid coordinate vector. This is used to determine the spatial center location of physical components in the geographic coordinate system; in parallel, a covariance matrix is ​​constructed based on discrete point data, eigenvalue decomposition is performed on this covariance matrix, the eigenvector corresponding to the smallest eigenvalue is selected and normalized to generate the component surface normal vector. The first step is to determine the geometric orientation and pose of the physical component in three-dimensional space. Then, the principal component axis alignment method is used to construct the minimum orientation bounding box that tightly encloses all discrete point data of the physical component, and the volume of the minimum orientation bounding box is calculated to generate the minimum bounding box volume. Finally, the feature data from the above three dimensions are encapsulated into an ordered geometric feature tensor. This completes the mapping from disordered discrete point clouds to entity features with high-dimensional geometric semantics.

[0066] To establish a mapping entity observation map reflecting the topological strength between various physical entities. Calculate the topological adjacency relationships between each physical component to generate a set of physical space associated edges. For any two physical nodes, i.e., the first... physical nodes and the physical nodes The connection edge between two physical nodes is calculated using a pre-defined distance metric algorithm. Physical spatial correlation .

[0067] The physical space correlation The numerical value is inversely proportional to the physical distance. The specific calculation logic is as follows: A spatial indexing algorithm, such as an R-tree index, is called to calculate the minimum bounding box volume of the two physical components. Intersection detection and neighborhood search are performed to generate a binary physical adjacency discriminant operator, which serves as the logical gate condition for determining the existence of associated edges. Then, assuming an adjacency relationship exists (i.e., the physical adjacency discriminant operator is set to 1), the centroid coordinate vectors of the two physical components are extracted. The system performs Euclidean distance difference calculations to obtain absolute spatial distance values. Then, it adds this spatial distance value to a preset numerical stability constant and performs a reciprocal operation to convert the distance dimension into a weight dimension. The numerical stability constant is a very small positive floating-point number preset by the system to prevent computational overflow or program errors caused by a zero denominator when the centroids of two entities coincide or the distance approaches zero. This calculation logic ensures that entities with closer spatial distances have greater association weights, constructing a weighted topology network that maps adjacency relationships in the physical world, providing a quantitative data foundation for subsequent steps.

[0068] Based on the above calculations, the final output contains a set of physical nodes. Physical space associated edge set and the geometric feature tensors between each physical node Observation map of the surveyed entity .

[0069] Step S2000, based on the semantic expectation graph of the certificate and mapping entity observation map The semantic nodes and physical nodes are projected onto the latent feature space to generate an initial probability correlation matrix. Construct a potential energy in memory that includes the boundary violation. and measuring deformation strain energy Composite energy function and the composite energy function Execute the Sinkhorn iterative algorithm to generate the optimal flexible mapping matrix. and global minimum deformation energy value .

[0070] Specifically, this step aims to transform the semantic expectation graph of the certificate constructed in step S1100. The flexible topological semantics and the mapping entity observation map constructed in step S1200 The rigid geometric data is mapped to the same computable high-dimensional manifold space. Based on this, an optimization algorithm is used to find the equilibrium state that minimizes the total system energy—the conflict cost between legal constraints and the physical reality—and to calculate the optimal flexible mapping matrix that can tolerate nonlinear deformation. and the global minimum deformation energy value of quantifying the degree of difference. This enables interpretable digital identification of reasonable measurement errors and substantial boundary violations.

[0071] Further, step S2000 includes:

[0072] Step S2100: Invoke the graph attention network to process the semantic expectation graph of the certificate. and surveying entity observation map Parallel feature encoding is performed, projecting heterogeneous semantic and physical nodes onto a unified latent feature space to generate semantic embedding vector sequences and geometric embedding vector sequences, respectively. The matching confidence is then calculated based on the two embedding vector sequences. Generate the initial probability correlation matrix .

[0073] Specifically, this step aims to address the incommensurability of data source dimensions between the flexible topological semantics of historical electronic certificates and the rigid geometric data of current surveying. By establishing a high-dimensional latent feature space, the certificate semantic expectation graph constructed in step S1100 is transformed... Natural language description and mapping entity observation map constructed in step S1200 The discrete point cloud entities are uniformly mapped to numerical vectors within this space, establishing preliminary probabilistic connections between heterogeneous nodes at the mathematical level, i.e., the initial probability correlation matrix. .

[0074] In the specific implementation process, this step calls a pre-built Graph Attention Network (GAT) as the feature encoder. This GAT network contains parallel semantic encoding layers and geometric encoding layers. The semantic expectation graph is then processed. semantic node set in The input is fed into the semantic encoding layer to generate the entity observation map. physical node set in and geometric feature tensor Input geometric coding layer.

[0075] By utilizing the nonlinear activation function and weight matrix in the graph attention network, input features from different data dimensions are projected onto the same high-dimensional latent feature space, generating semantic embedding vector sequences respectively. and geometric embedding vector sequence .

[0076] The semantic embedding vector sequence The generation logic is as follows: For the semantic expectation graph of the certificate... For each semantic node, an attention mechanism is used to aggregate its own lexical attributes and the feature information of its topological neighbors. A weighted summation operation is then used to generate a semantic embedding vector containing contextual information. This transforms discrete symbolic text into continuous numerical vectors. Among them, Indicates the first A semantic embedding vector; The semantic node index is represented by a value ranging from 1 to... Integer variables are used to uniquely identify the semantic expectation graph of the certificate. Each independent semantic node in, where, This represents the total number of semantic nodes.

[0077] The geometric embedding vector sequence The generation logic is as follows: for the surveyed entity observation map For each physical node, the same attention mechanism is used to aggregate the feature information of its own geometry and its physical spatial neighbors to generate a geometric embedding vector containing the local spatial structure. .in, Indicates the first One geometric embedding vector; This represents the physical node index, with values ​​ranging from 1 to... An integer variable used to uniquely identify the observation map of the surveyed entity. Each independent physical node in, where This represents the total number of physical nodes.

[0078] Based on this, perform fully connected feature similarity calculations to construct a set of connected semantic nodes. and physical node set initial probability correlation matrix For the aforementioned set of semantic nodes The first in semantic embedding vectors and the set of physical nodes The first in Geometric embedding vectors Calculate the matching confidence between the two. The matching confidence level It is stored in the initial probability correlation matrix The Middle Line 1 The double-precision floating-point value of the column is used to characterize the first value in the latent feature space. The semantic node and the first The probability estimate of which physical nodes belong to the same physical entity, and its value range is limited to a closed interval. .

[0079] This matching confidence level The specific calculation logic is as follows: Extract the first... semantic embedding vectors and the Geometric embedding vectors First, a vector dot product operation is performed to obtain the original similarity score between the two entities in the latent feature space. This original similarity score reflects the degree of overlap between the text description and the physical entity in the feature direction. Then, a division scaling operation is performed on the original similarity score using the square root of a preset feature space dimension constant to prevent gradient vanishing due to numerical accumulation caused by excessively large vector dimensions. Finally, a Softmax normalization transformation is performed on the scaled original similarity score to convert the similarity score within any real number range into a standard probability value, i.e., the matching confidence score. The feature space dimension constant is a preset positive integer, such as 64, 128, or 256, used to define the length of the embedding vector.

[0080] For example, suppose the certificate semantic expectation graph The semantic feature of semantic node A is "north to the passage", while the surveyed entity observation map The physical node B has a geometric feature described as a "long, narrow concrete road surface." In conventional techniques, the textual term "channel" and the geometric term "road surface" cannot be directly matched due to differences in name and data type. In this step, a graph attention network is used to encode semantic node A into a semantic embedding vector. This semantic embedding vector not only numerically encodes the passage function attribute of the "channel" but also aggregates its topological features, including its extensibility and connection to other boundaries. Similarly, physical node B is encoded into a geometric embedding vector, encoding its planar, long, narrow geometric feature. Although "channel" and "road surface" have different names, in the high-dimensional latent space, their feature vector directions are highly consistent; that is, the dot product of the semantic embedding vector and the geometric embedding vector is large because they play similar topological connection roles in their respective graphs. Finally, the matching confidence is calculated. Approaching 0.98, it can automatically identify the "channel" in the electronic certificate as the "cement road" in the actual surveying and mapping entity observation without human intervention.

[0081] Specifically, traditional techniques often rely on isolated explicit feature matching, such as text-to-text or shape-to-shape matching, which is easily affected by unclear descriptions or partial occlusion. This step, however, utilizes a graph attention network to aggregate the semantic topological information generated in step S1100 and the geometric adjacency information generated in step S1200. This allows the system to focus not on the "name" or "shape" of a single node, but on its "topological role" in the network, such as a strip connecting two main buildings. This enables the system to identify implicit relationships that humans can only discover intuitively, achieving the technical effect of accurate identification even if the names do not match or the shapes are damaged, as long as the topological relationship is consistent. This solves the technical pain point that heterogeneous matching cannot be performed based solely on literal definitions or single geometric features.

[0082] Step S2200, based on the initial probability correlation matrix Spatial constraint vector Geometric feature tensor Construct a potential energy in memory that includes the boundary violation. and measuring deformation strain energy Composite energy function .

[0083] Specifically, this step aims to address the common problem of nonlinear misalignment between rights description and current status mapping caused by minor crustal changes or reference object displacements in old residential areas and historical sites. It constructs a topological-geometric elastic coupling field based on boundary rights constraints in memory, structuring the textual descriptions in electronic certificates into a logical topological framework with differentiated stiffness attributes, and mapping the point cloud data collected by LiDAR into a geometric entity network with deformation degrees of freedom. The initial probability correlation matrix generated in step S2100 is used as the basis for this process. As a prior probability distribution benchmark for optimization, the soft connection state between heterogeneous nodes is initialized, based on the certificate semantic expectation graph constructed in step S1100. Spatial constraint vector and the survey entity observation map constructed in step S1200 geometric feature tensor Instantiate a differentiable composite energy function in computer memory. The composite energy function Quantifying the numerical cost of the deviation of the current physical state from the expected value of rights provides a mathematical optimization target for the subsequent search for the globally optimal elastic equilibrium point.

[0084] In the specific implementation process, this step reads the initial probability correlation matrix generated in step S2100. The spatial constraint vector generated in step S1100 and the geometric feature tensor generated in step S1200 Based on this, a composite energy function to be optimized is instantiated in computer memory. The composite energy function Its construction follows the principle of minimum energy in physics, based on the boundary violation potential energy. and measuring deformation strain energy It consists of two linearly weighted parts.

[0085] To quantify the nonlinear digital discrepancy between the description of rights boundaries in electronic certificates and the current physical entity state, a boundary violation potential energy is constructed in computer memory. During this process, the spatial constraint vector is invoked. elastic stiffness coefficient As the potential energy of this boundary violation The dynamic penalty weight is calculated as follows:

[0086] First, obtain the reference benchmark. This is done from the spatial constraint vector. Extract semantic orientation vector This semantic orientation vector This represents the theoretical expectations and direction from a legal perspective.

[0087] Second, obtain the observation state. From the geometric feature tensor Extracted component surface normal vectors And use the mapping matrix to be solved to determine the surface normal vector of the component. A spatial transformation is performed to generate a transformed physical normal vector, which represents the actual physical orientation of the surveyed entity after simulated deformation. The mapping matrix to be solved is typically a double random matrix or an affine transformation matrix, used to describe the set of physical nodes in the surveyed point cloud. How to map to the semantic node space of certificates through translation, rotation and non-rigid deformation.

[0088] Third, calculate the topological deviation. Calculate the semantic orientation vector. The cosine similarity or angle difference between the transformed physical normal vector and the transformed physical normal vector is used to generate the topological direction deviation value.

[0089] Fourth, apply a stiffness penalty. This is based on the elastic stiffness coefficient. The topological orientation deviation values ​​are nonlinearly weighted. When the elastic stiffness coefficient... When the value approaches 1, corresponding to strong constraints such as "adjacent" or "red line" in electronic certificates, an exponentially increasing penalty function is used to process the topological direction deviation value. This causes a small directional mismatch to generate a huge potential energy value, forcing the optimization algorithm to strictly maintain the geometric relationship of the boundary. When the elastic stiffness coefficient... When the value approaches 0, corresponding to weak rights constraints such as "nearby" or "approximately" in electronic certificates, a logarithmic or linearly decaying penalty function is used to process the topological direction deviation value, allowing the physical status quo to have a large direction deviation within a preset tolerance threshold.

[0090] Simultaneously, to prevent the system from irrationally distorting the surveying data in the process of pursuing a perfect match with the electronic certificate text description, a surveying deformation strain energy is constructed in the computer memory. The specific computational logic is as follows: First, the geometric feature tensor is called. Extract the centroid coordinate vectors of each physical component. To construct the physical node coordinate matrix; simultaneously, based on the semantic expectation graph of the certificate. The topological structure is used to generate a semantic node reference coordinate matrix that maintains the relative positional relationships using a graph embedding algorithm. Subsequently, the Frobenius Norm is used to calculate the overall displacement of the physical node coordinate matrix relative to the semantic node reference coordinate matrix under the action of the mapping matrix to be solved, thereby numerically representing the degree of translation, rotation or non-rigid scaling of the surveyed entity.

[0091] Based on this, the boundary violation potential energy and the measured deformation strain energy Perform linear weighted fusion and instantiate the composite energy function in computer memory. .

[0092] Step S2300, using the initial probability correlation matrix Starting from the point of iteration, the Sinkhorn iterative algorithm is used to iterate the composite energy function. Perform gradient descent and bidirectional normalization constraint updates until the system energy converges, and output the optimal flexible mapping matrix. and global minimum deformation energy value Based on the aforementioned global minimum deformation energy value The comparison results with the boundary legality tolerance threshold are used to output the judgment conclusion on the boundary status of the real estate unit.

[0093] Specifically, this step aims to simulate the physical energy dissipation process in computer memory, using the initial probability correlation matrix generated in step S2100. As the starting point for iteration, the composite energy function constructed using S2200 is... As a loss function, its objective is not to seek an absolutely zero-error match of geometric coordinates, but rather to search for a topological equilibrium point with the lowest system energy, and to calculate and output the optimal flexible mapping matrix. and global minimum deformation energy value .

[0094] In the specific implementation process, this step will verify the semantic expectation graph. and mapping entity observation map The registration task is transformed into a mathematical optimal transport problem. To address the issues of non-differentiability and high computational complexity in traditional combinatorial optimization algorithms, this step employs the Sinkhorn iterative algorithm to solve for the composite energy function. The global minimum value.

[0095] The system stores the initial probability correlation matrix in memory. Assign the value to the mapping matrix of the current iteration step, denoted as matrix. ,initial This serves as the starting point for the prior probability distribution in the optimization solution. Subsequently, the system enters an iterative optimization loop. In each iteration... The computational logic executed in the process is as follows:

[0096] First, the gradient calculation of the boundary violation energy. This is done using the Automatic Differentiation (AD) mechanism to calculate the gradient of the composite energy function. Perform a mapping matrix for the current iteration step. Partial differential operations are used to generate the dynamic energy gradient matrix. The dynamic energy gradient matrix Each element in the value quantifies the potential energy of a tiny probability perturbation in the matching relationship between a specific electronic certificate and a physical entity on the boundary violation. and measuring deformation strain energy The sum of the effects rate indicates the optimization direction where energy decreases the fastest.

[0097] Second, the matching probability is updated in the negative gradient direction. To approximate the system energy convergence to the global minimum state, the matching probability needs to be adjusted in the opposite direction of the energy gradient. This is based on the boundary violation potential constructed in step S2200. Derivation of the address topology cost matrix Combine it with the dynamic energy gradient matrix Linear superposition is performed to generate a generalized cost matrix representing the matching resistance in the current state. Subsequently, a negative exponential mapping operation is performed on this generalized cost matrix, mapping high resistance values ​​to low matching probabilities and low resistance values ​​to high matching probabilities, generating an intermediate state matrix. This mathematically achieves a numerical approximation towards a direction with a higher degree of fit between boundary constraints and the physical state. The boundary topology cost matrix... The matrix elements in the equation are equal to the static topological difference potential energy between semantic nodes and physical nodes; the static topological difference potential energy is the deformation strain energy measured in the system. In the initial state of zero, the semantic orientation vector of the semantic node and the component surface normal vector of the physical node The topological orientation deviation between them, via the elastic stiffness coefficient The weighted penalty value.

[0098] Third, Sinkhorn normalization constraints. While the intermediate state matrix reduces energy, it may violate the physical constraints of the probability matrix, for example, leading to a situation where one physical entity corresponds to multiple rights descriptions. Therefore, the system applies Sinkhorn bidirectional normalization constraints to the intermediate state matrix. Specifically, it calculates row scaling factor vectors and column scaling factor vectors, and performs alternating multiplication scaling on the intermediate state matrix, forcing the sum of each row and the sum of each column to strictly converge to 1. This enforces a one-to-one logical constraint between real estate unit ownership and surveyed entities at the data level. The row scaling factor vectors and column scaling factor vectors are both Lagrange multipliers in the Sinkhorn bidirectional normalization constraint algorithm, respectively constraining the completeness of the electronic certificate description and the uniqueness of the surveyed entity, preventing logical conflicts such as one right for multiple properties or one property for multiple rights.

[0099] Fourth, convergence determination of the energy equilibrium state. Calculate the total energy difference between two adjacent iterations. When the total energy difference When the numerical convergence threshold is less than the preset threshold or the number of iterations reaches the preset upper limit, the system is determined to have reached a numerical convergence state that is compatible with the topological logic and physical status, and the iteration stops.

[0100] Finally, the optimal flexible mapping matrix is ​​output. That is, the mapping matrix of the current iteration step when the iteration converges. .

[0101] Meanwhile, based on the composite energy function and the optimal flexible mapping matrix Output the global minimum deformation energy value The calculated global minimum deformation energy value The value is compared with a preset boundary legality tolerance threshold. The boundary legality tolerance threshold is a positive real scalar preset by the system or trained based on a historical case library. It is used to define the maximum energy tolerance limit in real estate management for nonlinear deformation caused by minor crustal changes, road construction, or insufficient historical measurement accuracy.

[0102] If the global minimum deformation energy value If the coordinate deviation is less than or equal to the boundary legality tolerance threshold, the system determines that the current coordinate deviation is within the elastic deformation range. This indicates that although there are differences in absolute coordinates between the current survey data and the historical certificate description, these differences can be resolved through small physical deformation costs and extremely low boundary violation costs. In this case, the system outputs a reasonable error judgment, concluding that although the real estate unit has experienced physical displacement, such as natural settlement or reasonable measurement errors, its relative topological relationship still conforms to the rights description in the electronic certificate and is therefore legal.

[0103] If the global minimum deformation energy value If the coordinate deviation exceeds the boundary legality tolerance threshold, the system determines that the current coordinate deviation has caused system energy overload, resulting in plastic failure. This indicates that in order to force current mapping data to conform to historical documentation descriptions, the system must incur a huge energy cost, such as breaking high-stiffness boundary lines or stretching the physical point cloud to severe distortion. In this situation, the system outputs a substantial violation judgment, determining that the real estate unit has substantially encroached upon or seriously deviated from the rigid boundary, constituting an infringement status requiring manual intervention for verification.

[0104] Step S3000, based on the optimal flexible mapping matrix Semantic Expectation Graph of Certificates and surveying entity observation map The local residual stress index is calculated and compared to generate compliance classification labels based on the local residual stress index and the current mapping point cloud data stream. Perform dual-channel data processing to generate a differential attribution heatmap. and compliance diagnostic report .

[0105] Specifically, this step aims to use a data reverse mapping algorithm to construct the semantic expectation graph of the certificate based on step S1100. The survey entity observation map constructed in step S1200 The optimal flexible mapping matrix output in step S2300 Combined with the current mapping point cloud data stream collected in step S1200 This is decomposed into local residual numerical signals of each entity node in physical space. This process transforms the convergence state of the black box into a visualized differential attribution heatmap. and readable, structured compliance diagnostic reports This enables a direct distinction and digital documentation between reasonable measurement errors and substantial boundary violations.

[0106] Further, step S3000 includes:

[0107] Step S3100, based on the optimal flexible mapping matrix Semantic Expectation Graph of Certificates Solve the survey and mapping entity observation map The local residual stress index of physical nodes is analyzed by comparing the local residual stress index with the elastic stiffness coefficient using a difference attribution classifier. This classifies physical nodes into different compliance states and outputs a node stress dataset containing compliance classification labels such as "Compliance Passed," "Violation Warning," and "Questionable and Pending Investigation." .

[0108] Specifically, this step aims to analyze the optimal flexible mapping matrix output in step S2300. Combined with the semantic expectation graph of the certificate constructed in step S1100 The survey entity observation map constructed in step S1200 The system decomposes the stress into the local residual stress index of each physical entity node. This local residual stress index not only reflects the offset of the physical location but also integrates the rigidity constraints of legal rights at the data level. Based on the local residual stress index, the system constructs a difference attribution classifier, automatically classifying the surveyed entities into three states: "elastic tolerance," "hard constraint conflict," or "topological outlier," and outputs a node stress dataset. This enables intelligent translation from mathematical calculation results to business compliance conclusions.

[0109] In the specific implementation process, this step traverses the surveyed entity observation map. Each physical node in the optimal flexible mapping matrix. The semantic node with the highest posterior matching probability is retrieved from the matrix, and a physical-semantic retrieval pair is established. The posterior matching probability is extracted from the optimal flexible mapping matrix. The columns and rows are used to ensure that the deviation is only included in the effective node stress when there is a high probability of correspondence between physical nodes and semantic nodes, thus filtering out noise interference from irrelevant nodes.

[0110] After identifying the best search pair, the system calls the document semantic expectation graph. Spatial constraint vectors in Using the currently registered high-confidence anchor points as the spatial reference origin, such as known and unchanged road intersections, semantic orientation vectors are utilized. and expected topological distance value By performing vector addition and coordinate transformation operations, the theoretical expected coordinates of the semantic node in the current physical coordinate system are derived. These theoretical expected coordinates represent the legal geometric location of the real estate boundary point in three-dimensional space, under the premise of strictly adhering to the electronic certificate's description of rights.

[0111] Subsequently, based on the theoretical expected coordinates and the actual physical coordinates of the current measurement, a quantitative calculation of the local residual stress index is performed. The local residual stress index is a non-negative floating-point scalar stored in system memory, used to quantify the virtual deformation cost that each physical entity is forced to bear to conform to the rights description in the electronic certificate, reflecting the degree to which the measured status deviates from the legal boundary. The specific calculation logic of the local residual stress index is as follows: First, the certificate semantic expectation graph is traversed. All semantic nodes are selected using posterior matching probabilities as a filtering gate to identify semantic nodes that form a valid match with the current physical node; subsequently, the centroid coordinate vector of the component of that physical entity is calculated. The Euclidean distance between the coordinates and the theoretical expected coordinates is multiplied by a preset displacement dimension normalization coefficient to obtain the geometric displacement components; simultaneously, the component surface normal vectors of the physical entity are calculated. and the semantic orientation vector of the rights description The topological direction residuals between them are used, and the elastic stiffness coefficient corresponding to the semantic node is utilized. The topological direction residual is nonlinearly amplified or suppressed, and multiplied by a preset angle normalization coefficient to obtain the topological direction component. Finally, the geometric displacement component and the topological direction component are added together and subjected to probability weighted summation to generate a unique local residual stress index, thereby realizing a difference measurement that conforms to the legal logic of real estate registration.

[0112] Based on this, a difference attribution classifier is constructed in memory to numerically compare the local residual stress index with a preset stress melting threshold, and an elastic stiffness coefficient is introduced. As an adjustment factor for the decision weight, physical nodes in the survey entity observation map are automatically mapped to the following three mutually exclusive digital compliance states: "flexible tolerance", "hard constraint conflict" or "topological outlier". The specific mapping logic is as follows:

[0113] When the maximum posterior matching probability of a physical node is greater than or equal to the preset confidence lower limit, the following comparison is performed:

[0114] First, it is determined to be "elastic tolerance". This occurs when the local residual stress exponent of a physical node is less than the stress fusing threshold, or when, although the local residual stress exponent exceeds the stress fusing threshold, its corresponding elastic stiffness coefficient... When the stiffness tolerance limit is less than the preset limit, the system determines that the physical node is in an elastic tolerance state. The physical meaning of this state is that although the physical entity has displaced, it is located in an area with weak legal constraints, or the amount of displacement is within the topological deformation range allowed by the algorithm. The system identifies it as a reasonable measurement error, natural settlement of the foundation, or non-substantial natural displacement, and assigns it the "compliance passed" label.

[0115] Second, it is determined to be a "hard constraint conflict". This occurs when the local residual stress exponent of a physical node is greater than or equal to the stress melting threshold, and its corresponding elastic stiffness coefficient... When the stiffness tolerance limit is greater than or equal to the limit, the system determines that the physical node is in a hard constraint conflict state. The physical meaning of this state is that the physical entity still generates huge and unresolvable deformation stress under the strict constraints of high-stiffness legal provisions, such as "red lines" or "walls". The system identifies this as a substantial boundary violation, illegal alteration or serious measurement coordinate error and assigns a "violation warning" label.

[0116] When the maximum posterior matching probability of a physical node is less than the preset confidence threshold, the system determines that the physical node is in a topological outlier state. The physical meaning of this state is that the mapping entity is in the certificate semantic expectation graph. If no corresponding node with topological isomorphism can be found in the logical network, the system identifies it as a newly added unregistered feature in the current survey or a substantial missing historical certificate data, and assigns it the "questionable and pending investigation" label.

[0117] Finally, the system encapsulates the above data in a structured manner to generate a nodal stress dataset. The stress dataset of this node This includes a unique identifier for each physical component, a local residual stress index, and a compliance classification label, which will be used to generate a visual attribution heatmap for later visualization. and a readable compliance diagnostic report The data foundation. The compliance classification labels are "Compliance Passed," "Violation Warning," and "Questionable and Pending Investigation."

[0118] Step S3200, based on the nodal stress dataset and current mapping point cloud data stream Dual-channel data processing is performed. In the visualization channel, a nonlinear transfer function is constructed to map the local residual stress exponent into a color rendering vector, which is then combined with the current survey point cloud data stream. Generate differential attribution heatmap In the readable channel, a slot filling algorithm is executed based on compliance classification tags to generate a compliance diagnostic report. .

[0119] Specifically, this step aims to process the nodal stress dataset output in step S3100. The abstract mathematical calculation results are superimposed as data attributes onto the current mapping point cloud data stream collected in step S1200. The above transforms dry surveying coordinate data into a visualized difference attribution heatmap with both legal and physical semantics. and readable, structured compliance diagnostic reports This enables digital delivery from underlying data computation to upper-level human-computer interaction, helping non-professionals to intuitively identify reasonable measurement errors and substantial boundary infringements.

[0120] In the specific implementation process, this step executes parallel dual-channel data processing logic, namely the visualization channel and the readability channel.

[0121] The processing logic of the visualization channel is to create a dataset of stress from the nodes in the computer memory. The range of the local residual stress exponent is mapped to a nonlinear transfer function in the RGB color space. This involves traversing the current point cloud data stream. For data belonging to the nodal stress dataset For each discrete laser point of each physical component, the local residual stress index corresponding to that physical component is called to perform numerical calculations and assign a unique color rendering vector to each laser point.

[0122] The color rendering vector is a three-dimensional vector containing red, green, and blue components stored in memory. It is used to drive the display device to draw each laser point in the view, intuitively mapping the compliance status of the entity to which each laser point belongs. The calculation logic of the color rendering vector is as follows: calculate the stress difference value of the local residual stress index relative to a preset benchmark visual tolerance threshold, and scale the stress difference value using a preset display sensitivity coefficient; then, use an S-shaped activation function to convert the scaled stress difference value into a risk weight coefficient between 0 and 1; finally, use the risk weight coefficient as a scaling factor to perform linear interpolation on the safety color reference vector and the warning color reference vector to finally calculate the color rendering vector. The reference visual tolerance threshold is a preset positive real constant used to define the starting point of the insensitive area for heatmap rendering. When the local residual stress index is less than this value, color changes will not be significantly triggered, simulating the human eye's ability to ignore minute measurement errors. The display sensitivity coefficient is a preset positive real constant used to adjust the slope of the S-curve, controlling the heatmap's response rate and visual sensitivity to stress changes. The safety color reference vector is a preset fixed color constant, for example, green is... , corresponding to the nodal stress dataset The elastic tolerance state in the code is used as the lower limit color reference for the compliance area in interpolation calculations; the warning color reference vector is a preset fixed color constant, for example, red is... , corresponding to the nodal stress dataset The hard constraint conflict states in the data are used as the upper limit warning color reference for the violation area in the interpolation operation. The calculation mechanism of this color rendering vector ensures that the heatmap can dynamically reflect the conflict situation where the greater the boundary stress, the more the color tends to be warning red, thus realizing an intuitive and visual output of the conflict status of real estate rights.

[0123] Finally, the current mapping point cloud data stream after traversal will be... The calculated color rendering vectors are encapsulated in a data structure to generate a point cloud model file with intelligent color encoding, i.e., a difference attribution heatmap. It is used for visual output on the display interface.

[0124] The processing logic of the readable channel is to traverse the node stress dataset. For each record, in each iteration, a unique identifier, local residual stress index, and compliance classification label of the corresponding physical component are extracted, transforming discrete calculation results into structured feature data usable for text generation. Based on the compliance classification label, a pre-built library of standardized semantic units is matched using an algorithm. Next, a slot filling (SF) algorithm is used to inject the extracted local residual stress index and compliance classification label into the pre-defined slots of the corresponding semantic units, generating a single text fragment with a diagnostic conclusion. Finally, all generated text fragments are logically assembled to generate a structured compliance diagnostic report. This compliance diagnostic report It not only includes the final judgment and key data quantification information, but also constitutes a complete digital evidence chain with legal basis, solving the pain point of the inexplicability of traditional judgment results.

[0125] Step S4000: Set the global minimum deformation energy value. Perform nonlinear mapping calculations to generate dynamic confidence scores. And instantiate verifiable state nodes Based on the compliance diagnostic report The compliance classification labels and preset conditional control logic automatically trigger hierarchical business flow control instructions. .

[0126] Specifically, this step aims to utilize the global minimum deformation energy value output in step S2300. As a confidence quantification benchmark, it is combined with the compliance diagnostic report generated in step S3200. Instantiate verifiable state nodes And based on the compliance diagnostic report The compliance classification label automatically triggers the corresponding business flow control instructions. This ensures the data integrity, decision traceability, and business execution flexibility of the Real Estate Data Chain (DAG).

[0127] Further, step S4000 includes:

[0128] Step S4100: Set the global minimum deformation energy value. Perform nonlinear mapping calculations to generate dynamic confidence scores. and the dynamic confidence score Embedded into the real estate data version chain DAG, and instantiated as a verifiable state node. .

[0129] Specifically, this step aims to obtain the global minimum deformation energy value output in step S2300. As a scalar measure of registration conflict, an inverse exponential decay mapping is performed to convert it into a dynamic confidence score representing the reliability of this match. Finally, the dynamic confidence score will be... Embedded into the real estate data version chain DAG, and verifiable state nodes with confidence weights are generated. This ensures that every mapping data record has a traceable calculation basis, addressing the technical deficiency in traditional data management of the lack of credibility verification for the output results of complex algorithms.

[0130] In the specific implementation process, this step defines a monotonically decreasing function that follows an inverse exponential decay model, used to calculate the global minimum deformation energy value. Mapped to dynamic confidence scores The dynamic confidence score The mapping logic specifically involves using a preset legality tolerance threshold to determine the global minimum deformation energy value. The system performs a division operation to convert the absolute energy cost into a relative risk ratio. Then, it uses a preset attenuation factor to perform a nonlinear scaling transformation on this relative risk ratio, and inverts the transformation result to generate a negative exponential term. Finally, it performs an exponential mapping operation on this negative exponential term, transforming the generalized risk value into a data reliability score between 0 and 1, generating a dynamic confidence score. This involves the traceable quantification of the reliability of real estate boundary association results. The legality tolerance threshold is a preset positive real constant used to define the maximum allowable energy tolerance limit in real estate management, serving as a normalization benchmark for the global minimum deformation energy value. It is converted into a comparable dimensionless relative conflict ratio; the attenuation factor is a preset positive real constant, usually greater than 1, used to adjust the nonlinear sensitivity of energy to confidence conversion and control the rate at which confidence decreases with increasing conflict energy.

[0131] Finally, the calculated dynamic confidence score will be... Embedded into the real estate data version chain DAG, instantiate a new verifiable state node. This finalizes the registration result. This verifiable state node... The instantiation logic is as follows: First, the key metadata of this registration task is encapsulated, such as electronic certificate ID, survey data ID, processing timestamp, and dynamic confidence score. This serves as the node payload data. Next, a cryptographic hash operation is performed on the encapsulated node payload data to generate a unique hash value for the node; simultaneously, the node records a hash pointer to the preceding verifiable state node to establish an immutable sequential dependency relationship.

[0132] Step S4200: Analyze the compliance diagnostic report. The compliance classification label is determined based on preset condition control logic. If the compliance classification label is "Compliance Passed", a business flow control instruction is triggered. For "automatic release," if the compliance classification label is "violation warning" or "questionable and pending investigation," a business flow control instruction will be triggered. This is a "circuit breaker warning".

[0133] Specifically, this step aims to analyze the compliance diagnostic report generated in step S3200. Based on the conclusions and the hierarchical threshold, the verifiable state nodes instantiated in step S4100 are... Executing a state lock or release operation automatically triggers the corresponding business flow control command. This ensures that reasonable errors can be automatically allowed, while data chains are immediately locked for substantial violations, addressing the pain point of traditional technologies lacking an interpretable and flexible judgment mechanism.

[0134] In the specific implementation process, the compliance diagnostic report is parsed. The compliance classification label is set, and the following conditional control logic is executed:

[0135] If the compliance diagnostic report If the compliance classification label is "Compliance Passed," meaning the compliance status is "Flexible Tolerance," it indicates that the conflict costs are within the legally permissible range of non-substantive differences. The system is configured to automatically generate an explanatory difference list containing quantitative data such as displacement and stress values, and store this list in the database as audit documentation, while simultaneously triggering business flow control instructions. The command is set to "automatic release". This verifiable state node Mark it as verified and remove business restrictions on subsequent change registration or archiving operations.

[0136] If the compliance diagnostic report If the compliance classification label is "Violation Warning" or "Questionable and Pending Investigation," meaning the compliance status is "Hard Constraint Conflict" or "Topological Outlier," it indicates that the cost of the conflict has exceeded the preset legal tolerance threshold. The system executes a data locking procedure, automatically setting this verifiable status node. The write protection flag is set, thus prohibiting any subsequent automated business operations based on this node. Simultaneously, the system triggers a business flow control command. This is a "circuit breaker warning". This is a business flow control instruction. Warning notifications and visual heatmaps will be sent to the manual review terminal until manual intervention is provided to resolve the conflict or new legal supporting documents are provided.

[0137] Example 2

[0138] This embodiment, based on Embodiment 1, provides an intelligent management system that links real estate surveying data with electronic certificates, such as... Figure 2 As shown, the system includes a heterogeneous map construction module, an elastic registration solution module, a difference attribution delivery module, and a business flow control module;

[0139] The heterogeneous map construction module is used to acquire historical electronic certificate data packages. and current mapping point cloud data stream Historical electronic certificate data packets are parsed using a spatial semantic constraint encoder. Spatial constraints to construct the semantic expectation graph of the certificate Simultaneously, a 3D semantic segmentation network is used to extract the current mapping point cloud data stream. The physical entity characteristics are used to construct a mapping entity observation map. .

[0140] The elastic registration solution module is based on the semantic expectation graph of certificates. and surveying entity observation map The semantic nodes and physical nodes are projected onto the latent feature space to generate an initial probability correlation matrix. Construct a potential energy in memory that includes the boundary violation. and measuring deformation strain energy Composite energy function and the composite energy function Execute the Sinkhorn iterative algorithm to generate the optimal flexible mapping matrix. and global minimum deformation energy value .

[0141] The difference attribution delivery module is based on the optimal flexible mapping matrix. Semantic Expectation Graph of Certificates and surveying entity observation map The local residual stress index is calculated and compared to generate compliance classification labels based on the local residual stress index and the current mapping point cloud data stream. Perform dual-channel data processing to generate a differential attribution heatmap. and compliance diagnostic report .

[0142] The business flow control module is used to control the global minimum deformation energy value. Perform nonlinear mapping calculations to generate dynamic confidence scores. And instantiate verifiable state nodes Based on the compliance diagnostic report The compliance classification labels and preset conditional control logic automatically trigger hierarchical business flow control instructions. .

[0143] The parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0144] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent management method linking real estate surveying data with electronic certificates, characterized in that, include: The system acquires historical electronic certificate data packages and current survey point cloud data streams. It uses a spatial semantic constraint encoder to parse the spatial constraints of the historical electronic certificate data packages to construct a certificate semantic expectation map. Simultaneously, it uses a 3D semantic segmentation network to extract the physical entity features of the current survey point cloud data streams to construct a survey entity observation map. Based on the semantic expectation graph of the certificate and the observation graph of the surveyed entity, the semantic nodes and physical nodes are projected onto the latent feature space to generate an initial probability correlation matrix. A composite energy function containing the boundary violation potential energy and the surveyed deformation strain energy is constructed in memory. The Sinkhorn iterative algorithm is executed on the composite energy function to generate the optimal flexible mapping matrix and the global minimum deformation energy value. Based on the optimal flexible mapping matrix, the semantic expectation map of the certificate, and the observation map of the surveyed entity, the local residual stress index is calculated and compared to generate a compliance classification label. Based on the local residual stress index and the current surveyed point cloud data stream, dual-channel data processing is performed to generate a difference attribution heatmap and a compliance diagnosis report. Perform nonlinear mapping calculations on the global minimum deformation energy value to generate dynamic confidence scores and instantiate verifiable state nodes; automatically trigger hierarchical business flow control instructions based on the compliance classification labels in the compliance diagnostic report and preset conditional control logic.

2. The intelligent management method for linking real estate surveying data with electronic certificates according to claim 1, characterized in that, The method for constructing the semantic expectation graph of the certificate includes performing optical character recognition on historical electronic certificate data packets to generate a text sequence to be processed, instantiating the text sequence to be processed into a set of semantic nodes using a spatial semantic constraint encoder, constructing a set of directed edges connecting the set of semantic nodes based on a dependency parsing algorithm, calculating the elastic stiffness coefficient by fusing static context components and dynamic context components, generating a spatial constraint vector by combining the semantic orientation vector and the expected topological distance value, and constructing a semantic expectation graph of the certificate containing a set of semantic nodes, a set of directed edges, and a set of stiffness attributes encapsulated by the elastic stiffness coefficient. The method for constructing the survey entity observation map includes: preprocessing the current survey point cloud data stream; calling a three-dimensional semantic segmentation network to cluster coordinate points to generate a set of physical nodes; using principal component analysis algorithm to solve the geometric feature tensor containing the component centroid coordinate vector, component surface normal vector, and minimum bounding box volume; calculating the physical spatial correlation degree based on the adjacency relationship between physical nodes to determine the set of physical spatial correlation edges; and constructing a survey entity observation map containing the set of physical nodes, the set of physical spatial correlation edges, and the geometric feature tensor.

3. The intelligent management method for linking real estate surveying data with electronic certificates according to claim 2, characterized in that, The spatial constraint vector is constructed by taking the directed edges in the semantic node set that connect the source semantic node to the target semantic node and constructing an ordered triplet containing the semantic orientation vector, the desired topological distance value, and the elastic stiffness coefficient. The semantic orientation vector is obtained by extracting the orientation descriptors from the text sequence to be processed, and mapping the orientation descriptors into unit vectors in the Cartesian coordinate system according to a preset orientation mapping table. The desired topological distance value is obtained by parsing the length value in the text sequence to be processed. The elastic stiffness coefficient is calculated by retrieving a deterministic score of the orientation descriptor from a pre-set legal dictionary, performing a normalization function on the deterministic score, and multiplying it by a preset lexical factor weight coefficient to generate a static semantic component. The normalized attention value of each word in the text sequence to be processed is extracted using a spatial semantic constraint encoder. The context uncertainty entropy is calculated based on the Shannon information entropy formula. The context uncertainty entropy is then reversed and normalized using the logarithm of the context window length of the text sequence to be processed as a benchmark to obtain the context deterministic operator. Finally, it is multiplied by a preset context factor weight coefficient to generate dynamic context components. The static semantic component and the dynamic context component are added together to obtain the elastic stiffness coefficient.

4. The intelligent management method for linking real estate surveying data with electronic certificates according to claim 2, characterized in that, The method for calculating the physical space correlation degree includes: Call the spatial indexing algorithm for the first The physical node and the first The minimum bounding box volume of each physical node is used to perform intersection detection and neighborhood search to generate a binary physical adjacency discriminant operator. In response to the physical adjacency discrimination operator being true, the first... The physical node and the first The component centroid coordinate vectors of each physical node are obtained, and Euclidean distance difference calculation is performed to obtain the absolute spatial distance value; The absolute spatial distance value and a preset numerical stability constant are added together, and the result of the addition is counted inversely to generate the physical spatial correlation degree.

5. The intelligent management method for linking real estate surveying data with electronic certificates according to claim 1, characterized in that, The method for generating the optimal flexible mapping matrix and the global minimum deformation energy value includes: The graph attention network is invoked to perform parallel feature encoding on the semantic expectation graph of the certificate and the observation graph of the surveying entity. The heterogeneous semantic nodes and physical nodes are projected onto a unified latent feature space to generate semantic embedding vector sequences and geometric embedding vector sequences respectively. The matching confidence is calculated based on the semantic embedding vector sequences and geometric embedding vector sequences to generate an initial probability association matrix. Based on the initial probability correlation matrix, spatial constraint vector, and geometric feature tensor, a composite energy function containing the boundary violation potential energy and the measured deformation strain energy is constructed in memory. Starting with the initial probability correlation matrix as the iteration starting point, the Sinkhorn iterative algorithm is used to perform gradient descent and bidirectional normalization constraint updates on the composite energy function until the system energy converges, outputting the optimal flexible mapping matrix and the global minimum deformation energy value; based on the comparison result between the global minimum deformation energy value and the boundary legality tolerance threshold, the judgment conclusion on the boundary state of the real estate unit is output.

6. The intelligent management method for linking real estate surveying data with electronic certificates according to claim 5, characterized in that, The steps for constructing the boundary violation potential energy are as follows: extracting semantic orientation vectors from spatial constraint vectors and extracting component surface normal vectors from geometric feature tensors; performing spatial transformation operations on the component surface normal vectors using the mapping matrix to be solved to generate transformed physical normal vectors; and calculating the vector similarity between the semantic orientation vectors and the transformed physical normal vectors to generate topological orientation deviation values. The elastic stiffness coefficient in the spatial constraint vector is used as an exponential adjustment factor to perform nonlinear weighting on the topological direction deviation value, thereby generating the boundary violation potential energy. The mapping matrix to be solved is a double random matrix or an affine transformation matrix, which represents the mapping relationship between the physical node set in the survey point cloud and the certificate semantic node space. The steps for constructing the measured deformation strain energy are as follows: extracting the component centroid coordinate vector from the geometric feature tensor to construct the physical node coordinate matrix, and generating the semantic node reference coordinate matrix based on the semantic expectation graph using a graph embedding algorithm; using the Frobenius norm to calculate the overall displacement of the physical node coordinate matrix relative to the semantic node reference coordinate matrix under the action of the mapping matrix to be solved, thereby generating the measured deformation strain energy.

7. The intelligent management method for linking real estate surveying data with electronic certificates according to claim 1, characterized in that, The methods for generating the difference attribution heatmap and compliance diagnostic report include: Based on the optimal flexible mapping matrix and the semantic expectation graph of the certificate, the local residual stress index of the physical nodes in the survey entity observation map is calculated. The local residual stress index and elastic stiffness coefficient are compared and analyzed by the differential attribution classifier. The physical nodes are divided into different compliance states and a node stress dataset containing compliance classification labels of "compliance passed", "violation warning" and "questionable pending investigation" is output. Based on the node stress dataset and the current survey point cloud data stream, dual-channel data processing is performed. In the visualization channel, a nonlinear transfer function is constructed to map the local residual stress index into a color rendering vector. Combined with the current survey point cloud data stream, a difference attribution heatmap is generated. In the readable channel, a slot filling algorithm is executed based on compliance classification labels to generate a compliance diagnostic report.

8. The intelligent management method for linking real estate surveying data with electronic certificates according to claim 7, characterized in that, The method for classifying compliance categories includes: If the maximum posterior matching probability of a physical node is less than the preset confidence level, the physical node is determined to be a topological outlier and is assigned a "questionable" label; the posterior matching probability is extracted from the columns and rows of the optimal flexible mapping matrix; If the maximum posterior matching probability of a physical node is greater than or equal to the preset confidence lower limit, the following comparison is performed: if the local residual stress index is less than the preset stress melting threshold, or if the local residual stress index is greater than or equal to the stress melting threshold and the elastic stiffness coefficient is less than the preset stiffness tolerance limit, then the physical node is determined to be in an elastic tolerance state and is given the "compliance passed" label. If the local residual stress index is greater than or equal to the stress melting threshold and the elastic stiffness coefficient is greater than or equal to the stiffness tolerance limit, the physical node is determined to be in a hard constraint conflict state and is given a "violation warning" label.

9. The intelligent management method for linking real estate surveying data with electronic certificates according to claim 1, characterized in that, The triggering method for the hierarchical business flow control instruction includes parsing the compliance classification label of the compliance diagnostic report, judging according to the preset condition control logic, if the compliance classification label is "compliance passed", then the business flow control instruction is "automatic release" is triggered, and if the compliance classification label is "violation warning" or "questionable and pending investigation", then the business flow control instruction is "circuit breaker warning".

10. An intelligent management system linking real estate surveying data and electronic certificates, used to implement the intelligent management method for linking real estate surveying data and electronic certificates as described in any one of claims 1-9, characterized in that, The system includes a heterogeneous map construction module, an elastic registration solution module, a difference attribution delivery module, and a business flow control module. The heterogeneous map construction module is used to acquire historical electronic certificate data packages and current survey point cloud data streams, and to parse the spatial constraints of historical electronic certificate data packages through a spatial semantic constraint encoder to construct a certificate semantic expectation map. Simultaneously, a three-dimensional semantic segmentation network is used to extract the physical entity features of the current survey point cloud data stream to construct a survey entity observation map. The elastic registration solution module: based on the certificate semantic expectation map and the surveyed entity observation map, projects the semantic nodes and physical nodes to the latent feature space to generate an initial probability correlation matrix, constructs a composite energy function in memory that includes the boundary violation potential energy and the surveyed deformation strain energy, and executes the Sinkhorn iterative algorithm on the composite energy function to generate the optimal flexible mapping matrix and the global minimum deformation energy value. The difference attribution delivery module: based on the optimal flexible mapping matrix, the certificate semantic expectation map and the survey entity observation map, calculates the local residual stress index and compares and analyzes it to generate a compliance classification label. Based on the local residual stress index and the current survey point cloud data stream, it performs dual-channel data processing to generate a difference attribution heatmap and a compliance diagnosis report. The business flow control module is used to perform nonlinear mapping calculations on the global minimum deformation energy value to generate dynamic confidence scores and instantiate verifiable state nodes; and to automatically trigger hierarchical business flow control instructions based on the compliance classification labels of the compliance diagnosis report and preset conditional control logic.

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