A multi-end collaborative work method for an engineering digital delivery platform

Abstract

The application discloses a multi-terminal collaborative work method for an engineering digital delivery platform, comprising the following steps: S1, establishing a unified central data model on the server side of the digital delivery platform, and performing structured processing on the unified central data model, thereby constructing the unified central data model and completing the structured processing, collecting multi-dimensional engineering data, unified format standards, quickly positioning data changes and responsible subjects, greatly reducing the difficulty of auditing and investigation, constructing a dynamic permission mapping model, abandoning fixed static authorization, taking into account work efficiency and data security, supporting audit tracking to realize full-process trace, adopting an intelligent difference synchronization and incremental push mode to replace traditional full-amount transmission, accurately identifying the change range for structured and unstructured data, transmitting only the incremental package, greatly reducing network load, guaranteeing consistency of multi-terminal data views, and improving cross-terminal collaborative accuracy.

Description

Technical Field

[0001] This application relates to the technical field of engineering digitization, and in particular to a multi-terminal collaborative working method for engineering digitization delivery platforms. Background Technology

[0002] With the deepening of digital transformation in fields such as industrial engineering and infrastructure engineering, digital delivery throughout the entire project lifecycle has gradually replaced traditional paper-based and discrete electronic delivery models, becoming a core trend in the industry's standardized development. Modern large-scale engineering projects involve collaborative operations across multiple stages, including design, procurement, construction, testing, and acceptance. Participants include internal management teams, on-site personnel, and third-party collaborators, and the operational terminals are also diversified: PC management terminals handle back-end coordination, mobile terminals handle on-site execution, on-site data collection terminals handle real-time data acquisition, and third-party collaboration terminals handle cross-unit data exchange. However, current digital delivery methods for engineering projects still have many shortcomings.

[0003] First, most existing digital delivery methods for engineering projects adopt a discrete data storage model that is segmented and system-specific. Engineering data such as design drawings, procurement lists, and construction records are scattered across different business systems. Data standards are not uniform and formats are incompatible at each stage, which easily leads to data silos. Engineering objects lack lifecycle metadata tags, making it difficult for managers to grasp the full picture of the entire engineering process. When problems occur, it is impossible to quickly locate the responsible party and data change nodes, making problem investigation extremely difficult.

[0004] Secondly, the common practice of using static permission allocation mechanisms, which divide permissions based solely on fixed roles without dynamically adjusting them according to the project task stage, data sensitivity, and terminal security environment, leads to several problems. On the one hand, static permissions are valid for a long time, which can easily result in redundant permissions, unauthorized access, and unauthorized operations, posing a risk of data leakage and tampering. On the other hand, the lack of a mechanism for temporary activation and automatic expiration of permissions means that permissions cannot be updated synchronously after the task stage changes, resulting in either insufficient permissions causing delays in operations or excessive permissions leading to security risks.

[0005] In addition, most existing multi-terminal data synchronization methods adopt the full synchronization mode, which requires the transmission of complete data packets regardless of the size of the data change, consuming a large amount of network bandwidth. In scenarios with poor network signal and insufficient bandwidth at the engineering site, synchronization timeout and interruption problems are very likely to occur. Moreover, the synchronization method lacks the ability to identify differences, and cannot accurately locate the changes in structured data and unstructured files. The asynchronous data updates of multiple terminals lead to inconsistent data views on each terminal.

[0006] Application content

[0007] This application aims to address, at least to some extent, the technical problems in the related art.

[0008] To achieve the above objectives, this application proposes a multi-terminal collaborative working method for a digital engineering delivery platform, including the following steps: S1, establishing a unified central data model on the server side of the digital delivery platform, and performing structured processing on the unified central data model to form a digital mainline containing multi-dimensional engineering objects and their related relationships, including design, procurement, construction, and inspection.

[0009] S2 is configured to support multi-terminal adaptation interfaces that allow access from PC management terminals, mobile operation terminals, field acquisition terminals, and third-party collaboration terminals. Based on terminal type, engineering role, and security policy, it dynamically loads differentiated functional components and data interaction protocols for various terminals to complete terminal access binding and status monitoring.

[0010] S3. Construct a dynamic permission mapping model based on engineering roles, task stages, and data sensitivity to assign real-time data operation permissions, task execution permissions, and process approval permissions to each terminal session, thereby achieving fine-grained permission isolation and audit tracking for cross-terminal operations.

[0011] S4. Based on the unified central data model and the preset delivery rules, the overall deliverables are decomposed into atomic collaborative sub-tasks through the adaptive task distribution engine. The sub-tasks and associated data resource packages are dynamically scheduled to the optimal terminal by combining the real-time capability status, geographical location and load of each terminal.

[0012] S5. Each terminal executes the distributed sub-tasks and uploads the generated structured data, unstructured files, and operation traceability logs through an encrypted channel. The platform server adopts an intelligent differential synchronization strategy to identify the semantics and scope of data changes and pushes incremental update packages to each relevant terminal as needed to maintain the consistency of multi-terminal data views.

[0013] S6 aggregates the outputs of multiple subtasks, automatically assembles, verifies, and generates a deliverable engineering digital asset package in the cloud based on predefined deliverable templates and version rules, electronically signs and stores the asset package on the blockchain, archives it to the central database, and triggers a delivery completion notification to the designated acceptance terminal.

[0014] In addition, the application may also include the following additional technical features:

[0015] Specifically, the structured processing in step S1 includes: attaching metadata tags to engineering objects containing lifecycle stages, responsible parties, version numbers, and compliance status, and establishing a network of references, dependencies, and parent-child relationships between objects to form a traceable digital thread.

[0016] Specifically, the construction of the dynamic permission mapping model in step S3 includes: setting a permission rule engine, which receives the context information of the terminal session, including the user role, the current task stage, the sensitivity level of the accessed data object, and the terminal security environment score, and calculates and binds a set of temporary permissions that automatically expire over time or with the task status in real time.

[0017] Specifically, the adaptive task distribution engine in step S4 operates by: defining the required terminal capability profile for each atomized collaborative subtask, wherein the capability profile includes the required hardware capabilities, software functions, network conditions, and personnel skill tags; collecting the performance status, network bandwidth, geographical location, and historical task completion quality data of each online terminal in real time to generate the real-time capability status of the terminal; matching and scoring the required capability profile of the subtask with the real-time capability status of each terminal, and selecting the target terminal for task scheduling based on the scoring results, the current load balancing strategy, and the urgency of the task.

[0018] Specifically, the intelligent difference synchronization strategy in step S5 includes: using field-level difference identification for structured data, using hash verification and fragment comparison for unstructured files to accurately locate data changes, using encrypted and compressed transmission during synchronization to reduce network load, and generating a synchronization log after synchronization to retain the time, subject, and content traceability information of data changes.

[0019] Specifically, the automatic assembly in step S6 includes: automatically capturing the verified sub-task outputs according to the structure of the deliverable template, filling them into the corresponding positions in the template, and automatically generating a directory, index, and relationship diagram to form a complete engineering digital asset package.

[0020] Specifically, step S6 also includes a multi-level delivery verification mechanism: after automatically assembling the digital asset package, format compliance verification and data integrity verification are performed first, and then logical verification at the engineering professional level is triggered. If the verification fails, it will automatically fall back to the corresponding sub-task terminal, mark the abnormal problem points and push rectification instructions, until all verifications pass before electronic signature and evidence storage are performed.

[0021] In summary, the beneficial effects of the multi-terminal collaborative working method for a digital engineering delivery platform proposed in this application are as follows:

[0022] 1. Construct a unified central data model and complete structured processing, collect multi-dimensional engineering data, unify format standards, eliminate data silos, and attach lifecycle metadata tags to engineering objects to build relationships and form a digital mainline, making it easier for managers to grasp the data of the whole process, quickly locate data changes and responsible parties, and significantly reduce the difficulty of auditing and investigation.

[0023] 2. Construct a dynamic permission mapping model, abandon fixed static authorization, adjust permissions in real time based on terminal context, and eliminate the risk of unauthorized access and data leakage through a mechanism for the automatic activation and deactivation of temporary permissions. Achieve multi-dimensional fine-grained permission isolation, and update permissions synchronously with the task stage to balance work efficiency and data security. Supporting audit trails to achieve full traceability.

[0024] 3. Employing intelligent differential synchronization and incremental push mode to replace traditional full transmission, it accurately identifies the scope of changes for both structured and unstructured data, transmitting only incremental packets, significantly reducing network load, adapting to weak network environments, avoiding synchronization interruptions, and eliminating version conflicts by pushing update packages on demand, ensuring data visibility across multiple devices. Figure 1 This will improve the accuracy of cross-terminal collaboration. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0026] Figure 1 This is a flowchart illustrating a multi-terminal collaborative working method for a digital engineering delivery platform, as described in this application.

[0027] Figure 2 This application presents a flowchart of an adaptive task distribution engine for a multi-terminal collaborative working method for a digital engineering delivery platform.

[0028] Figure 3 This application presents an automatic assembly and multi-level delivery verification flowchart for a multi-terminal collaborative working method for a digital engineering delivery platform. Detailed Implementation

[0029] To make the technical means, inventive features, objectives, and effects of this application easier to understand, the application is further described below with reference to specific illustrations. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0030] The present application will now be described in further detail with reference to the accompanying drawings.

[0031] like Figure 1 As shown in the figure, a multi-terminal collaborative working method for an engineering digital delivery platform according to an embodiment of this application includes the following steps: S1, establishing a unified central data model on the server side of the digital delivery platform, and performing structured processing on the unified central data model to form a digital mainline containing multi-dimensional engineering objects and their related relationships, including design, procurement, construction, and inspection.

[0032] It should be noted that, based on the principles of data governance throughout the entire engineering lifecycle, this approach breaks down the discrete data barriers between design, procurement, construction, and testing stages. Fragmented engineering entities and business information are abstracted into standardized data objects. Through structured analysis, the relationships and mappings between these objects are established, ultimately forming a digital thread that runs through the entire process from project initiation to delivery. This digital thread serves as the sole reliable source of engineering data, enabling unified collection, standardization, and management of all business data, and eliminating conflicts and duplicate entries from multiple data sources.

[0033] This step uses a digital thread to connect business data across all stages, making engineering changes, responsibility attribution, and version iterations readily apparent. This facilitates project review, problem identification, and compliance auditing. Structured storage reduces the cost of data retrieval, retrieval, and reuse, supports rapid access to target data across multiple terminals and roles, reduces repetitive workload, and provides underlying data support for subsequent task distribution, data synchronization, and delivery verification through multi-dimensional object relationships.

[0034] S2 is configured to support multi-terminal adaptation interfaces that allow access from PC management terminals, mobile operation terminals, field acquisition terminals, and third-party collaboration terminals. Based on terminal type, engineering role, and security policy, it dynamically loads differentiated functional components and data interaction protocols for various terminals to complete terminal access binding and status monitoring.

[0035] It should be noted that, by adopting the principle of platform-based layered adaptation and dynamic component loading, a standardized interface adaptation layer is built to address the differences in hardware characteristics, usage scenarios, and network environments of four types of terminals: PC management terminals, mobile operation terminals, field data collection terminals, and third-party collaboration terminals. Through terminal identity recognition and role permission prediction, matching functional components and data interaction protocols are dynamically loaded to achieve flexible access with a one-terminal-one-policy approach, while monitoring the terminal status throughout the process to ensure access security.

[0036] This step is adaptable to diverse scenarios such as engineering back-end management, on-site mobile operations, and third-party collaboration, breaking time and space limitations and enabling collaborative operations anytime and anywhere. It loads functional components in a differentiated manner to avoid redundant functions occupying terminal resources and improves the smoothness of operation of on-site mobile devices and low-configuration terminals.

[0037] S3. Construct a dynamic permission mapping model based on engineering roles, task stages, and data sensitivity to assign real-time data operation permissions, task execution permissions, and process approval permissions to each terminal session, thereby achieving fine-grained permission isolation and audit tracking for cross-terminal operations.

[0038] It should be noted that by integrating the principles of role-based access control and dynamic context adaptation, and abandoning the traditional static access control model, an access control rule engine is built by combining the role division of engineering business, task stage, data sensitivity level, and terminal security environment. By parsing session context information in real time, it dynamically calculates and binds temporary permissions, so that permissions can be automatically adjusted according to tasks, scenarios, and states, balancing the rigidity of control with the flexibility of operation.

[0039] This process aligns with the phased operational needs of engineering projects, avoiding data leakage risks caused by redundant permissions and preventing operational efficiency issues due to insufficient permissions. It precisely controls the operational scope of different roles, strictly prevents unauthorized access and unauthorized modification of core engineering data, ensures data security, and features an automatic expiration mechanism for temporary permissions to reduce residual permissions and meet the stringent security and compliance requirements of the engineering industry.

[0040] S4. Based on a unified central data model and preset delivery rules, the overall deliverables are decomposed into atomic collaborative sub-tasks through an adaptive task distribution engine. The sub-tasks and associated data resource packages are dynamically scheduled to the optimal terminal based on the real-time capability status, geographical location and load of each terminal.

[0041] It should be noted that, based on the principles of task atomic decomposition, bidirectional capability matching, and load balancing scheduling, the complex overall delivery task is decomposed into independent, executable, and clearly defined atomic subtasks. By constructing task requirement profiles and terminal capability profiles, and combining the terminal's real-time status, load conditions, and geographical location, the algorithm scores the task to achieve optimal scheduling, allowing the right terminal to undertake the right task.

[0042] This step breaks down complex tasks into atomic parts, reducing the workload on a single terminal, simplifying workflows, improving overall collaboration efficiency, matching specialized tasks with specialized terminals and personnel, ensuring work quality, reducing the probability of rework, avoiding uneven workloads on terminals, maximizing the use of multi-terminal hardware and human resources, and accelerating the overall progress of the project.

[0043] S5. Each terminal executes the distributed sub-tasks and uploads the generated structured data, unstructured files, and operation traceability logs through an encrypted channel. The platform server adopts an intelligent differential synchronization strategy to identify the semantics and scope of data changes and pushes incremental update packages to each relevant terminal as needed to maintain the consistency of multi-terminal data views.

[0044] It should be noted that by adopting the principle of incremental differential synchronization and hierarchical verification and identification, the inefficient mode of traditional full synchronization is abandoned. Differential verification methods are used for structured business data and unstructured engineering files to accurately locate data changes. Network load is reduced by encrypted and compressed transmission, enabling incremental updates of data across multiple terminals and maintaining real-time consistency of data views across all terminals.

[0045] This step replaces full synchronization with incremental synchronization, significantly reducing data transmission volume and improving synchronization speed. It is suitable for weak network environments at engineering sites, pushes incremental updates in real time from multiple devices, avoids data conflicts and version inconsistencies, eliminates coordination errors, and uses encrypted channels to prevent leakage and tampering during engineering data transmission, ensuring the security of core data.

[0046] S6 aggregates the outputs of multiple subtasks, automatically assembles, verifies, and generates a deliverable engineering digital asset package in the cloud based on predefined deliverable templates and version rules, electronically signs and stores the asset package on the blockchain, archives it to the central database, and triggers a delivery completion notification to the designated acceptance terminal.

[0047] It should be noted that, based on the principles of template-based automated assembly, multi-level compliance verification, and blockchain notarization, and using standardized delivery templates as a framework, all sub-task outputs are automatically collected; the compliance of deliverables is ensured through a multi-level verification mechanism; and finally, the immutability and permanent archiving of delivered assets are achieved through electronic signatures and blockchain notarization, thus completing the digital delivery closed loop.

[0048] This step replaces the tedious work of manually sorting and assembling deliverables with automated delivery, reducing human error and significantly shortening the delivery cycle. It ensures that the delivered assets are qualified and compliant, meeting the project acceptance standards, by checking the format, completeness, and professional logic at every level. Electronic signatures and blockchain notarization enable the permanent retention and immutability of delivered digital assets, giving them legal effect. It is also suitable for project audits and long-term archiving. The entire delivery process is traceable, and the rectification, verification, signing, and notarization stages are clearly traceable, achieving closed-loop management of delivery.

[0049] In one embodiment of this application, the structuring process of step S1 includes: attaching metadata tags containing lifecycle stages, responsible parties, version numbers and compliance status to engineering objects, and establishing a network of references, dependencies and parent-child relationships between objects to form a traceable digital thread.

[0050] Specifically, standardized metadata tags are uniformly attached to all types of engineering objects (including design models, construction drawings, process documents, acceptance documents, bills of materials, test reports, and other engineering data and entity mapping objects). These tags not only include lifecycle stage, responsible party, version number, and compliance status fields, but can also be extended with additional attributes such as iteration records, approval nodes, and reasons for changes according to the engineering scenario, so as to achieve full-dimensional information labeling of a single engineering object.

[0051] Based on this, a network of reference associations, dependency bindings, and parent-child relationships between different engineering objects is established through topological associations, hierarchical mappings, and association bindings. This clarifies the business logic and data flow links between objects and constructs a traceable digital mainline that runs through the entire process of project initiation, design, construction, acceptance, and operation and maintenance, providing underlying data support for subsequent data management, permission allocation, and task collaboration.

[0052] In one embodiment of this application, step S3 of constructing a dynamic permission mapping model includes: setting a permission rule engine, which receives context information of a terminal session, including user role, current task stage, sensitivity level of accessed data objects, and terminal security environment score, and calculates and binds a set of temporary permissions that automatically expire over time or task status in real time.

[0053] Specifically, a rule-driven lightweight permission rule engine is built. This engine is deployed using a microservice architecture and has low latency and high concurrency real-time processing capabilities. It can seamlessly receive multi-dimensional context information of terminal sessions. The context information not only covers user roles, current task stages, sensitivity levels of accessed data objects, and terminal security environment scores, but can also be compatible with accessing auxiliary information such as identity verification results, operation behavior trajectories, and project permission boundaries.

[0054] Based on a preset permission control algorithm, the engine performs real-time parsing and weighted calculations on context information to quickly calculate and generate a temporary permission set adapted to the current scenario. This permission set has strict timeliness and state binding characteristics, and will automatically expire and unbind as the task progresses, time passes, session ends, or scenario changes. It follows the principle of least privilege throughout the process, which not only ensures the smoothness of legitimate access, but also eliminates security risks such as unauthorized access and data leakage.

[0055] In one embodiment of this application, such as Figure 2 As shown, the working method of the adaptive task distribution engine in step S4 includes: defining the required terminal capability profile for each atomized collaborative subtask, the capability profile including the required hardware capabilities, software functions, network conditions, and personnel skill tags; collecting the performance status, network bandwidth, geographical location, and historical task completion quality data of each online terminal in real time to generate the real-time capability status of the terminal; matching and scoring the required capability profile of the subtask with the real-time capability status of each terminal, and selecting the target terminal for task scheduling based on the scoring results, the current load balancing strategy, and the task urgency.

[0056] Specifically, the overall engineering task is first modularly decomposed into several independent and executable atomic collaborative subtasks. A unique terminal capability profile is customized for each subtask. This profile not only quantifies the required hardware capabilities such as computing power and storage capacity, software functions such as office software and professional engineering tools, and network conditions such as network bandwidth, latency, and stability, but also refines and matches personnel skill tags such as professional qualifications, operational experience, and historical performance, thus clarifying the hard thresholds for task execution.

[0057] Secondly, the terminal monitoring plugin collects dynamic data from each online terminal in real time, covering core indicators such as real-time performance load, network bandwidth usage, geographical distribution, historical task completion rate, and error rate, and dynamically updates and generates a real-time capability status profile of the terminal.

[0058] Finally, the capability profiles required for subtasks are matched and scored with the real-time capability status of each terminal in a multi-dimensional weighted manner. Combined with preset load balancing strategies, task urgency priority, and geographical proximity scheduling rules, the optimal target terminal is selected to complete the task distribution and scheduling, thereby maximizing resource utilization and improving task execution efficiency.

[0059] In one embodiment of this application, the intelligent difference synchronization strategy in step S5 includes: using field-level difference identification for structured data, using hash verification and fragment comparison for unstructured files to accurately locate the data change content, using encrypted compression transmission during the synchronization process to reduce network load, and generating a synchronization log after synchronization is completed to retain the time, subject, and content traceability information of the data change.

[0060] Specifically, for structured engineering data (such as bills of materials, parameter ledgers, approval records, etc.), field-level incremental difference identification technology is adopted. Through key-value comparison and field snapshot retention, the newly added, modified, or deleted individual data fields are accurately located, avoiding the waste of resources in full data synchronization.

[0061] For unstructured engineering files (such as design drawings, model files, audio and video acceptance data, etc.), combining hash verification and fragment comparison technology, we first use hash value to quickly determine whether the file has been changed, and then we fragment and compare the different files to pinpoint the specific changed segments.

[0062] The synchronization transmission process employs a combination of national cryptographic encryption algorithms and streaming compression technology, which not only ensures the confidentiality of data transmission but also significantly reduces transmission volume and network load. After synchronization is completed, a standardized synchronization log is automatically generated, which records all essential information such as the timestamp of data changes, the subject of the operation, the content of the changes, and the synchronization results, providing a complete traceability basis for subsequent data auditing, problem tracing, and error rectification.

[0063] In one embodiment of this application, such as Figure 3 As shown, the automatic assembly in step S6 includes: automatically capturing the verified subtask outputs according to the structure of the deliverable template, filling them into the corresponding positions in the template, and automatically generating a directory, index, and relationship diagram to form a complete engineering digital asset package.

[0064] Specifically, standardized deliverable templates are pre-customized based on industry standards and project requirements. The templates have built-in fixed structural frameworks, field placeholders, layout formats and archiving requirements. In the automatic assembly process, the data interface accurately captures the sub-task outputs that have completed quality verification and compliance review, and fills them into the corresponding module placeholder areas with one click according to the preset logical relationships and position rules of the template.

[0065] Once the data is populated, the system automatically generates a hierarchical catalog, intelligently compiles an index, and draws a data relationship diagram. It also sorts out the business relationships and logical links between the various outputs, and finally packages them into a unified, complete, and directly archiveable engineering digital asset package. This significantly reduces the time spent on manual sorting and improves the standardization and uniformity of the deliverables.

[0066] In one embodiment of this application, such as Figure 3 As shown, step S6 also includes a multi-level delivery verification mechanism: after automatically assembling the digital asset package, format compliance verification and data integrity verification are performed first, and then the logical verification of the engineering professional dimension is triggered. If the verification fails, it will automatically fall back to the corresponding sub-task terminal, mark the abnormal problem points and push the rectification instructions, until all verifications pass before electronic signature and evidence storage are performed.

[0067] Specifically, after the digital asset package is automatically assembled, a three-level progressive verification process is initiated. First, a basic format compliance verification is conducted to check whether the file format, naming conventions, and layout style meet the template requirements. Second, a data integrity verification is conducted to check for missing files, blank fields, broken data links, and other issues to ensure that no content in the asset package is missing. Finally, an engineering professional dimension logic verification is triggered, which verifies the compliance and rationality of data logic, professional parameters, and business processes in accordance with industry technical standards, project design parameters, construction specifications, and other requirements.

[0068] If any step fails the verification, the system immediately terminates the delivery process, automatically rolls the asset package back to the execution terminal of the corresponding subtask, accurately marks the abnormal problem points, rectification requirements and deadlines, and pushes visual rectification instructions; after the terminal completes the rectification, the verification process is retried, forming a verification-abnormality-rectification-review process, until all verification items pass. Then, the system automatically executes electronic signature and blockchain notarization to ensure the tamper-proof nature and legal validity of the delivered assets.

[0069] It should be noted that, in this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0070] The present application and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present application. The actual structure is not limited to this. In conclusion, if a person skilled in the art is inspired by this description and designs a similar structure and embodiment without departing from the spirit of the present application, such design should fall within the protection scope of the present application.

Claims

1. A multi-terminal collaborative working method for a digital engineering delivery platform, characterized in that, Includes the following steps: S1. Establish a unified central data model on the server side of the digital delivery platform, and perform structured processing on the unified central data model to form a digital mainline that includes multi-dimensional engineering objects and their related relationships, including design, procurement, construction, and testing. S2. Configure a multi-terminal adaptation interface that supports access from PC management terminal, mobile operation terminal, field acquisition terminal and third-party collaboration terminal. Based on terminal type, engineering role and security policy, dynamically load differentiated functional components and data interaction protocols for various terminals to complete terminal access binding and status monitoring. S3. Construct a dynamic permission mapping model based on engineering roles, task stages, and data sensitivity to assign real-time data operation permissions, task execution permissions, and process approval permissions to each terminal session, thereby achieving fine-grained permission isolation and audit tracking for cross-terminal operations. S4. Based on the unified central data model and the preset delivery rules, the overall deliverables are decomposed into atomic collaborative sub-tasks through the adaptive task distribution engine, and the sub-tasks and associated data resource packages are dynamically scheduled to the optimal terminal in combination with the real-time capability status, geographical location and load of each terminal. S5. Each terminal executes the distributed sub-tasks and uploads the generated structured data, unstructured files, and operation traceability logs through an encrypted channel. The platform server adopts an intelligent differential synchronization strategy to identify the semantics and scope of data changes and pushes incremental update packages to each relevant terminal as needed to maintain the consistency of multi-terminal data views. S6 aggregates the outputs of multiple subtasks, automatically assembles, verifies, and generates a deliverable engineering digital asset package in the cloud based on predefined deliverable templates and version rules, electronically signs and stores the asset package on the blockchain, archives it to the central database, and triggers a delivery completion notification to the designated acceptance terminal.

2. The multi-terminal collaborative working method for a digital engineering delivery platform according to claim 1, characterized in that, The structured processing in step S1 includes: attaching metadata tags to engineering objects containing lifecycle stages, responsible parties, version numbers, and compliance status, and establishing a network of references, dependencies, and parent-child relationships between objects to form a traceable digital thread.

3. The multi-terminal collaborative working method for a digital engineering delivery platform according to claim 1, characterized in that, The construction of the dynamic permission mapping model in step S3 includes: setting a permission rule engine, which receives the context information of the terminal session, including the user role, the current task stage, the sensitivity level of the accessed data object, and the terminal security environment score, and calculates and binds a set of temporary permissions that automatically expire over time or with the task status in real time.

4. The multi-terminal collaborative working method for a digital engineering delivery platform according to claim 1, characterized in that, The working method of the adaptive task distribution engine in step S4 includes: Define the required terminal capability profile for each atomized collaborative subtask, the capability profile including the required hardware capabilities, software functions, network conditions and personnel skill tags; Real-time collection of performance status, network bandwidth, geographical location and historical task completion quality data of each online terminal to generate real-time terminal capability status; The required capabilities profiles of subtasks are matched and scored with the real-time capability status of each terminal. Based on the scoring results, the current load balancing strategy, and the urgency of the task, the target terminal is selected for task scheduling.

5. A multi-terminal collaborative working method for an engineering digital delivery platform according to claim 1, characterized in that, The intelligent difference synchronization strategy in step S5 includes: using field-level difference identification for structured data, using hash verification and fragment comparison for unstructured files to accurately locate data changes, using encrypted and compressed transmission during synchronization to reduce network load, and generating a synchronization log after synchronization to retain the time, subject, and content traceability information of data changes.

6. The multi-terminal collaborative working method for a digital engineering delivery platform according to claim 1, characterized in that, The automatic assembly in step S6 includes: automatically capturing the verified sub-task outputs according to the structure of the deliverable template, filling them into the corresponding positions in the template, and automatically generating a directory, index, and relationship diagram to form a complete engineering digital asset package.

7. A multi-terminal collaborative working method for an engineering digital delivery platform according to claim 1, characterized in that, Step S6 also includes a multi-level delivery verification mechanism: after automatically assembling the digital asset package, format compliance verification and data integrity verification are performed first, and then logical verification at the engineering professional level is triggered. If the verification fails, it will automatically fall back to the corresponding sub-task terminal, mark the abnormal problem points and push rectification instructions, until all verifications pass before electronic signature and evidence storage are performed.

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