Data twin collaborative management system of urban renewal intelligent construction management platform

Abstract

The application discloses a data twin cooperative management system of an urban renewal intelligent construction management platform, and relates to the technical field of intelligent construction and construction management. The system accesses multi-source data in an urban renewal project, analyzes and converts the data from different business systems, generates unified data objects, and builds data twin nodes on the basis. The system builds a construction stage data twin chain according to the construction stage sequence, and builds a data cooperative topology model according to the dependency relationship between data. When a data node is updated, the system detects conflicts of related nodes according to the cooperative topology model, and triggers a data synchronization operation when a data conflict is detected. The application realizes the cooperative management between data in the whole process of the urban renewal project through the data twin chain and the data cooperative topology model, thereby improving data consistency and data cooperative efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent construction and construction management technology, and in particular to a data twin collaborative management system for an intelligent construction management platform for urban renewal. Background Technology

[0002] With the continuous advancement of urbanization in my country, urban renewal has become an important strategic direction for high-quality urban development. National and local governments have successively introduced relevant policies on urban renewal, promoting the large-scale implementation of projects such as the renovation of existing buildings, the improvement of old residential communities, and the upgrading of urban infrastructure. Compared with new construction projects, urban renewal projects have unique characteristics such as complex construction environments, numerous constraints, and a lack of historical data, which places higher demands on project data management.

[0003] Furthermore, with the continuous expansion of urban renewal projects, these projects are increasingly characterized by long construction periods, numerous participating entities, and complex data sources. During the implementation of urban renewal projects, multiple participating entities are often involved, including planning and design units, construction units, monitoring units, and equipment management units. Each entity generates a large amount of data at different stages of the project. For example, the planning stage generates planning and design data, the design stage generates BIM model data, the construction stage generates construction task data and equipment operation data, and the monitoring stage generates structural monitoring data and environmental monitoring data. This data is typically stored in different business systems, with significant differences in data structure and standards, making unified management and collaborative utilization of the data difficult.

[0004] In terms of data modeling, digital twin technology has received widespread attention in the field of engineering construction in recent years. Digital twins establish digital mapping models of physical entities, enabling real-time data interaction and state synchronization between the physical and digital worlds. However, existing engineering digital twin applications mostly focus on the state monitoring of single devices or components, and have not yet been effectively extended to the collaborative management of multi-stage data throughout the entire process of urban renewal projects. Existing digital twin systems typically treat twin nodes as independent data mapping units, lacking systematic modeling of data inheritance relationships between twin nodes at different construction stages, and failing to structurally express cross-node data dependencies through topological structures. This limits the depth of application of digital twin technology in the full-process data management of urban renewal projects.

[0005] In existing technologies, data management for engineering construction projects typically employs centralized management via data platforms or data integration systems. These systems generally aggregate data from different business systems into a unified database through data interfaces, enabling data utilization through data querying or sharing. However, these data platforms usually only achieve centralized data storage and simple data exchange, failing to establish inheritance relationships between data from different construction stages or dependencies between different data sets. Therefore, when data at a certain stage is updated, the system struggles to identify other data objects associated with that data, easily leading to inconsistencies in data versions between different systems, thus affecting the accuracy and reliability of engineering data.

[0006] Furthermore, complex relationships often exist between data at different stages during the implementation of urban renewal projects. For example, construction task data in the construction phase typically relies on BIM model data from the design phase, while construction monitoring data depends on structural information from the construction phase. When design phase data is adjusted, without an effective data collaboration and management mechanism, relevant construction and monitoring data may still reference older versions of the data, leading to inconsistencies between construction and design information, and potentially even construction safety risks. In existing technologies, data updates usually require manual notification of relevant systems for data synchronization, or updates via periodic data synchronization. This approach is not only inefficient but also struggles to guarantee the timeliness and consistency of data updates.

[0007] In terms of multi-party collaborative management, the business systems used by various stakeholders in urban renewal projects are often independently developed by different vendors, lacking a unified data interaction standard. The data generated by each stakeholder exhibits significant differences in collection frequency, data granularity, and field definitions, leading to significant challenges in cross-system data integration. Existing integration solutions typically employ point-to-point data interfaces or middleware for system connection, resulting in high interface maintenance costs and the need to redevelop adaptation logic when new data sources are integrated, leading to poor system scalability. Furthermore, the lack of a unified data version management mechanism makes it difficult to keep data versions synchronized between different stakeholders, a problem particularly pronounced in scenarios involving multiple parties working in parallel.

[0008] Meanwhile, existing systems often lack effective data conflict detection mechanisms during engineering data updates. When multiple systems update the same data object, it becomes difficult for the system to identify data version conflicts in a timely manner, easily leading to inconsistencies in the data content stored in different systems, thus affecting the reliability of engineering data. Especially in urban renewal projects, due to the complexity of construction site conditions and frequent data updates, the lack of a mechanism that can automatically identify data dependencies and coordinate data updates often necessitates manual data verification and adjustment, thereby increasing project management costs.

[0009] In summary, existing technologies have the following shortcomings in data management for urban renewal projects: First, they lack a unified modeling mechanism for multi-source heterogeneous data, making it difficult to organize data from different business systems into data objects with a unified structure and consistent semantics; second, they lack explicit modeling of the inheritance relationships between data at different construction stages, resulting in a lack of structured support for data flow between stages; third, they lack a topological expression of the dependencies between data nodes, making it impossible to automatically locate affected related data when data is updated; and fourth, they lack an effective data conflict detection and automatic synchronization mechanism, making it difficult to ensure data consistency in multi-party collaborative scenarios. Summary of the Invention

[0010] Currently, there is an urgent need for a system capable of collaborative management of urban renewal projects to solve the aforementioned technical problems. The purpose of this invention is to achieve collaborative management of data at different stages within an intelligent construction management platform for urban renewal, automatically identifying dependencies between data during updates and promptly synchronizing the data, thereby improving data consistency and collaborative efficiency in urban renewal projects.

[0011] To achieve the above objectives, this invention proposes a data twin collaborative management system for an intelligent construction management platform for urban renewal, comprising: a data acquisition module for collecting multi-source data from urban renewal projects;

[0012] The data stage identification module is used to identify the construction stage to which the data belongs;

[0013] A data twin node generation module is used to generate corresponding construction stage data twin nodes based on the data.

[0014] The data twin chain construction module is used to construct the data twin chain of the construction stages according to the construction stage sequence, and to establish the data inheritance relationship between the data twin nodes of each construction stage.

[0015] The data collaboration topology construction module is used to construct a data collaboration topology model based on the data dependencies between the data twin nodes in the construction phase.

[0016] The data collaboration control module is used to realize data sharing and data updating according to the data collaboration topology model;

[0017] The data conflict detection module is used to detect data conflicts during the data update process based on the data collaboration topology model.

[0018] The data traceability module is used to achieve full-process data traceability of the project based on the data twin chain of the construction stage.

[0019] The data acquisition module is used to access multiple data sources through the system interface and import data from different data sources into the platform. These data sources include the planning management system, BIM modeling system, construction monitoring system, equipment operation monitoring system, and on-site sensing equipment system.

[0020] The data acquisition module performs a unified data structure transformation on data from different data sources when importing data. This data structure transformation includes:

[0021] Parse the raw data to extract data fields;

[0022] Map data fields from different data sources to a unified set of data fields according to preset data field mapping rules;

[0023] The data structure is reconstructed based on the unified data field set to generate a unified data object;

[0024] The unified data object is then stored in the platform's data warehouse to achieve unified management of data from different data sources.

[0025] The data twin node generation module is used to generate a unique data identifier for each piece of data when the data enters the system, and to generate a corresponding data twin node based on the data identifier.

[0026] The data identifier includes the data source identifier, construction stage identifier, timestamp information, and data version number.

[0027] The data twin chain building module is used to connect multiple data twin nodes in the order of construction stages to form a construction stage data twin chain.

[0028] The data twin chain construction module is used to establish a data inheritance relationship between data twin nodes in adjacent construction stages, so that data twin nodes in subsequent stages inherit the basic data attributes in the nodes of the previous stage.

[0029] The data collaboration topology construction module is used to construct a data collaboration topology graph, in which data twin nodes serve as topology nodes and data dependencies between nodes serve as topology edges.

[0030] The data conflict detection module is used to detect the data version difference between the updated data twin node and its dependent nodes when data is updated, and to determine whether there is a data conflict based on the data version difference.

[0031] When a data conflict is detected, the data collaboration control module triggers a data synchronization operation on the data twin nodes with dependencies based on the data collaboration topology model.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] This application proposes a data twin collaborative management system for an intelligent construction management platform for urban renewal. By uniformly accessing and modeling data from different business systems, the system transforms raw data into unified data objects and generates corresponding data twin nodes. This organizes multi-source data from urban renewal projects into a computable data node structure. Furthermore, the system constructs a data twin chain for each construction phase, connecting data nodes from different phases according to their sequence. This establishes a clear data inheritance relationship between data from different phases, thereby achieving unified organization of data throughout the entire urban renewal project process.

[0034] Compared with existing technologies, the data twin collaborative management system of the urban renewal intelligent construction management platform provided in this application generates data twin nodes and constructs a data twin chain for the construction phase. This enables the system to organize data generated at different stages according to the construction phase sequence, thereby quickly locating the phase data associated with the data when the data is updated, and improving the structured nature of data management throughout the entire process of urban renewal projects.

[0035] Meanwhile, this application establishes data dependencies between data twin nodes by constructing a data collaboration topology model, enabling the system to identify the collaboration relationships between different data nodes. When a data node is updated, the system can automatically locate the set of nodes that have dependencies on that node based on the data collaboration topology model, thereby performing data synchronization processing on the relevant nodes, avoiding data version inconsistencies between different systems, and improving the system's data collaboration capabilities.

[0036] Furthermore, this application establishes a data conflict detection mechanism to detect the data version information between the updated node and its dependent nodes during the data update process, and determines whether a data conflict exists by calculating the collaborative correlation between computing nodes. When a data conflict is detected, the system can automatically trigger a data synchronization operation, thereby ensuring the consistency of data at different stages and improving the system's data reliability.

[0037] Furthermore, this application organizes data in urban renewal projects into a combined structure of data twin chains and data collaboration topology diagrams for each construction phase. This enables the system to not only manage data inheritance between vertical construction phases but also to collaboratively manage horizontal data dependencies, thus forming a data collaboration management mechanism covering the entire lifecycle of urban renewal projects. Through this approach, the system can achieve automatic collaboration and dynamic updates of multi-source data without manual intervention, thereby improving the data management efficiency and system stability of the intelligent construction management platform for urban renewal.

[0038] Therefore, this application constructs a full-process data collaborative management mechanism based on data twin nodes, construction phase data twin chains, and data collaborative topology models, enabling multi-source data in urban renewal projects to form a unified data organization structure. This not only improves the consistency and traceability of engineering data but also enhances the data collaboration capabilities and data management level of the urban renewal intelligent construction management platform. Attached Figure Description

[0039] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings:

[0040] Figure 1 Architecture diagram of a data twin collaborative management system for an intelligent construction management platform for urban renewal. Detailed Implementation

[0041] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0042] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0043] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0044] Example 1

[0045] This embodiment provides a data twin collaborative management system for an intelligent construction management platform for urban renewal, applied to the full lifecycle data management scenario of comprehensive urban renewal projects. The urban renewal project involves data flow and collaboration across multiple construction phases, specifically including the demolition and clearing phase, foundation construction phase, main structure construction phase, electromechanical installation phase, and final acceptance phase—a total of five construction phases. The data types generated at each phase are diverse and from scattered sources, and there are complex data dependencies and inheritance relationships between phases, placing high demands on data consistency, traceability, and cross-phase collaborative management.

[0046] To achieve full-process data collaborative management of the aforementioned urban renewal projects, the system described in this embodiment includes eight functional modules: a data acquisition module, a data stage identification module, a data twin node generation module, a data twin chain construction module, a data collaborative topology construction module, a data collaborative control module, a data conflict detection module, and a data traceability module.

[0047] The data acquisition module is responsible for connecting to multiple data sources in urban renewal projects. Through system interfaces, it introduces multi-source data from planning management systems, BIM modeling systems, construction monitoring systems, equipment operation monitoring systems, and on-site sensing equipment systems into the platform. It also performs unified data structure transformation on the raw data from each data source, generates standardized unified data objects, and stores them in the platform's data warehouse.

[0048] The data stage identification module determines the construction stage affiliation of unified data objects in the platform's data warehouse, identifies and marks each data item to its corresponding construction stage, and provides a stage information basis for the subsequent generation of data twin nodes.

[0049] When data enters the system, the data twin node generation module generates a unique data identifier for each piece of data, which includes a data source identifier, a construction stage identifier, a timestamp, and a data version number. Based on the unique data identifier, a corresponding data twin node is generated, and each data twin node serves as a digital mapping unit for that piece of data in the system.

[0050] The data twin chain construction module connects the data twin nodes of each stage in sequence according to the construction order of demolition and cleaning stage, foundation construction stage, main structure construction stage, electromechanical installation stage, and final acceptance stage, to form a construction stage data twin chain that runs through the entire project process. It also establishes a data inheritance relationship between the data twin nodes of adjacent construction stages, so that the data twin nodes of subsequent stages can inherit the basic data attributes of the nodes of the previous stage.

[0051] The data collaboration topology construction module uses the data twin nodes of each construction stage as topology nodes and the data dependencies between nodes as topology edges to construct a data collaboration topology graph, forming a complete data collaboration topology model, which provides topological support for subsequent data sharing, data updates and data conflict detection.

[0052] The data collaboration control module, based on the data collaboration topology model, enables collaborative control of data sharing and data updates among the data twin nodes. When a data conflict is detected, the data collaboration control module further triggers data synchronization operations on the data twin nodes with dependencies based on the dependencies in the topology model to ensure data consistency.

[0053] During the data update process, the data conflict detection module continuously detects the data version differences between the updated data twin node and its dependent nodes, and determines whether there is a data conflict based on the version difference results, and feeds back the conflict detection results to the data collaboration control module.

[0054] The data traceability module relies on the data twin chain of the construction phase to trace the generation, transmission, inheritance and update records of any data item in the entire project process, realizing the complete data link traceability capability from the completion and acceptance stage back to the demolition and cleanup stage.

[0055] The above eight functional modules work together to form the data twin collaborative management system of the urban renewal intelligent construction management platform described in this embodiment, realizing the closed-loop management of the entire chain of urban renewal projects from data collection, stage identification, twin node generation, twin chain construction, topology collaboration to conflict detection and data traceability.

[0056] In this embodiment, the data acquisition module accesses five types of data sources through the system interface: the planning management system, the BIM modeling system, the construction monitoring system, the equipment operation monitoring system, and the on-site sensing equipment system. It introduces raw data from different data sources into the platform, performs unified data structure transformation on the raw data, generates standardized unified data objects, and then stores them in the platform's data warehouse.

[0057] The planning management system provides urban renewal projects with planning approval data, land boundary data, and construction indicator data. This data is transmitted to the platform in structured message format via the planning management system interface. The BIM modeling system provides building information model data, including component geometry information, material property information, and spatial topology data. This data is transmitted to the platform in IFC standard format files or via a BIM platform-specific interface. The construction monitoring system provides construction progress data, quality inspection data, and safety monitoring data. This data is transmitted to the platform in real-time in structured data tables or via interface messages. The equipment operation monitoring system provides construction equipment operating status data, equipment location data, and equipment operating parameter data. This data is continuously uploaded to the platform in the form of equipment monitoring protocol messages. The on-site sensing equipment system, through temperature sensors, displacement sensors, stress sensors, and video acquisition equipment deployed at the construction site, provides environmental and structural monitoring data for the construction site. This data is transmitted to the platform in real-time in the form of sensor protocol data streams.

[0058] Because the data formats, data structures, and field definitions of the above five types of data sources differ, the data acquisition module performs three steps in sequence—data format parsing, data field mapping, and data structure reconstruction—on the raw data from different data sources when importing data, to generate a unified data object.

[0059] In the data format parsing step, the data acquisition module calls the corresponding format parser for different data source formats. For structured message data from the planning management system and construction monitoring system, the structured message parser is called to extract data fields and field values; for IFC format files from the BIM modeling system, the IFC format parser is called to extract data fields such as component identifiers, geometric attributes, and relationships; for equipment monitoring protocol messages from the equipment operation monitoring system, the protocol parser is called to extract data fields such as equipment number, operating parameters, and timestamps; for sensor protocol data streams from the field sensing equipment system, the sensor data parser is called to extract data fields such as sensor number, collected values, and collection time. Through the above parsing steps, the raw data from each data source is converted into a set of field-level data that can be processed subsequently.

[0060] In the data field mapping step, the data acquisition module maps data fields from different data sources to a unified data field set predefined by the platform according to preset data field mapping rules. The unified data field set includes a data source identifier field, a construction phase association field, a data type field, a data content field, a data acquisition time field, and a data quality identifier field. Taking the component identifier field of the BIM modeling system as an example, the field mapping rule maps it to a data content field in the unified data field set, and adds the data source identifier field value to the BIM modeling system identifier and the data type field value to the component information type identifier. Similarly, taking the sensor acquisition value field of the on-site sensing equipment system as an example, the field mapping rule maps it to a data content field in the unified data field set, and adds the data source identifier field value to the on-site sensing equipment system identifier and the data type field value to the sensing monitoring data type identifier. For non-mandatory fields with missing values ​​in the original data, the field mapping rule assigns them preset default values; for fields without corresponding mapping relationships in the original data, the field mapping rule records them in the data quality identifier field for subsequent data quality review.

[0061] In the data structure reconstruction step, the data acquisition module reconstructs the structure of each data entry based on the unified data field set after field mapping, generating a unified data object containing all fields of the aforementioned unified data field set. This unified data object is stored using a standardized data structure, ensuring that data from different data sources has a consistent data structure after entering the platform's data warehouse, thereby achieving unified management and subsequent processing of data from different data sources.

[0062] After completing the unified data structure conversion, the data acquisition module writes the generated unified data objects into the platform data warehouse. The platform data warehouse partitions and stores the unified data objects according to data source type and construction stage to support efficient querying and retrieval by the data stage identification module. For real-time data streams from on-site sensing equipment systems and equipment operation monitoring systems, the data acquisition module continuously writes the unified data objects into the platform data warehouse using a streaming write method. For batch data from planning management systems, BIM modeling systems, and construction monitoring systems, the data acquisition module periodically writes the unified data objects into the platform data warehouse using a batch write method, thus balancing the acquisition needs of both real-time and batch data.

[0063] In this embodiment, when a unified data object enters the system, the data twin node generation module generates a unique data identifier for each unified data object and generates a corresponding data twin node based on the unique data identifier.

[0064] The unique data identifier consists of four parts: data source identifier, construction stage identifier, timestamp information, and data version number.

[0065] The data source identifier is used to mark the source system of the data. In this embodiment, the planning management system, BIM modeling system, construction monitoring system, equipment operation monitoring system and field sensing equipment system are respectively associated with preset data source identifiers, such as PS, BIM, CM, EM and SS, to distinguish data generated by different data sources.

[0066] The construction stage identifier is used to mark the construction stage to which the data belongs. In this embodiment, the demolition and cleaning stage, the foundation construction stage, the main structure construction stage, the electromechanical installation stage, and the completion and acceptance stage are respectively corresponding to preset construction stage identifiers, such as P01, P02, P03, P04, and P05 in sequence. The construction stage identifier is determined by the identification result of the data stage identification module and then transmitted to the data twin node generation module.

[0067] The timestamp information records the precise time when the data was collected and entered into the system, expressed in year, month, day, hour, minute, second, and millisecond format. It is used to distinguish data entries in the time dimension within the same data source and the same construction phase.

[0068] The data version number is used to mark the version status of the data in the system. When the data first enters the system, the version number is recorded as V1. Each time the data is updated, the version number is incremented sequentially, recorded as V2, V3, and so on, to support the subsequent data conflict detection module in judging the differences in data versions.

[0069] The above four parts are combined in the order of data source identifier, construction stage identifier, timestamp information, and data version number to form a unique data identifier for the data. Taking a component attribute data from the BIM modeling system, belonging to the main structure construction stage, and entering the system for the first time at a specific time as an example, its unique data identifier can be represented as BIM-P03-20240815143022001-V1, thus uniquely identifying the data within the entire system.

[0070] The data twin node generation module generates a corresponding data twin node for each unified data object based on its unique data identifier. The data twin node, as a digital mapping unit for that data in the system, includes the following attributes: node identifier attribute, storing the unique data identifier corresponding to the node; node data attribute, storing the complete data content of the unified data object corresponding to the node; node status attribute, recording the current data version status and data validity identifier of the node; and node association attribute, used to store the association relationship between the node and other nodes in the data twin chain and data collaborative topology model. The node association attribute is updated and written after the data twin chain construction module and data collaborative topology construction module complete their construction.

[0071] After each data twin node is generated, the data twin node generation module registers it to the system node registry so that it can be called by the data twin chain construction module and the data collaboration topology construction module.

[0072] In this embodiment, the data twin chain construction module connects the data twin nodes of each stage in sequence according to the construction phase sequence of the urban renewal project, constructs the construction phase data twin chain, and establishes data inheritance relationships between the data twin nodes of adjacent construction phases.

[0073] The data twin chain construction module reads all registered data twin nodes from the system node registry and categorizes the nodes into stages according to the construction stage identifier in the unique data identifier of each node: demolition and cleaning stage, foundation construction stage, main structure construction stage, electromechanical installation stage, and final acceptance stage.

[0074] After completing the stage classification, the data twin chain construction module establishes directed connections between data twin nodes of adjacent construction stages according to the construction stage sequence. That is, the data twin node of the previous construction stage is used as the source node and the data twin node of the next construction stage is used as the target node. A directed link is established from the source node to the target node, thereby connecting all data twin nodes in the order of construction stages to form a construction stage data twin chain that runs through the demolition and cleaning stage to the final acceptance stage.

[0075] When multiple data twin nodes exist within the same construction phase, the data twin chain construction module establishes an ordered connection between the data twin nodes within the same phase based on the timestamp information in the unique data identifier of the node and the order in which the data enters the system, forming a node sequence within the phase. Then, the first node of the node sequence within the phase is connected to the last node of the node sequence of the previous construction phase, and the last node of the node sequence within the phase is connected to the first node of the node sequence of the next construction phase, thereby completely constructing the data twin chain of the construction phase.

[0076] The data twin chain construction module establishes a data inheritance relationship between data twin nodes in adjacent construction stages while constructing the data twin chain during the construction phase, so that data twin nodes in subsequent stages can inherit the basic data attributes in the nodes of the previous stage.

[0077] The process of establishing the data inheritance relationship is as follows: The data twin chain construction module presets a set of basic data attributes, which includes project number attributes, component identification attributes, spatial location attributes, and planning indicator attributes. These attributes are the core basic attributes that run through all construction stages of the urban renewal project. After the directed link between adjacent construction stage nodes is established, the data twin chain construction module writes the attribute values ​​contained in the set of basic data attributes of the data twin node in the previous stage into the corresponding inherited attribute field in the node data attributes of the data twin node in the next stage, thereby completing the cross-stage inheritance of basic data attributes.

[0078] Taking data inheritance between the foundation construction stage and the main structure construction stage as an example, the project number attribute, foundation component identification attribute, and spatial location attribute recorded in the data twin node of the foundation construction stage will be inherited as basic data attributes to the corresponding data twin node of the main structure construction stage. This ensures that the data twin node of the main structure construction stage already has the basic data background from the previous stage when it is generated, eliminating the need for repeated collection, thereby reducing data redundancy and ensuring data consistency and continuity.

[0079] Subsequent stages follow the same pattern: the data twin nodes of the electromechanical installation stage inherit the basic data attributes of the main structure construction stage, and the data twin nodes of the completion and acceptance stage inherit the basic data attributes of the electromechanical installation stage, ultimately forming a complete data inheritance chain covering the five construction stages of the entire urban renewal project.

[0080] To accurately describe the data dependencies between data twin nodes at each construction stage, this embodiment defines the data collaboration topology model as a directed weighted graph, denoted as: ;

[0081] in: Let be the set of topological nodes, representing the set of all data twin nodes in the system, denoted as: ;

[0082] in Indicates the first A data twin node, This represents the total number of data twin nodes in the system. Each node... Carrying attribute tuples: ;

[0083] in This serves as the data source identifier for the node. This serves as a marker for the construction phase of a node. For the timestamp information of the node, This is the current data version number of the node.

[0084] Taking the BIM component attribute data node (node ​​A) during the main structure construction phase in this embodiment as an example, its attribute tuples are specifically represented as follows: ;

[0085] Data source identifier Construction phase markings timestamp 20240901090000001, Initial data version number .

[0086] E is the set of topological edges, representing the set of directed edges that establish data dependencies between nodes, denoted as:

[0087] ;

[0088] Among them, the directed edges From node Pointing to node , representing a node The data content depends on the nodes The data content. Based on the dependency type, It is further divided into three subsets: ;

[0089] in It is a subset of inherited topological edges. It is a subset of cooperative topological edges. It is a cross-stage collaborative topological edge subset.

[0090] Taking this embodiment as an example, there is a collaborative dependency between node A (BIM component attribute data node in the main structure construction phase) and node B (equipment positioning data node in the electromechanical installation phase), and an inheritance dependency between node B and node C (as-built drawing data node in the completion and acceptance phase). The corresponding topological edges are as follows: ;

[0091] Therefore, in this embodiment, the topological edge set includes the two directed edges mentioned above and other dependent edges, forming a complete topological edge set. .

[0092] The topological edge weight function is defined as follows: ;

[0093] For any topological edge Its weight Represents a node For nodes The strength of data dependency. Preset weight base values ​​are assigned based on dependency type, and the weight base values ​​for the three types of topological edges satisfy: ;

[0094] Taking this embodiment as an example, the preset base value of the inherited topology edge weight is... Basic values ​​of edge weights in collaborative topology Cross-stage collaborative topology edge weight base value The weight of the collaborative topological edge between node A and node B is: ;

[0095] The inherited topological edge weights between node B and node C are:

[0096] ;

[0097] The weights reflect the priority of data synchronization operations. When multiple topological edges trigger data synchronization simultaneously, the system prioritizes dependencies with higher weights. In this embodiment, if an update to node A simultaneously triggers synchronization with node B and other collaborating nodes, the system sorts the topological edges by weight value and executes the synchronization operation with the highest weight first.

[0098] As construction progresses, the system continuously connects new data twin nodes, and the topology model... Dynamic expansion. Let the first... The updated topology model is denoted as . Then the first The updated topology model is as follows:

[0099] ;

[0100] Taking this embodiment as an example, let the initial topology model of the system be... The data already includes node A and node B, as well as the cooperative topological edges between them. The node set and edge set are respectively:

[0101] ;

[0102] When the as-built drawing data node (node ​​C) from the completion and acceptance phase is newly registered to the system, the data collaboration topology construction module identifies that node C has an inherited dependency on node B, triggering the first incremental update. The updated topology model is as follows:

[0103] ;

[0104] Thus, the topology model continuously and dynamically expands with node registration events during the construction process, always maintaining consistency with the actual data state of the system.

[0105] This embodiment introduces a data version difference measurement function to quantitatively determine data conflicts. Let node... The current data version number is ,node Middle of nodes The version reference number is Define nodes Relative to node Version difference value for: ;

[0106] when At that time, node Reference version and node The current version is consistent, and there are no data conflicts; when At that time, node The referenced version is behind the node. The current version has data conflicts and requires data synchronization.

[0107] Taking this embodiment as an example, due to design changes, the data version number of node A is changed from... Updated to ,Right now The version reference number of node A in node B is still [previous version number]. ,Right now Substitute into the formula: ;

[0108] Calculation results If a data conflict is found between node B and node A, a data synchronization operation needs to be triggered.

[0109] Furthermore, this embodiment defines nodes. After a version update, the overall conflict level of all its dependent nodes for:

[0110] ;

[0111] in For nodes All directly dependent node sets, For the corresponding topological edge weights, This is an indicator function; it takes the value 1 when the condition is true, and 0 otherwise.

[0112] Taking this embodiment as an example, the set of nodes that directly depend on node A is: Node B has version differences. Corresponding topological edge weights Substitute into the formula: ;

[0113] Overall conflict level This indicates that the version update of node A has a high impact on the consistency of system data. The system assigns a high response priority to this update event and prioritizes triggering the data synchronization operation for node B.

[0114] In this embodiment, the process of the data collaboration control module triggering data synchronization operation is defined as the process of triggering data collaboration topology graph. The breadth-first traversal process. Let the node... When a version update occurs, define a set of synchronization operations. The initial state of the synchronization queue to be processed is as follows: ;

[0115] Taking this embodiment as an example, after node A is updated, its direct dependent node is node B and The initial synchronization queue is: ;

[0116] In the In the next synchronous iteration, from the queue Extract the highest priority node from the list. Perform data synchronization operation: ;

[0117] In the 0th iteration of this embodiment ( For example, from the queue Retrieve node B from the list, perform a synchronization operation, update the version reference number of node B to node A with the current version number of node A, and increment the version number of node B itself: ;

[0118] That is, the version reference number of node B is... Updated to Node B's own version number is determined by Increment to The 0th iteration of synchronization has been completed.

[0119] After completing the synchronization operation on node B, retrieve the set of nodes that directly depend on node B. Calculate the version differences between node C and node B:

[0120] ;

[0121] Node C has version differences; add it to the next round of synchronization queue: ;

[0122] In the first iteration ( In the queue Extract node C from the list and perform a synchronization operation: ;

[0123] That is, the version reference number of node C is... Updated to The version number of node C itself is determined by Increment to The first iteration of synchronization has been completed.

[0124] Retrieve the set of nodes that directly depend on node C. In this embodiment, node C has no further dependent nodes, therefore:

[0125] ;

[0126] The synchronization queue is empty, the iteration terminates, the synchronization operation is complete, and a total of 2 iterations are executed.

[0127] Due to the data collaborative topology model Since the graph is a directed acyclic graph (DAG), and there are no directed cycles in the topology, the above recursive data synchronization iteration process must terminate within a finite number of steps. Specifically, let the topology graph... The total number of nodes is The maximum number of iterations for synchronization operations does not exceed Second-rate: ;

[0128] Taking this embodiment as an example, the total number of nodes in the topology graph (Node A, Node B, Node C), the upper bound of the maximum number of iterations for synchronization operations is: ;

[0129] The actual number of iterations performed in this synchronization operation The convergence rate exactly reached the upper bound, consistent with the theoretical analysis, thus verifying the effectiveness of the convergence guarantee. After synchronization, the version status of nodes A, B, and C in the system is summarized as follows:

[0130] ;

[0131] ;

[0132] All version differences are reset to zero: ;

[0133] Data Collaborative Topology Model All nodes in the process have been restored to a state of data consistency, and the data collaborative update process caused by this design change has been completed in a closed loop.

[0134] In this embodiment, the data traceability module relies on the data twin chain of the construction phase to achieve complete traceability of any data item throughout the entire process of the urban renewal project.

[0135] When a platform user or other system module initiates a data tracing request, the data tracing module receives the request, which includes a unique data identifier or a data twin node identifier for the data to be traced. Based on the identifier, the data tracing module locates the target data twin node in the data twin chain during the construction phase and uses that node as the starting point for tracing.

[0136] The data tracing module supports both forward and reverse tracing directions. In forward tracing mode, the module starts from the target data twin node and traverses the data twin nodes of subsequent construction stages along the directed connection relationship of the construction stage data twin chain. It extracts the node data attributes and node status attributes of each node, forming a complete forward data flow record of the target data from its respective construction stage to the completion and acceptance stage. In reverse tracing mode, the module starts from the target data twin node and traverses the data twin nodes of previous construction stages along the reverse connection relationship of the construction stage data twin chain. It extracts the node data attributes and node status attributes of each node, forming a complete reverse data source record of the target data tracing back from its respective construction stage to the demolition and cleanup stage.

[0137] In addition to inter-stage tracing, the data tracing module also supports tracing the version history of a single data twin node. Based on the data version number in the unique data identifier of the target data twin node, the data tracing module retrieves historical version data records corresponding to each data update of that node from the platform's data warehouse, arranges them in version number order, and outputs them to form a complete version change history of that node from V1 to the current version. This includes the change time, change content, and reason for each version change. The reasons for triggering the change include two categories: proactive updates from the data source and data synchronization operations triggered by the data collaboration control module.

[0138] Taking the acceptance data node in the completion and acceptance stage as an example, when the acceptance personnel initiate a reverse traceability request for this node, the data traceability module traverses the data twin chain of the construction stage in reverse, and extracts the related data twin node data of the electromechanical installation stage, the main structure construction stage, the foundation construction stage, and the demolition and cleaning stage in sequence, forming a complete data source link for the acceptance data from site survey, foundation construction, structural construction to equipment installation, and outputs the data content and version status of key data nodes in each stage, realizing complete traceability of the entire project process data.

[0139] Example 2

[0140] Based on the system architecture described in Example 1, this embodiment takes a specific scenario of data version conflict caused by design changes between the main structure construction stage and the electromechanical installation stage as an example to further explain the operation process of the data conflict detection module and the data collaborative control module.

[0141] During the construction of a comprehensive urban renewal project, the main structure construction phase has completed the main frame construction. The BIM modeling system generated component attribute data for the main structure. After the data acquisition module completed the unified data structure conversion, the data twin node generation module generated a unique data identifier for it and generated a corresponding data twin node, denoted as node A. Its attribute tuple is as follows: ;

[0142] During the electromechanical installation phase, pipeline layout design is performed based on the spatial positioning information of the main structure components, generating equipment positioning data nodes, denoted as node B, whose attribute tuples are: ;

[0143] During the final acceptance phase, as-built drawing data nodes are generated based on the equipment positioning data from the electromechanical installation phase. This node is denoted as node C, and its attribute tuples are: ;

[0144] The above three nodes have been registered to the system node registry. After the data collaboration topology construction module completes the topology modeling, the data collaboration topology model in this embodiment is as follows: ;

[0145] Node B has a collaborative dependency on node A, and the topological edges... Weight Node C has an inherited dependency on node B, and the topological edges... Weight .

[0146] In the initial state, the version number and version reference number of each node are recorded as follows:

[0147] ;

[0148] ;

[0149] The version difference values ​​on all nodes are zero, indicating that the system is in a state of data consistency.

[0150] ;

[0151] ;

[0152] During the main structure construction phase, due to changes in on-site construction conditions, the design team revised the spatial positioning information of some main components. The BIM modeling system transmitted the updated component attribute data to the platform. After the data acquisition module completed the unified data structure conversion, the data twin node generation module changed the data version number of node A from... Increment to The attribute tuple for node A is updated to: ;

[0153] That is, the current version number of node A is updated to: At this point, the version reference number of node B to node A in the system still retains its initial recorded value. Section; Point A version update event triggers the data conflict detection module to start the conflict detection process.

[0154] The data conflict detection module retrieves the set of all directly dependent nodes of node A in the topology graph based on the data collaboration topology model: ;

[0155] Calculate the version difference between node B and node A: ;

[0156] because The system determines that there is a data version difference between node B and node A, indicating a data conflict.

[0157] Further calculate the overall conflict degree of the update event of node A. :

[0158] ;

[0159] ;

[0160] Overall conflict level The system assigns a higher response priority to this node A update event. The data conflict detection module generates the following data conflict record: ;

[0161] The data conflict record is then transmitted to the data coordination control module, triggering a data synchronization operation. Upon receiving the data conflict record, the data coordination control module constructs an initial synchronization queue. ;

[0162] 0th iteration ( ):

[0163] From queue Extract node B from the data and use it according to the cooperative topology edges. The marked data dependency type will synchronize the component spatial positioning data of node A (current version V2) to the corresponding component positioning reference field of node B, and perform a version update: ;

[0164] ;

[0165] That is, the version reference number of node B is... Updated to Node B's own version number is determined by Increment to .

[0166] Verify the version differences between node B and node A:

[0167] ;

[0168] Data conflicts between node B and node A are resolved. Retrieve the set of nodes that directly depend on node B:

[0169] ;

[0170] Version differences between compute node C and node B:

[0171] ;

[0172] Node C has version differences; add it to the next round of synchronization queue:

[0173] ;

[0174] The first iteration ( ):

[0175] From queue Extract node C from the topology and perform operations based on the inheritance-type topology edges. The marked data dependency type will synchronize the device location data of node B's current version V2 to the corresponding inherited attribute field of node C, and perform a version update:

[0176] ;

[0177] ;

[0178] That is, the version reference number of node C is... Updated to The version number of node C itself is determined by Increment to .

[0179] Verify the version differences between node C and node B:

[0180] ;

[0181] The data conflict between node C and node B is resolved. The set of nodes that directly depend on node C is retrieved. In this embodiment, node C has no further dependent nodes, therefore:

[0182] ;

[0183] The recursive iteration terminates when the synchronization queue is empty.

[0184] The total number of nodes in the topology graph in this embodiment The upper bound for the maximum number of iterations in a synchronization operation is:

[0185] ;

[0186] The actual number of iterations performed in this synchronization operation The convergence condition is met. After synchronization, the version status of all nodes in the system is summarized as follows:

[0187] ;

[0188] ;

[0189] All version differences are reset to zero: ;

[0190] After data synchronization is complete, the data tracing module initiates forward tracing for node A, sequentially traversing nodes B and C along the construction phase data twin chain, outputting the current version data content and version change history of each node. The tracing results show that the version numbers of nodes A, B, and C have all been updated to [version number missing]. The version reference relationship is complete, the data content of each node is consistent with the spatial positioning information of the component after the correction of node A, the data traceability link is completely closed, and the data collaborative update process caused by this design change is completely closed-loop.

Claims

1. A data twin collaborative management system for an intelligent construction management platform for urban renewal, characterized in that: include: The data acquisition module is used to collect multi-source data from urban renewal projects; The data stage identification module is used to identify the construction stage to which the data belongs; A data twin node generation module is used to generate corresponding construction stage data twin nodes based on the data. The data twin chain construction module is used to construct a construction stage data twin chain according to the construction stage sequence and establish the data inheritance relationship between the data twin nodes of each construction stage; the data twin chain construction module is used to connect multiple data twin nodes according to the construction stage sequence to form a construction stage data twin chain. The data collaboration topology construction module is used to construct a data collaboration topology model based on the data dependency relationship between the data twin nodes in the construction phase. Specifically, it constructs a data collaboration topology graph, in which the data twin nodes serve as topology nodes and the data dependency relationship between nodes serves as topology edges. The data collaboration control module is used to realize data sharing and data updating according to the data collaboration topology model; when a data conflict is detected, the data collaboration control module triggers data synchronization operation on the data twin nodes with dependencies according to the data collaboration topology model. The data conflict detection module is used to detect data conflicts during the data update process based on the data collaboration topology model. The data conflict detection module is used to detect the data version difference between the updated data twin node and its dependent nodes when the data is updated, and to determine whether there is a data conflict based on the data version difference. The data traceability module is used to achieve full-process data traceability of the project based on the data twin chain of the construction stage.

2. The data twin collaborative management system of the urban renewal intelligent construction management platform according to claim 1, characterized in that: The data acquisition module is used to access multiple data sources through the system interface and import data from different data sources into the platform.

3. The data twin collaborative management system of the urban renewal intelligent construction management platform according to claim 2, characterized in that: When importing data, the data acquisition module performs a unified data structure transformation on data from different data sources. The data structure transformation includes: parsing the data format of the original data to extract data fields. Map data fields from different data sources to a unified set of data fields according to preset data field mapping rules; The data structure is reconstructed based on the unified data field set to generate a unified data object; The unified data object is then stored in the platform's data warehouse to achieve unified management of data from different data sources.

4. The data twin collaborative management system of the urban renewal intelligent construction management platform according to claim 1, characterized in that: The data twin node generation module is used to generate a unique data identifier for each piece of data when the data enters the system, and to generate a corresponding data twin node based on the data identifier.

5. The data twin collaborative management system of the urban renewal intelligent construction management platform according to claim 4, characterized in that: The data identifier includes the data source identifier, construction stage identifier, timestamp information, and data version number.

6. The data twin collaborative management system of the urban renewal intelligent construction management platform according to claim 1, characterized in that: The data twin chain construction module is used to establish a data inheritance relationship between data twin nodes in adjacent construction stages, so that data twin nodes in subsequent stages inherit the basic data attributes in the nodes of the previous stage.

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