An engineering construction whole-cycle digital twin collaborative management and control method

Abstract

The application discloses an engineering construction whole-cycle digital twin cooperative management and control method, comprising the following steps: fusing BIM and CIM to construct a space-time digital bottom plate and generate a digital twin; receiving multi-source data update twin from edge node breakpoint continuation; according to layered permissions of each participating party, operation data is hashed and encrypted and chained to a consortium chain for evidence preservation and traceability; through preset geometry and progress deviation threshold, the twin is judged to be out of limit, and when the threshold is exceeded, the twin is updated and closed-loop chained after rectification and re-measurement; based on the space-time digital bottom plate, decision weight calculation, progress time sequence prediction and deviation analysis, and emergency path search are carried out, and consultation weight, progress warning and dispatch path are output. The application connects space and business data by fusing and constructing a digital twin, and combines a three-level permission consortium chain and edge-cloud breakpoint continuation to realize real-time linkage between twin monitoring and business processes, and forms intelligent, reliable and closed-loop whole-cycle management and control capability.

Description

Technical Field

[0001] This application relates to the field of engineering management and control technology, and in particular to a digital twin collaborative management and control method for the entire lifecycle of engineering construction. Background Technology

[0002] The entire construction cycle comprises five phases: planning, design, construction, delivery, and operation and maintenance. Digital twins provide a new approach to project management through bidirectional mapping between physical and digital models. BIM is used for component-level modeling, while CIM is used for city-level spatial foundations. As project scale expands, the need for cross-party data collaboration becomes urgent, but current digital management remains fragmented, making it difficult to form a unified digital twin.

[0003] An existing BIM+CIM visualization management system involves manually importing models and overlaying them with base maps, then periodically synchronizing business data via API. Users view the data according to fixed roles, and manual statistics and rectification are performed. This type of solution only achieves static display; BIM and CIM lack unified standards and real-time mapping, the digital twin lacks dynamic linkage capabilities, and collaboration efficiency is low.

[0004] The system employs fixed role-based permissions and lacks a three-tiered penetration mechanism encompassing macro (construction), meso (supervision / general contractor), and micro (subcontractor / work team) levels. The absence of blockchain-based evidence storage and traceability leads to a high risk of unauthorized access and untraceable operations. Scenarios such as consultations, progress tracking, and emergency responses lack intelligent algorithms, relying on human experience for decision-making. The centralized cloud architecture lacks edge nodes and breakpoint resume capabilities, making data susceptible to loss and experiencing high synchronization latency in weak network / outage environments.

[0005] Furthermore, the authentication of multiple participating parties is inconsistent, and permission configurations are fixed, making dynamic authorization by stage and business type impossible. There is no standardized trigger path, clear threshold, or automatic task dispatch loop for deviations detected by the twin database, leading to manual handling of anomalies. Interface protocols for integration with OA, master data, and document systems are disorganized and inconsistent, resulting in low synchronization efficiency and poor consistency.

[0006] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0007] The main purpose of this application is to provide a digital twin collaborative management and control method for the entire life cycle of engineering construction, which aims to solve the technical problems of existing BIM / CIM being disconnected from business processes and lacking reliable collaborative management and control.

[0008] To achieve the above objectives, this application proposes a digital twin collaborative management and control method for the entire lifecycle of engineering construction, the method comprising: By integrating the building information model of individual buildings with the urban space base of the city information model, a spatiotemporal digital base is constructed, and a digital twin that maps to the physical engineering entity is generated using the spatiotemporal digital base as the data base. Receive multi-source field data uploaded by edge nodes deployed at the construction site in a breakpoint resume mode, and update the digital twin based on the data; According to the hierarchical permissions of all parties involved in the project, the operation data is hashed and encrypted and uploaded to the project consortium blockchain for data storage and operation traceability. The digital twin is judged to exceed the limits by using preset thresholds for planar position deviation, elevation deviation, structural size deviation and progress deviation. When the limit is exceeded, a work order is automatically triggered and the digital twin is updated and the closed-loop record is uploaded to the blockchain after receiving the rectification and retest results. Based on the aforementioned spatiotemporal digital baseboard, the weight calculation of multi-party decision indicators, the time-series prediction and deviation analysis of construction progress, and the search and planning of emergency paths are performed respectively, and the consultation weight, progress warning and scheduling path are output.

[0009] In one embodiment, the step of integrating the building information model (BIM) individual building model and the city information model (CIM) urban spatial base to construct a spatiotemporal digital base includes: Building information model (BIM) data and city information model (CIM) data are fused through spatial coordinate alignment and semantic mapping. The BIM data is in an industrial basic class format, and the CIM data is in a city geospatial markup language (CGML) format. A three-dimensional spatial index is established based on fused data, and the three-dimensional spatial index includes geometric information, attribute information and association information; Using the three-dimensional spatial index as a framework, time-series data fields are superimposed to form a spatiotemporal digital base.

[0010] In one embodiment, the step of receiving multi-source field data uploaded by edge nodes deployed at the construction site in a breakpoint resume mode includes: The system receives data uploaded by edge nodes, which are deployed in power distribution boxes, monitoring rooms, and tower crane locations. The data includes IoT sensor data, video surveillance data, and construction machinery operating data after local filtering and noise reduction. If a network interruption is detected during the reception process, the hash check value of the received data and the location of the reception breakpoint at the time of the interruption are recorded. Once the network is restored, the breakpoint location and data hash verification value carried in the resume request re-uploaded by the edge node are compared with the hash verification value recorded locally. If the verification passes, subsequent data will continue to be received from the receiving breakpoint location.

[0011] In one embodiment, the parties involved in the project include the construction unit, the supervision unit, the general contractor, and the subcontractors. The step of hashing and encrypting the operation data according to the hierarchical permissions of the parties involved in the project and uploading it to the project consortium blockchain for data storage and operation traceability includes: Determine the data operation permissions of each party involved in the project, wherein each permission has different data operation strategies; Based on the data operation strategy, the operation data of the parties involved in the project are obtained. A hash algorithm is used to generate a fixed-length digest value for the operation data, and the digital signature of the operator is attached. After being packaged into a transaction, it is verified by the consensus node in the consortium blockchain and written into an immutable block.

[0012] In one embodiment, the step of determining whether the digital twin exceeds limits based on preset thresholds for planar position deviation, elevation deviation, structural dimension deviation, and progress deviation includes: Real-time collection of spatial positioning data and progress reporting data at the construction site, and difference calculation between the data and the corresponding design reference data in the digital twin, to obtain the planar position deviation, elevation deviation, structural dimension deviation and progress deviation; When any deviation exceeds the corresponding threshold, the event will be marked as pending, and the preset rectification responsibility unit and rectification time limit will be automatically matched according to the type of deviation and spatial location information. Based on the matched responsible unit and rectification deadline, combined with the coordinates of the over-limit location, the deviation value and the on-site image link, an electronic work order containing the above data is generated; A status tracking identifier is embedded in the electronic work order and sent to the terminal of the corresponding responsible unit through a mobile push interface. The status tracking identifier records the timing and operator of the entire process of receiving and closing the work order.

[0013] In one embodiment, the step of performing weight calculation of multi-party decision indicators based on the spatiotemporal digital baseboard includes: Multiple evaluation indicators for consultation and decision-making are extracted from the spatiotemporal digital base plate. These evaluation indicators include spatial constraint indicators, schedule indicators, investment indicators, and resource indicators. Obtain the subjective scores of the evaluation indicators, and calculate the objective information entropy of each evaluation indicator based on historical construction data; The subjective scores and objective information entropy are weighted and integrated to output the comprehensive weight of each evaluation indicator; The evaluation indicators are ranked according to their comprehensive weights, a consultation priority sequence is output, and the key constraints that restrict the construction progress are identified.

[0014] In one embodiment, the step of performing time-series prediction and deviation analysis of construction progress based on the spatiotemporal digital base plate includes: Extract the construction schedule baseline data, actual completion progress data, and building information model component process association data from the spatiotemporal digital base plate; Using the construction schedule baseline data and the actual completed progress data as inputs to the earned value management method, the budgeted cost, actual cost and planned value of the completed work are calculated, and the schedule deviation rate and investment deviation rate are output based on the calculation results. Historical progress time series data is used as input to the time series forecasting model to extrapolate the time series and output the predicted completion time value; The schedule deviation rate, the investment deviation rate, and the predicted completion time are compared with their corresponding thresholds, and the lag between them and the planned completion time is calculated. Based on the comparison results and the comparison of the lag calculation results with the corresponding threshold, when any result exceeds the corresponding threshold, a progress warning of the corresponding level is triggered, and a warning report is generated based on the progress warning.

[0015] In one embodiment, the steps of performing weight calculations for multi-party decision indicators, time-series prediction and deviation analysis of construction progress, and search and planning of emergency paths based on the spatiotemporal digital baseboard, and outputting consultation weights, progress warnings, and scheduling paths, include: The spatial topology network of the construction site is extracted from the spatiotemporal digital base plate. The spatial topology network includes roads, passages, tower crane operating paths, and emergency resource storage points. Based on the location of quality and safety anomalies or equipment failure points, determine the emergency target points, and combine them with the real-time location data of personnel and equipment at the construction site to construct a search space with the emergency target points as the endpoints; A heuristic path search algorithm is adopted to iteratively calculate and output the optimal emergency dispatch path in the search space with a preset optimization objective; The optimal emergency dispatch path is overlaid onto the spatiotemporal digital base to generate a three-dimensional navigation view, which is then pushed to relevant emergency personnel.

[0016] Furthermore, to achieve the above objectives, this application also proposes a digital twin collaborative management and control device for the entire lifecycle of engineering construction. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the digital twin collaborative management and control method for the entire lifecycle of engineering construction as described above.

[0017] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the digital twin collaborative management and control method for the entire life cycle of engineering construction as described above.

[0018] One or more technical solutions proposed in this application have at least the following technical effects: A spatiotemporal digital baseboard is constructed by integrating the building information model (BIM) individual model and the urban space baseboard of the city information model (CIM). A digital twin mapping to the physical engineering entity is generated using this spatiotemporal digital baseboard as the data foundation. Multi-source site data uploaded from edge nodes deployed at the construction site via a breakpoint resume method is received, and the digital twin is updated based on this data. Operational data is hashed and encrypted according to the hierarchical permissions of all parties involved in the project, and uploaded to the project consortium blockchain for data storage and operation traceability. The digital twin is judged to exceed limits based on preset thresholds for planar position deviation, elevation deviation, structural dimension deviation, and progress deviation. When a limit is exceeded, a work order is automatically triggered, and the digital twin is updated and the closed-loop record is uploaded to the blockchain after receiving the rectification and retest results. Based on the spatiotemporal digital baseboard, the weight calculation of multi-party decision indicators, the time-series prediction and deviation analysis of construction progress, and the search and planning of emergency paths are performed, outputting consultation weights, progress warnings, and scheduling paths.

[0019] The technical solution of this application realizes the trigger-based linkage between twin monitoring and business processes, solves the technical problem of the disconnect between BIM / CIM and business processes and the lack of reliable collaborative management, and completes the technical leap from static open loop to dynamic closed loop, from manual decision-making to intelligent driving, and from centralized transmission to reliable edge-cloud collaboration. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the first embodiment of the digital twin collaborative management and control method for the entire construction cycle of the project in this application. Figure 2 This is a detailed process diagram based on step S10 in the first embodiment; Figure 3This is a detailed process diagram based on step S20 in the first embodiment; Figure 4 This is a detailed schematic diagram of step S30 in the first embodiment; Figure 5 This is a detailed schematic diagram of step S40 based on the first embodiment; Figure 6 This is a detailed process diagram based on step S50 in the first embodiment; Figure 7 Overall architecture diagram of the digital twin collaborative management and control system for the entire lifecycle of engineering construction; Figure 8 A timing diagram for twin data synchronization; Figure 9 For the security authentication process flowchart; Figure 10 This is a diagram illustrating the penetration of three levels of access control. Figure 11 A twin-triggered business flow flowchart; Figure 12 A diagram illustrating collaborative decision-making and coordinated response; Figure 13 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the digital twin collaborative management and control method for the entire construction lifecycle in this application embodiment.

[0023] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0025] In the full lifecycle management of engineering construction, the integration of BIM and CIM mainly adopts a path of static model overlay and manual business association. This type of solution achieves only static display through manual model import, timed API synchronization of business data, fixed role-based access, and manual statistical rectification. Its core shortcomings are: fragmented BIM / CIM data, untraceable permissions and operations, unreliable transmission in weak network conditions, decision-making relying on human experience, and lack of automatic closure of twin bias, which fundamentally contradicts the needs of dynamic collaborative management throughout the entire lifecycle.

[0026] To address the aforementioned shortcomings, this application proposes a BIM / CIM-driven collaborative management and control method for digital twins. This method involves constructing a spatiotemporal digital baseboard and generating a digital twin by integrating BIM and CIM; deploying edge nodes for local filtering, caching, and breakpoint resume upload to update the twin; hashing and encrypting operational data according to macro, meso, and micro-level hierarchical permissions and uploading it to the consortium blockchain for evidence storage and traceability; judging whether the twin exceeds limits based on preset deviation thresholds, automatically issuing work orders when limits are exceeded, updating the twin after rectification and retesting, and completing the blockchain loop; and outputting consultation weights, progress warnings, and scheduling paths based on the spatiotemporal digital baseboard.

[0027] This application realizes the trigger-based linkage between twin monitoring and business processes, solves the technical problem of the disconnect between BIM / CIM and business processes and the lack of reliable collaborative management and control, and completes the technical leap from static open loop to dynamic closed loop, from manual decision-making to intelligent driving, and from centralized transmission to reliable edge-cloud collaboration.

[0028] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0029] Based on this, the embodiments of this application provide a digital twin collaborative management and control method for the entire lifecycle of engineering construction, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the digital twin collaborative management and control method for the entire engineering construction lifecycle of this application. In this embodiment, the digital twin collaborative management and control method for the entire engineering construction lifecycle includes steps S10 to S50: Step S10: Integrate the building information model (BIM) individual model and the city information model (CIM) urban space base to construct a spatiotemporal digital base, and use the spatiotemporal digital base as the data foundation to generate a digital twin that maps to the physical engineering entity. In this embodiment, the building information model (BIM) individual building model is integrated with the urban space base of the city information model (CIM) to construct a unified spatiotemporal digital base. This base serves as the data foundation for generating digital twins that are mapped in real-time to the physical engineering entities. This solves the problems of inconsistent BIM and CIM standards and incompatible data interfaces, providing a spatially and temporally consistent basic data environment for all subsequent collaborative management and control.

[0030] To achieve the aforementioned integration, the first step is to integrate Building Information Modeling (BIM) data in the Industrial Basic Class format with City Information Modeling (CIM) data in the City Geographic Markup Language (CML) format through spatial coordinate alignment and semantic mapping. Spatial coordinate alignment involves transforming the local coordinate system in the BIM model to the national geodetic coordinate system or local independent coordinate system used by CIM, using a seven-parameter Bursa model or a four-parameter transformation algorithm to unify the coordinate reference. Semantic mapping maps BIM component types (such as walls, beams, slabs, and columns) to the urban object classification system in CIM. For example, it maps structural columns in BIM to "building structural components" in CIM, and adds a spatial hierarchical code to each component to ensure logical interoperability between model data from different sources. After completing coordinate alignment and semantic mapping, the integrated data is written into a distributed spatiotemporal database, which employs a dual index structure of spatial grid and temporal partitioning. Spatially, the engineering area is divided into grid units of multiple scales based on the geospatial grid coding standard. Each unit stores the geometric and attribute information of BIM components within its corresponding range. Temporally, time partitions are established according to construction stages and project dates, ensuring that the status change records of the same spatial grid under different timestamps are fully preserved. On this basis, a spatiotemporal digital foundation is formed by overlaying time-series data fields with a three-dimensional spatial index as the framework, supporting both time-dimensional backtracking and spatial-dimensional querying.

[0031] After generating the spatiotemporal digital baseplate, a digital twin is further constructed using this baseplate as the data foundation. The digital twin is a digital mirror that maintains real-time mapping, data homology, and state synchronization with the physical engineering entity. It contains not only static geometric models but also dynamically linked business data such as construction progress, quality inspection, and equipment status. Specifically, during construction, an initial BIM / CIM fusion model is extracted from the spatiotemporal digital baseplate, establishing associations between model components and on-site IoT device collection points. For example, the unique BIM identifier of a concrete column is bound to the number of the strain sensor pre-embedded on that column, and this binding relationship is recorded through a smart contract. Subsequently, a digital twin instance is created for each component, containing the component's design parameters, construction records, real-time monitoring data stream address, and historical change logs. When the cloud receives on-site data from edge nodes, it quickly locates the corresponding digital twin instance using the component identifier and updates its status fields. For example, when the number of compaction passes in a certain area reaches the design requirements, the color of the corresponding road surface in the digital twin changes from yellow to green, and the progress percentage is automatically refreshed. Through the above data processing logic, the digital twin is no longer a static three-dimensional model, but an intelligent carrier that can evolve synchronously with the physical engineering and proactively drive business processes.

[0032] Step S20: Receive multi-source field data uploaded by edge nodes deployed at the construction site in a breakpoint resume mode, and update the digital twin based on the data; In this embodiment, multi-source field data uploaded by edge nodes deployed at the construction site's power distribution boxes, monitoring rooms, and tower crane locations via a breakpoint resume transmission method is received, and the cloud-based digital twin is updated in real time based on this data. Addressing the technical shortcomings of data loss and high synchronization latency in weak or offline network environments at construction sites, a mechanism combining edge preprocessing and cloud verification is used to achieve reliable data transmission and low-latency updates of the digital twin.

[0033] Edge nodes perform local data filtering, noise reduction, threshold determination, and edge caching at the construction site. Specifically, edge computing gateways deployed at key locations have built-in data preprocessing units. These units perform Kalman filtering on raw data collected by IoT sensors to eliminate random noise, compress video streams using H.265 encoding and extract key frame features, and perform anomaly detection on CAN bus data from construction machinery according to preset threshold ranges. When a data point exceeds a safety threshold, the edge node immediately triggers an audible and visual alarm locally and marks the anomaly as high priority. Under normal network conditions, the edge nodes upload the preprocessed standard data packets to the cloud data receiving interface in real time via 5G or 4G networks. The data packets are encapsulated using the MQTT-SN lightweight protocol, and the packet header includes a timestamp of data generation, device identifier, data length, and checksum.

[0034] When a network interruption or signal quality degradation occurs, the edge node automatically switches to breakpoint resume mode. The cloud continuously monitors the data stream status during reception. If a network interruption is detected, the cloud immediately records the hash checksum of the successfully received data up to the current moment, as well as the receiving breakpoint position corresponding to the interruption. This hash checksum is calculated using the SHA-256 algorithm, covering the content of all completed data blocks from the start of the transmission to before the interruption, uniquely representing the integrity of the received data. The cloud writes the breakpoint information, along with the corresponding device identifier and data batch number, to its local temporary storage area. After the network is restored, the edge node re-initiates a resume request, carrying the breakpoint position from the previous interruption and the hash checksum of the unuploaded data. Upon receiving the resume request, the cloud first extracts the breakpoint position and hash checksum from the request and compares them byte-by-byte with the locally recorded received hash checksum. If they match perfectly, it confirms that the received data has not been tampered with and is complete, and the cloud continues receiving subsequent data from the recorded receiving breakpoint position; if the comparison fails, the cloud notifies the edge node to retransmit from the beginning of the batch of data. Based on the above process, you can view... Figure 8 , Figure 8 The diagram shows the timing sequence of the twin data synchronization, clearly illustrating the complete timing from sensor acquisition to edge preprocessing and then to cloud breakpoint resume transmission.

[0035] Upon receiving data uploaded from the edge nodes, the cloud immediately initiates the digital twin update process. First, it parses the component identification codes in the data packet to quickly locate the corresponding spatial grid units on the spatiotemporal digital base. Then, it processes the data according to its type: for geometric deviation data, it compares it with the design values ​​and updates the spatial location or dimensional attributes of the BIM components; for progress data, it modifies the completion percentage of the corresponding tasks and synchronizes the planned start and end times to the process association diagram; for safety monitoring data, it highlights abnormal locations in the twin as bubbles and overlays real-time image links. All update operations are logged, including the update time, data source node identifier, and numerical comparisons before and after the update. Through this mechanism, even in poor network conditions at the construction site, the cloud-based digital twin can still obtain complete, continuous, and time-accurate on-site data, achieving millisecond-level synchronous mapping with the physical engineering entity.

[0036] Step S30: According to the hierarchical permissions of the parties involved in the project, the operation data is hashed and encrypted and uploaded to the project consortium blockchain for data storage and operation traceability. In this embodiment, the operation data is hashed and encrypted according to the hierarchical permissions of the parties involved in the project, and then uploaded to the project consortium blockchain to achieve data storage and operation traceability. This solves the technical problems of unauthorized access across participating parties, untraceable operations, and difficulty in defining responsibilities. By combining macro, meso, and micro three-level permission penetration with the immutability of blockchain, a trustworthy collaborative management and control environment is constructed.

[0037] First, a hierarchical permission system is defined for all parties involved in the project. Macro-level permissions correspond to the construction unit, granting them access to overall project data, decision-making, and comprehensive management of the entire process, including approving major changes, accessing all participating parties' operation records, and issuing stop-work orders. Meso-level permissions correspond to the supervision unit and general contractor, granting them permissions for on-site coordination, production scheduling, and process control. For example, the supervisor can view quality inspection reports and sign acceptance opinions, while the general contractor can adjust construction plans and issue work instructions to subcontractors. Micro-level permissions correspond to subcontractors and on-site work teams, granting them only permissions for on-site work execution, process data reporting, and on-site business operations. For example, the rebar team can only report the binding progress and self-inspection records of its assigned area. These three levels of permissions are written into the permission table of the consortium blockchain via smart contracts. Each level of permission can be further subdivided into read-only, read-write, and approval sub-permissions based on project stage, construction location, and data type. When a user logs into the system, the identity authentication module issues a JWT token based on the OAuth2.0 extended protocol. The token embeds the user's unit type, job role, and the effective permission scope for the current project stage. Before each data operation, the cloud-based permission verification engine extracts the permission identifier from the token and compares it with the permission rule table stored on the consortium blockchain. Only when the operation type, data object and user permissions are completely matched will execution be allowed.

[0038] For all operation data that passes the permission verification, hash encryption and on-chain notarization are further performed. Each operation record includes the operator's identity, operation timestamp, operation type (e.g., modifying deviation threshold, dispatching rectification work order, uploading acceptance images), data status before and after the operation, and associated engineering part identification code. The system first concatenates the above information into a standard format string, and then uses the SHA-256 hash algorithm to generate a fixed-length 256-bit digest value for this string. This digest value is equivalent to the "digital fingerprint" of the operation record; any slight data change will result in a completely different digest value. After generating the digest, the digest value is digitally signed using the operator's private key (stored in a hardware key or digital certificate), and the signature result is appended to the original data. Subsequently, the signed data packet is broadcast to the consensus node of the engineering consortium blockchain. The consortium blockchain is jointly participated in by the construction unit, supervision unit, general contractor, and subcontractors, and adopts an improved practical Byzantine fault-tolerant consensus algorithm. After receiving a data packet, the consensus nodes first verify the validity of the digital signature, i.e., decrypting the signature using the operator's public key and comparing it with the recalculated digest value; secondly, they check whether the operator's permissions meet the data writing rules for the current stage. When more than two-thirds of the consensus nodes have verified the data, the data is packaged into a new block. The block header contains the hash value, timestamp, and Merkle root of the previous block, while the block body stores the encrypted operation data and the operator's signature. Based on the above process, you can view... Figure 9 , Figure 9This is a security authentication flowchart, illustrating the complete process from unified login to token issuance and permission verification; you can also view... Figure 10 , Figure 10 This diagram illustrates the three-level permission penetration mechanism, visually showcasing the isolation and penetration relationship between macro, meso, and micro levels of permissions.

[0039] Blocks written to the consortium blockchain are synchronized to all nodes, and no single node can tamper with the block content. When it is necessary to trace an operation, quality inspectors or supervisors can read the original evidence data from the consortium blockchain using the block height and transaction index in the operation log, recalculate the hash value, and compare it with the hash value stored on the chain to verify whether the operation record has been tampered with. Simultaneously, by using the hash chain of the previous block in the block header, the entire sequence of operations from the start of the project to the current moment can be fully reconstructed. This mechanism ensures that the actions of all participating parties are fully traceable, responsibilities are clearly defined, and any attempt to forge or tamper with data will be exposed during hash verification.

[0040] Step S40: The digital twin is judged to exceed the limits by using preset thresholds for planar position deviation, elevation deviation, structural size deviation and progress deviation. When the limit is exceeded, a work order is automatically triggered and the digital twin is updated and the closed-loop record is uploaded to the blockchain after receiving the rectification and retest results. In this embodiment, the digital twin is judged to exceed limits by using preset thresholds for planar position deviation, elevation deviation, structural dimension deviation, and progress deviation. When a limit is exceeded, a work order is automatically triggered. After receiving the rectification and retest results, the digital twin is updated and the closed-loop record is uploaded to the blockchain. This step constructs a trigger-based business closed loop of "monitoring—early warning—work order dispatch—rectification—retesting—item clearance," solving the technical problems of the disconnect between digital twin monitoring and business processes, and the reliance on manual handling of anomalies.

[0041] The preset deviation thresholds specifically include: planar position deviation ±5 mm, elevation deviation ±3 mm, structural dimension deviation ±8 mm, and progress deviation ≥10%. These thresholds are set according to relevant national engineering specifications and project-specific construction plans, and can be dynamically adjusted for different project types and construction stages. The cloud receives spatial positioning data and progress reporting data from edge nodes in real time. Spatial positioning data is obtained through real-time dynamic measurement base stations of the Global Navigation Satellite System deployed at the construction site, achieving centimeter-level measurement accuracy and outputting the position coordinates of construction machinery or key components once per second. Progress reporting data comes from daily reporting by construction team mobile terminals or automatic detection of completion volume by IoT sensors. After receiving the data, the cloud performs point-by-point difference calculations between the measured values ​​and the design benchmark data stored in the digital twin. For planar position, the Euclidean distance between the measured coordinates and the design coordinates is calculated; for elevation, the absolute value of the difference between the measured elevation and the design elevation is calculated; for structural dimensions, the absolute deviation between the measured length, width, and thickness and the design values ​​is calculated; for progress, the percentage difference between the actual completed work and the planned work is calculated.

[0042] When any deviation exceeds the corresponding threshold, the system marks the event as pending. Based on the deviation type and spatial location information, the system automatically matches the corresponding responsible unit and rectification deadline from a pre-established mapping rule base. The mapping rule base is constructed using an engineering breakdown structure, associating each work unit with the subcontractor responsible for construction and the supervisor responsible for oversight. It also specifies rectification deadlines for various defects according to engineering specifications; for example, the rectification deadline for foundation deviations is 24 hours, and the rectification deadline for concrete component dimensional deviations is 48 hours. Upon successful matching, the system generates an electronic work order. The electronic work order includes the following fields: coordinates of the out-of-limit location (e.g., X=34567.89, Y=23456.78, Z=15.23), deviation values ​​(planar deviation +6.2 mm, progress deviation -12%), on-site image link (the address of an image or short video automatically captured by the edge node when the out-of-limit is detected), description of rectification requirements (automatically generated according to relevant specifications, such as "Please rework the foundation plane location in this area and adjust it to the design coordinate range"), and the name of the responsible unit, the contact person, and the rectification deadline. After the electronic work order is generated, it is sent in real time to the mobile terminal of the person in charge of the corresponding responsible unit via a mobile push interface (integrating APP message push and SMS gateway). At the same time, a status tracking identifier is embedded in the electronic work order. This identifier is a unique work order serial number, which the backend database uses to associate the entire lifecycle status of the work order. The initial status of the status tracking identifier is "Pending Receipt". When the responsible person opens the work order, the mobile terminal automatically reports the receipt confirmation information, and the status changes to "Processing". After the responsible person completes the on-site rectification, they upload the retest data (including new coordinate measurements, photos of the rectified work, and a self-inspection approval signature) via mobile device. Upon receiving the retest results, the cloud platform compares them again with the deviation threshold. If the threshold is met, the electronic work order status is updated to "Pending Acceptance" and pushed to the supervision unit for review. Once the supervision unit confirms compliance, the work order status changes to "Closed Loop." Based on the above process, you can view... Figure 11 , Figure 11 The diagram shows the entire process of a twin-triggered business flow, from monitoring to threshold judgment, to order dispatch and rectification, and finally to item clearance.

[0043] While the electronic work order is closed in a loop, the digital twin is simultaneously updated in the cloud. Specifically, the spatial location or dimensional attributes of the corresponding BIM components are corrected based on the re-measurement data, and this correction record is written into the historical version of the spatiotemporal digital base plate. At the same time, the entire closed-loop record (including the time of discovery of the over-limit, the time of work order issuance, the time of rectification completion, the re-measurement data, and the supervisor's signature) is packaged, hash-encrypted, and stored on the blockchain to generate an immutable closed-loop log. Through this mechanism, the digital twin not only reflects the latest status of the engineering entity in real time, but also fully records the discovery and elimination process of each deviation, achieving full controllability and traceability of quality and safety issues.

[0044] Step S50: Based on the spatiotemporal digital baseboard, perform weight calculation of multi-party decision indicators, time-series prediction and deviation analysis of construction progress, and search and planning of emergency paths, and output consultation weights, progress warnings and scheduling paths.

[0045] In this embodiment, based on a spatiotemporal digital baseboard, the weight calculation of multi-party decision indicators, the time-series prediction and deviation analysis of construction progress, and the search and planning of emergency paths are performed respectively, and the consultation weights, progress warnings, and scheduling paths are output. This step integrates three types of intelligent decision-making algorithms: improved analytic hierarchy process, improved earned value method combined with long short-term memory network, and improved A* algorithm, providing quantitative scientific basis for consultation decision-making, progress control, and emergency scheduling.

[0046] In calculating the weights of multi-party decision-making indicators, the system first extracts multiple evaluation indicators required for consultation and decision-making from a spatiotemporal digital base. These include spatial constraint indicators (such as the degree of overlap between adjacent work surfaces and equipment movement distance), schedule indicators (such as the schedule deviation rate on the critical path), investment indicators (such as the ratio of completed investment to planned investment), and resource indicators (such as the utilization rate of labor, materials, and machinery). These indicators are statistically analyzed using spatiotemporal grids, with each grid cell corresponding to a set of indicator values. Next, the system receives subjective scores from experts from each participating party for the evaluation indicators. The scoring uses a nine-level scaling method, with experts assigning scores based on the relative importance of each indicator through the consultation terminal. Simultaneously, the system calculates the objective information entropy of each evaluation indicator based on historical construction data. The information entropy is calculated by statistically analyzing the numerical distribution of the same indicator across different historical projects or construction phases; the more dispersed the values, the greater the information entropy, representing a higher amount of decision-making information contained in the indicator. Finally, the system weights and merges the subjective scores and objective information entropy to output the comprehensive weight of each evaluation indicator. Indicators with higher comprehensive weights have higher priority in consultation and decision-making. The evaluation indicators are ranked according to their comprehensive weights, a consultation priority sequence is output, and key constraints hindering construction progress are identified, such as "insufficient resource allocation" or "work process conflicts." Based on the above process, you can view... Figure 12 , Figure 12This diagram illustrates collaborative decision-making and coordinated response, showcasing the entire process from indicator input and weight calculation to priority output.

[0047] In terms of construction progress time-series prediction and deviation analysis, the system extracts construction progress plan baseline data (i.e., planned start time, planned end time, and planned workload for each process), actual completed progress data (actual completed volume reported through edge nodes), and building information model component process association data (i.e., the construction process number corresponding to each BIM component) from the spatiotemporal digital base plate. Using the construction progress plan baseline data and actual completed progress data as inputs to the earned value management method, the system calculates the budgeted cost of completed work, the actual cost of completed work, and the budgeted cost of planned work. The budgeted cost of completed work equals the actual completed work volume multiplied by the budgeted unit price; the actual cost of completed work equals the actual completed work volume multiplied by the actual unit price; and the budgeted cost of planned work equals the planned completed work volume multiplied by the budgeted unit price. Based on these three parameters, the schedule deviation rate is calculated: the schedule deviation rate equals the budgeted cost of completed work minus the budgeted cost of planned work, divided by the budgeted cost of planned work; the investment deviation rate equals the budgeted cost of completed work minus the actual cost of completed work, divided by the actual cost of completed work. Simultaneously, historical progress time-series data (the daily completed work volume over the past three months) is input into the pre-trained time series prediction model. This model employs a Long Short-Term Memory (LSTM) network structure, taking 30 days of time-series data as input and outputting a predicted completion time for the next 7 days. Model parameters are trained using data from the early stages of construction and updated incrementally periodically. The model compares the schedule deviation rate with a preset 10% threshold, the investment deviation rate with a preset 5% threshold, and calculates the lag between the predicted completion time and the planned completion time. If the schedule deviation rate exceeds -10% (i.e., the actual progress lags behind the planned progress by more than 10%), or the investment deviation rate exceeds -5%, or the predicted completion time is more than 3 days later than the planned completion time, a corresponding level of progress warning is automatically triggered. The warning report also includes a deviation cause analysis, using correlation analysis to pinpoint whether it's a material supply delay or a mechanical failure, and adjustment suggestions, such as increasing construction teams or optimizing process overlaps.

[0048] In terms of emergency route search and planning, the spatial topology network of the construction site is extracted from the spatiotemporal digital base map. This spatial topology network includes roads (width, direction of travel, height and weight restrictions of construction access roads), passages (safe passages within floors, stairwell locations), tower crane operating paths (slewing radius and lifting range of tower cranes), and emergency resource storage points (location and current inventory of first aid kits, fire extinguishers, and emergency material warehouses). Emergency target points are determined based on the location of quality and safety anomalies or equipment failure points. For example, when a fall from height occurs, the coordinates of the accident point are the emergency target point. The system combines real-time positioning data of personnel and equipment at the construction site (obtained through ultra-wideband positioning tags built into safety helmets) to construct a search space with the emergency target point as the endpoint. A heuristic path search algorithm is adopted, with the optimization objectives of minimizing path travel time and traffic obstruction, to iteratively calculate and output the optimal emergency dispatch route in this search space. The heuristic function uses the straight-line distance from the current node to the destination divided by the average travel speed, taking into account the real-time congestion level of the road segment. If a large number of people gather or equipment is parked on a certain road segment, the cost of that segment is increased. The algorithm expands the node with the lowest current cost value each time until the emergency target point is found. The output optimal emergency dispatch path not only includes a series of spatial coordinates, but also includes the estimated travel time for each step and risk warnings along the way. Finally, this optimal emergency dispatch path is overlaid on the spatiotemporal digital baseboard to generate a 3D navigation view, marked with red lines and arrows on the CIM map, and pushed to relevant emergency personnel, while simultaneously displaying the location and status of emergency resources. Based on the above process, it can be viewed... Figure 12 , Figure 12 The diagram shows the timing relationship of the collaborative decision-making algorithm, from the triggering of an anomaly to consultation, progress, parallel processing of emergency algorithms, and finally, the issuance of instructions.

[0049] Furthermore, the step of calculating the weights of multi-party decision-making indicators based on the spatiotemporal digital baseboard includes: Multiple evaluation indicators for consultation and decision-making are extracted from the spatiotemporal digital base plate. These evaluation indicators include spatial constraint indicators, schedule indicators, investment indicators, and resource indicators. Obtain the subjective scores of the evaluation indicators, and calculate the objective information entropy of each evaluation indicator based on historical construction data; The subjective scores and objective information entropy are weighted and integrated to output the comprehensive weight of each evaluation indicator; The evaluation indicators are ranked according to their comprehensive weights, a consultation priority sequence is output, and the key constraints that restrict the construction progress are identified.

[0050] This embodiment refines the weight calculation process for multi-party decision-making indicators. The system first extracts multiple evaluation indicators required for consultation and decision-making from the spatiotemporal digital base. The spatiotemporal digital base stores spatial and business data throughout the entire project lifecycle. Following preset indicator extraction rules, the system traverses each data layer in the base and extracts four core indicators. Spatial constraint indicators include the degree of overlap between adjacent work surfaces, the minimum safe distance between construction machinery, and the transfer distance between material storage areas and work areas. Schedule indicators include the deviation between the actual and planned durations of each work process on the critical path, and the proportion of total float consumption on non-critical paths. Investment indicators include the ratio of budgeted to actual costs for completed work, and the matching degree between the budgeted amount for remaining work and the progress of funding availability. Resource indicators include labor attendance rate, utilization rate of major construction machinery, and inventory turnover days for key materials. These indicators are statistically analyzed using the smallest work package in the project breakdown structure as the unit. Each work package corresponds to a set of indicator values, along with its spatial location and time labels.

[0051] Next, subjective scores for the aforementioned evaluation indicators are obtained. In a multi-party consultation scenario, experts from the construction unit, supervision unit, design unit, and general contractor each assign scores to each evaluation indicator's importance via a consultation terminal. A nine-level scale is used for scoring, with five qualitative levels—equally important, slightly important, significantly important, strongly important, and extremely important—and their adjacent intermediate values, corresponding to scores from 1 to 9. After each expert completes their score independently, the system collects all scores and calculates the arithmetic mean for each indicator, which serves as the initial subjective score for that indicator. To ensure the reasonableness of the scores, the system also removes outlier scores that deviate from the mean by more than two standard deviations.

[0052] Meanwhile, the system calculates the objective information entropy of each evaluation indicator based on historical construction data. The calculation of information entropy relies on the distribution characteristics of indicator values ​​across different construction phases or historical projects. Specifically, the system extracts the numerical sequences of the same evaluation indicator from historical versions of the spatiotemporal digital base or databases of similar projects across multiple samples. For spatial constraint indicators, it calculates the overlap time of work surfaces in different construction phases; for schedule indicators, it calculates the distribution of schedule deviations for each process; for investment indicators, it calculates the overspending of each sub-project; and for resource indicators, it calculates the idle rate distribution of various resources. Let the numerical sequence of the j-th indicator across n samples be x1, x2, ..., x... n The system first discretizes the sequence, dividing it into k intervals, and calculates the sample proportion p in each interval. i Then the entropy value of the j-th index is calculated using the following formula: e j = - (1 / ln k) × Σ i p i× ln p i ; When the indicator values ​​are evenly distributed across all intervals, the entropy value approaches 1, indicating that the indicator contains a large amount of objective information and should be given a higher weight in decision-making. Conversely, if the indicator values ​​are highly concentrated in a few intervals, the entropy value approaches 0, indicating that the indicator has low discrimination and its weight should be reduced accordingly. After calculating the entropy value of each indicator, the system subtracts the entropy value from 1 to obtain the difference coefficient of the indicator. The normalized difference coefficient is the objective weight.

[0053] After obtaining subjective scores and objective information entropy, the system weights and fuses them to output the comprehensive weight of each evaluation indicator. The weighted fusion uses a multiplicative normalization method: first, the product of the subjective score and the objective difference coefficient for each indicator is calculated; then, this product is divided by the sum of the products of all indicators to obtain the comprehensive weight. This fusion method respects expert experience while incorporating the distribution characteristics of the data itself, avoiding the bias of solely subjective weighting. A higher comprehensive weight indicates a more prominent importance of that indicator in the consultation and decision-making process.

[0054] Based on the comprehensive weights, the system sorts the evaluation indicators in descending order and outputs a consultation priority sequence. For example, if the schedule indicator has the highest comprehensive weight, the ranking is: schedule first, resources second, spatial constraints third, and investment fourth. Simultaneously, the system identifies key constraints hindering construction progress based on the comprehensive weight distribution and the real-time status of the indicator values. Specifically, the system compares the two indicators with the highest comprehensive weights with their current actual values. If the current value of an indicator deviates from the industry benchmark or the project's target value by a certain proportion, it is identified as a key constraint and highlighted in red on the consultation interface. For example, when the resource indicator has the highest weight and its actual utilization rate is below 60%, the system outputs "Insufficient resource allocation is the main constraint on the current progress"; when the spatial constraint indicator has the highest weight and the overlap time of work surfaces exceeds the planned value by 30%, it outputs "Work process conflicts lead to decreased efficiency." These outputs, along with the comprehensive weight values ​​of each indicator, are pushed to the consultation terminal, providing quantitative evidence for leadership decision-making.

[0055] And, the step of performing time-series prediction and deviation analysis of construction progress based on the spatiotemporal digital base plate includes: Extract the construction schedule baseline data, actual completion progress data, and building information model component process association data from the spatiotemporal digital base plate; Using the construction schedule baseline data and the actual completed progress data as inputs to the earned value management method, the budgeted cost, actual cost and planned value of the completed work are calculated, and the schedule deviation rate and investment deviation rate are output based on the calculation results. Historical progress time series data is used as input to the time series forecasting model to extrapolate the time series and output the predicted completion time value; The schedule deviation rate, the investment deviation rate, and the predicted completion time are compared with their corresponding thresholds, and the lag between them and the planned completion time is calculated. Based on the comparison results and the comparison of the lag calculation results with the corresponding threshold, when any result exceeds the corresponding threshold, a progress warning of the corresponding level is triggered, and a warning report is generated based on the progress warning.

[0056] This embodiment refines the process of time-series prediction and deviation analysis of construction progress. The system first extracts baseline data for the construction progress plan, actual progress data, and building information model (BIM) component process association data from the spatiotemporal digital base plate. The baseline data for the construction progress plan is stored in the time-series layer of the base plate, including the planned start time, planned end time, planned workload, and planned budget cost for each process. The actual progress data is obtained by summarizing the completed work volume reported daily by edge nodes, including the actual completed amount, actual start and end times, and actual costs incurred for each process. The BIM component process association data is a mapping table that associates the unique identifier of each BIM component with the corresponding construction process number, enabling the progress data to pinpoint specific spatial locations and component objects.

[0057] Using baseline data from the construction schedule plan and actual progress data as inputs to Earned Value Management (EVM), the system calculates three core parameters. The first parameter is the budgeted cost of work completed, which equals the actual completed work quantity multiplied by the budgeted unit price, reflecting the value of the completed work calculated at the budgeted price. The second parameter is the actual cost of work completed, which equals the actual completed work quantity multiplied by the actual unit price, reflecting the actual cost incurred. The third parameter is the budgeted cost of planned work, which equals the planned completed work quantity multiplied by the budgeted unit price, reflecting the value of the work that should have been completed by the current time point according to the plan. Based on these three parameters, the system further calculates the schedule deviation rate and the investment deviation rate. The schedule deviation rate equals the budgeted cost of work completed minus the budgeted cost of planned work, divided by the budgeted cost of planned work, and then multiplied by 100%. A negative schedule deviation rate indicates that the actual progress lags behind the planned progress. The investment deviation rate equals the budgeted cost of work completed minus the actual cost of work completed, divided by the actual cost of work completed, and then multiplied by 100%. A negative investment deviation rate indicates that the actual cost has exceeded the budget.

[0058] Meanwhile, the system uses historical progress time-series data as input to the time series prediction model, extrapolating future time series and outputting predicted completion times. Historical progress time-series data is extracted from historical versions of the spatiotemporal digital base, recording the actual completed work or cumulative completion percentage for each day over the past three months, on a daily basis. The time series prediction model employs a Long Short-Term Memory (LSTM) network structure, specifically designed to handle time-series data with long-term dependencies. The network consists of an input layer, two LSM hidden layers, and a fully connected output layer. The input window length is set to 30 days, using the daily completion progress data from the past 30 days as input features. The output is the predicted completion amount for each day of the next 7 days; the predicted completion time is obtained by extrapolating the cumulative amount over the next 7 days. The network model is trained using completed progress data in the early stages of construction, with the training objective being to minimize the mean squared error between the predicted and actual values. During construction, the system continuously monitors the prediction error; when the error continuously exceeds a preset threshold, it triggers an incremental update of the model, using the most recent data to fine-tune the network parameters.

[0059] After obtaining the schedule deviation rate, investment deviation rate, and predicted completion time, the system compares these three results with their corresponding thresholds and calculates the lag between the predicted and planned completion times. The preset threshold for the schedule deviation rate is -10%, meaning an alert is triggered when the actual progress lags behind the planned progress by more than 10%. The preset threshold for the investment deviation rate is -5%, meaning an alert is triggered when the actual cost overrun exceeds 5%. The preset threshold for the predicted completion time lag is three days, meaning an alert is triggered when the predicted completion date is more than three days later than the planned completion date. The system calculates the difference between the schedule deviation rate and the threshold, the difference between the investment deviation rate and the threshold, and the difference between the number of days the completion time lag is and three days.

[0060] Based on the comparison results and the calculation results of the lag, and the comparison with the corresponding thresholds, a progress warning of the corresponding level is triggered when any result exceeds the corresponding threshold. The warning levels are divided into three levels: when only one indicator exceeds the threshold and the exceedance is within 20%, a level three yellow warning is triggered and sent to the progress management personnel of the general contractor; when two indicators exceed the threshold or a single indicator exceeds 20%, a level two orange warning is triggered and sent to the supervision unit; when all three indicators exceed the threshold or any one exceeds 50%, a level one red warning is triggered and sent to the construction unit and all participating parties. After the warning is triggered, the system generates a warning report based on the type of the exceeding indicator and related data. The warning report includes the following: the name and actual value of the exceeding indicator, the percentage deviation from the threshold, the possible causes identified through correlation analysis, and recommended adjustment measures. Cause analysis is achieved by mining related records in the spatiotemporal digital base. For example, when the progress deviation rate exceeds the limit, the system traces the material arrival records, mechanical failure records, and weather records within that time period to identify the main factors causing the delay.

[0061] Furthermore, you can also view Figure 2 , Figure 2 This is a detailed process diagram based on step S10 in the first embodiment. Figure 2 The step of integrating the building information model (BIM) individual model and the city information model (CIM) urban spatial base to construct a spatiotemporal digital base includes S11~S13: Step S11: The building information model data and the city information model data are fused through spatial coordinate alignment and semantic mapping, wherein the building information model data is in the industrial basic class format and the city information model data is in the urban geo-marking language format. Step S12: Establish a three-dimensional spatial index based on the fused data. The three-dimensional spatial index includes geometric information, attribute information, and association information. Step S13: Using the three-dimensional spatial index as a skeleton, time-series data fields are superimposed to form a spatiotemporal digital base plate.

[0062] In this embodiment, the process of constructing a spatiotemporal digital base plate is further refined into the following steps: sequentially completing the data fusion of the building information model and the city information model, establishing the three-dimensional spatial index, and superimposing the temporal dimension, ultimately forming a base plate structure that supports bidirectional spatial and temporal retrieval.

[0063] Building Information Modeling (BIM) data and City Information Modeling (CIM) data are fused through spatial coordinate alignment and semantic mapping. BIM data uses an Industrial Basic Class (IBC) format, which defines building components and their geometric, material, and cost attributes in an object-oriented manner. CIM data uses a City Geographic Markup Language (CGML) format, specifically designed to represent the terrain, buildings, roads, and vegetation of 3D city objects. Because the two datasets use different coordinate systems—BIM typically uses a local coordinate system, while CIM uses a national geodetic coordinate system or a local independent coordinate system—spatial coordinate alignment is performed first. The system extracts the coordinates of control points in the BIM, such as building corner points or column center coordinates, and uses a seven-parameter Bursa model to calculate the transformation parameters from the local coordinate system to the CIM coordinate system, including three translations, three rotation angles, and a scale factor. After coordinate transformation, the geometries of both models are correctly superimposed under a unified spatial reference. Next, semantic mapping is performed, establishing the correspondence between BIM component types and CIM object types. For example, a "wall" component in the Building Information Model (BIM) is mapped to a "building wall" object in the City Information Model (CIM), and a "beam" in the BIM is mapped to a "structural beam" object. For components that cannot be directly mapped, the system adds custom object types by extending the application mode of the CIM, ensuring that all BIM data can be reasonably represented in the CIM spatial base. Through spatial coordinate alignment and semantic mapping, the two originally isolated data types are merged into a logically complete and spatially consistent 3D scene. Based on the above process, you can view... Figure 1 , Figure 1 This is the overall system architecture diagram, in which the BIM / CIM fusion library in the data layer is the output carrier of this step.

[0064] A three-dimensional spatial index is built based on the aforementioned fused data. This index comprises three levels: geometric information, attribute information, and relational information. Geometric information stores the external dimensions and spatial location of each component in the form of triangular meshes or parametric surfaces, supporting rapid spatial queries and visualization rendering. Attribute information records the non-spatial characteristics of components in key-value pairs, including material type, concrete strength grade, supplier, construction date, and design life. This information is stored in a distributed database and associated with the geometric information through a unique component identifier. Relational information expresses the connections and dependencies between components, such as the support relationship between beams and columns, the overlap relationship between floor slabs and walls, the connection relationship between pipes and equipment, and the sequential logical relationships between construction processes. The aforementioned relational information is stored using a graph database, where nodes represent components or construction processes, and edges represent relationship types and constraints. When constructing the three-dimensional spatial index, the system uses octree partitioning technology to recursively divide the engineering area into cubic units of varying sizes. The root node of the octree covers the entire engineering area, and each partitioning evenly divides the parent node into eight child nodes until the number of components contained in the child node is less than a preset threshold or the node size reaches the minimum resolution. Each octree node stores a list of component identifiers within its spatial range, along with the bounding box coordinates of that range, thus enabling spatial partitioning management of geometric information. Simultaneously, each octree node is linked to a corresponding attribute database record and associated edges in the graph database via pointers. Through this 3D spatial index, when a user clicks on any component in the 3D model, the system can locate the node from the octree within milliseconds, thereby extracting complete attribute and association information. Conversely, when querying for a component based on attribute conditions, its spatial location can also be quickly located by using the reverse index from the attribute database to the octree node.

[0065] Using the aforementioned three-dimensional spatial index as a framework, time-series data fields are overlaid to form a spatiotemporal digital base. The time-series data fields record the state changes of each component or spatial region at different points in time or within time intervals. Specifically, the system adds a time-series array field to the data record corresponding to each leaf node of the octree. This array stores key events occurring within the spatial unit in chronological order, along with their corresponding timestamps and state values. For example, for the foundation portion within a grid unit, the time-series array records the following entries: pouring time, curing start time, curing temperature curve data points, strength test time and test value, and acceptance time and acceptance conclusion. For construction progress control, the time-series array also records the planned start time, actual start time, planned completion time, actual completion time, and any deviation correction records during this period. The time-series data fields support two query modes: time-point query, which returns the instantaneous state of all spatial units at a given timestamp; and time interval query, which returns all change records within a given start and end time. To achieve efficient time-dimensional retrieval, the system establishes a timestamp-based B+ tree index on the time-series array. When edge nodes upload sensing data, the cloud uses the timestamp and spatial coordinates carried in the data to quickly locate the corresponding octree node through a time index. A new status record is then appended to the node's time-series array, and the node's current status field is updated. Through the overlay of this three-dimensional spatial index and time-series data fields, the spatiotemporal digital baseboard supports both conventional spatial queries and analysis, as well as the backtracking and simulation of the entire construction process, providing a unified data foundation for deviation determination and decision-making calculations in subsequent steps.

[0066] Furthermore, you can also view Figure 3 , Figure 3 This is a detailed process diagram based on step S20 in the first embodiment. Figure 3 The step of receiving multi-source field data uploaded by edge nodes deployed at the construction site in a breakpoint resume mode includes S21~S23: Step S21: Receive data uploaded by edge nodes, which are deployed in power distribution boxes, monitoring rooms and tower crane locations. The data includes IoT sensor data, video surveillance data and construction machinery operating data after local filtering and noise reduction. Step S22: If a network interruption is detected during the receiving process, the hash check value of the received data and the receiving interruption point at the time of the interruption are recorded. Step S23: After the network is restored, the breakpoint position and data hash verification value carried in the resume request re-uploaded by the edge node are compared with the hash verification value recorded locally. If the verification is successful, subsequent data will continue to be received from the receiving breakpoint position.

[0067] In this embodiment, the receiving edge node uploads data in a breakpoint resume mode. Specifically, this includes completing normal data reception and preprocessing, recording the status when the network is interrupted, and resuming verification and data connection after the network is restored, which together construct a reliable breakpoint resume mechanism on the cloud side.

[0068] The cloud receives data uploaded from edge nodes. These edge nodes are not general-purpose edge servers, but rather strategically deployed at construction sites, such as power distribution boxes, monitoring rooms, and tower crane locations. Power distribution boxes are located near power monitoring equipment for easy collection of voltage, current, and energy consumption data; monitoring rooms centrally manage multiple video streams, facilitating preliminary intelligent analysis; and tower crane locations cover the mechanical status and spatial positioning data of high-altitude work areas. The data uploaded by these edge nodes falls into three categories: First, IoT sensor data after local filtering and noise reduction, such as time-series data like temperature, humidity, vibration, and strain processed by Kalman filtering; second, video surveillance data after compression and keyframe extraction, typically using H.265 encoding and extracting image frames containing abnormal behaviors such as personnel intrusion or not wearing safety helmets; and third, construction machinery operating data, including the lifting capacity, slewing angle, and anemometer readings of tower cranes, the pumping pressure and displacement of concrete pump trucks, and the compaction speed and vibration frequency of road rollers. When the cloud receives this data, it first performs protocol parsing—the data packets are encapsulated using a lightweight message queue telemetry transmission protocol, and the packet header includes the device identifier, data generation timestamp, data type encoding, and checksum. After parsing, the cloud writes the data to a distributed message queue and then distributes it to subsequent processing pipelines, including updating the digital twin, storing it in a spatiotemporal digital baseboard, and triggering the early warning rule engine.

[0069] When the cloud detects a network interruption during reception, it proactively records the hash checksum of the received data and the location of the reception breakpoint at the moment of interruption. Network interruption detection is achieved through a heartbeat mechanism: edge nodes send heartbeat packets every second. If the cloud does not receive a heartbeat for more than five consecutive seconds or the sequence number of the received data packets changes, a network interruption is determined. Once an interruption is determined, the cloud immediately performs the following recording operations: First, it calculates the hash checksum of all successfully received data from the start of the transmission to the moment of interruption. The calculation method is as follows: each data packet is concatenated into a continuous binary stream in the order of reception, and a 256-bit digest value is calculated using the secure hash algorithm SHA-256. This digest value uniquely represents the complete content of the received data; any lost or out-of-order data packets will result in a completely different digest value. Second, the cloud records the location of the reception breakpoint at the moment of interruption. The breakpoint location is represented by the data packet sequence number, i.e., the number of the last successfully received and acknowledged data packet before the interruption. The system writes this breakpoint location, along with the corresponding device identifier, data batch number, current session identifier, and the calculated hash checksum, into the cloud's breakpoint resumption status table. This table uses key-value pairs for storage. The key is a combination of device identifier and batch number, and the value is a structure containing the breakpoint location, hash check value, and record timestamp. These records provide the cloud with accurate data for subsequent data recovery.

[0070] Once the network is restored, the cloud compares the breakpoint location and data hash checksum carried in the re-uploaded resume request from the edge node with the hash checksum recorded locally. If the verification passes, subsequent data reception resumes from the breakpoint location. Network recovery is also determined based on a heartbeat mechanism. When the cloud receives the edge node's heartbeat again and successfully sends a probe packet, it considers the link restored. At this time, the edge node automatically initiates a resume request, which carries the following fields: device identifier, data batch number, sequence number of the last successfully sent data packet before the interruption (i.e., the breakpoint location), and the hash checksum of the unuploaded data. Upon receiving the resume request, the cloud first queries its local resume status table based on the device identifier and batch number in the request, retrieving the breakpoint location and hash checksum recorded locally. Then, it performs two layers of verification: the first layer verifies whether the breakpoint location matches, i.e., whether the breakpoint location in the request is consistent with the breakpoint location recorded locally; the second layer verifies whether the hash value matches, i.e., calculating the hash value from the start of the data to the breakpoint location in the request and comparing it with the hash checksum recorded locally. If both layers of verification pass, it indicates that the received data is complete and has not been tampered with. The cloud returns an acknowledgment response to the edge node, which includes the locally recorded breakpoint position, instructing the edge node to retransmit from the next data packet at that position. If the hash checksum does not match, it indicates that the received data is corrupted or missing. The cloud returns a full retransmission instruction, requiring the edge node to retransmit all data starting from the beginning of that batch.

[0071] Through the aforementioned collaboration, the cloud can still guarantee the integrity and accuracy of data reception even in weak or offline network environments. Edge nodes do not require complex state management; they only need to resend the resume request after recovery and resend data according to the cloud's instructions. By actively recording verification information, the cloud becomes the dominant party in resume control, avoiding duplicate reception or data loss caused by inconsistencies between the edge node and cloud states. This mechanism is particularly suitable for harsh environments with frequent network fluctuations and uneven signal coverage at construction sites, ensuring that the digital twin can obtain a complete, orderly, and reliable on-site data stream.

[0072] Furthermore, you can also view Figure 4 , Figure 4 This is a detailed process diagram based on step S30 in the first embodiment. Figure 4 The parties involved in the project include the construction unit, the supervision unit, the general contractor, and the subcontractors. The steps of hashing and encrypting the operation data according to the hierarchical permissions of the parties involved in the project and uploading it to the project consortium blockchain for data storage and operation traceability include S31~S32: Step S31: Determine the data operation permissions of each of the project participants, wherein each permission has a different data operation strategy; Step S32: Based on the data operation strategy, obtain the operation data of the parties involved in the project, use a hash algorithm to generate a fixed-length digest value for the operation data, attach the digital signature of the operator, package it into a transaction, verify it through the consensus node in the consortium blockchain, and write it into an immutable block.

[0073] This embodiment details the specific process of hashing and encrypting data according to the hierarchical permissions of all parties involved in the project and storing it on the blockchain, including determining the permission policy and executing the storage.

[0074] First, the data operation permissions of all parties involved in the project are determined. These parties include the construction unit, supervision unit, general contractor, and subcontractors. The construction unit, as the investor and ultimate responsible party, has the authority to view overall data, approve major changes, and manage the entire process. The supervision unit, entrusted by the construction unit, is responsible for on-site supervision of quality, safety, and progress, and has the authority to review inspection batch data, sign acceptance opinions, and issue rectification orders. The general contractor is responsible for on-site production organization and coordination, and has the authority to prepare construction plans, allocate resources, and report process data. Subcontractors are responsible for the construction of specific work areas and only have the authority to report their own team's self-inspection data and view their own task list. These permissions are not fixed but dynamically divided according to the project stage, data type, and the role of each party. The system defines permissions as the Cartesian product of operation type and data object. Operation types include six basic actions: create, read, update, delete, approve, and export. Data objects are categorized according to the project breakdown structure: project level, unit project level, sub-project level, itemized project level, and inspection batch level. The construction unit is granted permissions for all operation types at the project level; the supervision unit is granted read, update, and approval permissions at the sub-project level and above; the general contractor is granted creation, read, and update permissions at the sub-item level and above; and the subcontractor is only granted read and creation permissions at the inspection batch level. These permission rules are written into the smart contract of the consortium blockchain in JSON format, and any permission change requires dual signature confirmation from both the construction unit and the supervision node. Through this layered and hierarchical permission system, different participating parties can only operate data within their own scope of responsibility, and unauthorized access is rejected at the contract level.

[0075] Based on the aforementioned data operation strategy, the system acquires operation data from all parties involved in the project. A hash algorithm is used to generate a fixed-length digest value for the operation data, which is then appended with the operator's digital signature. This digest is packaged into a transaction, verified by consensus nodes in the consortium blockchain, and written into an immutable block. In practice, when any participating party initiates a data operation request through a client—for example, the general contractor uploading a concrete pouring record—the system first verifies whether the operation complies with the defined permission policy. If the verification passes, the system serializes the operation data into a string according to a standard format. The operation data includes the operator's identifier, operation timestamp, operation type, data status before and after the operation, associated component identifiers, spatial coordinates, and equipment fingerprints. The serialized string is used as the input to the hash algorithm. The system employs the secure hash algorithm SHA-256, which compresses an input of arbitrary length into a fixed-length output of 256 bits, i.e., the digest value. This digest value is equivalent to the digital fingerprint of the operation record; any slight modification to the original data will cause a cascading change in the digest value. After generating the digest, the system invokes the operator's digital signature module. This module is typically a hardware key or software certificate conforming to the Public Key Infrastructure (PKI) standard. It uses the operator's private key to perform a signature operation on the digest value, generating a signature value. The signature value is appended to the original data, forming the data packet to be uploaded to the blockchain.

[0076] Once the data packet is constructed, it is broadcast to the consensus nodes of the engineering consortium blockchain. The consortium blockchain consists of nodes jointly formed by the construction unit, supervision unit, major participating parties, and an independent third-party certification authority. Upon receiving the data packet, the consensus nodes first verify the signature: using the operator's public key to decrypt the signature value, obtaining the decrypted digest, and then independently calculating the hash value of the original data, comparing the two for consistency. If they match, it proves that the data was indeed generated by the claimed operator and has not been tampered with. Secondly, the consensus nodes verify whether the operator's permissions meet the data writing rules for the current stage; this verification is completed by calling a smart contract deployed on the chain. When more than two-thirds of the consensus nodes have verified and reached a consensus, the data packet is assembled into a new block. The block consists of a block header and a block body: the block header contains the hash value, timestamp, Merkle root, and version number of the previous block; the block body stores the hash-encrypted operation data and the operator's signature. The newly generated block is broadcast to all consortium blockchain nodes via a peer-to-peer network, and each node appends it to the end of its maintained blockchain ledger after verifying the block's legitimacy.

[0077] Through the above, once any operational data is recorded on the blockchain, the hash value of the previous block contained in its block header forms a dependent chain structure, making it impossible for anyone to tamper with the historical record without altering all subsequent blocks. When auditing or tracing an operation is required, the original data can be read from the blockchain, the hash recalculated, and compared with the hash value stored on the blockchain to verify the integrity and authenticity of the operation. Furthermore, because the operator's digital signature is non-repudiable, responsibility can be precisely traced back to the specific participating party and individual. This mechanism effectively solves the technical problems of easily tampered data, difficulty in defining responsibility, and low traceability efficiency in traditional project management.

[0078] Furthermore, you can also view Figure 5 , Figure 5 This is a detailed process diagram based on step S40 in the first embodiment. Figure 5 The step of determining whether the digital twin exceeds the limits based on preset thresholds for planar position deviation, elevation deviation, structural dimension deviation, and progress deviation includes S41~S44: Step S41: Collect spatial positioning data and progress reporting data of the construction site in real time, and calculate the difference with the corresponding design reference data in the digital twin to obtain the plane position deviation, elevation deviation, structural dimension deviation and progress deviation. Step S42: When any deviation exceeds the corresponding threshold, the excess event is marked as pending handling, and the preset rectification responsibility unit and rectification time limit are automatically matched according to the excess type and spatial location information. Step S43: Based on the matched responsible unit for rectification and the rectification deadline, combined with the coordinates of the over-limit location, the deviation value and the on-site image link, generate an electronic work order containing the above data; Step S44: Embed a status tracking identifier in the electronic work order and send it to the terminal of the corresponding responsible unit through the mobile push interface. The status tracking identifier records the timing and operator of the entire process of receiving and closing the work order.

[0079] In this embodiment, during the process of determining the digital twin to exceed the limit by setting a preset deviation threshold and triggering the issuance of a work order, the deviation calculation and limit determination, the matching of responsible units, the generation of electronic work orders, and the issuance and status tracking of work orders are completed in sequence, thus constructing a complete link from anomaly discovery to the implementation of handling tasks.

[0080] The system collects real-time spatial positioning data and progress reports from the construction site, and calculates the differences between these data and the corresponding design baseline data in the digital twin to obtain the planar position deviation, elevation deviation, structural dimension deviation, and progress deviation. The spatial positioning data originates from a real-time dynamic measurement base station of the Global Navigation Satellite System deployed at the construction site. This base station operates as a base station plus a rover station. The rover station is installed on the measuring prism of construction machinery or key components, outputting three-dimensional coordinates with centimeter-level accuracy once per second. The system receives the coordinate stream in real-time through a message queue and extracts the design coordinates of the same location from the digital twin based on the engineering decomposition structure to which the coordinates belong. The planar position deviation is calculated as the Euclidean distance between the measured coordinates and the design coordinates on the horizontal plane; the elevation deviation is the absolute value of the vertical difference between the measured elevation and the design elevation; the structural dimension deviation requires calculating the actual length, width, and thickness of the component by combining the coordinates of multiple measuring points, and then subtracting this from the design dimensions to obtain the absolute deviation value. Progress reporting data is obtained through daily reporting by construction team mobile terminals or automatic detection of completed quantities by IoT sensors. The system compares this with the planned completion quantity to calculate the progress deviation rate, which is calculated by subtracting the planned completion quantity from the actual completion quantity, dividing by the planned completion quantity, and then multiplying by 100%. All deviation calculation results are written in real time to the time sequence field of the spatiotemporal digital base plate, along with a timestamp of the calculation time.

[0081] When any deviation exceeds the corresponding threshold, the system marks the event as pending and automatically matches the pre-set responsible unit and rectification time limit based on the deviation type and spatial location information. The pre-set deviation thresholds are as described above: planar position deviation ±5 mm, elevation deviation ±3 mm, structural dimension deviation ±8 mm, and progress deviation ≥10%. The system uses a rule engine for matching: a pre-established mapping rule library is created, which outputs the responsible unit type and standard rectification time limit based on the deviation type and location type. For example, if the planar position deviation occurs in the foundation, the mapping rule outputs the responsible unit as the earthwork subcontractor, with a rectification time limit of 24 hours; if the structural dimension deviation occurs in the concrete column, the responsible unit is the carpentry subcontractor, with a rectification time limit of 48 hours; if the progress deviation occurs during the main structure construction stage, the responsible unit is the general contractor, with a rectification time limit of 72 hours. Simultaneously, the system further refines the specific work team or responsible person of the responsible unit based on the spatial location information. For example, by reverse-looking the construction area division map using location coordinates, it determines which work team or section the area belongs to. After matching is completed, the system generates a structured record containing the name of the responsible unit, the contact information of the responsible person, and the required completion deadline, which is then stored in association with the overdue event.

[0082] Based on the matched responsible unit and rectification deadline, and combined with the coordinates of the exceeding limit location, deviation value, and on-site image link, an electronic work order containing the above data is generated. The coordinates of the exceeding limit location are given in three-dimensional coordinates in the engineering coordinate system, with an accompanying text description, such as "500 mm west of column No. 3 in Area A". The deviation value includes not only the specific value exceeding the standard, but also the percentage of the value exceeding the threshold, so that the responsible unit can judge the severity. The on-site image link is automatically captured by the edge node when the exceeding limit is detected, and the link points to an image or short video file stored in cloud object storage, the content of which is a real-time record of the exceeding limit area. The main body of the electronic work order is organized according to a standard format: the top is the work order number and generation time; the middle is the problem description, including the location, deviation type, measured value, design value, and deviation percentage; the bottom is the handling requirements, including suggested rectification measures, responsible person, and rectification deadline; the bottom is a signature field, reserving space for electronic signature confirmation of receipt and completion.

[0083] A status tracking identifier is embedded in the electronic work order and sent to the terminal of the corresponding responsible unit via a mobile push interface. The status tracking identifier is a globally unique string, typically generated using a universally unique identification code algorithm, with a length of 32 characters. This identifier corresponds one-to-one with the work order record in the backend database and is used to track the entire lifecycle status of the work order. The initial status of the status tracking identifier is "Pending Receipt," which then sequentially progresses to "Received," "Processing," "Pending Retesting," "Pending Acceptance," "Loop Closed," and possibly "Rejected" or "Timeout." The system encapsulates the electronic work order in rich text format and sends it to the mobile application or WeChat mini-program of the corresponding responsible person through an integrated mobile push channel. When pushing, in addition to the work order content itself, a lightweight reminder message is also attached, including the location of the violation and the urgency level. After the responsible person opens the work order, the mobile terminal automatically reports receipt confirmation, and the cloud updates the status tracking identifier to "Received" and records the receipt timestamp. After the responsible person completes the on-site rectification, they need to upload the retest data and rectification photos to the work order, and the system compares again to see if the deviation has been eliminated. After the supervision unit passes the online acceptance, the status tracking indicator changes to "closed loop." The operator, operation time, and operation location throughout the entire process are recorded in the work order log and cannot be tampered with. Based on the above process, you can view... Figure 4 , Figure 4 This is a twin-triggered business workflow flowchart, which clearly shows the entire path from deviation calculation and threshold determination to work order generation, issuance, rectification, retesting and finally item clearance.

[0084] Furthermore, you can also view Figure 6 , Figure 6 This is a detailed process diagram based on step S50 in the first embodiment. Figure 6The steps of performing weight calculation of multi-party decision indicators, time-series prediction and deviation analysis of construction progress, and search and planning of emergency paths based on the spatiotemporal digital baseboard, and outputting consultation weights, progress warnings and scheduling paths, include S51~S54: Step S51: Extract the spatial topology network of the construction site from the spatiotemporal digital base plate. The spatial topology network includes roads, passages, tower crane operating paths, and emergency resource storage points. Step S52: Determine the emergency target location based on the abnormal location of quality and safety or the equipment failure point, and construct a search space with the emergency target location as the endpoint by combining the real-time positioning data of personnel and equipment at the construction site. Step S53: Using a heuristic path search algorithm, the optimal emergency dispatch path is iteratively calculated and output in the search space with a preset optimization objective. Step S54: The optimal emergency dispatch path is overlaid onto the spatiotemporal digital base plate to generate a three-dimensional navigation view, which is then pushed to relevant emergency personnel.

[0085] This embodiment details the specific process of emergency route search planning, which is divided into spatial topology network extraction, target point and search space construction, heuristic route search, and navigation view generation.

[0086] The spatial topology network of the construction site is extracted from the spatiotemporal digital base. The spatiotemporal digital base stores the geometric information and relationships of all spatial entities at the construction site. The system extracts four types of elements according to entity type: road elements include the centerline, width, direction of travel, height and weight restrictions, and road surface smoothness coefficient of construction access roads; passageway elements include the location coordinates and width of safety passages, stairwells, and temporary ramps within floors; tower crane operation path elements include the slewing radius of the tower crane, the boom coverage sector, and the connecting path between the lifting point and the unloading point; emergency resource storage point elements include the location of first aid kits, fire equipment cabinets, and emergency material warehouses, as well as the current inventory status of these resource points. After extracting the above elements, the system uniformly converts them into a weighted directed graph structure. The nodes of the graph represent road intersections, passageway entrances / exits, tower crane operating platforms, or resource points, and the edges represent the traversable paths between nodes. The weight of each edge is initialized to the Euclidean distance of the path and adjusted based on factors such as road surface smoothness, slope, and current congestion level. For example, the travel speed on a muddy road section is reduced to 0.6 times that of a normal road section, and the edge weight corresponding to that section is multiplied by a correction factor of 1.67. Through the above processing, the spatial topology network can accurately reflect the actual traffic conditions at the construction site.

[0087] Emergency target locations are determined based on the locations of quality and safety anomalies or equipment failure points. Combined with real-time location data of personnel and equipment at the construction site, a search space is constructed with these emergency target locations as the endpoints. Quality and safety anomalies are identified from deviation exceeding limits; for example, when the displacement of a foundation pit slope exceeds a threshold, this location becomes a potential accident point. Equipment failure points are identified from abnormal signals reported by IoT sensors, such as a sudden rise in tower crane motor temperature or abnormal pressure fluctuations in concrete pump trucks. The location of the faulty equipment is the emergency target location. The system reads the coordinates of these points from a spatiotemporal digital baseboard and sets them as the endpoint of the search space. Simultaneously, the system uses indoor positioning base stations or GPS positioning terminals deployed at the construction site to obtain the real-time locations of emergency response team members, safety officers, and supervising engineers, as well as the locations of dispatchable vehicles and equipment. These personnel and equipment locations are used as the starting point set of the search space. The search space is confined within the constructed spatial topology network; all reachable paths between the starting and ending points and their intermediate nodes constitute a subgraph of the search space. To reduce computational load, the system employs a geographically partitioned pruning strategy, retaining only nodes and edges within a buffer zone with the line connecting the start and end points as the axis. The buffer zone width is set to 50 meters.

[0088] A heuristic pathfinding algorithm is employed to iteratively calculate and output the optimal emergency dispatch path within the search space, with a preset optimization objective. The preset optimization objective is to minimize path travel time while also minimizing obstruction. The heuristic pathfinding algorithm uses a modified A* algorithm. This algorithm maintains a priority queue containing nodes to be expanded. Each node has an actual cost g(n) from the starting point to the current node and a heuristically estimated cost h(n) from the current node to the destination. The total cost f(n) is equal to the sum of g(n) and h(n). The algorithm iteratively expands the node with the smallest f(n) from the priority queue until the destination is reached. The actual cost g(n) is calculated based on the edge weights set in step S51, accumulating the weighted distances of all edges traversed from the starting point along the selected path to the current node. The heuristic estimation h(n) is calculated by dividing the Euclidean distance between the current node and the destination by a preset maximum travel speed. A real-time congestion factor is also introduced for correction. If real-time location data around the current node shows a large number of people gathering or equipment malfunctioning, h(n) is multiplied by a coefficient greater than 1 to reduce the search priority of that direction. The algorithm also supports multi-objective optimization. When the difference in path travel time is within 5%, the path with less obstruction (i.e., fewer obstacles or hazards) is prioritized. After iterative search, the algorithm outputs a sequence of nodes from the starting point to the destination and its corresponding edge sequence; this sequence represents the optimal emergency dispatch path.

[0089] The optimal emergency dispatch route described above is overlaid onto a spatiotemporal digital base to generate a 3D navigation view, which is then pushed to relevant emergency personnel. The spatiotemporal digital base already includes the 3D terrain of the construction site, building information model components, and a city information model base map. The system renders the optimal emergency dispatch route as a highlighted red line in the aforementioned 3D scene, marking the estimated arrival time at each node of the route, and indicating dangerous areas along the route with red semi-transparent warning spheres. Simultaneously, directional arrows and text prompts are added at key turning points of the route, such as "Turn left ahead into the safety passage." The generated 2D or 3D navigation view is pushed to the emergency personnel's mobile terminal application via streaming rendering technology. The terminal displays the view in augmented reality mode, overlaying virtual path arrows onto the live camera feed to guide personnel quickly to the accident site. For situations requiring simultaneous dispatch of multiple emergency resources, the system calculates the optimal route for each starting point and displays them in different colors on the same view to avoid route conflicts. Emergency personnel can also report the current traffic situation in real time through their terminals, and the system dynamically adjusts the recommended routes for the remaining personnel based on the reported information. Through the above steps, the system achieves a second-level response from the occurrence of an anomaly to the generation of an emergency resource dispatch path, significantly improving emergency response efficiency.

[0090] Furthermore, you can also view Figure 7 , Figure 7 The overall architecture diagram of the digital twin collaborative management and control system for the entire life cycle of engineering construction is shown in this application. The system adopts a five-layer progressive architecture from bottom to top, namely, perception layer, edge layer, data layer, platform service layer and application layer. Data flow and business collaboration between each layer are realized through standard interfaces.

[0091] The perception layer is deployed at the construction site and is responsible for collecting raw data across the entire area. This layer includes IoT sensors, such as temperature, humidity, vibration, and strain sensors; video surveillance equipment, such as high-definition cameras and thermal imagers; modeling terminals for Building Information Modeling (BIM) and City Information Modeling (CIM); and various on-site data acquisition devices, such as handheld measuring terminals and GPS receivers. The types of data collected by the perception layer cover spatial positioning data, environmental parameters, equipment operating conditions, and construction images, forming a multi-source, heterogeneous raw data pool.

[0092] The edge layer consists of distributed edge nodes deployed in power distribution boxes, monitoring rooms, and tower crane locations. Each edge node has a built-in computing and caching unit to perform local filtering, noise reduction, threshold determination, and feature extraction on the raw data uploaded from the perception layer, reducing the amount of invalid data uploaded. Simultaneously, when the network is interrupted, the edge nodes write critical data to their local cache and automatically resume transmission once the network is restored. This layer enables reliable data transmission and real-time local alarms in weak network environments.

[0093] The data layer serves as the unified collection and storage center for all data. This layer comprises four sub-repositories: a fusion repository of Building Information Modeling (BIM) and City Information Modeling (CIM) data, storing 3D model data aligned to spatial coordinates and semantically mapped; a business repository, storing project progress, investment, contracts, and quality and safety records; a blockchain evidence repository, storing hash-encrypted operation records and consortium blockchain consensus data; and a document repository, storing unstructured documents such as engineering drawings, change orders, and acceptance images. The data layer employs a hierarchical storage architecture, storing real-time streaming data, near-line historical data, and long-term archived data separately and establishing multi-dimensional indexes.

[0094] The platform service layer encapsulates universal twin technology service capabilities across all scenarios. This layer integrates six core engines: a twin construction engine responsible for generating the spatiotemporal digital base and updating the digital twin in real time; an access control engine that implements macro, meso, and micro level access verification and dynamic authorization; a multi-party collaboration engine that supports message push and task distribution across participating parties; an intelligent decision-making engine that runs an improved analytic hierarchy process (AHP), an improved earned value method (EVA) combined with long short-term memory networks (LSTM) and an improved A* algorithm; a business process engine that manages the flow and closed-loop clearance of electronic work orders triggered by deviations exceeding limits; and an interface gateway that provides a unified secure hypertext transfer protocol application programming interface (API) for connecting to external office automation systems, master data systems, and archive systems.

[0095] The application layer addresses multi-level management needs at the bureau, center, and project levels, supporting specific business scenarios. This layer comprises five application modules: a multi-level hierarchical control module enabling data penetration queries based on administrative levels; an online consultation and decision-making module providing multi-party expert scoring and comprehensive weight ranking interfaces; a full-cycle construction progress control module displaying earned value analysis curves and progress warning information; an emergency dispatch module outputting optimal path planning and resource allocation solutions; and a full-process digital delivery module supporting integrated archiving and transfer of building information modeling, drawings, and documents. After edge layer preprocessing, data layer aggregation, and platform service layer empowerment, the on-site perceived data ultimately forms a closed-loop control system at the application layer, encompassing "on-site perception—edge processing—cloud governance—middle platform empowerment—business application."

[0096] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the digital twin collaborative management and control method for the entire life cycle of engineering construction in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0097] This application provides a digital twin collaborative management and control device for the entire lifecycle of engineering construction. The device includes: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the digital twin collaborative management and control method for the entire lifecycle of engineering construction described in Embodiment 1 above.

[0098] The following is for reference. Figure 13 The diagram illustrates a structural schematic of a digital twin collaborative management and control device suitable for implementing the embodiments of this application. The digital twin collaborative management and control device for the entire lifecycle of engineering construction in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 13 The digital twin collaborative management and control equipment for the entire construction process shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0099] like Figure 13As shown, the digital twin collaborative management and control equipment for the entire construction project lifecycle may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in read-only memory (ROM) 1002 or programs loaded from storage device 1003 into random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the digital twin collaborative management and control equipment for the entire construction project lifecycle. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the engineering construction lifecycle digital twin collaborative management equipment to exchange data with other devices wirelessly or via wired communication. Although various engineering construction lifecycle digital twin collaborative management devices are shown in the figure, it should be understood that implementation or possession of all of them is not required. More or fewer may be implemented alternatively.

[0100] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0101] The engineering construction full-cycle digital twin collaborative management and control equipment provided in this application, employing the engineering construction full-cycle digital twin collaborative management and control method in the above embodiments, can solve the technical problems of existing BIM / CIM being disconnected from business processes and lacking reliable collaborative management and control. Compared with the prior art, the beneficial effects of the engineering construction full-cycle digital twin collaborative management and control equipment provided in this application are the same as the beneficial effects of the engineering construction full-cycle digital twin collaborative management and control method provided in the above embodiments, and other technical features in this engineering construction full-cycle digital twin collaborative management and control equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0102] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0104] This application provides a storage medium, which is a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon. The computer-readable program instructions are used to execute the digital twin collaborative management and control method for the entire life cycle of engineering construction in the above embodiments.

[0105] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be executed by instructions, used by devices, or used in conjunction with them. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0106] The aforementioned computer-readable storage medium may be included in the digital twin collaborative management and control equipment for the entire construction project lifecycle; or it may exist independently and not be assembled into the digital twin collaborative management and control equipment for the entire construction project lifecycle.

[0107] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the engineering construction full-cycle digital twin collaborative management and control device, the engineering construction full-cycle digital twin collaborative management and control device realizes the technical content of the engineering construction full-cycle digital twin collaborative management and control method embodiment shown above.

[0108] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using dedicated hardware-based implementations that perform the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions.

[0110] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0111] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described digital twin collaborative management and control method for the entire engineering construction lifecycle. This solves the technical problem of existing BIM / CIM being disconnected from business processes and lacking reliable collaborative management and control. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the digital twin collaborative management and control method for the entire engineering construction lifecycle provided in the above embodiments, and will not be repeated here.

Claims

1. A digital twin collaborative management and control method for the entire lifecycle of engineering construction, characterized in that, The digital twin collaborative management and control method for the entire construction cycle includes the following steps: A spatiotemporal digital baseboard is constructed by integrating a building information model (BIM) individual model and a city information model (CIM) urban spatial baseboard. A digital twin mapping to the physical engineering entity is then generated using this spatiotemporal digital baseboard as the data foundation. The steps of integrating the BIM individual model and the CIM urban spatial baseboard to construct the spatiotemporal digital baseboard include: fusing BIM data and CIM data through spatial coordinate alignment and semantic mapping; wherein the BIM data is in an industrial basic format and the CIM data is in a city geospatial markup language format; establishing a three-dimensional spatial index based on the fused data, the three-dimensional spatial index containing geometric information, attribute information, and association information; and overlaying time-series data fields onto the three-dimensional spatial index as a skeleton to form the spatiotemporal digital baseboard. The system receives multi-source field data uploaded by edge nodes deployed at the construction site using a breakpoint-resume transmission method, and updates the digital twin based on the data. The step of receiving the multi-source field data uploaded by edge nodes deployed at the construction site using a breakpoint-resume transmission method includes: receiving data uploaded by edge nodes deployed at distribution boxes, monitoring rooms, and tower crane locations; the data includes IoT sensor data, video surveillance data, and construction machinery operating data after local filtering and noise reduction; during the reception process, if a network interruption is detected, the hash check value of the received data and the reception breakpoint position at the time of interruption are recorded; after the network is restored, the breakpoint position and data hash check value carried in the re-uploaded transmission request from the edge node are compared with the locally recorded hash check value; if the verification passes, subsequent data is received from the reception breakpoint position. According to the hierarchical permissions of the parties involved in the project, the operation data is hashed, encrypted, and uploaded to the project consortium blockchain for data storage and operation traceability. The parties involved in the project include the construction unit, the supervision unit, the general contractor, and the subcontractors. The steps of hashing and encrypting the operation data according to the hierarchical permissions of the parties involved in the project and uploading it to the project consortium blockchain for data storage and operation traceability include: determining the data operation permissions of each of the parties involved in the project, wherein each permission has different data operation strategies; obtaining the operation data of each party involved in the project based on the data operation strategies; generating a fixed-length digest value for the operation data using a hash algorithm; attaching the digital signature of the operator; packaging it into a transaction; verifying it through the consensus nodes in the consortium blockchain; and writing it into an immutable block. The consensus nodes of the consortium blockchain are jointly participated in by the construction unit, the supervision unit, the general contractor, and the subcontractors. The digital twin is evaluated for exceeding limits using preset thresholds for planar position deviation, elevation deviation, structural dimension deviation, and schedule deviation. When a limit is exceeded, a work order is automatically triggered. Upon receiving the rectification and retest results, the digital twin is updated, and the closed-loop record is uploaded to the blockchain. The step of evaluating the digital twin for exceeding limits using preset thresholds for planar position deviation, elevation deviation, structural dimension deviation, and schedule deviation includes: real-time collection of spatial positioning data and progress reporting data from the construction site, and calculation of the difference between these and the corresponding design baseline data in the digital twin to obtain the planar position deviation. The system identifies deviations in elevation, structural dimensions, and schedule. When any deviation exceeds its corresponding threshold, the event is marked as pending action. Based on the type and location of the deviation, a pre-defined responsible unit and deadline for rectification are automatically assigned. The system then generates an electronic work order containing the aforementioned data, based on the matched responsible unit and deadline, the coordinates of the deviation location, the deviation value, and on-site image links. A status tracking identifier is embedded in the electronic work order and sent to the terminal of the corresponding responsible unit via a mobile push interface. This status tracking identifier records the timing and operator of the entire process of receiving and closing the work order. Based on the aforementioned spatiotemporal digital baseboard, the weight calculation of multi-party decision indicators, the time-series prediction and deviation analysis of construction progress, and the search and planning of emergency paths are performed respectively, and the consultation weight, progress warning and scheduling path are output.

2. The digital twin collaborative management and control method for the entire construction lifecycle as described in claim 1, characterized in that, The step of calculating the weights of multi-party decision indicators based on the spatiotemporal digital baseboard includes: Multiple evaluation indicators for consultation and decision-making are extracted from the spatiotemporal digital base plate. These evaluation indicators include spatial constraint indicators, schedule indicators, investment indicators, and resource indicators. Obtain the subjective scores of the evaluation indicators, and calculate the objective information entropy of each evaluation indicator based on historical construction data; The subjective scores and objective information entropy are weighted and integrated to output the comprehensive weight of each evaluation indicator; The evaluation indicators are ranked according to their comprehensive weights, a consultation priority sequence is output, and the key constraints that restrict the construction progress are identified.

3. The digital twin collaborative management and control method for the entire construction lifecycle as described in claim 1, characterized in that, The steps of performing time-series prediction and deviation analysis of construction progress based on the spatiotemporal digital base plate include: Extract the construction schedule baseline data, actual completion progress data, and building information model component process association data from the spatiotemporal digital base plate; Using the construction schedule baseline data and the actual completed progress data as inputs to the earned value management method, the budgeted cost, actual cost and planned value of the completed work are calculated, and the schedule deviation rate and investment deviation rate are output based on the calculation results. Historical progress time series data is used as input to the time series forecasting model to extrapolate the time series and output the predicted completion time value; The schedule deviation rate, the investment deviation rate, and the predicted completion time are compared with their corresponding thresholds, and the lag between them and the planned completion time is calculated. Based on the comparison results and the comparison of the lag calculation results with the corresponding threshold, when any result exceeds the corresponding threshold, a progress warning of the corresponding level is triggered, and a warning report is generated based on the progress warning.

4. The digital twin collaborative management and control method for the entire construction lifecycle as described in claim 1, characterized in that, The steps of performing weight calculations for multi-party decision indicators, time-series prediction and deviation analysis of construction progress, and search and planning of emergency paths based on the spatiotemporal digital baseboard, and outputting consultation weights, progress warnings, and scheduling paths, include: The spatial topology network of the construction site is extracted from the spatiotemporal digital base plate. The spatial topology network includes roads, passages, tower crane operating paths, and emergency resource storage points. Based on the location of quality and safety anomalies or equipment failure points, determine the emergency target points, and combine them with the real-time location data of personnel and equipment at the construction site to construct a search space with the emergency target points as the endpoints; A heuristic path search algorithm is adopted to iteratively calculate and output the optimal emergency dispatch path in the search space with a preset optimization objective; The optimal emergency dispatch path is overlaid onto the spatiotemporal digital base to generate a three-dimensional navigation view, which is then pushed to relevant emergency personnel.

5. A digital twin collaborative management and control device for the entire lifecycle of engineering construction, characterized in that, The digital twin collaborative management and control device for the entire construction cycle of an engineering project stores a computer program, which, when executed by a processor, implements the digital twin collaborative management and control method for the entire construction cycle of an engineering project as described in any one of claims 1-4.

6. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the digital twin collaborative management and control method for the entire lifecycle of engineering construction as described in any one of claims 1-4.

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