Engineering collaborative design management and control method and system based on digital twinning

Abstract

The invention discloses an engineering collaborative design management and control method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: presenting a project design in a digital twinning model through a virtual reality environment based on a collaborative management matrix, carrying out the interactive design review and optimization, and generating an optimized design scheme; based on the optimized design scheme, the project team arranges task execution according to the design progress, and generates a task execution plan and resource scheduling data; monitoring actual execution conditions of the task execution plan and the resource scheduling data, and generating a quality control report by analyzing execution deviation and performing quality control; by automatically generating the task execution plan and the resource scheduling data, the design quality and the project management efficiency are improved, and the flexibility and the real-time performance in the project execution process are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning, in particular to an engineering collaborative design management and control method and system based on digital twinning. BACKGROUND

[0002] With the rapid development of information technology and digital technology, the field of engineering design gradually adopts digital twinning technology to improve the visualization, collaboration efficiency and accuracy of the design process; digital twinning technology closely combines physical entities with their virtual models to realize real-time monitoring, analysis and feedback, and has been widely applied in intelligent manufacturing, construction, aviation and other industries. In most engineering design projects, design teams share tasks and data through collaborative platforms and combine virtual reality and other technologies for review and optimization, which has achieved certain results. Although the application of digital twinning technology has brought significant progress in design collaboration, in the current collaborative design practice based on digital twinning, due to the lack of a unified task collaboration management mechanism, design changes are difficult to quickly spread and consistently update in the digital twinning environment, which further affects the timeliness of overall design optimization efficiency and quality control. Most existing systems focus on data visualization or process management in a single link during the design phase, and fail to organically couple key processes such as design review, task scheduling, resource allocation and quality control in a unified collaborative framework, which limits the effective traceability and closed-loop optimization capabilities of project life cycle data. SUMMARY

[0003] In view of the above existing problems, the present application is proposed.

[0004] Therefore, the present application provides an engineering collaborative design management and control method based on digital twinning to solve the problem of design change propagation lag and multi-link process fragmentation caused by the lack of a unified collaborative management mechanism.

[0005] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a digital twin-based engineering collaborative design management method, which comprises collecting requirements of each design team, constructing a digital twin model, integrating each design task into a unified matrix to obtain a collaborative management matrix; based on the collaborative management matrix, presenting project design in a virtual reality environment in the digital twin model and performing interactive design review and optimization to generate an optimized design scheme; based on the optimized design scheme, a project team arranges task execution according to design progress to generate a task execution plan and resource scheduling data; the actual execution of the task execution plan and the resource scheduling data is monitored, execution deviation is analyzed and quality control is performed to generate a quality control report; the optimized design scheme, the task execution plan and the quality control report are archived to generate complete project archival documents; after the project is completed and delivered, operation data are provided in real time and design modification and optimization are performed to generate an operation data report.

[0006] As a preferred scheme of the digital twin-based engineering collaborative design management method, the collecting requirements of each design team and the constructing a digital twin model specifically comprise the following steps, collecting design inputs, functional requirements and technical constraint conditions of design teams of building, structure, mechanical and electrical and heating and ventilation disciplines to obtain multi-source heterogeneous requirement data; cleaning and semantically modeling the multi-source heterogeneous requirement data to form a requirement knowledge graph; based on the requirement knowledge graph, associating geometric and semantic information of BIM with real-time monitoring data of IoT sensors according to spatial positions and component identifiers, and generating a preliminary digital twin model through parameterized modeling and semantic mapping, and automatically checking and detecting conflicts by using a machine learning algorithm to obtain the digital twin model.

[0007] As a preferred scheme of the digital twin-based engineering collaborative design management method, the obtaining a collaborative management matrix specifically comprises the following steps, normalizing the multi-source heterogeneous requirement data to obtain standardized project data, and performing multi-dimensional hierarchical task decomposition of the project through a work breakdown structure and a product breakdown structure, assigning unique code identifiers, and establishing a logical relationship network between tasks; storing structured task nodes, design components, project documents and personnel role elements in the logical relationship network between tasks in association, and establishing semantic relationships between personnel role elements through semantic rules to form the collaborative management matrix.

[0008] As a preferred scheme of the digital twin-based engineering collaborative design management method, the generating an optimized design scheme specifically comprises the following steps, The geometry and semantic data of the digital twin model and the shareable design task data in the collaborative management matrix and the collaborative environment configuration information are converted and rendered in real time through a virtual reality rendering method to generate a virtual reality environment; In the virtual reality environment, the parts that need to be modified are marked to obtain modification opinions and optimization tasks; the original design scheme is adjusted according to the modification opinions and optimization tasks to generate an alternative design scheme, and after comparison and confirmation in the virtual reality environment, an optimized design scheme is generated.

[0009] As a preferred scheme of the engineering collaborative design management and control method based on digital twinning, wherein: the original design scheme is adjusted according to the modification opinions and optimization tasks to generate an alternative design scheme, and after comparison and confirmation in the virtual environment, an optimized design scheme is generated, the specific steps are as follows, Modification parameters and optimization targets are extracted from the modification opinions and optimization tasks, and a multi-objective particle swarm optimization algorithm is called to perform iterative operation to generate an alternative design scheme; The alternative design scheme is pushed to the virtual reality environment, and a multi-attribute decision analysis algorithm is used to comprehensively evaluate and sort the performance indicators of the alternative design scheme to generate an optimized design scheme.

[0010] As a preferred scheme of the engineering collaborative design management and control method based on digital twinning, wherein: the task execution plan and resource scheduling data are generated, and the specific steps are as follows, The design change content in the optimized design scheme is identified, the design task list is updated, the newly added, modified and canceled task items are marked, the available resource status is combined, the time arrangement of each task is calculated, and human and equipment resources are allocated to each task; The human and equipment resources are converted into a Gantt chart, and automatic verification is performed through resource allocation rules to generate task execution plan and resource scheduling data, and the data are synchronized to the digital twin model through an API interface.

[0011] As a preferred scheme of the engineering collaborative design management and control method based on digital twinning, wherein: the quality control report is generated, and the specific steps are as follows, The actual execution of the task execution plan and resource scheduling data is monitored through on-site sensor data and is spatio-temporally aligned and fused with automatic inspection data to obtain multi-source quality fusion data; Based on the multi-source quality fusion data, the construction quality is analyzed in real time, the quality deviation is recorded, and the classification and severity are evaluated to obtain a quality control report.

[0012] As a preferred scheme of the engineering collaborative design management and control method based on digital twinning, wherein: the complete project archival document is generated, and the specific steps are as follows, Integrate the optimized design scheme, task execution plan and quality control report, and perform version solidification processing to generate an unalterable data snapshot; Based on the component coding system of the unalterable data snapshot and the digital twin model, the association index between the design scheme, the plan node, the quality problem and the related responsible party is established; the association index and the unalterable data snapshot are organized and encapsulated according to the project management specification to form a complete project archive document.

[0013] As a preferred scheme of the engineering collaborative design management and control method based on digital twinning, wherein: the operation data report is generated, and the specific steps are as follows, A standardized data interface is established between the digital twin model and the operation team, and the equipment running state, energy consumption monitoring, environmental parameters and operation data are collected in real time, and an operation feedback receiving channel is set up, and equipment failure records and use improvement suggestions are collected; Based on the equipment failure records and use improvement suggestions, performance evaluation is carried out in collaboration, scheme modification and simulation verification are carried out through the digital twin environment, the equipment running state and the optimized design scheme are integrated regularly to form the operation data report.

[0014] In a second aspect, the present application provides an engineering collaborative design management and control system based on digital twinning, comprising: an integration module for collecting the requirements of each design team, building a digital twin model, integrating each design task into a unified matrix to obtain a collaborative management matrix; a collaboration module for presenting project design in a digital twin model through a virtual reality environment based on the collaborative management matrix, and performing interactive design review and optimization to generate an optimized design scheme; a resource scheduling module for generating a task execution plan and resource scheduling data based on the optimized design scheme, and arranging tasks according to the design progress; a monitoring module for monitoring the actual execution of the task execution plan and the resource scheduling data, analyzing the execution deviation and performing quality control to generate a quality control report; a management module for archiving the optimized design scheme, the task execution plan and the quality control report to generate a complete project archive document; and a feedback module for providing operation data in real time and performing design modification and optimization after the project is completed to generate an operation data report.

[0015] The present application has the advantages that: by combining the digital twin model with virtual reality technology, the optimization and interactive review of the design scheme are realized, the accuracy of the design and the team collaboration efficiency are improved, the task execution plan and the resource scheduling data are automatically generated, not only the design quality and the project management efficiency are improved, but also the flexibility and real-time performance in the project execution process are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0017] Fig. 1 A flow chart of an engineering collaborative design management and control method based on digital twinning.

[0018] Fig. 2 A schematic diagram of an engineering collaborative design management and control system based on digital twinning.

[0019] Fig. 3 A flow chart of constructing a digital twinning model.

[0020] Fig. 4 A flow chart of design optimization in a virtual reality environment. DETAILED DESCRIPTION

[0021] In order to make the above objectives, features and advantages of the present application more apparent and comprehensible, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0022] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0023] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0024] Reference Figs. 1-4 For one embodiment of the present application, the embodiment provides an engineering collaborative design management and control method based on digital twinning, comprising the following steps: S1. Collecting requirements of each design team, constructing a digital twinning model, integrating each design task into a unified matrix, and obtaining a collaborative management matrix.

[0025] S1.1. Collecting design inputs, functional requirements and technical constraint conditions of design teams of building, structure, mechanical and electrical and heating and ventilation professions, and obtaining multi-source heterogeneous requirement data.

[0026] Specifically, detailed communication with each design team is conducted to collect functional requirements and technical requirements of architectural, structural, mechanical and electrical, and heating and ventilation designs. For example, the architectural design team provides space planning, room layout, functional zoning, selection of building materials, and structural requirements; the structural design team provides foundation bearing capacity, seismic design standards, building height requirements; the mechanical and electrical design team provides power, lighting, air conditioning system configuration and pipeline arrangement requirements; the heating and ventilation design team provides temperature control requirements, air circulation scheme and ventilation equipment requirements, and clearly defines the technical constraints behind the requirements, including structural safety, space limitations for equipment arrangement, load requirements of pipeline and power system, etc. The collected requirement data from different design teams is sorted and classified to form a multi-source heterogeneous requirement dataset.

[0027] S1.2. Cleaning and semantic modeling of multi-source heterogeneous requirement data to form a requirement knowledge graph.

[0028] Specifically, the collected multi-source heterogeneous requirement data is cleaned to remove redundant, repetitive and irrelevant information. Data cleaning includes standardizing different sources of requirement data, such as unifying units of area, power, temperature control, etc., and eliminating inconsistent data entries. During the data cleaning process, missing data is filled in, for example, when a design team does not provide a specific requirement, it needs to be supplemented through communication or reference to relevant standards. Semantic modeling is performed to identify the relationships between key elements (such as users, functions, rules) in the design team's requirements, define and connect them using a standard format (such as JSON or knowledge graph), and convert them into structured semantic information. Through natural language processing, key requirements, functional requirements and constraint conditions are extracted to generate a requirement knowledge graph.

[0029] S1.3. Based on the requirement knowledge graph, the geometric and semantic information of BIM and the real-time monitoring data of IoT sensors are associated according to spatial location and component identification, and a preliminary digital twin model is generated through parameterized modeling and semantic mapping. Machine learning algorithms are used for automatic checking and conflict detection to obtain a digital twin model.

[0030] Specifically, the geometric information and spatial layout in BIM and the real-time monitoring data provided by IoT (such as equipment operating status, environmental temperature and humidity, etc.) are used to comprehensively describe the design task and the on-site operating conditions. Through parameterized modeling, each design element has variability and flexibility, and is updated as the requirements and on-site data change. Through semantic mapping, the requirements in the requirement knowledge graph are semantically related to the elements in BIM, allowing the design scheme to accurately match the actual requirements, forming a preliminary digital twin model. After generating the preliminary digital twin model, an automatic check and conflict detection is performed using a machine learning algorithm; the machine learning algorithm identifies potential conflicts between the design and actual data (such as conflicts between equipment layout and space limitations, contradictions between functional requirements and structural constraints, etc.), and gives optimization suggestions or automatically adjusts the preliminary digital twin model. After completing the optimization of the preliminary digital twin model, appropriate permissions are assigned to each design team to ensure that each team can access and modify the design parts related to their responsibilities, ensuring information security and management in collaborative work.

[0031] It should be noted that semantic mapping is to build an accurate translation and connection bridge between the requirement knowledge graph and the design model (BIM + parametric model), and to associate abstract requirement statements with specific design elements and real-time data through unified semantic standards.

[0032] S1.4. Normalize the multi-source heterogeneous requirement data to obtain standardized project data, and perform multi-dimensional hierarchical task decomposition on the project through work breakdown structure and product breakdown structure, assign unique code identifiers, and establish a logical relationship network between tasks.

[0033] Specifically, data cleaning techniques are used to remove outliers and duplicates, format conversion tools (such as BIM professional tools) are used to unify data structure and file types, and unit conversion rules are used to ensure that all measurement units conform to a unified standard (such as IFC standard), forming standardized project data; Based on standardized project data, a combination of work breakdown structure and product breakdown structure is used to divide tasks into the first level according to project stages and professional fields (such as space layout, facade design, etc.), refine the second level according to physical components (such as design work for foundation components, column components), and perform third-level decomposition for specific operations and deliverables (such as analyzing geological survey reports); each minimum task unit is assigned a unique structured code identifier (such as the coding rule example "project number-professional code-sequence number" PRJ2023-STR-001), and a logical relationship network is established according to the technical dependency between tasks.

[0034] It should be noted that the combination of work breakdown structure (WBS) and product breakdown structure (PBS) is a method of matrix association between the work required to complete the project (WBS) and the physical product components required to deliver the project (PBS); PBS ensures that all physical components (such as beams, plates, columns of buildings, pipes, valves of equipment) are clearly defined and covered, and WBS decomposes each component into specific design, procurement, installation, etc. task package.

[0035] S1.5. The structured task nodes, design components, project documents and personnel role elements in the logical relationship network between tasks are stored in association, and the semantic relationship between the personnel role elements is established through semantic rules to form a collaborative management matrix.

[0036] Specifically, based on the task unit coding and associated relationship in the logical relationship network between tasks, the structured task nodes with unique coding identification, design components (including building components and equipment elements) in digital twin models, project documents (including design specifications and calculation books) and personnel role elements are stored in node form through a graph database (such as Neo4j), and semantic rules (such as "task unit A is responsible for generating design component B", "personnel role C approves project document D") are defined according to industry standards (such as IFC standard), and the semantic properties of the relationship between nodes are given through semantic mapping in the storage process, forming a collaborative management matrix integrated with multiple elements based on semantic association relationship.

[0037] It should be noted that permission policies are configured for different data objects and functional operations in the collaborative management matrix, and the collaborative management matrix is encapsulated as a standardized RESTful API service and a message queue interface, and real-time access services are provided to each participant through web and mobile applications, forming a shareable design task data and collaboration environment.

[0038] S2. Based on the collaborative management matrix, the project design is presented in a virtual reality environment in the digital twin model, and interactive design review and optimization are performed to generate an optimized design scheme.

[0039] S2.1. The geometric and semantic data of the digital twin model and the shareable design task data and collaboration environment configuration information in the collaborative management matrix are converted and rendered in real time through virtual reality rendering methods to generate a virtual reality environment.

[0040] Specifically, structured geometric information and semantic labels are extracted from the geometric and semantic data of the digital twin model, including the three-dimensional shape, size and spatial layout of buildings, equipment, pipelines, etc., and semantic data includes information such as various design tasks, functional requirements and technical constraints; The data related to project progress, task relationship, resource allocation, etc. are obtained from the shareable design task data and collaboration environment in the collaborative management matrix; The data connection with the virtual reality platform is established through the RESTful API interface, the shareable design task data (including task unit code, responsibility allocation, progress status) in the collaborative management matrix and the collaborative environment configuration information are completely transmitted to the virtual reality rendering method, the three-dimensional project scene is reconstructed in the virtual reality rendering engine according to the geometric data and semantic information of the digital twin model, and the immersive roaming is realized through the virtual reality device (such as the head-mounted display device and the handle controller), and the spatial relationship of the building structure, the installation layout of the equipment component and the pipeline design are interactively viewed, wherein the building structure shows the beam column positioning and the spatial size, the equipment layout presents the installation position of the mechanical and electrical equipment and the maintenance channel, and the pipeline design scheme displays the pipeline routing and the elevation information, forming a virtual reality environment.

[0041] It should be noted that the virtual reality rendering method is a technical process of converting the geometric data and semantic information of the digital twin model into an immersive three-dimensional environment through a real-time graphics engine, importing the mesh data of the BIM model using a game engine (such as Unity or Unreal), applying material mapping, lighting and shadow effects to build a basic scene; left and right eye images are generated respectively through stereoscopic rendering technology, and depth perception is presented through a head-mounted display; at the same time, a physics engine is integrated to realize collision detection and spatial navigation, and based on semantic information, interactive functions such as component attribute query and section analysis are realized, scene roaming, component selection and information retrieval are realized through a handle control, forming an immersive collaborative verification environment supporting multi-scale and multi-dimensional information display.

[0042] S2.2. In the virtual reality environment, mark the parts that need to be modified, obtain modification opinions and optimization tasks; according to the modification opinions and optimization tasks, adjust the original design scheme to generate an alternative design scheme, and after comparison and confirmation in the virtual reality environment, generate an optimized design scheme.

[0043] S2.2.1. Extract modification parameters and optimization targets from the modification opinions and optimization tasks, and call the multi-objective particle swarm optimization algorithm for iterative operation to generate an alternative design scheme.

[0044] Specifically, the modification parameters of the component size adjustment value and the material attribute change requirement and the multiple optimization targets of cost control and performance improvement are extracted from the modification opinions and optimization tasks, the multi-objective particle swarm optimization algorithm (MOPSO) is called to set the population size, for example, the value is 100, the iteration number is 200, and the parameters such as the value are set, the parameterized components (such as beam column section, pipe diameter, equipment arrangement coordinates) in the digital twin model are driven to automatically iterate and operate, and the digital twin model is automatically adjusted in each iteration, the specification verification and performance simulation are executed, and an alternative design scheme is generated.

[0045] S2.2.2. Push the alternative design schemes to the virtual reality environment, and comprehensively evaluate and rank the performance indicators of the alternative design schemes by a multi-attribute decision analysis algorithm to generate an optimized design scheme.

[0046] Specifically, the alternative design schemes are pushed to the virtual reality environment, and the multi-dimensional performance indicators (such as the building cost example value is ten thousand yuan, the energy efficiency example value is kWh / m² / year, and the structural safety factor example value is ) of the alternative design schemes are weighted and calculated and comprehensively evaluated by a multi-attribute decision analysis algorithm (such as the TOPSIS method) to generate a priority ranking result of the alternative design schemes; according to the ranking result, the scheme with the highest comprehensive score is automatically selected to generate an optimized design scheme.

[0047] It should be noted that the weighted calculation and comprehensive evaluation by the multi-attribute decision analysis algorithm are as follows: a decision matrix containing the performance indicators of each scheme is constructed, and the cost-type (such as building cost) and benefit-type (such as safety factor) indicators are normalized; the objective weights are automatically calculated according to the discrete degree of the indicator data by the entropy weight method to avoid subjective bias; the Euclidean distances of each scheme from the positive ideal solution (the optimal value combination of each indicator) and the negative ideal solution (the worst value combination of each indicator) are calculated by the TOPSIS method, and the relative closeness (the closeness to the positive ideal solution) is used as the comprehensive score for ranking to select the optimal scheme.

[0048] It should be noted that the expression for calculating the relative closeness of each alternative design scheme to the optimal scheme in the ideal solution is as follows: ; wherein, represents the relative closeness of the i-th alternative design scheme, represents the weighted Euclidean distance of the i-th scheme to the negative ideal solution, represents the weighted Euclidean distance of the i-th scheme to the positive ideal solution, represents the index variable of the i-th scheme. S3. Based on the optimized design scheme, the project team arranges task execution according to the design progress to generate a task execution plan and resource scheduling data. S3.1. Identify the design change content in the optimized design scheme, update the design task list, mark the added, modified, and canceled task items, calculate the time arrangement of each task in combination with the available resource status, and allocate human and equipment resources to each task.

[0049] S3.1. Identify the design change content in the optimized design scheme, update the design task list, mark the added, modified, and canceled task items, calculate the time arrangement of each task in combination with the available resource status, and allocate human and equipment resources to each task.

[0050] S3.1. Identify the design change content in the optimized design scheme, update the design task list, mark the added, modified, and canceled task items, calculate the time arrangement of each task in combination with the available resource status, and allocate human and equipment resources to each task.

[0051] ​​Specifically, by comparing the design data (such as BIM models, drawing files or parameterized data sets) of two versions through the version comparison algorithm, the newly added, modified or deleted design content in the optimized design scheme is identified, and based on the identification result, the corresponding new task item is created in the design task list, the to-be-updated task item is marked, and the invalid task item is cancelled; combined with the available resource state (including the skill level of human resources and work load, equipment resource available time period), the start time, end time and float time of each task are calculated, and specific human resources (clearly responsible person and participants) and equipment resources (clearly equipment type, quantity and use time period) are allocated for each task.

[0052] It should be noted that the human resource state is derived from the employee skill profile of the HR and the work hour reporting data in the project management system, forming a comprehensive portrait including skill level certification, current task load and future time availability; the equipment resource state comes from the scheduling calendar, maintenance records and Internet of Things sensor data of the equipment management system, which real-time tracks the equipment location, running state and booked period, and jointly builds the available resource state.

[0053] S3.2. Convert human and equipment resources into Gantt chart, automatically check through resource allocation rules, generate task execution plan and resource scheduling data, and synchronize to digital twin model through API interface.

[0054] Specifically, according to the human and equipment resources, the human and equipment resources required for each task are sorted out, the allocation of human resources includes the number of personnel required for each task, skill requirements, work duration, etc., the allocation of equipment resources includes the type, quantity and use duration of equipment required for each task, and are input into the Gantt chart to obtain the conversion of human and equipment resources into Gantt chart. After converting human and equipment resources into Gantt chart, according to the resource allocation rules, the task execution plan is automatically checked, the resource configuration is automatically adjusted through analyzing resource availability, task dependency relationship and time arrangement, the task execution plan and resource scheduling data are generated, and are synchronized to the digital twin model through the API interface.

[0055] It should be noted that the resource allocation rules are defined according to the task breakdown structure in the collaborative management matrix, identify the human, equipment and other resources required for each task, conduct resource demand analysis combined with the project resource pool (such as available personnel, equipment quantity, equipment use time), consider task priority, time window between tasks, resource use restrictions and task dependency relationship, develop rules to optimize the allocation of resources, and form the resource allocation rules.

[0056] S4. Monitor the task execution plan and resource scheduling data for quality control, and generate a quality control report.

[0057] S4.1. Monitor the actual execution of task execution plans and resource scheduling data through on-site sensor data, and perform spatiotemporal alignment and fusion processing with automated inspection data to obtain multi-source quality fusion data.

[0058] Specifically, the actual execution status of task execution plans and resource scheduling data is monitored through on-site sensor data (the actual situation of planned tasks and resource scheduling in the real world). The actual execution status of task execution plans and resource scheduling data is synchronized with the data collected by automated inspection through timestamp and spatial coordinate registration to obtain multi-source heterogeneous data. The multi-source heterogeneous data is then processed by weighted averaging to eliminate data redundancy and conflicts, forming spatiotemporally aligned multi-source quality fusion data containing multi-dimensional information such as geometric size deviation, resource consumption status, and quality indicators.

[0059] S4.2. Based on multi-source quality fusion data, conduct real-time analysis of construction quality, record quality deviations, classify and assess their severity, and obtain a quality control report.

[0060] Specifically, based on spatiotemporally aligned multi-source quality fusion data and anomaly detection algorithms, the system learns the distribution patterns of normal data to locate anomalies that significantly deviate from these patterns. It identifies quality deviations such as dimensional errors and material defects during construction, classifies and evaluates the identified deviations according to their type (e.g., geometric deviations, material non-compliance) and severity, records the specific parameters, location information, and evaluation results of all deviations, and generates a structured quality control report containing deviation details, classification levels, and handling suggestions.

[0061] S5. Archive the optimized design scheme, task execution plan, and quality control report to generate a complete project archive document.

[0062] S5.1. Integrate and optimize the design scheme, task execution plan and quality control report, and perform version solidification to generate an unalterable data snapshot.

[0063] Specifically, the optimized design scheme, task execution plan, and quality control report are integrated, and a digital fingerprint is generated using a hash algorithm (such as SHA-256). The data content and digital fingerprint are packaged together and timestamped. The data packet is then encrypted using digital signature technology to form an immutable data snapshot.

[0064] S5.2. Based on the component coding system of immutable data snapshots and digital twin models, establish an association index between design schemes, planning nodes, quality issues and relevant responsible parties; organize and encapsulate the association index and immutable data snapshots in accordance with project management specifications to form a complete project archive document.

[0065] Specifically, based on an immutable data snapshot and digital twin model component coding system, a two-way association index is established between design components in the design scheme, planning nodes in the task execution plan, quality issues in the quality control report, and relevant responsible parties; Organize and encapsulate the associated indexes and tamper-proof data snapshots in accordance with the ISO 19650 project management standard, add standardized metadata annotations (including project phase, professional classification, archiving time, and responsible person), and form a complete project archive document.

[0066] It should be noted that the bidirectional association index between relevant responsible parties is to establish a bidirectional traceable map of rights and responsibilities for each entity (such as components, tasks, and issues) and relevant parties (such as design, construction, and supervision units) in the project.

[0067] S6. After the project is completed and delivered, provide operational data in real time and make design modifications and optimizations based on operational feedback, and generate operational data reports.

[0068] S6.1. Establish a standardized data interface between the digital twin model and the operations team, and collect equipment operating status, energy consumption monitoring, environmental parameters and operational data in real time. At the same time, set up an operations feedback receiving channel and collect equipment fault records and usage improvement suggestions.

[0069] Specifically, a standardized data interface based on RESTful API is deployed between the digital twin model and the operations team. This interface enables the real-time transmission of equipment operation status monitoring data, energy consumption monitoring readings, environmental parameter collection data, and other operational data. Simultaneously, an operational feedback receiving channel is established, using a combination of web forms and message queues to collect equipment fault records, reports, and suggestions for improvement.

[0070] S6.2. Based on equipment fault records and usage improvement suggestions, conduct collaborative performance evaluations, modify solutions and perform simulation verification through a digital twin environment, and regularly integrate equipment operating status and optimized design solutions to generate operational data reports.

[0071] Specifically, based on equipment fault records and usage improvement suggestions, parameters are adjusted and solutions are modified for problematic or needing optimization components in a digital twin environment. The modified solutions are then verified through multi-dimensional simulation using a digital twin model. The equipment operating status and optimized design solutions are regularly integrated and analyzed to generate operational data reports.

[0072] This embodiment also provides an engineering collaborative design and management system based on digital twins, including: an integration module for collecting requirements from various design teams, constructing a digital twin model, and integrating various design tasks into a unified matrix to obtain a collaborative management matrix; a collaboration module for presenting project designs in the digital twin model through a virtual reality environment based on the collaborative management matrix, and conducting interactive design reviews and optimizations to generate optimized design schemes; a resource scheduling module for scheduling tasks according to the design progress based on the optimized design schemes, generating task execution plans and resource scheduling data; a monitoring module for monitoring the actual execution of task execution plans and resource scheduling data, analyzing execution deviations and performing quality control to generate quality control reports; a management module for archiving optimized design schemes, task execution plans, and quality control reports to generate complete project archive documents; and a feedback module for providing real-time operational data and modifying and optimizing designs after project completion and delivery to generate operational data reports.

[0073] In summary, this invention, by combining digital twin models with virtual reality technology, achieves design optimization and interactive review, improves design accuracy and team collaboration efficiency, and enhances design quality and project management efficiency by automatically generating task execution plans and resource scheduling data, thereby increasing the flexibility and real-time nature of project execution.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A collaborative engineering design and control method based on digital twins, characterized in that: include, Collect the requirements of each design team, build a digital twin model, and integrate each design task into a unified matrix to obtain a collaborative management matrix; Based on the collaborative management matrix, the project design is presented in a virtual reality environment in the digital twin model, and interactive design review and optimization are carried out to generate an optimized design scheme. Based on the optimized design, the project team schedules task execution according to the design progress and generates task execution plans and resource scheduling data. Monitor the actual execution of task execution plans and resource scheduling data, analyze execution deviations and perform quality control to generate quality control reports; The optimized design scheme, task execution plan, and quality control report are archived to generate a complete project archive document; After the project is completed and delivered, operational data is provided in real time, and design modifications and optimizations are made to generate operational data reports.

2. The engineering collaborative design and control method based on digital twins as described in claim 1, characterized in that: The specific steps for collecting requirements from various design teams and constructing a digital twin model are as follows. Collect design inputs, functional requirements, and technical constraints from design teams of various disciplines, including architecture, structure, mechanical and electrical engineering, and HVAC, to obtain multi-source heterogeneous demand data; Clean and semantically model multi-source heterogeneous demand data to form a demand knowledge graph; Based on the demand knowledge graph, the geometric and semantic information of BIM is associated with the real-time monitoring data of IoT sensors according to spatial location and component identification. Through parametric modeling and semantic mapping, a preliminary digital twin model is generated. Then, machine learning algorithms are used for automatic verification and conflict detection to obtain the final digital twin model.

3. The engineering collaborative design and control method based on digital twins as described in claim 1, characterized in that: The specific steps to obtain the collaborative management matrix are as follows: Normalize multi-source heterogeneous demand data to obtain standardized project data, and decompose the project into multi-dimensional hierarchical tasks through work breakdown structure and product breakdown structure, assign unique code identifiers, and establish a logical relationship network between tasks. The structured task nodes, design components, project documents, and personnel role elements in the logical relationship network between tasks are associated and stored, and semantic relationships between personnel role elements are established through semantic rules to form a collaborative management matrix.

4. The engineering collaborative design and control method based on digital twins as described in claim 1, characterized in that: The specific steps for generating the optimized design scheme are as follows: The geometric and semantic data of the digital twin model, the shareable design task data and collaborative environment configuration information in the collaborative management matrix are converted and rendered in real time using virtual reality rendering methods to generate a virtual reality environment. In the virtual reality environment, the parts that need to be modified are marked to obtain modification suggestions and optimization tasks; based on the modification suggestions and optimization tasks, the original design scheme is adjusted to generate alternative design schemes; after comparison and confirmation in the virtual reality environment, the optimized design scheme is generated.

5. The engineering collaborative design and control method based on digital twins as described in claim 4, characterized in that: Based on the modification suggestions and optimization tasks, the original design scheme is adjusted to generate alternative design schemes. After comparison and confirmation in a virtual environment, an optimized design scheme is generated. The specific steps are as follows. Extract modification parameters and optimization objectives from modification suggestions and optimization tasks, and call the multi-objective particle swarm optimization algorithm for iterative calculation to generate alternative design schemes; The alternative design schemes are pushed into the virtual reality environment, and the performance indicators of the alternative design schemes are comprehensively evaluated and ranked by a multi-attribute decision analysis algorithm to generate an optimized design scheme.

6. The engineering collaborative design and control method based on digital twins as described in claim 1, characterized in that: The specific steps for generating the task execution plan and resource scheduling data are as follows: Identify the design changes in the optimized design scheme, update the design task list, mark the added, modified and canceled task items, calculate the time schedule for each task based on the available resource status, and allocate human and equipment resources for each task; Human and equipment resources are transformed into Gantt charts, and resource allocation rules are used for automated verification to generate task execution plans and resource scheduling data, which are then synchronized to the digital twin model via API interfaces.

7. The engineering collaborative design and control method based on digital twins as described in claim 1, characterized in that: The specific steps for generating the quality control report are as follows: By monitoring the actual execution of task execution plans and resource scheduling data using on-site sensor data and performing spatiotemporal alignment and fusion processing with automated inspection data, multi-source quality fusion data is obtained. The construction quality is analyzed in real time based on multi-source quality fusion data, quality deviations are recorded, and they are classified and their severity is assessed to obtain a quality control report.

8. The engineering collaborative design and control method based on digital twins as described in claim 1, characterized in that: The specific steps for generating a complete project archive are as follows. Integrate and optimize the design scheme, task execution plan and quality control report, and perform version solidification to generate an unalterable data snapshot; Based on an immutable data snapshot and digital twin model, a component coding system is established to create an association index between design schemes, planning nodes, quality issues, and relevant responsible parties. The associated indexes and tamper-proof data snapshots are organized and packaged in accordance with project management standards to form a complete project archive.

9. The engineering collaborative design and control method based on digital twins as described in claim 1, characterized in that: The specific steps for generating the operational data report are as follows: Establish a standardized data interface between the digital twin model and the operations team, and collect equipment operating status, energy consumption monitoring, environmental parameters and operational data in real time. At the same time, set up an operations feedback receiving channel and collect equipment failure records and usage improvement suggestions. Based on equipment fault records and usage improvement suggestions, we collaboratively conduct performance assessments, modify solutions and perform simulation verification through a digital twin environment, and regularly integrate equipment operating status and optimized design schemes to generate operational data reports.

10. A digital twin-based collaborative engineering design and control system, based on the digital twin-based collaborative engineering design and control method according to any one of claims 1 to 9, characterized in that: include, The integration module is used to collect the requirements of various design teams, build a digital twin model, and integrate various design tasks into a unified matrix to obtain a collaborative management matrix. The collaboration module is used to present project designs in a virtual reality environment within a digital twin model based on a collaborative management matrix, and to conduct interactive design reviews and optimizations to generate optimized design solutions. The resource scheduling module is used by the project team to arrange task execution based on the optimized design scheme and the design progress, and to generate task execution plans and resource scheduling data. The monitoring module is used to monitor the actual execution of task execution plans and resource scheduling data, analyze execution deviations and perform quality control to generate quality control reports; The management module is used to archive optimized design schemes, task execution plans, and quality control reports, generating complete project archive documents; The feedback module is used to provide real-time operational data and make design modifications and optimizations after the project is completed and delivered, generating operational data reports.

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