Collaborative management method, system and device for bim base integrated data

Abstract

The present application relates to the technical field of data processing, and specifically includes a BIM base integrated data collaborative management method, system and device, which comprises: setting a digital base, integrating thermal power engineering entity data and a BIM model mapping channel, extracting thermal power engineering full-cycle heterogeneous data, establishing a component association with the BIM model, and obtaining a thermal power engineering BIM tracking space; obtaining a collaborative management task, and generating task feedback data results. The technical problems of lacking a digital base, being difficult to realize accurate mapping with a BIM model, being unable to support dynamic updating and efficient calling of data, and the accuracy of collaborative management task execution processing being limited are solved, the technical effects of realizing a thermal power engineering BIM tracking space through a BIM collaborative management digital base, positioning parameter decomposition of a collaborative management task, improving the accuracy of task processing, enhancing the timeliness of abnormal processing, and guaranteeing the fine management efficiency and decision-making scientificity of a thermal power engineering full cycle are achieved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a collaborative management method, system, and device for BIM-based integrated data. Background Technology

[0002] The entire life cycle of thermal power projects covers multiple stages such as design, construction, operation and maintenance, involving massive amounts of heterogeneous information such as BIM model data, progress data, cost data, real-time monitoring data and documents. With the acceleration of digital transformation, the decentralized storage management model can no longer meet the project's needs for real-time data sharing, accurate correlation and collaborative decision-making.

[0003] Currently, the application of BIM and data management in thermal power projects lacks a standardized system. The coding rules for heterogeneous data are not uniform, making it difficult to achieve accurate matching through semantic association. Furthermore, there is a lack of tracking and simulation capabilities that support the spatiotemporal dimensions. In addition, collaborative management is mostly limited to simple information transmission, resulting in fragmented decision-making basis and low management efficiency, which makes it difficult to meet the refined needs of collaborative management throughout the entire life cycle of thermal power projects.

[0004] In summary, existing technologies suffer from the following technical problems: lack of a digital foundation, difficulty in achieving accurate mapping with BIM models, inability to support dynamic data updates and efficient data retrieval, and limited accuracy in collaborative management task execution. Summary of the Invention

[0005] This application provides a collaborative management method, system, and equipment for BIM-based integrated data, aiming to solve the technical problems in existing technologies such as the lack of a digital foundation, difficulty in achieving accurate mapping with BIM models, inability to support dynamic data updates and efficient data retrieval, and limited accuracy in collaborative management task execution.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows: In a first aspect, this application provides a collaborative management method for BIM-based integrated data, wherein the method includes: constructing a BIM collaborative management platform as a digital foundation; integrating thermal power plant entity data with a BIM model mapping channel based on the digital foundation; extracting heterogeneous data from the entire lifecycle of the thermal power plant through data middleware, encoding the data according to preset encoding rules, and establishing component associations with the BIM model; updating the data mapping through the mapping channel based on the component associations to obtain the thermal power plant BIM tracking space; acquiring collaborative management tasks, performing parameter decomposition and positioning on the collaborative management tasks, performing management task data matching simulation through the thermal power plant BIM tracking space, and generating task feedback data results.

[0007] Preferably, a multi-layer data model framework is constructed, including a basic model layer, a business data layer, a real-time data layer, and a document knowledge layer; based on the hierarchical structure of the multi-layer data model framework, a mapping channel between the thermal power plant entity data and the BIM model is established, the mapping channel corresponds to the hierarchical structure, and each mapping channel has a hierarchical association label.

[0008] Preferably, the basic model layer is used to store the geometric information of the BIM model; the business data layer is used to store business data related to the components, such as progress, cost, quality, and safety, through the component codes in the BIM model; the real-time data layer is used to receive real-time monitoring data from IoT sensor devices through the component codes in the BIM model; and the document knowledge layer is used to store related drawings, instructions, and regulations through the component codes in the BIM model.

[0009] Preferably, each component is assigned a unique code based on the BIM model; the extracted data is identified as a component and its data type, and matched with the coding library according to the identified components to determine the matching component code and its corresponding data type; based on the matching component code, data coding is performed in combination with the data type to establish the association between the extracted data and the components of the BIM model, wherein the data coding includes the associated component code and the data type identification code.

[0010] Preferably, based on the thermal power engineering ontology, component concepts, data type concepts, and their attributes and relationships are defined to construct a thermal power engineering knowledge graph; natural language processing technology is used to parse the thermal power engineering data extracted by the data middleware to identify and extract entity information; the entity information is semantically matched with the component concepts in the knowledge graph to determine the unique target component corresponding to the entity information, and the data type is determined according to the node context relationship of the entity information in the knowledge graph.

[0011] Preferably, a multi-dimensional simulation space is constructed, including a time grid and a spatial grid. The time grid is established based on the time sequence of the project progress cycle, and the spatial grid is established based on the spatial structure sequence of the BIM model. The time grid is mapped based on the full-cycle nodes of the thermal power project corresponding to the extracted data, and the spatial grid is mapped based on the BIM spatial location of the component associated with the component, so as to obtain the BIM tracking space of the thermal power project, which supports bidirectional query and backtracking simulation calculation of the component status by time and spatial dimensions.

[0012] Preferably, the collaborative management tasks are parsed for task type, including progress simulation, collision detection, safety warning, and quality assessment; based on the parsed task types and historical sample data, correlation parameters and target component analysis are performed to obtain task correlation parameters; according to the task correlation parameters, the simulation engine of the thermal power engineering BIM tracking space is called to load the target components and their correlation parameters to perform simulation calculations and generate the task feedback data results, which include simulation reports, warning information, and optimization schemes.

[0013] Preferably, based on the task association parameters, the time series and spatial grid codes associated with the target component are extracted from the BIM tracking space of the thermal power project to obtain the spatiotemporal context environment for task execution; according to the spatiotemporal context environment, dynamic data within the corresponding time interval and spatial region are loaded, including: historical status data and future plan data of the target component within the specified time interval; status data of other components in the same or adjacent spatial grid as the target component; the task association parameters, the target component and its associated data are placed in the spatiotemporal context environment for calculation in the simulation engine to simulate the execution process and mutual influence of the task within a preset time and spatial range.

[0014] In a second aspect, this application provides a collaborative management system for BIM-based integrated data, wherein the system comprises: a digital base construction module: constructing a BIM collaborative management platform as a digital base, and integrating thermal power plant entity data and BIM model mapping channels based on the digital base; a data encoding module: extracting heterogeneous data of the entire lifecycle of the thermal power plant through data middleware, encoding the data according to preset encoding rules, and establishing component associations with the BIM model; a data mapping update module: performing data mapping updates based on the component associations through the mapping channels to obtain the thermal power plant BIM tracking space; and a parameter decomposition and positioning module: acquiring collaborative management tasks, performing parameter decomposition and positioning on the collaborative management tasks, performing management task data matching simulation through the thermal power plant BIM tracking space, and generating task feedback data results.

[0015] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described collaborative management method for BIM base integrated data.

[0016] In summary, one or more technical solutions provided in this application achieve the technical effect of centralized integration of heterogeneous data throughout the entire lifecycle of thermal power projects through a BIM collaborative management digital base, combined with a multi-layer data model framework and standardized mapping channels. This constructs a BIM tracking space for thermal power projects, decomposes and locates parameters for collaborative management tasks, improves the accuracy of task processing, enhances the timeliness of anomaly handling, and ensures the efficiency of refined management and the scientific nature of decision-making throughout the entire lifecycle of thermal power projects. Attached Figure Description

[0017] Figure 1 This application provides a flowchart illustrating the collaborative management method for BIM base integrated data.

[0018] Figure 2 A schematic diagram of the collaborative management system for BIM base integrated data is provided for this application.

[0019] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0020] Explanation of reference numerals in the attached drawings: Digital base construction module M100, data encoding module M200, data mapping update module M300, parameter decomposition and positioning module M400, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. Detailed Implementation

[0021] Example 1: The present application will be described in detail below with reference to the accompanying drawings, as follows... Figure 1 As shown, this application provides a collaborative management method for BIM base integrated data, wherein the method includes: S1: Construct a BIM collaborative management platform as a digital foundation, and integrate the mapping channel between thermal power engineering entity data and BIM model based on the digital foundation.

[0022] Specifically, the BIM collaborative management platform is used to manage BIM models and related data throughout the entire lifecycle of thermal power projects. It supports multi-user collaboration and provides functions such as data sharing, model updates, and task allocation, serving as the core tool for achieving collaborative data management. The digital foundation refers to the underlying data architecture that provides basic infrastructure support for the BIM collaborative management platform, supporting data storage, processing, and transmission to ensure data integrity and consistency. The mapping channel is a mechanism that associates thermal power project entity data, including schedule data, cost data, and real-time monitoring data, with components in the BIM model. Through the mapping channel, dynamic binding between data and the model is achieved, ensuring real-time data updates and queries.

[0023] Execution Steps: A BIM collaborative management platform is constructed as the digital foundation. This platform categorizes, stores, and manages various data from thermal power projects through a multi-layered data model framework. This framework enables standardized data storage and efficient retrieval. Preferably, by constructing mapping channels, the entity data of the thermal power project is precisely associated with the components in the BIM model, achieving dynamic data updates and bidirectional queries. Specifically, when the progress data of a component is updated, the BIM model can reflect this change in real time, deeply binding data and the model. This improves data management efficiency, provides precise data support for collaborative management tasks, and ensures the accuracy and timeliness of task execution.

[0024] S2: Extract heterogeneous data of the entire life cycle of thermal power projects through data middleware, encode the data according to preset coding rules, and establish component association with the BIM model; S3: Based on the component association, perform data mapping update through the mapping channel to obtain the BIM tracking space of thermal power projects.

[0025] Specifically, data middleware is used to extract, transform, and load data between different data sources and target systems. In thermal power engineering, data middleware can extract data from various heterogeneous data sources and convert it into a unified format. Pre-defined encoding rules refer to encoding standards predefined during data processing to standardize the extracted data, ensuring data consistency and identifiability, and facilitating subsequent data association and querying. Component association refers to binding the extracted data with specific components in the BIM model. By assigning each component a unique code and matching it according to data type and component code, accurate association between data and model is achieved. Data mapping update refers to updating the associated data to the BIM model through mapping channels to ensure that the data in the model is up-to-date. The BIM tracking space is used to store and manage all data related to the BIM model, supporting bidirectional querying and retrospective simulation calculations of component status by time and space dimensions, providing a dynamic spatiotemporal context for collaborative management tasks.

[0026] Execution steps: Through data middleware, heterogeneous data from the entire lifecycle of thermal power projects is extracted, encoded according to preset coding rules, and component associations are established with the BIM model. Specifically, the data middleware extracts heterogeneous data from various stages of thermal power projects. This heterogeneous data comes from different systems and formats, such as cost data from ERP systems and real-time monitoring data from SCADA systems. According to preset coding rules, the extracted data is standardized. Furthermore, each component is assigned a unique code, and data types are identified and classified to ensure data consistency and identifiability. Preferably, unlike general classification codes, the KKS (Kraftwerk KenngeichenSystem, Power Plant Identification System) coding of the power industry is deeply integrated, giving each entity, from a steam turbine to a bolt, a unique digital ID card throughout its entire lifecycle.

[0027] The coded data is associated with components in the BIM model. By matching component codes and data types, a precise binding relationship between data and the model is established. Furthermore, the progress data of a component is associated with the geometric information of its BIM model, enabling dynamic data updates and queries. Through mapping channels, the associated data is updated in the BIM model, constructing a BIM tracking space for thermal power projects. This space supports bidirectional queries and retrospective simulation calculations of component status along both time and spatial dimensions. Preferably, retrospective simulation calculations are performed within the thermal power project BIM tracking space, providing accurate data support for collaborative management tasks and ensuring the efficiency of refined management and the scientific nature of decision-making throughout the entire lifecycle of thermal power projects.

[0028] S4: Obtain collaborative management tasks, perform parameter decomposition and positioning of the collaborative management tasks, perform management task data matching simulation through the thermal power project BIM tracking space, and generate task feedback data results.

[0029] Specifically, collaborative management tasks refer to specific tasks that require collaboration among multiple stakeholders throughout the entire lifecycle of a thermal power project. These tasks involve various aspects such as schedule management, quality control, safety warnings, and cost control, and typically require the integration of information from multiple data sources. Parameter decomposition and positioning involves breaking down collaborative management tasks into specific parameters and target components, identifying the key data and components involved, and further analyzing the task type while combining historical data and knowledge graphs to determine the associated parameters. Management task data matching simulation refers to using a simulation engine to perform data matching and simulation calculations within the BIM tracking space of the thermal power project, based on the decomposed parameters and target components, to generate task feedback data results that support decision-making. Task feedback data results refer to reports, warning information, and optimization schemes generated through simulation calculations, used to guide actual project management and decision-making, including schedule simulation reports, clash check results, and safety warning information.

[0030] Execution steps: Receive collaborative management task requests from different stakeholders, including progress simulation, clash detection, safety alerts, and quality assessment. Specifically, progress management tasks require checking whether the progress of a certain construction phase conforms to the plan. Analyze the collaborative management tasks to determine the key parameters and target components involved. Specifically, for progress management tasks, the task type is analyzed as progress simulation. Based on historical sample data and knowledge graphs, determine the parameters associated with the task, such as the progress data of the target component, planned time, and actual time. Based on the decomposed parameters and target components, call the simulation engine of the thermal power plant BIM tracking space for data matching and simulation. The simulation engine loads the target component and its associated parameters and performs calculations within a spatiotemporal context. Specifically, in the progress simulation, it loads the historical progress data and future plan data of the target component, calculates the deviation between the actual progress and the planned progress, and generates task feedback data results after the simulation is completed. This provides a scientific basis for project management and decision-making. The task feedback data results include simulation reports. Furthermore, the progress simulation report shows that the actual progress of a certain construction stage lags behind the planned progress. Optimization measures such as increasing resources are taken, which significantly improves the accuracy of collaborative management tasks and the timeliness of anomaly handling, ensuring the efficiency of refined management and the scientific nature of decision-making throughout the entire lifecycle of thermal power projects.

[0031] Furthermore, based on the digital foundation integrating the physical data of thermal power plants with the BIM model mapping channel, the method of this application includes: A multi-layered data model framework is constructed, including a basic model layer, a business data layer, a real-time data layer, and a document knowledge layer. Based on the hierarchical structure of the multi-layered data model framework, a mapping channel between the thermal power plant entity data and the BIM model is established. The mapping channel corresponds to the hierarchical structure, and each mapping channel has a hierarchical association label.

[0032] Specifically, the multi-layered data model framework is used to organize and store various types of data in thermal power engineering, including a basic model layer, a business data layer, a real-time data layer, and a document knowledge layer. Each layer has specific data storage and management functions. The basic model layer stores the geometric information of the BIM model, including basic attributes such as the shape, size, and location of components. The business data layer stores business data related to the components of the BIM model, such as schedule, cost, quality, and safety data. The real-time data layer stores real-time monitoring data from IoT sensor devices, such as temperature, pressure, and vibration data. The document knowledge layer stores knowledge-based materials related to the BIM model, such as drawings, manuals, and regulations. The mapping channel is a mechanism that associates the entity data of thermal power engineering with the components in the BIM model. Through the mapping channel, dynamic binding between data and the model is achieved, ensuring real-time data updates and queries. The hierarchical association label is a marker in the mapping channel used to identify the hierarchical relationship between data and BIM model components, which helps to quickly locate and query data.

[0033] Execution steps: Construct a multi-layered data model framework. Further, construct a multi-layered data model framework comprising a basic model layer, a business data layer, a real-time data layer, and a document knowledge layer. Each layer has specific data storage and management functions to ensure that various types of data can be stored in an orderly manner and retrieved efficiently. Specifically, the basic model layer stores the geometric information of the BIM model, including the shape, size, and location of components; the business data layer stores business data related to components, including schedule, cost, quality, and safety; the real-time data layer stores real-time monitoring data from IoT sensor devices, including temperature, pressure, vibration, etc.; and the document knowledge layer stores knowledge-based materials related to the BIM model, such as drawings, manuals, and regulatory documents.

[0034] Based on the hierarchical structure of the aforementioned multi-layer data model framework, mapping channels between the entity data of the thermal power project and the BIM model are established. Specifically, each mapping channel has a hierarchical association label to identify the hierarchical relationship between the data and the components of the BIM model. The basic model layer mapping channel is used to associate the geometric information of the BIM model with the entity data to ensure the accuracy and consistency of the geometric information. The business data layer mapping channel is used to associate business data with the components of the BIM model, supporting the dynamic updating and querying of business data such as progress, cost, quality, and safety. The real-time data layer mapping channel is used to associate real-time monitoring data with the components of the BIM model, supporting the dynamic updating and querying of real-time data. The document knowledge layer mapping channel is used to associate knowledge materials such as drawings, manuals, and regulations with the components of the BIM model, supporting the dynamic updating and querying of knowledge materials. In the above steps, the mapping channel accurately associates the entity data of thermal power projects with the components in the BIM model. When it is necessary to query the progress of a component, the progress data in the business data layer is quickly located through the mapping channel, and a progress report is generated by combining the real-time monitoring data in the real-time data layer. The mapping channel corresponds to the hierarchical structure, which improves the efficiency of data management.

[0035] Furthermore, the method of this application includes: The basic model layer is used to store the geometric information of the BIM model; the business data layer is used to store business data related to the progress, cost, quality, and safety of components through the component codes in the BIM model; the real-time data layer is used to receive real-time monitoring data from IoT sensor devices through the component codes in the BIM model; and the document knowledge layer is used to store related drawings, instructions, and regulations through the component codes in the BIM model.

[0036] Specifically, BIM model geometric information refers to the basic information such as the three-dimensional geometry, dimensions, and location that constitute the building information model. It is the foundation of the BIM model and is used to describe the physical form and spatial relationships of engineering components. Component codes are unique identifiers assigned to each component in the BIM model. Through component codes, data from different sources are associated with specific components, achieving precise data binding. Schedule, cost, quality, and safety business data are business data related to thermal power engineering components, used to record and manage information on project progress, cost control, quality inspection, and safety monitoring, respectively. IoT sensor devices refer to sensors deployed on the construction site or equipment for real-time monitoring of engineering parameters such as temperature, pressure, and vibration. IoT sensor devices transmit data to the data management system through IoT technology. Drawings, manuals, and regulations are documents related to thermal power engineering, including design drawings, equipment manuals, and operating procedures.

[0037] Execution steps: The basic model layer stores the geometric information of the BIM model, including the shape, size, and location of components. It is the core of the BIM model and provides the foundation for the visualization and spatial analysis of the project. Specifically, in thermal power engineering, the basic model layer stores the three-dimensional geometric models of major equipment such as boilers and steam turbines, supporting the visualization of engineering design and construction phases. The business data layer is used to store business data related to components, such as progress, cost, quality, and safety, through the component codes in the BIM model. Specifically, for pipeline components, the business data layer can store their installation progress, cost budget, quality inspection results, and safety inspection records. The associated storage method makes the data closely integrated with the model, facilitating direct querying and management of relevant business data in the BIM model.

[0038] The real-time data layer is used to store real-time monitoring data received from IoT sensor devices by associating component codes in the BIM model. Specifically, for high-pressure boilers, the real-time data layer stores real-time data from temperature and pressure sensors. After this real-time data is associated with components in the BIM model, the operating status of the equipment is displayed in real time in the BIM model, supporting real-time monitoring and early warning. The document knowledge layer is used to store related drawings, manuals, and regulations by associating component codes in the BIM model. Specifically, for valve components, the document knowledge layer can store their design drawings, installation instructions, and operating procedures, allowing direct access to component-related documents in the BIM model, providing comprehensive support for project management and decision-making. In the above steps, the basic model layer, business data layer, real-time data layer, and document knowledge layer have a clear structure. Preferably, data of different granularities are properly placed, satisfying both macro-management and micro-traceability. Through integrated applications under the multi-layer data model framework, the status and related information of the equipment can be fully understood, thereby making scientific and reasonable maintenance decisions and improving the efficiency and safety of project operation and maintenance.

[0039] Furthermore, the method in this application includes encoding data according to preset encoding rules and establishing component associations with the BIM model: Each component is assigned a unique code based on the BIM model; the extracted data is identified by component and data type, and matched with the coding library according to the identified components to determine the matching component code and its corresponding data type; based on the matching component code, data coding is performed in combination with the data type to establish the association between the extracted data and the components of the BIM model, wherein the data coding includes the associated component code and the data type identification code.

[0040] Specifically, unique coding refers to the unique identifier assigned to each component in the BIM model, used to uniquely identify each component in the data management system. Unique coding is the foundation for achieving accurate association between data and the model; component and data type identification; coding library refers to a database that stores component codes and related information, used to provide reference during data identification and matching; data coding refers to encoding the identified data according to certain rules, including associated component codes and data type identification codes, to ensure accurate binding between data and components and facilitate subsequent data processing and querying; component association refers to associating the coded data with specific components in the BIM model, so that the data can be directly reflected on the corresponding components in the model, realizing dynamic interaction between data and the model.

[0041] Execution steps: Based on the BIM model, assign a unique code to each component. Specifically, during the BIM model creation process, assign a unique code to each component. This unique code is the component's unique identifier in the data management system, ensuring accurate association between data and components. For instance, in thermal power engineering, each pipe, valve, equipment, and other component has a unique code. Identify the components and data types in the extracted data, and match the identified components with the code library. Specifically, from the data extracted by the data middleware, natural language processing technology and knowledge graphs are used to identify the components and data types involved in the data. Preferably, develop and utilize data middleware to extract, clean, and transform data from multiple systems including PDMS, Aveva, Revit, SAP, and P6, and write it into a unified BIM database according to established standards, rather than simply linking files. This achieves automated and structured data integration, ensuring the uniqueness and accuracy of the data source.

[0042] The identified components are matched with a coding library to determine their unique code in the BIM model. Specifically, by parsing text data, the component mentioned is identified as a boiler, and the data type is temperature. Based on the matched component code, data coding is performed in conjunction with the data type to establish an association between the extracted data and the component in the BIM model. Specifically, the data is coded by combining the matched component code and the data type. Furthermore, the boiler component's code in the coding library is BOILER-001; the temperature data of the identified boiler component is coded as BOILER-001-TEMPERATURE. This coding method identifies the component and data type to which the data belongs, facilitating subsequent data processing and querying. The data code is then associated with the component code in the BIM model. Furthermore, when viewing the boiler component in the BIM model, its associated temperature data is directly obtained. Through these steps, dynamic interaction between data and the BIM model is achieved, providing accurate data support for the refined management and decision-making of thermal power projects.

[0043] Furthermore, the method of this application includes identifying the components and data types of the extracted data: Based on the ontology of thermal power engineering, the concepts of components and data types are defined, along with their attributes and relationships, to construct a knowledge graph for thermal power engineering. Natural language processing technology is used to parse the thermal power engineering data extracted by the data middleware, identify and extract entity information. The entity information is semantically matched with the component concepts in the knowledge graph to determine the unique target component corresponding to the entity information, and the data type is determined based on the node context relationship of the entity information in the knowledge graph.

[0044] Specifically, the ontology of thermal power engineering refers to a framework that systematically and structurally describes the knowledge and information within the field of thermal power engineering, including various entities, attributes, and relationships between entities. The component concept is an abstract description of various components in thermal power engineering, including their names, functions, and attributes; for example, boilers, steam turbines, and pipelines are all component concepts in thermal power engineering. The data type concept is the classification and description of data related to components, such as schedule data, cost data, quality data, and real-time monitoring data. A knowledge graph is a structured semantic knowledge base used to describe entities and their relationships. In thermal power engineering, a knowledge graph can contain information such as components, data types, and attributes, as well as the semantic relationships between them. Natural language processing technology is used to parse text data and extract entity information and semantic relationships. Entity information refers to specific information extracted from text data, such as component names, data types, and values, which are matched and associated with concepts in the knowledge graph. Semantic matching refers to analyzing the semantic content of entity information and matching it with concepts in the knowledge graph to determine the unique target component and data type corresponding to the entity information.

[0045] Execution steps: Based on the thermal power engineering ontology, define component concepts, data type concepts, and their attributes and relationships to construct a thermal power engineering knowledge graph. Further, define boilers as having attributes such as temperature and pressure, with the corresponding data type being real-time monitoring data. Through these definitions, construct a knowledge graph containing knowledge in the field of thermal power engineering. After extracting thermal power engineering data from the data middleware, use natural language processing technology to parse this data and extract entity information. Specifically, extract entity information such as boilers and temperature from text data.

[0046] Semantic matching of entity information with component concepts in the knowledge graph is performed to determine the unique target component corresponding to the entity information. Specifically, the extracted boiler is matched with the boiler concept in the knowledge graph to determine its unique target component. At the same time, the data type is determined based on the node context relationship of the entity information in the knowledge graph. Specifically, based on the position of temperature in the knowledge graph, it is determined to be a real-time monitoring data type. Through semantic matching, the unique target component and data type corresponding to the entity information are determined, realizing the precise binding of data and model. In the above steps, the semantic matching and data association based on the knowledge graph improve the accuracy of data processing.

[0047] Furthermore, the method for obtaining the BIM tracking space of thermal power projects in this application includes: A multi-dimensional simulation space is constructed, including a time grid and a spatial grid. The time grid is established based on the time sequence of the project progress cycle, and the spatial grid is established based on the spatial structure sequence of the BIM model. The time grid is mapped based on the full-cycle nodes of the thermal power project corresponding to the extracted data, and the spatial grid is mapped based on the BIM spatial location of the component association to obtain the BIM tracking space of the thermal power project. It supports bidirectional query and backtracking simulation calculation of component status by time and spatial dimensions.

[0048] Specifically, the multi-dimensional simulation space is used to simulate and analyze the state and behavior of components in thermal power engineering, including spatial and temporal dimensions, supporting dynamic simulation and analysis of component states. The time grid is a time interval divided according to the project schedule time series, dividing the entire project cycle into multiple time points or time periods for tracking and analyzing the state of components at different time points. The spatial grid is a spatial region divided based on the spatial structure sequence of the BIM model, dividing the project site into multiple spatial units for determining the location and interrelationships of components in space. Time grid mapping refers to mapping extracted data to the time grid to determine the data's location in the time dimension, which helps analyze the state changes of components at different time points. Spatial grid mapping refers to mapping components to the spatial grid to determine the component's location in the spatial dimension, which helps analyze the distribution and interrelationships of components in space. The BIM tracking space is a virtual space constructed through time grid mapping and spatial grid mapping, used to store and manage component state information, supporting bidirectional querying and backtracking simulation calculations of component states by time and spatial dimensions.

[0049] Execution steps: Construct a multi-dimensional simulation space, specifically including a time grid and a spatial grid. The time grid is divided according to the project schedule time series, and the spatial grid is divided based on the spatial structure sequence of the BIM model, dividing the thermal power project cycle into multiple construction phases. The spatial grid, based on the spatial structure sequence of the BIM model, divides the thermal power project site into different areas or floors. Map the extracted data to the time grid to determine the data's position in the time dimension. Furthermore, for the component's progress data, map it to specific time points in the project schedule cycle to analyze the component's progress status at different time points.

[0050] By mapping components to a spatial grid, the spatial position of the components is determined. Specifically, based on the spatial position of the components in the BIM model, they are mapped to specific areas in the spatial grid to analyze the distribution and interrelationships of the components in space. Through time and spatial grid mapping, a BIM tracking space for thermal power projects is constructed. Furthermore, the time and spatial dimensions corresponding to construction tasks are associated, indirectly achieving dynamic binding between construction tasks and BIM model components. Further, based on the time axis, the construction status and component information within a specific space at any given time can be queried, or based on spatial location, the construction and installation history of any component can be traced back. The thermal power project BIM tracking space stores the status information of components in both time and space dimensions, supporting bidirectional querying and backtracking simulation calculations of component status along both time and spatial dimensions. In the above steps, the BIM tracking space is constructed to support dynamic querying and simulation of boiler status, providing support for the refined management of thermal power projects.

[0051] Furthermore, the method of this application includes: acquiring collaborative management tasks, decomposing and locating the parameters of the collaborative management tasks, performing management task data matching simulation through the BIM tracking space of the thermal power plant project, and generating task feedback data results. The collaborative management tasks are analyzed for task types, including progress simulation, collision detection, safety warning, and quality assessment. Based on the analyzed task types and historical sample data, correlation parameters and target components are analyzed to obtain task correlation parameters. According to the task correlation parameters, the simulation engine of the thermal power engineering BIM tracking space is called to load the target components and their correlation parameters to perform simulation calculations and generate the task feedback data results, which include simulation reports, warning information, and optimization schemes.

[0052] Specifically, task type analysis refers to classifying and identifying collaborative management tasks to determine their specific types, including schedule simulation, collision detection, safety alerts, and quality assessment. Related parameter and target component analysis involves identifying key parameters and target components related to the task based on its type and historical sample data, helping to clarify the specific requirements and scope of the task. Task-related parameters are specific parameters associated with collaborative management tasks, representing crucial information required for task execution, including time frame, spatial scope, and data type. The simulation engine can be used for tasks such as schedule simulation, collision detection, and safety alerts. Task feedback data results refer to the output generated by the simulation, including simulation reports, alert information, and optimization schemes, providing support for project management and decision-making.

[0053] Execution steps: Classify and identify collaborative management tasks to determine their specific types; based on task types and historical sample data, determine the key parameters and target components involved in the task; further, for schedule simulation tasks, the associated parameters include schedule data under time range, spatial range, and data type; through analysis, clarify the specific parameter requirements of the task, such as time range, spatial range, and data type, which will serve as inputs for simulation calculations; based on the task-related parameters, call the simulation engine for thermal power engineering BIM tracking space, which will load the target components and their associated parameters and execute the simulation calculations.

[0054] The simulation engine performs simulation calculations based on the input parameters and data. Specifically, in the progress simulation, the engine loads the historical progress data and future plan data of the target component to determine the deviation between the actual progress and the planned progress. After the simulation calculation is completed, task feedback data results are generated, including simulation reports, early warning information, and optimization solutions. Specifically, the progress simulation report shows that the actual progress of a certain construction stage lags behind the planned progress. The preferred simulation and feedback mechanism based on BIM tracking space improves the accuracy of collaborative management tasks and the timeliness of anomaly handling, ensuring the efficiency of refined management and the scientific nature of decision-making throughout the entire life cycle of thermal power projects.

[0055] Furthermore, the method of this application includes invoking the simulation engine of the BIM tracking space of thermal power plant projects based on the task association parameters: Based on the task association parameters, the time series and spatial grid codes associated with the target component are extracted from the BIM tracking space of the thermal power project to obtain the spatiotemporal context of task execution. According to the spatiotemporal context, dynamic data within the corresponding time interval and spatial region are loaded, including: historical status data and future plan data of the target component within the specified time interval; status data of other components in the same or adjacent spatial grid as the target component; the task association parameters, target component and its associated data are placed in the spatiotemporal context in the simulation engine for calculation to simulate the execution process and mutual influence of the task within a preset time and spatial range.

[0056] Specifically, the spatiotemporal context refers to all background information and conditions related to the target component within a specific time and space range, including time series and spatial grid coding, used to describe the state of the target component in a specific time and space; the time series refers to data points arranged in chronological order, used to describe the state changes of the target component at different points in time; the spatial grid coding refers to the location identifiers of the target component and its surrounding components in a spatial grid, used to describe the distribution and interrelationships of components in space; dynamic data refers to the state data of the target component and its surrounding components within a specific time interval and spatial region; the simulation engine is used to perform simulation calculations, capable of performing calculations in the spatiotemporal context environment based on the input task-related parameters, the target component and its associated data, and generating simulation results.

[0057] Execution steps: Based on the task-related parameters, extract the time series and spatial grid codes associated with the target component from the BIM tracking space of the thermal power project. Specifically, for the progress simulation task, extract the historical status data of the target component in the past week and the planned data for the next week, as well as the location information of the target component within a specific spatial grid. Based on the extracted spatiotemporal context, load the dynamic data within the corresponding time interval and spatial area, including: determining the historical status data and future planned data of the target component, such as loading the actual progress data of the target component in the past week and the planned progress data for the next week; determining the status data of other components in the same or adjacent spatial grid as the target component, such as loading the status data of other components adjacent to the target component to assess the impact of the status data of other components adjacent to the target component on the target component.

[0058] In the simulation engine, task-related parameters, target components, and their associated data are placed within a spatiotemporal context for computation. Furthermore, based on these parameters, the engine simulates the execution process and mutual influences of the task within a preset time and spatial range. The engine determines the deviation between the actual and planned progress of the target component and assesses the impact of adjacent components on its progress. After simulation, results are generated, including simulation reports, early warning information, and optimization schemes. These results provide a scientific basis for project management and decision-making; for example, a progress simulation report might show that the actual progress of a certain construction phase lags behind the planned progress. In these steps, the simulation and feedback mechanism based on the spatiotemporal context improves the accuracy of collaborative management tasks and the timeliness of anomaly handling.

[0059] In summary, the beneficial effects of the embodiments of this application are: By employing a BIM collaborative management platform as a digital foundation, and integrating entity data of thermal power plants with BIM model mapping channels based on this digital foundation, heterogeneous data from the entire lifecycle of thermal power plants is extracted through data middleware. This data is encoded according to preset coding rules to establish component associations with the BIM model. Based on these component associations, data mapping is updated through the mapping channels to obtain the BIM tracking space for thermal power plants. Collaborative management tasks are acquired, parameters are decomposed and located for these tasks, and data matching simulation is performed using the BIM tracking space to generate task feedback data results. This application provides a collaborative management method, system, and equipment for data integration using a BIM foundation. It achieves centralized integration of heterogeneous data from the entire lifecycle of thermal power plants through a BIM collaborative management digital foundation, combined with a multi-layered data model framework and standardized mapping channels. The constructed BIM tracking space for thermal power plants allows for parameter decomposition and location of collaborative management tasks, improving the accuracy of task processing, enhancing the timeliness of anomaly handling, and ensuring the efficiency and scientific nature of refined management throughout the entire lifecycle of thermal power plants.

[0060] Example 2, based on the same inventive concept as the collaborative management method of BIM base integrated data in the aforementioned examples, such as... Figure 2 As shown in the embodiment of this application, a collaborative management system for BIM base integrated data is provided, wherein the system includes: Digital Base Construction Module M100: Constructs a BIM collaborative management platform as a digital base, and integrates the mapping channel between thermal power engineering entity data and BIM model based on the digital base.

[0061] Data Encoding Module M200: Through data middleware, it extracts heterogeneous data from the entire lifecycle of thermal power projects, encodes the data according to preset encoding rules, and establishes component associations with the BIM model.

[0062] Data mapping update module M300: Based on the component association, data mapping is updated through the mapping channel to obtain the BIM tracking space of thermal power project.

[0063] Parameter decomposition and positioning module M400: acquires collaborative management tasks, performs parameter decomposition and positioning on the collaborative management tasks, performs management task data matching simulation through the thermal power project BIM tracking space, and generates task feedback data results.

[0064] Furthermore, the digital docking station construction module M100 is used to perform the following methods: A multi-layered data model framework is constructed, including a basic model layer, a business data layer, a real-time data layer, and a document knowledge layer. Based on the hierarchical structure of the multi-layered data model framework, a mapping channel between the thermal power plant entity data and the BIM model is established. The mapping channel corresponds to the hierarchical structure, and each mapping channel has a hierarchical association label.

[0065] Furthermore, the digital docking station construction module M100 is also used to perform the following methods: The basic model layer is used to store the geometric information of the BIM model; the business data layer is used to store business data related to the progress, cost, quality, and safety of components through the component codes in the BIM model; the real-time data layer is used to receive real-time monitoring data from IoT sensor devices through the component codes in the BIM model; and the document knowledge layer is used to store related drawings, instructions, and regulations through the component codes in the BIM model.

[0066] Furthermore, the data encoding module M200 is used to perform the following method: Each component is assigned a unique code based on the BIM model; the extracted data is identified by component and data type, and matched with the coding library according to the identified components to determine the matching component code and its corresponding data type; based on the matching component code, data coding is performed in combination with the data type to establish the association between the extracted data and the components of the BIM model, wherein the data coding includes the associated component code and the data type identification code.

[0067] Furthermore, the data encoding module M200 is also used to perform the following method: Based on the ontology of thermal power engineering, the concepts of components and data types are defined, along with their attributes and relationships, to construct a knowledge graph for thermal power engineering. Natural language processing technology is used to parse the thermal power engineering data extracted by the data middleware, identify and extract entity information. The entity information is semantically matched with the component concepts in the knowledge graph to determine the unique target component corresponding to the entity information, and the data type is determined based on the node context relationship of the entity information in the knowledge graph.

[0068] Furthermore, the data mapping update module M300 is used to perform the following method: A multi-dimensional simulation space is constructed, including a time grid and a spatial grid. The time grid is established based on the time sequence of the project progress cycle, and the spatial grid is established based on the spatial structure sequence of the BIM model. The time grid is mapped based on the full-cycle nodes of the thermal power project corresponding to the extracted data, and the spatial grid is mapped based on the BIM spatial location of the component association to obtain the BIM tracking space of the thermal power project. It supports bidirectional query and backtracking simulation calculation of component status by time and spatial dimensions.

[0069] Furthermore, the parameter decomposition and positioning module M400 is used to perform the following method: The collaborative management tasks are analyzed for task types, including progress simulation, collision detection, safety warning, and quality assessment. Based on the analyzed task types and historical sample data, correlation parameters and target components are analyzed to obtain task correlation parameters. According to the task correlation parameters, the simulation engine of the thermal power engineering BIM tracking space is called to load the target components and their correlation parameters to perform simulation calculations and generate the task feedback data results, which include simulation reports, warning information, and optimization schemes.

[0070] Furthermore, the parameter decomposition and positioning module M400 is also used to perform the following method: Based on the task association parameters, the time series and spatial grid codes associated with the target component are extracted from the BIM tracking space of the thermal power project to obtain the spatiotemporal context of task execution. According to the spatiotemporal context, dynamic data within the corresponding time interval and spatial region are loaded, including: historical status data and future plan data of the target component within the specified time interval; status data of other components in the same or adjacent spatial grid as the target component; the task association parameters, target component and its associated data are placed in the spatiotemporal context in the simulation engine for calculation to simulate the execution process and mutual influence of the task within a preset time and spatial range.

[0071] Example 3: Based on the same inventive concept as the collaborative management method of BIM base integrated data in Example 1, the present invention also provides an electronic device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the method described in Example 1.

[0072] like Figure 3As shown, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, connecting various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0073] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A collaborative management method for BIM-based integrated data, characterized in that, include: A BIM collaborative management platform is constructed as a digital foundation, and a mapping channel between thermal power plant entity data and BIM model is integrated based on the digital foundation. Through data middleware, heterogeneous data from the entire lifecycle of thermal power projects are extracted, coded according to preset coding rules, and component associations with the BIM model are established. Based on the component association, data mapping and updating are performed through the mapping channel to obtain the BIM tracking space for thermal power engineering. Acquire collaborative management tasks, decompose and locate the parameters of the collaborative management tasks, perform management task data matching simulation through the BIM tracking space of the thermal power project, and generate task feedback data results. The acquisition of the BIM tracking space for thermal power projects includes: A multi-dimensional simulation space is constructed, including a time grid and a spatial grid. The time grid is established based on the time series of the project progress cycle, and the spatial grid is established based on the spatial structure sequence of the BIM model. Based on the extracted data, time grid mapping is performed on the full life cycle nodes of the thermal power project. Spatial grid mapping is performed according to the BIM spatial location of the component associated with the component to obtain the BIM tracking space of the thermal power project. It supports bidirectional query and backtracking simulation calculation of component status according to time and spatial dimensions. The collaborative management task is acquired, its parameters are decomposed and located, and the management task data is matched and simulated using the BIM tracking space of the thermal power plant project. The task feedback data results are then generated, including: The collaborative management tasks are analyzed for task types, including progress simulation, collision detection, security warning, and quality assessment. Based on the task type obtained from the analysis and combined with historical sample data, the correlation parameters and target components are analyzed to obtain the task correlation parameters. The simulation engine of the thermal power engineering BIM tracking space is invoked according to the task-related parameters, the target component and its related parameters are loaded and the simulation calculation is performed to generate the task feedback data results, which include simulation reports, early warning information and optimization schemes. The simulation engine for the BIM tracking space of the thermal power plant is invoked based on the task association parameters, including: Based on the task association parameters, the time series and spatial grid codes associated with the target components are extracted from the BIM tracking space of the thermal power project to obtain the spatiotemporal context of task execution. Based on the spatiotemporal context, dynamic data within the corresponding time interval and spatial region is loaded, including: historical status data and future plan data of the target component within the specified time interval; and status data of other components located in the same or adjacent spatial grid as the target component. The task-related parameters, target components, and their associated data are placed in the spatiotemporal context environment for calculation in the simulation engine to simulate the execution process and mutual influence of the task within a preset time and space range.

2. The collaborative management method for BIM base integrated data according to claim 1, characterized in that, Based on a digital platform, an integrated mapping channel between thermal power plant entity data and BIM model is provided, including: Construct a multi-layered data model framework, including a basic model layer, a business data layer, a real-time data layer, and a document knowledge layer; Based on the hierarchical structure of the multi-layer data model framework, a mapping channel between the thermal power plant entity data and the BIM model is established. The mapping channel corresponds to the hierarchical structure, and each mapping channel has a hierarchical association label.

3. The collaborative management method for BIM base integrated data according to claim 2, characterized in that, The basic model layer is used to store the geometric information of the BIM model; the business data layer is used to store business data related to the progress, cost, quality, and safety of components through the component codes in the BIM model; the real-time data layer is used to receive real-time monitoring data from IoT sensor devices through the component codes in the BIM model; and the document knowledge layer is used to store related drawings, instructions, and regulations through the component codes in the BIM model.

4. The collaborative management method for BIM base integrated data according to claim 2, characterized in that, Data is encoded according to preset encoding rules, and component associations with the BIM model are established, including: Each component is assigned a unique code based on the BIM model; The extracted data is identified in terms of components and data types. The identified components are matched with the coding library to determine the matching component codes and their corresponding data types. Based on the matching component code, data encoding is performed in combination with data type to establish the association between the extracted data and the components of the BIM model, wherein the data encoding includes the associated component code and the data type identification code.

5. The collaborative management method for BIM base integrated data according to claim 4, characterized in that, The extracted data is subjected to component and data type identification, including: Based on the ontology of thermal power engineering, we define the concepts of components and data types, as well as their attributes and relationships, and construct a knowledge graph of thermal power engineering. Natural language processing technology is used to parse the thermal power engineering data extracted by the data middleware, and entity information is identified and extracted. The entity information is semantically matched with the component concepts in the knowledge graph to determine the unique target component corresponding to the entity information, and the data type is determined based on the node context relationship of the entity information in the knowledge graph.

6. A collaborative management system for BIM-based integrated data, characterized in that, The system is used for implementing the collaborative management method of BIM base integrated data according to any one of claims 1-5, wherein the system comprises: Digital Foundation Construction Module: Constructs a BIM collaborative management platform as the digital foundation, and integrates the mapping channel between thermal power plant entity data and BIM model based on the digital foundation; Data encoding module: Through data middleware, it extracts heterogeneous data from the entire lifecycle of thermal power projects, encodes the data according to preset encoding rules, and establishes component associations with the BIM model; Data mapping update module: Based on the component association, data mapping updates are performed through the mapping channel to obtain the BIM tracking space of thermal power engineering; Parameter decomposition and positioning module: acquires collaborative management tasks, performs parameter decomposition and positioning on the collaborative management tasks, performs management task data matching simulation through the thermal power project BIM tracking space, and generates task feedback data results.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the collaborative management method for BIM base integrated data as described in any one of claims 1-5.

Images