Electric power construction design management system and method based on constraint graph twinning and anti-fact optimization

Abstract

The invention discloses an electric power construction design management system and method based on constraint graph twinning and anti-fact optimization, and relates to the technical field of electric power engineering construction and design management, and the method comprises the following steps: collecting electric power construction standardized design data; abstracting an electric power construction design object into a graph structure and defining constraint conditions according to the electric power construction standardized design data to obtain a constraint graph twinborn model; performing causal modeling based on the twin model of the constraint graph, generating an anti-fact sample, and performing risk sensitivity analysis to obtain an analysis result; and according to the analysis result, optimizing the design scheme to obtain a candidate solution, and when the candidate solution violates the constraint condition, generating a graph difference patch to repair the candidate solution to obtain a compliant optimization design scheme, and completing the electric power construction design management. According to the method, the problem that unified constraint check and risk analysis are lacked in the existing electric power construction design process can be solved.

Description

Technical Field

[0001] This invention relates to the field of power engineering construction and design management technology, and in particular to a power construction design management system and method based on constraint graph twinning and counterfactual optimization. Background Technology

[0002] In the construction of power engineering projects, the design phase is a crucial stage that determines the project's quality, schedule, and cost. With the increasing scale and complexity of projects, design work not only involves collaboration among multiple disciplines such as civil engineering, electrical engineering, and structural engineering, but also requires consideration of comprehensive indicators such as safety, environmental protection, and carbon emissions. How to effectively manage multi-source heterogeneous data during the design phase and achieve comprehensive control over design constraints and risks has become an important focus for the industry.

[0003] Currently, traditional power construction design and management methods generally rely on manual experience and single-discipline design tools, lacking a unified data integration and constraint verification mechanism. In complex environments, design data is scattered across different systems, making cross-discipline consistency management difficult; risk analysis is often only discovered during the construction or operation phases, resulting in delays; and design version change records are incomplete, making traceability difficult and easily leading to inconsistencies and inefficient collaboration. Given these issues, existing technologies struggle to achieve integrated management and real-time constraint verification of multi-source data during the design phase, and cannot identify potential risks in advance or effectively optimize solutions, resulting in data silos, delayed risk identification, and insufficient version collaboration during the design phase. Summary of the Invention

[0004] The purpose of this invention is to provide a power construction design management system and method based on constraint graph twins and counterfactual optimization, so as to solve the problem of lack of unified constraint verification and risk analysis in the existing power construction design process.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a power construction design management method based on constraint graph twins and counterfactual optimization includes the following steps: Collect standardized design data for power construction; Based on the standardized design data for power construction, the power construction design object is abstracted into a graph structure and constraints are defined to obtain a constraint graph twin model; Causal modeling is performed based on the constraint graph twin model, and after generating counterfactual samples, risk sensitivity analysis is conducted to obtain the analysis results. Based on the analysis results, the design scheme is optimized to obtain candidate solutions. When a candidate solution violates the constraints, a graph difference patch is generated to repair the candidate solution, thereby obtaining a compliant optimized design scheme and completing the power construction design management.

[0006] In some implementations, the following steps are also included: Based on the aforementioned differential patch, traceability and archiving are performed, and multi-disciplinary collaborative management is completed; Based on the compliant optimized design scheme, a 3D display and risk heat map overlay are performed, and interactive annotations and scheme comparisons are completed.

[0007] In some implementations, the standardized design data for power construction includes: geological survey information, building information model files, meteorological and environmental data, transmission line mapping data, and power grid access parameters; The steps for collecting standardized design data for power construction specifically include: The access data is acquired, and the access data is cleaned, format converted, and consistency verified to obtain standardized design data for power construction.

[0008] In some implementations, the abstraction of power construction design objects into a graph structure and the definition of constraints specifically include: Towers, foundations, conductors, busbars, and civil engineering components are abstracted as nodes of the structure shown in the diagram; Electrical constraints, construction constraints, and structural constraints are defined as attributes of the edges connecting the nodes in the graph structure.

[0009] In some implementations, the method further includes: performing real-time consistency checks on at least one of the electrical constraints, construction constraints, and structural constraints in the constraint graph twin model based on a predefined rule base.

[0010] In some implementations, the step of performing causal modeling based on the constraint graph twin model, generating counterfactual samples, and then performing risk sensitivity analysis specifically includes: Construct a set of uncertain scenarios that includes at least one of the following: meteorological conditions, construction conditions, supply chain conditions, and operation and maintenance conditions. Based on the design variables and outcome variables in the constraint diagram twin model, a causal relationship model is established. Under the constraints of the causal relationship model, counterfactual samples are generated for the set of uncertain scenarios, and a risk sensitivity report is formed based on the counterfactual samples as the analysis result.

[0011] In some implementations, optimizing the design to obtain candidate solutions specifically includes: The optimization is carried out with cost, construction period, safety indicators, environmental impact indicators and carbon emission indicators as objectives, and a min-max robust optimization strategy is adopted to generate candidate solutions.

[0012] In some implementations, the graph difference patch includes an object identifier, parameters before modification, parameters after modification, reason for repair, and generation timestamp.

[0013] Secondly, a power construction design management system based on constraint graph twinning and counterfactual optimization includes: The data acquisition and integration module is used to collect standardized design data for power construction; specifically, it is used to collect and integrate geological survey information, building information model files, transmission line mapping data, meteorological and environmental data, and power grid access parameters. The constraint graph twin modeling module is used to abstract power construction design objects into a graph structure and define constraints based on the standardized power construction design data, thereby obtaining a constraint graph twin model; specifically, it is used to abstract power construction design objects into a graph structure and define electrical constraints, construction constraints, and structural constraints. The counterfactual risk twin module is used to perform causal modeling based on the constraint graph twin model, generate counterfactual samples, and then perform risk sensitivity analysis to obtain analysis results; specifically, it is used to generate counterfactual samples based on causal modeling and carry out risk sensitivity analysis. The robust multi-objective optimization and compliance repair module is used to optimize the design scheme based on the analysis results to obtain candidate solutions. When the candidate solution violates the constraints, a graph difference patch is generated to repair the candidate solution, thereby obtaining a compliant optimized design scheme and completing the power construction design management. Specifically, it is used to realize the multi-objective optimization and specification verification of the design scheme through the main loop robust optimization and secondary loop compliance repair mechanism.

[0014] In some implementations, it also includes: The version tracing and collaboration module is used to trace and archive the differential patches in the diagram and to complete multi-disciplinary collaborative management; specifically, it is used to trace and archive the modification patches of the design scheme and to manage multi-disciplinary collaborative management. The visualization and interaction module is used to display the compliant optimized design scheme in 3D and overlay risk heat maps, and to complete interactive annotations and scheme comparisons; specifically, it is used to realize 3D design display, risk heat map overlay, interactive annotations, and scheme comparisons.

[0015] The data acquisition and integration module provides unified structured data to the constraint graph twin modeling module. The constraint graph twin modeling module interacts with the counterfactual risk twin module to conduct risk scenario analysis. The analysis results of the counterfactual risk twin module serve as input to the robust multi-objective optimization and compliance repair module. The optimization and repair results are recorded and collaboratively managed by the version traceability and collaboration module. Finally, the visualization and interaction module provides three-dimensional display and interactive feedback, thereby forming a data management, constraint verification, risk analysis, optimization decision-making, and collaborative traceability system covering the entire process of power construction design.

[0016] Furthermore, the data acquisition and integration module includes a data interface unit, a data cleaning unit, and a data storage unit. The data interface unit supports IFC, GeoJSON, SCD, and CSV formats. The data cleaning unit uses interpolation to complete missing data and uses statistical methods to detect and replace abnormal data. The data storage unit uses a combination of relational database and time-series database to store static and dynamic data.

[0017] Furthermore, the constraint graph twin modeling module abstracts the power construction design object into a graph structure. The nodes include towers, foundations, conductors, busbars and civil engineering components, and the edges include electrical constraints, construction constraints and structural constraints. The consistency check unit realizes real-time verification of clearance distance, phase spacing and foundation bearing capacity based on rule base and algorithmic calculation model.

[0018] Furthermore, the risk twin module includes a scenario generation unit, a causal modeling unit, and a counterfactual generation unit. The scenario generation unit constructs a set of uncertain scenarios under meteorological, construction, supply chain, and operation and maintenance conditions. The causal modeling unit establishes the causal relationship between design variables and outcome variables. The counterfactual generation unit generates counterfactual samples under causal constraints and forms a risk sensitivity report.

[0019] Furthermore, the optimization module adopts a dual-loop mechanism of robust optimization of the main loop and compliance repair of the sub-loop. The main loop generates candidate solutions based on the min-max robust optimization strategy under the objectives of cost, schedule, safety, environment and carbon emissions. The sub-loop generates graph difference patches and completes the repair when the candidate solutions violate the constraints.

[0020] Furthermore, the graph difference patch includes an object identifier, parameters before modification, parameters after modification, repair reason, and generation timestamp. The patch has reversibility and dependency chain description, which is used to support version rollback and multiple patch inheritance calls.

[0021] Furthermore, the management module uses a combination of on-chain fingerprinting and offline storage to trace design versions. On-chain storage includes patch hashes, authors, timestamps, and compliance summaries, while off-chain storage includes BIM files and simulation data, which are linked through hash verification values.

[0022] Furthermore, the collaborative mechanism merges multi-professional modifications based on object-level node identifiers. When a conflict is detected, the conflict arbitration unit processes it according to the priority of the rule base, or generates a branch version for manual review.

[0023] Furthermore, the visualization module includes a 3D design display unit and a risk heatmap display unit. The 3D design display unit presents the design modification results in real time, while the risk heatmap display unit maps the sensitivity analysis results onto the 3D scene to mark high-risk areas.

[0024] Furthermore, the visualization module further includes an interactive annotation unit and a scheme comparison unit. The interactive annotation unit allows designers to add annotations to the 3D model and form design patches. The scheme comparison unit displays the differences between multiple optimization schemes through radar charts, indicator curves, and 3D difference highlights, and synchronizes the comparison results to the management module for archiving.

[0025] Compared with the prior art, the present invention has the following beneficial effects: This invention abstracts the design object of power construction into a graph structure and defines constraints to construct a constraint graph twin model, achieving a unified, structured, and computable expression of the design object and multi-source constraints. This solves the problems of scattered data and difficulty in unified constraint verification in traditional design. Based on this model, causal modeling is performed and counterfactual samples are generated for risk sensitivity analysis, enabling proactive identification of risk-sensitive points under multiple scenarios during the design phase, overcoming the shortcomings of delayed risk discovery and passive response. Furthermore, by optimizing based on the analysis results and automatically generating graph difference patches for repair when candidate solutions violate constraints, a "analysis-optimization-repair" closed loop is formed. This achieves multi-objective robust optimization while ensuring that the design scheme always complies with specifications, significantly improving design efficiency and the compliance and reliability of the results. Therefore, this invention solves the problem of the lack of unified constraint verification and risk analysis in the existing power construction design process. Attached Figure Description

[0026] Figure 1 A flowchart of a power construction design management method based on constraint graph twinning and counterfactual optimization provided in an embodiment of the present invention; Figure 2 A schematic diagram of the modules of the power construction design management system based on constraint graph twinning and counterfactual optimization provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a constraint graph twin modeling process provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a robust multi-objective optimization and compliance repair closed-loop process provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0028] like Figure 1 As shown, this embodiment provides a power construction design management method based on constraint graph twinning and counterfactual optimization, characterized by the following steps: S1, collects standardized design data for power construction; Specifically, the access data is acquired, and the access data is cleaned, format converted, and consistency verified to obtain standardized design data for power construction.

[0029] S2, Based on the standardized design data for power construction, the power construction design object is abstracted into a graph structure and constraints are defined to obtain a constraint graph twin model; Specifically, towers, foundations, conductors, busbars, and civil engineering components are abstracted as nodes of the structure shown in the diagram; Electrical constraints, construction constraints, and structural constraints are defined as attributes of the edges connecting the nodes in the graph structure. Based on a predefined rule base, at least one of the electrical constraints, construction constraints, and structural constraints in the constraint graph twin model is checked for consistency in real time.

[0030] S3, causal modeling is performed based on the constraint graph twin model, counterfactual samples are generated, and risk sensitivity analysis is performed to obtain the analysis results; Specifically, an uncertain scenario set is constructed, which includes at least one of the following: meteorological conditions, construction conditions, supply chain conditions, and operation and maintenance conditions. Based on the design variables and outcome variables in the constraint diagram twin model, a causal relationship model is established. Under the constraints of the causal relationship model, counterfactual samples are generated for the set of uncertain scenarios, and a risk sensitivity report is formed based on the counterfactual samples as the analysis result.

[0031] S4. Based on the analysis results, the design scheme is optimized to obtain candidate solutions. When the candidate solution violates the constraints, a graph difference patch is generated to repair the candidate solution, thereby obtaining a compliant optimized design scheme and completing the power construction design management.

[0032] Specifically, optimization is performed with cost, schedule, safety, environmental impact, and carbon emission targets as objectives, and a min-max robust optimization strategy is used to generate candidate solutions. The graph difference patch includes object identifier, parameters before modification, parameters after modification, reason for repair, and generation timestamp.

[0033] The method further includes the following steps: Based on the aforementioned differential patch, traceability and archiving are performed, and multi-disciplinary collaborative management is completed; Based on the compliant optimized design scheme, a 3D display and risk heat map overlay are performed, and interactive annotations and scheme comparisons are completed.

[0034] This embodiment provides a power construction design management system based on constraint graph twins and counterfactual optimization. The system consists of multiple functional modules, covering the entire process of power construction design, including data management, constraint verification, risk analysis, optimization decision-making, and version collaboration. For example... Figure 2 As shown, the system mainly includes a data acquisition and integration module, a constraint graph twin modeling module, a counterfactual risk twin module, a robust multi-objective optimization and compliance remediation module, a version traceability and collaboration module, and a visualization and interaction module. The modules interact according to data flow and logical relationships: the data acquisition and integration module provides unified data input to the constraint graph twin modeling module; the constraint graph twin modeling module interacts with the counterfactual risk twin module to achieve risk scenario modeling and counterfactual inference; the analysis results from the counterfactual risk twin module are input to the robust multi-objective optimization and compliance remediation module for optimization and compliance remediation; the optimization results are transmitted to the version traceability and collaboration module for archiving and multi-professional collaboration; finally, the visualization and interaction module provides 3D display and interactive feedback, forming a complete closed-loop system.

[0035] To make the technical solution of the present invention clearer, the various functional modules in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that these embodiments are only used to illustrate the technical solution of the present invention, and not to limit the present invention. Specifically, the system consists of six core modules, namely, a data acquisition and integration module, a constraint graph twin modeling module, a counterfactual risk twin module, a robust multi-objective optimization and compliance repair module, a version traceability and collaboration module, and a visualization and interaction module. The structure and working principle of the above modules will be described in detail below.

[0036] In the embodiments of the present invention, the data acquisition and integration module, as the front-end basic link of the system, is mainly used to access, clean and standardize the multi-source data required for power construction design.

[0037] This module consists of three parts: a data interface unit, a data cleaning unit, and a data storage unit. 1. Data Interface Unit Responsible for connecting geological survey data, building information model (BIM) files, power transmission line mapping data, meteorological and environmental information, and power grid access parameters.

[0038] The data interface supports multiple standard formats, including IFC format, GeoJSON format, SCD file, and Excel / CSV project data table.

[0039] A unified data bus protocol enables synchronous access to data from different sources.

[0040] 2. Data Cleaning Unit Perform format conversion and consistency verification on the accessed data to ensure that data of different formats are converted into a unified structured representation.

[0041] For missing data, interpolation or imputation methods based on statistical distribution are used; for outlier data, standard deviation detection or clustering detection methods are used to identify and replace them.

[0042] The cleaned data is labeled to facilitate subsequent constraint diagram modeling.

[0043] 3. Data storage unit It uses a combination of relational databases and time-series databases for storage.

[0044] Static design data is stored in a relational database, while dynamic meteorological data and sensor data are stored in a time-series database.

[0045] The data storage unit is connected to the subsequent constraint graph twin modeling module via an API interface, supporting real-time invocation and batch querying.

[0046] By implementing this module, the present invention can ensure that the design data input into the system is accurate, complete, and has a uniform format, laying the foundation for subsequent constraint twin modeling and risk optimization analysis.

[0047] In embodiments of the present invention, the constraint graph twin modeling module is used to transform power construction design objects into graph models with constraint information and maintain real-time synchronization with the geometric twin model, thereby achieving dynamic compliance verification of the design scheme. Figure 3 As shown, the constraint graph twin modeling process includes node abstraction, constraint definition, and consistency check unit, and finally outputs the compliance verification result.

[0048] 1. Modeling Structure This module includes a graph modeling unit, a constraint definition unit, a consistency check unit, and an algorithmic calculation unit.

[0049] Graph modeling unit: Abstracts various design objects in power construction into nodes of a graph model, including towers, foundations, conductors, busbars, transformers, circuit breakers, disconnect switches, support structures, and civil engineering components.

[0050] Constraint definition unit: Abstracting electrical, construction, and structural requirements into edge attributes to form a complete constraint relationship network. For example, the clearance distance between a conductor and the ground forms a constraint edge between a node and a topographic point, and the foundation bearing capacity forms a constraint edge between a node and geological conditions.

[0051] Consistency check unit: responsible for calling the rule base and logical expressions to determine whether the current design parameters meet the relevant power specifications and construction standards.

[0052] Alternate calculation unit: Using a trained regression model or neural network, it performs approximate calculations on complex electromagnetic field distributions, tower stress, and foundation settlement, reducing the amount of computation required for real-time verification.

[0053] 2. Constraints on expression form To achieve rapid detection, constraints are expressed in a formal logical manner and mathematical inequalities: Electrical clearance constraints:

[0054] in To design the clearance value, Minimum clearance requirements are determined by voltage level and terrain.

[0055] Phase spacing constraints:

[0056] in The phase conductor spacing, It is a function based on voltage level and meteorological load.

[0057] Foundation bearing constraints:

[0058] in Based on the actual bearing stress, These are the allowable values ​​corresponding to the soil category.

[0059] 3. Operating Mechanism When a user modifies design parameters (such as tower height, foundation dimensions, or route alignment) in the BIM system, the graphical modeling unit instantly updates the attributes of the corresponding nodes and edges.

[0060] The consistency check unit calls the rule base to compare all constraints one by one; if a violation is detected, the system will highlight the conflict location in the 3D model through the visualization module and generate a prompt message.

[0061] The proxy computation unit calls a lightweight proxy model in each iteration, ensuring that the detection speed is within seconds, which is suitable for interactive design processes.

[0062] 4. Technical Effects Through the constraint graph twin modeling module, the present invention can achieve instant compliance verification of the power construction plan in the design stage, avoiding rework caused by traditional manual drawing review or post - facto correction, and improving the intelligence and standardization level of the design link.

[0063] In the embodiments of the present invention, the counterfactual risk twin module is used to simulate the engineering performance under different uncertain scenarios in the design stage, generate counterfactual samples through causal reasoning, so as to realize the sensitivity analysis and early warning of the potential risks of the design scheme. This module is deeply coupled with the constraint graph twin modeling module and can identify the vulnerable links of the design scheme under extreme conditions in advance on the premise of ensuring constraint compliance.

[0064] 1. Module Composition This module includes a scenario generation unit, a causal modeling unit, a counterfactual generation unit, and a risk analysis unit.

[0065] Scenario generation unit: Based on historical monitoring data and statistical distributions, construct meteorological extreme scenarios (storm, icing, heavy rainfall), construction condition scenarios (insufficient construction resources, traffic obstruction), supply chain scenarios (delay or price increase of key materials), operation and maintenance condition scenarios (temporary power outage or emergency maintenance requirements), etc.

[0066] Causal modeling unit: Use a structural causal model (SCM) to establish the causal relationship between design variables and result variables. Design variables include tower height, conductor type, foundation type, construction plan sequence, etc., and result variables include cost, construction period, safety index, environmental impact, and carbon emission.

[0067] Counterfactual generation unit: Under the constraint of the causal model, generate counterfactual results for a given design scheme when "several pre - variables X take different values". For example, when the original design is a common foundation, generate risk results under the condition of "if a deeper foundation is adopted" or "if the construction time is extended by 30 days".

[0068] Risk analysis unit: By comparing the result differences under different decisions, calculate the risk sensitivity index and output a report to guide the optimization module to modify the scheme.

[0069] 2. Method Steps Step 1: Scenario set construction The system generates various possible meteorological conditions according to historical meteorological data and probability models. For example, in the design of a substation supporting a 330MW drum - type boiler unit, generate extreme conditions with a wind speed of 30m / s and low - temperature conditions with an ice - coating thickness of 30mm.

[0070] Step 2: Causal relationship modeling Construct a causal graph, where nodes represent design decisions and outcome metrics, and edges represent causal relationships. Train the graph on historical project data to generate causal strength parameters.

[0071] Step 3: Generating Counterfactual Results Intervene in the existing design scheme and calculate the risk changes under different conditions. The mathematical expression is:

[0072] in This indicates the differences in outcome variables under different decision-making conditions.

[0073] Step 4: Sensitivity Analysis and Risk Report Output The system generates sensitivity curves for the changes in risk under various scenarios, marks the design variables that have the greatest impact on cost, schedule or safety, and generates risk heat maps through a 3D visualization interface.

[0074] 3. Technical Effects This module enables the invention to identify high-risk design elements in advance during the design phase, avoiding the discovery of problems only during the construction or operation and maintenance phases as is done in traditional methods. The results are directly fed back as input to the robust multi-objective optimization and compliance remediation module, giving the optimization process risk awareness capabilities.

[0075] In the embodiments of the present invention, the robust multi-objective optimization and compliance repair module adopts a double-ring structure combining a main ring and a sub-ring, which ensures that the design scheme is optimized globally for multiple objectives while always complying with the industry standards and engineering requirements defined by the constraint diagram twin modeling module.

[0076] 1. Module Composition This module consists of a main loop robust optimization unit, a secondary loop compliance repair unit, and a patch archive unit.

[0077] Main loop robust optimization unit: responsible for joint optimization of indicators such as cost, schedule, safety, environmental impact and carbon emissions under uncertain scenario set.

[0078] Sub-loop compliance repair unit: When a candidate solution generated by the main loop optimization violates the constraints, a repair mechanism is triggered to make local adjustments to the design scheme.

[0079] Patch Archive Unit: Records the repair process as a graph difference patch and archives it in subsequent version traceability and collaboration modules.

[0080] like Figure 4 As shown, this invention adopts a dual-loop closed-loop mechanism of robust optimization of the main loop and compliance repair of the secondary loop. After compliance check, candidate solutions enter different paths. If they do not comply with the rules, patch repair is triggered and the solution returns to the optimization stage.

[0081] 2. Optimization Objectives and Mathematical Model The system's optimization objective function is defined as:

[0082] in: Design scheme x in scenario The cost below; Construction period; Safety indicators; Environmental impact indicators; Carbon emission indicators; : A set of uncertain scenarios generated by the counterfactual risk twin module.

[0083] The objective function employs a min-max robust optimization strategy to ensure that the final design remains stable in all scenarios.

[0084] 3. Optimize Algorithm Implementation The main loop adopts a distributed island model optimization architecture, with each island running different evolutionary algorithms, including NSGA-II, Rao-2, ant colony optimization, and particle swarm optimization.

[0085] The islands periodically exchange solutions to increase solution diversity and avoid local optima.

[0086] During the optimization process, each generation of solutions requires calling the constraint graph twin modeling module. Figure 1 The compliance check unit performs compliance verification.

[0087] 4. Compliance Remediation Mechanism When a candidate solution is detected to violate constraints (such as insufficient phase spacing or excessive foundation bearing capacity), the secondary loop triggers a repair mechanism.

[0088] The repair mechanism automatically generates graph-diff patches, which include the object identifier, original parameters, corrected parameters, reason for correction, and generation timestamp.

[0089] Example: If the wire spacing of a certain solution is less than 25 cm, the secondary loop repair mechanism automatically generates a patch of "wire spacing + 25 cm" and applies it to the candidate solution.

[0090] 5. Patch archives and dependency chains All generated patches are stored in a patch archive unit, and patches are reversible and have dependency chain descriptions. The dependency chain is used to record the inheritance relationship between patches, such as the relationship between the basic bearing patch and the geological parameter correction patch, ensuring the integrity of the version evolution process.

[0091] 6. Technical Effects Through this module, the present invention takes into account both robustness and compliance during the optimization process. It can not only automatically find a balanced solution in terms of cost, schedule and safety, but also immediately fix design defects when regulatory conflicts are detected, thereby achieving a closed-loop combination of optimization and compliance.

[0092] In embodiments of the present invention, the version traceability and collaboration module is used to ensure the traceability of the entire process of power construction design schemes and the consistency of cross-professional collaboration. This module ensures that the design evolution process is transparent, reliable, and meets audit requirements through object-level patch management, chain-based evidence storage, and compliance model arbitration.

[0093] 1. Module Composition This module includes a patch management unit, an on-chain evidence storage unit, a collaborative merging unit, and a conflict arbitration unit.

[0094] Patch Management Unit: Provides unified storage and version management for graph difference patches generated by the Robust Multi-Objective Optimization and Compliance Repair Module.

[0095] On-chain evidence storage unit: Using blockchain technology, the hash value, author, timestamp, and compliance summary of the patch are stored in the consortium blockchain to ensure that the data is immutable.

[0096] Collaborative Merging Unit: Based on object-level node identifiers, merge versions of cross-disciplinary design modifications.

[0097] Conflict Arbitration Unit: When conflicting modifications occur, the relevant designers will review the changes through rule arbitration or by generating a branch version.

[0098] 2. Storage Structure and Mechanism On-chain evidence storage: The system employs a combination of lightweight on-chain storage and large off-chain object storage. On-chain storage only stores patch fingerprint information, including hash value, author, timestamp, dependencies, and compliance summary; off-chain storage uses an object storage system to store BIM files, simulation results, and large-scale design documents. On-chain and off-chain data are bidirectionally bound through hash verification values.

[0099] Patch dependency chain: Each patch records the preceding patch number and its dependencies when it is generated, ensuring a complete chain of version evolution. For example, a tower foundation load-bearing patch depends on a previously generated geological data correction patch, and the system will automatically load the relevant dependencies when it is called.

[0100] 3. Collaboration Mechanism When different disciplines (structural, electrical, civil, and construction) make modifications to the same design object, the system will merge them based on node identifiers.

[0101] If a conflict arises during the merging process, such as when the civil engineering department adjusts the foundation dimensions, resulting in a discrepancy with the clearance defined by the electrical engineering department, the conflict arbitration unit will determine the priority based on the rule base, or automatically generate a branch version and submit it to the designer for review.

[0102] All merger processes and conflict resolution are recorded on-chain, ensuring clear accountability.

[0103] 4. Technical Effects This module enables end-to-end version traceability and collaborative management. Every modification during the design process can be traced back to the responsible person and the specific reason, avoiding the risks caused by version loss or information inconsistency in traditional design management. Simultaneously, through on-chain evidence storage mechanisms and object-level patch management, transparent, secure, and efficient design evolution management is achieved, providing technical assurance for the compliance and auditability of power construction projects.

[0104] In embodiments of the present invention, the visualization and interaction module provides users with intuitive 3D displays and interactive functions, ensuring that designers, construction units, and reviewers can fully understand and participate efficiently during the design phase. This module not only enables real-time visualization of design schemes but also provides risk distribution display and interactive annotation functions, thereby enhancing collaborative efficiency.

[0105] 1. Module Composition The visualization and interaction module includes a 3D design display unit, a risk heatmap display unit, an interactive annotation unit, and a solution comparison unit.

[0106] 3D Design Display Unit: Based on the combination of BIM geometric model and constraint diagram twin, a panoramic 3D model of the power construction object is generated, and the design modification results are displayed in real time.

[0107] Risk Heatmap Display Unit: Presents the sensitivity results calculated by the counterfactual risk twin module in a spatial distribution manner, for example, by marking high-risk construction areas or weak load-bearing areas through color depth or heat regions.

[0108] Interactive annotation unit: Allows designers to directly select nodes or edges in a 3D scene, add annotation information, modification comments, or confirm results, and can solidify the annotations as design patches.

[0109] Solution Comparison Unit: Supports the parallel display of multiple optimized design solutions, and highlights the differences between solutions through indicator radar charts, comparison curves, and 3D difference highlights.

[0110] 2. Data Interaction Process When designers modify parameters (such as tower height or foundation dimensions) in the 3D interface, the modified data is transmitted in real time to the constraint diagram twin modeling module, which then performs compliance verification.

[0111] If a constraint conflict is triggered, the visual interface will highlight or flash the object that violates the rule, along with a description of the conflict.

[0112] The risk heatmap display unit overlays counterfactual risk analysis results with 3D design scenarios, allowing designers to intuitively perceive potential risks.

[0113] When users submit comments in the interactive annotation unit, the system will generate corresponding patches and pass them to the version tracking and collaboration module for archiving.

[0114] 3. Technical Implementation Details The 3D rendering uses a lightweight engine and supports browsing on both web and desktop.

[0115] The spatial mapping of risk distribution dynamically updates the risk distribution results by establishing a correspondence between three-dimensional coordinates and constraint nodes.

[0116] Interactive annotations and solution comparisons are synchronized with the database in real time to ensure consistency in collaboration.

[0117] 4. Technical Effects This module allows designers to intuitively view design schemes and risk distributions, significantly improving their understanding and control over complex power construction projects. Interactive annotation and multi-scheme comparison features ensure efficient design discussions and decision-making, giving the system not only optimization and traceability capabilities but also excellent usability and collaboration.

[0118] In summary, the power construction design management system based on constraint graph twins and counterfactual optimization of this invention forms an intelligent management system covering the entire power construction design process through the collaborative work of six functional modules: data acquisition and integration, constraint graph twin modeling, counterfactual risk twinning, robust multi-objective optimization and compliance repair, version traceability and collaboration, and visualization and interaction. This provides intelligent support for the entire power construction design process. Compared with existing technologies, this embodiment achieves real-time constraint verification of design schemes through constraint graph twin modeling, enabling the discovery of potential non-compliance issues in electrical, structural, and construction aspects during the design phase. Through counterfactual risk twinning, it achieves multi-scenario risk analysis based on causal modeling, effectively avoiding the defects of delayed risk identification. The dual-loop mechanism of robust optimization in the main loop and compliance repair in the secondary loop not only ensures the stability of the design under multiple objectives such as cost, schedule, safety, and environment, but also guarantees that the scheme can be automatically repaired and form traceable patches when constraints are violated. Furthermore, this embodiment achieves full-process traceability of design evolution and cross-disciplinary consistency through version tracking and collaboration mechanisms, avoiding management risks caused by version loss and conflicts. Through 3D visualization and interactive functions, designers can intuitively view design schemes and risk distributions, and conduct efficient collaboration. Therefore, this invention improves data management efficiency and collaborative design level while ensuring design compliance and robustness, demonstrating significant engineering application value.

[0119] In this system, the data acquisition and integration module ensures the unity and integrity of multi-source heterogeneous data; the constraint graph twin modeling module abstracts power engineering design objects into a graphical model containing electrical, construction, and structural constraints, enabling real-time compliance verification; the counterfactual risk twin module generates counterfactual samples through causal reasoning, placing risk prediction and sensitivity analysis ahead of the design stage; the robust multi-objective optimization and compliance remediation module adopts a dual-loop mechanism combining main-loop optimization and secondary-loop remediation, ensuring that the design always meets the specifications while performing robust multi-objective optimization; the version traceability and collaboration module achieves full-process traceability and cross-professional conflict handling through object-level patching and chain-based evidence storage; and the visualization and interaction module provides intuitive 3D display, risk distribution presentation, and interactive annotation functions, ensuring the efficiency and transparency of multi-party collaboration.

[0120] Through the above technical solution, the present invention effectively overcomes the problems of scattered design data, low efficiency of manual verification, delayed risk identification and untraceable design versions in the prior art, and significantly improves the level of intelligence, standardization and collaboration in the power construction design stage, and has strong application value and promotion significance.

[0121] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power construction design management method based on constraint graph twins and counterfactual optimization, characterized in that, Includes the following steps: Collect standardized design data for power construction; Based on the standardized design data for power construction, the power construction design object is abstracted into a graph structure and constraints are defined to obtain a constraint graph twin model; Causal modeling is performed based on the constraint graph twin model, and after generating counterfactual samples, risk sensitivity analysis is conducted to obtain the analysis results. Based on the analysis results, the design scheme is optimized to obtain candidate solutions. When a candidate solution violates the constraints, a graph difference patch is generated to repair the candidate solution, thereby obtaining a compliant optimized design scheme and completing the power construction design management.

2. The power construction design management method based on constraint graph twinning and counterfactual optimization according to claim 1, characterized in that, It also includes the following steps: Based on the aforementioned differential patch, traceability and archiving are performed, and multi-disciplinary collaborative management is completed; Based on the compliant optimized design scheme, a 3D display and risk heat map overlay are performed, and interactive annotations and scheme comparisons are completed.

3. The power construction design management method based on constraint graph twinning and counterfactual optimization according to claim 1, characterized in that, The standardized design data for power construction includes: geological survey information, building information model files, meteorological and environmental data, transmission line mapping data, and power grid access parameters; The steps for collecting standardized design data for power construction specifically include: The access data is acquired, and the access data is cleaned, format converted, and consistency verified to obtain standardized design data for power construction.

4. The power construction design management method based on constraint graph twinning and counterfactual optimization according to claim 1, characterized in that, The abstraction of power construction design objects into a graph structure and the definition of constraints specifically include: Towers, foundations, conductors, busbars, and civil engineering components are abstracted as nodes of the structure shown in the diagram; Electrical constraints, construction constraints, and structural constraints are defined as attributes of the edges connecting the nodes in the graph structure.

5. A power construction design management method based on constraint graph twinning and counterfactual optimization according to claim 4, characterized in that, It also includes: performing real-time consistency verification on at least one of the electrical constraints, construction constraints, and structural constraints in the constraint graph twin model based on a predefined rule base.

6. The power construction design management method based on constraint graph twinning and counterfactual optimization according to claim 1, characterized in that, The process of performing causal modeling based on the constraint graph twin model, generating counterfactual samples, and then conducting risk sensitivity analysis specifically includes: Construct a set of uncertain scenarios that includes at least one of the following: meteorological conditions, construction conditions, supply chain conditions, and operation and maintenance conditions. Based on the design variables and outcome variables in the constraint diagram twin model, a causal relationship model is established. Under the constraints of the causal relationship model, counterfactual samples are generated for the set of uncertain scenarios, and a risk sensitivity report is formed based on the counterfactual samples as the analysis result.

7. The power construction design management method based on constraint graph twinning and counterfactual optimization according to claim 1, characterized in that, The optimization of the design scheme to obtain candidate solutions specifically includes: The optimization is carried out with cost, construction period, safety indicators, environmental impact indicators and carbon emission indicators as objectives, and a min-max robust optimization strategy is adopted to generate candidate solutions.

8. The power construction design management method based on constraint graph twinning and counterfactual optimization according to claim 1, characterized in that, The graph difference patch includes object identifier, parameters before modification, parameters after modification, reason for repair, and generation timestamp.

9. A power construction design management system based on constraint graph twins and counterfactual optimization, characterized in that, include: The data acquisition and integration module is used to collect standardized design data for power construction. The constraint graph twin modeling module is used to abstract the power construction design object into a graph structure and define the constraint conditions based on the power construction standardized design data to obtain the constraint graph twin model; The counterfactual risk twin module is used to perform causal modeling based on the constraint graph twin model, generate counterfactual samples, perform risk sensitivity analysis, and obtain analysis results. The robust multi-objective optimization and compliance repair module is used to optimize the design scheme based on the analysis results to obtain candidate solutions. When the candidate solution violates the constraints, a graph difference patch is generated to repair the candidate solution, thereby obtaining a compliant optimized design scheme and completing the power construction design management.

10. A power construction design management system based on constraint graph twinning and counterfactual optimization according to claim 9, characterized in that, Also includes: The version tracing and collaboration module is used to trace and archive the differential patches in the graph and to complete multi-professional collaborative management. The visualization and interaction module is used to perform 3D display and risk heat map overlay based on the compliant optimization design scheme, and to complete interactive annotation and scheme comparison.

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