Engineering information management system based on BIM

Abstract

A BIM-based engineering information management system of the present invention relates to the field of constructional engineering informatization management, and comprises a data processing module, a model construction module, a block chain evidence storage module, a verification module, a data fusion module and an authority management module. The data processing module receives original engineering data and generates a structured data signal. And the model construction module generates a BIM model according to the structured data signal. And the block chain evidence storage module encrypts and stores the metadata signal, generates an evidence storage completion signal and feeds back the evidence storage completion signal to the model construction module. And the verification module analyzes the dynamic operation log signal, and generates a risk early warning signal and a repair suggestion signal when abnormality is detected. And the data fusion module generates a fusion data signal, transmits the fusion data signal to the model construction module and triggers BIM model updating. And the authority management module adjusts the user authority according to the risk early warning signal. According to the invention, the problems of data islands, safety risks, low cooperation efficiency and insufficient intelligence in engineering information management can be solved.

Description

Technical Field

[0001] The present invention relates to the field of construction engineering information management, and in particular to a BIM-based engineering information management system. Background Art

[0002] Building Information Modeling (BIM) technology has become a core tool for modern project management. It has significantly improved project efficiency through functions such as 3D modeling, multi-disciplinary collaboration, and construction simulation. However, with the expansion of project scale and the acceleration of digital transformation, the shortcomings of traditional BIM systems in data management, security, and intelligence have become increasingly prominent. First, project data is scattered among different professional teams and software platforms, and asynchronous model version updates lead to frequent data silos. For example, when the structural design and mechanical and electrical installation teams modify the same model node, the lack of a real-time collaboration mechanism leads to version conflicts, requiring a lot of time to manually merge data, which seriously slows down the construction progress.

[0003] Secondly, BIM models typically utilize a centralized storage architecture, which presents a single point of failure risk. Their crude permission management system makes critical data susceptible to tampering or leakage. For example, unauthorized modification of model parameters by construction parties could potentially compromise project quality, but traditional audit traceability is insufficient, making it difficult to quickly identify the responsible party. Furthermore, model compliance checks rely heavily on manual experience, and issues such as pipeline collisions and structural overruns are often discovered only during the construction phase, resulting in extremely high rework costs. Emerging real-time data from IoT sensors and drone point clouds lacks deep integration with BIM models, making it impossible to dynamically optimize construction plans. Existing improvement proposals include some patents that attempt to incorporate blockchain technology to store critical model data, but these only address static metadata and fail to cover the integrity verification of dynamic operation logs. Other solutions employ machine learning for model verification, but the AI algorithms are disconnected from business processes, making it impossible to integrate permission control or trigger automatic repairs. Furthermore, multi-source data integration often relies on customized interfaces and lacks a universal adaptability architecture, limiting system scalability and compatibility with sensors or GIS platforms from different vendors.

[0004] When it comes to rights management, traditional role-based divisions are unable to adapt to the dynamic demands of complex engineering scenarios, such as time-sensitive access control for temporary contractors or the need for fine-grained data sharing during cross-regional team collaboration. These challenges compound the existing BIM system's inability to support refined management and control throughout the project lifecycle. An innovative solution integrating trusted evidence, intelligent verification, and dynamic collaboration is urgently needed to achieve efficient cross-platform, multi-role collaboration and real-time decision-making while ensuring data security. Summary of the Invention

[0005] In light of the shortcomings of the above-mentioned existing technologies, the present invention aims to provide a BIM-based engineering information management system to address the issues of data silos, security risks, low collaboration efficiency, and insufficient intelligence in engineering information management. This invention ensures data credibility through blockchain encryption and evidence storage, uses AI to verify model defects in real time, dynamically integrates multi-source data to improve collaboration efficiency, and implements fine-grained permission control through federated learning.

[0006] The present invention provides a BIM-based engineering information management system, comprising: A data processing module receives raw engineering data and generates structured data signals; Model construction module, which generates a BIM model based on structured data signals and extracts model metadata signals and dynamic operation log signals; The blockchain evidence storage module encrypts and stores the metadata signal and generates an evidence completion signal to feed back to the model construction module; Verification module, which analyzes dynamic operation log signals and generates risk warning signals and repair suggestion signals when anomalies are detected; Data fusion module: The data fusion module integrates model metadata signals, dynamic operation log signals and repair suggestion signals to generate fused data signals, which are transmitted to the model construction module and trigger BIM model updates; The permission management module adjusts user permissions based on risk warning signals, generates operation interception signals to the federated learning node to train the abnormal access model, and after training, the federated learning node sends permission policy update signals to the permission management module and synchronizes them to the blockchain evidence module to leave traces.

[0007] In one embodiment of the present invention, the data processing module includes a data cleaning unit and a format conversion unit. The data cleaning unit filters redundant fields in the original engineering data through a preset regular expression template and a machine learning model, and generates a cleaned intermediate data signal. After receiving the intermediate data signal, the format conversion unit converts the unstructured data into a uniformly encoded structured data signal according to the BIM model construction standard, wherein the structured data signal includes geometric parameters, material properties and construction progress labels. After completing the conversion, the format conversion unit sends a data ready signal to the model construction module, and simultaneously encrypts and transmits the metadata of the structured data signal to the blockchain evidence module for pre-storage.

[0008] In one embodiment of the present invention, a parametric modeling engine is installed in the model construction module. The parametric modeling engine generates a three-dimensional model skeleton based on the geometric parameters in the structured data signal, and loads the corresponding physical property calculation model through the material properties to generate the geometric property signal and material parameter signal of the BIM model. At the same time, it records the user's editing operations on the model to form a dynamic operation log signal. The dynamic operation log signal includes the operation timestamp, user ID and modified content hash value. When concurrent editing operations on the same model node are detected, the model construction module sends a version conflict warning signal to the blockchain evidence storage module, and triggers the version lock signal to suspend the editing permission of the current node.

[0009] In one embodiment of the present invention, the blockchain evidence module uses an asymmetric encryption algorithm to shard the metadata signal, stores the sharded data blocks in the private chain nodes and the distributed file system respectively, and writes the hash value of each data block into the blockchain ledger to form an evidence chain. The version lock signal is implemented through a smart contract. When more than three related metadata blocks are written continuously into the evidence chain, the smart contract automatically generates a version lock signal and transmits it to the model construction module. The version lock signal is revoked by the blockchain evidence module only after receiving the risk release confirmation signal sent by the verification module.

[0010] In one embodiment of the present invention, the verification module has a built-in defect recognition model and a rule engine. The defect recognition model is trained through a historical engineering defect data set and is used to identify pipeline collisions, structural overruns or specification conflicts in dynamic operation log signals. The rule engine loads the industry standard specification library and compares it with the BIM model attributes in real time. When an anomaly is detected, the verification module generates a risk warning signal containing risk level, positioning coordinates and repair priority labels, and at the same time calls a preset simulation engine to simulate the repair plan and generate a repair suggestion signal containing parameter adjustment suggestions, alternative path planning and cost impact analysis. The repair suggestion signal triggers the data fusion module to start the model update process.

[0011] In one embodiment of the present invention, the data fusion module includes a spatiotemporal alignment unit and a conflict resolution unit. The spatiotemporal alignment unit normalizes the timestamps of IoT (Internet of Things) sensor data, drone point cloud data, and GIS (geographic information system) data, and maps them to the corresponding positions of the BIM model through spatial coordinate system conversion to generate spatiotemporal synchronized fusion data signals. When the conflict resolution unit detects that the deviation between the sensor data and the preset threshold of the BIM model exceeds 10%, it automatically generates a data anomaly report and triggers a secondary verification of the verification module. The fused data signal drives the model construction module to iteratively optimize the material parameters or structural topology of the BIM model, and at the same time sends real-time update instructions to the visualization module.

[0012] In one embodiment of the present invention, the rights management module includes a geo-fence engine and a behavior analysis model. The geo-fence engine generates a dynamic electronic fence based on the construction area boundary in the GIS data. When the GPS (Global Positioning System) coordinates of the user terminal exceed the fence range, its data access rights are automatically downgraded and an operation interception signal is generated. The behavior analysis model obtains encrypted user operation history data through the federated learning node and trains to generate an abnormal access detection model. When an unconventional operation sequence is detected, the rights management module dynamically restricts the user's editing rights to the key nodes of the BIM model and encrypts the interception log and transmits it to the blockchain evidence storage module.

[0013] In one embodiment of the present invention, the federated learning node uses homomorphic encryption technology to aggregate multi-terminal user behavior data. After training the abnormal access detection sub-model locally, the model gradient parameters are uploaded to the central server for aggregation update, and a global permission policy update signal is generated. The permission policy update signal includes a user role permission matrix, a data sensitivity classification table, and an operation blacklist and whitelist. After receiving the update signal, the permission management module initiates secondary identity authentication for users with security risks exceeding the threshold, and writes the updated policy version hash value into the audit-dedicated chain of the blockchain evidence storage module.

[0014] In one embodiment of the present invention, the model construction module further includes a dynamic version merging unit. After receiving the version locking signal sent by the blockchain evidence storage module, the dynamic version merging unit automatically parses the concurrent modification records of multiple users on the same BIM model node, compares the differences in geometric properties, material parameters and associated logic in each version through a conflict resolution algorithm, and generates a version integration plan including weight scores and merging priority labels.

[0015] In one embodiment of the present invention, after the integration scheme verifies the structural stability through a lightweight simulation engine, it triggers the model construction module to generate an updated BIM model version, and encrypts and transmits the merge log to the blockchain evidence module to generate a new evidence chain branch, and at the same time sends a version merge completion signal to the authority management module to lift the editing operation restrictions.

[0016] The present invention provides a BIM-based engineering information management system that ensures data credibility through blockchain encryption and evidence storage, AI real-time verification of model defects, dynamic fusion of multi-source data to improve collaboration efficiency, and federated learning to achieve fine-grained authority control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a system architecture diagram of a BIM-based engineering information management system. DETAILED DESCRIPTION

[0019] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0020] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0021] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0022] See Figure 1 , shown is a BIM-based engineering information management system according to the present invention. The BIM-based engineering information management system according to the present invention includes a data processing module, a model construction module, a blockchain evidence storage module, a verification module, a data fusion module, and a rights management module. The data processing module receives raw engineering data and generates a structured data signal. The model construction module generates a BIM model based on the structured data signal and extracts the model metadata signal and dynamic operation log signal. The blockchain evidence storage module encrypts and stores the metadata signal and generates an evidence storage completion signal, which is fed back to the model construction module. The verification module analyzes the dynamic operation log signal and generates a risk warning signal and a repair suggestion signal when an anomaly is detected. The data fusion module integrates the model metadata signal, the dynamic operation log signal, and the repair suggestion signal to generate a fused data signal, which is transmitted to the model construction module and triggers a BIM model update. The rights management module adjusts user rights based on the risk warning signal and generates an operation interception signal to the federated learning node to train the abnormal access model. After training, the federated learning node sends a rights policy update signal to the rights management module and synchronizes it to the blockchain evidence storage module to leave a trace.

[0023] like Figure 1 As shown, the data processing module serves as the system entry point, receiving raw engineering data from user terminals (such as design software and mobile devices) or external devices (such as sensors and drones). This data includes design drawings, bills of materials, construction logs, and point cloud scan data. This module preprocesses the raw data using a data cleaning unit. Specifically, this unit uses regular expression templates to match redundant information in unstructured text (e.g., duplicate records or invalid symbols in construction logs) and identifies outliers (e.g., concrete strength values outside a reasonable range) using machine learning models (e.g., LSTM-based sequence classifiers). This generates cleaned intermediate data signals. Subsequently, a format conversion unit converts the intermediate data into structured data signals that comply with BIM standards. For example, this involves mapping layer information in CAD drawings to geometric parameters in the IFC (Industry Foundation Classes) format and converting Excel material tables into standardized attribute tables containing strength grades and supplier codes. The structured data signals contain not only static information such as geometric parameters and material properties, but also construction phase tags (e.g., "foundation construction period" and "steel structure installation period") for subsequent model version management. After the conversion is completed, the data processing module sends a data-ready signal to the model building module, and simultaneously encrypts and transmits the metadata of the structured data (such as data source, conversion timestamp) to the blockchain evidence storage module for pre-storage to ensure that the data traceability is reliable.

[0024] Furthermore, the model construction module generates a BIM 3D model based on structured data signals. Its core is a parametric modeling engine. This engine first analyzes geometric parameters (such as beam and column cross-sectional dimensions and pipeline routing coordinates) to construct a 3D model skeleton. It then loads material parameters (such as steel elastic modulus and concrete compressive strength) and associates them with physical property calculation models (such as finite element analysis models) to generate a complete BIM model containing geometric attribute signals (vertex coordinates and topological relationships) and material parameter signals (mechanical properties and durability indicators). During model editing, the module records user operations (such as component movement and property modifications) in real time, generating dynamic operation log signals. Each log entry includes the operation timestamp, user identity (ID), a hash value of the modified content (e.g., generated using the SHA-256 algorithm), and an impact scope indicator (e.g., "only affects the electrical system"). If multiple users are detected concurrently editing the same model node (e.g., a fire protection pipe on a specific floor), the model construction module sends a version conflict warning signal to the blockchain notarization module and triggers a version lock signal, suspending editing permissions for that node until the conflict is resolved. In addition, the module features a built-in lightweight rendering engine that converts BIM models into lightweight formats (such as GLTF) suitable for display on mobile devices, while preserving key attribute information for subsequent analysis by the verification module. The blockchain evidence storage module ensures the immutability and traceability of BIM data. This module utilizes a hybrid storage architecture: Model metadata signals (such as geometric properties and material parameters) are sharded using an asymmetric encryption algorithm (such as RSA-2048). Data blocks are stored separately on private blockchain nodes (deployed on the engineering company's internal servers) and a distributed file system (such as IPFS). The hash value of each data block is written to the blockchain ledger to form an evidence chain. Dynamic operation log signals are batch-hashed using a Merkle tree structure, with the root hash periodically uploaded to the blockchain (e.g., every 10 minutes). When three or more related metadata blocks (such as design change records from the same construction phase) are consecutively written to the evidence chain, the module's built-in smart contract automatically generates a version lock signal, which is transmitted to the model construction module to freeze editing permissions for the current model version. Version locks are only released under two circumstances: first, when the verification module sends a confirmation signal that the risk has been resolved (e.g., a defect has been fixed), or second, when a manually authorized party (e.g., the project's chief engineer) unlocks the lock through a multi-signature verification mechanism. Furthermore, the module provides an audit interface that supports searching historical records by time range, user ID, or operation type, and generates legally binding audit reports.

[0025] In one embodiment of the present invention, the technical details of the data processing module are further defined. Its data cleaning unit and format conversion unit form a two-stage preprocessing pipeline. The data cleaning unit first uses regular expression templates to match noise information in the raw data. For example, in construction logs, it filters out non-standard date formats (such as "2023 / 13 / 32") or invalid characters (such as garbled characters). It then uses a pretrained LSTM model to identify anomalous data. The model input is a text sequence (such as a material inspection report), and the output is an abnormal probability value. When the probability exceeds a threshold (such as 0.9), the data is automatically marked and notified for manual review. The format conversion unit combines a rule engine with machine learning. For highly structured data (such as CAD drawings), it directly converts it using predefined IFC mapping rules (such as mapping "Layer 1" to "Structural Beam"). For unstructured data (such as handwritten notes by construction workers), it uses optical character recognition (OCR) and then inputs it into a BERT model to extract key entities (such as "Concrete pouring thickness 30cm"), which are then converted into standard attribute fields. During the conversion process, the module generates a data lineage map in real time, documenting the mapping between raw and structured data. This map is encapsulated in JSON-LD format and encrypted for transmission to the blockchain evidence module, providing a basis for subsequent data traceability. A dynamic version merging mechanism is proposed to address version conflicts within the model construction module. When the blockchain evidence module triggers a version lock signal, the dynamic version merging unit within the model construction module activates. It first extracts concurrent edit records from the blockchain ledger and analyzes differences in geometric properties, material parameters, and associated logic between versions (e.g., User A changes the pipe diameter to 200mm, while User B adds a support bracket at the same location). The conflict resolution algorithm generates a weighted score based on preset priority rules (e.g., the designer's authority is higher than the contractor's) and physical feasibility assessments (e.g., finite element analysis verifies structural strength). The algorithm also assigns a merging priority label (e.g., "Must Keep" or "Optional Improvement"). Once the integration solution is verified by the lightweight simulation engine (e.g., fluid simulation verifies flow loss after pipeline modification), the model construction module is triggered to generate a new version of the BIM model. Simultaneously, the merge log (including conflicting nodes, resolution logic, and final parameters) is encrypted and transmitted to the blockchain evidence storage module, generating an evidence chain branch (e.g., "V2.1_merge") for subsequent audit and traceability. Furthermore, after the version merge is complete, the module sends a signal to the permissions management module, removing editing restrictions for the relevant users and updating their operational permissions based on the merge results (e.g., downgrading the permissions of users who frequently cause conflicts).

[0026] like Figure 1As shown, the technical implementation of the verification module is further defined. This module integrates a dual verification mechanism: a deep learning-based defect recognition model and an industry-standard driven rules engine. The defect recognition model is trained using a historical engineering defect dataset covering typical issues such as pipeline collisions, structural stress violations, and fire separation violations. The model utilizes a hybrid convolutional neural network and graph neural network architecture to analyze the geometric topological relationships and physical property associations within BIM models. For example, this can detect collision risks at intersections of different elevations within a pipeline system or assess the deformation thresholds of steel structure nodes under load conditions. The rules engine incorporates an extensible library of industry standards, including building fire protection codes, seismic design standards, and green building evaluation metrics. The engine compares BIM model parameters against regulatory requirements in real time and generates risk warning signals containing risk levels, location coordinates, and repair priority labels when deviations are detected. For example, if an escape route is detected to be insufficiently wide, it is marked as a red high-risk and linked to evacuation simulation results. After identifying the anomaly, the verification module calls the lightweight simulation engine to simulate the repair plan, such as automatically adjusting the pipeline elevation or adding structural support, and generates a repair suggestion signal that includes parameter adjustment range, alternative path planning, and cost impact analysis. This signal triggers the data fusion module to start the model update process, and at the same time sends a risk level change notification to the authority management module to adjust the corresponding user's operating permissions. The verification module also supports manual review and intervention. When the repair suggestion is rejected by the user, the module records the decision reason and re-evaluates the risk factor. If manual operation causes the risk to escalate, a second verification is forced to be triggered and the editing permissions of the relevant models are frozen until the risk is resolved. The spatiotemporal alignment and conflict resolution capabilities of the data fusion module are refined. The module includes a spatiotemporal alignment unit and a multi-source data conflict resolution unit. The spatiotemporal alignment unit first normalizes the timestamps of real-time data from IoT sensors, point cloud data collected by drones, and GIS geographic information data. It uses the Network Time Protocol to synchronize the clocks of all devices and unifies the time base of all data to the main time axis of the BIM model. During the spatial coordinate alignment phase, a coordinate system conversion algorithm is used to convert the spatial reference system of data from different sources to the engineering coordinate system used by the BIM model. For example, GPS WGS-84 coordinates are converted to the local construction coordinate system. The point cloud data is then aligned with the geometric surfaces of the BIM model using the ICP registration algorithm. The conflict resolution unit monitors deviations between the fused data and preset thresholds of the BIM model. For example, if the concrete curing temperature monitored by a sensor exceeds the model's preset range, the unit automatically triggers a data credibility assessment process, correcting the original data based on sensor calibration records and environmental factors. If significant deviations still exist after correction, a data anomaly report is generated and submitted to the verification module for secondary verification, while suspending the automatic update of relevant model parameters.The data fusion module also drives the model construction module to iteratively optimize the BIM model. For example, it adjusts the foundation model stiffness parameters according to real-time settlement monitoring data, or updates the construction progress simulation logic based on meteorological data. The optimized model parameters are used to generate a new version of the evidence record through the blockchain evidence module, and the difference is compared with the original version for audit traceability.

[0027] like Figure 1 As shown, the collaborative control mechanism of geo-fencing and behavioral analysis of the permission management module is specified in detail. The geo-fencing engine generates dynamic electronic fences based on the construction area boundary coordinates in the GIS data. The fence range is automatically adjusted as the construction phase progresses. For example, the foundation construction period is limited to 50 meters around the foundation pit, and the main structure period is extended to the entire building outline. When the GPS coordinates of the user terminal exceed the fence range, the engine downgrades its data access rights in real time, converts the original design editing rights to read-only mode, and generates an operation interception signal to interrupt the sensitive operations it is performing, such as model parameter modification or drawing export requests. The behavioral analysis model obtains encrypted user operation history data through the federated learning node and uses a time series pattern mining algorithm to identify abnormal access sequences, such as frequent switching of access model nodes in a short period of time or attempts to download data from unauthorized areas. When abnormal behavior is detected, the model dynamically adjusts the user's permission level, such as limiting its editing rights to key structural nodes or extending the operation approval process. The permission management module also integrates a multi-factor authentication interface. When a user triggers a high-risk operation (such as deleting historical versions or exporting a complete model), biometric verification or dynamic token confirmation is mandatory. All interception operations and permission change records are encrypted and transmitted to the blockchain evidence storage module to generate an unalterable audit log. The log contains the operation time, device fingerprint and associated risk score.

[0028] Furthermore, the workflow and encryption strategy for federated learning nodes are defined. Federated learning nodes utilize homomorphic encryption to process user behavior data uploaded by each terminal, ensuring that data remains encrypted during model training. Each terminal locally trains an abnormal access detection sub-model, adds noise to the gradient parameters using differential privacy, and then uploads the data to a central server. The server aggregates the gradients across all terminals to update global model parameters and generate a global permission policy update signal containing a user role permission matrix, a data sensitivity classification table, and operation blacklists and whitelists. This permission policy update signal is then sent to the permission management module, which adjusts user permissions based on the latest policy. For example, this may reduce data download limits for users who frequently trigger low-risk alerts or enable real-time screen recording monitoring for high-risk roles. The node also sets trigger conditions for model updates. When a new attack pattern (such as abnormal access from a specific IP address segment) or a project phase change (such as transitioning from design to construction) is detected, an emergency training process is automatically initiated to generate a temporary permission policy. The validity period of the temporary policy is tied to the associated project milestone and automatically rolls back to the baseline permission settings if the policy expires. Federated learning nodes are deeply integrated with the blockchain evidence storage module's audit chain. Each policy update generates a digital fingerprint and writes it to the blockchain, ensuring traceability and non-repudiation of permission changes. This also supports third-party auditors verifying policy compliance through the authorization interface. Through refined technical design, the system enhances defect identification accuracy, multi-source data fusion reliability, dynamic permission control intelligence, and federated learning security, creating a comprehensive closed-loop management chain from data verification to access control. The verification module's double-check mechanism reduces the need for manual intervention. The data fusion module's spatiotemporal alignment and conflict resolution ensure multi-source data consistency. The permission management module's geofencing and behavioral analysis enable scenario-adaptive control. Federated learning nodes enhance the generalization capabilities of anomaly detection models while preserving privacy. Signal interaction between modules forms a linkage mechanism. For example, anomaly reports from the data fusion module trigger secondary checks in the verification module, and the verification results drive permission policy adjustments. Ultimately, blockchain evidence storage ensures data integrity and auditability throughout the entire process, providing a highly robust solution for information management in complex engineering environments.

[0029] like Figure 1The figure shows the system's closed-loop feedback and dynamic optimization mechanism. When the visualization module detects that a user has rejected the repair suggestion generated by the verification module three or more times in a row, it automatically generates a manual intervention request signal and transmits it to the project management terminal, triggering the project supervisor to intervene and review the disputed node. This manual intervention request signal includes a historical version comparison view of the disputed operation, the evolving risk level trend, and a cost-impact analysis of the repair solution. After passing multi-factor authentication, the supervisor can choose to maintain the original suggestion, adopt the user's modification, or propose a third-party optimization solution within the arbitration interface. The decision result forms a manual arbitration signal and is fed back to the verification module. Upon receiving the arbitration signal, the verification module reassesses the risk level. If the arbitration solution introduces new conflicting factors, it invokes a lightweight simulation engine for multi-dimensional verification, such as simultaneous testing of structural strength, pipeline flow, and fire escape routes. A new repair suggestion signal with a verification report is generated, and the full log of the arbitration process is encrypted and stored in an independent audit branch of the blockchain evidence storage module. Furthermore, the system implements a dynamic learning mechanism. When there is a significant discrepancy between the manual arbitration result and the AI recommendation, it automatically extracts features from the disputed case and incorporates them into the training set of the defect recognition model. Through incremental learning, the model's decision weights are optimized, reducing the probability of manual intervention in similar scenarios in the future. An environmental perception and supply chain collaboration module is introduced. This module is connected to the meteorological data platform, building materials supplier database and construction machinery Internet of Things system through the API interface to achieve real-time integration of external environment and resource data. The environmental perception unit dynamically collects temperature, humidity and wind speed data at the construction site and matches it with the construction process parameters in the BIM model. For example, when continuous heavy rain is detected, the concrete pouring plan is automatically adjusted and associated with the model progress label. At the same time, a construction plan change suggestion signal is generated and transmitted to the authority management module, triggering a temporary upgrade of the authority of the relevant subcontractors to speed up the approval process. The supply chain collaboration unit monitors the inventory and logistics status of building materials in real time. When a delivery delay of key materials (such as prestressed steel strands) is detected, the alternative solution analysis process is automatically initiated. By comparing the parameters of alternative materials in the supplier database (such as tensile strength and elongation), compliant alternatives are screened and a material substitution impact report is generated, including BIM model parameter adjustment suggestions, cost change estimates and construction schedule rescheduling plans. This module also drives the blockchain evidence module to strengthen the evidence of supply chain change records, and generates structured evidence data packets after OCR recognition of supplier qualification documents, logistics receipts and quality inspection reports. Each data packet contains multiple layers of hash check codes to ensure the non-tamperability of data in the entire supply chain.

[0030] Furthermore, global sharing of experience data is achieved by constructing a distributed knowledge graph. The knowledge transfer engine extracts the BIM model version evolution records, risk management logs, and permission policy change trajectories of historical projects from the blockchain evidence storage module, and uses graph neural networks to extract common features (such as foundation design patterns under specific geological conditions and high-frequency construction conflict types) to generate reusable engineering knowledge units. Each knowledge unit contains three parts: one is the characteristic vector of the problem scenario (such as the selection parameters of the soft soil deep foundation pit support scheme), the second is the association rules of the solution (such as the functional relationship between the support pile spacing and soil parameters), and the third is the quantitative evaluation index of the implementation effect (such as the percentage improvement of settlement control accuracy). When a new project imports the initial BIM model, the system automatically matches similar scenarios in the knowledge graph, pushes the associated knowledge units to the model construction module, and drives the parametric modeling engine to pre-fill the optimization parameters. For example, when a high-rise steel structure project is identified, the historical preferred design scheme of the wind vibration control node is automatically loaded. The multi-project collaboration framework allows different project teams to share desensitized knowledge units in an encrypted environment. Federated learning nodes perform differential privacy processing on the knowledge data contributed by each project, aggregate it to generate a global optimization strategy, and reversely update the local system's defect identification model and rule engine, forming a positive cycle of cross-project experience. The framework also provides a knowledge trading market based on smart contracts. Enterprises can pay digital tokens to obtain expert solutions for specific scenarios. All transaction records and knowledge usage traces are recorded in the consortium chain of the blockchain evidence storage module, ensuring clear intellectual property ownership and an automatically enforceable profit-sharing mechanism.

[0031] The BIM-based engineering information management system of the present invention ensures data credibility through blockchain encryption and evidence storage, AI verifies model defects in real time, dynamically integrates multi-source data to improve collaboration efficiency, and implements fine-grained authority control through federated learning.

[0032] Therefore, the BIM-based engineering information management system of the present invention can solve the problems of data silos, security risks, low collaboration efficiency and lack of intelligence in engineering information management.

[0033] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A BIM-based engineering information management system, characterized in that: include: a data processing module, the data processing module receiving raw engineering data and generating a structured data signal; A model construction module, wherein the model construction module generates a BIM model according to the structured data signal and extracts a model metadata signal and a dynamic operation log signal; A blockchain evidence storage module, which encrypts and stores the metadata signal and generates an evidence storage completion signal to feed back to the model construction module; a verification module, the verification module analyzing the dynamic operation log signal and generating a risk warning signal and a repair suggestion signal when an anomaly is detected; a data fusion module, the data fusion module integrating the model metadata signal, the dynamic operation log signal, and the repair suggestion signal to generate a fused data signal, the fused data signal being transmitted to the model construction module and triggering the BIM model update; The rights management module adjusts user rights according to the risk warning signal, generates an operation interception signal to the federated learning node to train the abnormal access model, and after training, the federated learning node sends a rights policy update signal to the rights management module and synchronizes it to the blockchain evidence storage module to leave a trace.

2. The engineering information management system based on BIM (a data-based tool used in engineering design and construction) according to claim 1, characterized in that: The data processing module includes a data cleaning unit and a format conversion unit. The data cleaning unit filters the redundant fields in the original engineering data through a preset regular expression template and a machine learning model, and generates a cleaned intermediate data signal. After receiving the intermediate data signal, the format conversion unit converts the unstructured data into a uniformly encoded structured data signal according to the BIM model construction standard, wherein the structured data signal includes geometric parameters, material properties and construction progress labels. After completing the conversion, the format conversion unit sends a data ready signal to the model construction module, and simultaneously encrypts and transmits the metadata of the structured data signal to the blockchain evidence storage module for pre-storage.

3. The BIM-based engineering information management system according to claim 1, characterized in that: The model construction module is equipped with a parametric modeling engine. The parametric modeling engine generates a three-dimensional model skeleton according to the geometric parameters in the structured data signal, and loads the corresponding physical property calculation model through the material properties to generate the geometric property signal and material parameter signal of the BIM model. At the same time, it records the user's editing operations on the model to form a dynamic operation log signal. The dynamic operation log signal includes the operation timestamp, user ID and modified content hash value. When concurrent editing operations on the same model node are detected, the model construction module sends a version conflict warning signal to the blockchain evidence storage module and triggers a version lock signal to suspend the editing permission of the current node.

4. The BIM-based engineering information management system according to claim 1, characterized in that: The blockchain evidence module uses an asymmetric encryption algorithm to shard the metadata signal, stores the sharded data blocks in the private chain nodes and distributed file system respectively, and writes the hash value of each data block into the blockchain account book to form an evidence chain. The version lock signal is implemented through a smart contract. When more than three related metadata blocks are written continuously in the evidence chain, the smart contract automatically generates a version lock signal and transmits it to the model construction module. The version lock signal is revoked by the blockchain evidence module only after receiving the risk release confirmation signal sent by the verification module.

5. The BIM-based engineering information management system according to claim 1, characterized in that: The verification module has a built-in defect recognition model and a rule engine. The defect recognition model is trained with a historical engineering defect data set to identify pipeline collisions, structural overruns, or specification conflicts in dynamic operation log signals. The rule engine loads an industry standard specification library and compares it with BIM model attributes in real time. When an anomaly is detected, the verification module generates a risk warning signal containing risk level, positioning coordinates, and repair priority labels, and simultaneously calls a preset simulation engine to simulate the repair plan, generating the repair suggestion signal containing parameter adjustment suggestions, alternative path planning, and cost impact analysis. The repair suggestion signal triggers the data fusion module to start the model update process.

6. The BIM-based engineering information management system according to claim 1, characterized in that: The data fusion module includes a spatiotemporal alignment unit and a conflict resolution unit. The spatiotemporal alignment unit normalizes the timestamps of IoT (Internet of Things) sensor data, drone point cloud data, and GIS (Geographic Information System) data, and maps them to the corresponding positions of the BIM model through spatial coordinate system conversion to generate spatiotemporal synchronized fusion data signals. When the conflict resolution unit detects that the deviation between the sensor data and the preset threshold of the BIM model exceeds 10%, it automatically generates a data anomaly report and triggers a secondary verification of the verification module. The fusion data signal drives the model construction module to iteratively optimize the material parameters or structural topology of the BIM model, and at the same time sends real-time update instructions to the visualization module.

7. The BIM-based engineering information management system according to claim 6, characterized in that: The rights management module includes a geo-fence engine and a behavior analysis model. The geo-fence engine generates a dynamic electronic fence based on the construction area boundary in the GIS data. When the GPS (Global Positioning System) coordinates of the user terminal exceed the fence range, its data access rights are automatically downgraded and an operation interception signal is generated. The behavior analysis model obtains encrypted user operation history data through the federated learning node and trains to generate an abnormal access detection model. When an unconventional operation sequence is detected, the rights management module dynamically restricts the user's editing rights to the key nodes of the BIM model and encrypts the interception log and transmits it to the blockchain evidence storage module.

8. The BIM-based engineering information management system according to claim 1, characterized in that: The federated learning node uses homomorphic encryption technology to aggregate multi-terminal user behavior data. After training the abnormal access detection sub-model locally, the model gradient parameters are uploaded to the central server for aggregation update, and a global permission policy update signal is generated. The permission policy update signal includes a user role permission matrix, a data sensitivity classification table, and an operation blacklist and whitelist. After receiving the update signal, the permission management module initiates secondary identity authentication for users with security risks exceeding the threshold, and writes the updated policy version hash value into the audit-dedicated chain of the blockchain evidence storage module.

9. The BIM-based engineering information management system according to claim 1, characterized in that: The model construction module further includes a dynamic version merging unit. After receiving the version locking signal sent by the blockchain evidence storage module, the dynamic version merging unit automatically analyzes the concurrent modification records of multiple users on the same BIM model node, compares the differences in geometric properties, material parameters and association logic in each version through a conflict resolution algorithm, and generates a version integration plan including weight scores and merging priority tags.

10. The BIM-based engineering information management system according to claim 9, characterized in that: After verifying the structural stability through the lightweight simulation engine, the integration solution triggers the model construction module to generate an updated BIM model version, and encrypts and transmits the merge log to the blockchain evidence module to generate a new evidence chain branch. At the same time, it sends a version merge completion signal to the authority management module to lift the editing operation restrictions.

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