A building unit-based code assignment plot management method and system

Abstract

This invention relates to the field of building asset management information system technology, and particularly to a method and system for code-based mapping management of individual buildings. The method includes the following steps: S1: Obtaining unique identifier information, core spatial graphic data, and key attribute data for each individual building, and determining the central individual building based on the unique identifier information; extracting the management department and identification time based on the key attribute data, and forming a time-series set of individual building data records based on the management department and identification time. This invention achieves dynamic quantification of individual building associations through time-series data organization and multi-dimensional correlation analysis; constructs a weighted network to optimize resource screening and sorting; combines geometric comparison to achieve accurate spatial mapping; establishes a cross-departmental linkage mechanism to achieve automatic change synchronization, comprehensively improving collaboration efficiency, data consistency, and real-time response, thus contributing to digital urban governance.

Description

Technical Field

[0001] This invention relates to the field of building asset management information system technology, and in particular to a code-based mapping management method and system for individual buildings. Background Technology

[0002] Building unit coding and mapping management is a fundamental component of modern urban digital governance, involving multiple administrative departments such as planning, housing and construction, and municipal administration. Currently, the mainstream method is to assign a unique identifier to each building unit and input the spatial graphics and attribute information of each building unit (i.e., each specific building) into the respective department's database system, thus initially realizing the digital archiving and querying of building unit information.

[0003] However, the existing management model involves independent management processes in each department, and the data update mechanism is limited to within the department itself, lacking cross-system collaboration and linkage. When a building needs to update its data due to renovation, demolition, or change of use, it cannot automatically trigger data synchronization and correction in other related management departments, resulting in inconsistent business data across departments, forming information silos, and seriously affecting the execution efficiency of cross-departmental collaborative administrative workflows and the accuracy of data-based decision-making.

[0004] Therefore, it is urgent to break through the current decentralized management paradigm and build a management mechanism that can automatically identify related changes and realize cross-departmental business data collaboration, so as to solve the problem of business management lag caused by asynchronous data updates and improve the automation level of cross-departmental administrative workflow and the real-time and reliability of collaborative management in the specific business scenario of building asset management. Summary of the Invention

[0005] To overcome the shortcomings of isolated data update workflows and difficulties in collaboration in cross-departmental administrative management processes, this invention provides a code-based mapping management method and system for individual buildings.

[0006] The technical solution of this invention is: a method for managing the coding and mapping of individual buildings, comprising the following steps:

[0007] S1: Obtain the unique identification information, core spatial graphic data, and key attribute data of the building unit, and determine the central building unit based on the unique identification information; extract the management department and identification time based on the key attribute data, and form a time-seriesd set of building unit data records based on the management department and the identification time;

[0008] S2: Calculate the time difference of the identification time of adjacent data in the building unit data record set according to the building unit data record set, obtain the calculation result, and obtain the association relationship sorting result according to the calculation result; obtain the core spatial graphic data of the building unit according to the building unit data record set, and obtain the spatial association relationship result according to the core spatial graphic data;

[0009] S3: Match the set of building unit data records with a preset business rule base, and obtain business association results based on the business rule base; based on the association ranking results, the spatial association results, and the business association results, obtain a highly associated set with building units as the core;

[0010] S4: Based on the highly correlated set of the target building units, construct a baseline spatial graphic queue for the target building units; determine the management method for assigning codes and placing images according to the baseline spatial graphic queue.

[0011] Preferably, the step of obtaining the unique identifier information, core spatial graphic data, and key attribute data of a single building, and determining the central building based on the unique identifier information, includes:

[0012] Within a spatially adjacent area, obtain a preset number of individual buildings;

[0013] For each of the aforementioned building units, obtain unique identification information, core spatial graphic data, and key attribute data;

[0014] The number of management department identifiers is extracted based on the unique identifier information. The building unit with the most management department identifiers is taken as the central building unit, and the remaining building units are taken as associated building units.

[0015] Preferably, the step of extracting the management department and identification time based on the key attribute data, and forming a time-series set of building unit data records based on the management department and the identification time, includes:

[0016] All the individual buildings are grouped according to the name of the management department, thereby constructing a set of individual buildings corresponding to each unique management department name;

[0017] All the individual buildings are grouped using the management department as the key, and a mapping relationship from the management department to the set of individual buildings is established;

[0018] For each set of building units corresponding to each management department, using the identification time of the central building unit as the sorting benchmark, all the building units in the set of building units are sorted according to the identification time to form a time-seriesd set of building unit data records;

[0019] The term "collection of building units" refers to a group of building units that share the same management department attribute.

[0020] The mapping relationship refers to the corresponding association between the management department and the collection of individual buildings;

[0021] The set of data records for individual buildings refers to an ordered set containing all attribute data of individual buildings, arranged in chronological order.

[0022] Preferably, the step of calculating the time difference of the identification time of adjacent data in the building unit data record set based on the building unit data record set, obtaining the calculation result, and obtaining the association relationship sorting result based on the calculation result includes:

[0023] Based on the calculation results, extract individual building data records that are less than or equal to a preset time difference threshold, and re-sort the individual building data records according to the identified time to obtain the association relationship sorting results;

[0024] The association ranking result refers to an ordered list of potentially related building unit data records, filtered according to the time difference of the identifier and arranged in chronological order of the identifier time.

[0025] Preferably, the step of obtaining the core spatial graphic data of the building unit based on the building unit data record set, and obtaining the spatial relationship result based on the core spatial graphic data, includes:

[0026] Calculate the spatial topological relationships between the individual buildings based on the core spatial graphic data, and obtain the spatial relationship calculation results;

[0027] The spatial topology relationship refers to the adjacent, connected, and contained spatial positional relationships between individual buildings based on spatial graphic data, specifically obtained by calculating the spatial geometric relationships between the core spatial graphic data of the individual buildings;

[0028] Based on the spatial relationship calculation results, extract individual building data records with preset spatial relationship types to obtain spatial association results;

[0029] The spatial association result refers to a set of spatially dependent building unit data records formed after filtering based on spatial topological relationships.

[0030] Preferably, the step of matching the building unit data record set with a preset business rule base and obtaining business relationship results based on the business rule base includes:

[0031] The data record set of individual buildings is matched with the preset business rule base according to the business rule base. Based on the matching result, the change operation of the attribute data of the individual buildings is executed or the early warning information is generated. The records of the change operation and the early warning information together constitute the business association result.

[0032] Preferably, obtaining a highly correlated set with individual buildings as the core based on the correlation ranking results, the spatial correlation results, and the business correlation results includes:

[0033] Based on the ranking results of the association relationships, the spatial association results, and the business association results, a comprehensive quantitative assessment is made of the degree of association between any two units in the set of building units.

[0034] The comprehensive quantitative assessment assigns weight coefficients to temporal correlation, spatial topology correlation, and business rule correlation, and calculates the final comprehensive correlation value by weighted summation.

[0035] A symmetric matrix is ​​constructed based on the comprehensive correlation value. The symmetric matrix is ​​used to reflect the correlation strength between every two individual buildings.

[0036] The symmetric matrix is ​​converted into a weighted undirected network graph, in which nodes represent individual buildings and the edges between nodes represent the comprehensive correlation value between two corresponding buildings.

[0037] For each building node in the network, calculate the comprehensive correlation value between each building node and all other nodes, and sort them in descending order to obtain the descending sort result;

[0038] Based on the descending sorting results, all associated building units with a comprehensive correlation value exceeding a preset threshold are selected, forming a high correlation set with the building unit node as the core.

[0039] Preferably, constructing a baseline spatial graphic queue of the target building unit based on a set of highly correlated target building units includes:

[0040] Based on a highly correlated set of target building units, extract the core spatial graphic data of all correlated building units within the set;

[0041] Using the comprehensive correlation value between each associated building unit and the target building unit as the sorting key, all core spatial graphic data are sorted in descending order to construct a baseline spatial graphic queue for the target building unit; the baseline spatial graphic queue serves as the benchmark for subsequent spatial range calibration.

[0042] Preferably, the method for determining the coding and image placement management based on the reference spatial graphics queue includes:

[0043] If there is a new building or an existing building data update, the core spatial graphic data of the new or updated building will be compared with the corresponding baseline spatial graphic queue at each level and the tolerance will be judged.

[0044] If a deviation is detected, automatic geometric correction is performed or a warning message requiring manual intervention is generated. After calibration, a unique identifier is assigned or updated for the building unit, and the calibrated core spatial graphic data and all key attribute data are plotted and stored in the database.

[0045] At the same time, the creation or update operation will trigger all associated building units to initiate their own baseline spatial graphics queue update and data calibration process.

[0046] A code-based mapping management system for individual buildings includes:

[0047] The building unit data acquisition and preprocessing module obtains the unique identification information, core space graphic data and key attribute data of building units in adjacent spaces. It determines the center and related building units based on the number of management department identifications, and forms a time-series data record set by grouping by management department and sorting by identification time.

[0048] The multi-dimensional correlation analysis module calculates the time difference of the identified time in the data record set to obtain the time-series correlation result, calculates the spatial topology relationship based on the core spatial graphic data to obtain the spatial correlation result, and matches the business rule base to trigger data changes or warnings to obtain the business correlation result.

[0049] The integrated network construction module combines temporal, spatial, and business relevance, calculates the comprehensive relevance value with weights, constructs a symmetric matrix and a weighted undirected network graph, and filters a set of high relevance with the target building as the core.

[0050] The dynamic coding and calibration execution module constructs a benchmark spatial graphics queue based on a highly correlated set. It performs geometric comparison and tolerance judgment on the graphics data of newly created or updated units with the queue, performs correction or warning, completes coding and mapping, and triggers the correlation data update process.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] This invention effectively solves the problems of isolated data updates and low collaborative efficiency in cross-departmental management of individual buildings. Through time-series data organization and multi-dimensional correlation analysis, this invention achieves, for the first time, the dynamic quantification of correlation rules between individual buildings; it optimizes the intelligent screening and sorting mechanism of highly correlated resources using a weighted network model; and it achieves automatic calibration and accurate mapping of spatial data by combining geometric comparison and tolerance judgment techniques. Particularly noteworthy is the construction of a cross-departmental linkage triggering mechanism, which enables automatic synchronization and process refactoring of business data changes, significantly improving the overall collaborative efficiency, data consistency, and real-time business response of urban building asset management, providing a reliable data foundation and decision support for urban digital governance. Attached Figure Description

[0053] Figure 1 This is a flowchart of the coding and mapping management method for individual buildings based on the present invention;

[0054] Figure 2 This is a structural diagram of the code assignment and mapping management system based on individual buildings according to the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1

[0057] A method for managing the mapping of individual buildings using codes, such as... Figure 1 As shown, it includes the following steps:

[0058] S1-1: Obtain the unique identification information, core spatial graphic data and key attribute data of the building unit, and determine the central building unit based on the unique identification information;

[0059] Within a spatially adjacent area, obtain a preset number of individual buildings;

[0060] For each individual building, obtain unique identification information, core spatial graphic data, and key attribute data;

[0061] The number of management department identifiers is extracted based on the unique identifier information. The building with the most management department identifiers is designated as the central building, and the remaining building units are designated as associated building units.

[0062] It should be noted that this invention obtains multi-source data of multiple building units through spatial adjacency relationships. The unique identification information includes building codes and real estate unit numbers; core spatial graphic data includes building outline coordinates and elevation information; and key attribute data covers management departments, construction time, and usage classification. The preset quantity is dynamically set based on the regional building density, and the spatial adjacency range ensures substantial correlation between buildings. By statistically analyzing the number of management departments associated with each building unit, the building with the most concentrated cross-departmental management needs is selected as the central building unit, as changes to the data of the central building unit will generate the greatest linkage effect. The central building unit is the core node triggering multi-departmental collaborative updates within the region, and associated building units are building units that have spatial adjacency with the central building unit and need to be updated synchronously. For example, if a historical building is simultaneously associated with three departments—the Cultural Relics Bureau, the Planning Bureau, and the Housing and Construction Bureau—then when the historical building's facade is repaired, multi-departmental data synchronization updates will be automatically triggered.

[0063] S1-2: Extract the management department and identification time based on key attribute data, and form a time-series collection of individual building data records based on the management department and identification time;

[0064] All individual buildings are grouped according to the name of the management department, thereby constructing a set of individual buildings corresponding to each unique management department name;

[0065] All individual buildings are grouped using management departments as the key, and a mapping relationship is established from management departments to the set of individual buildings;

[0066] For each management department's set of building units, the identification time of the central building unit is used as the sorting benchmark. All building units in the set are sorted according to their identification time to form a time-seriesd set of building unit data records.

[0067] A collection of individual buildings refers to a group of individual buildings that share the same management department attributes.

[0068] Mapping relationship refers to the corresponding association between management departments and a collection of individual buildings;

[0069] A building unit data record set refers to an ordered set containing all attribute data of a building unit, arranged in chronological order.

[0070] It should be noted that by analyzing the key attribute data of individual buildings, the names of the management departments and the identification time information are extracted. Management departments include functional agencies of the Planning Bureau, Housing and Construction Bureau, and Municipal Bureau. The identification time refers to the entry or latest update time of the building data. A clear correspondence of authority and responsibility is established based on the management departments, forming a set of building units under departmental jurisdiction, facilitating cross-departmental data collaboration. Establishing a mapping relationship from management departments to the set of building units enables rapid location of all building units under the jurisdiction of each management department, providing a data foundation for subsequent change coordination. Using the identification time of the central building unit as the sorting benchmark is because it has the most comprehensive departmental correlation, ensuring temporal consistency. The time-series data record set formed by sorting by identification time clearly shows the building data change process, providing an important basis for the retrospective analysis and prediction of the impact of building changes. This time-series data organization method effectively solves the problem of asynchronous cross-departmental data updates.

[0071] S2-1: Calculate the time difference between adjacent data identifiers in the building unit data record set based on the building unit data record set, obtain the calculation result, and obtain the association relationship sorting result based on the calculation result;

[0072] Based on the calculation results, extract individual building data records that are less than or equal to the preset time difference threshold, and re-sort the individual building data records according to the identified time to obtain the association relationship sorting results;

[0073] The association ranking result refers to an ordered list of potentially related building data records, filtered based on the time difference of the identifiers and arranged in chronological order of the identifiers.

[0074] It should be noted that by calculating the time difference between the identification times of individual building units, buildings updated within similar time periods are identified. These buildings have undergone related changes due to the same engineering project or policy adjustments. The preset time difference threshold is dynamically set according to the business update frequency, and is set to the average time of adjacent update cycles. The selected buildings are then reordered according to their identification times, clearly showing groups of buildings with temporal correlations, providing a time-based data foundation for subsequent spatial and business correlation analysis. This temporal correlation sorting can effectively capture multi-building collaborative changes triggered by the same event, improving the accuracy and efficiency of cross-departmental data linkage.

[0075] Example: In a data set of individual building records for a certain area, the identification times of buildings A, C, and F are found to be October 5th, 2023, October 7th, and October 20th, 2023, respectively. The calculated time difference between adjacent buildings is 2 days for buildings A and C, and 13 days for buildings C and F. With a preset time difference threshold of 7 days, the data records for buildings A and C are extracted. These two records are then reordered according to their identification times to form a relational sorting result. This result indicates that buildings A and C are potentially related building units, and because their update times are very close, they belong to the same phase of comprehensive renovation project.

[0076] S2-2: Obtain the core spatial graphic data of a building based on the set of building data records, and obtain the spatial relationship results based on the core spatial graphic data;

[0077] The spatial topological relationships between individual buildings are calculated based on the core spatial graphic data to obtain the spatial relationship calculation results.

[0078] Spatial topology refers to the adjacent, connected, and contained spatial positional relationships between individual buildings based on spatial graphic data. Specifically, it is obtained by calculating the spatial geometric relationships between the core spatial graphic data of individual buildings.

[0079] Based on the spatial relationship calculation results, extract the data records of individual buildings with preset spatial relationship types to obtain the spatial association results;

[0080] Spatial association results refer to a set of spatially dependent building unit data records formed after filtering based on spatial topological relationships.

[0081] It should be noted that spatially dependent building groups are identified by analyzing the core spatial graphic data of individual buildings and calculating the spatial topological relationships between them. Spatial topological relationships include adjacent, connected, and contained spatial relationships, obtained by calculating the geometric relationships between building outlines. Preset spatial relationship types are set according to actual business needs, commonly including adjacent relationships within a distance threshold, connected relationships with adjacent boundaries, and outline-containing relationships. Spatial distance formulas are used in the calculations. Determine the adjacent relationships, where, This represents the Euclidean distance between two points. : Represents the planar coordinates of any point on the outline of the first building unit. : Represents the planar coordinates of any point on the outline of the second building unit, when When they are determined to be adjacent, This refers to the distance tolerance threshold set based on building size. This spatial correlation analysis can effectively identify building groups that have interconnected influences due to physical proximity, providing a spatial dimension for cross-departmental data updates.

[0082] For example, based on core spatial graphic data, calculations show that the minimum edge distance between the outlines of building D and building E is 1.5 meters, which is less than the preset 2-meter adjacent tolerance threshold. Therefore, it is determined that the two have an adjacent spatial topological relationship. Based on the spatial relationship calculation results, the data records of buildings D and E are extracted to obtain the spatial association results. This result includes the data records of all spatially dependent building units, indicating that buildings D and E form a spatially related group due to their close physical proximity. Any graphic change to one of them must consider its spatial impact on the other.

[0083] S3-1: Match the pre-defined business rule base with the set of individual building data records, and obtain the business relationship results based on the business rule base;

[0084] The data record set of individual buildings is matched with the preset business rule base according to the business rule base. Based on the matching result, the change operation of the attribute data of individual buildings is executed or the early warning information is generated. The records of the change operation and the early warning information together constitute the business relationship result.

[0085] It should be noted that the preset business rule base is built based on the digital construction of management specifications of various departments, including business logic for changes in building use and updates to safety standards. When individual building data matches a business rule, attribute data change operations are automatically triggered, such as updating building classification codes or usage status, and warning information is generated, such as compliance check anomaly alerts. The business relationship result is a collection of data update records and warning messages generated after the rule engine matches building data with business rules one by one. This result is obtained by comparing building attributes with the business rule base in real time, ensuring that cross-departmental business changes can be reflected in building data in a timely manner, realizing dynamic data management driven by business rules.

[0086] For example, when matching building unit data records with the business rule base, it was found that the usage attribute of a certain building unit had changed from residential to catering. The rule base stipulates that a change of usage must trigger a fire safety level update operation and generate an early warning message requiring fire safety filing. The attribute change operation was automatically executed, and the record of this change operation and the generated early warning message were stored together, forming a business relationship result. This result records the data changes and early warnings driven by business rules, ensuring the linkage of business compliance management.

[0087] S3-2: Based on the ranking results of association relationships, spatial association relationship results, and business association relationship results, obtain a set of highly associated buildings as the core;

[0088] Based on the ranking results of association relationships, spatial association results, and business association results, a comprehensive quantitative assessment is conducted on the degree of association between any two units in the building unit set.

[0089] The comprehensive quantitative assessment assigns weight coefficients to temporal correlation, spatial topology correlation, and business rule correlation, and calculates the final comprehensive correlation value by weighted summation.

[0090] A symmetric matrix is ​​constructed based on the comprehensive correlation value. The symmetric matrix is ​​used to reflect the correlation strength between every two individual buildings.

[0091] The symmetric matrix is ​​converted into a weighted undirected network graph. In the weighted undirected network graph, nodes represent individual buildings, and the edges between nodes represent the comprehensive correlation value between the corresponding two buildings.

[0092] For each building node in the network, calculate the comprehensive correlation value between each building node and all other nodes, and sort them in descending order to obtain the descending sort result;

[0093] Based on the descending sorting results, all associated building units with a comprehensive correlation value exceeding a preset threshold are selected, forming a high correlation set with the building unit node as the core.

[0094] It should be noted that 'association strength' refers to the numerical value of the comprehensive association degree calculated by the weighted summation formula. This value is used to quantitatively characterize the comprehensive association degree between any two building units in terms of temporal, spatial, and business rule dimensions. The higher the value, the greater the association strength between the two units.

[0095] It should be noted that the above comprehensive quantitative assessment of the degree of association between any two entities is based on the data obtained from the association ranking results, spatial association results, and business association results in the preceding steps.

[0096] Specifically, the temporal correlation score is obtained using the following formula. ,in, For time-series correlation scoring, To indicate the time difference, This is a scaling factor used to control the attenuation factor; recommended values ​​are provided. =0.5 (example), then: =0 =1, =2 =1 / (1+1)=0.5, =7 o'clock =1 / (1+3.5)=0.222. Before performing the calculation, it is necessary to... Perform normalization (e.g., divide by a typical timescale parameter, such as...) , transformed This is used to eliminate the influence of units of measurement, ensuring that the score has consistent mathematical meaning across different business scenarios. When faster decay is required, the following formula is used: ,in, For time scale (example) =3 days).

[0097] Spatial topological correlation score is obtained using the following formula. ,in, Scoring spatial topological correlation, The minimum edge distance of the building outline (meters). Spatial scale (example) =2.0 meters), in The score is 1 when the value is 0, and it drops rapidly with increasing distance. For discrete rules (adjacent, connected, contained), the corresponding benchmark is [missing information - likely a criterion]. =0.95, connected =0.85, adjacent =0.7, otherwise =0.

[0098] The business rule relevance score is obtained using the following formula: ,in, Scoring the relevance of business rules. , The set of rules (or change / alert items) that are triggered in the rule engine. If two individuals trigger the exact same set of rules, the score is 1; if there are no common triggering rules, the score is 0.

[0099] Methods for setting weighting coefficients: Method 1, based on experience / scenario default values; example, urban planning scenario. =0.2, =0.6, =0.2 (emphasizing the relative importance of space), in the context of engineering approval. =0.5, =0.3, =0.2, fire safety compliance scenario =0.1, =0.3, =0.6, the weights in the example are dynamically adjusted by the system administrator in the system configuration interface, and are the default suggested values ​​for easy implementation. Method 2, in order to achieve an objective allocation of the correlation weights of the three dimensions of time series, space and business, requires supervised model training based on historical labeled data. The specific steps are as follows: First, collect historical building individual change events, and have domain experts manually label and construct "substantial correlation" and "non-correlation" sample pairs; for each pair of samples, calculate its time series correlation score ( Spatial topological correlation score ) and business rule-related scoring ( This forms a feature-label dataset. Logistic Regression or XGBoost algorithms are used for training to fit an association probability prediction model. , This represents the probability value predicted by the model that the pair of building units (i,j) belongs to a "substantial association". The sigmoid function is the activation function used in logistic regression models; it is also called the logistic function. , , , These are the model coefficients. For the intercept term, , , Corresponding features , , The regression coefficients were calculated. Model performance was evaluated using five-fold cross-validation, requiring an AUC of at least 0.85 and precision and recall both greater than 0.75 to ensure good discriminative ability and stability. After training, the standardized coefficients of the model were extracted. , , According to the formula () This is converted into weight allocation for each dimension, thereby achieving quantitative integration of the overall correlation.

[0100] Press the correct calculate , , Overall correlation , To assess overall relevance, For time-series correlation scoring, Scoring spatial topological associations, Associating scores with business rules , , The importance weights are assigned to the three dimensions of time sequence, space, and business.

[0101] To achieve a networked representation and efficient filtering of the relationships between individual buildings, the comprehensive correlation value is transformed into a graph structure and then filtered. The specific steps are as follows: First, construct an n×n symmetric matrix M, where n is the total number of individual buildings within the current spatial range. Matrix elements... That is, the first in the matrix Line number The column's element value is a building unit. With individual buildings The overall correlation value between them, the elements on the main diagonal of the matrix (i.e. = () is meaningless and is usually set to 0 or ignored.

[0102] Subsequently, the symmetric matrix M is transformed into a weighted undirected graph G(V,E). Here, the vertex set V consists of all individual buildings, with each building serving as a vertex; the edge set E consists of all relationships, where each relationship is defined for any two distinct buildings. and ,like If the weight is greater than 0, then an edge is created between them, and the weight of the edge is... That is At this point, the multi-dimensional relationships between individual buildings have been fully mapped into a weighted network graph.

[0103] Finally, a high-association set is selected based on this weighted undirected graph. For each vertex in the graph (i.e., the target building), the weights of the edges between it and all other vertices in the graph (i.e., the overall association value) are calculated and sorted in descending order. The selection strategy should employ a clear threshold: set an absolute threshold for the overall association value (e.g., only retain associations with a weight greater than the average of all edge weights), or set a relative proportion threshold (e.g., only retain the top k% of vertices most closely associated with the target vertex, where k is typically set to 10 to 20 based on network density and business requirements). Ultimately, the selected vertices constitute a high-association set centered around the target building.

[0104] Example: To illustrate the implementation process of this method, suppose that in the management of a certain area, it is found that the identification time of buildings A and C differs by only 2 days (less than the preset threshold of 7 days), and they are identified as having a high temporal correlation, suspected to belong to the same phase of comprehensive renovation project; through spatial analysis, it is found that the minimum edge distance of the outlines of buildings D and E is 1.5 meters, less than the tolerance threshold of 2 meters, and they are judged to have an adjacent spatial topological relationship. Furthermore, when the use of a building changes from residential to catering, after matching with the business rule base, a fire safety level update is automatically triggered and a filing warning is generated, forming a business association record. Based on these multi-dimensional association results, the weights obtained from training (such as...) are used... =0.2, =0.6, =0.2) Calculate the comprehensive correlation value A weighted undirected network graph is constructed. Based on this, a set of highly correlated target building units M, {N1, N2, N3, N4}, is selected. Then, a baseline spatial graphic queue [N1, N2, N3, N4] is constructed, ordered by their comprehensive correlation values ​​(0.92, 0.85, 0.78, 0.65). When a new building unit X is constructed, its graphic data is geometrically compared with this queue level by level. If a deviation of 1.8 meters (exceeding the 1.5-meter tolerance) is found between X and N1 and N2, geometric correction is automatically performed. After calibration, the data is coded (BldX-2024-001) and stored in the database. Simultaneously, all related buildings are automatically triggered to initiate their own baseline queue update and data verification processes, thereby achieving cross-departmental data linkage updates and business collaboration.

[0105] It should be noted that, based on the weighted undirected network graph constructed using comprehensive correlation values, the sum of the comprehensive correlation values ​​between each building node and all other nodes in the network is calculated to obtain the total correlation of each building node. The total correlation values ​​of all building nodes are compared, and the building node with the highest total correlation value is identified. If this highest total correlation value is higher than the second highest value and exceeds a preset difference threshold, then this building node is selected as the new central building node. If the difference between the highest total correlation value and the total correlation value of one or a preset number of nodes does not exceed the preset difference threshold, then the building node with the highest total correlation value among these high-correlation nodes is selected as the new central building node. This decision-making process ensures the accuracy and robustness of the selection of the central building node.

[0106] The preset difference threshold is recommended to be set using a relative proportion method, that is, taking 10% to 20% of the maximum total correlation value in the network as the threshold. This method can adapt to networks of different sizes and avoid the limitations of setting absolute values. The preset number of nodes is recommended to be dynamically adjusted according to the total number of nodes in the network: when the total number of nodes is less than 50, the first 3 nodes are fixed for comparison; when the total number of nodes is greater than or equal to 50, the first 5% of nodes are selected (rounded up). This setting method balances the accuracy of judgment and computational efficiency, ensuring the universality and operability of the central node identification process.

[0107] If the difference between the maximum total correlation value and the total correlation value of one or a preset number of nodes does not exceed a preset difference threshold, then selecting the building unit with the highest total correlation value among these highly correlated nodes as the new central building unit refers to the collaborative decision-making mechanism adopted by the system when there is no node with an absolutely significant advantage in the network. Specifically, if the correlation values ​​of the highest correlation node and several of its immediate successors (i.e., the "preset number") nodes are nearly identical (the difference does not exceed the threshold), it indicates that these nodes collectively constitute the highly correlated core group of the network, with comparable advantages. In this case, the system no longer makes subtle distinctions but follows the principle of "choosing the highest rather than the lowest," directly selecting the node with the highest correlation value among them as the central unit. This is a robust strategy based on group comparison, effectively avoiding misjudgment or frequent switching of the central node due to minor data fluctuations.

[0108] This method achieves dynamic, objective, and stable identification of central building units by introducing network centrality analysis and differential comparison. Its technical logic lies in transforming the traditional method of determining the core, which relies on subjective or static rules, into a data-driven, objective calculation and selection process based on the overall network's correlation strength. This is crucial for solving the "information silos" and "collaboration difficulties" problems in the background technology: it ensures that the initiating point (central node) of each data update is the most critical unit in the current network that can affect the overall situation, thus making the subsequent cross-departmental collaborative update process accurate in its objectives and reasonable in its scope. This significantly improves the accuracy, efficiency, and consistency of cross-departmental business data collaboration from the source.

[0109] S4-1: Based on the highly correlated set of target building units, construct a baseline spatial graphic queue of target building units;

[0110] Based on a highly correlated set of target building units, extract the core spatial graphic data of all correlated building units within the set;

[0111] Using the comprehensive correlation between each associated building unit and the target building unit as the sorting key, all core spatial graphic data are sorted in descending order to construct a baseline spatial graphic queue for the target building unit; the baseline spatial graphic queue serves as the benchmark for subsequent spatial range calibration.

[0112] It should be noted that, based on the highly correlated set of the target building unit, core spatial graphic data of all related building units are extracted, including geometric features such as building outline coordinates and elevation information. Using the pre-calculated comprehensive correlation value between each related building unit and the target building unit as the sorting criterion, all core spatial graphic data are arranged in descending order of correlation strength, constructing a baseline spatial graphic queue for the target building unit. The spatial graphic data at the top of this queue comes from building units with the strongest temporal, spatial, and business correlations with the target building unit, best representing the spatial characteristics of the area. This baseline spatial graphic queue serves as a reference standard for spatial geometric comparison and tolerance judgment when constructing or updating building units, ensuring the consistency and accuracy of spatial data updates, and providing a unified spatial benchmark for cross-departmental collaborative management.

[0113] It should be noted that the specific construction process of the benchmark spatial graphic queue is as follows: Core spatial graphic data of all associated building units, including their outline coordinates and elevation information, are extracted from the high-association set of the target building unit. Based on the pre-calculated comprehensive correlation value between each associated building unit and the target building unit, all core spatial graphic data are arranged in descending order of correlation strength, thus forming the benchmark spatial graphic queue for the target building unit. The size of this queue is equal to the number of building units in the high-association set, and the order of elements within the queue is determined by the comprehensive correlation value; the higher the value, the earlier the element appears in the queue. This queue serves as the benchmark for subsequent spatial calibration, and comparisons are performed step-by-step in this order during geometric comparison.

[0114] For example, continuing from the previous example, the high-correlation set of the target building unit M includes N1, N2, N3, and N4. The core spatial graphic data of all four associated building units within this set are extracted, namely the building outline coordinates and elevation information of the associated building units. Using the pre-calculated comprehensive correlation values ​​(0.92, 0.85, 0.78, and 0.65) between each associated building unit and the target building unit M as the sorting key, all core spatial graphic data are sorted in descending order. The sorting result is N1, N2, N3, and N4, which forms the baseline spatial graphic queue for the target building unit M. This queue defines the priority reference order for spatial calibration, providing an authoritative benchmark for subsequent geometric comparisons.

[0115] S4-2: Determine the management method for assigning codes to graphs based on the baseline spatial graph queue.

[0116] If there is a new building or an existing building data update, the core spatial graphic data of the new or updated building will be compared with the corresponding baseline spatial graphic queue at each level and the tolerance will be judged.

[0117] If a deviation is found, automatic geometric correction is performed or a warning message that requires manual handling is generated. After calibration, a unique identification information is assigned or updated for the building unit, and the calibrated core spatial graphic data and all key attribute data are plotted and stored in the database.

[0118] Meanwhile, the new construction or update operation will trigger all associated building units to start their own baseline spatial graphic queue update and data calibration processes.

[0119] It should be noted that when a building unit is newly constructed or updated, the core spatial graphic data of the newly constructed or data-updated building unit is compared with the baseline spatial graphic queue of the target building unit at different levels in terms of spatial geometry. By calculating the spatial position deviation and comparing it with a preset tolerance threshold, geometric correction is automatically performed or a warning message that requires manual handling is generated when the deviation exceeds the threshold. After spatial calibration, a unique identification information of the building unit that has just completed spatial calibration is assigned or updated for the building unit, and the calibrated spatial graphic data and key attribute data are plotted and stored in the database. This operation simultaneously triggers all associated building units to start the update and data calibration processes of the baseline spatial graphic queue, ensuring the coordination and consistency of associated building groups in terms of spatial data and attribute data. This dynamic code assignment and plotting mechanism realizes the real-time linkage update of cross-department building data, effectively solving the problem of data inconsistency caused by the change of a single building.

[0120] It should be noted that in the traditional mode, the data update process only proceeds linearly within a department. Through multi-dimensional correlation analysis, comprehensive network construction, and dynamic triggering mechanism, the present invention reconstructs this isolated linear process into an intelligent, networked and linked automated workflow. When the data of any management node changes, other affected management nodes (departments) are automatically identified, and the corresponding data update sub-process is triggered, thus achieving business closure and process reengineering at the administrative management level, greatly improving the efficiency and accuracy of administrative collaboration.

[0121] It should be noted that the process of performing step-by-step spatial geometric comparison and tolerance judgment between the core spatial graphic data of newly built or updated building units and the corresponding benchmark spatial graphic queue is as follows: First, the core spatial graphic data of the newly built building unit X is obtained, including the outline coordinates and elevation information of the newly built building unit X. This data is then compared with the core spatial graphic data of the first-ranked associated building unit N1 in the benchmark spatial graphic queue of the target building unit M, and the minimum edge distance between the outline of the newly built building unit X and the outline of the associated building unit N1 is calculated. If this distance is less than or equal to the preset tolerance threshold of 1.5 meters, it is determined that the tolerance requirement is met, and the comparison continues with the next associated building unit N2 in the queue; if the distance is greater than the tolerance threshold, geometric correction is automatically performed to adjust the outline coordinates of the newly built building unit X so that the spatial relationship between the outline of the newly built building unit X and the outline of the associated building unit N1 meets the tolerance requirement. After completing this round of comparison, the comparison with N2 is performed according to the same rules. This process is performed step-by-step according to the ranking of the benchmark spatial graphic queue until all associated building units in the queue have completed the comparison or triggered the correction.

[0122] For example, when constructing a new building unit X, the core spatial graphic data of X is acquired and geometrically compared step-by-step with the baseline spatial graphic queue of the target building unit M. Calculations reveal a 1.8-meter positional deviation between the outline of the new building unit X and the baseline graphics of queues N1 and N2, exceeding the 1.5-meter tolerance threshold. Geometric correction is automatically performed to calibrate X's graphic data to match the baseline queue. After calibration, X is assigned a unique identifier BldX-2024-001, and the calibrated graphic data, along with its purpose and key attribute data of the management department, are mapped and stored in the database. Simultaneously, this construction operation automatically triggers the baseline spatial graphic queue update and data verification process for associated building units N1, N2, N3, and N4.

[0123] It should be noted that the preset time difference threshold is usually set to 7 days, and adjusted within the range of 3 to 30 days depending on the frequency of regional business updates. The preset tolerance threshold ε is set according to the measurement accuracy and building density, and the value range is usually 0.5 meters to 3.0 meters. For example, 1.0 meter is used in high-precision urban areas, and 2.5 meters is used in suburbs or for large buildings. The preset threshold for comprehensive correlation value screening is usually taken as the value corresponding to the top 15% of the correlation values ​​of all individual buildings in descending order, or dynamically adjusted to the average value of all edge weights according to the network density.

[0124] It should be noted that the "hierarchical spatial geometric comparison and tolerance judgment" is implemented by calling standard algorithm libraries in the Geographic Information System (GIS) field. Specifically, the minimum edge distance between the outline of the new building and the reference graphic is calculated using functions such as Distance() from the GEOS library; the spatial relationships between graphics (such as overlap and intersection) are judged using functions such as Intersects() and Within(). The "automatic geometric correction" strategy includes, but is not limited to: automatically translating or rotating the graphic of the new building to ensure that its spatial relationship with the reference graphic meets the tolerance requirements; or using algorithms such as Iterative Closest Point (ICP) for high-precision automatic registration.

[0125] When geometric deviations exceed the tolerance threshold and automatic correction fails, or business rule validation fails, a structured early warning message is generated. This message includes the early warning number, triggering building ID, early warning type (e.g., "Graphic Deviation Exceeds Limit," "Rule Conflict"), early warning level, detailed description, handling suggestions, and timestamp. The early warning message is pushed in real-time to the relevant management departments' business systems in JSON format via a message middleware (e.g., Apache Kafka or RabbitMQ). The relevant departments' front-end interfaces display the early warning as pop-ups, highlighted lists, or message center notifications, prompting staff to conduct manual review and handling. After handling, personnel update the status in the system, forming a closed management loop.

[0126] The "map placement and database entry" operation refers to: writing the calibrated spatial graphic data into a specific data table in a spatial database (such as PostGIS); writing the unique identifier and key attribute data into the business table in a relational database (such as MySQL); and publishing the updated graphic data as a map service (such as WMS / WFS) through a spatial data engine (such as GeoServer) to ensure that various electronic maps can visualize the latest building information in real time, thus completing the final step of "map placement".

[0127] Example 2

[0128] Based on Example 1, a code-based mapping management system for individual buildings, such as... Figure 2 As shown, it includes:

[0129] The building unit data acquisition and preprocessing module obtains the unique identification information, core space graphic data and key attribute data of building units in adjacent spaces. It determines the center and related building units based on the number of management department identifications, and forms a time-series data record set by grouping by management department and sorting by identification time.

[0130] The multi-dimensional correlation analysis module calculates the time difference of the identified time in the data record set to obtain the time-series correlation results, calculates the spatial topology relationship based on the core spatial graphic data to obtain the spatial correlation results, and matches the business rule base to trigger data changes or warnings to obtain the business correlation results.

[0131] The integrated network construction module combines temporal, spatial, and business relevance, calculates the comprehensive relevance value with weights, constructs a symmetric matrix and a weighted undirected network graph, and filters a set of high relevance with the target building as the core.

[0132] The dynamic coding and calibration execution module constructs a benchmark spatial graphics queue based on a highly correlated set. It performs geometric comparison and tolerance judgment on the graphics data of newly created or updated units with the queue, performs correction or warning, completes coding and mapping, and triggers the correlation data update process.

[0133] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for managing the coding and mapping of individual buildings, characterized in that, Includes the following steps: S1: Obtain the unique identification information, core spatial graphic data, and key attribute data of the building unit, and determine the central building unit based on the unique identification information; extract the management department and identification time based on the key attribute data, and form a time-seriesd set of building unit data records based on the management department and the identification time; S2: Calculate the time difference of the identification time of adjacent data in the building unit data record set according to the building unit data record set, obtain the calculation result, and obtain the association relationship sorting result according to the calculation result; The core spatial graphic data of the building is obtained from the data record set of the building unit, and the spatial relationship results are obtained from the core spatial graphic data. S3: Match the set of individual building data records with a preset business rule base, and obtain the business association results based on the business rule base; Based on the ranking results of the association relationships, the spatial association results, and the business association results, a high-association set centered on individual buildings is obtained, including: comprehensively and quantitatively evaluating the degree of association between any two buildings in the set based on the ranking results of the association relationships, the spatial association results, and the business association results; the comprehensive quantitative evaluation assigns weight coefficients to temporal association, spatial topological association, and business rule association respectively, and calculates the final comprehensive association value through weighted summation; constructs a symmetric matrix based on the comprehensive association value, which reflects the association strength between every two buildings; converts the symmetric matrix into a weighted undirected network graph, where nodes represent individual buildings, and edges between nodes represent the comprehensive association value between corresponding two buildings; calculates the comprehensive association value between each individual building node and all other nodes in the network, and sorts them in descending order to obtain a descending order ranking result; based on the descending order ranking result, selects all associated individual buildings with a comprehensive association value exceeding a preset threshold, forming a high-association set centered on that individual building node. S4: Based on the high correlation set of the target building unit, construct a benchmark spatial graphic queue for the target building unit, including: extracting the core spatial graphic data of all related building units in the set based on the high correlation set of the target building unit; sorting all core spatial graphic data in descending order using the comprehensive correlation value between each related building unit and the target building unit as the sorting key to construct the benchmark spatial graphic queue for the target building unit; the benchmark spatial graphic queue serves as the benchmark for subsequent spatial range calibration; and determining the management method for coding and mapping based on the benchmark spatial graphic queue, including: if there is a newly built building unit or an existing building unit undergoes data updates, performing a step-by-step spatial geometric comparison and tolerance judgment between the core spatial graphic data of the newly built or updated building unit and the corresponding benchmark spatial graphic queue; if a deviation is found, performing automatic geometric correction or generating a warning message requiring manual handling; after calibration, assigning or updating a unique identifier information to the building unit, and mapping and storing the calibrated core spatial graphic data and all key attribute data in the database; simultaneously, this new or updated operation will trigger all related building units to start their own benchmark spatial graphic queue update and data calibration process.

2. The method for code assignment and mapping management based on individual buildings according to claim 1, characterized in that, The process of obtaining the unique identifier information, core spatial graphic data, and key attribute data of individual buildings, and determining the central building based on the unique identifier information, includes: Within a spatially adjacent area, obtain a preset number of individual buildings; For each of the aforementioned building units, obtain unique identification information, core spatial graphic data, and key attribute data; The number of management department identifiers is extracted based on the unique identifier information. The building unit with the most management department identifiers is taken as the central building unit, and the remaining building units are taken as associated building units.

3. The method for code assignment and mapping management based on individual buildings according to claim 1, characterized in that, The step of extracting the management department and identification time based on the key attribute data, and forming a time-series set of building unit data records based on the management department and identification time, includes: All the individual buildings are grouped according to the name of the management department, thereby constructing a set of individual buildings corresponding to each unique management department name; All the individual buildings are grouped using the management department as the key, and a mapping relationship from the management department to the set of individual buildings is established; For each set of building units corresponding to each management department, using the identification time of the central building unit as the sorting benchmark, all the building units in the set of building units are sorted according to the identification time to form a time-seriesd set of building unit data records; The term "collection of building units" refers to a group of building units that share the same management department attribute. The mapping relationship refers to the corresponding association between the management department and the collection of individual buildings; The set of data records for individual buildings refers to an ordered set containing all attribute data of individual buildings, arranged in chronological order.

4. The method for code assignment and mapping management based on individual buildings according to claim 1, characterized in that, The step of calculating the time difference of the identified time of adjacent data in the building unit data record set based on the building unit data record set, obtaining the calculation result, and obtaining the association relationship sorting result based on the calculation result includes: Based on the calculation results, extract individual building data records that are less than or equal to a preset time difference threshold, and re-sort the individual building data records according to the identified time to obtain the association relationship sorting results; The association ranking result refers to an ordered list of potentially related building unit data records, filtered according to the time difference of the identifier and arranged in chronological order of the identifier time.

5. The method for code-based mapping management of individual buildings according to claim 1, characterized in that, The step of obtaining the core spatial graphic data of the building unit based on the building unit data record set, and obtaining spatial relationship results based on the core spatial graphic data, includes: Calculate the spatial topological relationships between the individual buildings based on the core spatial graphic data, and obtain the spatial relationship calculation results; The spatial topology relationship refers to the adjacent, connected, and contained spatial positional relationships between individual buildings based on spatial graphic data, specifically obtained by calculating the spatial geometric relationships between the core spatial graphic data of the individual buildings; Based on the spatial relationship calculation results, extract individual building data records with preset spatial relationship types to obtain spatial association results; The spatial association result refers to a set of spatially dependent building unit data records formed after filtering based on spatial topological relationships.

6. The method for code-based mapping management of individual buildings according to claim 1, characterized in that, The step of matching the building unit data record set with a preset business rule base and obtaining business relationship results based on the business rule base includes: The data record set of individual buildings is matched with the preset business rule base according to the business rule base. Based on the matching result, the change operation of the attribute data of the individual buildings is executed or the early warning information is generated. The records of the change operation and the early warning information together constitute the business association result.

7. A code-based mapping management system for individual buildings, used to implement the code-based mapping management method for individual buildings as described in any one of claims 1-6, characterized in that, include: The building unit data acquisition and preprocessing module obtains the unique identification information, core space graphic data and key attribute data of building units in adjacent spaces. It determines the center and related building units based on the number of management department identifications, and forms a time-series data record set by grouping by management department and sorting by identification time. The multi-dimensional correlation analysis module calculates the time difference of the identified time in the data record set to obtain the time-series correlation result, calculates the spatial topology relationship based on the core spatial graphic data to obtain the spatial correlation result, and matches the business rule base to trigger data changes or warnings to obtain the business correlation result. The integrated network construction module combines temporal, spatial, and business relevance, calculates the comprehensive relevance value with weights, constructs a symmetric matrix and a weighted undirected network graph, and filters a set of high relevance with the target building as the core. The dynamic coding and calibration execution module constructs a benchmark spatial graphics queue based on a highly correlated set. It performs geometric comparison and tolerance judgment on the graphics data of newly created or updated units with the queue, performs correction or warning, completes coding and mapping, and triggers the correlation data update process.

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