Land full life cycle tracing and ownership fitting method based on identification code

Abstract

The application discloses a land full life cycle tracing and right ownership matching method based on an identification code, belongs to the technical field of big data resource services, data mining software and cloud fusion application running support platform software, and improves operation decision efficiency when daily registration data and annual update results are fused by analyzing feature modes of daily registration records and taking analysis results as the only determination condition of operation types. Through transaction boundary encapsulation and atomic linkage execution of space operation and tracing operation, synchronous update of a land parcel present situation change and a relationship edge in a right ownership tracing graph is realized, so that the problem that consistency of a tracing result and a layer state cannot be guaranteed in a traditional scheme is solved, and a real-time and accurate right ownership base map is provided for construction land approval.

Description

Technical Field

[0001] This application relates to the fields of big data resource services, data mining software, and cloud-integrated application operation support platform software technology, and in particular to a method for tracing and matching the entire life cycle of land ownership based on identification codes. Background Technology

[0002] In the approval process for construction land, it is necessary to perform spatial overlay analysis between the approval scope and the latest ownership map to confirm the ownership status. However, there is a time lag between the annual updated ownership results and routine registration changes—the actual ownership has changed through routine procedures such as initial registration, transfer registration, change registration, and cancellation registration, while the annual results have not yet reflected these changes, resulting in inconsistencies between the base map on which the approval relies and the actual ownership status.

[0003] Among related technologies, the identification code association scheme uses the real estate unit code as an index to link data from various business stages, but no operation-driven relationship is established between its identification code and registration type; the time-series map scheme can trace the evolution history of land parcels, but its tracing relationship construction and spatial data fusion operation are executed independently; the layer overlay analysis scheme can perform ownership verification, but its analysis rules lack dynamic association with the semantics of registration type.

[0004] The above-mentioned scheme has problems such as low operational efficiency and difficulty in ensuring consistency between traceability results and layer status when integrating daily registration data and annual results. Summary of the Invention

[0005] This application provides a method for tracing and matching the entire life cycle of land ownership based on identification codes. The technical solution is as follows: On the one hand, a method for tracing and matching the entire life cycle of land ownership based on identification codes is provided, the method comprising: The real estate unit code, registration type, and original real estate unit code carried in the daily registration records are analyzed for feature patterns to obtain the value feature combination corresponding to the daily registration records and the association status between the original real estate unit code and the current land parcels in the annual update result layer. Based on the value feature combination and the associated state, and using the value feature combination itself as the sole criterion for determining the operation type, a spatial operation descriptor and a traceability operation descriptor are generated. The spatial operation descriptor is used to describe the state change type of the target parcel in the annual update result layer, and the traceability operation descriptor is used to describe the edge relationship change type of the corresponding node of the target parcel in the ownership traceability map. The spatial operation descriptor and the trace operation descriptor are encapsulated by transaction boundaries to obtain spatial operation instructions and trace operation instructions with atomic linkage relationships. The atomic linkage relationship constrains the spatial operation instructions and the trace operation instructions to be executed in conjunction within the same transaction boundary. Within the same transaction boundary, the spatial operation command is applied to the annual update result layer to change the current status of the target parcel, and the traceability operation command is simultaneously applied to the ownership traceability map to update the traceability relationship edge associated with the target parcel, so that the status change of the target parcel and the generation of the traceability relationship edge are completed atomically in the same transaction. Attached Figure Description

[0006] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0007] Figure 1 This is a flowchart of a land lifecycle tracing and ownership matching method based on an identifier code, provided in an embodiment of this application. Figure 2 This is a flowchart of another method for tracing and matching the entire life cycle of land based on an identifier code, provided in an embodiment of this application; Figure 3 This is a flowchart of another method for tracing and matching the ownership of land based on an identifier code, provided in an embodiment of this application; Figure 4 This is a flowchart of another method for tracing and matching the ownership of land based on an identifier code, provided in an embodiment of this application. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0009] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "nth," nor are there any restrictions on quantity or execution order.

[0010] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0011] In the approval process for construction land, when spatially overlaying the approval scope with the latest ownership map, inconsistencies often arise between the annual update and routine registration changes, leading to discrepancies between the map on which the approval relies and the actual ownership status. Related technical solutions face challenges in merging routine registration data with annual results, including low operational efficiency and difficulty in ensuring consistency between traceability results and layer status.

[0012] To address this, this application proposes a method for tracing and matching the entire lifecycle of land ownership based on identification codes, see [link to relevant documentation]. Figure 1 This includes the following steps.

[0013] 101. Perform feature pattern analysis on the real estate unit code, registration type and original real estate unit code carried in the daily registration records to obtain the value feature combination corresponding to the daily registration records and the association status between the original real estate unit code and the current land parcels in the annual update result layer.

[0014] 102. Based on the combination of value features and associated states, and using the combination of value features itself as the sole criterion for determining the operation type, generate spatial operation descriptors and retrospective operation descriptors. Spatial operation descriptors are used to describe the type of state change of the target parcel in the annual update result layer, and retrospective operation descriptors are used to describe the type of edge relationship change of the corresponding node of the target parcel in the ownership retrospective map.

[0015] 103. Encapsulate the spatial operation descriptor and the trace operation descriptor within the transaction boundary to obtain spatial operation instructions and trace operation instructions with atomic linkage. The atomic linkage constraint ensures that the spatial operation instructions and the trace operation instructions are executed in conjunction within the same transaction boundary.

[0016] 104. Within the same transaction boundary, apply spatial operation commands to the annual update result layer to change the current status of the target parcel, and simultaneously apply traceability operation commands to the ownership traceability map to update the traceability relationship edges associated with the target parcel, so that the status change of the target parcel and the generation of traceability relationship edges are completed atomically in the same transaction.

[0017] For ease of understanding, the following explains some key terms in this embodiment: Real estate unit code: refers to the code used to uniquely identify a real estate unit, which serves as the core index in real estate registration business.

[0018] Registration type: refers to the category of real estate registration business, such as initial registration, transfer registration, change registration, cancellation registration, etc., which determines the nature of the registration operation.

[0019] Original Real Estate Unit Code: Refers to the original identification code of a real estate unit that is affected or replaced by the current registration business in certain registration business.

[0020] Routine registration records refer to business data generated during the real estate registration process, which includes information such as the real estate unit code, registration type, and original real estate unit code.

[0021] Annual Updated Results Layer: Refers to the spatial data set that is regularly submitted and released by the real estate management department, reflecting the current status of real estate parcels at a specific point in time.

[0022] Current land parcel: refers to a land parcel unit that is currently in a valid state and has complete ownership information in the annual update results layer.

[0023] Value feature combination: refers to the structured data that can characterize the operational intent and state transition pattern of a record by parsing and encoding key fields in daily registration records.

[0024] Association Status: This refers to whether there is a corresponding relationship between the original real estate unit code and the current land parcels in the annual update result layer, and the validity of this relationship.

[0025] Spatial operation descriptor: refers to a data structure used to abstractly describe what kind of status change operation is performed on a target parcel in the annual update results layer.

[0026] Traceability operation descriptor: refers to a data structure used to abstractly describe what kind of edge relationship change operation is performed on the node corresponding to the target parcel in the ownership traceability graph.

[0027] Ownership tracing map: refers to a graph database constructed with real estate parcels as nodes and the ownership evolution relationship between parcels as edges, used to record the entire life cycle tracing information of parcels.

[0028] Target parcel: refers to a specific parcel unit whose spatial status or traceability needs to be changed during the current routine registration and record processing.

[0029] Spatial operation instructions: These are execution units encapsulated by spatial operation descriptors, which can be directly applied to the annual update result layer to perform parcel status changes.

[0030] Trace operation instructions: These are execution units encapsulated by trace operation descriptors, which can be directly applied to the ownership traceability graph to update traceability relationship edges.

[0031] Atomic linkage: refers to the fact that spatial operation instructions and trace operation instructions must be an inseparable whole during execution, either all succeed or all fail, in order to ensure data consistency.

[0032] Transaction boundary: refers to the logical or physical boundary that limits the execution scope of atomic linkage relationships, ensuring that operations performed within it have the ACID properties of transactions.

[0033] This embodiment provides a method for tracing and matching the entire life cycle of land ownership based on identification codes, and its specific implementation is as follows: Feature pattern parsing is performed on the real estate unit codes, registration types, and original real estate unit codes carried in daily registration records to obtain the value feature combinations corresponding to the daily registration records and the association status between the original real estate unit codes and the current land parcels in the annual update result layer. In one implementation, a preset rule set can be used to match and extract fields from the daily registration records one by one. For example, the format of the real estate unit code can be identified through string matching, the registration type can be determined through enumerated value comparison, and the annual update result layer can be directly queried to determine whether the original real estate unit code exists. The extracted information is then simply concatenated or encoded to form preliminary value feature combinations, and the association status is marked as "existent" or "non-existent" based on the query results. In another implementation, a keyword-based parser can be constructed. This parser identifies the real estate unit codes, registration types, and original real estate unit codes from the text content of the daily registration records based on a preset keyword list and structures them. A direct database query is then performed on the annual update result layer to determine whether the original real estate unit code exists in the current land parcels, and the association status is determined accordingly.

[0034] Based on the aforementioned combinations of value features and associated states, spatial operation descriptors and retrospective operation descriptors are generated, using the value feature combination itself as the sole criterion for determining the operation type. The spatial operation descriptor describes the type of state change for the target parcel in the annual update result layer, while the retrospective operation descriptor describes the type of edge relationship change for the corresponding node of the target parcel in the ownership retrospective map. Specifically, a mapping table can be pre-established, directly mapping each possible combination of value features to a specific spatial operation type (e.g., "add parcel", "modify parcel attributes", "delete parcel") and a specific retrospective edge relationship type (e.g., "generate edge", "consume edge", "attribute change edge"). After parsing the value feature combination, the corresponding spatial operation descriptor and retrospective operation descriptor are obtained directly by looking up the table. For example, if the value feature combination represents "first registration" and "no association with the original real estate unit code", it is directly determined as a spatial operation of "add parcel" and a retrospective operation of "generate edge". As another implementation approach, a decision tree model can be designed. This model takes the combination of value features and associated states as input, and outputs the corresponding spatial operation descriptor and trace operation descriptor through a series of preset judgment nodes.

[0035] Spatial operation descriptors and trace operation descriptors are encapsulated within transaction boundaries to obtain spatial operation instructions and trace operation instructions with atomic linkage. This atomic linkage constrains the spatial operation instructions and trace operation instructions to execute in tandem within the same transaction boundary. For example, the contents of the spatial operation descriptors and trace operation descriptors can be converted into executable scripts or API call parameters for their respective systems, and then these two independent execution units can be packaged into a single logical transaction. For instance, a batch script can be created containing update commands for the spatial database and update commands for the graph database, ensuring that these two commands are executed within the same database transaction. If any command fails, the entire batch transaction will be rolled back. As an optional implementation, each spatial operation descriptor and trace operation descriptor can be assigned a unique transaction ID, and during execution, a transaction coordinator can monitor these two operations to ensure they are logically treated as a single unit.

[0036] Within the same transaction boundary, spatial operation instructions are applied to the annual update result layer to change the current status of the target parcel, while simultaneously, retrospective operation instructions are applied to the ownership retrospective graph to update the retrospective relationship edges associated with the target parcel. This ensures that the change in the target parcel's status and the generation of retrospective relationship edges are completed atomically within the same transaction. Specifically, when a spatial operation instruction is executed to modify the current status of the target parcel in the annual update result layer, such as changing the parcel status from "pending approval" to "approved," the retrospective operation instruction is immediately triggered, creating or modifying the retrospective relationship edges related to the target parcel in the ownership retrospective graph, such as adding an edge indicating "approved." These two operations are performed within the same database transaction or distributed transaction, making the update of the spatial layer status and the generation of relationship edges in the retrospective graph logically synchronous and atomic. For example, if the spatial operation instruction successfully updates the parcel status, but the retrospective operation instruction fails to create a retrospective edge due to a network failure, the entire transaction will be rolled back, and the parcel status update will be undone, thus avoiding data inconsistency.

[0037] This application improves the efficiency of operational decision-making when integrating daily registration data with annual update results by parsing the feature patterns of daily registration records and using the parsing results as the sole criterion for determining the operation type. By encapsulating the transaction boundaries of spatial and traceability operations and achieving atomic linkage execution, it ensures that changes in the current status of land parcels are synchronized with the updates of relational edges in the ownership traceability map. This solves the problem of inconsistent traceability results with layer status in traditional solutions, providing a real-time and accurate ownership base map for construction land approval.

[0038] In some of the embodiments described above in this application, a feature pattern parsing method is proposed to generate value feature combinations and associated states by parsing the real estate unit code, registration type, and original real estate unit code carried in daily registration records. However, in its implementation, due to the lack of an effective semantic prediction mechanism and consistency verification means, the existence status judgment of the original real estate unit code in the annual update result layer may be inaccurate, thereby affecting the reliability of the associated state, reducing the efficiency of subsequent spatial operations and traceability operations, and threatening the consistency between land ownership traceability and layer status changes.

[0039] To address this, this application further proposes a method for feature pattern parsing of the real estate unit code, registration type, and original real estate unit code carried in routine registration records, in order to obtain the value feature combination corresponding to the routine registration record and the association status between the original real estate unit code and the current land parcels in the annual update result layer. See [link to relevant documentation]. Figure 2 The method includes the following steps: 201. Using the registration type as a priori constraint, perform semantic prediction on the existence status of the original real estate unit code in the annual update result layer. This semantic prediction is used to determine the expected existence status that the original real estate unit code should present under the constraint of the registration type.

[0040] 202. Using the original real estate unit code as an index, extract the actual existence status of the original real estate unit code in the annual update result layer, and perform consistency verification between the expected existence status and the actual existence status to obtain the consistency verification result.

[0041] 203. Based on the consistency verification result, determine whether the association status between the original real estate unit code and the current land parcel belongs to one of the following: valid association, invalid association, or no association.

[0042] 204. The registration type, the category of the associated state, and the consistency verification results of the expected state and the actual state are fused and encoded to obtain the value feature combination with the ability to determine state transition.

[0043] For example, using the registration type as a priori constraint serves to provide guidance for the subsequent semantic prediction of the existence status of the original real estate unit code. For instance, for routine registration records of the "initial registration" type, the priori constraint might indicate that the original real estate unit code should be empty. Conversely, for routine registration records of the "transfer registration" type, the priori constraint might indicate that the original real estate unit code should exist. This priori constraint can be implemented through a pre-defined rule base; for example, by establishing a mapping table that associates different registration types with the corresponding existence constraints of the original real estate unit code. Alternatively, it can be trained using a machine learning model that learns and outputs priori constraints for specific registration types based on historical registration data and the corresponding existence status of the original real estate unit codes.

[0044] Semantic prediction of the existence status of the original real estate unit code in the annual update result layer aims to predict its expected status in the annual update result layer based on the prior constraints of the registration type. This prediction process can be based on a set of expert system rules; for example, if the registration type is "cancellation registration," the original real estate unit code is predicted to be in a "cancelled" or "non-existent" state. Alternatively, an ontology-based reasoning engine can be used to associate the registration type, business rules, and land parcel status ontology, and the expected existence status of the original real estate unit code can be derived through reasoning.

[0045] Determining the expected existence state of the original real estate unit code under the registration type constraint is the direct output of semantic prediction. This expected existence state can be a simple Boolean value, such as "should exist" or "should be empty". It can also be a more refined state description, such as "should exist and be a current land parcel", "should exist but be a historical land parcel", or "should be empty".

[0046] Extracting the actual existence status of the original property unit code in the current year's updated result layer, using the original property unit code as an index, is to obtain the true status of that code in the current year's updated result layer. This can be achieved by performing an exact match query in the attribute database of the annual update result layer using the original property unit code as the query key. Alternatively, it can be done through spatial queries. For example, if the original property unit code is associated with the spatial geometric information of a certain land parcel, the actual existence status of that land parcel can be quickly located and extracted using a spatial index.

[0047] The consistency check between the expected state and the actual state aims to compare the predicted result with the actual situation and identify potential data inconsistencies. This check can be achieved by directly comparing whether the two state values ​​are equal. For example, if the expected state is "should exist" while the actual state is "does not exist," the check result is inconsistent. Another approach is to use a fuzzy matching algorithm, which allows for minor differences within a certain tolerance range to accommodate slight deviations in the data.

[0048] The consistency check result is the output of the above check process. This result can be a simple Boolean value (e.g., true for consistency, false for inconsistency) or an enumeration type (e.g., consistent, partially consistent, inconsistent). The result can also be a quantifiable metric, such as a value between 0 and 1 representing the degree of consistency.

[0049] Determining the association status between the original real estate unit code and the current land parcel based on the consistency verification result—whether it is a valid association, a invalid association, or no association—is a further classification and interpretation of the verification result. For example, if the consistency verification result is completely consistent, the association status may be determined as "valid association." If it is expected to exist but does not actually exist, it may be determined as "invalid association." If it is expected to be empty and is actually empty, it may be determined as "no association." This classification process can be implemented through a pre-defined decision rule table, directly mapping different combinations of verification results to the corresponding association status category. Alternatively, a classifier, such as a support vector machine or neural network, can be trained to classify based on the consistency verification result and other auxiliary features.

[0050] The goal of this fusion encoding is to integrate multiple key pieces of information into a unified feature representation with rich semantics, by fusing the registration type, the category of the associated state, and the consistency verification result between the expected and actual states. This fusion encoding can be achieved by bit encoding or hash encoding of this information. For example, a unique numerical value or binary bit string can be assigned to each registration type, associated state category, and verification result, and then these can be concatenated or subjected to bit operations to obtain a comprehensive code. Another approach is to use feature vectors, treating this information as different dimensions of a vector, and generating a combination of value features with state transition determination capabilities through weighted summation or nonlinear transformation.

[0051] The resulting combination of value features, capable of determining state transitions, is a product of the aforementioned fusion coding. The coding structure of this combination can directly or indirectly reflect the types of land parcel state transition events implicit in daily registration records. For example, a specific coding value might directly correspond to a "newly created land parcel" event, while another coding value might correspond to a "land parcel merger" event. This coding method makes subsequent operation type determination more efficient and accurate, providing a solid data foundation for tracing the entire lifecycle of land and aligning ownership.

[0052] By introducing prior constraints on registration types for semantic prediction, the above technical solution can more accurately predict the expected existence status of the original real estate unit code in the annual update result layer, avoiding blind queries and judgments. By extracting the actual existence status and performing consistency checks, the deviation between expectations and reality can be objectively identified, thus accurately identifying multiple association states—effective, ineffective, or unrelated—improving the reliability of feature pattern parsing. The registration type, association status category, and consistency check results are fused and encoded to generate a value feature combination with state transition judgment capabilities. This combination can dynamically and comprehensively reflect the complex patterns of parcel state transitions, providing more accurate and robust input for subsequent generation of spatial operation descriptors and traceability operation descriptors. This not only solves the problem of inaccurate state judgment caused by the lack of effective semantic prediction and consistency checks in traditional methods, but also lays a solid foundation for the atomic linkage execution of subsequent spatial operation instructions and traceability operation instructions, ensuring a high degree of consistency between land ownership traceability results and layer state changes, thereby improving the efficiency and accuracy of the entire traceability and overlay method.

[0053] In some of the embodiments described above in this application, semantic prediction is proposed to determine the expected existence status of the original real estate unit code in the annual update result layer. However, in its implementation process, due to the lack of specific rule-driven and evaluation mechanisms, the prediction efficiency is low and the accuracy is insufficient.

[0054] In response, this application further proposes a step for semantically predicting the existence status of the original real estate unit code in the annual update result layer by using the registration type as a priori constraint. This step includes: inputting the field value of the registration type into the state constraint rule body, driving the state constraint rule body to output the original code existence constraint condition corresponding to the registration type; substituting the current field value of the original real estate unit code into the original code existence constraint condition for truth evaluation; and determining, based on the evaluation result, whether the expected existence status is an empty state or a state that should exist and points to the current state.

[0055] In this context, "registration type" refers to the identifier used to classify different business types within real estate registration, such as initial registration, transfer registration, change registration, cancellation registration, and correction registration. Each registration type corresponds to a specific set of business rules and data processing logic. As a priori constraint, it provides the basic contextual information for subsequent semantic prediction and is a key input for determining the expected state of the original real estate unit code. The registration type can be an enumerated set of values, each representing a specific registration business type, or a string or code, whose corresponding business semantics are identified through table lookup or parsing.

[0056] This state constraint rule body is a module or system containing predefined logical rules used to dynamically generate or match corresponding source code existence constraints based on the input registration type field value. It encapsulates the business logic of the existence state that the original real estate unit code should have under different registration types. This state constraint rule body is the core component for achieving automated and accurate semantic prediction, capable of transforming abstract registration types into concrete, executable constraints. This state constraint rule body can be a rule engine-based system, which pre-defines mapping rules between various registration types and source code existence constraints. When a registration type is input, the rule engine matches and infers based on these rules, outputting the corresponding constraints. Alternatively, it can be a configurable lookup table or decision tree, taking the registration type as input and directly querying or traversing to obtain the corresponding source code existence constraints.

[0057] The original code existence constraint is determined based on the registration type and is a set of logical rules or conditions used to determine the appropriate state of the original property unit code in the annual update result layer. For example, for "initial registration," the original code should "not exist." For "transfer registration," the original code should "exist." This original code existence constraint serves as the basis for truth value evaluation, clarifying the specific criteria for evaluating the original property unit code. The original code existence constraint can be a Boolean expression, such as "original code_IS_NULL" or "original code_EXISTS_IN_CURRENT_LAYER." Alternatively, it can be a function or method call that takes the original property unit code as a parameter and returns its expected existence state in a specific layer.

[0058] Truth evaluation refers to the process of substituting the current field values ​​of the original real estate unit code into the existence constraints of the original code, and determining whether the code satisfies the constraints through logical judgment or calculation. The result is usually a Boolean value or a quantitative value indicating the degree of compliance. Truth evaluation applies abstract constraints to specific data to arrive at a quantitative judgment of the expected existence state. Truth evaluation can be a logical judgment module that directly executes a preset logical expression, or a database query operation that constructs a query statement based on the constraints and makes judgments based on the query results.

[0059] The expected existence status is the result of truth value assessment. It clarifies the state that the original real estate unit code "should" present in the annual update result layer under specific registration types and constraints. This includes "should be empty" or "should exist and point to the current state." This expected existence status provides a benchmark for subsequent comparison with the actual existence status and is a prerequisite for consistency verification. The expected existence status can be an enumerated value, such as EXPECTED_NULL or EXPECTED_EXIST_AND_POINT_TO_CURRENT, or a Boolean flag combined with additional descriptive fields to indicate the specific status.

[0060] By introducing state constraint rule bodies and a truth value evaluation mechanism, this application can dynamically generate existence constraints of the original code based on the field values ​​of the registration type, and objectively and quantitatively evaluate the current field values ​​of the original real estate unit code. This solves the problems of low efficiency and insufficient accuracy of semantic prediction in traditional methods. For example, the state constraint rule body closely links the prediction logic with the semantics of the registration type, avoiding the limitations of manual intervention and static rules, and improving the automation and targeting of the prediction. Truth value evaluation provides clear logical judgment basis, making the evaluation process objective and reliable, and reducing subjective bias. In this way, this application can efficiently and accurately determine the expected existence state of the original real estate unit code, providing a solid and reliable foundation for subsequent consistency verification and generation of value feature combinations, thereby improving the overall accuracy and efficiency of the land life cycle tracing and ownership matching method.

[0061] In some of the solutions mentioned above in this application, the association status is determined based on the consistency verification results to accurately classify the association status between the original real estate unit code and the current land parcel. However, in its implementation, the quantification of the status deviation may be inaccurate, leading to classification errors. In particular, when the association fails, the classification judgment threshold remains fixed and cannot adapt to the historical deviation distribution, thus affecting the classification accuracy.

[0062] In response, this application further proposes a method for determining the association status between the original real estate unit code and the current land parcel, comprising: comparing the expected existing status with the actual existing status to obtain a status deviation descriptor, which is used to quantify the degree of deviation between the expected existing status and the actual existing status in the null value dimension and the non-null pointing dimension. The status deviation descriptor is input into an association status classification component, which then outputs a classification result indicating whether the association status between the original real estate unit code and the current land parcel is valid, invalid, or unrelated. When the classification result is invalid association, the target status deviation descriptor corresponding to the invalid association is extracted, and this target status deviation descriptor is fed back to the classification judgment threshold of the association status classification component, so that the classification judgment threshold is adaptively shifted based on the cumulative distribution characteristics of the target status deviation descriptor.

[0063] For example, in the step of comparing the expected state with the actual state to obtain a state deviation descriptor, which quantifies the degree of deviation between the expected and actual states in the null value dimension and the non-null pointing dimension, the state deviation descriptor is a quantitative indicator used to accurately characterize the degree of difference between the expected and actual states. This difference is not limited to simple consistency, but considers the deviation in the "null value dimension" (i.e., whether the state is null) and the "non-null pointing dimension" (i.e., whether the specific entity or attribute pointed to when the state is non-null is consistent). By finely quantifying the state deviation, richer and more accurate input information can be provided for subsequent associated state classification, avoiding classification errors caused by coarse judgments. One implementation is to encode the expected and actual states into multi-dimensional feature vectors. For example, the first dimension of the vector represents the null value state (0 for non-null, 1 for null), and the second dimension represents the hash value pointing to the entity ID. The state deviation descriptor can be obtained by calculating the Euclidean distance or cosine similarity between these two vectors, thus comprehensively reflecting the deviation in the null value and pointing dimensions. Another approach is to pre-define a set of state deviation patterns, such as "expected non-empty, actually empty", "expected empty, actually not empty", "expected to point to A, actually points to B", etc. Logical judgment maps the comparison results between the expected and actual states to these pre-define patterns, and assigns a quantized deviation score or a one-hot encoded vector to each pattern; this score or vector is the state deviation descriptor.

[0064] In the step of inputting the state deviation descriptor into the association state classification component, which then outputs a classification result indicating whether the association state between the original real estate unit code and the current land parcel is valid, invalid, or unrelated, the association state classification component is a functional module whose core responsibility is to receive and parse the state deviation descriptor, and then determine whether the association relationship between the original real estate unit code and the current land parcel belongs to the category of valid association, invalid association, or unrelated. This component is key to achieving automated and intelligent association state determination; it transforms complex deviation information into clear classification results, providing a decision-making basis for subsequent business processing. One implementation approach is to integrate an expert rule system within the association state classification component. These rules are based on predefined logical conditions, such as "if the state deviation descriptor indicates no deviation in both the null and non-null pointing dimensions, it is determined to be a valid association. If it indicates inconsistency in the non-null pointing dimension, it is determined to be an invalid association. If it indicates that the expected existence is actually completely missing, it is determined to be an unrelated association." Another implementation approach is for the association state classification component to employ a machine learning-based model, such as a support vector machine (SVM) or decision tree. This model is trained using historical data to learn the mapping relationship between state bias descriptors and associated state classification results. At runtime, by taking a new state bias descriptor as input, the model can predict the corresponding associated state classification result.

[0065] In the step of extracting the target state deviation descriptor corresponding to the failure association when the classification result is a failure association, and feeding the target state deviation descriptor back to the classification decision threshold of the association state classification component, so that the classification decision threshold is adaptively shifted based on the cumulative distribution characteristics of the target state deviation descriptor, the classification decision threshold is the numerical limit on which the association state classification component makes classification decisions. This adaptive shift means that these thresholds can be dynamically adjusted according to the cumulative distribution characteristics of the target state deviation descriptors in historical data, rather than remaining fixed. Through adaptive shift, the classification criteria can be continuously learned and optimized, especially when facing constantly changing business scenarios or data patterns, which can improve the accuracy and robustness of failure association identification and reduce false positives or false negatives. One implementation is to store the corresponding target state deviation descriptor in a sliding window or historical queue when a failure association is identified. The system periodically (e.g., every N failure associations processed or at regular intervals) calculates the statistical distribution of the target state deviation descriptors within the window, such as calculating its mean, median, or a certain percentile. Then, based on these statistical values, the classification threshold is adjusted with a preset step size and direction to better reflect the actual distribution of current failure associations. Another approach is to employ iterative optimization based on a feedback mechanism. Each time a failure association is identified, its target state deviation descriptor is used to update an internal model parameter, which directly affects the calculation of the classification threshold. For example, a weighted average of failure association deviations can be maintained, and this average can be used as the benchmark for the classification threshold. Newly identified failure association deviations update this average with a certain decay factor, thereby achieving smooth adaptive adjustment of the threshold.

[0066] The above technical solution effectively addresses the problems of inaccurate state deviation quantification and fixed classification thresholds. For example, by finely comparing the expected state with the actual state and generating a state deviation descriptor, this descriptor comprehensively quantifies the deviation between null and non-null pointing dimensions, providing a more objective and accurate input for subsequent classification. The associated state classification component can efficiently output classification results of valid association, invalid association, or no association based on this state deviation descriptor. Furthermore, when the classification result is an invalid association, the corresponding target state deviation descriptor can be extracted and fed back to the classification threshold of the associated state classification component. This feedback mechanism allows the classification threshold to adaptively shift based on the cumulative distribution characteristics of the target state deviation descriptor, thereby dynamically adjusting the classification criteria. This improves the accuracy and adaptability of identifying invalid associations, especially when facing complex and ever-changing data patterns. It can self-optimize to adapt to new deviation distributions, enhancing the accuracy and robustness of determining the association status between the original real estate unit code and the current land parcel, thereby improving the accuracy and reliability of land lifecycle tracing and ownership matching.

[0067] In some of the embodiments described above in this application, when the classification result is a failure association, the target state deviation descriptor is extracted and fed back to the classification decision threshold so that the threshold is adaptively shifted. However, in this process, the threshold shift is based on only a single deviation descriptor, which may lead to inaccurate shift and unstable classification results, affecting the accuracy of subsequent spatial operations and tracing operations.

[0068] To address this, this application further proposes that the aforementioned method also includes: collecting multiple target state deviation descriptors historically determined to be associated with failure, forming a failure deviation descriptor set; calculating the deviation amplitude distribution of the failure deviation descriptor set on the non-empty pointing dimension, and determining the offset direction and offset step size of the classification judgment threshold based on the deviation amplitude distribution; and updating the classification judgment threshold of the associated state classification component with the offset direction and the offset step size.

[0069] For example, in the step of collecting multiple target state deviation descriptors that have been historically identified as failure associations to form a failure deviation descriptor set, the aim is to systematically aggregate the target state deviation descriptors corresponding to past events identified as "failure associations." These descriptors are indicators that quantify the degree of deviation between the expected state and the actual state. By collecting multiple historical instances, rather than relying on a single event, a statistically representative dataset can be constructed, providing a more comprehensive and reliable sample foundation for subsequent analysis. One implementation method is to maintain a persistent data storage module in the system background, such as a historical event log database or data warehouse. Whenever the association state classification component determines that a daily registration record is a "failure association," its generated target state deviation descriptor is automatically captured and appended to this storage module. This module can be configured to periodically archive or clean up old data to optimize storage efficiency. Another implementation method is to utilize a stream processing framework or message queue mechanism. When a "failure association" event occurs, the corresponding target state deviation descriptor is published as a message to a specific topic. A consumer service continuously listens for and accumulates these messages. When the accumulated number reaches a preset threshold or a specific time window has passed, it aggregates these descriptors into a set of failure deviation descriptors and passes them to the subsequent processing module.

[0070] In the step of calculating the distribution of deviation magnitudes of the set of failure deviation descriptors on the non-null pointing dimension, the core of this step lies in the statistical analysis of the collected set of failure deviation descriptors to reveal their numerical distribution characteristics on the "non-null pointing dimension." The "non-null pointing dimension" quantifies the degree of deviation between the actual and expected states of the original real estate unit codes in the annual update result layer, especially in terms of pointing to valid land parcels. By calculating its distribution, we can understand the magnitude, central tendency, dispersion, and possible patterns of deviation magnitudes in failure-related events. One approach is to use traditional statistical analysis methods, such as constructing histograms to visualize the frequency distribution of deviation magnitudes, calculating the mean, median, and mode to characterize central tendency, and calculating the variance, standard deviation, and quartile range to measure dispersion. These statistics provide a quantitative understanding of deviation patterns. Another approach is to use more advanced probability density estimation techniques, such as kernel density estimation (KDE), to smoothly estimate the probability density function of deviation magnitudes, thereby more accurately capturing their distribution patterns, including complex features such as multimodal or skewed distributions. In addition, the cumulative distribution function (CDF) can be calculated to analyze the cumulative probability of different deviation magnitudes.

[0071] The step of determining the offset direction and offset step size for the classification threshold based on the distribution of the deviation magnitude aims to translate statistical insights into the distribution of failure deviation magnitudes into specific parameters for adjusting the classification threshold. The offset direction indicates which direction the threshold should move (e.g., increase or decrease), while the offset step size quantifies the magnitude of the movement. This process makes threshold adjustment data-driven and responsive to observed failure association patterns. One implementation approach is to set decision logic based on statistical rules. For example, if the average deviation magnitude of the failure deviation descriptor set is higher than the current classification threshold, the offset direction is determined to be positive (increasing the threshold), and the offset step size can be set as a proportion of the difference between the average deviation magnitude and the current threshold. Conversely, if the average deviation magnitude is lower than the threshold, a negative offset may be necessary. Another implementation approach is to use adaptive algorithms or optimization models. For example, by defining a loss function to measure the accuracy or misclassification rate of classification at the current threshold, gradient descent or other optimization methods can be used to iteratively adjust the offset direction and step size based on the impact of the deviation magnitude distribution on the loss function to minimize the loss function, thereby finding the optimal threshold adjustment strategy.

[0072] In the step of updating the classification threshold of the associated state classification component with the offset direction and the offset step size, this is the process of actually applying the adjustment parameters determined in the previous steps to the system. By applying the calculated offset direction and offset step size to the classification threshold inside the associated state classification component, the component can dynamically adjust its judgment logic to more accurately distinguish between valid associations, invalid associations, and no associations. Implementation Method 1: The most direct way is to perform an arithmetic update. For example, if the offset direction is positive, the current classification threshold is added to the offset step size. If the offset direction is negative, the offset step size is subtracted. This update can take effect immediately or at the start of the next processing cycle. Implementation Method 2: To increase the smoothness and robustness of the update, strategies such as weighted averaging or exponential smoothing can be used. For example, the new classification threshold can be obtained by weighted averaging the old threshold and the target new threshold calculated based on the offset direction and step size, where the weighting factor can be dynamically adjusted according to the stability of historical data or the confidence level of the update to avoid excessive threshold fluctuations.

[0073] By collecting multiple target state deviation descriptors historically identified as failure associations and constructing a failure deviation descriptor set, this application obtains a more comprehensive and representative sample of failure modes, avoiding the accidental impact of a single event on threshold adjustment. Calculating the deviation amplitude distribution of this set along a non-empty pointing dimension allows for precise quantification of the typical deviation range and trend of failure associations, thus providing solid data support for the adaptive adjustment of classification thresholds. Based on this deviation amplitude distribution, a more reasonable and accurate offset direction and offset step size can be determined, ensuring that the threshold adjustment matches the actual data distribution. Updating the classification threshold of the associated state classification component with the determined offset direction and offset step size enables the classifier to dynamically adapt to constantly changing business scenarios and data characteristics, improving the accuracy and stability of associated state classification. This adaptive threshold adjustment mechanism effectively solves the inaccuracy and instability problems caused by adjusting based solely on a single deviation descriptor, thereby ensuring the accuracy and consistency of the generation and execution of subsequent spatial operation instructions and traceability operation instructions, and improving the reliability of land lifecycle traceability and ownership matching.

[0074] In some of the embodiments described above in this application, a method is proposed to fuse and encode the registration type, the category of the associated state, and the consistency verification results of the expected state and the actual state to generate a combination of value features with state transition determination capability. However, in its implementation, the fusion encoding may not fully consider the adjustment effect of the associated state and the dynamic influence of the deviation pattern, resulting in the generated combination of value features being unable to accurately distinguish the operation subtype range, affecting the accuracy of subsequent state transition determination, thereby reducing the linkage efficiency of spatial operation and traceability operation.

[0075] To address this, this application further proposes a fusion coding method for fusing and coding the registration type, the category of the associated state, and the consistency verification result between the expected state and the actual state, to obtain the value feature combination with state transition determination capability. The fusion coding method includes the following steps: Using the registration type as an index, the operation base value is extracted from the preset operation type base value table. This preset operation type base value table is a predefined data structure used to store the basic encoded values ​​corresponding to different registration types. Its purpose is to provide an initial, standardized operation base value for subsequent modulation processes, ensuring that the starting point of encoding is consistent with the semantics of the registration type. For example, this table can be a hash table, where the key is the registration type (such as "initial registration", "transfer registration", "cancellation registration", etc.), and the value is the corresponding operation base value (such as an integer or a specific encoded string). Alternatively, it can be a relational database table containing a "registration type" field and an "operation base value" field, extracted through a query operation. The operation base value refers to the initial value or code extracted from the preset operation type base value table and associated with a specific registration type. As the starting point for subsequent modulation processes, this operation base value represents the basic operational semantics of the registration type under ideal conditions. For example, it can be a predefined integer value, such as 100 for initial registration, 200 for transfer registration, and 300 for cancellation registration. Alternatively, it can be a binary code with specific bit segment meanings, where certain bit segments represent the primary category of the registration type.

[0076] Using the classification of the associated state as a modulating factor, the operational base value is subjected to a first amplitude modulation to obtain a first intermediate encoded value. The modulating factor corresponding to the classification of the associated state shifts this first intermediate encoded value towards the valid or abnormal operational range. Here, the modulating factor is a parameter that adjusts the operational base value based on the classification of the associated state (e.g., valid association, invalid association, no association). In the first amplitude modulation, the modulating factor initially shifts the operational base value according to the health or abnormality of the associated state, bringing it closer to the valid or abnormal operational range, thus reflecting the influence of the associated state on the operational semantics. For example, the modulating factor can be a numerical multiplier or addend, such as a multiplier of 1.0 for valid association, 0.8 for invalid association, and 0.5 for no association. Alternatively, it can be a mapping function that maps different classifications to different sets of modulation parameters. This first amplitude modulation refers to using the classification of the associated state as a modulating factor to perform an initial numerical adjustment on the operational base value. The purpose of this modulation is to perform preliminary "calibration" of the operating base value based on the validity or abnormality of the associated state, so that it numerically reflects the "health" level or potential risk of the operation, thereby initially guiding the encoded value to the valid or abnormal operating range. For example, this modulation can be achieved by multiplying the operating base value with a regulation factor (e.g., first intermediate encoded value = operating base value * regulation factor). Alternatively, it can be achieved by adding or subtracting the operating base value with a regulation factor (e.g., first intermediate encoded value = operating base value + regulation factor). The first intermediate encoded value refers to the encoded value obtained after the first amplitude modulation, which already contains the semantic information of the registration type and associated state. This first intermediate encoded value serves as the input to the second amplitude modulation, carrying the modulation result of the previous stage and providing a basis for subsequent, more refined bias mode modulation. For example, it can be a floating-point number or an integer, and its numerical range and precision depend on the specific encoding scheme. Alternatively, it can be a structure or object containing numerical values ​​and some metadata for subsequent processing. The valid or abnormal operation range refers to a predefined range of values ​​used to distinguish between normal, executable operations and operations with potential problems that require special handling. By guiding the first intermediate encoded value to these ranges, the nature of the current operation can be preliminarily determined, providing direction for subsequent fine-tuning. For example, it can be a set of numerical ranges (e.g., [100, 199] for valid operations, [0, 99] for abnormal operations). Alternatively, it can be determined by a threshold, such as greater than a certain threshold for valid operations and less than a certain threshold for abnormal operations.

[0077] Using the deviation pattern characterized by the consistency check result as an adjustment factor, the first intermediate encoded value is subjected to a second amplitude modulation to obtain the value feature combination. This second amplitude modulation causes the encoded value of the value feature combination to fall into the operation subtype range corresponding to the deviation type. Here, the deviation pattern refers to the specific type or degree of difference between the expected state and the actual state revealed by the consistency check result. This deviation pattern, as the adjustment factor for the second amplitude modulation, is used to more finely distinguish operation subtypes, such as null deviation, pointing deviation, and redundant deviation. For example, it can be an enumeration type representing different deviation types (such as EXPECTED_EMPTY_ACTUAL_EXIST, EXPECTED_EXIST_ACTUAL_EMPTY, POINTER_MISMATCH). Alternatively, it can be a numerical value quantifying the severity or type of the deviation. The second amplitude modulation refers to further numerically adjusting the first intermediate encoded value using the deviation pattern characterized by the consistency check result as an adjustment factor. Based on the first amplitude modulation, this modulation refines the encoded value according to the specific deviation pattern, ensuring it falls within the operation subtype interval corresponding to the specific deviation type, thus achieving a more precise distinction of operation semantics. For example, it can directly map the value feature combination to the deviation pattern and the first intermediate encoded value using a lookup table. Alternatively, it can combine the parameters of the deviation pattern (such as deviation amplitude and deviation direction) with the first intermediate encoded value using complex mathematical functions. The operation subtype interval refers to a smaller numerical range further subdivided within the valid operation interval or abnormal operation interval according to the specific deviation type. This operation subtype interval enables the value feature combination to accurately indicate the detailed semantics of the operation, including the deviation type involved, thus providing a finer-grained basis for the subsequent generation of spatial operation descriptors and trace operation descriptors. For example, it can be a more subdivided numerical range (e.g., within the valid operation interval, [100, 120] represents "normal operation without deviation," and [121, 140] represents "slightly pointing deviation correction operation"). Alternatively, it can use a bitmask to reserve specific segments in the encoded value to represent different subtypes of operations.

[0078] The above technical solution effectively addresses the problem that fusion coding fails to fully consider the moderating effect of associated states and the dynamic influence of deviation patterns, resulting in the generated value feature combinations being unable to accurately distinguish operation subtype intervals. For example, using the registration type as an index to extract operation base values ​​from a preset operation type base value table provides a standardized starting point consistent with basic operation semantics, enhancing coding stability. Using the associated state's category as a modulating factor, the operation base value undergoes a first amplitude modulation, allowing the first intermediate coded value to initially shift towards the valid or abnormal operation interval based on the health or abnormality of the associated state. This mechanism dynamically incorporates the influence of associated states, enabling timely identification and differentiation of potentially problematic operations, avoiding the misinterpretation of abnormal associations as normal operations, thereby improving coding accuracy and the ability to predict operational risks. Using the deviation pattern represented by the consistency verification result as a modulating factor, the first intermediate coded value undergoes a second amplitude modulation, further refining the coding results, ensuring that the value feature combinations accurately fall within the operation subtype interval corresponding to the specific deviation type. This multi-level, dynamic modulation process results in highly semantically rich and granular combinations of value features. It not only includes the main category information of the registration type but also more accurately reflects the health level and specific deviation patterns of the associated status. Therefore, when generating spatial operation descriptors and retrospective operation descriptors, this precise combination of value features can more accurately determine the status change type of the target parcel and the change type of node edge relationships in the ownership retrospective map. This ensures that the atomic linkage between spatial operations and retrospective operations can be executed more reliably and efficiently, avoiding operational errors or breaks in the retrospective chain due to inaccurate coding, and improving the accuracy and efficiency of land lifecycle retrospective and ownership alignment.

[0079] In some of the above-mentioned schemes of this application, the operation base value is modulated with the category of the associated state as an adjustment factor to generate a first intermediate code value. However, in this process, the modulation depth fails to fully quantify the actual deviation of the associated state, resulting in the first intermediate code value failing to accurately reflect the difference between the failed association and the effective association, affecting the accuracy of subsequent value feature combinations and the reliability of state transition determination.

[0080] In response, this application further proposes a method for obtaining a first intermediate encoded value by using the associative state's category as an adjustment factor to perform a first amplitude modulation on the operating base value, including: When the category to which the associated state belongs is a valid association, the first deviation magnitude between the expected state and the actual state on the non-empty pointing dimension is extracted.

[0081] When the association state belongs to the category of failure association, the second deviation magnitude between the expected existence state and the actual existence state on the non-null pointing dimension is extracted, and the second deviation magnitude is greater than the first deviation magnitude.

[0082] Using the first deviation amplitude or the second deviation amplitude as the modulation depth, the operating base value is scaled to obtain the first intermediate coding value. The modulation depth makes the magnitude of the first intermediate coding value positively correlated with the degree of failure of the associated state.

[0083] For example, when the association status is determined to be a valid association, the first deviation magnitude between the expected and actual states on the non-empty pointing dimension is extracted. Here, the "non-empty pointing dimension" refers to whether the target parcel pointed to by the real estate unit code is empty and the specific parcel identifier it points to. The first deviation magnitude is used to quantify the minor differences or expected changes that may exist between the expected and actual states under a valid association. For example, the first deviation magnitude can be determined by calculating the spatial overlap or attribute similarity between the expected and actual parcels, or a small fixed value can be set as the first deviation magnitude to reflect their high consistency.

[0084] When the association state is classified as a failed association, the system extracts a second deviation magnitude between the expected and actual states on the non-empty pointing dimension. A failed association implies an inconsistency between the expected and actual states; for example, the expected state may exist but be empty, or the expected state may point to A but actually point to B. Therefore, this second deviation magnitude is designed to be larger than the first deviation magnitude to highlight the severity of this inconsistency. The second deviation magnitude can be calculated using a more refined difference analysis model. For example, when the expected pointed-to parcel no longer exists or has been completely replaced by another parcel, a larger fixed deviation value can be assigned. Alternatively, the degree of deviation can be quantified based on the topological relationship between the expected and actual pointed-to parcels (such as complete non-intersection or partial overlap).

[0085] Using the extracted first or second deviation amplitude as the modulation depth, the operational base value is scaled to obtain the first intermediate encoded value. This operational base value is an initial value extracted from a preset operational type base value table based on the registration type, representing the operational tendency of that registration type under ideal conditions. By using the deviation amplitude as the modulation depth, the operational base value can be adjusted so that its value is positively correlated with the failure level of the associated state. For example, amplitude scaling can be achieved by multiplying the operational base value by a scaling factor calculated based on the modulation depth, such as first intermediate encoded value = operational base value * (1 + modulation depth), where the modulation depth is the normalized deviation amplitude. Another approach is to map the modulation depth to the increment of the operational base value using a nonlinear function, ensuring that the first intermediate encoded value increases accordingly with the increase in failure level, thus numerically reflecting the reliability or abnormality level of the associated state.

[0086] The above technical solution can accurately quantify the actual deviation of the association status, solving the problem that the modulation depth cannot fully reflect the difference between invalid and valid associations. By distinguishing between valid and invalid associations and extracting different magnitudes of deviation amplitude as modulation depth, the first intermediate code value can more accurately reflect the "health" or "abnormality" of the land parcel association status. When the association status is valid, a smaller first deviation amplitude is used to ensure that the first intermediate code value fluctuates within the valid operation range, avoiding excessive impact on normal operations. When the association status is invalid, a larger second deviation amplitude is used, causing the first intermediate code value to shift towards the abnormal operation range, thereby clearly identifying potential problematic land parcels or abnormal operations in subsequent value feature combinations. This refined modulation mechanism improves the accuracy of value feature combinations and the reliability of state transition determination, providing more accurate input for the subsequent generation of spatial operation descriptors and traceability operation descriptors, thereby improving the overall robustness and accuracy of the land life cycle traceability and ownership overlay method.

[0087] In some of the embodiments described above in this application, a method for generating spatial operation descriptors and trace operation descriptors based on the combination of value features and associated states is proposed to perform spatial and trace operations in a coordinated manner. However, in its implementation, since the combination relationship between the value feature combination and the associated state is not clearly defined and parsed, the operation type determination may be non-unique or inaccurate, thereby affecting the synchronization and consistency of spatial operations and trace operations.

[0088] To address this, this application further proposes a method for generating spatial operation descriptors and retrospective operation descriptors based on combinations of value features and associated states, using the combination of value features itself as the sole criterion for determining the operation type. This method includes: parsing the parcel state migration event category corresponding to the daily registration record from the combination of value features; the parcel state migration event category being implicitly contained within the encoding structure of the value feature combination; using the parcel state migration event category as a direct driving signal to determine the state change type performed on the target parcel in the annual update result layer, and generating a spatial operation descriptor based on the state change type; and determining the edge relationship change type performed on the corresponding node of the target parcel in the ownership retrospective map based on the combination pattern of the parcel state migration event category and associated states, and generating a retrospective operation descriptor based on the edge relationship change type.

[0089] For example, when parsing the parcel status migration event category corresponding to the daily registration record from the combination of value features, this parcel status migration event category refers to the event type describing the change of a parcel from one state to another, such as the creation, merger, division, cancellation, and attribute change of a parcel. This combination of value features is the encoded value obtained after parsing the feature pattern of the daily registration record in the previous step, and its encoding structure is designed to implicitly represent these event categories. This step aims to accurately and uniquely identify the specific event type that causes the change of parcel status from the highly abstract and encoded combination of value features, providing a core basis for subsequent spatial generation and tracing operations. In practical applications, a series of numerical intervals can be predefined, each interval corresponding to a parcel status migration event category. By comparing the encoded value of the combination of value features with these predefined intervals, it is determined which interval it falls into, thereby identifying the corresponding event category. For example, an encoded value in [0, 100) represents a "new event," [100, 200) represents a "replacement event," and so on. Alternatively, the encoding structure of this combination of value features can be in the form of a bitmask, where different bits or combinations of bits represent different event features. By performing bit operations on the combination of value features, specific bit patterns can be extracted, thereby parsing the category of land parcel status migration events. For example, a bit set to 1 for one feature indicates "creation," another bit set to 1 indicates "cancellation," and the combined bit pattern indicates "change."

[0090] When using parcel state migration event categories as direct driving signals to determine the type of state change to be performed on a target parcel in the annual update results layer, and generating spatial operation descriptors based on these categories, the state change type refers to the specific sequence of operations or set of instructions to be performed on the target parcel in the annual update results layer at the spatial data level, such as "create parcel geometry," "update parcel attributes," or "delete parcel geometry." The spatial operation descriptor is a structured representation of these operations, containing information such as the operation type, parameters, and target parcel identifier. The purpose of this step is to directly and accurately translate the identified parcel state migration event categories into specific operation instructions at the spatial data level, ensuring that the spatial layer update is consistent with actual business events. For example, a set of rules can be preset to associate each parcel state migration event category with a set of predefined state change types (such as a series of spatial operation primitives). When a specific parcel state migration event category is received, the rule engine triggers the corresponding state change type. Alternatively, a state change type template can be predefined for each parcel state migration event category, containing a series of spatial operations to be executed sequentially. Once the parcel state migration event category is determined, the corresponding operation template is directly loaded and instantiated to form a spatial operation descriptor.

[0091] When determining the edge relationship change type to be performed on the corresponding node of the target parcel in the ownership tracing graph based on the combination pattern of parcel state migration event categories and associated states, and generating a tracing operation descriptor based on the edge relationship change type, the edge relationship change type refers to the type of edge operation to be performed on the node corresponding to the target parcel in the ownership tracing graph at the graph data level, such as "create tracing edge", "update edge attribute", "delete tracing edge", etc. The tracing operation descriptor is a structured representation of these edge operations, containing information such as the operation type, source node, target node, and edge attributes. The associated state is the result of judging the association between the original real estate unit code and the current parcel in the previous step (such as valid association, invalid association, no association). The purpose of this step is to comprehensively consider the business event type and the actual situation of parcel association to generate precise operation instructions for the tracing relationship edges in the ownership tracing graph, ensuring that the graph can accurately reflect the evolution history and ownership relationship of the parcel. For example, a decision matrix can be constructed, where rows represent parcel state migration event categories and columns represent the attribution category of associated states. Each cell in the matrix stores the corresponding edge relationship change type. By consulting this matrix, the type of edge relationship change can be directly determined based on the combination of parcel state migration event categories and associated states. Alternatively, a series of conditional statements can be used to progressively deduce the type of edge relationship change based on different combinations of parcel state migration event categories and associated states.

[0092] By refining the generation logic of operation descriptors, the aforementioned technical solutions effectively address the problem of ambiguous operation type determination, thereby enabling precise linkage between spatial operations and retrospective operations. For example, parcel state migration event categories are parsed from value feature combinations. Utilizing the implicit characteristics of the value feature combination coding structure, event information can be extracted directly and accurately, avoiding external interference and ensuring the uniqueness and accuracy of event determination. Using parcel state migration event categories as direct driving signals to determine the state change type performed on target parcels in the annual update result layer simplifies the determination process, eliminates intermediate conversion steps, and improves the response efficiency of spatial operations. Based on the combination pattern of parcel state migration event categories and associated states, the edge relationship change type performed on the corresponding nodes of target parcels in the ownership retrospective map is determined. This comprehensively considers the contextual information of state associations, allowing retrospective operations to better reflect actual ownership changes, thus improving the overall generation accuracy of spatial operation descriptors and retrospective operation descriptors, and further enhancing the reliability and consistency of transaction execution.

[0093] In some of the solutions mentioned above in this application, the parcel state migration event category is parsed from the combination of value features to determine the operation type. However, in its implementation, when the coded value of the combination of value features falls in the overlapping area of ​​two adjacent numerical intervals, the initial judgment result may be classified incorrectly due to the fuzzy interval boundary, resulting in inaccurate event category parsing. This, in turn, affects the correctness of the subsequent generation of spatial operation descriptors and trace operation descriptors, and reduces the consistency and efficiency of transaction execution.

[0094] To address this, this application further proposes a method for parcel status migration event category corresponding to the daily registration record from the combination of value features. This method includes: extracting the location of the encoded value of the combination of value features in a preset numerical space; determining an initial judgment result for the parcel status migration event category based on the numerical interval of the location; the initial judgment result indicating whether the parcel status migration event category belongs to a new event interval, a replacement event interval, a modified event interval, or a terminated event interval; when the location is located in the overlapping area of ​​two adjacent numerical intervals, extracting the deviation magnitude corresponding to the category of the associated state in the combination of value features; correcting the initial judgment result with the deviation magnitude; and determining the corrected judgment result as the parcel status migration event category.

[0095] For example, extracting the location of the encoded value of this combination of value features in a preset numerical space aims to transform the abstract combination of value features into quantifiable spatial coordinates, providing a precise location basis for subsequent event category determination. This combination of value features is an encoded value that integrates registration type, the category of associated status, and consistency verification results; its encoding structure itself implicitly contains multiple characteristics of land parcel status migration. Extracting its location in the preset numerical space can be achieved in several ways. For instance, the encoded value of this combination of value features can be directly used as a value on a one-dimensional or multi-dimensional coordinate axis. This numerical space can be a continuous real number space or a discrete integer space. Another approach is to map a specific segment or the overall value of this combination of value features to a specific point in the numerical space using a preset mapping function or lookup table. This mapping function can be a simple linear transformation or a complex nonlinear mapping to ensure that different feature combinations can be mapped to different location points, thereby forming a discriminative distribution in the numerical space.

[0096] Based on the numerical range of the landing point location, an initial determination result is made regarding the land parcel state migration event category. This initial determination result indicates whether the land parcel state migration event belongs to the emerging event range, replacement event range, correction event range, or termination event range. This step aims to quickly identify the possible corresponding land parcel state migration event categories based on the rough distribution of landing point locations. The numerical range is a predefined range of values ​​corresponding to different event categories (such as emerging events, replacement events, correction events, and termination events). The initial determination result can be achieved in the following ways: One way is to divide the preset numerical space into several non-overlapping or partially overlapping intervals, each interval corresponding to a specific land parcel state migration event category. When the landing point location falls within an interval, the event category corresponding to that interval is determined as the initial determination result. Another way is to use a rule-based classifier, which contains a series of conditional statements, to determine which event category interval it belongs to based on the numerical value of the landing point location or its distance from other key points. For example, if the landing point location is less than a certain threshold, it is determined to be in the emerging event range. If the value is greater than another threshold, it is determined to be a termination event interval; if it is between the two, it may be a replacement or correction event interval.

[0097] Furthermore, when the landing point is located in the overlapping area of ​​two adjacent numerical intervals, the deviation amplitude corresponding to the category of the associated state in the combination of value features is extracted. This deviation amplitude is used to correct the initial judgment result, and the corrected judgment result is determined as the category of the land parcel state migration event. This step aims to solve the problem that the initial judgment result may be inaccurate when the landing point is in a vague boundary area by introducing the deviation amplitude of the associated state for fine correction. The overlapping area refers to the part where two or more adjacent numerical intervals overlap numerically, so that the landing point may simultaneously meet the judgment conditions of multiple intervals, resulting in an unclear initial judgment result. The deviation amplitude can be extracted by consulting a preset deviation amplitude table or by calculation. This deviation amplitude quantifies the degree of deviation between the original real estate unit code and the current land parcel associated state. The implementation of correcting the initial judgment result may include: one way is to dynamically adjust the interval boundary according to the magnitude and direction of the deviation amplitude when the landing point is in the overlapping area, so that the landing point can be clearly divided into one of the intervals. For example, if the deviation magnitude indicates a significant failure in the associated state, the landing point might be pushed towards the "termination event" or "correction event" range. Another approach is to use the deviation magnitude as a weighting factor to weight multiple event categories that might correspond to the overlapping area, selecting the category with the highest weight as the corrected judgment result.

[0098] The above technical solution effectively solves the problem of classification ambiguity and inaccuracy caused by the overlapping area of ​​the encoded value of the value feature combination in the parcel state migration event category analysis process. For example, by extracting the landing position of the encoded value of the value feature combination in the preset numerical space, a quantitative basis for event category determination is provided, avoiding the introduction of subjective judgment. The initial determination result of the parcel state migration event category is determined based on the numerical interval where the landing position is located, realizing rapid preliminary classification of event categories. Furthermore, when the landing position is located in the overlapping area of ​​two adjacent numerical intervals, this application introduces the deviation amplitude corresponding to the category of the associated state as a correction basis. This deviation amplitude can reflect the degree of deviation of the associated state in the actual data, thereby dynamically correcting the initial determination result. This correction mechanism enables the event category analysis to no longer rely on rigid interval division in scenarios with ambiguous boundaries, but can adaptively adjust according to the actual deviation of the associated state, improving the accuracy and robustness of parcel state migration event category determination. The revised judgment results can more accurately reflect the land status migration event categories corresponding to the daily registration records, providing more reliable input for the generation of subsequent spatial operation descriptors and traceability operation descriptors, thereby improving the consistency and efficiency of the entire transaction execution and effectively supporting the accuracy of land life cycle traceability and ownership matching.

[0099] In some of the solutions described above in this application, when the landing point is located in the overlapping area of ​​two adjacent numerical intervals, the deviation amplitude of the associated state is used to correct the initial judgment result, thereby determining the category of the land parcel state migration event. However, in this process, there are shortcomings in how to use the deviation amplitude for accurate correction to avoid misjudgment of the event category and ensure the consistency and efficiency of subsequent operations. For example, although the deviation amplitude can reflect the degree of abnormality of the associated state, the lack of a quantitative mechanism to guide boundary adjustment may cause the correction result to deviate from the actual migration event, affecting the accuracy of spatial operations and tracing relationships.

[0100] In response, this application further proposes a method for correcting the initial judgment result based on the deviation magnitude and determining the corrected judgment result as the parcel status migration event category. This method includes: Obtain the first geometric distance between the landing point position and the boundary of the first numerical interval corresponding to the initial judgment result.

[0101] Using the deviation magnitude as a boundary offset factor, the boundary of the first numerical interval is offset to obtain the corrected boundary of the second numerical interval.

[0102] When the landing point is within the new interval defined by the modified second numerical interval boundary, the parcel status migration event category is determined as the event category corresponding to the second numerical interval adjacent to the first numerical interval boundary.

[0103] For example, the landing point location refers to the specific coordinates or values ​​mapped from the combination of value features in daily registration records within a preset numerical space. This landing point location is the direct input for determining the category of land parcel status migration events. It can be implemented by mapping the combination of value features to a specific point in a one-dimensional or multi-dimensional numerical space using a hash function, or by concatenating the various components of the combination of value features (such as registration type, the category of associated status, and consistency verification results) bit by bit or segment by using encoding rules to form a single value, which is the landing point location.

[0104] The initial determination result is the category of the land parcel state migration event based on the numerical range in which the landing point is located. For example, if the landing point falls within the "new event range," the initial determination result is "new event." This can be achieved by pre-setting a series of non-overlapping or partially overlapping numerical ranges, each corresponding to a land parcel state migration event category, and determining the category by comparing the landing point's location with the boundaries of these ranges. Alternatively, a decision tree or lookup table can be used, taking the landing point's location as input and directly outputting the corresponding land parcel state migration event category.

[0105] The boundary of the first numerical interval is the boundary value adjacent to the numerical interval corresponding to the initial judgment result. For example, if the initial judgment result is "new event interval", then the boundary of the first numerical interval may be the dividing point between the "new event interval" and the "replacement event interval". This can be achieved by predefining these numerical intervals and their boundary values ​​during system initialization and storing them in a configuration table. Alternatively, it can be achieved by using machine learning algorithms to perform cluster analysis on historical data to dynamically determine the numerical intervals and their boundaries for different event categories.

[0106] This first geometric distance quantifies the numerical difference between the landing point location and the boundary of the first numerical interval. It reflects the proximity of the landing point location to the current decision interval boundary and serves as an indicator for assessing decision ambiguity. It can be implemented by directly calculating the absolute value of the difference between the numerical value of the landing point location and the numerical value of the first numerical interval boundary. Alternatively, in a multidimensional numerical space, geometric distances such as Euclidean distance and Manhattan distance can be calculated.

[0107] The deviation amplitude is a quantitative indicator that measures the degree of abnormality in the associated state, such as the first deviation amplitude or the second deviation amplitude. It reflects the effectiveness or ineffectiveness of the association between the original real estate unit code and the current land parcel. Its implementation can be achieved by pre-setting different deviation amplitude values ​​based on the category of the associated state (effective association, ineffective association, no association). Alternatively, continuous deviation amplitude values ​​can be obtained by finely quantifying the difference between the expected and actual states in the non-null pointing dimension.

[0108] The boundary offset factor refers to the weight or strength of the deviation magnitude as the adjustment of the boundary of the first numerical interval. It directly maps the degree of anomalousness of the associated state to the strength of the boundary adjustment. This can be achieved by directly using the value of the deviation magnitude as the offset, or by converting the deviation magnitude into a more suitable factor as the offset using a mapping function (such as a linear or exponential function).

[0109] This positional offset refers to the numerical adjustment of the boundary of the first numerical interval based on a boundary offset factor (i.e., the deviation magnitude). Its function is to dynamically change the judgment threshold of the event category, making it more adaptable to actual abnormal situations in associated states. This can be achieved by adding or subtracting the boundary offset factor from the value of the first numerical interval boundary to move the boundary. Alternatively, the direction of the deviation magnitude (e.g., positive or negative deviation) can determine whether the boundary contracts inward or expands outward.

[0110] The revised second numerical interval boundary is a new decision boundary obtained after the position offset operation. This boundary is dynamically adjusted, reflecting a more precise threshold for event category classification after considering the degree of abnormality in associated states. It can be implemented by performing arithmetic operations (addition, subtraction, multiplication, and division) on the first numerical interval boundary and the boundary offset factor to obtain the new boundary value. Alternatively, it can be obtained directly from a table lookup or rule engine based on the combination of the first numerical interval boundary and the deviation magnitude.

[0111] The new interval is defined by the modified second numerical interval boundary. This new interval is dynamic; it may be wider or narrower than the original interval, or it may be shifted entirely. It can be implemented by combining the modified second numerical interval boundary with another unshifted boundary to form a single interval. Alternatively, if both adjacent boundaries are shifted, the interval can be defined by both modified second numerical interval boundaries.

[0112] The second numerical interval adjacent to the boundary of the first numerical interval refers to the numerical interval corresponding to another parcel state migration event category adjacent to the boundary of the first numerical interval. For example, if the boundary of the first numerical interval is the boundary between the "new event interval" and the "replacement event interval", then the second numerical interval adjacent to the boundary of the first numerical interval may be the "replacement event interval". This can be achieved by finding the next or previous interval adjacent to the boundary of the first numerical interval in a preset list of numerical intervals, or by searching using the interval ID or index.

[0113] The above technical solution obtains the first geometric distance between the landing point location and the boundary of the first numerical interval corresponding to the initial judgment result. This quantifies the degree of judgment deviation, providing a basic measurement basis for subsequent correction and avoiding the uncertainty caused by subjective judgment. Using the deviation amplitude as a boundary offset factor, the degree of abnormality of the associated state is directly transformed into a driving factor for boundary adjustment, ensuring that the correction process is closely related to actual data changes and enhancing the pertinence of the correction. The boundary of the first numerical interval is offset to generate the corrected boundary of the second numerical interval, dynamically adapting to the deviation amplitude, making the new interval more consistent with the actual distribution of migration events, and solving the risk of misjudgment caused by boundary rigidity. When the landing point location is within the new interval, the event category is determined as the event category corresponding to the second numerical interval adjacent to the boundary of the first numerical interval. The attribution judgment is made based on the corrected logical boundary, ensuring the matching degree between the event category and the migration characteristics. This dynamic, data-driven correction mechanism effectively solves the problem of misjudgment caused by boundary ambiguity and abnormal associated state when determining the category of land parcel state migration events in the overlapping area of ​​numerical intervals, improving the accuracy and reliability of the determination of the category of land parcel state migration events. This further improves the accuracy of subsequent spatial operation descriptors and traceability operation descriptors, thereby enhancing the overall consistency and operational efficiency of the land lifecycle traceability and ownership matching method.

[0114] In some embodiments described above, this application proposes using parcel status migration event categories as direct driving signals to determine the type of status change performed on a target parcel in the annual update result layer. However, parcel status migration event categories only characterize the migration stage of a parcel throughout its entire lifecycle. The same event category may require different combinations of spatial operations under different layer conditions. If only event categories and operation types are statically mapped, ignoring the actual current state of the target parcel in the layer, it will lead to a mismatch between operation instructions and the current layer status, causing operation execution failures or data inconsistencies, and reducing the automation and reliability of ownership overlay processing.

[0115] In response, this application further proposes a method for determining the type of state change performed on a target parcel in the annual update result layer, the steps of which include: Using the parcel state migration event category as an index, retrieve the operation primitive sequence bound to the parcel state migration event category from the spatial operation primitive library. The operation primitive sequence consists of at least one spatial operation primitive arranged in a preset order.

[0116] Based on the current status identifier of the target parcel in the annual update result layer, the execution preconditions of the first spatial operation primitive in the operation primitive sequence are verified.

[0117] When the precondition verification passes, the combination of spatial operations described by the sequence of operational primitives is determined as the state change type.

[0118] To more clearly illustrate the above scheme, the key technical features involved will be explained in detail below.

[0119] Land parcel state transition event categories are abstract classifications describing the transformation of a land parcel from one state to another, such as "land parcel creation," "land parcel merging," "land parcel splitting," and "land parcel cancellation." The spatial operation primitive library is a collection of predefined, atomic-level spatial operations. Each operation primitive represents an indivisible minimum spatial data processing unit, such as "create land parcel geometry," "delete land parcel geometry," "modify land parcel attributes," and "merge land parcel geometry." Retrieving an operation refers to retrieving a series of atomic operations associated with an event category from the library. For example, a mapping table or configuration rule file can be created to bind each land parcel state transition event category to one or more spatial operation primitive sequences. When the system identifies a specific event category, it directly queries the mapping table to retrieve the corresponding operation primitive sequence. Alternatively, a semantic parsing-based approach can be used to perform semantic analysis on land parcel state transition event categories, dynamically generating or combining spatial operation primitives. For example, a rule engine or expert system can intelligently select and assemble sequences from a finer-grained set of operation primitives based on the semantic features of the event categories.

[0120] This sequence of spatial operation primitives refers to an ordered set of spatial operation primitives. The pre-defined order means that these primitives have a fixed execution order within the sequence to ensure the logical correctness and data consistency of complex spatial operations. For example, a "parcel partitioning" event might include primitives such as "create new parcel geometry," "modify existing parcel geometry," and "update parcel attributes," and these primitives must be executed in a specific order. The order of this sequence can be predefined in the spatial operation primitive library as part of the metadata binding the operation sequence to each event category. For example, each primitive in the sequence and its execution order can be described using structured data formats such as XML and JSON. The order of this sequence can also be managed by a workflow engine, where each spatial operation primitive is encapsulated as a task node in the workflow. By defining the dependencies and control flow between tasks, the pre-defined execution order is achieved.

[0121] The target parcel is the parcel entity targeted by the current operation. The annual update result layer is a spatial layer containing the latest ownership data. The current status identifier is the real-time status information of the target parcel in this layer, such as "existing," "cancelled," or "pending approval." Execution preconditions are the conditions that must be met for the first spatial operation primitive to be executed safely and correctly. For example, if the first operation is "modify parcel attributes," the preconditions might be that the target parcel must "exist" and "not be locked." Validation refers to checking whether the current layer status meets these preset conditions before executing the operation. Specifically, one or more precondition rules can be defined for each spatial operation primitive, taking the current status identifier of the target parcel as input. Before execution, the system obtains the current status identifier of the target parcel and matches it against the precondition rules of the first operation primitive. Alternatively, a state machine model can be used to manage the current status of parcels and the legality of operations. Each state machine node represents a parcel current status, and each state transition arc represents a legal operation. The verification process involves checking whether the current parcel status contains a state transition arc leading to the first operational primitive.

[0122] A state change type determines the specific set of spatial operations to be performed on the target parcel. When the preconditions of the first operation primitive pass validation, it means that the current layer state and the operation logic implicit in the event category are compatible. At this point, the entire sequence of operation primitives is considered a valid and executable combination of spatial operations. For example, after validation, the system directly encapsulates the retrieved sequence of operation primitives into an executable state change type object, which contains all atomic operations and their execution order. Alternatively, after validation, the sequence of operation primitives can be further optimized or adapted. For instance, based on the specific attributes or spatial relationships of the target parcel, some primitive parameters in the sequence can be fine-tuned, and then this can be determined as the state change type.

[0123] The above technical solution addresses the issue of insufficient operational adaptability for the same event category under varying layer conditions, improving the accuracy and robustness of state change type determination. For example, using the parcel state migration event category as an index, a sequence of bound operational primitives is retrieved from the spatial operation primitive library. This establishes a structured association between the event category and atomic-level spatial operation primitives, upgrading the determination of state change types from a single operation label to a combination of primitives with temporal dependencies, enhancing the granularity of operation expression. This sequence of operational primitives consists of at least one spatial operation primitive arranged in a preset order, ensuring that the execution order of each sub-operation in a composite spatial operation can be systematically managed and scheduled, avoiding operation omissions or sequence errors. Furthermore, the preconditions for executing the first spatial operation primitive are verified based on the current status identifier of the target parcel in the annual update result layer. The actual layer state is used as a gate condition for operation execution, completing state consistency verification during the operation generation stage, rather than passively responding to anomalies during the execution stage, thereby reducing the risk of invalid operations contaminating the layer. When the precondition verification passes, the spatial operation combination described by the operation primitive sequence is determined as the state change type. This ensures that the determined spatial operation matches both the event semantics and the current layer status, providing a reliable operational basis for subsequent instruction execution and guaranteeing the accuracy and transaction consistency of spatial data changes during the ownership overlay process. This not only improves the automation level of operation execution but also enhances the reliability of data processing, avoiding operation failures or data inconsistencies caused by layer state mismatches.

[0124] In some of the solutions described above in this application, a method is proposed to verify the execution preconditions of the first spatial operation primitive in the operation primitive sequence based on the current status identifier of the target parcel, in order to ensure the correct execution of the state change operation. However, in its implementation, when the current status identifier is inconsistent with the expected status identifier, the verification failure may cause the entire operation sequence to be interrupted or rolled back, affecting execution efficiency and system stability. At the same time, it cannot adaptively adjust the operation sequence to cope with state changes, resulting in the risk of inconsistency between layer status and traceability relationship.

[0125] To address this, this application further proposes a method for verifying the execution preconditions of the first spatial operation primitive in the operation primitive sequence based on the current status identifier of the target parcel in the annual update result layer. This method includes: comparing the current status identifier with the expected status identifier bound to the first spatial operation primitive. When the current status identifier matches the expected status identifier, the execution precondition verification is deemed successful. When the current status identifier does not match the expected status identifier, the first spatial operation primitive is removed from the operation primitive sequence, and the removed operation primitive sequence is used as the spatial operation combination corresponding to the status change type.

[0126] For example, the current status identifier of a target land parcel in the annual update results layer refers to the status marker of a specific target land parcel that is currently identified and recorded by the system within the annual update results layer. This identifier can be the land parcel's lifecycle status (e.g., "exists," "cancelled," "pending approval"), attribute status (e.g., "confirmed ownership," "not confirmed ownership"), or spatial status (e.g., "complete," "divided"). In practical applications, this identifier can be an enumeration type, such as ParcelStatus.Active or ParcelStatus.Archived, stored in the attribute fields of the land parcel object. Alternatively, it can be a status code, for example, using the number 0 to represent "does not exist," 1 to represent "exists," and 2 to represent "changed," stored in the land parcel record in the database. This identifier serves as the basis for determining the current actual situation of the land parcel and is fundamental to the correct execution of subsequent operations.

[0127] The precondition for the first spatial operation primitive in this sequence refers to a series of preset conditions that the system must meet before executing a spatial operation primitive. These conditions are typically related to the current state of the target parcel, the legality of the operation, and system resources. For example, the precondition can be a predefined set of rules configured in JSON or XML format, containing key-value pairs such as "precondition":{"status":"expected_status", "permission":"required_permission"}. Alternatively, it can be hard-coded into the conditional statement in the operation primitive logic, such as if(targetParcel.status==ExpectedStatus){execute();}. The purpose of this precondition is to ensure that the operation is executed in the correct context and state, preventing illegal or invalid operations.

[0128] Comparing the current status identifier with the expected status identifier bound to the first spatial operation primitive means comparing the actual state of the target parcel with the state that the spatial operation primitive is pre-defined in its design and upon which its correct execution depends. This comparison can be done directly using string or numerical comparisons, such as `currentStatus.equals(expectedStatus)`. Alternatively, it can be done using a state machine or state diagram to check whether the current state is a valid predecessor state of the expected state. This step aims to identify whether the current parcel state meets the execution requirements of the operation primitive.

[0129] When the current state identifier matches the expected state identifier, the precondition check is considered passed, meaning the actual state and the expected state are perfectly matched, and the execution environment of the operation primitive meets the requirements. At this point, a boolean value of true or a success status code can be returned, allowing the operation primitive sequence to continue execution.

[0130] When the current state identifier does not match the expected state identifier, the first spatial operation primitive is removed from the sequence of operation primitives. This means that when the actual state and the expected state do not match, the first operation primitive cannot be executed in the current environment. To avoid operation interruption, the system chooses to remove the inapplicable operation. For example, if the sequence of operation primitives is stored in a list or array data structure, the first element can be removed using methods such as remove(index) or subList(1, end). Alternatively, a pointer or index pointing to the currently pending operation primitive can be maintained; when a mismatch occurs, the pointer or index is moved to the next operation primitive, thus skipping the first operation primitive.

[0131] The process of using the removed operation primitives as the spatial operation combination corresponding to this state change type means that after removing the first inapplicable operation primitive, the remaining operation primitives constitute a new spatial operation combination adapted to the current parcel state. At this point, the modified list or array can be returned directly as the new operation combination, or a new operation sequence object can be created containing the removed operation primitives.

[0132] The above technical solution introduces a dynamically adjusted verification mechanism, resolving the potential operational interruption issue caused by inconsistent land parcel status identifiers. By comparing the current status identifier with the expected status identifier, consistency can be directly detected, avoiding errors caused by blind execution. When both are consistent, the verification passes, ensuring the integrity of the operational primitive sequence and maintaining the accuracy of status changes. More importantly, when the two are inconsistent, this application removes the first inapplicable spatial operational primitive from the operational primitive sequence and uses the remaining sequence as a new spatial operation combination. This allows for adaptive adjustment of the operational process, avoiding operational interruptions or rollbacks, and improving execution efficiency and system robustness. This mechanism not only strengthens the fault tolerance of the verification process but also ensures a smooth transition of layer status changes, thereby maintaining the consistency and synchronization of spatial data and traceability relationships throughout the entire land lifecycle tracing process.

[0133] In some of the solutions mentioned above in this application, a combination pattern of parcel status migration event categories and associated status is proposed to determine the edge relationship change type to generate a traceability operation descriptor. However, in this process, additional attributes of the traceability edge, such as confidence level identifiers and annotation fields, may be ignored, resulting in insufficient accuracy of the traceability relationship or lack of contextual information, which affects the reliability and understandability of the ownership traceability map.

[0134] To address this, this application further proposes a combination pattern based on the parcel state migration event category and associated state to determine the edge relationship change type performed on the corresponding node of the target parcel in the ownership tracing map. This includes: determining the basic type of the tracing edge based on the parcel state migration event category, where the basic type includes at least one of generating edge, consuming edge, attribute changing edge, or terminating edge; determining the additional attributes of the tracing edge based on the attribution category of the associated state, where the additional attributes include an edge confidence identifier and an edge annotation field; and merging the basic type of the tracing edge with the additional attributes to obtain the edge relationship change type.

[0135] For example, determining the basic type of traceability edges based on parcel status migration event categories aims to assign the most basic semantic type to the edge relationships in the ownership tracing map based on the core status changes of parcels reflected in daily registration records. Parcel status migration event categories, such as new occurrence events, replacement events, correction events, or termination events, directly indicate key turning points in the parcel's lifecycle. For instance, when a parcel status migration event category indicates a "new occurrence event," it can be mapped to a "generation edge," indicating that a new parcel entity or a new parcel state has been created. When it indicates a "termination event," it can be mapped to a "termination edge" or "consumption edge," indicating that the parcel entity no longer has momentum or has been replaced by another parcel. The basic type of traceability edges can be determined by looking up a pre-defined mapping table, i.e., directly retrieving the corresponding basic type of traceability edge from the table based on different parcel status migration event categories. Another approach is to use a rule engine for judgment. For example, a series of conditional rules can be defined, and when a parcel status migration event category meets specific conditions, the corresponding rules are triggered to determine the basic type of traceability edges.

[0136] The basic types of traceability edges include at least one of the following: generation edges, consumption edges, attribute change edges, or termination edges. These basic types constitute the core semantics of edge relationships in the ownership traceability map. "Generation edges" are typically used to represent the creation of a new land parcel, such as the relationship between a new parcel formed through initial registration or parcel division and the original parcel. "Consumption edges" indicate the demise or replacement of an existing parcel, such as operations like parcel mergers, divisions, or cancellations that cause the original parcel to cease to exist independently. When the non-spatial attributes of a parcel change, such as land use or rights holder information, but its spatial form and subject remain fundamentally unchanged, "attribute change edges" can be used to represent this. Furthermore, "termination edges" can serve as a special type of consumption edge, explicitly indicating the end of a parcel's lifecycle, such as the complete cancellation of a parcel. These basic types can be used individually or combined according to complex business scenarios to more accurately describe the evolutionary relationships between parcels.

[0137] Determining the attached attributes of traceable edges based on the attribution category of the association status aims to provide deeper contextual information and reliability assessment for edge relationships in the traceability map. The attribution category of the association status, such as valid association, invalid association, or no association, reflects the quality and effectiveness of the association between the original property unit code and the current land parcels in the annual update result layer. For example, when the attribution category of the association status is "valid association," it indicates a good match between the original code and the current land parcel. In this case, a high value can be assigned to the edge confidence identifier in the attached attributes of the traceability edge (e.g., set to "high" or a value of 1.0), and the edge annotation field can record "valid association, consistent data." Conversely, when the attribution category of the association status is "invalid association," it may mean that the land parcel pointed to by the original code no longer exists or there is inconsistency. In this case, the edge confidence identifier can be set to a lower value (e.g., "low" or a value of 0.5), and the reason for the invalidation should be explained in detail in the edge annotation field, such as "the original land parcel has been merged" or "the original land parcel has been cancelled." One way to determine additional attributes is through predefined mapping rules, mapping different association state categories to specific edge confidence identifiers and edge annotation field templates. Another approach is to dynamically generate the content of the edge annotation fields based on the detailed diagnostic results of the association states; for example, filling in annotation information based on the specific deviation patterns of consistency check results.

[0138] The additional attributes of the traceability edge include an edge confidence flag and an edge annotation field. The edge confidence flag is a quantitative indicator used to assess the reliability of the traceability relationship. It can be a discrete level (e.g., high, medium, low) or a continuous numerical value (e.g., a floating-point number between 0 and 1), with higher values ​​indicating a more reliable traceability relationship. For example, a high confidence flag can be set when there is a clear and unambiguous valid association between the original property unit code and the current land parcel. A medium flag can be set when there is a vague or partial match. A low flag is set when the association status is invalid. The edge annotation field is a text description field used to record supplementary information, background information, or anomalies related to the traceability relationship. For example, it can record the specific business type, operator, and timestamp that caused the change in land parcel status, or, when the association status is invalid, the specific cause and diagnostic information of the failure. These additional attributes greatly enrich the information content of the traceability edge, making it not just a simple connection, but a connection with rich semantics and quality assessment.

[0139] The basic type of the traceability edge is merged with its additional attributes to obtain the edge relationship change type. This step integrates the previously determined basic semantics of the traceability edge and its additional context and reliability information into a complete, structured edge relationship description. The merging can be achieved by encapsulating the basic type and additional attributes in a single data structure, such as a composite object or a record containing multiple fields. For example, an edge relationship change type could be represented as {Basic Type: "Edge Generation", Confidence: "High", Annotation: "New Parcel, First Registration"}. This merging ensures that when generating the traceability operation descriptor, the described edge relationship not only has a clear type but also includes detailed descriptions of its reliability and background, thus forming a comprehensive and easily understood edge relationship change type.

[0140] The above technical solutions effectively address the issues of inaccurate tracing relationships or lack of contextual information. By determining the basic type of tracing edges based on the category of land parcel status migration events, the edge relationships in the tracing map accurately reflect the core changes in land parcel status, avoiding the ambiguity of edge types that may result from relying solely on combination patterns. Determining additional attributes such as edge confidence indicators and edge annotation fields based on the attribution category of associated states compensates for the shortcomings of traditional solutions that neglect these key information, making the tracing relationships verifiable and interpretable. For example, when a target land parcel in the annual update result layer changes, if its original real estate unit code has an "invalid association" with the current land parcel, the edge relationship change type in the generated tracing operation descriptor will not only indicate a "consumed edge" or "attribute change edge," but will also include a lower "edge confidence indicator" and a detailed "edge annotation field" explaining the specific reason for the failure. This edge relationship with rich additional attributes enables the ownership tracing map to provide more accurate and contextually informative tracing paths, greatly improving the reliability and understandability of the map. This synergizes with the scheme that generates traceability operation descriptors based on the combination pattern of land parcel state migration event categories and associated states. This allows for the full utilization of detailed information parsed in the early stages when generating traceability operation descriptors, enabling the construction of more insightful traceability relationships. Consequently, in subsequent transaction boundary encapsulation and atomic linkage execution, the consistency and integrity of spatial operations and traceability operations are ensured, providing a solid foundation for the traceability of the entire land lifecycle.

[0141] In some of the solutions mentioned above in this application, transaction boundary encapsulation is proposed for spatial operation descriptors and trace operation descriptors to ensure atomic linkage execution. However, in this process, since spatial operations and trace operations may be executed independently, there is a lack of mandatory association between spatial data changes and ownership traceability updates, which leads to execution inconsistency problems and affects the synchronization of the overall ownership status and traceability graph.

[0142] To address this, this application further proposes a method for encapsulating transaction boundaries of spatial operation descriptors and trace operation descriptors, resulting in spatial operation instructions and trace operation instructions with atomic linkage relationships. (See [link to relevant documentation]). Figure 3 The method includes: 301. Extract the parcel identifier of the target parcel from the spatial operation descriptor and embed the parcel identifier into the node positioning field of the trace operation descriptor so that the trace operation descriptor obtains a spatial anchoring relationship with the target parcel.

[0143] 302. Extract the edge relationship change type from the trace operation descriptor, determine the constraint conditions on the spatial operation descriptor based on the edge relationship change type, and embed the constraint conditions back into the verification parameter segment of the spatial operation descriptor.

[0144] 303. Compile the mutually embedded space operation descriptors and trace operation descriptors together to generate space operation instructions and trace operation instructions. The space operation instructions and trace operation instructions share the reference links established by mutual embedding during execution.

[0145] A spatial operation descriptor is a data structure or object used to encapsulate detailed information about spatial change operations performed on target parcels in the annual update results layer. Its purpose is to explicitly specify the geometric or attribute modifications to be performed, such as parcel creation, splitting, merging, attribute updating, or deletion. This descriptor can be implemented in various forms; for example, it can be a structured JSON object containing the operation type, target parcel identifier, new geometric data (such as coordinate point sets, topological relationships), and attribute key-value pairs to be updated. Alternatively, it can be an XML document defining the various components of the operation using tags.

[0146] The traceability operation descriptor is also a data structure or object that encapsulates detailed information about edge relationship change operations performed on the corresponding nodes of a target parcel in the ownership traceability graph. Its purpose is to define how to update the relationships between parcels during their historical evolution, such as the parcel's origin, destination, and attribute changes. This descriptor can be implemented, for example, as a graph database operation instruction template containing source node identifiers, target node identifiers, edge types (such as "generated from", "consumed from", "attribute change"), and edge attributes (such as timestamp, operator). Alternatively, it can be an instance in an object-oriented programming language, whose attributes correspond to the edge information in the graph.

[0147] Transaction boundary encapsulation refers to the process of managing a series of logically related operations (in this case, spatial operations and trace operations) as an indivisible unit. Its purpose is to ensure that these operations either all succeed or all fail and roll back, thereby maintaining data consistency and integrity. Implementation methods can include, for example, utilizing transaction mechanisms provided by the database management system (such as SQL's BEGIN TRANSACTION, COMMIT, and ROLLBACK statements) to incorporate spatial database operations and graph database operations into the same distributed transaction. Alternatively, an application-level transaction coordinator can be used to explicitly manage the commit and rollback of operations within the business logic.

[0148] Atomic linkage refers to a strong coupling between spatial operation instructions and traceability operation instructions, making them treated as a single, uninterruptible logical unit during execution. This relationship ensures that any change to spatial data is necessarily accompanied by an update of the corresponding relationship in the ownership traceability graph, and vice versa, thus preventing data inconsistency. This relationship is achieved through a transaction boundary encapsulation mechanism. For example, when a spatial operation instruction attempts to modify the parcel status, the traceability operation instruction must synchronously update its edge relationships in the graph. If either operation fails, the entire transaction is rolled back, ensuring that the states of both remain synchronized.

[0149] Spatial operation commands are sequences of commands that have been processed from spatial operation descriptors and are directly executable. They are used to modify parcel data in the annual update result layer. Their function is to drive the spatial database or GIS system to perform specific spatial data operations. For example, they can be SQL UPDATE, INSERT, or DELETE statements for the spatial database, used to modify the geometry or attributes of parcels. Alternatively, they can be functions that call GIS platform APIs to implement spatial editing capabilities for parcels.

[0150] Tracing operation instructions are directly executable command sequences formed by processing tracing operation descriptors, used to actually update edge relationships in the ownership tracing graph. Their function is to drive the graph database system to perform specific graph structure operations. For example, it can be a Cypher query statement for the graph database (such as CREATE(a)-[r:RELATION]->(b)) used to create, modify, or delete edges between nodes. Alternatively, it can be a function call to the graph database API to achieve dynamic updates of the graph.

[0151] A land parcel identifier is a code or string used to uniquely identify a target land parcel. Its function is to serve as a unique reference for the land parcel across different systems and data layers, ensuring precise targeting of operations. For example, a land parcel identifier can be a real estate unit code or a globally unique identifier (UUID) generated internally by the system.

[0152] The node location field is a specific field in the tracing operation descriptor used to store the identifier of the node in the ownership tracing map corresponding to the target parcel. Its purpose is to ensure that the tracing operation can accurately locate and operate on the correct node representing the parcel in the map. For example, this field can be a string type, directly storing the parcel identifier, or a reference to a node object in the map.

[0153] Spatial anchoring refers to the direct and explicit association established between a traceability operation descriptor and the target parcel. Its function is to ensure that the semantics of the traceability operation are closely bound to the actual spatial entity, preventing the traceability information from becoming disconnected from the spatial entity. This relationship is achieved by embedding the parcel identifier into the node positioning field. For example, during the initialization phase of the traceability operation descriptor, the parcel identifier extracted from the spatial operation descriptor is directly assigned to the node positioning field of the traceability operation descriptor.

[0154] Edge relationship change types are category definitions for modifying edge relationships in a property ownership tracing map. Their purpose is to clarify the semantics of tracing operations and guide how the map updates the historical evolution relationships between parcels. For example, edge relationship change types can include "creating edge" (indicating the creation of a new parcel), "consuming edge" (indicating the termination or merger of a parcel), "attribute change edge" (indicating a change in the attributes of a parcel), or "terminating edge" (indicating the end of the parcel's life cycle), etc.

[0155] Constraints are rules or restrictions that must be met for spatial operation descriptors. Their purpose is to ensure that the execution of spatial operations conforms to traceability logic and business rules, preventing illegal spatial changes. For example, when the edge relationship change type indicates that a parcel is "consumed," the constraint might require the spatial operation to include a "historicization" of the target parcel's current state. When the parcel is "created," the constraint might require the spatial operation to include a "new parcel graphic insertion."

[0156] The validation parameter section is an area within the spatial operation descriptor used to store constraints. Its purpose is to provide pre-validation for the execution of spatial operations, ensuring that validity checks are performed according to traceability logic before the actual execution of the spatial operation. For example, the validation parameter section can be a list storing multiple Boolean expressions or function references, which are evaluated before the spatial operation is executed.

[0157] Union compilation is the process of integrating interleaved space operation descriptors and trace operation descriptors and converting them into executable instructions. Its purpose is to generate a unified, intrinsically related execution plan, ensuring that space operations and trace operations remain logically and data-wise synchronized. For example, union compilation can be a software module that parses the structure of two descriptors, identifies shared data points between them, and generates intermediate code containing shared variables or reference pointers, converting it into an executable sequence of instructions.

[0158] A reference chain is a data dependency or sharing mechanism established between space operation instructions and trace operation instructions. Its function is to ensure that during execution, two instructions can access and manipulate the same logical data or state, thereby achieving atomic linkage. For example, a reference chain can be implemented at compile time by allocating the same memory address or data storage slot for shared data, allowing modifications made by space operation instructions to that address to be detected and recorded by trace operation instructions.

[0159] The above technical solution effectively addresses the inconsistency issue caused by the lack of mandatory correlation between spatial data changes and ownership tracing updates. For example, by extracting the target parcel's identifier from the spatial operation descriptor and embedding it into the node positioning field of the tracing operation descriptor, this application establishes a strong logical correlation between spatial operations and tracing operations, enabling the tracing operation descriptor to obtain a spatial anchoring relationship with the target parcel. This allows any tracing operation to accurately locate its corresponding spatial entity, avoiding a disconnect between tracing information and the actual parcel status, thereby ensuring the accuracy of the tracing map.

[0160] By extracting edge relationship change types from the tracing operation descriptor and determining the constraints on the spatial operation descriptor based on these types, and then embedding these constraints back into the verification parameter segment of the spatial operation descriptor, this application implements reverse constraints of tracing logic on spatial operations. This means that spatial operations must meet the semantic requirements defined by the tracing relationship before execution. For example, if the tracing logic indicates that a parcel has been "consumed," then the spatial operation must include corresponding historical processing. This two-way constraint mechanism greatly enhances the consistency of the system and prevents spatial changes that do not conform to the historical evolution logic from occurring.

[0161] Furthermore, the embedded spatial operation descriptors and traceability operation descriptors are jointly compiled to generate spatial operation instructions and traceability operation instructions with shared reference links, enabling atomic linkage at the execution level. This joint compilation mechanism allows the two instructions to share the same data dependencies during execution. For example, operations modifying land parcel status and recording traceability relationship edges will reference the same data or status. This fundamentally eliminates the time lag and inconsistency that may result from independent execution, ensuring that land parcel status changes and the generation of traceability relationship edges are completed atomically within the same transaction. Through this tightly coupled design, this application achieves a high degree of synchronization and consistency between the annual update result layer and the ownership traceability map, improving the reliability and data integrity of land lifecycle traceability.

[0162] In some of the embodiments described above in this application, constraints on spatial operation descriptors are determined based on edge relationship change types to ensure atomic linkage of transactions. However, in its implementation, since edge relationship change types may contain a variety of complex semantics, directly determining constraints lacks fine-grained parsing, which may result in inaccurate or incomplete constraint coverage, thereby affecting the synchronous execution efficiency and state consistency of spatial operations and trace operations.

[0163] In response, this application further proposes a method for determining the constraints on the spatial operation descriptor based on the edge relationship change type, which includes: The relationship change type is resolved to at least one trace semantic atom, which is either a consumption semantic atom or a generation semantic atom.

[0164] When the consumed semantic atom is obtained through parsing, it is determined that the constraint includes performing a historicization modification on the current state identifier of the target parcel.

[0165] When the semantic atom is obtained through parsing, the constraint condition is determined to include performing a new parcel graphic insertion into the annual update result layer.

[0166] When both the consumed semantic atom and the generated semantic atom are obtained through parsing, the constraint condition is determined to include performing a historical modification of the current status identifier of the target parcel and inserting a new parcel graphic into the annual update result layer.

[0167] For example, the edge relationship change type can be parsed into at least one traceable semantic atom, which can be either a consumption semantic atom or a generation semantic atom. Edge relationship change types refer to specific categories describing changes in relationships between parcel nodes in the ownership traceability graph, such as parcel merging, splitting, cancellation, and attribute changes. These change types may have complex semantics, and directly mapping them to spatial operation constraints may not be precise enough. Traceable semantic atoms are the basic, indivisible semantic units that constitute these complex change types, mainly divided into consumption semantic atoms and generation semantic atoms. Consumption semantic atoms represent the occupation, invalidation, or removal of existing parcel resources, such as parcel merging or cancellation. Generation semantic atoms represent the generation of new parcel resources or the updating of existing parcel resources, such as the creation of a new parcel after parcel splitting or the creation of a new state record after parcel attribute changes. Decomposing complex edge relationship change types into these basic semantic atoms helps to more clearly understand their inherent operational intent, thus providing a foundation for subsequent precise determination of spatial operation constraints. One implementation method is to use a predefined rule engine for parsing. This rule engine contains a series of mapping rules that map specific edge relationship change types (such as "splitting") to one or more traceable semantic atoms (such as "consumption semantic atom" indicating that the original parcel is consumed, and "generation semantic atom" indicating that a new parcel is generated). Another implementation approach is to use an ontology-based semantic parsing method. By constructing an ontology model containing knowledge of parcel change domains, edge relationship change types are matched with concepts in the ontology to identify the consumption or generation semantic atoms they imply.

[0168] When the consumption semantic atom is parsed, the constraint condition is determined to include performing a historicization modification on the current status identifier of the target parcel. The consumption semantic atom indicates that the target parcel will no longer exist as a currently valid entity in the ownership tracing map, or its current status will undergo a fundamental change. Therefore, to maintain consistency between the annual update result layer and the ownership tracing map, and to ensure the traceability of historical data, it is necessary to historicize the current status identifier of the target parcel in the annual update result layer. Historicization modification means changing the target parcel from a "current" status to a "historical" or "non-current" status, but the data itself is not deleted; instead, it is marked as historical data for subsequent queries and tracing. One implementation is to add a "status" field to the target parcel's attribute table, and when the consumption semantic atom is parsed, update the value of this field from "current" to "historical" or "cancelled." Another implementation is to move the spatial and attribute data of the target parcel from the current layer to a dedicated historical layer or historical database, and remove the parcel from the current layer or mark it as inactive.

[0169] When the semantic atom is generated, determining the constraint involves inserting a new parcel graphic into the annual update result layer. The semantic atom indicates that a new parcel entity will be created in the ownership tracing map, or that an existing parcel entity will undergo geometric changes, such as the creation of a new sub-parcel after parcel division, or the formation of a new parcel after parcel merger. To accurately reflect these changes in the annual update result layer, a new parcel graphic insertion operation needs to be performed. This involves adding the spatial geometric information and associated attribute data of the newly generated parcel to the annual update result layer, making it part of the current parcels. One implementation is to insert the geometric data (such as polygon coordinates) and attribute data (such as property unit code, area, etc.) of the new parcel into the parcel table of the annual update result layer through the insertion operation of the spatial database. Another implementation is to use the editing functions of a Geographic Information System (GIS) to draw or import new parcel graphics into the annual update result layer and assign them corresponding attribute information.

[0170] When both the consumption semantic atom and the generation semantic atom are obtained through parsing, the constraint condition is determined to include both performing a historicization modification on the current status identifier of the target parcel and inserting a new parcel graphic into the annual update result layer. Certain complex edge relationship change types, such as parcel splitting or parcel merging, may simultaneously involve both consumption and generation semantics. For example, in a parcel splitting operation, the original parcel is "consumed" (historized), while multiple new sub-parcels are generated (new graphic insertion). In this case, to ensure complete synchronization between the annual update result layer and the ownership tracing map, both historicization modification and new parcel graphic insertion constraint operations need to be performed simultaneously. This ensures that the original parcel's status is correctly updated to historic, while the newly generated parcels are accurately added to the current status layer. One implementation approach is to encapsulate the historicization modification and new parcel graphic insertion operations within an atomic transaction, ensuring that both either succeed simultaneously or fail simultaneously to maintain data consistency. Another approach is to use a coordinator or workflow engine to execute these two operations in a preset order (e.g., first historical modifications, then new graphics insertion), and monitor the results to ensure a rollback is possible if either step fails.

[0171] Through the aforementioned technical solution, complex edge relationship change types are parsed into more fundamental and explicit traceability semantic atoms, namely, consumption semantic atoms or generation semantic atoms. This fine-grained parsing enables the constraints on spatial operation descriptors to be accurately matched and dynamically determined based on the actual semantics of the change. For example, when a consumption semantic atom is identified, constraints for historical modification can be accurately applied, ensuring that the current status identifier of the target parcel in the annual update result layer is correctly updated to the historical status, thereby maintaining the integrity and traceability of historical data. When a generation semantic atom is identified, the system can accurately apply constraints for inserting new parcel graphics, ensuring that newly generated parcels or modified parcel graphics are reflected in the annual update result layer in a timely and accurate manner. For complex changes that simultaneously involve both consumption and generation semantics, this application can simultaneously apply constraints for historical modification and new parcel graphic insertion, comprehensively covering various complex change scenarios in the parcel lifecycle. This constraint determination mechanism based on semantic atom parsing improves the accuracy and coverage of spatial operation descriptor constraints, effectively solving the problem of inaccurate or incomplete constraints caused by the complexity of edge relationship change types. In view of this, this application can better ensure the atomic linkage execution of spatial operation instructions and traceability operation instructions within the same transaction boundary, thereby improving the efficiency of operation execution and greatly ensuring the consistency of the status between the annual update result layer and the ownership traceability map, providing a solid technical foundation for land life cycle traceability and ownership overlay.

[0172] In some of the solutions mentioned above in this application, the edge relationship change type is parsed into at least one traceable semantic atom to determine the constraints on the spatial operation descriptor. However, in its implementation, due to the lack of clear parsing rules and classification logic, the parsing results may be inconsistent or erroneous, affecting the accuracy of the constraints and thus destroying the atomic linkage relationship in the transaction boundary encapsulation.

[0173] To address this, this application further proposes resolving the edge relationship change type into at least one trace semantic atom, including: extracting the trace edge basic type from the edge relationship change type; generating a consumption semantic atom when the trace edge basic type is a consumption edge; generating a production semantic atom when the trace edge basic type is a production edge; generating both a consumption semantic atom and a production semantic atom when the trace edge basic type is an attribute change edge; and generating a consumption semantic atom when the trace edge basic type is a termination edge.

[0174] The extraction of basic traceability edge types from edge relationship change types aims to identify the core, standardized traceability operation types from high-level edge relationship change descriptions. This can be achieved in several ways. For example, a pre-defined rule base can be used to perform pattern matching or searching between the text descriptions or codes of edge relationship change types and predefined basic traceability edge types (such as "generating edge," "consuming edge," "attribute change edge," and "terminating edge") to determine their attribution. Alternatively, semantic parsing techniques can be used to semantically understand edge relationship change types, identify key verbs or concepts, and then infer the corresponding basic traceability edge types.

[0175] When the basic type of a traceable edge is determined to be a consumable edge, a consumable semantic atom is generated. A consumable semantic atom represents the semantic operation of deprecating, removing, or using an entity or resource. This atom can be generated by directly instantiating a predefined consumable semantic atom data structure or object and populating it with relevant operation identifiers, timestamps, and other attributes. Alternatively, in an event-driven architecture, when a consumable edge type is detected, a dedicated function or service is triggered to create and return the consumable semantic atom.

[0176] Similarly, when the traced edge's basic type is a generating edge, a generating semantic atom will be generated. A generating semantic atom represents the semantics of a new entity or resource being created, generated, or introduced. Its generation can be similar to that of a consuming semantic atom, for example, by instantiating a generating semantic atom object or by calling a specific generating function.

[0177] When the basic type of the traced edge is an attribute change edge, since an attribute change usually means the invalidation (consumption) of the old attribute value and the activation (generation) of the new attribute value, this application generates both consumption semantic atoms and generation semantic atoms simultaneously. This dual-atom generation mechanism enables a complete semantic expression of the attribute change operation. In specific implementation, after identifying the attribute change edge, the logic for generating consumption semantic atoms and generation semantic atoms can be called separately, and these two atoms can be processed as components of a composite event.

[0178] Furthermore, when the basic type of the tracing edge is a terminating edge, the system will also generate consumption semantic atoms. A terminating edge represents the end of an entity or its lifecycle, which is semantically equivalent to "consuming" or invalidating that entity. By uniformly mapping terminating edges to consumption semantic atoms, subsequent semantic processing logic can be simplified, and semantic consistency can be ensured. This can be achieved by reusing the logic for generating consumption semantic atoms, or by internally converting the terminating edge type into a special consumption type.

[0179] The above technical solution provides clear and standardized parsing rules and classification logic for the conversion from edge relationship change types to traceability semantic atoms. This effectively solves the problem of inconsistent or erroneous parsing results caused by the lack of unified standards in traditional solutions. Since traceability semantic atoms are the basis for determining the constraints of spatial operation descriptors (such as determining the constraints on spatial operation descriptors based on edge relationship change types in the above method), the solution in this application ensures that the generated constraints are more accurate and reliable. Furthermore, in the process of encapsulating the transaction boundaries of spatial operation descriptors and traceability operation descriptors in the above method, accurate constraints can effectively guarantee the atomic linkage between spatial operation instructions and traceability operation instructions. This ensures that the current status change of the target parcel in the annual update result layer and the update of the traceability relationship edges in the ownership traceability map are completed atomically in the same transaction, avoiding inconsistencies between spatial data and traceability relationship data, and improving the accuracy and reliability of land lifecycle traceability and ownership overlay.

[0180] In some of the solutions mentioned above in this application, it is proposed to jointly compile mutually embedded spatial operation descriptors and trace operation descriptors to generate spatial operation instructions and trace operation instructions and share reference links. However, in its implementation, there is a lack of a mechanism for associating and pairing attribute items and sharing data dependency links, which may lead to the inability to process spatial state changes and trace relationship updates synchronously when instructions are executed, causing data inconsistency risks and reducing operational efficiency.

[0181] In response, this application further proposes to jointly compile the mutually embedded space operation descriptors and trace operation descriptors to generate space operation instructions and trace operation instructions. This process includes: The spatial operation descriptor and the trace operation descriptor, which are embedded with each other, are combined into a joint descriptor intermediate representation. The joint descriptor intermediate representation uses the parcel identifier as the primary key and the operation content of the spatial operation descriptor and the edge operation content of the trace operation descriptor as the joint attribute set.

[0182] Traverse the set of joint attributes represented in the middle of the joint descriptor, associate and pair the first attribute item in the operation content of the spatial operation descriptor that involves the change of the current status identifier of the target parcel with the second attribute item in the edge operation content of the trace operation descriptor that involves the change type of edge relationship, and establish an attribute mapping pair.

[0183] The intermediate representation of the union descriptor is compiled into instructions. During the compilation process, the attribute mapping pair is expanded into the instruction body of the spatial operation instruction and the instruction body of the trace operation instruction. The two expanded instruction bodies share the data dependency link between the attribute items in the attribute mapping pair, thus obtaining the spatial operation instruction and the trace operation instruction.

[0184] For example, the interleaved spatial operation descriptors and trace operation descriptors are combined into a joint descriptor intermediate representation. This joint descriptor intermediate representation uses the parcel identifier as the primary key and the operation content of the spatial operation descriptor and the edge operation content of the trace operation descriptor as the joint attribute set. The joint descriptor intermediate representation is a unified data structure that integrates information from spatial and trace operation descriptors. Its function is to provide a centralized view for subsequent collaborative processing of spatial and trace operations. For example, using the parcel identifier as the unique primary key enables precise identification and management of operations on specific parcels. It aggregates the operation content of the spatial operation descriptor (e.g., changes in parcel geometry, attribute updates, etc.) with the edge operation content of the trace operation descriptor (e.g., creation, modification, or deletion of trace relationships, etc.) into a joint attribute set. This structured merging method, for example, using JSON, XML, or custom data objects, allows all parcel-related spatial and trace operation details to be efficiently accessed and processed within a single logical entity, laying the foundation for subsequent instruction generation and data synchronization.

[0185] After obtaining the intermediate representation of the joint descriptor, its joint attribute set is traversed. This step aims to identify and establish data associations between spatial operations and retrospective operations. Specifically, it identifies the first attribute item in the spatial operation descriptor that involves changes in the current status of the target parcel (e.g., parcel status code, lifecycle stage, etc.), and the second attribute item in the retrospective operation descriptor that involves changes in the type of edge relationship (e.g., type, direction, attributes, etc. of the retrospective edge). Through semantic analysis, pre-defined rule matching, or based on metadata definitions, these logically related attribute items are associated and paired to establish attribute mapping pairs. For example, the attribute item "parcel status changes from 'pending approval' to 'approved'" in a spatial operation will be paired with the attribute item "create a retrospective edge of type 'approved'" in a retrospective operation. This pairing mechanism ensures that every critical change in spatial status can find a corresponding relationship update in the retrospective graph, thereby achieving consistency between spatial and retrospective data at the logical level.

[0186] The compilation process generates instructions for the intermediate representation of the union descriptor. The core of this compilation process lies in transforming abstract attribute mapping pairs into concrete, executable instruction bodies for spatial operations and trace operation operations. Crucially, during the expansion process, these two instruction bodies ensure that they share the data dependency chain between attribute items in the attribute mapping pair. This means that for any attribute associated in the attribute mapping pair, the corresponding operation in both the spatial and trace operation instructions will reference or manipulate the same underlying data source or data value. For example, if the "new state value" is a shared attribute for both spatial and trace operations, the spatial instruction will write this value when updating the current state of the parcel, while the trace operation instruction will read or use this value when creating or modifying trace edges, and both will point to the same storage location or variable. This mechanism of sharing data dependency chains, for example, by allocating a unified memory address or data reference for shared attributes at compile time, fundamentally guarantees the atomicity and synchronization of spatial state changes and trace relationship updates at the execution level, generating strongly correlated spatial and trace operation instructions.

[0187] The above technical solution effectively solves the problem that spatial state changes and retrospective relationship updates may not be processed synchronously during instruction execution, leading to data inconsistency risks and reduced operational efficiency. For example, by embedding the spatial operation descriptors and retrospective operation descriptors into a joint descriptor intermediate representation, and using the parcel identifier as the primary key and the operation content as the joint attribute set, a unified and structured data view is provided for spatial and retrospective operations. This avoids information fragmentation and redundancy from the source, improving data processing efficiency. By traversing the joint attribute set, the first attribute item involving changes in the current state identifier of the target parcel is associated and paired with the second attribute item involving changes in edge relationship type, establishing a clear attribute mapping pair. This ensures that every critical change in spatial state can find a corresponding relationship update in the retrospective graph, thereby achieving a high degree of consistency between spatial and retrospective data at the logical level. Furthermore, during the instruction generation and compilation process, by making the instruction body of the expanded spatial operation instruction and the instruction body of the retrospective operation instruction share the data dependency links between attribute items in the attribute mapping pair, this application guarantees the atomic linkage between spatial operations and retrospective operations at the execution level. This means that when a spatial operation command modifies the current status of a land parcel, the tracing operation command will synchronously update the tracing relationship edges based on the same data source, thereby completely eliminating the risk of data inconsistency caused by data asynchrony. This tightly coupled compilation and execution mechanism not only ensures high data consistency and reliability but also greatly simplifies subsequent transaction management and error rollback logic, thus improving the robustness and operational efficiency of the land lifecycle tracing and ownership matching method as a whole.

[0188] In some of the embodiments described above in this application, it is proposed to jointly compile mutually embedded spatial operation descriptors and trace operation descriptors to generate spatial operation instructions and trace operation instructions. However, in its implementation, if the attribute items in the attribute mapping pair do not share the data dependency link, the data dependency relationship may be unstable when the instruction is expanded, thereby causing data inconsistency between spatial operation and trace operation during execution and destroying the atomic linkage of transactions.

[0189] To address this, this application further proposes expanding the attribute mapping pair into instruction bodies for spatial operation instructions and trace operation instructions during the compilation process, ensuring that the two expanded instruction bodies share the data dependency links between attribute items in the attribute mapping pair, thus obtaining spatial operation instructions and trace operation instructions. For example, the method includes: allocating an attribute value storage slot for each attribute item in the attribute mapping pair during compilation, and pointing the paired first and second attribute items in the attribute mapping pair to the same attribute value storage slot. Expanding the first attribute item into an attribute modification instruction sequence within the instruction body of the spatial operation instruction, binding the operation target address of the attribute modification instruction sequence to the attribute value storage slot. Expanding the second attribute item into an attribute recording instruction sequence within the instruction body of the trace operation instruction, binding the read source address of the attribute recording instruction sequence to the same attribute value storage slot. Encapsulating the expanded instruction bodies of the spatial operation instruction and the trace operation instruction respectively to obtain the spatial operation instruction and the trace operation instruction.

[0190] In this context, allocating a storage slot for the attribute value of each attribute item in the attribute mapping pair during compilation refers to reserving specific memory space or a location in a data structure for the data attribute value during the instruction generation phase. This aims to provide a unified and stable data access point for the execution of subsequent instructions, ensuring data consistency throughout the entire processing flow. For example, this can be achieved by creating a unique memory address or register allocation for each attribute item in the compiler's symbol table, or by defining a shared data buffer or structure and reserving a field or offset for each attribute item within it.

[0191] Pointing the first and second attribute items in an attribute mapping pair to the same attribute value storage slot means that these two attributes, which may logically represent different operations, share the same data source in physical storage. The first attribute item typically refers to the attribute in the spatial operation descriptor that involves changes to the current status identifier of the target parcel, while the second attribute item refers to the attribute in the trace operation descriptor that involves changes to the edge relationship type. By making them point to the same storage slot, a data dependency link is forcibly established between them, ensuring that any modification or retrieval of data in that slot reflects the same latest state. This can be achieved by setting the data access addresses in the instructions for the generated first and second attribute items to the address of the shared storage slot during the compiler's code generation phase. Alternatively, it can be achieved through pointer or reference mechanisms, where the internal representations of both the first and second attribute items store pointers or references to the shared slot.

[0192] Expanding the first attribute item into a sequence of attribute modification instructions within the instruction body of spatial operation instructions, and binding the target address of this sequence of instruction operations to the attribute value storage slot, means transforming the intention to modify spatial data (such as the current status indicator of a land parcel) represented by the first attribute item into a series of executable low-level operation instructions, such as "read old value," "calculate new value," and "write new value." Binding the target address to the attribute value storage slot ensures that these modification operations directly affect the shared data source, thereby guaranteeing that changes to spatial data are reflected in the shared slot in real time. For example, when generating assembly code or intermediate code, the compiler can set the target operand of modification instructions (such as MOV, STORE, etc.) to the address of this attribute value storage slot. Alternatively, in a high-level language environment, the generated method calls or assignment statements internally operate on variables or object members pointing to this shared slot.

[0193] Expanding the second attribute item into a sequence of attribute record instructions within the instruction body of a traceability operation, with the read source address of this sequence bound to the same attribute value storage slot, means transforming the recording intent of the traceability relationship (e.g., edge relationship change type) represented by the second attribute item into a series of executable low-level operation instructions, such as "read current value," "format record," or "write to log or graph database." Binding the read source address to the same attribute value storage slot ensures that the traceability record operation obtains the latest state consistent with the data modified by the spatial operation, thereby guaranteeing the accuracy and real-time nature of the traceability relationship. For example, when generating assembly code or intermediate code, the compiler can set the source operand of record instructions (such as LOAD, READ, etc.) to the address of this attribute value storage slot. Alternatively, in a high-level language environment, the generated method call or data access statement's internal implementation will read data from the variable or object member pointing to this shared slot.

[0194] Encapsulating the instruction bodies of the expanded spatial operation instructions and the trace operation instructions separately to obtain spatial operation instructions and trace operation instructions means packaging the instruction sequence and its related metadata (such as operation type, parameters, execution context, etc.) into independent, executable units. Separate encapsulation of spatial operation instructions and trace operation instructions ensures their logical independence, allowing them to be scheduled and executed as independent tasks or functions, while maintaining a strong data-level association through shared attribute value storage slots. For example, the instruction sequence can be encapsulated as independent functions, methods, or objects that operate on shared attribute value storage slots during execution. Alternatively, the instruction sequence can be compiled into independent binary modules or scripts that access shared slots at runtime through shared memory or inter-process communication mechanisms.

[0195] Through the above technical solution, a unified attribute value storage slot is allocated to each attribute item in the attribute mapping pair during the compilation stage, and the paired first and second attribute items are forced to point to the same storage slot, thereby establishing a robust data dependency link between spatial operations and tracing operations at the underlying data level. When the first attribute item is expanded into an attribute modification instruction sequence of a spatial operation instruction, its operation target address is directly bound to this shared slot, ensuring that any changes to the spatial state are written to this slot in real time. When the second attribute item is expanded into an attribute record instruction sequence of a tracing operation instruction, its read source address is also bound to the same shared slot, ensuring that the tracing record always obtains the latest data synchronized with the spatial operation. This design avoids data fragmentation caused by independent storage during instruction expansion, fundamentally solving the problem of data inconsistency. By encapsulating the expanded instruction bodies separately, the logical independence of spatial operation instructions and tracing operation instructions is maintained, while the shared data dependency link ensures their atomic linkage during execution, thereby effectively guaranteeing the high consistency and integrity of spatial data and tracing relationship data during the land lifecycle tracing and ownership matching process.

[0196] In some of the solutions mentioned above in this application, spatial operations and trace operations are performed within the same transaction boundary to ensure atomic linkage. However, in this process, how to efficiently capture state transition events and accurately parse trace relationship edges to ensure that the trace relationship has been correctly created when the transaction is committed, and avoid operation rollback or data inconsistency due to processing delays or errors.

[0197] In response, this application further proposes a method for tracing and matching the entire life cycle of land ownership based on identification codes, see [link to relevant documentation]. Figure 4 Its specific implementation methods include: 401. When executing the spatial operation command and modifying the current status identifier of the target parcel in the annual update result layer, intercept the state transition event generated by the modification operation. The state transition event carries the parcel identifier of the target parcel, the first state value before modification, and the second state value after modification.

[0198] "Executing the spatial operation instruction" refers to the system modifying the spatial or attribute data of the target parcel in the annual update result layer according to preset logic. This can be achieved by calling the API interface of the Geographic Information System (GIS) platform, for example, by directly updating the parcel spatial table and attribute table in the database by executing SQL statements. Alternatively, it can be achieved through a specific function encapsulated in the business service layer, which is responsible for handling operations such as geometric editing and attribute updates of the parcel. "Intercepting the state transition event generated by the modification operation" refers to the ability to capture and record the modification behavior and its related data in real time when the current status identifier of the target parcel changes. This can be achieved by configuring triggers at the database level, automatically executing a preset stored procedure when the current status identifier field of the target parcel is updated, and this stored procedure is responsible for encapsulating the state transition event. Alternatively, it can be achieved by performing aspect-oriented programming (AOP) programming on the business logic of modifying the current status identifier of the target parcel at the application layer, inserting event capture logic before, during, or after the execution of the modification operation, thereby intercepting the state transition event. The phrase "a state transition event carrying the parcel identifier, the first state value before modification, and the second state value after modification" means that a state transition event is a structured data packet containing key information about the parcel's state change, used to fully describe a state change. The event can be encapsulated as a JSON object containing fields such as "parcelId" (parcel identifier), "oldStatus" (first state value), and "newStatus" (second state value). Alternatively, the event can be defined as a specific data structure or message protocol, such as ProtocolBuffers or Avro, to ensure efficient and consistent data transmission.

[0199] 402. Asynchronously deliver the state transition event to the graph event processing pipeline. The graph event processing pipeline will then parse the edge type and edge direction of the trace relationship edge to be created based on the combination pattern of the first state value and the second state value.

[0200] The phrase "asynchronously delivering the state transition event to the graph event processing pipeline" refers to sending the event to a dedicated processing channel without blocking the current main business process. The graph event processing pipeline is a module specifically designed to handle event streams related to the graph. This can be achieved by using message queues (such as Kafka or RabbitMQ) as an event bus to publish intercepted state transition events to specific topics or queues, with the graph event processing pipeline acting as a consumer to subscribe to and receive these events. Alternatively, an independent thread pool or coroutine mechanism can be used to submit event processing tasks to background threads for asynchronous execution, while the main thread continues to perform subsequent operations, thereby improving system response speed. The phrase "the graph event processing pipeline parses the edge type and direction of the traceability relationship edge to be created based on the combination pattern of the first and second state values" means that the graph event processing pipeline intelligently determines the type and direction of traceability edges to be established in the ownership traceability graph based on the changes in the land parcel's state. The graph event processing pipeline can maintain a state transition rule engine containing a series of predefined rules. Each rule maps a specific "first state value - second state value" combination to a specific edge type (such as "generate", "consume", "change") and edge direction (such as "points to new parcel" or "points to old parcel"). Alternatively, a machine learning-based model can be used, trained on historical state transition data, to identify different state value combination patterns and predict the corresponding traceability edge types and directions.

[0201] 403. During the transaction commit phase of this spatial operation instruction, poll the processing results of the graph event processing pipeline. When it is confirmed that the traceability relationship edge has been created in the ownership traceability graph, commit the transaction of this spatial operation instruction. Otherwise, roll back the modification of the current status identifier by this spatial operation instruction.

[0202] The phrase "polling the graph event processing pipeline during the transaction commit phase of the spatial operation instruction" refers to the polling mechanism ensuring that asynchronously processed graph events are confirmed to be complete before the main transaction commits, thus maintaining data consistency. Before committing, the main transaction can periodically query the graph event processing pipeline for the processing status of specific events until it receives confirmation of completion. Alternatively, after the graph event processing pipeline completes processing, it can use a callback mechanism or publish the completion event to another message queue, allowing the main transaction to listen for this completion event, thereby avoiding the overhead of active polling. The phrase "committing the transaction of the spatial operation instruction when it is confirmed that the traceability edge has been created in the ownership traceability graph; otherwise, rolling back the modification of the current state identifier by the spatial operation instruction" is a crucial step in ensuring the atomicity of spatial and traceability operations—either both succeed or both fail. This can be achieved using the database's distributed transaction management capabilities, incorporating spatial and graph operations into a single global transaction, with the transaction coordinator managing commits and rollbacks uniformly. Alternatively, a two-phase commit (2PC) protocol can be used. In the first phase, spatial operations and graph operations prepare for commit separately. In the second phase, if both are ready, the coordinator issues a commit command; otherwise, it issues a rollback command.

[0203] The above technical solution enables real-time capture of every subtle change in land parcel status, transforming it into structured state transition events. This provides a complete and accurate data foundation for subsequent traceability relationship construction, preventing issues like event omissions or incomplete data that could break the traceability chain. By asynchronously delivering state transition events to the graph event processing pipeline, spatial operations and traceability relationship construction are decoupled, improving system processing efficiency and preventing the main business process from being blocked while waiting for graph operations. The graph event processing pipeline can intelligently parse the type and direction of traceability relationship edges based on the previous and subsequent combination patterns of land parcel status, ensuring the accuracy and semantic completeness of the traceability relationships. Furthermore, a polling mechanism is introduced during the transaction commit phase of spatial operation commands, determining whether to commit or rollback the transaction based on the graph event processing results. This ensures a high degree of consistency between spatial data and the ownership traceability graph while maintaining atomic linkage relationships, avoiding data inconsistencies caused by processing delays or errors, and providing a solid technical guarantee for land lifecycle traceability.

[0204] In some of the solutions described above in this application, a graph event processing pipeline is proposed to parse the edge type and direction of the traceability relationship edge to be created based on the combination pattern of the first state value and the second state value, in order to ensure the accurate generation of the traceability relationship and transaction consistency. However, in its implementation, the complexity of the state value combination pattern may lead to inaccurate parsing, especially when there are multiple candidate edge types. It is difficult to dynamically determine the primary and secondary edge types based on state changes, thereby affecting the accuracy of the traceability relationship and increasing the risk of transaction rollback.

[0205] In response, this application further proposes a step whereby the graph event processing pipeline parses the edge type and edge direction of the traceability relationship edge to be created based on the combination pattern of the first state value and the second state value, including: The first state value and the second state value are differentially encoded to obtain the state transition feature code. The encoded bits of the state transition feature code represent the direction and span of the transition from the first state value to the second state value.

[0206] Using the state transition feature code as an index, the corresponding edge type and edge direction are matched in the state transition edge type mapping table, which stores the correspondence between the state transition feature code and the edge type and edge direction.

[0207] When the state transition feature code matches multiple candidate edge types in the state transition edge type mapping table, the main edge type is determined based on the coding bit with the largest transition span in the state transition feature code, and the edge type corresponding to the coding bit with the second largest transition span is determined as the auxiliary edge type. The combination of the main edge type and the auxiliary edge type is output as the parsing result.

[0208] For example, differential encoding is performed on the first and second state values ​​to obtain a state transition feature code. The encoded bits of this feature code represent the direction and span of the transition from the first to the second state value. Differential encoding aims to quantify complex land parcel state changes into a structured, computable feature representation, thereby avoiding the ambiguity of directly handling combinations of original state values. For instance, bit vector encoding can be used, where each encoded bit represents a change in a specific attribute of the land parcel state (such as parcel type, ownership status, or right of use type). When an attribute changes, the corresponding encoded bit is activated, and its value indicates the type of change (such as increase, decrease, or modification) and the degree of change (i.e., the transition span). Another implementation is differential encoding, which directly calculates the differences between the first and second state values ​​across various dimensions and encodes these differences and their signs into specific fields of the state transition feature code. The absolute magnitude of the difference reflects the transition span, and the sign reflects the transition direction. In addition, state value pairs can be mapped to feature codes through hash functions. These hash functions are carefully designed to map the differences between states to specific bits or intervals of the feature codes, thereby indirectly representing the direction and span of migration.

[0209] Using state transition feature codes as indexes, the corresponding edge types and directions are matched in a state transition edge type mapping table. This mapping table stores the correspondence between state transition feature codes and edge types and directions. The state transition edge type mapping table is a pre-built knowledge base used to associate quantized state transition feature codes with specific traceability edge types (e.g., generating edges, consuming edges, attribute change edges, terminating edges, etc.) and their directions (e.g., from old parcel to new parcel, or from new parcel to old parcel). This mapping table can be implemented as a database table, an in-memory hash table, or a configuration based on a rule engine. For example, when a state transition feature code is received, it is used as a key to quickly look up the predefined edge type and direction in the mapping table. Another implementation method is to use a decision tree or decision forest model, taking each encoded bit of the state transition feature code as input features, and performing classification prediction through a trained model to output the corresponding edge type and direction.

[0210] When a state transition feature code matches multiple candidate edge types in the state transition edge type mapping table, the primary edge type is determined based on the encoding bit with the largest transition span in the state transition feature code, and the edge type corresponding to the encoding bit with the second largest transition span is determined as the secondary edge type. The combination of the primary edge type and the secondary edge type is output as the parsing result. In some complex state transition scenarios, a state transition feature code may simultaneously satisfy the matching conditions of multiple edge types, resulting in multiple candidate edge types. To resolve this ambiguity, this application introduces a decision mechanism based on transition span priority. For example, a set of priority rules can be preset, defining a weight or priority for each edge type in the mapping table. When multiple matches occur, the edge type with the highest weight is selected as the primary edge type, and the one with the second highest weight is selected as the secondary edge type. Another approach is to assign different weights to different coding bits in the coding design of the state transition feature code to reflect the "importance" or "influence" of the state attribute change they represent. When multiple candidate edge types appear, the correlation between these candidate edge types and the coding bit with the largest transition span (i.e. the most significant state change) in the state transition feature code is analyzed. The one with the highest correlation is selected as the main edge type, and the one with the second highest correlation is selected as the auxiliary edge type.

[0211] The above technical solution effectively solves the problem of inaccurate parsing of state value combination patterns, improving the accuracy and efficiency of traceability relationship generation. By differentially encoding the first and second state values, complex land parcel state changes are quantified into state migration feature codes with clear migration directions and spans. This avoids the ambiguity of direct combination pattern parsing and provides a solid foundation for subsequent accurate matching. Matching is performed in the state migration edge type mapping table using the state migration feature codes as indexes. The pre-stored correspondences enable fast and regular lookups, ensuring the efficiency and consistency of the traceability relationship parsing process. Furthermore, when multiple candidate edge types are matched, the primary edge type is determined based on the encoding bit with the largest migration span in the state migration feature codes, and the edge type corresponding to the encoding bit with the second largest migration span is determined as the secondary edge type. This dynamic priority decision-making mechanism solves the decision ambiguity when there are multiple candidate edge types, enabling the parsing results to more comprehensively and accurately reflect the actual state migration process, thus ensuring the complete expression of the traceability relationship. This precise and efficient ability to parse traceability relationship edges ensures that during the transaction commit phase, the traceability relationship edges in the ownership traceability map are highly consistent with the land parcel status changes in the annual update result layer, reducing the risk of transaction rollback and improving the robustness and reliability of the entire land lifecycle traceability and ownership overlay method.

[0212] In some of the solutions mentioned above in this application, the coordinated execution of spatial operation instructions and traceability operation instructions is proposed to ensure the atomicity of land ownership changes. However, in this process, since historical operation anomalies are not detected in advance, operation failure and inefficiency may occur.

[0213] To address this, this application further proposes a method that includes: before applying the spatial operation instruction to the annual update result layer, traversing the persistent traceability relationship edges in the ownership traceability map, and extracting historical operation anomaly features recorded in the traceability relationship edges. These historical operation anomaly features include the operation rollback rate corresponding to a specific registration type, the degree of edge density anomaly of parcels in a specific area, and the frequency of link breakage of current parcel nodes. Based on these historical operation anomaly features, an operation risk blocking threshold is generated for the target parcel. This operation risk blocking threshold is used to determine whether to allow the spatial operation instruction to continue execution before the spatial operation instruction is executed. When the parcel identifier of the target parcel matches a high-risk parcel marked in the historical operation anomaly features, or when the operation rollback rate corresponding to the registration type of the daily registration record exceeds the operation risk blocking threshold, the execution of the spatial operation instruction is blocked within the transaction boundary.

[0214] For example, before applying spatial operation commands to the annual update result layer, a preliminary risk detection step is performed. This step traverses the persistent traceability edges in the ownership traceability map to identify potential risk factors. Traversing the persistent traceability edges in the ownership traceability map means accessing all historical traceability records stored in the graph database or other persistent storage. These records document the historical trajectory of ownership changes, divisions, mergers, and other operations at different points in time for the land parcel. Extracting historical operation anomalies refers to identifying patterns from these historical records that deviate from normal operation patterns or indicate potential problems. For example, periodic or real-time data mining tasks can be used to analyze the edge attributes, node attributes, and graph structure in the ownership traceability map, pre-calculating and storing various anomaly indicator indicators. Alternatively, before each spatial operation command is executed, the local graph structure related to the target land parcel can be dynamically traversed and analyzed.

[0215] The historical operational anomaly characteristics are a set of indicators used to quantify and describe unexpected or high-risk patterns in the system's historical behavior. These include the operation rollback rate for specific registration types, the degree of edge density anomaly in specific regional parcels, and the frequency of link breaks in current parcel nodes. Specifically, the operation rollback rate for specific registration types refers to the frequency with which a certain type of registration business is revoked or rolled back after execution. A high rollback rate may indicate defects in the operational process of this type of business, data quality issues, or a high risk of human error. The degree of edge density anomaly in specific regional parcels refers to the difference between the number or complexity of traceability relationship edges between parcels within a certain geographical area and the average or expected level of that area. This may indicate a high incidence of ownership disputes, illegal operations, or abnormal data concentration in that area. The frequency of link breaks in current parcel nodes refers to the frequency with which the traceability link between the node representing the currently valid parcel and historical parcel nodes in the ownership traceability map is interrupted or incomplete. Link breaks may indicate data loss, operational omissions, or errors in the map construction logic. These characteristics can be calculated using pre-defined statistical models and rules. For example, the operation rollback rate can be obtained by analyzing the commit and rollback records of specific registration types of operations in the statistical database transaction log. Alternatively, machine learning models can be trained on historical operation data to automatically identify and quantify these anomalous characteristics.

[0216] Based on the historical operational anomaly characteristics, an operational risk blocking threshold is generated for the target parcel. This operational risk blocking threshold is a dynamically adjusted value or set of rules used to assess the risk level of the current operation before executing spatial operation instructions. Generating this threshold means that it is not fixed but adaptively adjusted based on historical anomaly characteristics to more accurately reflect the risk status of the current operational environment. For example, a rule-based expert system can be used to generate the threshold, preseting different risk levels and corresponding blocking thresholds based on combinations of operation rollback rate, edge density anomaly degree, and link breakage frequency. Alternatively, statistical analysis or machine learning methods can be used to dynamically generate the threshold. By analyzing historical normal and abnormal operation data, a risk scoring model can be established, and the blocking threshold can be dynamically adjusted based on the model's output.

[0217] This operational risk blocking threshold is used to determine whether to allow a spatial operation instruction to proceed before execution. The core function of this threshold is to act as a "safety gate," performing a risk assessment before the spatial operation instruction is submitted to the annual update result layer for actual modification. This mechanism ensures that only operations that pass the risk assessment can continue, thus preventing high-risk or potentially erroneous operations from impacting core data. For example, during the preprocessing stage of a spatial operation instruction, the risk assessment module is invoked. This module compares the current operation's context information with the generated blocking threshold; if the assessment result is higher than the threshold, it returns an "execution not allowed" instruction. Alternatively, the risk blocking logic can be integrated into the transaction management framework as a pre-check hook before transaction commit.

[0218] When the parcel identifier of the target parcel matches a high-risk parcel marked in the historical operation anomaly features, or when the rollback rate of the operation corresponding to the registration type of the daily registration record exceeds the operation risk blocking threshold, the execution of the spatial operation instruction will be blocked within the transaction boundary. This is the specific risk blocking triggering condition and execution mechanism. For example, after receiving a spatial operation instruction, the high-risk parcel list is queried to determine if the target parcel is included. The historical operation rollback rate of the current registration type is queried and compared with the dynamically generated blocking threshold. If any condition is met, a rollback or rejection instruction is issued to the transaction manager to prevent the execution of the spatial operation instruction. Alternatively, this can be handled through a unified risk assessment service. This service receives the target parcel identifier and registration type as input, combines historical anomaly features and dynamic thresholds, and outputs a Boolean value. If the output is "Blocked," the transaction manager captures this signal and immediately terminates the current transaction. Once a block is triggered, the entire spatial operation instruction and its associated retrospective operation instructions will be canceled, without causing any modification to the annual update result layer and ownership retrospective map, thus ensuring data consistency and integrity.

[0219] The aforementioned technical solutions effectively identify and prevent potentially high-risk or invalid operations, avoiding resource waste and data inconsistencies caused by abnormal operations. Risk assessment before executing spatial operation commands ensures that only verified, low-risk operations proceed to the subsequent atomic linkage execution stage, thereby improving the reliability and efficiency of the entire land lifecycle tracing and ownership alignment method. This proactive blocking mechanism not only reduces rollback costs due to operational failures but also enhances system robustness and data quality assurance capabilities by utilizing historical data for predictive intervention in future operations.

[0220] In some embodiments described above in this application, an operational risk blocking threshold is proposed to determine whether execution is permitted based on historical operational anomaly characteristics before the execution of space operation instructions, thereby preventing high-risk operations and ensuring data consistency. However, in the process of generating this operational risk blocking threshold, if each anomaly characteristic is considered in isolation without analyzing the correlation between them, the operational risk blocking threshold may be set inaccurately. For example, excessive blocking may reduce operational efficiency, while insufficient blocking may fail to avoid the risk of data inconsistency.

[0221] To address this, this application further proposes a method for generating an operational risk blocking threshold for a target parcel based on historical operational anomaly characteristics. This method includes: performing a correlation analysis between the operational rollback rate corresponding to a specific registration type and the degree of edge density anomaly in the specific area of ​​the parcel to obtain an anomaly coupling coefficient. This anomaly coupling coefficient characterizes the co-occurrence strength of operational rollback rate anomalies and edge density anomalies in spatial distribution. When the anomaly coupling coefficient exceeds a coupling threshold, the target parcel is determined to be within an anomaly coupling area, and the operational risk blocking threshold is lowered from the base threshold by a first magnitude. When the anomaly coupling coefficient does not exceed the coupling threshold, and the link breakage frequency of the current parcel node exceeds a breakage frequency threshold, the operational risk blocking threshold is lowered from the base threshold by a second magnitude, where the second magnitude is smaller than the first magnitude.

[0222] The rollback rate for a specific registration type refers to the frequency or proportion of transaction rollbacks during the execution of business operations for a specific registration type (e.g., transfer registration, change registration, cancellation registration, etc.) due to various reasons (such as data verification failure, business rule conflicts, user cancellation, etc.). It can be expressed as the percentage of rollback operations for a specific registration type out of the total number of operations within a certain time period, or calculated by statistically analyzing records of specific registration types in the rollback log. For example, a rollback log table can be maintained to record the registration type and whether a rollback occurred for each operation, thereby calculating the rollback rate for various registration types in real time or periodically. The anomaly degree of edge density of land parcels in a specific region refers to the degree to which the number or tightness of traceability edges between land parcel nodes within a specific geographical area deviates from the normal level in the ownership tracing map. The normal level can be determined based on historical data or the average edge density of adjacent regions. The quantification of the anomaly degree can be achieved by calculating the average in-degree and out-degree and clustering coefficient of land parcel nodes within a specific region and comparing them with the regional average or global average to obtain a deviation index. For example, if the number of traceability edges between land parcel nodes in a region is much higher than the average, it may indicate that there are frequent ownership changes or data anomalies in that region.

[0223] Correlation analysis is a statistical method used to assess the existence, strength, and direction of a statistical association between two or more variables. In this application, its role is to reveal whether a co-occurrence relationship exists between the operation rollback rate corresponding to a specific registration type and the degree of edge density anomaly in a specific area. Implementation methods may include, but are not limited to: using the Pearson Correlation Coefficient to measure linear correlation, or using Spearman's Rank Correlation Coefficient to measure nonlinear or monotonic correlation. By calculating these coefficients, the synchronous changing trend of two anomalous characteristics in spatial distribution can be quantified. This anomaly coupling coefficient, a result of correlation analysis, quantifies the co-occurrence strength of operation rollback rate anomalies and edge density anomalies in spatial distribution. The magnitude of this coefficient reflects the probability or degree of simultaneous occurrence of the two anomalies. For example, a high positive value may indicate that when the operation rollback rate is high in a certain area, its edge density anomaly also tends to be high. The calculation of this coefficient can be based on the results of correlation analysis, such as directly using the correlation coefficient, or by weighting and normalizing the correlation coefficient to adapt to a specific risk assessment model. This coupling threshold is a preset value used to determine whether an abnormal coupling coefficient reaches a standard sufficient to identify the target land parcel as being in an "abnormal coupling zone." When the abnormal coupling coefficient exceeds this threshold, it indicates that the correlation between the abnormal operation rollback rate and the abnormal edge density is strong enough, requiring more stringent risk control measures. This threshold can be set based on historical data analysis, expert experience, or risk tolerance. For example, by statistically analyzing historical abnormal events, the critical value of the coupling coefficient that leads to serious data inconsistencies can be identified.

[0224] The operational risk blocking threshold is a dynamically adjusted value used to determine whether to allow a space operation instruction to proceed before execution. Its function is to act as a safeguard against potential data inconsistencies or errors caused by high-risk operations to the annual update results layer and ownership traceability map. This threshold can be a comprehensive score or a combination of multiple independent indicators. When the risk score of the operation to be executed exceeds this threshold, the operation will be blocked. The base threshold is the initial setting or default value of the operational risk blocking threshold. It represents the general tolerance for operational risk when no specific anomalies or risk factors are found. During subsequent risk assessments, this base threshold will be adjusted upwards or downwards based on detected anomalies to adapt to different risk scenarios. The first and second amplitudes are quantitative values ​​used to adjust the operational risk blocking threshold. They represent the degree to which the base threshold is lowered under different risk scenarios. The first amplitude is typically used to address more severe and more coupled risk scenarios, therefore its downward adjustment is larger, resulting in a lower blocking threshold and making it easier to trigger blocking. The second amplitude is used to address relatively independent or minor risk scenarios, with a smaller downward adjustment than the first amplitude, making the blocking threshold relatively lenient. The specific values ​​for these amplitudes can be finely set based on risk assessment models, historical data analysis, and a trade-off between system stability and operational efficiency. The link breakage frequency of the current land parcel node refers to the frequency at which traceability relationship edges (links) related to the current land parcel node in the ownership traceability map are broken or incomplete. Link breaks may manifest as interrupted traceability paths, isolated nodes, or traceability relationship edges pointing to invalid nodes. This frequency can be calculated by periodically checking the connectivity of land parcel nodes and the integrity of traceability paths in the map, and counting the number of breakage events found. For example, a link breakage log can be maintained to record each detected breakage event and its associated land parcel node. The breakage frequency threshold is a preset value used to determine whether the link breakage frequency of the current land parcel node reaches a standard sufficient to identify an independent risk. When the link breakage frequency exceeds this threshold, it indicates that the land parcel node or its associated traceability relationships have high instability or potential problems, requiring attention even without coupling with other abnormal features and potentially triggering risk blocking. This threshold can also be set based on historical data analysis, expert experience, or risk tolerance.

[0225] The above technical solution, when generating operational risk blocking thresholds, no longer assesses historical operational anomalies in isolation, but introduces the analysis of correlations between different anomalies. For example, by performing correlation analysis on the operational rollback rate corresponding to a specific registration type and the degree of edge density anomalies in a specific area, the co-occurrence strength of these two anomalies in spatial distribution can be effectively captured, thus obtaining an anomaly coupling coefficient. When this anomaly coupling coefficient exceeds a preset coupling threshold, it indicates that the target parcel is in a high-risk anomaly coupling area. At this time, the operational risk blocking threshold is significantly lowered from the basic threshold by a first magnitude to more strictly limit operations that may lead to data inconsistencies. In addition, even if no anomaly coupling occurs, if the link breakage frequency of the current parcel node exceeds the breakage frequency threshold, an independent risk factor is identified. At this time, the operational risk blocking threshold is lowered from the basic threshold by a second magnitude, and the second magnitude is smaller than the first magnitude, reflecting the differentiated treatment of different risk levels. This mechanism, based on multi-dimensional anomaly feature correlation analysis and hierarchical adjustment, makes the setting of operational risk blocking thresholds more precise and intelligent, avoiding the reduction in operational efficiency caused by excessive blocking, and also effectively preventing the risk of data inconsistency caused by insufficient blocking, thereby improving the robustness and data quality of the land life cycle tracing and ownership matching method.

[0226] The following example will provide a more detailed explanation of the above technical solution: Suppose there exists a land management system whose annual update results layer records a current land parcel with the property unit code "330100001001GB00001F00000000". Now, the system receives a routine registration record, the registration type of which is "transfer registration", where the original property unit code is "330100001001GB00001F00000000" and the property unit code is "330100001001GB00001F00000000". This routine registration record aims to transfer the ownership of this land parcel from the original owner to a new owner.

[0227] Feature pattern analysis is performed on this routine registration record. Specifically, the registration type "transfer registration" is used as a priori constraint and input into the state constraint rule body. This rule body drives the output of the original code existence constraint, that is, the original real estate unit code "330100001001GB00001F00000000" should exist and point to the current state, which determines the expected existence state. The system uses the original real estate unit code as an index to extract its actual existence state in the annual update result layer and finds that the land parcel does exist and is a current land parcel. The expected existence state and the actual existence state are checked for consistency, and the result is "consistent". Based on this check result, the system determines that the association state between the original real estate unit code and the current land parcel belongs to "effective association". The registration type "transfer registration", the association state category "effective association", and the consistency check result are fused and encoded. For example, the operation base value is extracted using the registration type as an index, and then the operation base value is modulated by the first amplitude using "effective association" as an adjustment factor to obtain the first intermediate encoded value. Because it is an effective association, the modulation depth is relatively small, causing the first intermediate code value to shift towards the effective operating range. Using the unbiased mode characterized by the consistency check result as an adjustment factor, the first intermediate code value is subjected to second amplitude modulation to obtain a value feature combination with state transition determination capability. This process solves the problem of the lack of operational driving relationship between the identification code and the registration type in related technologies, and clarifies the association between the original code and the current land parcel through semantic prediction and consistency check.

[0228] Based on the combination of value features and associated states, the system uses the combination of value features itself as the sole criterion for determining the operation type, generating spatial operation descriptors and retrospective operation descriptors. The system parses the parcel state migration event category from the value feature combination, such as a "replacement event." This event category is implicit in the encoding structure of the value feature combination, solving the problem of lack of dynamic correlation between analysis rules and registration type semantics in related technologies. Using the "replacement event" as a direct driving signal, the system determines the type of state change performed on the target parcel in the annual update result layer, such as marking the original parcel as a historical state and inserting a new parcel with new ownership information, and generates a spatial operation descriptor based on this. Based on the combination pattern of "replacement event" and "effective association," the system determines the type of edge relationship change performed on the corresponding node of the target parcel in the ownership retrospective map, such as generating "consumption edges" and "generation edges," and generates a retrospective operation descriptor based on this. This step overcomes the problem of independent execution of retrospective relationship construction and spatial data fusion operations in related technologies, realizing the linked description of retrospective operations and spatial operations.

[0229] The spatial operation descriptor and the retrospective operation descriptor are encapsulated at transaction boundaries to obtain spatial operation instructions and retrospective operation instructions with atomic linkage. The system extracts the parcel identifier "330100001001GB00001F000000000" from the spatial operation descriptor and embeds it into the node positioning field of the retrospective operation descriptor, enabling the retrospective operation descriptor to obtain a spatial anchoring relationship with the target parcel. The edge relationship change type (consuming edge and generating edge) is extracted from the retrospective operation descriptor, and based on this, constraints on the spatial operation descriptor are determined, such as the requirement to perform historical modification on the original parcel and insert new parcel graphics into the annual update result layer. These constraints are embedded back into the verification parameter section of the spatial operation descriptor. The mutually embedded spatial operation descriptors and the retrospective operation descriptors are jointly compiled to generate spatial operation instructions and retrospective operation instructions. During the compilation process, the first attribute item involving the change of current status identifier is associated and paired with the second attribute item involving the edge relationship change type, and they point to the same attribute value storage slot to ensure that the two instructions share the data dependency link. By encapsulating transaction boundaries and sharing reference links, this solution enables atomic linkage of instructions, further resolving the issue of independent execution of traceability relationship construction and spatial data fusion operations.

[0230] Before applying spatial operation commands to the annual update result layer, the system traverses the persistent traceability edges in the ownership traceability map to extract historical operation anomaly features. These features include the operation rollback rate for the specific registration type "transfer registration," the degree of edge density anomalies in specific areas of land parcels, and the frequency of link breaks in current land parcel nodes. Based on these historical operation anomaly features, the system generates operation risk blocking thresholds for target land parcels. For example, if the operation rollback rate for "transfer registration" exceeds a preset threshold, or if the target land parcel is located within an abnormally coupled region, the execution of spatial operation commands will be blocked within the transaction boundary, thereby enhancing the system's robustness and data quality.

[0231] Within the same transaction boundary, a spatial operation instruction is executed to change the current status identifier of the target parcel in the annual update result layer from "Current" to "Historical". During this modification process, a resulting state transition event is intercepted. This event carries the parcel identifier, the first state value "Current" before modification, and the second state value "Historical" after modification. This state transition event is asynchronously delivered to the graph event processing pipeline. The graph event processing pipeline performs differential encoding based on the combination pattern of the first and second state values ​​to obtain a state transition feature code, and uses this as an index to match the corresponding edge type (e.g., "Historized Edge") and edge direction in the state transition edge type mapping table. During the transaction commit phase of the spatial operation instruction, the processing results of the graph event processing pipeline are polled. When it is confirmed that the traceability edge (e.g., a "Historized Edge" pointing from the original parcel node to the historical parcel node) has been created in the ownership traceability graph, the transaction of the spatial operation instruction is committed. If the creation of the traceability edge fails, the modification of the current status identifier by the spatial operation instruction is rolled back. This atomic operation enables the synchronous completion of spatial data changes and traceability map updates, solving the problem of difficulty in ensuring consistency between traceability results and layer status in related technologies.

[0232] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0233] The above are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for tracing and matching the entire life cycle of land ownership based on identification codes, characterized in that, The method includes: The real estate unit code, registration type, and original real estate unit code carried in the daily registration records are analyzed for feature patterns to obtain the value feature combination corresponding to the daily registration records and the association status between the original real estate unit code and the current land parcels in the annual update result layer. Based on the value feature combination and the associated state, and using the value feature combination itself as the sole criterion for determining the operation type, a spatial operation descriptor and a traceability operation descriptor are generated. The spatial operation descriptor is used to describe the state change type of the target parcel in the annual update result layer, and the traceability operation descriptor is used to describe the edge relationship change type of the corresponding node of the target parcel in the ownership traceability map. The spatial operation descriptor and the trace operation descriptor are encapsulated by transaction boundaries to obtain spatial operation instructions and trace operation instructions with atomic linkage relationships. The atomic linkage relationship constrains the spatial operation instructions and the trace operation instructions to be executed in conjunction within the same transaction boundary. Within the same transaction boundary, the spatial operation command is applied to the annual update result layer to change the current status of the target parcel, and the traceability operation command is simultaneously applied to the ownership traceability map to update the traceability relationship edge associated with the target parcel, so that the status change of the target parcel and the generation of the traceability relationship edge are completed atomically in the same transaction.

2. The method according to claim 1, characterized in that, The step of parsing the real estate unit code, registration type, and original real estate unit code carried in the daily registration records to obtain the value feature combination corresponding to the daily registration records and the association status between the original real estate unit code and the current land parcels in the annual update result layer includes: Using the registration type as a priori constraint, a semantic prediction is made on the existence status of the original real estate unit code in the annual update result layer. The semantic prediction is used to determine the expected existence status that the original real estate unit code should present under the registration type constraint. Using the original real estate unit code as an index, the actual existence status of the original real estate unit code is extracted from the annual update result layer, and the consistency between the expected existence status and the actual existence status is verified to obtain the consistency verification result; based on the consistency verification result, the association status between the original real estate unit code and the current land parcel is determined to belong to one of the following: valid association, invalid association, or no association; The registration type, the category of the associated state, and the consistency verification results between the expected state and the actual state are fused and encoded to obtain the value feature combination with state transition determination capability.

3. The method according to claim 2, characterized in that, The step of determining the association status between the original real estate unit code and the current land parcel based on the consistency verification result, which belongs to one of valid association, invalid association, or no association, includes: The expected existence state is compared with the actual existence state to obtain a state deviation descriptor. The state deviation descriptor is used to quantify the degree of deviation between the expected existence state and the actual existence state in the null value dimension and the non-null pointing dimension. The state deviation descriptor is input into the associated state classification component, which drives the associated state classification component to output the classification result of whether the association state between the original real estate unit code and the current land parcel belongs to effective association, invalid association, or no association. When the classification result is a failure association, the target state deviation descriptor corresponding to the failure association is extracted, and the target state deviation descriptor is fed back to the classification judgment threshold of the association state classification component, so that the classification judgment threshold is adaptively shifted based on the cumulative distribution characteristics of the target state deviation descriptor.

4. The method according to claim 2, characterized in that, The step of fusing and encoding the registration type, the category of the associated state, and the consistency verification results between the expected state and the actual state to obtain the value feature combination with state transition determination capability includes: Using the registration type as an index, extract the operation base value from the preset operation type base value table; Using the category of the associated state as an adjustment factor, the operation base value is subjected to a first amplitude modulation to obtain a first intermediate code value. The adjustment factor corresponding to the category of the associated state causes the first intermediate code value to shift towards the effective operation range or the abnormal operation range. Using the deviation pattern represented by the consistency check result as an adjustment factor, the first intermediate encoded value is subjected to second amplitude modulation to obtain the value feature combination. The second amplitude modulation causes the encoded value of the value feature combination to fall into the operation subtype range corresponding to the deviation type.

5. The method according to claim 2, characterized in that, The step of generating spatial operation descriptors and trace operation descriptors based on the combination of value features and the associated state, using the combination of value features itself as the sole criterion for determining the operation type, includes: The parcel status migration event category corresponding to the daily registration record is parsed from the value feature combination, and the parcel status migration event category is implicit in the coding structure of the value feature combination; Using the parcel status migration event category as a direct driving signal, determine the type of status change to be performed on the target parcel in the annual update result layer, and generate the spatial operation descriptor based on the status change type; Based on the combination pattern of the parcel state migration event category and the associated state, determine the edge relationship change type to be performed on the node corresponding to the target parcel in the ownership tracing map, and generate the tracing operation descriptor based on the edge relationship change type.

6. The method according to claim 1, characterized in that, The step of encapsulating the spatial operation descriptor and the trace operation descriptor with transaction boundaries to obtain spatial operation instructions and trace operation instructions with atomic linkage includes: Extract the parcel identifier of the target parcel from the spatial operation descriptor, and embed the parcel identifier into the node positioning field of the trace operation descriptor, so that the trace operation descriptor obtains a spatial anchoring relationship with the target parcel; Extract the edge relationship change type from the traceability operation descriptor, determine the constraint conditions on the spatial operation descriptor based on the edge relationship change type, and embed the constraint conditions back into the verification parameter segment of the spatial operation descriptor; The spatial operation descriptor and the trace operation descriptor, which are embedded with each other, are jointly compiled to generate the spatial operation instruction and the trace operation instruction. The spatial operation instruction and the trace operation instruction share the reference link established by the mutual embedding when they are executed.

7. The method according to claim 6, characterized in that, The step of jointly compiling the mutually embedded spatial operation descriptors and the trace operation descriptors to generate the spatial operation instructions and the trace operation instructions includes: The spatial operation descriptors and the trace operation descriptors, which are embedded with each other, are combined into a joint descriptor intermediate representation. The joint descriptor intermediate representation uses the parcel identifier as the primary key and the operation content of the spatial operation descriptor and the edge operation content of the trace operation descriptor as a joint attribute set. Traverse the joint attribute set represented by the intermediate of the joint descriptor, associate and pair the first attribute item in the operation content of the spatial operation descriptor that involves the change of the current status identifier of the target parcel with the second attribute item in the edge operation content of the trace operation descriptor that involves the change type of edge relationship, and establish an attribute mapping pair; The intermediate representation of the union descriptor is compiled into instructions. During the compilation process, the attribute mapping pair is expanded into the instruction body of the spatial operation instruction and the instruction body of the trace operation instruction, and the two expanded instruction bodies share the data dependency link between the attribute items in the attribute mapping pair, thus obtaining the spatial operation instruction and the trace operation instruction.

8. The method according to claim 1, characterized in that, The step of applying the spatial operation command to the annual update result layer to change the current status of the target parcel, and simultaneously applying the tracing operation command to the ownership tracing map to update the tracing relationship edges associated with the target parcel, includes: When executing the spatial operation command, when modifying the current status identifier of the target parcel in the annual update result layer, the state transition event generated by the modification operation is intercepted. The state transition event carries the parcel identifier of the target parcel, the first state value before modification, and the second state value after modification. The state transition event is asynchronously delivered to the graph event processing pipeline, which then parses the edge type and direction of the trace relationship edge to be created based on the combination pattern of the first state value and the second state value. During the transaction submission phase of the spatial operation instruction, the processing results of the graph event processing pipeline are polled. When it is confirmed that the traceability relationship edge has been created in the ownership traceability graph, the transaction of the spatial operation instruction is submitted; otherwise, the modification of the current status identifier by the spatial operation instruction is rolled back.

9. The method according to claim 8, characterized in that, The graph event processing pipeline parses the edge type and direction of the traceability relationship edge to be created based on the combination pattern of the first state value and the second state value, including: The first state value and the second state value are differentially encoded to obtain a state transition feature code. The encoded bits of the state transition feature code represent the direction and span of the transition from the first state value to the second state value. Using the state transition feature code as an index, the corresponding edge type and edge direction are matched in the state transition edge type mapping table, which stores the correspondence between the state transition feature code and the edge type and edge direction. When the state transition feature code matches multiple candidate edge types in the state transition edge type mapping table, the main edge type is determined based on the coding bit with the largest transition span in the state transition feature code, and the edge type corresponding to the coding bit with the second largest transition span is determined as the auxiliary edge type. The combination of the main edge type and the auxiliary edge type is output as the parsing result.

10. The method according to claim 9, characterized in that, The method further includes: Before applying the spatial operation command to the annual update result layer, traverse the persistent traceability relationship edges in the ownership traceability map, and extract the historical operation anomaly features recorded in the traceability relationship edges. The historical operation anomaly features include the operation rollback rate corresponding to a specific registration type, the degree of edge density anomaly of land parcels in a specific area, and the frequency of link breakage of current land parcel nodes. Based on the historical operational anomaly characteristics, an operational risk blocking threshold is generated for the target parcel. The operational risk blocking threshold is used to determine whether to allow the spatial operation instruction to continue to be executed before the spatial operation instruction is executed. When the parcel identifier of the target parcel matches the high-risk parcel marked in the historical operation anomaly features, or when the operation rollback rate corresponding to the registration type of the daily registration record exceeds the operation risk blocking threshold, the execution of the spatial operation instruction is blocked within the transaction boundary.

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