Water conservancy project cost intelligent analysis and evaluation system

Abstract

The invention discloses a hydraulic engineering cost intelligent analysis and evaluation system, and belongs to the technical field of hydraulic engineering construction management and cost control. The hydraulic engineering cost intelligent analysis and evaluation system realizes intelligent analysis and evaluation of hydraulic engineering cost through the following steps: establishing a unified data specification, and realizing intelligent fusion and real-time updating of multi-source heterogeneous data; engineering quantity automatic calculation is executed, and a detailed engineering quantity list is generated; multi-dimensional dynamic cost analysis and prediction are realized, and a cost prediction result is output; establishing a dynamic risk monitoring and early warning mechanism; monitoring a risk change trend in real time and generating a risk assessment report; the balance optimization of the construction cost and the resource allocation is realized; generating an optimal decision scheme; and adjusting a project implementation strategy according to the optimal decision scheme, and continuously updating project data through the data integration standardization module to form closed-loop optimization control until the project cost reaches a preset optimization target.

Description

Technical Field

[0001] This application relates to the field of water conservancy project construction management and cost control technology, and more specifically, to an intelligent analysis and evaluation system for water conservancy project costs. Background Technology

[0002] As water conservancy projects expand in scale and construction technologies become increasingly complex, the difficulty of project cost management is also increasing. Traditional cost estimation relies mainly on manual experience and static data, which often fails to accurately reflect the actual situation of the project. This is especially true when data sources are scattered and information is inconsistent, significantly impacting the accuracy of quantity calculations and cost forecasts. Furthermore, various risk factors exist during construction, such as technical challenges, environmental changes, and management uncertainties. Existing methods struggle to identify and effectively address these risks in a timely manner, resulting in unsatisfactory cost control.

[0003] In recent years, with the development of Building Information Modeling (BIM) and intelligent technologies, automated quantity surveying and dynamic cost analysis have gradually become possible. However, in practical applications, problems such as inconsistent data standards, incomplete risk assessments, and insufficient decision support still exist. These issues limit the efficiency and accuracy of cost management in water conservancy projects, making it difficult to meet the current project requirements for scientific management.

[0004] In summary, how to achieve unified integration of various data, automatically and accurately calculate project quantities, dynamically predict project costs, and effectively control project costs by combining risk assessment and optimization decisions has become an urgent technical problem to be solved. Summary of the Invention

[0005] In order to overcome a series of defects in the existing technology, the purpose of this application is to provide an intelligent analysis and evaluation system for water conservancy project costs, including a data integration and standardization module, a quantity calculation module, a cost analysis and prediction module, a risk assessment and control module, and an optimization decision-making module. The intelligent analysis and evaluation system for water conservancy project costs realizes intelligent analysis and evaluation of water conservancy project costs through the following steps.

[0006] The data integration and standardization module acquires multi-source heterogeneous data of water conservancy projects, including BIM model data, CAD drawing data, geological exploration data, geographical environment data, and market price data. It then performs semantic mapping and standardization processing on the multi-source heterogeneous data to establish unified data specifications and achieve intelligent fusion and real-time updates of the multi-source heterogeneous data.

[0007] The engineering quantity calculation module receives standardized data provided by the data integration and standardization module, automatically identifies the geometric features and material properties of the engineering structure, performs automatic engineering quantity calculation, and generates a detailed engineering quantity list.

[0008] Based on the bill of quantities output by the quantity calculation module in response to the cost analysis and prediction module, and combined with real-time market price information, construction process parameters, and the geographical environment and schedule of the project site, multi-dimensional dynamic cost analysis and prediction are realized, and the cost prediction results are output.

[0009] The risk assessment and control module responds to the prediction results of the cost analysis and prediction module, identifies and quantifies various risk factors in the project implementation process based on a pre-built risk factor rule base, analyzes the risk transmission path and its impact on project cost, and establishes a dynamic risk monitoring and early warning mechanism.

[0010] If a high-risk factor is identified, an early warning mechanism is triggered through the risk assessment and control module to monitor risk trends in real time and generate a risk assessment report.

[0011] The optimization decision-making module utilizes the cost prediction results and the risk assessment report to construct a multi-objective optimization model, thereby achieving a balanced optimization of project cost and resource allocation.

[0012] The optimization decision module generates the optimal decision scheme and provides an interactive visual interface to display the cost analysis results, risk assessment data, and optimization decision scheme.

[0013] The project implementation strategy is adjusted according to the optimal decision-making scheme, and the project data is continuously updated through the data integration and standardization module to form a closed-loop optimization control until the project cost reaches the preset optimization target.

[0014] Furthermore, the data integration standardization module adopts a layered data fusion architecture, including a physical layer data acquisition unit, a semantic layer mapping and transformation unit, and an application layer standardization output unit. Specifically: the physical layer data acquisition unit is configured to extract structured data from different database systems and parse unstructured CAD drawing annotation information and geological exploration report text content using OCR image recognition and natural language processing; the semantic layer mapping and transformation unit constructs an ontology knowledge graph, defines specialized vocabulary, conceptual relationships, and attribute constraint rules for the water conservancy engineering field, and uses semantic reasoning to map heterogeneous data to a unified conceptual space; the application layer standardization output unit generates standardized data formats conforming to industry standards based on the GB50500 engineering quantity list pricing specification and the regulations for compiling design estimates for water conservancy and hydropower projects.

[0015] Furthermore, the quantity calculation module includes a geometric model analysis unit, a component classification and identification unit, and a quantity statistics calculation unit. Specifically: the geometric model analysis unit, based on computational geometry principles, uses boundary representation and structural solid geometry methods to spatially decompose the BIM model and extract the three-dimensional geometric parameters of various hydraulic structures; the component classification and identification unit, based on a deep convolutional neural network, identifies different types of hydraulic engineering components and automatically labels material properties and construction process requirements; and the quantity statistics calculation unit, based on the component identification results, automatically calculates the quantity of each sub-item of the project and generates a comprehensive list report covering earthwork excavation, concrete pouring, steel reinforcement fabrication and installation, and metal structure installation.

[0016] Furthermore, based on the principles of computational geometry, the BIM model is spatially decomposed using boundary representation and structural solid geometry methods to extract the three-dimensional geometric parameters of various hydraulic structures, including the following steps.

[0017] The BIM model undergoes geometric and attribute analysis and processing. By unifying the coordinate system, verifying accuracy, repairing defects, and eliminating redundancy, a clear and accurate basic structure for the model is constructed.

[0018] Complex geometries are transformed into boundary representation structures based on vertices, edges, and faces. The topological consistency of the geometry is verified using a half-edge data structure and Euler's formula, accurately describing the geometric boundaries and internal volume of hydraulic structures.

[0019] By identifying the basic structural units in complex hydraulic structures through Boolean operations, a CSG tree structure based on the construction process and hierarchical relationship is constructed to realize the recursive decomposition of complex irregular structures into standard geometric units.

[0020] Large hydraulic structures are spatially decoupled according to material type, structural function, and construction zoning to achieve rapid positioning of geometric entities and efficient spatial decomposition and parallel processing of complex models.

[0021] For the decomposed geometric units, key geometric parameters and shape features are automatically extracted and analyzed to generate a standardized geometric parameter data table.

[0022] The extracted geometric parameters are classified and organized according to the design specifications for water conservancy and hydropower projects, generating structured data files, and establishing a bidirectional association index between the geometric parameters and BIM model elements.

[0023] Furthermore, based on the component identification results, the quantities of each sub-item project are automatically calculated, and a comprehensive bill of quantities report covering earthwork excavation, concrete pouring, steel reinforcement fabrication and installation, and metal structure installation is generated, including the following steps.

[0024] The system automatically matches the identified hydraulic structure components with the corresponding engineering quantity calculation rules, analyzes the calculation formulas, units of measurement and deduction principles for various components, and establishes a standardized calculation rule library covering earthwork, concrete, steel reinforcement and metal structures.

[0025] By using Boolean difference calculations between the digital ground model and the BIM model, the scope and depth of earthwork excavation can be accurately defined. Combined with material classification, the engineering quantity is automatically calculated, and temporary and auxiliary excavation quantities that are not measured are eliminated, so as to achieve standardized and accurate calculation of excavation engineering quantities.

[0026] Based on material properties, the application scope and quantity of different grades of concrete are identified, the volume occupied by embedded parts, holes and steel bars is automatically deducted, and the pouring volume is summarized according to the construction process.

[0027] Based on the reinforcement information in the BIM model and design drawings, the system automatically identifies the specifications and distribution of steel bars, accurately calculates the length of steel bars, takes into account various additional lengths and losses, classifies and statistically analyzes the quantities of fabrication and installation work, and automatically generates a steel bar material list and processing list.

[0028] Based on geometric dimensions, material density, and classification, the system automatically calculates the fabrication and installation quantities of metal structural components, generating a detailed list of metal structural components and installation quantity data.

[0029] Integrate the quantity data of each sub-item of the project to generate a standardized bill of quantities report containing project code, name, unit of measurement, quantity of work and characteristic description. Establish the traceability relationship between the quantity data and the components of the BIM model, and output a comprehensive quantity file that can be directly used for cost analysis and bidding.

[0030] Furthermore, the cost analysis and prediction module adopts a hybrid neural network architecture, including a time-series prediction network, a spatial correlation analysis network, and a multi-factor fusion decision network. Specifically: the time-series prediction network, based on long short-term memory networks and attention mechanisms, analyzes the time-series characteristics of historical cost data to capture the periodic patterns and trends of cost fluctuations; the spatial correlation analysis network is responsible for modeling the relationship between engineering projects and spatial factors, realizing the expression and analysis of spatial dependence; the multi-factor fusion decision network comprehensively considers the combined impact of material price fluctuations, construction technical difficulty, environmental protection requirements, and construction period constraints on costs, generating a dynamically adjusted cost prediction model and realizing uncertainty quantification analysis.

[0031] Furthermore, the modeling of the relationship between the engineering project and spatial factors, realizing the expression and analysis of spatial dependence, includes the following steps.

[0032] A grid-based coding method is used to divide the geospatial space into regular units and generate a unique multidimensional feature vector for each spatial unit, thus constructing a standardized spatial feature representation.

[0033] Based on the principles of geographical proximity and functional similarity, the spatial connectivity between each grid cell is calculated, and then an adjacency matrix reflecting the spatial relationships of the engineering project is constructed.

[0034] Spatial autocorrelation analysis is used to process the adjacency matrix, quantitatively assess the spatial dependence of engineering cost data, and identify its spatial clustering characteristics and anomalous distribution areas.

[0035] We design a spatial relationship modeling framework based on graph convolutional neural networks. The adjacency matrix is ​​used as the input to the graph structure, and the spatial neighborhood information is effectively aggregated and learned through multi-layer graph convolution operations.

[0036] A hierarchical spatial dependency modeling architecture is constructed to capture spatial dependency features at three scales: local neighborhood, regional scope, and macro environment. Multi-level spatial information is then integrated to form a unified comprehensive spatial relational representation.

[0037] Furthermore, the risk assessment and control module includes a risk factor database construction unit, a risk transmission analysis unit, and a dynamic monitoring and early warning unit. Specifically: the risk factor database construction unit establishes a comprehensive risk factor classification system covering technical risks, economic risks, environmental risks, policy risks, and management risks based on historical case data and expert knowledge from water conservancy project construction; it uses fuzzy hierarchical analysis to determine the weight and impact score of each risk factor; the risk transmission analysis unit constructs a causal relationship diagram between risk factors based on Bayesian network theory, analyzes the chain reaction of a single risk event on the overall project cost through related chains, and calculates the probability distribution and expected loss value of risk transmission; the dynamic monitoring and early warning unit monitors changes in key risk indicators during project progress in real time, and generates and pushes a risk assessment report when the risk indicator exceeds a preset threshold.

[0038] Furthermore, based on Bayesian network theory, a causal relationship diagram between risk factors is constructed to analyze the chain effect of a single risk event on the overall project cost through the associated chain, and to calculate the probability distribution of risk transmission and the expected value of loss, including the following steps.

[0039] Collect and organize historical risk events and project cost data, identify each risk factor node and its possible value range, and establish a node list and status set.

[0040] Determine the directed edges between risk factors, construct a preliminary network topology, and characterize the causal relationships and dependency paths among risk factors.

[0041] By using historical case data, maximum likelihood estimation is employed to calculate the conditional probability distribution of each node under different states of the parent node, thereby improving the probability parameters of the Bayesian network.

[0042] In the constructed Bayesian network, a certain risk node is fixed in the "occurrence" state, and the probability influence is transmitted to downstream nodes through belief propagation to obtain the posterior distribution of each associated node.

[0043] By using network inference, the transmission path probability from the initial risk event to each key cost factor is extracted, the probability distribution of its impact on each component of the overall project cost is quantified, and key intermediary nodes in the risk chain are identified.

[0044] By combining the cost of each component and the corresponding risk transmission probability distribution, the expected loss value of each transmission path is calculated and accumulated to finally obtain the overall expected loss of a single risk event on the overall project cost.

[0045] Furthermore, the optimization decision module includes an objective function builder, a constraint manager, and a Pareto optimal solution set generator. Specifically: the objective function builder dynamically adjusts the weight coefficients of each objective according to the specific needs of the project to construct a comprehensive evaluation function; the constraint manager is responsible for managing multiple types of constraints to ensure the feasibility of the optimization scheme; and the Pareto optimal solution set generator generates multiple non-dominated solutions through iterative optimization and ranks and evaluates the schemes using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods.

[0046] Compared with existing technologies, this application has the following beneficial effects: This application integrates and standardizes engineering data from different sources, automatically calculates engineering quantities using BIM models, and dynamically predicts engineering costs by combining multiple factors; at the same time, it establishes a risk assessment and early warning mechanism to analyze the impact of various risks that may occur during construction on costs, and finally balances costs and resource allocation by optimizing the model, thereby achieving comprehensive, real-time and scientific management of water conservancy project costs, improving the accuracy of assessments and the rationality of decisions. Attached Figure Description

[0047] Figure 1 This is a module communication timing diagram of an intelligent analysis and evaluation system for water conservancy project costs disclosed in an embodiment of this application.

[0048] Figure 2 This is a schematic diagram illustrating the process of intelligent analysis and evaluation of water conservancy project costs in an embodiment of this application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0050] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0052] like Figure 1 As shown, a smart analysis and evaluation system for water conservancy project costs includes the following modules.

[0053] The data integration and standardization module is used to acquire multi-source heterogeneous data from water conservancy projects, including BIM model data, CAD drawing data, geological exploration data, geographic environment data, and market price data. It establishes unified data specifications through semantic mapping and standardization processing to achieve intelligent fusion and real-time updates of multi-source heterogeneous data.

[0054] The quantity calculation module is configured to receive standardized data provided by the data integration and standardization module, automatically identify the geometric features and material properties of the engineering structure, perform automatic quantity calculation, and generate a detailed bill of quantities.

[0055] The cost analysis and prediction module responds to the quantity data output by the quantity calculation module, and realizes multi-dimensional dynamic cost analysis and prediction based on real-time market price information, construction process parameters, and the geographical environment and schedule of the project location.

[0056] The risk assessment and control module responds to the prediction results of the cost analysis and prediction module. Based on a pre-built risk factor rule base, it identifies and quantifies various risk factors in the project implementation process, analyzes the risk transmission path and its impact on project cost, and establishes a dynamic risk monitoring and early warning mechanism.

[0057] The optimization decision-making module, based on the prediction results of the cost analysis and prediction module and the risk data of the risk assessment and control module, constructs a multi-objective optimization model to achieve a balance between project cost and resource allocation, generates the optimal decision-making scheme, and provides an interactive visualization interface to assist in the formulation of cost management strategies.

[0058] Among them, the data integration and standardization module, the engineering quantity calculation module, the cost analysis and prediction module, the risk assessment and control module, and the optimization decision-making module work together to solve the problems of dynamic analysis, risk control, and optimization decision-making in water conservancy projects.

[0059] In this embodiment, the core of the data integration standardization module lies in achieving intelligent fusion and unified management of multi-source heterogeneous data. Water conservancy projects involve a wide variety of data types, including BIM models, CAD drawings, geological exploration information, geographic environmental data, and market price information. These data sources differ significantly in structure, format, and semantics. This module, by introducing semantic mapping technology, can effectively identify and match the semantic correspondences between different data sources, solving the problems of data silos and information fragmentation. Standardization further unifies data formats and specifications, enabling subsequent modules to seamlessly access and call multi-source data. This module not only improves the automation and real-time nature of data acquisition but also significantly enhances data quality and consistency. Through a real-time update mechanism, it can dynamically respond to project progress and market changes, achieving dynamic data synchronization and timeliness assurance. Furthermore, the establishment of unified data specifications provides an accurate and complete data foundation for subsequent engineering quantity calculations and cost predictions, avoiding calculation errors or information delays caused by data differences or mismatches. Therefore, the data integration standardization module effectively improves the intelligence and reliability of water conservancy project cost analysis, ensuring the efficient collaboration and continuous operation of the entire system.

[0060] In this embodiment, the key to the quantity calculation module lies in its automated identification and accurate calculation of the geometric features and material properties of engineering structures. Relying on the unified and standardized data provided by the data integration and standardization module, intelligent identification can accurately extract geometric information from BIM models and CAD drawings, enabling automatic calculation of the spatial dimensions and morphological parameters of structures. Simultaneously, combined with material property data, it automatically determines the types and quantities of materials required for engineering components, accurately generating a bill of quantities. This module effectively overcomes the inefficiency and error-prone nature of traditional manual quantity calculation, improving the accuracy and timeliness of quantity statistics. Its automated feature extraction method supports detailed breakdown of complex structures, covering various engineering materials such as concrete, steel reinforcement, and earthwork, meeting the diverse needs of water conservancy projects. The output bill of quantities from this module is not only detailed but also highly structured, facilitating subsequent use by the cost analysis module and forming a closed-loop data flow. Furthermore, automated quantity calculation shortens the initial data preparation time for projects, reduces the impact of human intervention on data quality, and provides a solid digital foundation for engineering cost assessment. In summary, the quantity calculation module, through highly automated and accurate calculations, effectively promotes the scientific and refined management level of water conservancy engineering cost assessment.

[0061] In this embodiment, a cost analysis and prediction module enables multi-dimensional, dynamic, and accurate project cost assessment and prediction. This module closely relies on detailed quantity data provided by the quantity calculation module, combined with real-time updated market price information, to dynamically reflect the cost fluctuation trends of raw materials and construction labor. Simultaneously, the module integrates construction process parameters, deeply considering the impact of different construction methods on costs, achieving coupled analysis of construction plans and costs. Geographical environmental factors such as topography and climate conditions are also taken into consideration, reflecting the actual impact of regional characteristics on project costs. Furthermore, in conjunction with the project schedule, the module dynamically adjusts the cost prediction model over time, supporting multi-stage, multi-node cost control and timely feedback on potential cost overrun risks. This module also supports multivariate sensitivity analysis, revealing key cost drivers and assisting management in grasping the core drivers of cost changes. Multi-dimensional dynamic cost analysis greatly improves the scientific rigor and foresight of budgeting, reducing risks caused by information lag or estimation errors. Through real-time, dynamic cost prediction, the project management team can achieve precise fund allocation and resource allocation, providing strong data support for ensuring the economic benefits of the project. Overall, the cost analysis and prediction module achieves intelligent, accurate, and dynamic cost assessment by integrating multi-source information and multi-dimensional model analysis.

[0062] In this embodiment, the risk assessment and control module, based on cost analysis and forecasting results, conducts risk identification, quantification, and dynamic management, significantly improving the systematicness and real-time nature of risk control in water conservancy projects. This module constructs a rule base covering various risk factors, including technical, market, environmental, and schedule risks, enabling automatic identification of potential risk points during project implementation. The analysis of risk transmission paths deeply reveals the correlations and cascading effects between risk factors, providing a scientific basis for risk management. By quantifying risk impact, the module can assess the specific degree of impact of different risks on the overall project cost and support risk prioritization. The dynamic risk monitoring mechanism tracks project progress and environmental changes in real time, and, combined with early warning threshold settings, promptly issues risk warnings to help project managers respond quickly and adjust strategies. The module's risk control function not only reduces potential losses but also improves the transparency and controllability of project execution. The close integration of risk management and cost forecasting enhances the robustness of cost control and avoids budget oversight due to risk negligence. Through risk identification, analysis, and early warning, the module significantly improves the preventative and proactive nature of water conservancy project cost management, contributing to the safe and sustainable control of project costs.

[0063] In this embodiment, the optimization decision-making module comprehensively utilizes cost prediction data and risk assessment results provided by the front-end module to construct a multi-objective optimization model, achieving intelligent balance and optimal scheduling between project cost and resource allocation. This module, through a multi-objective optimization algorithm, considers multiple indicators such as cost, schedule, and resource utilization to form a balanced compromise solution that meets the diverse needs of different project stages. The interactive visual interface greatly enhances the transparency and convenience of the decision-making process, enabling project managers to intuitively understand the cost composition and risk distribution of each option, supporting dynamic adjustments and option comparisons. This module supports decision prediction based on simulation and scenario analysis, facilitating the evaluation of the effectiveness of different management strategies and strengthening the scientific and forward-looking nature of decision-making. Through coordinated scheduling of resource allocation and cost control, it achieves efficient resource utilization and cost minimization. It also possesses adaptive capabilities, adjusting the optimization model based on project implementation feedback to ensure the matching degree between the decision-making solution and the actual environment. The optimization decision-making module effectively improves the intelligent level of water conservancy project management, promotes the transformation of cost control from passive response to proactive optimization, and achieves a dual improvement in project economic benefits and risk management.

[0064] In summary, this intelligent analysis and evaluation system for water conservancy project costs constructs a complete closed loop through the collaborative work of five modules, covering data collection, automatic calculation of engineering quantities, dynamic cost prediction, risk assessment and real-time control, and intelligent optimization decision-making. Each module performs its specific function while supporting each other, forming a technical architecture that combines data-driven and model-driven approaches, effectively improving the scientific rigor, accuracy, and intelligence of water conservancy project cost management. The system not only improves data processing efficiency and the automation of engineering quantity calculation but also achieves multi-dimensional dynamic cost analysis and proactive risk warning, further ensuring the rationality and security of project fund utilization. Through the multi-objective optimization decision-making module, it can make optimal resource allocation and cost control plans under complex constraints and uncertainties, promoting the intelligent and digital transformation of water conservancy project management. Overall, this system provides strong technical support for the water conservancy engineering field, effectively addressing the challenges brought by various changing factors during project implementation, and achieving refined and dynamic engineering cost management.

[0065] Furthermore, the data integration standardization module adopts a layered data fusion architecture, including a physical layer data acquisition unit, a semantic layer mapping and transformation unit, and an application layer standardization output unit. Specifically: the physical layer data acquisition unit is configured to extract structured data from different database systems and parse unstructured CAD drawing annotation information and geological exploration report text content using OCR image recognition and natural language processing; the semantic layer mapping and transformation unit constructs an ontology knowledge graph, defines specialized vocabulary, conceptual relationships, and attribute constraint rules for the water conservancy engineering field, and uses semantic reasoning to map heterogeneous data to a unified conceptual space; the application layer standardization output unit generates standardized data formats conforming to industry standards based on the GB50500 engineering quantity list pricing specification and the regulations for compiling design estimates for water conservancy and hydropower projects.

[0066] In summary, a layered fusion architecture was adopted to achieve efficient integration and standardized processing of multi-source heterogeneous data from water conservancy projects. At the physical layer, not only can structured data be automatically collected from various database systems, but OCR technology and natural language processing methods are also innovatively introduced to accurately parse unstructured annotations in CAD drawings and geological exploration report text, greatly enriching the coverage of data sources. At the semantic layer, a dedicated knowledge graph of water conservancy engineering ontology is constructed. By clearly defining domain terms and their inherent relationships, semantic consistency conversion and unified mapping of heterogeneous data are achieved, ensuring deep semantic fusion between different data types. At the application layer, in strict accordance with national engineering quantity list pricing specifications and relevant design budget standards, standardized data formats that meet industry requirements are output, providing a standardized and reliable data foundation for subsequent cost analysis and calculation modules.

[0067] Furthermore, by constructing an ontology knowledge graph, defining specialized terms, conceptual relationships, and attribute constraint rules for the field of water conservancy engineering, and using semantic reasoning to map heterogeneous data to a unified conceptual space, the following steps are included.

[0068] Based on industry standards and expert knowledge in water conservancy engineering, a hierarchical concept classification system is constructed, including the engineering entity layer, attribute layer, and relationship layer, forming a core concept vocabulary library covering the entire life cycle of water conservancy projects, ensuring the uniqueness and completeness of concept definitions.

[0069] The semantic relationships between concepts are defined using the OWL description language, and logical reasoning rules are constructed to form a complete semantic network structure.

[0070] Based on the technical specifications and design guidelines for water conservancy projects, attribute constraints are defined for each concept, and complex constraints are formally expressed using the SWRL rule language.

[0071] Construct a mapping rule base for multi-source data to ontology concepts, automatically mapping IFC entities in BIM models, layer identifiers in CAD drawings, and terms in geological exploration reports to unified ontology concepts.

[0072] Semantic reasoning and consistency verification are performed on the mapped data, implicit semantic relationships are automatically mined, data conflicts are identified and missing information is supplemented, and knowledge is automatically expanded and verified through reasoning rules.

[0073] Multi-source data that has undergone semantic reasoning processing is uniformly stored in an RDF triple database to form a standardized concept space representation.

[0074] In summary, by defining specialized terminology, conceptual relationships, and attribute constraints in the field of water conservancy engineering, a hierarchical concept classification system covering the entire lifecycle was constructed, ensuring the uniqueness and completeness of core concepts. The OWL description language and SWRL rules were used to formally express semantic relationships and complex constraints, forming a rigorous semantic network structure and logical reasoning framework. Through mapping rules from multi-source data to ontology concepts, heterogeneous data from BIM, CAD, and geological reports were automatically unified into a unified concept space, improving the semantic consistency of the data. Semantic reasoning and consistency verification not only uncover implicit semantic relationships and supplement missing information but also effectively identify and resolve data conflicts, achieving automatic knowledge expansion and dynamic verification. Finally, the reasoned data was stored in an RDF triple database, forming a structured and standardized concept space representation. This series of technical measures significantly improved the semantic fusion depth and quality of water conservancy engineering data, providing a solid semantic foundation and data guarantee for subsequent intelligent analysis.

[0075] Furthermore, the quantity calculation module includes a geometric model analysis unit, a component classification and identification unit, and a quantity statistics calculation unit. Specifically: the geometric model analysis unit, based on computational geometry principles, uses boundary representation and structural solid geometry methods to spatially decompose the BIM model and extract the three-dimensional geometric parameters of various hydraulic structures; the component classification and identification unit, based on a deep convolutional neural network, identifies different types of hydraulic engineering components and automatically labels material properties and construction process requirements; and the quantity statistics calculation unit, based on the component identification results, automatically calculates the quantity of each sub-item of the project and generates a comprehensive list report covering earthwork excavation, concrete pouring, steel reinforcement fabrication and installation, and metal structure installation.

[0076] In summary, through multi-level technology integration, efficient and accurate automated calculation of water conservancy project quantities has been achieved. The geometric model analysis unit utilizes computational geometry principles, employing boundary representation and 3D construction techniques to accurately decompose and extract the three-dimensional spatial geometric parameters from the BIM model, ensuring the completeness and accuracy of the spatial dimensions and morphological information of engineering components. The component classification and recognition unit introduces a deep convolutional neural network, possessing powerful feature learning and recognition capabilities. It can automatically distinguish different types of water conservancy project components, accurately labeling their material properties and corresponding construction process requirements, significantly improving the automation and intelligence level of recognition. The quantity calculation unit, based on the classification results, automatically summarizes and calculates various sub-items such as earthwork, concrete, reinforcement, and metal structures, generating detailed and structured quantity list reports, providing a solid data foundation for subsequent cost analysis. Overall, this quantity calculation module effectively solves the inefficiency and error problems of traditional manual quantity calculation, realizing automatic quantity statistics based on intelligent algorithms and geometric analysis, greatly improving the digital management and cost control accuracy of water conservancy projects.

[0077] Furthermore, based on the principles of computational geometry, the BIM model is spatially decomposed using boundary representation and structural solid geometry methods to extract the three-dimensional geometric parameters of various hydraulic structures, including the following steps.

[0078] The BIM model undergoes geometric and attribute analysis and processing. By unifying the coordinate system, verifying accuracy, repairing defects, and eliminating redundancy, a clear and accurate basic structure for the model is constructed.

[0079] Complex geometries are transformed into boundary representation structures based on vertices, edges, and faces. The topological consistency of the geometry is verified using a half-edge data structure and Euler's formula, accurately describing the geometric boundaries and internal volume of hydraulic structures.

[0080] By identifying the basic structural units in complex hydraulic structures through Boolean operations, a CSG tree structure based on the construction process and hierarchical relationship is constructed to realize the recursive decomposition of complex irregular structures into standard geometric units.

[0081] Large hydraulic structures are spatially decoupled according to material type, structural function, and construction zoning to achieve rapid positioning of geometric entities and efficient spatial decomposition and parallel processing of complex models.

[0082] For the decomposed geometric units, key geometric parameters and shape features are automatically extracted and analyzed to generate a standardized geometric parameter data table.

[0083] The extracted geometric parameters are classified and organized according to the design specifications for water conservancy and hydropower projects, generating structured data files, and establishing a bidirectional association index between the geometric parameters and BIM model elements.

[0084] In summary, high-precision extraction of 3D geometric parameters of hydraulic structures was achieved through geometric and attribute analysis steps. First, the model preprocessing stage, through a unified coordinate system, accuracy verification, and defect repair, ensured the structural integrity and accuracy of the BIM model, laying a solid foundation for subsequent analysis. Subsequently, complex geometries were converted into boundary representation structures based on vertices, edges, and faces. Combined with a half-edge data structure and Euler's formula, the topological consistency of the geometry was verified, accurately depicting the geometric boundaries and internal volumes of the structure, effectively avoiding geometric errors and model mismatches. Through Boolean operations and CSG tree structure construction, complex irregular structures were recursively decomposed into standard geometric units, achieving hierarchical expression and efficient management of complex structures. Spatial decoupling technology was used to break down large buildings according to materials, functions, and construction zones, supporting rapid positioning and parallel processing of geometric entities, significantly improving the efficiency and flexibility of model analysis. The decomposed geometric units automatically extracted key parameters and shape features, generating standardized data tables that meet engineering design specifications. Finally, a bidirectional index between geometric parameters and BIM model elements was established to ensure data interconnectivity and dynamic updating capabilities.

[0085] Furthermore, based on the component identification results, the quantities of each sub-item project are automatically calculated, and a comprehensive bill of quantities report covering earthwork excavation, concrete pouring, steel reinforcement fabrication and installation, and metal structure installation is generated, including the following steps.

[0086] The system automatically matches the identified hydraulic structure components with the corresponding engineering quantity calculation rules, analyzes the calculation formulas, units of measurement and deduction principles for various components, and establishes a standardized calculation rule library covering earthwork, concrete, steel reinforcement and metal structures.

[0087] By using Boolean difference calculations between the digital ground model and the BIM model, the scope and depth of earthwork excavation can be accurately defined. Combined with material classification, the engineering quantity is automatically calculated, and temporary and auxiliary excavation quantities that are not measured are eliminated, so as to achieve standardized and accurate calculation of excavation engineering quantities.

[0088] Based on material properties, the application scope and quantity of different grades of concrete are identified, the volume occupied by embedded parts, holes and steel bars is automatically deducted, and the pouring volume is summarized according to the construction process.

[0089] Based on the reinforcement information in the BIM model and design drawings, the system automatically identifies the specifications and distribution of steel bars, accurately calculates the length of steel bars, takes into account various additional lengths and losses, classifies and statistically analyzes the quantities of fabrication and installation work, and automatically generates a steel bar material list and processing list.

[0090] Based on geometric dimensions, material density, and classification, the system automatically calculates the fabrication and installation quantities of metal structural components, generating a detailed list of metal structural components and installation quantity data.

[0091] Integrate the quantity data of each sub-item of the project to generate a standardized bill of quantities report containing project code, name, unit of measurement, quantity of work and characteristic description. Establish the traceability relationship between the quantity data and the components of the BIM model, and output a comprehensive quantity file that can be directly used for cost analysis and bidding.

[0092] In summary, this automated quantity calculation process, based on component identification results, utilizes rule matching and precise calculation to achieve comprehensive and standardized statistics on various sub-items of the project. Firstly, by constructing a standardized calculation rule library covering earthwork, concrete, reinforcement, and metal structures, it automatically matches and identifies the corresponding measurement formulas, units of measurement, and deduction principles for each component, ensuring the standardization and consistency of the calculation process. For earthwork excavation, it combines Boolean difference operations between the digital ground model and the BIM model to accurately define the excavation range and depth, and automatically eliminates temporary and auxiliary excavations, ensuring accurate calculation of excavation quantities. Concrete quantity calculation relies on intelligent identification of the application range of different grades of concrete based on material properties, automatically deducting the volume occupied by embedded parts, holes, and reinforcement, and combining this with construction technology to classify and summarize the pouring volume, reflecting the meticulousness and technological adaptability of the quantity calculation. The calculation of reinforcement fabrication and installation quantities is based on detailed reinforcement information in the BIM and design drawings, automatically identifying reinforcement specifications and layout, accurately calculating lengths, fully considering additional length and loss factors, and generating reinforcement material and processing lists, greatly improving the comprehensiveness and practicality of the statistics. The quantity statistics for metal structural components rely on geometric dimensions, material density, and classification to automatically calculate fabrication and installation quantities, generating detailed bill of quantities data. Finally, the quantity data for each sub-item is integrated to generate a standardized bill of quantities report containing project code, name, unit of measurement, quantity, and characteristic description. A two-way traceability relationship between quantities and BIM components is established to ensure data transparency and traceability.

[0093] Furthermore, the cost analysis and prediction module adopts a hybrid neural network architecture, including a time-series prediction network, a spatial correlation analysis network, and a multi-factor fusion decision network. Specifically: the time-series prediction network, based on long short-term memory networks and attention mechanisms, analyzes the time-series characteristics of historical cost data to capture the periodic patterns and trends of cost fluctuations; the spatial correlation analysis network is responsible for modeling the relationship between engineering projects and spatial factors, realizing the expression and analysis of spatial dependence; the multi-factor fusion decision network comprehensively considers the combined impact of material price fluctuations, construction technical difficulty, environmental protection requirements, and construction period constraints on costs, generating a dynamically adjusted cost prediction model and realizing uncertainty quantification analysis.

[0094] In summary, a hybrid neural network architecture enables multi-dimensional, dynamic, and accurate prediction of water conservancy project costs. The time-series prediction network, combining Long Short-Term Memory (LSTM) and attention mechanisms, delves into the time-series characteristics of historical cost data, accurately capturing the periodic and trend changes in cost fluctuations and enhancing the ability to predict future cost changes. The spatial correlation analysis network constructs a complex relationship model between the project and its geographical and environmental spatial factors, effectively expressing and analyzing the spatial dependence of different regional characteristics on costs, enhancing the spatial adaptability and refinement of the prediction. The multi-factor fusion decision-making network comprehensively integrates factors such as material price fluctuations, construction technical difficulty, environmental protection requirements, and construction period constraints to form a dynamically adjusted cost prediction model, taking into account the interaction of multiple influencing factors and improving the model's comprehensiveness and practicality. Simultaneously, this network achieves uncertainty quantification analysis, helping managers identify potential risks and fluctuation ranges, providing a scientific basis for decision-making. Overall, by integrating time-series analysis, spatial correlation modeling, and multi-factor comprehensive decision-making, the accuracy and dynamic response capability of cost prediction are significantly improved.

[0095] Furthermore, based on Long Short-Term Memory (LSTM) networks and attention mechanisms, the time-series characteristics of historical construction cost data are analyzed to capture the periodic patterns and trends of cost fluctuations. This includes the following steps: collecting and organizing historical construction cost data for water conservancy projects, performing missing value imputation and normalization preprocessing; inputting the preprocessed time-series data into a multi-layer LSTM network, effectively capturing the long-short-term dependencies and temporal dynamics in the data through its gating mechanism; assigning different weights to the output features of each time step of the LSTM using the attention mechanism to highlight the impact of key time periods on cost fluctuations; and predicting future cost trends and periodic fluctuations based on the weighted time-series representation, reflecting its dynamic characteristics.

[0096] In summary, by combining Long Short-Term Memory (LSTM) networks with an attention mechanism, the time-series characteristics of historical cost data for water conservancy projects can be effectively analyzed. First, the historical cost data undergoes integrity restoration and standardization to ensure the quality and consistency of the input data. A multi-layer LSTM network utilizes its gating structure to capture long-term and short-term dependencies in the data, accurately reflecting the dynamic changes in cost over time. The attention mechanism assigns differentiated weights to the outputs at different time steps, highlighting the significant impact of key periods on cost fluctuations and improving the model's sensitivity to important information. Finally, based on a weighted time-series representation, the model can accurately predict future cost trends and cyclical changes, reflecting the dynamic fluctuation characteristics of costs.

[0097] Furthermore, the modeling of the relationship between the engineering project and spatial factors, realizing the expression and analysis of spatial dependence, includes the following steps.

[0098] A grid-based coding method is used to divide the geospatial space into regular units and generate a unique multidimensional feature vector for each spatial unit, thus constructing a standardized spatial feature representation.

[0099] Based on the principles of geographical proximity and functional similarity, the spatial connectivity between each grid cell is calculated, and then an adjacency matrix reflecting the spatial relationships of the engineering project is constructed.

[0100] Spatial autocorrelation analysis is used to process the adjacency matrix, quantitatively assess the spatial dependence of engineering cost data, and identify its spatial clustering characteristics and anomalous distribution areas.

[0101] We design a spatial relationship modeling framework based on graph convolutional neural networks. The adjacency matrix is ​​used as the input to the graph structure, and the spatial neighborhood information is effectively aggregated and learned through multi-layer graph convolution operations.

[0102] A hierarchical spatial dependency modeling architecture is constructed to capture spatial dependency features at three scales: local neighborhood, regional scope, and macro environment. Multi-level spatial information is then integrated to form a unified comprehensive spatial relational representation.

[0103] In summary, by dividing geospatial space into regular grid units and generating multi-dimensional feature vectors for each unit, a standardized expression of spatial information is achieved. Based on geographical proximity and functional similarity, an adjacency matrix reflecting the spatial relationships of engineering projects is constructed, and spatial autocorrelation analysis is used to quantify the spatial dependence of cost data, identifying clustering features and anomalous regions. A graph convolutional neural network is introduced to perform deep learning on the adjacency matrix; multi-layer graph convolution operations effectively integrate spatial neighborhood information, improving the accuracy of spatial relationship modeling. Through a hierarchical spatial dependency modeling framework, spatial features at local, regional, and macro-scales are captured and fused to form a comprehensive spatial association representation, effectively revealing the complex dependency between engineering costs and spatial factors, providing solid technical support for refined cost prediction and regional resource optimization.

[0104] Furthermore, the multi-factor fusion decision network is constructed based on a deep neural network architecture enhanced by an attention mechanism, including a feature extraction layer, an attention weight calculation layer, and a decision fusion layer. Specifically: the feature extraction layer uses a multi-layer fully connected neural network to perform deep feature mining on each input factor and extract potential nonlinear relationships; the attention weight calculation layer is based on a self-attention mechanism and dynamically calculates the weights of each factor in combination with the specific features of the current project to achieve adaptive adjustment of the factor influence; the decision fusion layer performs nonlinear combination of the weighted factor features and generates the final cost prediction result through activation function processing.

[0105] In summary, by introducing an attention mechanism and a deep neural network structure, efficient modeling and dynamic fusion of multiple factors influencing engineering costs are achieved. The feature extraction layer utilizes multi-layer neural networks to deeply explore the complex nonlinear relationships between various input factors, enhancing the model's ability to identify the essential features of variables. The attention weight calculation layer employs a self-attention mechanism, enabling dynamic allocation of weights for different factors based on specific project conditions, achieving adaptive optimization of influence. Finally, the decision fusion layer nonlinearly combines the weighted features to output cost prediction results that better reflect reality. This architecture strengthens the model's ability to identify and respond to key factors in complex environments, improving the accuracy and adaptability of cost prediction.

[0106] Furthermore, the risk assessment and control module includes a risk factor database construction unit, a risk transmission analysis unit, and a dynamic monitoring and early warning unit. Specifically: the risk factor database construction unit establishes a comprehensive risk factor classification system covering technical risks, economic risks, environmental risks, policy risks, and management risks based on historical case data and expert knowledge from water conservancy project construction; it uses fuzzy hierarchical analysis to determine the weight and impact score of each risk factor; the risk transmission analysis unit constructs a causal relationship diagram between risk factors based on Bayesian network theory, analyzes the chain reaction of a single risk event on the overall project cost through related chains, and calculates the probability distribution and expected loss value of risk transmission; the dynamic monitoring and early warning unit monitors changes in key risk indicators during project progress in real time, and generates and pushes a risk assessment report when the risk indicator exceeds a preset threshold.

[0107] In summary, by constructing a comprehensive and quantifiable risk assessment and control mechanism, various potential risks in water conservancy projects can be effectively identified and dynamically responded to. The risk factor database construction unit, based on historical cases and expert knowledge, forms a classification system covering multi-dimensional risk types and uses fuzzy hierarchical analysis to quantify the impact of various risks, providing a scientific basis for subsequent analysis. The risk transmission analysis unit uses Bayesian networks to establish causal chains between risk factors, accurately simulates risk diffusion paths, and calculates the probability of risk propagation and expected cost losses under different scenarios. The dynamic monitoring and early warning unit tracks key risk indicators in real time, promptly detects risk exceeding limits, and automatically generates early warning reports, significantly improving the predictability, quantification, and response efficiency of engineering risks, providing strong support for cost control and project safety.

[0108] Furthermore, based on Bayesian network theory, a causal relationship diagram between risk factors is constructed to analyze the chain effect of a single risk event on the overall project cost through the associated chain, and to calculate the probability distribution of risk transmission and the expected value of loss, including the following steps.

[0109] Collect and organize historical risk events and project cost data, identify each risk factor node and its possible value range, and establish a node list and status set.

[0110] Determine the directed edges between risk factors, construct a preliminary network topology, and characterize the causal relationships and dependency paths among risk factors.

[0111] By using historical case data, maximum likelihood estimation is employed to calculate the conditional probability distribution of each node under different states of the parent node, thereby improving the probability parameters of the Bayesian network.

[0112] In the constructed Bayesian network, a certain risk node is fixed in the "occurrence" state, and the probability influence is transmitted to downstream nodes through belief propagation to obtain the posterior distribution of each associated node.

[0113] By using network inference, the transmission path probability from the initial risk event to each key cost factor is extracted, the probability distribution of its impact on each component of the overall project cost is quantified, and key intermediary nodes in the risk chain are identified.

[0114] By combining the cost of each component and the corresponding risk transmission probability distribution, the expected loss value of each transmission path is calculated and accumulated to finally obtain the overall expected loss of a single risk event on the overall project cost.

[0115] In summary, a systematic analysis of the risk transmission path in water conservancy projects was achieved by constructing a causal relationship graph of risk factors based on Bayesian networks. By collecting historical risk events and cost data, risk nodes and their states were identified, and a network topology reflecting risk dependence was constructed to reveal the causal relationships between various risk factors. Maximum likelihood estimation was used to determine conditional probabilities, achieving accurate fitting of network parameters. After fixing specific risk events as occurrence states, a belief propagation algorithm was used to infer their probability of impact on associated risk nodes, dynamically reflecting the risk transmission process. Through network inference, the probability distribution of the impact of risk events on each cost item was quantified, key intermediary nodes were identified, and the focus of risk diffusion was clarified. Combining the cost data of each item, the expected loss of the transmission path was calculated, achieving a quantitative assessment of the risk's impact on the overall cost. This improved the scientific rigor and precision of risk impact assessment, providing accurate decision-making basis for project risk management and cost control.

[0116] Furthermore, the dynamic monitoring and early warning unit sets up three levels of early warning: yellow, orange, and red, based on the risk level, each corresponding to different threshold ranges and response measures. Specifically: a yellow warning indicates a slight abnormality in the risk indicator, which is automatically recorded and continuously monitored; an orange warning indicates a significant abnormality in the risk indicator, which automatically notifies the relevant responsible persons and pushes a risk analysis report; and a red warning indicates a severe abnormality in the risk indicator, which immediately initiates the emergency response process and notifies all relevant personnel.

[0117] In summary, by establishing a three-tiered risk early warning mechanism, real-time monitoring and graded response to key risk indicators of water conservancy projects have been achieved. Yellow alerts are used to detect minor anomalies, automatically recording and continuously monitoring them to ensure potential risks receive timely attention. Orange alerts, targeting significant anomalies, not only issue automatic notifications but also push detailed risk analysis reports, prompting relevant personnel to take timely measures. Red alerts, for serious anomalies, rapidly activate emergency response procedures, notifying all relevant personnel to ensure efficient handling of the problem. This tiered early warning system effectively improves the early identification and response efficiency of risk events, strengthens the initiative and accuracy of project risk management, and helps reduce potential losses caused by risks.

[0118] Furthermore, the optimization decision module includes an objective function builder, a constraint manager, and a Pareto optimal solution set generator. Specifically: the objective function builder dynamically adjusts the weight coefficients of each objective according to the specific needs of the project to construct a comprehensive evaluation function; the constraint manager is responsible for managing multiple types of constraints to ensure the feasibility of the optimization scheme; and the Pareto optimal solution set generator generates multiple non-dominated solutions through iterative optimization and ranks and evaluates the schemes using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods.

[0119] In summary, by dynamically constructing a comprehensive evaluation objective function, multi-objective optimization tailored to specific project needs is achieved. The objective function builder flexibly adjusts the weights of each objective, effectively reflecting the priority of different interests. The constraint manager comprehensively manages various constraints, ensuring that all generated optimization solutions meet the feasibility requirements of actual engineering. The Pareto optimal solution set generator employs iterative optimization to uncover multiple non-dominated solutions, and combines the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods to scientifically rank and evaluate the solutions, helping decision-makers weigh the pros and cons among multiple alternatives and select the optimal strategy. Overall, the technology improves the balance between engineering cost and resource allocation, enhancing the practicality and decision support level of the optimized solutions.

[0120] Furthermore, multiple non-dominated solutions are generated through iterative optimization, and the solutions are ranked and evaluated by combining the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method, including the following steps.

[0121] First, the objective function and its constraints of the multi-objective optimization problem are defined, and corresponding evaluation indicators are set for each optimization objective. An initial population is generated through random initialization.

[0122] Secondly, a multi-objective evolutionary algorithm is used to perform selection, crossover, and mutation operations on the population, continuously iterating and updating the solution set, retaining the non-dominated solutions of the current generation, until the convergence criterion is met or the maximum number of iterations is reached.

[0123] Subsequently, non-dominated solutions on the Pareto front are extracted from the final population, and a set of candidate solutions that are not simultaneously dominated by other schemes on all objectives is constructed.

[0124] Next, a judgment matrix is ​​constructed, the relative weights of each evaluation index are calculated, and a consistency check is performed to ensure the rationality and consistency of the weights.

[0125] Based on this, the index values ​​of each non-dominated solution are fuzzified, the membership degree values ​​of the indexes are calculated based on the membership degree function, and a weighted sum is performed in combination with the weights to obtain the comprehensive evaluation score of each solution.

[0126] Finally, all non-dominated solutions are ranked according to the fuzzy comprehensive evaluation score, the solution with the highest score is selected as the optimal solution, and the ranking results and the basis for recommendation are output.

[0127] In summary, a multi-objective evolutionary algorithm is used to generate multiple non-dominated solutions, and analytic hierarchy process (AHP) and fuzzy comprehensive evaluation are introduced to rank and optimize these solutions. First, the objective function and constraints of the optimization problem are constructed, and iterative optimization is performed using an evolutionary algorithm to extract the solution set on the Pareto front. Then, an AHP-based judgment matrix is ​​constructed to determine the weights of each indicator, and consistency is verified to ensure the rationality of the evaluation system. Based on this, fuzzification is used to convert the indicator values ​​of each solution into membership degrees, and then weighted summaries are performed to finally obtain the comprehensive score for each solution.

[0128] like Figure 2 As shown, the intelligent analysis and evaluation system for water conservancy project costs disclosed in this implementation achieves intelligent analysis and evaluation of water conservancy project costs through the following steps.

[0129] The data integration and standardization module acquires multi-source heterogeneous data of water conservancy projects, including BIM model data, CAD drawing data, geological exploration data, geographical environment data, and market price data. It then performs semantic mapping and standardization processing on the multi-source heterogeneous data to establish unified data specifications and achieve intelligent fusion and real-time updates of the multi-source heterogeneous data.

[0130] The engineering quantity calculation module receives standardized data provided by the data integration and standardization module, automatically identifies the geometric features and material properties of the engineering structure, performs automatic engineering quantity calculation, and generates a detailed engineering quantity list.

[0131] Based on the bill of quantities output by the quantity calculation module in response to the cost analysis and prediction module, and combined with real-time market price information, construction process parameters, and the geographical environment and schedule of the project site, multi-dimensional dynamic cost analysis and prediction are realized, and the cost prediction results are output.

[0132] The risk assessment and control module responds to the prediction results of the cost analysis and prediction module, identifies and quantifies various risk factors in the project implementation process based on a pre-built risk factor rule base, analyzes the risk transmission path and its impact on project cost, and establishes a dynamic risk monitoring and early warning mechanism.

[0133] If a high-risk factor is identified, an early warning mechanism is triggered through the risk assessment and control module to monitor risk trends in real time and generate a risk assessment report.

[0134] The optimization decision-making module utilizes the cost prediction results and the risk assessment report to construct a multi-objective optimization model, thereby achieving a balanced optimization of project cost and resource allocation.

[0135] The optimization decision module generates the optimal decision scheme and provides an interactive visual interface to display the cost analysis results, risk assessment data, and optimization decision scheme.

[0136] The project implementation strategy is adjusted according to the optimal decision-making scheme, and the project data is continuously updated through the data integration and standardization module to form a closed-loop optimization control until the project cost reaches the preset optimization target.

[0137] In summary, this intelligent cost analysis and evaluation system for water conservancy projects achieves intelligent management of the entire process, from data collection and quantity calculation to cost prediction, risk assessment, and optimization decision-making, through multi-module collaboration. First, it integrates heterogeneous data from multiple sources, performing semantic mapping and standardization to achieve efficient data fusion and dynamic updates. Utilizing automatic identification technology, it accurately calculates quantities, providing a detailed foundation for cost analysis. The cost analysis module combines real-time market and environmental information to dynamically reflect project cost trends. The risk assessment module identifies and quantifies potential risks through a rule base, establishing a monitoring and early warning mechanism to ensure risk control. The optimization decision-making module, based on cost and risk data, uses a multi-objective optimization model to generate reasonable resource allocation schemes and provides an interactive interface to assist decision-making, achieving transparency and scientific rigor. This forms a data-driven closed-loop optimization control system, enabling dynamic adjustments to project implementation strategies, effectively pushing project costs closer to expected targets, and improving overall project management efficiency and economic benefits.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart analysis and evaluation system for water conservancy project costs, comprising a data integration and standardization module, a quantity calculation module, a cost analysis and prediction module, a risk assessment and control module, and an optimization decision-making module, characterized in that, The intelligent analysis and evaluation system for water conservancy project costs achieves intelligent analysis and evaluation of water conservancy project costs through the following steps: The data integration and standardization module acquires multi-source heterogeneous data of water conservancy projects, including BIM model data, CAD drawing data, geological exploration data, geographical environment data and market price data. It also performs semantic mapping and standardization processing on the multi-source heterogeneous data, establishes unified data specifications, and realizes intelligent fusion and real-time updating of multi-source heterogeneous data. The engineering quantity calculation module receives standardized data provided by the data integration and standardization module, automatically identifies the geometric features and material properties of the engineering structure, performs automatic engineering quantity calculation, and generates a detailed engineering quantity list. Based on the bill of quantities output by the bill of quantities in response to the bill of quantities calculation module, the cost analysis and prediction module combines real-time market price information, construction process parameters, and the geographical environment and schedule of the project location to achieve multi-dimensional dynamic cost analysis and prediction, and output the cost prediction results. The risk assessment and control module responds to the prediction results of the cost analysis and prediction module, identifies and quantifies various risk factors in the project implementation process based on the pre-built risk factor rule base, analyzes the risk transmission path and its impact on project cost, and establishes a dynamic risk monitoring and early warning mechanism. If a high-risk factor is identified, an early warning mechanism is triggered through the risk assessment and control module to monitor risk changes in real time and generate a risk assessment report. The optimization decision-making module utilizes the cost prediction results and the risk assessment report to construct a multi-objective optimization model, thereby achieving a balanced optimization of project cost and resource allocation. The optimization decision module generates the optimal decision scheme and provides an interactive visual interface to display the cost analysis results, risk assessment data and optimization decision scheme. The project implementation strategy is adjusted according to the optimal decision-making scheme, and the project data is continuously updated through the data integration and standardization module to form a closed-loop optimization control until the project cost reaches the preset optimization target.

2. The intelligent analysis and evaluation system for water conservancy project costs according to claim 1, characterized in that, The data integration and standardization module adopts a layered data fusion architecture, including a physical layer data acquisition unit, a semantic layer mapping and transformation unit, and an application layer standardization output unit. Specifically: the physical layer data acquisition unit is configured to extract structured data from different database systems and parse unstructured CAD drawing annotation information and geological exploration report text content using OCR image recognition and natural language processing; the semantic layer mapping and transformation unit constructs an ontology knowledge graph, defines specialized vocabulary, conceptual relationships, and attribute constraint rules for the water conservancy engineering field, and uses semantic reasoning to map heterogeneous data to a unified conceptual space; the application layer standardization output unit generates standardized data formats conforming to industry standards based on the GB50500 engineering quantity list pricing specification and the regulations for compiling design estimates for water conservancy and hydropower projects.

3. The intelligent analysis and evaluation system for water conservancy project costs according to claim 1, characterized in that, The quantity calculation module includes a geometric model analysis unit, a component classification and identification unit, and a quantity statistics calculation unit. Specifically: the geometric model analysis unit, based on computational geometry principles, uses boundary representation and structural solid geometry methods to spatially decompose the BIM model and extract the three-dimensional geometric parameters of various hydraulic structures; the component classification and identification unit, based on deep convolutional neural networks, identifies different types of hydraulic engineering components and automatically labels material properties and construction process requirements; and the quantity statistics calculation unit, based on the component identification results, automatically calculates the quantity of each sub-item of the project and generates a comprehensive list report covering earthwork excavation, concrete pouring, steel reinforcement fabrication and installation, and metal structure installation.

4. The intelligent analysis and evaluation system for water conservancy project costs according to claim 3, characterized in that, Based on the principles of computational geometry, the BIM model is spatially decomposed using boundary representation and structural solid geometry methods to extract the three-dimensional geometric parameters of various hydraulic structures. This includes the following steps: The BIM model undergoes geometric and attribute analysis and processing. By unifying the coordinate system, verifying accuracy, repairing defects, and eliminating redundancy, a clear and accurate basic structure of the model is constructed. Complex geometries are transformed into boundary representation structures based on vertices, edges, and faces. The topological consistency of the geometry is verified using a half-edge data structure and Euler's formula, accurately describing the geometric boundaries and internal volume of hydraulic structures. By identifying the basic structural units in complex hydraulic structures through Boolean operations, a CSG tree structure based on the construction process and hierarchical relationship is constructed to realize the recursive decomposition of complex irregular structures into standard geometric units. Large hydraulic structures are spatially decoupled according to material type, structural function and construction zoning to achieve rapid positioning of geometric entities and efficient spatial decomposition and parallel processing of complex models; For the decomposed geometric units, key geometric parameters and shape features are automatically extracted and analyzed to generate a standardized geometric parameter data table; The extracted geometric parameters are classified and organized according to the design specifications for water conservancy and hydropower projects, generating structured data files, and establishing a bidirectional association index between the geometric parameters and BIM model elements.

5. The intelligent analysis and evaluation system for water conservancy project costs according to claim 3, characterized in that, Based on the component identification results, the quantities of each sub-item of the project are automatically calculated, generating a comprehensive bill of quantities report covering earthwork excavation, concrete pouring, rebar fabrication and installation, and metal structure installation. This includes the following steps: The system automatically matches the identified hydraulic structure components with the corresponding engineering quantity calculation rules, analyzes the calculation formulas, units of measurement and deduction principles corresponding to various components, and establishes a standardized calculation rule library covering earthwork, concrete, steel reinforcement and metal structures. By using the digital ground model and the BIM model to perform Boolean difference calculations, the scope and depth of earthwork excavation can be accurately defined. Combined with material classification, the engineering quantity can be automatically calculated, and temporary and auxiliary excavation quantities that are not measured can be eliminated, so as to achieve standardized and accurate excavation quantity calculation. Based on material properties, the application scope and quantity of different grades of concrete are identified, the volume occupied by embedded parts, holes and steel bars is automatically deducted, and the pouring volume is summarized according to the construction process. Based on the steel reinforcement information in the BIM model and design drawings, the steel reinforcement specifications and distribution are automatically identified, the steel reinforcement length is accurately calculated, and various additional lengths and losses are considered. The production and installation quantities are classified and statistically analyzed, and a steel reinforcement material list and processing list are automatically generated. Based on geometric dimensions, material density, and classification, the system automatically calculates the fabrication and installation quantities of metal structural components, generating a detailed list of metal structural components and installation quantity data. Integrate the quantity data of each sub-item of the project to generate a standardized bill of quantities report containing project code, name, unit of measurement, quantity of work and characteristic description. Establish the traceability relationship between the quantity data and the components of the BIM model, and output a comprehensive quantity file that can be directly used for cost analysis and bidding.

6. The intelligent analysis and evaluation system for water conservancy project costs according to claim 1, characterized in that, The cost analysis and prediction module adopts a hybrid neural network architecture, including a time-series prediction network, a spatial correlation analysis network, and a multi-factor fusion decision network. Specifically: the time-series prediction network is based on long short-term memory networks and attention mechanisms to analyze the time-series characteristics of historical cost data and capture the periodic patterns and trends of cost fluctuations; the spatial correlation analysis network is responsible for modeling the relationship between engineering projects and spatial factors, realizing the expression and analysis of spatial dependence; the multi-factor fusion decision network generates a dynamically adjusted cost prediction model by comprehensively considering the combined impact of material price fluctuations, construction technical difficulty, environmental protection requirements, and construction period constraints on costs, and realizes the quantitative analysis of uncertainty.

7. The intelligent analysis and evaluation system for water conservancy project costs according to claim 6, characterized in that, The modeling process establishes the relationship between engineering projects and spatial factors, enabling the expression and analysis of spatial dependencies. This includes the following steps: A grid-based coding method is used to divide the geospatial space into regular units, and a unique multidimensional feature vector is generated for each spatial unit to construct a standardized spatial feature representation. Based on the principles of geographical proximity and functional similarity, the spatial connectivity between each grid cell is calculated, and then an adjacency matrix reflecting the spatial relationship of the engineering project is constructed. Spatial autocorrelation analysis is used to process the adjacency matrix, quantitatively assess the spatial dependence of engineering cost data, and identify its spatial clustering characteristics and abnormal distribution areas. Design a spatial relationship modeling framework based on graph convolutional neural networks, using the adjacency matrix as the graph structure input, and achieving effective aggregation and learning of spatial neighborhood information through multi-layer graph convolution operations; A hierarchical spatial dependency modeling architecture is constructed to capture spatial dependency features at three scales: local neighborhood, regional scope, and macro environment. Multi-level spatial information is then integrated to form a unified comprehensive spatial relational representation.

8. The intelligent analysis and evaluation system for water conservancy project costs according to claim 1, characterized in that, The risk assessment and control module includes a risk factor database construction unit, a risk transmission analysis unit, and a dynamic monitoring and early warning unit. Specifically: the risk factor database construction unit establishes a comprehensive risk factor classification system covering technical, economic, environmental, policy, and management risks based on historical case data and expert knowledge from water conservancy engineering construction. It also uses fuzzy hierarchical analysis to determine the weight and impact score of each risk factor. The risk transmission analysis unit constructs a causal relationship diagram between risk factors based on Bayesian network theory, analyzes the chain reaction of a single risk event on the overall project cost, and calculates the probability distribution and expected loss value of risk transmission. The dynamic monitoring and early warning unit monitors changes in key risk indicators during project progress in real time. When a risk indicator exceeds a preset threshold, it generates a risk assessment report and pushes it out.

9. The intelligent analysis and evaluation system for water conservancy project costs according to claim 8, characterized in that, Based on Bayesian network theory, a causal relationship diagram between risk factors is constructed. The chain reaction effect of a single risk event on the overall project cost is analyzed, and the probability distribution and expected value of risk transmission are calculated. The steps include: Collect and organize historical risk events and project cost data, identify each risk factor node and its possible value range, and establish a node list and status set; Determine the directed edges between risk factors, construct a preliminary network topology, and characterize the causal relationships and dependency paths among risk factors; Using historical case data, maximum likelihood estimation is employed to calculate the conditional probability distribution of each node under different states of the parent node, thereby improving the probability parameters of the Bayesian network. In the constructed Bayesian network, a certain risk node is fixed as the "occurrence" state, and the probability influence is transmitted to downstream nodes through belief propagation to obtain the posterior distribution of each associated node. Through network inference, the transmission path probability from the initial risk event to each key cost factor is extracted, the probability distribution of its impact on each item of the overall project cost is quantified, and key intermediary nodes in the risk chain are identified. By combining the cost of each component and the corresponding risk transmission probability distribution, the expected loss value of each transmission path is calculated and accumulated to finally obtain the overall expected loss of a single risk event on the overall project cost.

10. The intelligent analysis and evaluation system for water conservancy project costs according to claim 1, characterized in that, The optimization decision module includes an objective function builder, a constraint manager, and a Pareto optimal solution set generator. Specifically: the objective function builder dynamically adjusts the weight coefficients of each objective according to the specific needs of the project to construct a comprehensive evaluation function; the constraint manager is responsible for managing multiple types of constraints to ensure the feasibility of the optimization scheme; and the Pareto optimal solution set generator generates multiple non-dominated solutions through iterative optimization and ranks and evaluates the schemes using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods.

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