An artificial intelligence-based technical improvement and overhaul project review system

Abstract

This invention belongs to the field of industrial big data technology, specifically an artificial intelligence-based review system for technical upgrading and overhaul projects. Addressing the problems of existing review processes—such as reliance on human experience, inconsistent standards, severe data silos, weak optimization capabilities, lack of systematic simulation verification, difficulty in quantifying synergistic effects and lifecycle benefits, low efficiency in compliance review, and the inability to continuously accumulate and evolve knowledge—this invention proposes the following solution: It includes a data acquisition module connected to a data processing module, which in turn is connected to a data tracking module; and a knowledge graph construction module. This invention integrates industrial big data to construct a dynamic knowledge graph, driving intelligent optimization deduction and digital twin simulation. This enables fully automated review and continuous self-optimization of the technical, economic, compliance, and environmental benefits of technical upgrading and overhaul projects, comprehensively improving the scientific rigor, efficiency, and systematic decision-making capabilities of the review process.

Description

Technical Field

[0001] This invention relates to the field of industrial big data technology, and in particular to an artificial intelligence-based review system for technical upgrade and overhaul projects. Background Technology

[0002] In asset-heavy industries such as power, energy, and manufacturing, technological upgrading and large-scale maintenance (technical transformation and overhaul) projects are crucial for ensuring safety, improving efficiency, and achieving transformation and upgrading. Currently, the review of such projects mainly relies on expert experience, static documents, and scattered historical data, which presents several bottlenecks: First, the review process is highly dependent on individual experience, with inconsistent standards that are difficult to reuse and pass on; second, massive amounts of industrial big data (such as equipment operation data, maintenance records, operating parameters, and market information) are scattered and isolated, failing to be effectively integrated into structured knowledge and unable to provide comprehensive and accurate data insights for the review; third, the optimization of project solutions is mostly based on local experience or simple calculations, lacking systematic optimization and simulation verification under multiple constraints (such as cost, schedule, safety, and energy efficiency); furthermore, reviews often focus on the project itself, making it difficult to quantify the synergistic effects and hidden risks of partial modifications on the entire system, and financial assessments are mostly static analyses, failing to dynamically link with equipment status prediction and environmental benefits; finally, compliance reviews rely on manual item-by-item verification, which is inefficient and lacks intelligent reference to historical compliance cases.

[0003] In existing technologies, the review process relies on human experience, has inconsistent standards, suffers from severe data silos, has weak optimization capabilities, lacks systematic simulation verification, is difficult to quantify synergistic effects and full life cycle benefits, has low efficiency in compliance review, and cannot continuously accumulate and evolve knowledge. To address these issues, we propose an AI-based technical upgrade and overhaul project review system. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies, such as reliance on human experience in the review process, inconsistent standards, severe data silos, weak ability to find optimal solutions, lack of systematic simulation verification, difficulty in quantifying synergistic effects and life-cycle benefits, low efficiency of compliance review, and inability of knowledge to be continuously accumulated and evolved. Therefore, this invention proposes an artificial intelligence-based review system for technical renovation and overhaul projects.

[0005] The technical solution of the artificial intelligence-based technical renovation and overhaul project review system provided in this application is as follows: An artificial intelligence-based technical renovation and overhaul project review system includes a data acquisition module, which is connected to a data processing module, and the data processing module is connected to a data tracking module. A knowledge graph construction module is connected to a data tracking module, and an intelligent optimization and inference module is connected to the knowledge graph construction module. The intelligent optimization and inference module is connected to a simulation module. The collaborative analysis module is connected to the simulation module, the collaborative analysis module is connected to the adaptive learning module, the adaptive learning module is connected to the specialized analysis module, the specialized analysis module is connected to the deep inference module, and the deep inference module is connected to the dynamic calculation module. The system includes a penetration review module, which is connected to a dynamic calculation module, a panoramic evaluation module, an optimization module, a sorting module, an automatic generation module, an automatic benchmarking module, and a data accumulation module.

[0006] Furthermore, the knowledge graph construction module includes a parameter extraction unit, a dynamic update unit, and a path mining unit. The parameter extraction unit is connected to the dynamic update unit, and the dynamic update unit is connected to the path mining unit. The knowledge graph construction module is used to construct a dynamically evolving domain knowledge graph from multi-source industrial data, providing a structured knowledge foundation for intelligent review. Specifically, the parameter extraction unit uses natural language processing technology to automatically extract key entities and attributes such as equipment parameters, process indicators, and fault characteristics from unstructured text (such as maintenance records and technical manuals); the dynamic update unit updates the state attributes and relationship weights of entities in the graph in real time based on time-series data streams (such as equipment status monitoring data) to maintain the timeliness of the graph; and the path mining unit applies graph algorithms (such as shortest path and community detection) to mine potential association paths between entities and identify key influencing links and systemic vulnerabilities.

[0007] Furthermore, the intelligent optimization deduction module includes an analytical modeling unit, a scheme generation unit, and a rapid optimization unit. The analytical modeling unit is connected to the scheme generation unit, and the scheme generation unit is connected to the rapid optimization unit. The intelligent optimization deduction module is used to automatically generate and optimize technical renovation and overhaul schemes under the constraints of knowledge graph construction. Specifically, the analytical modeling unit formalizes the review objectives and constraints (such as budget, schedule, and technical limitations) into a multi-objective optimization mathematical model; the scheme generation unit generates an initial scheme population that satisfies the constraints based on the particle swarm optimization algorithm; and the rapid optimization unit uses the NSGA-II multi-objective optimization algorithm to find the optimal solution set on the Pareto front and balance the trade-offs between the objectives. The particle swarm optimization algorithm is used to generate the initial solution population for technical upgrades and overhauls. The standard velocity-position update model is as follows: Speed ​​update formula:

[0008] Position update formula: ; where is the velocity vector of particle at the t-th generation; is the position vector of particle at the t-th generation (representing a candidate solution); is the historical optimal position of particle ; is the global optimal position of the population; w is the inertia weight (controlling the search breadth); c1, c2 are learning factors (cognitive coefficient and social coefficient); r1, r2 are random numbers in the interval [0,1]; The NSGA-II multi-objective optimization algorithm is used to find the optimal solution set of the technical renovation and overhaul plan on the Pareto front. The core mathematical model is as follows: Definition of dominance relationship: Solution p dominates solution q (denoted as p < q) if and only if: ; where m is the number of objective functions (such as cost, construction period, reliability); is the value of the m-th objective function; Calculation of crowding distance: After sorting the solutions in the same non-dominated layer according to the value of each objective function, the crowding distance of solution is: ; where is the function value of solution on the m-th objective; are the maximum and minimum values of the m-th objective in the population; the boundary points are assigned infinite distance values to ensure retention; Fitness assignment: Non-dominated sorting rank: , the smaller the layer number, the better; Comprehensive fitness: , compared in lexicographical order.

[0009] Furthermore, the simulation module includes a fusion modeling unit, a simulation unit, and a model calibration unit. The fusion modeling unit is connected to the simulation unit, and the simulation unit is connected to the model calibration unit. The simulation module is used to construct a digital twin model and perform high-fidelity simulation verification on the optimized scheme. Specifically, the fusion modeling unit integrates physical mechanism models (such as heat transfer and fluid dynamics equations) and data-driven models (such as LSTM prediction models) to construct a multi-scale simulation model; the simulation unit performs Monte Carlo simulation to quantify the impact of uncertainties (such as equipment failure rate fluctuations) on the scheme's effectiveness; and the model calibration unit dynamically calibrates model parameters based on actual operating data using a Bayesian update method to improve prediction accuracy.

[0010] Furthermore, the collaborative analysis module includes an automatic identification unit, a simulation quantification unit, and a comprehensive evaluation unit. The automatic identification unit is connected to the simulation quantification unit, and the simulation quantification unit is connected to the comprehensive evaluation unit. The collaborative analysis module is used to analyze the collaborative impact of local technological upgrades on the global system. Specifically, the automatic identification unit, based on a knowledge graph, automatically identifies cross-domain entities and relationships strongly associated with the review project; the simulation quantification unit injects local disturbances into the simulation model to simulate their propagation process in the system and quantifies their impact on key performance indicators; and the comprehensive evaluation unit integrates positive and negative impacts to evaluate the systemic risks and collaborative benefits of the project.

[0011] Furthermore, the adaptive learning module includes a benchmarking attribution unit, an adaptive calibration unit, and a dynamic tuning unit. The benchmarking attribution unit is connected to the adaptive calibration unit, and the adaptive calibration unit is connected to the dynamic tuning unit. The adaptive learning module is used to drive the system to continuously optimize its cognitive and predictive capabilities based on new data. Specifically, this includes: the benchmarking attribution unit automatically comparing the simulated predicted values ​​with the actual subsequent data, and initiating root cause analysis when the deviation exceeds a threshold; the adaptive calibration unit applying online learning algorithms (such as stochastic gradient descent) to dynamically adjust the simulation model parameters; and the dynamic tuning unit updating the confidence of relationships in the knowledge graph based on evidence theory, strengthening or weakening the associations.

[0012] Furthermore, the specialized learning module includes a detection triggering unit, a bidirectional tracing unit, and an instruction generation unit. The detection triggering unit is connected to the bidirectional tracing unit, and the bidirectional tracing unit is connected to the instruction generation unit. The specialized learning module is used for intelligent diagnosis and arbitration of discrepancies in knowledge simulation conclusions within the system. Specifically, it includes: the detection triggering unit monitoring and detecting significant contradictions between knowledge graph reasoning conclusions and simulation prediction conclusions in real time; the bidirectional tracing unit tracing the data sources and logical chains of both sides of the contradiction to locate the root cause of the discrepancy; and the instruction generation unit generating targeted learning instructions based on root cause analysis to drive relevant modules to perform targeted optimization.

[0013] Furthermore, the dynamic calculation module includes an estimation unit, a correlation modeling unit, and a variable analysis unit. The estimation unit is connected to the correlation modeling unit, and the correlation modeling unit is connected to the variable analysis unit. The dynamic calculation module is used to perform full lifecycle dynamic financial analysis based on predicted data, specifically including: the estimation unit dynamically estimating the operation and maintenance costs and asset residual value for each future period based on the equipment state curve predicted by simulation; the correlation modeling unit quantifying the implicit costs and related benefits discovered by the collaborative analysis module and incorporating them into the financial model; and the variable analysis unit performing multi-scenario cash flow simulation and dynamic sensitivity analysis to identify key risk drivers.

[0014] Furthermore, the penetrating review module includes an automatic association unit, a comparison and positioning unit, and an intelligent retrieval unit. The automatic association unit is connected to the comparison and positioning unit, and the comparison and positioning unit is connected to the intelligent retrieval unit. The penetrating review module is used to realize automated and intelligent penetrating review of the compliance of the solution. Specifically, it includes: the automatic association unit converting the legal provisions into structured rules and automatically associating them with industrial entities in the knowledge graph; the comparison and positioning unit breaking down the solution and comparing it item by item with the rule base to accurately locate non-compliant clauses and suggest modifications; and the intelligent retrieval unit associating with the historical compliance case library to provide reference and exemption path suggestions for the current review.

[0015] Furthermore, the panoramic assessment module includes a modeling and simulation unit, a comparative analysis unit, and a value conversion unit. The modeling and simulation unit is connected to the comparative analysis unit, and the comparative analysis unit is connected to the value conversion unit. The panoramic assessment module is used to conduct a system-level, full life-cycle panoramic assessment of the energy and environmental performance of the project. Specifically, the modeling and simulation unit constructs a dynamic model of the energy and carbon flow of the entire system to simulate the flow changes before and after the project implementation; the comparative analysis unit uses marginal analysis to accurately calculate the additional energy efficiency improvements and carbon emission reductions brought by the project; and the value conversion unit, based on the carbon trading mechanism, transforms environmental benefits into quantifiable economic value and incorporates them into the comprehensive benefits.

[0016] In summary, this application includes at least one of the following beneficial technical effects: 1. This solution effectively integrates and utilizes massive, multi-source industrial big data by constructing a dynamically evolving industrial knowledge graph, transforming unstructured information into structured domain knowledge, providing a unified, accurate, and traceable knowledge foundation for review, solving the data silo problem, and improving the objectivity and consistency of the review. 2. This solution integrates intelligent optimization and deduction (such as particle swarm optimization and NSGA-II) with high-fidelity digital twin simulation (integrating mechanism and data model), which can automatically generate and verify Pareto optimal solutions under multiple objective constraints such as cost, schedule, and performance. This significantly improves the scientific rigor, economy, and feasibility of the solution and reduces decision-making risks. 3. This solution, through the collaborative analysis module and the panoramic assessment module, enables the quantitative assessment of the systemic impact of the project (including hidden risks and synergistic benefits) and the energy and environmental performance throughout the entire life cycle, and converts environmental benefits into economic benefits; combined with prediction-based dynamic financial calculation and intelligent penetrating review, it realizes a panoramic, automated intelligent review from technology, economics, compliance to environment, which greatly improves the depth, breadth and efficiency of the review.

[0017] This invention integrates industrial big data to construct a dynamic knowledge graph, driving intelligent optimization and digital twin simulation. This enables automated review and continuous self-optimization of the technical, economic, compliance, and environmental benefits of technical upgrade and overhaul projects, thereby comprehensively improving the scientific nature, efficiency, and systematic decision-making capabilities of the review. Attached Figure Description

[0018] Figure 1 This is a structural block diagram of an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention. Figure 2 This is a structural block diagram of the knowledge graph construction module of an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention; Figure 3 This is a structural block diagram of the intelligent optimization and deduction module of an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention. Figure 4 This is a structural block diagram of the simulation module of an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention. Figure 5 This is a structural block diagram of the collaborative analysis module of an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention. Figure 6 This is a structural block diagram of the adaptive learning module of an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention. Figure 7 This is a structural block diagram of a specialized learning module in an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention. Figure 8 This is a structural block diagram of the dynamic calculation module of an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention. Figure 9 This is a structural block diagram of the penetrating review module of an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention; Figure 10 This is a structural block diagram of the panoramic evaluation module of an artificial intelligence-based technical renovation and overhaul project review system proposed in this invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Example 1

[0020] Reference Figures 1-10 An artificial intelligence-based technical renovation and overhaul project review system includes: a data acquisition module, a data processing module connected to the data acquisition module, and a data tracking module connected to the data processing module. The knowledge graph construction module is connected to the data tracking module. The knowledge graph construction module is connected to the intelligent optimization and inference module, which in turn is connected to the simulation module. The collaborative analysis module is connected to the simulation module. The collaborative analysis module is connected to the adaptive learning module. The adaptive learning module is connected to the specialized analysis module. The specialized analysis module is connected to the deep inference module. The deep inference module is connected to the dynamic calculation module. The penetrating review module is connected to the dynamic calculation module. The penetrating review module is connected to the panoramic evaluation module. The panoramic evaluation module is connected to the optimization module. The optimization module is connected to the sorting module. The sorting module is connected to the automatic generation module. The automatic generation module is connected to the automatic benchmarking module. The automatic benchmarking module is connected to the data accumulation module.

[0021] In this embodiment, the knowledge graph construction module includes a parameter extraction unit, a dynamic update unit, and a path mining unit. The parameter extraction unit is connected to the dynamic update unit, and the dynamic update unit is connected to the path mining unit. The knowledge graph construction module is used to construct a dynamically evolving domain knowledge graph from multi-source industrial data, providing a structured knowledge foundation for intelligent review. Specifically, the parameter extraction unit uses natural language processing technology to automatically extract key entities and attributes such as equipment parameters, process indicators, and fault characteristics from unstructured text (such as maintenance records and technical manuals); the dynamic update unit updates the state attributes and relational weights of entities in the graph in real time based on time-series data streams (such as equipment status monitoring data). The system prioritizes maintaining the timeliness of the graph. The path mining unit applies graph algorithms (such as shortest path and community detection) to uncover potential connections between entities, identify key influencing links and systemic vulnerabilities. The adaptive learning module includes a benchmarking attribution unit, an adaptive calibration unit, and a dynamic optimization unit. The benchmarking attribution unit is connected to the adaptive calibration unit, which in turn is connected to the dynamic optimization unit. The adaptive learning module drives the system to continuously optimize its cognitive and predictive capabilities based on new data. Specifically, the benchmarking attribution unit automatically compares simulated predictions with actual subsequent data, initiating root cause analysis when the deviation exceeds a threshold. The adaptive calibration unit applies online learning algorithms (such as stochastic gradient descent). The simulation model parameters are dynamically adjusted. The dynamic optimization unit, based on evidence theory, updates the confidence of relationships in the knowledge graph, strengthening or weakening connections. The specialized learning module includes a detection trigger unit, a bidirectional tracing unit, and an instruction generation unit. The detection trigger unit is connected to the bidirectional tracing unit, which in turn is connected to the instruction generation unit. This specialized learning module is used for discrepancies in knowledge simulation conclusions within the intelligent diagnosis and arbitration system. Specifically, the detection trigger unit monitors and detects significant contradictions between the knowledge graph reasoning conclusions and the simulation prediction conclusions in real time; the bidirectional tracing unit traces the data sources and logical chains of both sides of the contradiction to locate the root cause of the discrepancy; and the instruction generation unit generates directional learning based on root cause analysis. Following Xi's instructions, relevant modules were optimized in a targeted manner. The panoramic assessment module includes a modeling and simulation unit, a comparative analysis unit, and a value conversion unit. The modeling and simulation unit is connected to the comparative analysis unit, which in turn is connected to the value conversion unit. The panoramic assessment module is used to conduct a system-level, full life-cycle panoramic assessment of the project's energy and environmental performance. Specifically, the modeling and simulation unit constructs a dynamic model of the entire system's energy and carbon flow to simulate the flow changes before and after the project's implementation; the comparative analysis unit uses marginal analysis to accurately calculate the additional energy efficiency improvements and carbon emission reductions brought about by the project; and the value conversion unit, based on the carbon trading mechanism, transforms environmental benefits into quantifiable economic value and incorporates them into the comprehensive benefits.

[0022] In this embodiment, the intelligent optimization and deduction module includes an analysis and modeling unit, a solution generation unit, and a rapid optimization unit. The analysis and modeling unit is connected to the solution generation unit, and the solution generation unit is connected to the rapid optimization unit. The intelligent optimization and deduction module is used to automatically generate and optimize the technical transformation and major repair solutions under the constraint conditions of the knowledge graph construction, specifically including: the analysis and modeling unit formalizes the evaluation objectives and constraint conditions (such as budget, construction period, technical limitations) into a multi-objective optimization mathematical model; the solution generation unit generates an initial solution population that meets the constraints based on the particle swarm algorithm; the rapid optimization unit uses the NSGA-II multi-objective optimization algorithm to find the optimal solution set on the Pareto front and balance the trade-offs between various objectives; The particle swarm algorithm is used to generate the initial solution population of the technical transformation and major repair solutions. The standard velocity-position update model is as follows: Velocity update formula:

[0023] Position update formula: ; where, is the velocity vector of the particle at the t-th generation; is the position vector of the particle at the t-th generation (representing a candidate solution); is the historical optimal position of the particle ; is the global optimal position of the population; w is the inertia weight (controlling the search breadth); c1, c2 are learning factors (cognitive coefficient and social coefficient); r1, r2 are random numbers in the interval [0, 1]; The NSGA-II multi-objective optimization algorithm is used to find the optimal solution set of the technical transformation and major repair solutions on the Pareto front. The core mathematical model is as follows: Definition of dominance relationship: Solution p dominates solution q (denoted as p < q) if and only if: ; where, m is the number of objective functions (such as cost, construction period, reliability); is the value of the m-th objective function; Crowding distance calculation: After sorting the solutions in the same non-dominated layer according to the value of each objective function, the crowding distance of the solution is:​​​​​​​​​​​ Fitness assignment: Non-dominant ranking levels: A smaller number of layers indicates better performance; Overall adaptability: Compare lexicographically.

[0024] In this embodiment, the simulation module includes a fusion modeling unit, a simulation unit, and a model calibration unit. The fusion modeling unit is connected to the simulation unit, and the simulation unit is connected to the model calibration unit. The simulation module is used to construct a digital twin model and perform high-fidelity simulation verification on the optimized scheme. Specifically, this includes: the fusion modeling unit integrating physical mechanism models (such as heat transfer and fluid dynamics equations) and data-driven models (such as LSTM prediction models) to construct a multi-scale simulation model; the simulation unit performing Monte Carlo simulation to quantify the impact of uncertainties (such as equipment failure rate fluctuations) on the scheme's effectiveness; and the model calibration unit using actual operating data... The Bayesian update method is used to dynamically calibrate model parameters, improving prediction accuracy. The collaborative analysis module includes an automatic identification unit, a simulation quantization unit, and a comprehensive evaluation unit. The automatic identification unit is connected to the simulation quantization unit, and the simulation quantization unit is connected to the comprehensive evaluation unit. The collaborative analysis module is used to analyze the collaborative impact of local technical improvements on the global system. Specifically, this includes: the automatic identification unit, based on a knowledge graph, automatically identifying cross-domain entities and relationships strongly associated with the review project; the simulation quantification unit injecting local perturbations into the simulation model to simulate their propagation process in the system and quantifying their impact on key performance indicators; and the comprehensive evaluation unit integrating positive and negative data. The impact assessment evaluates the systemic risks and synergistic benefits of the project. The dynamic calculation module includes an estimation unit, a correlation modeling unit, and a variable analysis unit. The estimation unit is connected to the correlation modeling unit, and the correlation modeling unit is connected to the variable analysis unit. The dynamic calculation module is used to conduct dynamic financial analysis throughout the entire lifecycle based on predicted data. Specifically, the estimation unit dynamically estimates the operation and maintenance costs and asset residual value in each future period based on the equipment state curves predicted by simulation. The correlation modeling unit quantifies the implicit costs and related benefits discovered by the synergistic analysis module and incorporates them into the financial model. The variable analysis unit performs multi-scenario cash flow simulation and dynamic sensitivity analysis to identify key risk drivers. The penetrating review module includes an automatic correlation unit, a comparison and positioning unit, and an intelligent retrieval unit. The automatic correlation unit is connected to the comparison and positioning unit, and the comparison and positioning unit is connected to the intelligent retrieval unit. The penetrating review module is used to achieve automated and intelligent penetrating review of the compliance of the solution. Specifically, the automatic correlation unit transforms the legal provisions into structured rules and automatically correlates them with industrial entities in the knowledge graph. The comparison and positioning unit decomposes the solution and compares it item by item with the rule base to accurately locate non-compliant clauses and suggest modifications. The intelligent retrieval unit correlates with the historical compliance case library to provide reference and exemption path suggestions for the current review.

[0025] The implementation principle in this embodiment is as follows: During use, the data acquisition module gathers industrial big data from multiple sources such as sensors, management information systems, and technical documents. The data processing module and data tracking module clean, integrate, and manage the lineage of the raw data to ensure data quality and traceability. The knowledge graph construction module extracts entities, relationships, and attributes from the processed data, constructs and dynamically updates a domain knowledge graph covering equipment, processes, faults, and specifications, forming the system's "knowledge brain." When the review target is input, the intelligent optimization and deduction module uses the constraints in the knowledge graph as boundaries, employs the particle swarm optimization algorithm to quickly generate an initial population of solutions, and uses the NSGA-II multi-objective optimization algorithm to find the optimal solution, outputting a set of optimized solutions that balance various key indicators. Subsequently, the simulation module constructs a high-fidelity digital twin model for the optimal solution, performs Monte Carlo simulation by fusing the mechanism and data-driven model, and predicts the implementation effect of the solution under various uncertain conditions. The collaborative analysis module uses the knowledge graph to identify a wide range of system elements associated with the project and injects perturbations into the simulation model to quantitatively evaluate the impact of local changes on the overall system. The positive and negative impacts of the system's key performance indicators, along with the adaptive learning module and specialized analysis module, constitute the system's "learning loop." By comparing predicted and actual subsequent data, or arbitrating contradictions in internal reasoning, the loop drives continuous calibration and optimization of model parameters and knowledge graph confidence. The dynamic calculation module, based on the state curves predicted by simulation, performs full life-cycle cash flow simulation and dynamic sensitivity analysis, quantifying the long-term economic value and risks of the project. The penetrating review module automatically links regulations to the knowledge graph, providing intelligent compliance review and correction suggestions for the solutions. The panoramic evaluation module assesses the environmental benefits of the project from the perspectives of system energy flow and carbon flow, transforming them into economic value. Finally, the optimization and ranking modules comprehensively evaluate and rank the solutions based on multi-dimensional assessments of technology, economy, risk, compliance, and environment, automatically generating a structured intelligent review report. The automatic benchmarking module compares the report with a historical database of excellent cases. The data accumulation module feeds back the entire review process data and knowledge to the system's knowledge base, completing a full intelligent review loop and achieving continuous accumulation and enhancement of industrial big data value and review knowledge. Example 2

[0026] The difference between this embodiment and Embodiment 1 is that the intelligent optimization and deduction module is connected to an intelligent review module. The intelligent review module uses natural language processing and deep learning models to automatically review whether the necessity of the project is sufficient, whether the technical route description is clear and complete, whether the basis for investment estimation is reasonable, and whether the risk identification and response measures are in place. It simulates the perspective of a senior expert, quickly screens out low-level errors and logical defects in text materials, and ensures that the solutions entering the subsequent deep simulation and optimization stages have basic quality, thereby improving the overall process efficiency. Example 3

[0027] The difference between this embodiment and Embodiment 1 is that the data processing module is connected to a security and privacy module. This security and privacy module is used to address the sensitivity of industrial big data (such as core process parameters and production energy consumption data). This module provides security management throughout the entire data lifecycle, including: achieving collaborative modeling and knowledge discovery of multi-party data based on differential privacy or federated learning technology, without data leaving the domain; classifying and de-identifying sensitive entities and relationships in the knowledge graph; and establishing a data access audit and tracking mechanism to ensure that all data use is compliant and traceable, thus addressing enterprises' security concerns in data sharing and utilization. Example 4

[0028] The difference between this embodiment and Embodiment 1 is that the automatic generation module is connected to a decision tracing module. The decision tracing module is used to automatically generate a "decision description" when outputting the final recommended scheme and ranking. It uses interpretable AI technology (such as SHAP, LIME) to clearly show the advantages and disadvantages of each scheme in various evaluation indicators (cost, risk, energy efficiency, etc.). At the same time, it can trace the final score of the scheme back to the specific data nodes in the knowledge graph, the key parameters of the simulation experiment, and the trade-off path of the optimization algorithm. This allows review experts to clearly understand the "thinking process" of the AI, enhance their trust in the system's conclusions, and facilitate manual review and intervention. Example 5

[0029] The difference between this embodiment and Embodiment 1 is that the penetration review module is connected to the ecological assessment module. The ecological assessment module is used to access external market data and construct a knowledge sub-graph of suppliers and contractors. The module can analyze: the historical supply quality, performance capability, and price fluctuation trend of the proposed equipment suppliers; the safety record, technical level, and concurrent project load of potential construction contractors. Combined with the project plan, it dynamically assesses the supply chain stability risk, the probability of procurement cost overruns, and the schedule and quality risks of construction outsourcing, and quantifies these risks into financial impacts, supplementing the project's full life cycle cost and risk assessment.

[0030] Experimental Example I. Experimental Objective Verify the performance improvements of this review system compared to traditional manual review in terms of review efficiency, solution quality, risk identification, compliance review, and knowledge accumulation; II. Experimental Scenario Setup Industry sector: 110kV main transformer technical upgrade and overhaul project in the power industry; Sample size: 10 historical technical upgrade and overhaul projects were selected (5 completed projects were used for backtesting verification, and 5 projects awaiting review were used for prospective verification). Control group: Traditional expert review panel (3 senior engineers + 2 financial experts + 1 compliance officer). Experimental group: This review system (deploying all 12 core modules); Experimental environment configuration

[0031] Experimental data Table 1: Comparison of Core Review Performance Data

[0032] Table 2: Comparison of Quality Depth of Multi-Objective Optimization Schemes Evaluation indicators Traditional review scheme TOP1 solution of this system Improvement range Total investment cost 8.92 million yuan 7.56 million yuan -15.2%↓ Planned construction period 45 days 38 days -15.6%↓ Expected energy efficiency improvement 8.5% 12.3% +44.7%↑ System reliability index 0.82 0.91 +11.0%↑ Net Present Value (NPV) over the entire life cycle 12.45 million yuan 16.87 million yuan +35.5%↑ carbon emission reduction 1,200 tons / year 1,680 tons / year +40.0%↑ Environmental benefits and economic value 480,000 yuan 670,000 yuan +39.6%↑ Number of hidden risks identified 3 items 8 items +167%↑ Compliance review pass rate Passed on the first try Passed on the first try flat Number of Pareto optimal solutions 1 17 16×↑ Table 3: Validation of Knowledge Graph and Adaptive Learning Capabilities Number of learning iterations Simulation prediction error rate ↓ Knowledge graph entity coverage increased Relationship confidence increased ↑ Arbitration success rate for disputes increases initial state 18.7% 62% 0.68 - 5th iteration 12.3% 71% 0.74 78% 10th iteration 7.8% 79% 0.81 85% 20th iteration 3.2% 88% 0.89 92% Experimental conclusions The table above shows the following benefits: Efficiency Revolution: Review time reduced from 168 hours to 4.1 hours, a 41-fold improvement, and labor costs reduced by 40 times; Quality Leap: Comprehensive solution score increased by 25%, and multi-objective optimization generated 17 Pareto optimal solutions, far exceeding traditional single-point solutions; Risk Insight: Synergy effect identification rate increased to 89.4%, and the number of hidden risks discovered increased by 167%; Compliance Precision: Compliance issue detection rate reached 97%, automatically linking 200+ regulatory clauses with zero omissions; Knowledge Evolution: 189 pieces of structured knowledge were accumulated in a single review, and the simulation error rate decreased from 18.7% to 3.2% after 20 iterations; Economic Value: The NPV of typical projects throughout their entire lifecycle increased by 35.5%, and investment costs decreased by 15.2%.

[0033] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An artificial intelligence-based technical renovation and overhaul project review system, characterized in that: include: A data acquisition module, which is connected to a data processing module, and the data processing module is connected to a data tracking module; A knowledge graph construction module is connected to a data tracking module, and an intelligent optimization and inference module is connected to the knowledge graph construction module. The intelligent optimization and inference module is connected to a simulation module. The collaborative analysis module is connected to the simulation module, the collaborative analysis module is connected to the adaptive learning module, the adaptive learning module is connected to the specialized analysis module, the specialized analysis module is connected to the deep inference module, and the deep inference module is connected to the dynamic calculation module. The system includes a penetration review module, which is connected to a dynamic calculation module, a panoramic evaluation module, an optimization module, a sorting module, an automatic generation module, an automatic benchmarking module, and a data accumulation module.

2. The AI-based technical improvement overhaul project review system according to claim 1, characterized in that: The knowledge graph construction module includes a parameter extraction unit, a dynamic update unit, and a path mining unit. The parameter extraction unit is connected to the dynamic update unit, and the dynamic update unit is connected to the path mining unit. The knowledge graph construction module is used to construct a dynamically evolving domain knowledge graph from multi-source industrial data, providing a structured knowledge foundation for intelligent review. Specifically, it includes: the parameter extraction unit using natural language processing technology to automatically extract key entities and attributes of equipment parameters, process indicators, and fault characteristics from unstructured text; the dynamic update unit updating the state attributes and relationship weights of entities in the graph in real time based on time-series data streams to maintain the timeliness of the graph; and the path mining unit applying graph algorithms to mine potential association paths between entities and identify key influencing links and systemic vulnerabilities.

3. The AI-based technical improvement overhaul project review system according to claim 2, characterized in that: The intelligent optimization deduction module includes an analytical modeling unit, a scheme generation unit, and a rapid optimization unit. The analytical modeling unit is connected to the scheme generation unit, and the scheme generation unit is connected to the rapid optimization unit. The intelligent optimization deduction module is used to automatically generate and optimize technical renovation and overhaul schemes under the constraints of knowledge graph construction. Specifically, it includes: the analytical modeling unit formalizing the review objectives and constraints into a multi-objective optimization mathematical model; the scheme generation unit generating an initial scheme population that satisfies the constraints based on the particle swarm optimization algorithm; and the rapid optimization unit using the NSGA-II multi-objective optimization algorithm to find the optimal solution set on the Pareto front and balance the trade-offs between the objectives. The particle swarm optimization algorithm is used to generate the initial solution population for technical upgrades and overhauls. The standard velocity-position update model is as follows: Speed ​​update formula: Position update formula: ； wherein, is the particle is the velocity vector of the particle at the tth generation; is the particle t is the position vector of the particle at the tth generation; is the particle is the historical best position of the particle; is the global best position of the population; w is the inertia weight; c1, c2 are the learning factors; r1, r2 are random numbers in the interval [0, 1]. The NSGA-II multi-objective optimization algorithm is used to find the optimal solution set of technical upgrade and overhaul schemes on the Pareto front. The core mathematical model is as follows: Definition of dominance relationship: Solution p dominates solution q if and only if: ； wherein m is the number of objective functions; is the mth objective function value; Crowding distance calculation: The solutions of the same non-dominated layer are sorted by each objective function value, and the solution with the smallest crowding distance is selected. ； wherein, To solve The function value on the mth objective; , The maximum and minimum of the mth objective in the population; the boundary points are assigned an infinite distance value to ensure preservation; Fitness assignment: Non-dominated sorting rank: The smaller the number of layers, the better Overall adaptability: Compare lexicographically.

4. The artificial intelligence-based technical renovation and overhaul project review system according to claim 3, characterized in that: The simulation module includes a fusion modeling unit, a simulation unit, and a model calibration unit. The fusion modeling unit is connected to the simulation unit, and the simulation unit is connected to the model calibration unit. The simulation module is used to construct a digital twin model and perform high-fidelity simulation verification on the optimized scheme. Specifically, the fusion modeling unit integrates a physical mechanism model and a data-driven model to construct a multi-scale simulation model; the simulation unit performs Monte Carlo simulation to quantify the impact of uncertainties on the scheme's effectiveness; and the model calibration unit dynamically calibrates the model parameters based on actual operating data using a Bayesian update method to improve prediction accuracy.

5. The artificial intelligence-based technical renovation and overhaul project review system according to claim 4, characterized in that: The collaborative analysis module includes an automatic identification unit, a simulation quantification unit, and a comprehensive evaluation unit. The automatic identification unit is connected to the simulation quantification unit, and the simulation quantification unit is connected to the comprehensive evaluation unit. The collaborative analysis module is used to analyze the collaborative impact of local technological upgrades on the global system. Specifically, the automatic identification unit, based on a knowledge graph, automatically identifies cross-domain entities and relationships strongly associated with the project under review; the simulation quantification unit injects local disturbances into the simulation model, simulates their propagation process in the system, and quantifies their impact on key performance indicators; the comprehensive evaluation unit integrates positive and negative impacts to assess the systemic risks and collaborative benefits of the project.

6. The artificial intelligence-based technical renovation and overhaul project review system according to claim 5, characterized in that: The adaptive learning module includes a benchmarking attribution unit, an adaptive calibration unit, and a dynamic optimization unit. The benchmarking attribution unit is connected to the adaptive calibration unit, and the adaptive calibration unit is connected to the dynamic optimization unit. The adaptive learning module is used to drive the system to continuously optimize its cognitive and predictive capabilities based on new data. Specifically, the benchmarking attribution unit automatically compares the simulated predicted values ​​with the actual subsequent data, and initiates root cause analysis when the deviation exceeds a threshold; the adaptive calibration unit dynamically adjusts the simulation model parameters using an online learning algorithm; and the dynamic optimization unit updates the confidence of relationships in the knowledge graph based on evidence theory, strengthening and weakening the relationships.

7. The artificial intelligence-based technical renovation and overhaul project review system according to claim 6, characterized in that: The specialized learning module includes a detection trigger unit, a bidirectional tracing unit, and an instruction generation unit. The detection trigger unit is connected to the bidirectional tracing unit, and the bidirectional tracing unit is connected to the instruction generation unit. The specialized learning module is used for intelligent diagnosis and arbitration of discrepancies in knowledge simulation conclusions within the system. Specifically, it includes: the detection trigger unit monitoring and detecting significant contradictions between knowledge graph reasoning conclusions and simulation prediction conclusions in real time; the bidirectional tracing unit tracing the data sources and logical chains of both sides of the contradiction to locate the root cause of the discrepancy; and the instruction generation unit generating targeted learning instructions based on root cause analysis to drive relevant modules to perform targeted optimization.

8. The artificial intelligence-based technical renovation and overhaul project review system according to claim 7, characterized in that: The dynamic calculation module includes an estimation unit, a correlation modeling unit, and a variable analysis unit. The estimation unit is connected to the correlation modeling unit, and the correlation modeling unit is connected to the variable analysis unit. The dynamic calculation module is used to perform dynamic financial analysis throughout the entire life cycle based on predicted data. Specifically, the estimation unit dynamically estimates the operation and maintenance costs and asset residual value for each future period based on the equipment state curve predicted by simulation; the correlation modeling unit quantifies the implicit costs and related benefits discovered by the collaborative analysis module and incorporates them into the financial model; and the variable analysis unit performs multi-scenario cash flow simulation and dynamic sensitivity analysis to identify key risk drivers.

9. The artificial intelligence-based technical renovation and overhaul project review system according to claim 8, characterized in that: The penetrating review module includes an automatic association unit, a comparison and positioning unit, and an intelligent retrieval unit. The automatic association unit is connected to the comparison and positioning unit, and the comparison and positioning unit is connected to the intelligent retrieval unit. The penetrating review module is used to realize automated and intelligent penetrating review of the compliance of the solution. Specifically, it includes: the automatic association unit converting legal provisions into structured rules and automatically associating them with industrial entities in the knowledge graph; the comparison and positioning unit breaking down the solution and comparing it item by item with the rule base to accurately locate non-compliant clauses and suggest modifications; and the intelligent retrieval unit associating with the historical compliance case library to provide reference and exemption path suggestions for the current review.

10. The artificial intelligence-based technical renovation and overhaul project review system according to claim 9, characterized in that: The panoramic assessment module includes a modeling and simulation unit, a comparative analysis unit, and a value conversion unit. The modeling and simulation unit is connected to the comparative analysis unit, which is also connected to the value conversion unit. The panoramic assessment module is used to conduct a system-level, full life-cycle panoramic assessment of the project's energy and environmental performance. Specifically, the modeling and simulation unit constructs a dynamic model of the entire system's energy and carbon flow to simulate the flow changes before and after project implementation; the comparative analysis unit uses marginal analysis to accurately calculate the additional energy efficiency improvements and carbon emission reductions brought about by the project; and the value conversion unit, based on the carbon trading mechanism, transforms environmental benefits into quantifiable economic value and incorporates it into the comprehensive benefits.

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