Water conservancy project construction project collaborative management method and system

Abstract

The invention relates to the technical field of water conservancy project construction management, and discloses a water conservancy project construction project collaborative management method and system, and the method comprises the steps: integrating multi-source initial data in an encryption state through employing a federal learning algorithm, and generating a global construction knowledge graph; task work order automatic circulation and information sharing of a construction project are carried out through a real-time communication engine, the construction project is disassembled into a plurality of subtasks, and the priority, the dependency relationship and the resource demand of each task are marked; based on the global construction knowledge graph, a key path method is combined with a particle swarm optimization algorithm, resource constraints and environmental factors are integrated, a construction progress reference plan capable of being dynamically adjusted is generated, and a task execution state is recorded through a block chain; a natural language technology is utilized to intelligently classify and retrieve design drawings, contract files and acceptance reports, so that all participants share information in real time; according to the invention, real-time sharing of project information and efficient communication and cooperation among participants are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy project construction management, and in particular to a water conservancy project construction project collaborative management method and system. Background Art

[0002] Water conservancy project construction projects are usually large in scale, long in cycle, involve multiple disciplines, and have complex construction environments. They involve many parties, including owners, design units, construction units, supervision units, etc. A large amount of information exchange and collaboration is required between the parties. Traditional collaborative management methods mainly rely on manual communication and paper document delivery, which has problems such as untimely, inaccurate, and inefficient information transmission, resulting in inefficient communication between the parties. Summary of the Invention

[0003] The purpose of the present invention is to solve the above problems and to design a collaborative management method and system for water conservancy project construction projects.

[0004] A first aspect of the present invention provides a method for collaborative management of water conservancy project construction projects, the method comprising the following steps: Obtain multi-source initial data through IoT sensors, BIM models, and progress management systems, and use federated learning algorithms to integrate these data in an encrypted state to generate a global construction knowledge graph. Automatically transfer and share information about construction project work orders through a real-time communication engine, breaking down construction projects into several subtasks and marking each task's priority, dependencies, and resource requirements. Based on the global construction knowledge graph, the critical path method is combined with the particle swarm optimization algorithm to integrate resource constraints and environmental factors to generate a dynamically adjustable construction progress baseline plan, and the task execution status is recorded through the blockchain; Natural language technology is used to intelligently classify and retrieve design drawings, contract documents, and acceptance reports, enabling all parties involved to share information in real time.

[0005] Optionally, in a first implementation of the first aspect of the present invention, obtaining multi-source initial data through IoT sensors, BIM models, and a progress management system, integrating the multi-source initial data in an encrypted state using a federated learning algorithm, and generating a global construction knowledge graph includes: Preprocessing the multi-source initial data to obtain preprocessed data, where preprocessing includes data cleaning and data standardization; The pre-processed data is subjected to feature extraction and encrypted using a layered encryption architecture, and then uploaded to the central server after processing; The central server generates a global model through a federated learning algorithm and aggregates data through the global model; Based on the integrated global data, a global construction knowledge graph containing multi-faceted knowledge is constructed, where nodes represent various knowledge entities and edges represent the relationships between entities.

[0006] Optionally, in a second implementation of the first aspect of the present invention, extracting features from the preprocessed data includes: For IoT sensor data, temporal features, anomaly features and spatiotemporal features are extracted; for BIM model data, geometric features, semantic features and association features are extracted; for progress management system data, task features, resource features and progress features are extracted.

[0007] Optionally, in a third implementation of the first aspect of the present invention, the encryption processing using a layered encryption architecture includes: The sensor feature data is encrypted using a homomorphic encryption algorithm; The BIM model feature data is first generated into a data fingerprint using a secure hash algorithm, and then encrypted using an elliptic curve encryption algorithm; Attribute-based encryption is used for the characteristic data of the progress management system based on the roles of the participants.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, the method of using a critical path method combined with a particle swarm optimization algorithm based on a global construction knowledge graph to integrate resource constraints and environmental factors to generate a dynamically adjustable construction progress baseline plan includes: Based on the entities in the global construction knowledge graph, the construction process is broken down into work packages, and the logical relationships between tasks are converted into a CPM network diagram, with critical and non-critical paths marked. Calculate the earliest start time, earliest finish time, latest start time and latest finish time of each task, and identify the tasks on the critical path; With minimization of total construction period, resource utilization and cost as the objective function, and resource constraints, environmental constraints and task logic constraints as constraints, the particle swarm optimization algorithm is used to generate the optimized construction schedule baseline plan, and the adjustment strategy of the critical path tasks is marked.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, the step of generating an optimized construction progress baseline plan using a particle swarm optimization algorithm and marking adjustment strategies for critical path tasks includes: Initialize the particle swarm, where each particle represents a construction schedule, and randomly generate the initial schedule; Calculate the fitness value of each particle, update the individual extreme value and the global extreme value, adjust the particle position and speed, repeat the iteration until the maximum number of iterations is reached, and output the optimized construction progress benchmark plan.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, the intelligent classification and retrieval of design drawings, contract documents, and acceptance reports using natural language technology includes: Obtain a document set including design drawings, contract documents, and acceptance reports, and perform preprocessing on the document set, including word segmentation, part-of-speech tagging, and stop word removal; Keywords, subject terms and semantic features are extracted from the preprocessed document set, and a classification model is constructed using support vector machine. The classification model is used to classify the documents into design, contract, acceptance, technical specification and safety management categories. After the classification is completed, corresponding classification labels are added to each document.

[0011] A second aspect of the present invention provides a water conservancy project construction project collaborative management system, the system comprising: The data integration module is used to obtain multi-source initial data through IoT sensors, BIM models, and progress management systems. It uses a federated learning algorithm to integrate the multi-source initial data in an encrypted state to generate a global construction knowledge graph; The task decomposition module is used to automatically transfer and share information about construction project work orders through a real-time communication engine, breaking down construction projects into several subtasks and marking each task's priority, dependencies, and resource requirements; The task execution module is used to generate a dynamically adjustable construction schedule baseline plan based on the global construction knowledge graph, using the critical path method combined with the particle swarm optimization algorithm, integrating resource constraints and environmental factors, and recording the task execution status through the blockchain; The sharing module is used to use natural language technology to intelligently classify and retrieve design drawings, contract documents, and acceptance reports, so that all parties involved can share information in real time.

[0012] The third aspect of the present invention provides a collaborative management device for water conservancy project construction projects, which includes a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory to enable the collaborative management device for water conservancy project construction projects to execute each step of the collaborative management method for water conservancy project construction projects as described in any one of the above items.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the various steps of the collaborative management method for water conservancy project construction projects as described in any one of the above items.

[0014] In the technical solution provided by the present invention, multi-source initial data is obtained through Internet of Things sensors, BIM models, and progress management systems, and the multi-source initial data is integrated in an encrypted state using a federated learning algorithm to generate a global construction knowledge graph; the task work orders of the construction project are automatically circulated and information is shared through a real-time communication engine, the construction project is broken down into several subtasks, and the priority, dependency and resource requirements of each task are marked; based on the global construction knowledge graph, the critical path method is combined with the particle swarm optimization algorithm to integrate resource constraints and environmental factors to generate a dynamically adjustable construction progress benchmark plan, and the task execution status is recorded through the blockchain; natural language technology is used to intelligently classify and retrieve design drawings, contract documents, and acceptance reports, so that all participants can share information in real time; the present invention realizes real-time sharing of project information and efficient communication and collaboration among all participants, improves the efficiency and accuracy of information transmission, ensures the smooth progress of the project, and improves the scientificity and intelligence of project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Various other advantages and benefits will become apparent to those skilled in the art by reading the following detailed description of the preferred embodiment.The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present invention.

[0016] Figure 1 A flowchart of a collaborative management method for water conservancy project construction projects provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a water conservancy project construction project collaborative management system provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a water conservancy project construction project collaborative management device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The terms "first," "second," "third," "fourth," and so forth (if any) in the description and claims of the present invention and in the accompanying drawings are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that shown or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device that includes a series of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, product, or device.

[0018] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1A flowchart of a collaborative management method for a water conservancy project construction project provided by an embodiment of the present invention, the method specifically comprising the following steps: Step 101: Obtain multi-source initial data through IoT sensors, BIM models, and progress management systems, and use a federated learning algorithm to integrate the multi-source initial data in an encrypted state to generate a global construction knowledge graph; In this embodiment, various types of IoT sensors are deployed in the water conservancy project construction area, including but not limited to water level sensors, flow sensors, pressure sensors, displacement sensors, temperature sensors, and humidity sensors. Water level sensors are installed in rivers, reservoirs and other water areas to monitor water level changes in real time; flow sensors are set in water pipes or channels to measure water flow; pressure sensors are arranged in key locations such as dam bodies and foundations to monitor soil pressure, structural stress, etc.; displacement sensors are installed on slopes, buildings, etc. to monitor their displacement and deformation; temperature and humidity sensors are distributed in the construction site to obtain environmental temperature and humidity information. Various sensors collect data according to a predetermined sampling frequency; During the design phase of a water conservancy project, professional BIM modeling software is used to construct a three-dimensional BIM model containing detailed information about each part of the project, including design data such as the building structure and the geometric dimensions, material properties, and spatial position relationships of equipment and facilities. During the construction phase, as the project progresses, information from the actual construction process, such as concrete pouring time, steel bar binding locations, and equipment installation progress, is entered into the BIM model in real time to form BIM model data for the construction phase. During the operation and maintenance phase, the BIM model is continuously updated to record operation and maintenance data such as the equipment's operating status, maintenance records, and fault information. In this embodiment, a customized progress management system is adopted, which includes a task management module, a resource management module, a progress tracking module and a report analysis module. The task management module is used to create construction tasks, assign task leaders, start time and end time, etc.; the resource management module records resource information such as manpower, materials, and equipment, including manpower such as the number of construction workers, types of work, skill levels, etc., materials such as material types, inventory levels, procurement plans, etc., and equipment such as equipment models, quantities, usage status, maintenance records, etc.; the progress tracking module obtains the actual progress data of the task in real time, such as the completed project volume and the completion status of key nodes, through manual entry or docking with the Internet of Things sensor system and the BIM model system; the report analysis module generates various progress reports, such as weekly progress reports and monthly progress reports, to intuitively display the progress of the project.

[0019] In this embodiment, multi-source initial data is obtained through IoT sensors, BIM models, and progress management systems, and a federated learning algorithm is used to integrate the multi-source initial data in an encrypted state to generate a global construction knowledge graph: the multi-source initial data is preprocessed to obtain preprocessed data, where the preprocessing includes data cleaning and data standardization; feature extraction is performed on the preprocessed data, and encryption is performed using a layered encryption architecture, and the data is uploaded to a central server after processing; the central server generates a global model through a federated learning algorithm, and data aggregation is performed through the global model; based on the integrated global data, a global construction knowledge graph containing various aspects of knowledge is constructed, where nodes represent various knowledge entities and edges represent the relationships between entities.

[0020] In this embodiment, temporal features, anomaly features, and spatiotemporal features are extracted from IoT sensor data; geometric features, semantic features, and association features are extracted from BIM model data; and task features, resource features, and progress features are extracted from progress management system data.

[0021] In this embodiment, a homomorphic encryption algorithm is used to encrypt sensor feature data; a data fingerprint is first generated for the BIM model feature data using a secure hash algorithm, and then encrypted using an elliptic curve encryption algorithm; attribute-based encryption is used for the progress management system feature data based on the roles of the participants.

[0022] In this embodiment, each participant locally generates a pair of public and private keys. The public key is used to encrypt data, and the private key is kept by the participant. The extracted feature vector is converted into a numerical array, and each numerical value is encrypted using the public key to generate a ciphertext vector. The central server receives the encrypted feature vector and can directly perform addition and multiplication operations in the ciphertext state. For example, if the average value of multiple sensor data is calculated, the operation result is still ciphertext. The server returns the encrypted calculation result to the data provider, which uses the private key to decrypt and obtain the real result. During the entire process, the server cannot obtain the original data content.

[0023] In this embodiment, the SHA-256 algorithm is used to generate a fixed-length hash value and data fingerprint for the feature vector to verify data integrity. The participating parties negotiate a shared key through the Diffie-Hellman key exchange algorithm to ensure the security of the communication process. The negotiated shared key and ECC algorithm are used to encrypt the feature vector to generate ciphertext data. The encrypted BIM feature data and hash fingerprint are uploaded to the server together. During verification, the data is first decrypted, and then the hash value is recalculated and compared with the original fingerprint to ensure that the data has not been tampered with.

[0024] In this embodiment, user attributes are assigned according to the organizational structure and responsibilities, such as construction team project manager and supervision engineer, and a corresponding key component is generated for each attribute. Access policies are defined for different types of progress data. For example, viewing the monthly progress report requires project manager or supervisor permissions. Access policies are used to encrypt feature data, such as milestone node completion status and resource consumption records, to generate ciphertext. Users can only use the key components corresponding to their own attributes to decrypt data that complies with the access policy. For example, a construction team can only decrypt the task data for which it is responsible. When the user role changes, the system dynamically updates its attribute key to achieve fine-grained control over data access rights, ensuring that sensitive information is visible only to authorized personnel.

[0025] In this embodiment, the statistical features such as the mean, variance, extreme value, and rate of change of the water level / pressure are calculated through a sliding window, and the periodic fluctuation feature time series features are extracted by combining Fourier transform; Use isolation forest or LSTM autoencoder to detect abnormal data points of the device, generate abnormal confidence features, and obtain abnormal features; Fusion of sensor geographic coordinates and timestamps to construct spatiotemporal grid features, such as spatial correlation features based on Delaunay triangulation, to obtain spatiotemporal features The spatial geometric parameters of the component, such as three-dimensional coordinates (x, y, z), volume, surface area, aspect ratio, etc., are extracted to obtain geometric features.

[0026] Parse the design description text through natural language processing, extract key words such as material type, strength grade, and construction process, and obtain semantic features; Construct component connection diagrams, such as the assembly relationship between gates and hoists, and use graph neural networks to generate structural embedding features to obtain associated features. The task decomposition structure is encoded into a one-hot vector, and a multi-dimensional task vector is constructed by combining task priority, duration, and resource requirements to obtain task features. Standardize the coding of manpower, materials, and equipment, use principal component analysis to reduce the dimension and generate resource constraint characteristics to obtain resource characteristics; Calculate the deviation rate between the actual progress of the task and the planned progress, the number of days of delay of key nodes and other dynamic characteristics to obtain the progress characteristics.

[0027] Step 102: Automatically transfer and share information about construction project work orders through a real-time communication engine, breaking the construction project into several subtasks and marking the priority, dependencies, and resource requirements of each task. In this embodiment, a real-time communication engine suitable for the complex environments of water conservancy project construction is selected, such as one based on the WebSocket protocol. This engine offers advantages such as real-time two-way communication, low latency, and support for multiple platforms and devices. This engine can meet the information exchange needs of dispersed construction sites and numerous participants. A real-time communication engine server is deployed on the construction project management platform, and each participant accesses the communication system by installing the corresponding client, enabling real-time online communication. In this embodiment, a work breakdown structure (WBS) approach is adopted to decompose the entire construction project into several subtasks layer by layer according to the structural composition and construction process of the water conservancy project. For example, the dam project is decomposed into first-level subtasks such as foundation excavation, concrete pouring, dam body filling, and gate installation. Foundation excavation is further decomposed into second-level subtasks such as surveying and setting out, earthwork excavation, rock blasting, and foundation treatment, until the task is decomposed into the smallest manageable and executable task unit. During the task decomposition process, the work content, scope of work, and construction technical requirements of each subtask are clearly defined. Develop a task priority evaluation index system, taking into account factors such as the urgency of the task schedule, the impact on project quality and safety, and special requirements of the owner or contract. Task priorities are divided into three levels: high priority, medium priority, and low priority. The priority of each task is determined by using the analytic hierarchy process. Analyze the logical relationships between subtasks and determine the types of task dependencies, including mandatory dependencies, optional dependencies, and external dependencies. Mandatory dependencies include the requirement to complete foundation pouring before superstructure construction. Optional dependencies include the option to install equipment or lay pipes first based on the construction plan. External dependencies include the dependence of construction tasks on external factors such as weather conditions and material supply. Use a precedence diagram or arrow diagram to draw a task dependency diagram to clearly identify the predecessor and successor tasks of each task. For each subtask, analyze the required resource types and quantities in detail. For human resources, clarify the required types of work and number of personnel. For material resources, list the required material names, specifications, models, and required quantities. For equipment resources, determine the required mechanical equipment models, quantities, and operating hours. At the same time, consider resource availability and supply cycles to provide a basis for subsequent schedule planning. When the construction project is disassembled and marked, a task work order is automatically generated. The work order content includes information such as the task name, work content, priority, dependencies, resource requirements, planned start time, planned end time, and person in charge. Based on the information of the task leader, the work order is pushed to the client of the corresponding construction team or manager. After receiving the work order, the person in charge views the task details. If there are any questions about the task, they can consult the publisher through the communication system. If there are no objections, the work order is confirmed. During the execution of the task, the construction team records the progress of the task in real time through the mobile APP and uploads the information to the system. The system automatically updates the task status. When a task is completed and meets the dependency conditions of the subsequent task, the system automatically triggers the distribution of the work order for the subsequent task. All participants can view the status, progress information and resource usage of the task work order in real time through the system, realizing transparent sharing of construction project information.

[0028] Step 103: Based on the global construction knowledge graph, the critical path method is combined with the particle swarm optimization algorithm to integrate resource constraints and environmental factors to generate a dynamically adjustable construction progress baseline plan, and the task execution status is recorded through the blockchain; In this embodiment, based on the entities in the global construction knowledge graph, the construction process is decomposed into work packages, the logical relationship between tasks is converted into a CPM network diagram, and the critical path and non-critical path are marked; the earliest start time, earliest finish time, latest start time and latest finish time of each task are calculated to determine the tasks on the critical path; with minimizing the total construction period, resource utilization and cost as the objective function, and resource constraints, environmental constraints and task logic constraints as constraints, the particle swarm optimization algorithm is used to generate an optimized construction progress baseline plan, and the adjustment strategy of the critical path tasks is marked.

[0029] In this embodiment, a particle swarm is initialized, with each particle representing a construction schedule plan. An initial schedule plan is randomly generated. The fitness value of each particle is calculated, the individual extreme value and the global extreme value are updated, the particle position and speed are adjusted, and the iteration is repeated until the maximum number of iterations is reached. The optimized construction schedule baseline plan is output.

[0030] In this embodiment, after the construction progress baseline plan is generated, the Internet of Things sensors are used to obtain real-time construction environment data, actual task progress data fed back by the progress management system, and resource supply status from the resource management system. When the following situations occur, the progress plan is automatically triggered to adjust dynamically: the deviation between the actual progress and the planned progress exceeds a certain threshold, the supply of key resources is delayed or interrupted, or force majeure events occur that affect the construction progress; the critical path method and particle swarm optimization algorithm are re-applied to generate a new progress plan based on the new situation, and the adjusted plan is notified to all participants through the real-time communication engine. At the same time, blockchain technology is used to record the task execution status. The start time, completion time, resource consumption and other information of each task are recorded on the blockchain in the form of transactions to ensure that the data cannot be tampered with and is traceable. The blockchain adopts a consortium chain architecture and is jointly maintained by all project participants to ensure data security and sharing.

[0031] In this embodiment, when recording task execution status via blockchain, a unique digital identifier is first created for each construction task within the consortium chain architecture. Key data generated during the construction process, such as task start / completion time, actual construction volume, resource consumption details, quality inspection results, and signatures of responsible individuals, is hashed and stored on-chain in timestamp order, forming an unalterable task log. A smart contract automatically triggers a status update mechanism: When a construction team uploads a progress photo of the task via a mobile device and marks the completion point, the system automatically invokes the smart contract to verify the supervisor's electronic signature. Once verified, the status change is recorded on the blockchain. If the task involves resource allocation, material collection records and equipment usage data are linked to the task ID and uploaded to the blockchain, enabling traceability of resource flows. Each participant uses private key access to the entire execution data for the corresponding task, enabling real-time sharing of progress information while ensuring data consistency through the decentralized nature of blockchain. Any status change requires multi-party consensus verification to prevent tampering by a single node. This provides trusted digital credentials for construction process audits, responsibility demarcation, and progress dispute resolution, while also supporting the dynamic updating and optimization of the subsequent construction knowledge graph.

[0032] Step 104: Use natural language technology to intelligently classify and retrieve design drawings, contract documents, and acceptance reports to enable all parties involved to share information in real time.

[0033] In this embodiment, a document set including design drawings, contract documents, and acceptance reports is obtained, and the document set is preprocessed, including word segmentation, part-of-speech tagging, and removal of stop words; keywords, subject terms, and semantic features are extracted from the preprocessed document set, and a classification model is constructed using a support vector machine. The classification model is used to classify the documents into design, contract, acceptance, technical specification, and safety management categories. After the classification is completed, a corresponding classification label is added to each document.

[0034] In this embodiment, document retrieval is established, supporting multiple search methods, including keyword-based retrieval, semantic retrieval, and full-text retrieval. When a user enters a search keyword, not only will documents containing the keyword be matched, but semantic analysis will also be used to retrieve documents related to the keyword, such as documents related to concrete pouring quality requirements. Furthermore, knowledge graph technology is used to associate documents with knowledge entities such as construction tasks, resources, and processes, enabling intelligent retrieval based on knowledge associations. For example, when searching for a specific construction task, the system automatically associates and displays relevant documents such as design drawings, construction specifications, and acceptance standards.

[0035] In this embodiment, permission management is used to ensure that all participants have real-time access to documents within the authorized scope. Construction personnel can quickly retrieve drawings and safety specifications related to on-site operations by scanning codes through the mobile app. Managers can retrieve progress-related contract payment terms and change documents in real time. Supervision units can verify the compliance of acceptance reports with design drawings online. Ultimately, through intelligent classification and dynamic sharing of documents, information silos are broken and the collaborative efficiency of all participants is improved.

[0036] See also Figure 2 , a schematic diagram of the structure of a water conservancy project construction project collaborative management system provided by an embodiment of the present invention, the system includes: The data integration module is used to obtain multi-source initial data through IoT sensors, BIM models, and progress management systems. It uses a federated learning algorithm to integrate the multi-source initial data in an encrypted state to generate a global construction knowledge graph; The task decomposition module is used to automatically transfer and share information about construction project work orders through a real-time communication engine, breaking down construction projects into several subtasks and marking each task's priority, dependencies, and resource requirements; The task execution module is used to generate a dynamically adjustable construction schedule baseline plan based on the global construction knowledge graph, using the critical path method combined with the particle swarm optimization algorithm, integrating resource constraints and environmental factors, and recording the task execution status through the blockchain; The sharing module is used to use natural language technology to intelligently classify and retrieve design drawings, contract documents, and acceptance reports, so that all parties involved can share information in real time.

[0037] Figure 3The diagram below is a schematic diagram of the structure of a collaborative management device for water conservancy project construction projects provided by an embodiment of the present invention. The collaborative management device 300 may vary significantly depending on configuration or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors), memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing applications 333 or data 332. The memory 320 and storage medium 330 may be either transient or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown), each of which may include a series of instructions and operations within the collaborative management device 300. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, executing the series of instructions and operations stored in the storage medium 330 on the collaborative management device 300 to implement the method provided in the aforementioned embodiment.

[0038] The water conservancy project construction project collaborative management device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating devices 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the water conservancy project construction project collaborative management equipment shown does not constitute a limitation on the computer equipment provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0039] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are run on a computer, the computer executes the various steps of the water conservancy project construction project collaborative management method provided in the above embodiments.

[0040] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0041] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0042] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A collaborative management method for water conservancy project construction projects, characterized in that: The method comprises the following steps: Obtain multi-source initial data through IoT sensors, BIM models, and progress management systems, and use federated learning algorithms to integrate these data in an encrypted state to generate a global construction knowledge graph. Automatically transfer and share information about construction project work orders through a real-time communication engine, breaking down construction projects into several subtasks and marking each task's priority, dependencies, and resource requirements. Based on the global construction knowledge graph, the critical path method is combined with the particle swarm optimization algorithm to integrate resource constraints and environmental factors to generate a dynamically adjustable construction progress baseline plan, and the task execution status is recorded through the blockchain; Natural language technology is used to intelligently classify and retrieve design drawings, contract documents, and acceptance reports, enabling all parties involved to share information in real time.

2. A collaborative management method for water conservancy project construction projects according to claim 1, characterized in that: The method uses IoT sensors, BIM models, and a progress management system to obtain multi-source initial data, and uses a federated learning algorithm to integrate the multi-source initial data in an encrypted state to generate a global construction knowledge graph, including: Preprocessing the multi-source initial data to obtain preprocessed data, where preprocessing includes data cleaning and data standardization; The pre-processed data is subjected to feature extraction and encrypted using a layered encryption architecture, and then uploaded to the central server after processing; The central server generates a global model through a federated learning algorithm and aggregates data through the global model; Based on the integrated global data, a global construction knowledge graph containing multi-faceted knowledge is constructed, where nodes represent various knowledge entities and edges represent the relationships between entities.

3. A collaborative management method for water conservancy project construction projects as claimed in claim 2, characterized in that: The feature extraction of the preprocessed data includes: For IoT sensor data, temporal features, anomaly features and spatiotemporal features are extracted; for BIM model data, geometric features, semantic features and association features are extracted; for progress management system data, task features, resource features and progress features are extracted.

4. A collaborative management method for water conservancy project construction projects as claimed in claim 2, characterized in that: The encryption process is performed using a layered encryption architecture, including: The sensor feature data is encrypted using a homomorphic encryption algorithm; The BIM model feature data is first generated into a data fingerprint using a secure hash algorithm, and then encrypted using an elliptic curve encryption algorithm; Attribute-based encryption is used for the characteristic data of the progress management system based on the roles of the participants.

5. A collaborative management method for water conservancy project construction projects according to claim 1, characterized in that: The global construction knowledge graph is based on the critical path method combined with the particle swarm optimization algorithm, which integrates resource constraints and environmental factors to generate a dynamically adjustable construction progress baseline plan, including: Based on the entities in the global construction knowledge graph, the construction process is broken down into work packages, and the logical relationships between tasks are converted into a CPM network diagram, with critical and non-critical paths marked. Calculate the earliest start time, earliest finish time, latest start time and latest finish time of each task, and identify the tasks on the critical path; With minimization of total construction period, resource utilization and cost as the objective function, and resource constraints, environmental constraints and task logic constraints as constraints, the particle swarm optimization algorithm is used to generate the optimized construction schedule baseline plan, and the adjustment strategy of the critical path tasks is marked.

6. A collaborative management method for water conservancy project construction projects as claimed in claim 4, characterized in that: The particle swarm optimization algorithm is used to generate an optimized construction schedule baseline plan and mark the adjustment strategy of the critical path tasks, including: Initialize the particle swarm, where each particle represents a construction schedule, and randomly generate the initial schedule; Calculate the fitness value of each particle, update the individual extreme value and the global extreme value, adjust the particle position and speed, repeat the iteration until the maximum number of iterations is reached, and output the optimized construction progress benchmark plan.

7. A collaborative management method for water conservancy project construction projects as claimed in claim 1, characterized in that: The use of natural language technology to intelligently classify and retrieve design drawings, contract documents, and acceptance reports includes: Obtain a document set including design drawings, contract documents, and acceptance reports, and perform preprocessing on the document set, including word segmentation, part-of-speech tagging, and stop word removal; Keywords, subject terms and semantic features are extracted from the preprocessed document set, and a classification model is constructed using support vector machine. The classification model is used to classify the documents into design, contract, acceptance, technical specification and safety management categories. After the classification is completed, corresponding classification labels are added to each document.

8. A water conservancy project construction project collaborative management system, characterized in that: The system includes: The data integration module is used to obtain multi-source initial data through IoT sensors, BIM models, and progress management systems. It uses a federated learning algorithm to integrate the multi-source initial data in an encrypted state to generate a global construction knowledge graph; The task decomposition module is used to automatically transfer and share information about construction project work orders through a real-time communication engine, breaking down construction projects into several subtasks and marking each task's priority, dependencies, and resource requirements; The task execution module is used to generate a dynamically adjustable construction schedule baseline plan based on the global construction knowledge graph, using the critical path method combined with the particle swarm optimization algorithm, integrating resource constraints and environmental factors, and recording the task execution status through the blockchain; The sharing module is used to use natural language technology to intelligently classify and retrieve design drawings, contract documents, and acceptance reports, so that all parties involved can share information in real time.

9. A water conservancy project construction project collaborative management device, characterized in that: The water conservancy project construction project collaborative management device includes a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the water conservancy project construction project collaborative management device executes each step of the water conservancy project construction project collaborative management method as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the collaborative management method for water conservancy project construction projects as described in any one of claims 1 to 7 are implemented.

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