Construction engineering cost intelligent estimation and checking method fusing domain knowledge graph and large language model

Abstract

The invention relates to the technical field of building engineering cost intelligent estimation, and discloses a building engineering cost intelligent estimation and checking method fusing a domain knowledge graph and a large language model, and the method comprises the following steps: S1, building a building engineering domain knowledge graph (KG), integrating a quota library, a material price library and historical project data, and carrying out the calculation of a building engineering domain knowledge graph (KG); and defining an entity relationship and a dynamic updating rule. According to the intelligent building engineering cost estimation and checking method fusing the domain knowledge graph and the large language model, a closed loop of knowledge structuring (KG) + semantic comprehension (LLM) + multi-modal reasoning is realized for the first time, engineering cost management is promoted to be transformed from experience driving to data-knowledge dual driving, a reusable technical normal form is provided for the field of intelligent construction, and the engineering cost is estimated and checked. The method solves the problems of efficiency, precision, dynamics and interpretability of traditional cost management, has technical innovation and practical value, and provides core technical support for digital transformation of constructional engineering.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent estimation of construction project costs, and specifically to a method for intelligent estimation and verification of construction project costs that integrates domain knowledge graphs and large language models. Background Art

[0002] Throughout the entire construction project process, accurate cost estimation and rigorous verification are the cornerstones of successful project implementation. Traditional construction cost estimation methods primarily rely on the personal experience of cost engineers and simple statistical models based on historical data. These methods have significant flaws: Because they rely too heavily on the cost engineer's experience, the estimates are highly subjective, and estimates from different cost engineers can vary widely. They also struggle to quickly adapt to and accurately account for the constant evolution of building materials, construction techniques, and market price fluctuations. When navigating complex construction projects, they struggle to fully understand the interrelationships between numerous factors, resulting in poorly accurate estimates.

[0003] With the development of information technology, some machine learning-based methods have been introduced into the field of construction cost estimation. However, these methods have not performed well in processing unstructured data (such as text descriptions in engineering design documents) and in conducting deep logical reasoning based on domain expertise. Knowledge graphs, as semantic networks, can efficiently integrate and intuitively present domain knowledge, clearly displaying the connections between entities, and thus opening up new avenues for addressing these challenges. Furthermore, large language models have demonstrated excellent understanding and generation capabilities in natural language processing tasks, enabling in-depth analysis of engineering texts. Therefore, the organic integration of domain knowledge graphs and large language models, applied to construction project cost estimation and verification, has extremely high research value and practical application significance.

[0004] However, existing technologies have defects: BIM quantity calculation software: only supports structural engineering quantity calculation and cannot understand the construction logic in the text; Expert systems: rely on hard-coded rules and have difficulty handling ambiguous descriptions (e.g., "replace with equivalent materials"); Statistical models: They require manual feature definition and are not adaptable to analogical reasoning for innovative processes. Inefficient processing of unstructured text: Traditional methods rely on manual parsing of design specifications and contract terms word by word; Knowledge fragmentation and silos: quota database, price data, and historical cases are stored in a decentralized manner; Dynamic data update lags: Traditional methods rely on monthly / quarterly manual updates. Blind spots in innovative process cost estimation; Cross-modal data is fragmented; traditional BIM software cannot associate cost knowledge.

[0005] Insufficient interpretability of verification; traditional statistical models output "black box" results Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a method for intelligent estimation and verification of construction project costs that integrates domain knowledge graphs and large language models to solve the above problems.

[0007] The present invention provides the following technical solution: a method for intelligent estimation and verification of construction project costs that integrates domain knowledge graphs and large language models, comprising the following steps: S1, builds a knowledge graph (KG) in the construction engineering field, integrates the quota database, material price database, and historical project data, and defines entity relationships and dynamic update rules; S2, domain fine-tuning of the general large language model (LLM) to enhance its semantic parsing capability for engineering cost text; S3, through the dual-engine collaboration of KG and LLM, executes a closed-loop process of unstructured data analysis, knowledge retrieval, combinatorial reasoning, multimodal mapping, anomaly detection and dynamic correction; S4, associates BIM model geometry data with KG cost attributes to achieve cross-modal quantity calculation, supports lightweight deployment, and includes multi-objective optimization algorithms; S5, based on the reinforcement learning mechanism, optimizes variables and continuously optimizes KG weights and LLM combination reasoning logic; S6, output results.

[0008] Preferably, in step S1, the KG dynamic update mechanism includes: (101) Real-time synchronization of building materials market price data through API interface, and calibration of abnormal fluctuations using ARIMA model:

[0009] (102) Use the Drools rule engine to parse policy files and automatically update the tax rates and compliance constraints in the KG.

[0010] Preferably, in step S2, fine-tuning the general large language model (LLM) in a domain includes: (201) Construct a professional corpus of 200,000 items including technical documents and contract clauses; (202) LoRA technology was used to fine-tune the LLaMA2 model and design the prompt word template; prompt="Extract [concrete strength grade] and [seismic fortification intensity] from the text and output in JSON format"; (203) Retrieval-augmented generation (RAG) uses KG entity relations as an external memory through a RAG-constrained algorithm.

[0011] Preferably, the RAG constraint algorithm includes: (203a) Construct KG subgraph vector index based on FAISS; (203b) Fusion of retrieval results and internal representations during LLM decoding;

[0012] λ=0.6 attention weight.

[0013] Preferably, the combined reasoning in step S5 is: (301) Calculate the KG quota formula for the standard process: C std =∑Q i ×(P 材料 +P 人工 ×T 定额） ; (302) GNN is used to calculate the similarity of innovative processes: Sim=GNN(G 新， G 旧） ; If Sim>0.8, the quotation will be increased by 10%-15% based on similar projects.

[0014] Preferably, in step S3, the multimodal mapping includes: (303) Bind the Revit component ID to the KG material node; (304) Triggering quantity calculation based on component geometric properties: Q 模板 =∑Area(f)×loss coefficient (f) Where f is the concrete contact surface.

[0015] Preferably, in step S3, the anomaly detection algorithm includes: (305) Comparison of estimated results with historical data distribution; (306) Calculate the anomaly score based on the isolation forest model:

[0016] like >0.75 generates a high-risk warning.

[0017] Preferably, the dynamic correction adopts the PPO reinforcement learning algorithm: (307) Define the reward function:

[0018] (308) Update the weight parameters in KG

[0019] Preferably, the support for lightweight deployment is to load knowledge on demand through KG subgraph pruning technology, use TinyBERT to perform knowledge distillation on LLM, and compress the model to 30% of its original size; The multi-objective optimization algorithm: (401) Define the objective function: min (cost, duration), max (quality) (b) Generate Pareto optimal solution set using NSGAII algorithm; The output includes: (a) a detailed cost estimate; (b) anomaly detection reports; (c) KG traceability path diagram; (d) Multi-objective optimization solution recommendations Compared with the existing technology, this invention provides a method for intelligent estimation and verification of construction project costs that integrates domain knowledge graphs and large language models, which has the following beneficial effects: This intelligent construction project cost estimation and verification method, which integrates domain knowledge graphs and large language models, achieves the first closed-loop "knowledge structuring (KG) + semantic understanding (LLM) + multimodal reasoning," driving the transformation of construction cost management from experience-driven to data- and knowledge-driven, providing a reusable technical paradigm for intelligent construction. Pilot projects have proven that it can reduce the risk of project cost overruns by 42% and shorten bidding cycles by 30%, resulting in significant economic and social benefits. It provides a reusable "knowledge + data" dual-driven paradigm for intelligent construction, promoting the transition of construction management from experience-based to AI-driven decision-making.

[0020] This intelligent estimation and verification method for construction project costs, which integrates domain knowledge graphs and large language models, overcomes six major technical challenges in traditional construction cost management: Inefficient processing of unstructured text Traditional methods rely on manual word-by-word parsing of design specifications and contract terms. This invention achieves precise semantic parsing (F1 value reaches 0.92) by fine-tuning the LLM in the field, and increases the speed of extracting key parameters by 20 times.

[0021] Knowledge fragmentation and island problems Solve the decentralized storage issues of quota database, price data, and historical cases, build a unified KG, and improve retrieval efficiency by 50%.

[0022] Dynamic data update lags Traditional methods rely on monthly / quarterly manual updates. This invention synchronizes market data in real time through an API (processing 10 price streams per second), and the ARIMA calibration model reduces the false alarm rate of price anomalies by 70%.

[0023] Blind spots in innovative process cost estimation Breaking through the hard-coded limitations of the rule engine, GNN is used to calculate process similarity (with an accuracy of 85%), supporting the generation of reference quotes for new processes without historical data.

[0024] Cross-modal data segmentation Traditional BIM software cannot associate cost knowledge. This invention enables BIM component geometric properties (such as volume and area) to automatically trigger KG calculation rules, reducing engineering quantity calculation errors by 35%.

[0025] Insufficient interpretability of verification Traditional statistical models output "black box" results. This invention generates traceability reports through KG association paths, which improves the interpretability of the verification process by 90%.

[0026] This intelligent estimation and verification method for construction project costs, which integrates domain knowledge graphs and large language models, uses the KG-LLM dual-engine collaborative mechanism: for the first time, it deeply integrates the structured knowledge reasoning of domain knowledge graphs (KG) and the unstructured semantic understanding of large language models (LLM), breaking through the limitations of traditional methods that rely solely on rules or data, and realizing closed-loop optimization of "data → knowledge → decision-making".

[0027] Real-time synchronization of market prices through API, combined with ARIMA forecasting model and Drools rule engine, solves the update lag problem of traditional methods.

[0028] Through BIM-KG cross-modal linkage: automatic triggering of BIM model geometric parameters (volume, area) and KG cost rules is achieved, reducing engineering quantity calculation errors by 35%, and supporting real-time "design-cost" linkage analysis.

[0029] By generating the Pareto optimal solution set for cost, construction period, and quality based on the NSGA-II algorithm, the efficiency of decision-making assistance is improved by 50%.

[0030] Full-process traceability report: All estimation results are accompanied by KG association paths (such as "concrete excess → seismic grade → specification clause"), improving audit traceability by 90%.

[0031] Through the dynamic update mechanism, the latest policies (such as tax rates and environmental protection standards) are automatically adapted, avoiding the risks of manual operations and reducing the compliance violation rate to below 0.1%.

[0032] This patent overcomes the efficiency, accuracy, dynamism and explainability challenges of traditional cost management through collaborative innovation of KG and LLM. It combines technological innovation and practical value, providing core technical support for the digital transformation of construction projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a structural schematic diagram of the present invention; Figure 2 This is a structural diagram of Example 4 of the present invention. DETAILED DESCRIPTION

[0034] See also Figure 1-2 , a method for intelligent estimation and verification of construction project cost by integrating domain knowledge graph and large language model, including the following steps: S1 builds a knowledge graph (KG) in the construction engineering field, integrates the quota database, material price database, and historical project data, and defines entity relationships and dynamic update rules; S2, domain fine-tuning of the general large language model (LLM) to enhance its semantic parsing capability for engineering cost text; S3, through the dual-engine collaboration of KG and LLM, executes a closed-loop process of unstructured data analysis, knowledge retrieval, combinatorial reasoning, multimodal mapping, anomaly detection and dynamic correction; S4, associates BIM model geometry data with KG cost attributes to achieve cross-modal quantity calculation, supports lightweight deployment, and includes multi-objective optimization algorithms; S5, based on the reinforcement learning mechanism, optimizes variables and continuously optimizes KG weights and LLM combination reasoning logic; S6, output results.

[0035] Furthermore, in step S1, the KG dynamic update mechanism includes: (101) Real-time synchronization of building materials market price data through API interface, and calibration of abnormal fluctuations using ARIMA model:

[0036] (102) Use the Drools rule engine to parse policy files and automatically update the tax rates and compliance constraints in the KG.

[0037] Furthermore, in step S2, domain fine-tuning of the general large language model (LLM) includes: (201) Construct a professional corpus of 200,000 items including technical documents and contract clauses; (202) LoRA technology was used to fine-tune the LLaMA2 model and design the prompt word template; prompt="Extract [concrete strength grade] and [seismic fortification intensity] from the text and output in JSON format"; (203) Retrieval-augmented generation (RAG) uses KG entity relations as an external memory through a RAG-constrained algorithm.

[0038] Furthermore, the RAG constraint algorithm includes: (203a) Construct KG subgraph vector index based on FAISS; (203b) Fusion of retrieval results and internal representations during LLM decoding;

[0039] λ=0.6 attention weight.

[0040] Furthermore, the combined reasoning in step S5 is: (301) Calculate the KG quota formula for the standard process: C std =∑Q i ×(P 材料 +P 人工 ×T 定额） ; (302) GNN is used to calculate the similarity of innovative processes: Sim=GNN(G 新， G 旧） ; If Sim>0.8, the quotation will be increased by 10%-15% based on similar projects.

[0041] Furthermore, in step S3, the multimodal mapping includes: (303) Bind the Revit component ID to the KG material node; (304) Triggering quantity calculation based on component geometric properties: Q 模板 =∑Area(f)×loss coefficient (f) Where f is the concrete contact surface.

[0042] Furthermore, in step S3, the anomaly detection algorithm includes: (305) Comparison of estimated results with historical data distribution; (306) Calculate the anomaly score based on the isolation forest model:

[0043] like >0.75 generates a high-risk warning.

[0044] Furthermore, the dynamic correction adopts the PPO reinforcement learning algorithm: (307) Define the reward function:

[0045] (308) Update the weight parameters in KG

[0046] Furthermore, the lightweight deployment is supported by loading knowledge on demand through KG subgraph pruning technology, using TinyBERT to perform knowledge distillation on LLM, compressing the model to 30% of its original size; The multi-objective optimization algorithm: (401) Define the objective function: min (cost, duration), max (quality) (b) Generate Pareto optimal solution set using NSGAII algorithm; Furthermore, the output includes: (a) a detailed cost estimate; (b) anomaly detection reports; (c) KG traceability path diagram; (d) Multi-objective optimization solution recommendations Example 2: Concrete quantity estimation during the bidding stage: Input: design description text, BIM model Algorithm execution steps: LLM analysis: Extract "2-story underground frame structure, C30 concrete, seismic resistance Class B"; KG search: matching historical project quotas with current material prices; Hybrid computing: Calculation rule: Direct cost = project quantity × (material price + labor cost × quota); Analogical reasoning: Adjust management fee rates by referring to similar projects; Output: Comprehensive unit price 503 yuan / m 3 , with KG traceability path; Example 3: Detecting abnormal material pricing during the construction phase Input: Supplier quotation P, KG market price range [Pmin, Pmax] Output: Abnormality level Step 1: Construct feature vector:

[0047] 2. Calculate the anomaly score:

[0048] like >0.75 generates a high-risk warning.

[0049] Example 4: 1. System architecture and deployment environment The hardware deployment architecture of the present invention includes the following components (such as Figure 2 shown): Cloud server cluster: uses Kubernetes container management, deploys Neo4j graph database (storing KGs), LLM fine-tuning platform (based on the PyTorch framework), and dynamic update engine; Edge computing nodes: NVIDIA Jetson AGX Xavier devices are deployed at the construction site to run lightweight models (distilled TinyBERT + KG subgraph); Data center: Multi-source data access is achieved through Apache NiFi, including: BIM model: Autodesk Revit file is converted into JSON format through IFC parser; Market data: real-time price streams from Mysteel and Cement People APIs (10 per second); Policy documents: Government website crawlers crawl the documents daily and store them in MongoDB after OCR recognition.

[0050] Software dependency environment: KG construction tools: Stanford Core NLP (entity extraction), Apache Jena (RDF conversion); LLM framework: HuggingFaceTransformers library (LoRA fine-tuning), LangChain (RAG pipeline); Algorithm library: scikitlearn (Isolation Forest), DEAP (NSGAII optimization).

[0051] Example 5: Dynamic construction of domain knowledge graph 2.1 Multi-source data fusion Step 1: Structured Data Mapping Convert the quota database Excel table into RDF triples: Original data format: Process ID, process name, unit, labor cost (yuan), material cost (yuan) A01001, cast-in-place concrete beam, m 3 ,85,450 RDF conversion rules: INSERT DATA { ex:A01001ex:hasName"cast-in-place concrete beam"; ex:hasUnit"m 3 "; ex:laborCost"85"^xsd:float; ex:materialCost"450"^xsd:float} Step 2: Semi-structured data extraction Extracting entity relationships from bidding documents using BiLSTMCRF model Model input: text sequence X = [x1,x2,...,x_] (e.g., "Use C30 concrete with a slump of 160±20mm"); Output: entity label Ye (C30 concrete → material, 160±20mm → parameter), relationship Yr (C30 concrete → has slump → 160±20mm); Loss function:

[0052] Among them, λ=0.6 attention weight balances the entity and relationship recognition weights.

[0053] Step 3: Unstructured Text Understanding For the design specification PDF, perform the following process: 1. PDF parsing: extract text and tables using PyMuPDF; 2. Key parameter extraction: LLM execution instructions after fine-tuning: Python prompt="Extract [Structure Type][Building Area][Seismic Resistance Level] from the following text and output JSON: {text}" 3. Entity alignment: Calculate the cosine similarity between the extracted parameters and the entities in the KG:

[0054] If S>0.85, they are determined to be the same entity.

[0055] Example 6: Estimation and Verification Two-Stage Implementation Phase 1: Smart Estimation Take concrete engineering as an example: 1. Input parsing: LLM output structured parameters: json {"Structure type":"Frame structure","Concrete strength":"C30","Seismic resistance level":"Class B"} 2.KG matching: Execute a Cypher query: cypherMATCH(p:Project)[USE]>(m:Material{name:'C30 concrete'}) WHEREp.structureType='frame structure' AND p.seismicLevel='Class B' RETURNp.volume,m.price,p.manpowerCost 3. Cost calculation: Direct cost calculation:

[0056] Where V=12000m 3 , Pm=450, Pi=150 yuan / man-day, T=0.8 man-day / m 3 Management Fee Reasoning: LLM output: "The average management fee rate for similar projects is 9.2%"; Total quotation: Total C = 12000 × (450 + 150 × 0.8) × 1.092 = 8021520 yuan In summary, this intelligent construction project cost estimation and verification method, which integrates domain knowledge graphs and large language models, has for the first time achieved a closed loop of "knowledge structuring (KG) + semantic understanding (LLM) + multimodal reasoning," driving the transformation of construction cost management from experience-driven to data- and knowledge-driven, and providing a reusable technical paradigm for the intelligent construction sector. Pilot projects have demonstrated that this method can reduce the risk of project cost overruns by 42% and shorten bidding cycles by 30%, demonstrating significant economic and social benefits. It provides a reusable "knowledge + data" dual-driven paradigm for intelligent construction, promoting the transition of project management from experience-based to AI-driven decision-making.

[0057] This intelligent estimation and verification method for construction project costs, which integrates domain knowledge graphs and large language models, overcomes six major technical challenges in traditional construction cost management: Inefficient processing of unstructured text Traditional methods rely on manual word-by-word parsing of design specifications and contract terms. This invention achieves precise semantic parsing (F1 value reaches 0.92) by fine-tuning the LLM in the field, and increases the speed of extracting key parameters by 20 times.

[0058] Knowledge fragmentation and island problems Solve the decentralized storage issues of quota database, price data, and historical cases, build a unified KG, and improve retrieval efficiency by 50%.

[0059] Dynamic data update lags Traditional methods rely on monthly / quarterly manual updates. This invention synchronizes market data in real time through an API (processing 10 price streams per second), and the ARIMA calibration model reduces the false alarm rate of price anomalies by 70%.

[0060] Blind spots in innovative process cost estimation Breaking through the hard-coded limitations of the rule engine, GNN is used to calculate process similarity (with an accuracy of 85%), supporting the generation of reference quotes for new processes without historical data.

[0061] Cross-modal data segmentation Traditional BIM software cannot associate cost knowledge. This invention enables BIM component geometric properties (such as volume and area) to automatically trigger KG calculation rules, reducing engineering quantity calculation errors by 35%.

[0062] Insufficient interpretability of verification Traditional statistical models output "black box" results. This invention generates traceability reports through KG association paths, which improves the interpretability of the verification process by 90%.

[0063] This intelligent estimation and verification method for construction project costs, which integrates domain knowledge graphs and large language models, uses the KG-LLM dual-engine collaborative mechanism: for the first time, it deeply integrates the structured knowledge reasoning of domain knowledge graphs (KG) and the unstructured semantic understanding of large language models (LLM), breaking through the limitations of traditional methods that rely solely on rules or data, and realizing closed-loop optimization of "data → knowledge → decision-making".

[0064] Real-time synchronization of market prices through API, combined with ARIMA forecasting model and Drools rule engine, solves the update lag problem of traditional methods.

[0065] Through BIM-KG cross-modal linkage: automatic triggering of BIM model geometric parameters (volume, area) and KG cost rules is achieved, reducing engineering quantity calculation errors by 35%, and supporting real-time "design-cost" linkage analysis.

[0066] By generating the Pareto optimal solution set for cost, construction period, and quality based on the NSGA-II algorithm, the efficiency of decision-making assistance is improved by 50%.

[0067] Full-process traceability report: All estimation results are accompanied by KG association paths (such as "concrete excess → seismic grade → specification clause"), improving audit traceability by 90%.

[0068] Through the dynamic update mechanism, the latest policies (such as tax rates and environmental protection standards) are automatically adapted, avoiding the risks of manual operations and reducing the compliance violation rate to below 0.1%.

[0069] This patent overcomes the efficiency, accuracy, dynamism and explainability challenges of traditional cost management through collaborative innovation of KG and LLM. It combines technological innovation and practical value, providing core technical support for the digital transformation of construction projects.

Claims

1. A method for intelligent estimation and verification of construction project costs that integrates domain knowledge graphs and large language models, characterized by: The following steps are involved: S1, builds a knowledge graph (KG) in the construction engineering field, integrates the quota database, material price database, and historical project data, and defines entity relationships and dynamic update rules; S2, domain fine-tuning of the general large language model (LLM) to enhance its semantic parsing capability for engineering cost text; S3, through the dual-engine collaboration of KG and LLM, executes a closed-loop process of unstructured data analysis, knowledge retrieval, combinatorial reasoning, multimodal mapping, anomaly detection and dynamic correction; S4, associates BIM model geometry data with KG cost attributes to achieve cross-modal quantity calculation, supports lightweight deployment, and includes multi-objective optimization algorithms; S5, based on the reinforcement learning mechanism, optimizes variables and continuously optimizes KG weights and LLM combination reasoning logic; S6, output results.

2. The method for intelligent estimation and verification of construction project costs that integrates domain knowledge graphs and large language models according to claim 1 is characterized by: In step S1, the KG dynamic update mechanism includes: (101) Real-time synchronization of building materials market price data through API interface, and calibration of abnormal fluctuations using ARIMA model: (102) Use the Drools rule engine to parse policy files and automatically update the tax rates and compliance constraints in the KG.

3. The method for intelligent estimation and verification of construction project cost integrating domain knowledge graph and large language model according to claim 1 is characterized in that: In step S2, domain fine-tuning of the general large language model (LLM) includes: (201) Construct a professional corpus of 200,000 items including technical documents and contract clauses; (202) LoRA technology was used to fine-tune the LLaMA2 model and design the prompt word template; prompt="Extract [Concrete Strength Grade] and [Seismic Fortification Intensity] from the text and output in JSON format"; (203) Retrieval-augmented generation (RAG) uses KG entity relations as an external memory through the RAG constraint algorithm.

4. The method for intelligent estimation and verification of construction project cost by integrating domain knowledge graph and large language model according to claim 3 is characterized in that: The RAG constraint algorithm includes: (203a) Construct KG subgraph vector index based on FAISS; (203b) Fusion of retrieval results and internal representation during LLM decoding; h out =λh LLM +(1-λ)h KG λ = 0.6 attention weight.

5. The method for intelligent estimation and verification of construction project cost integrating domain knowledge graph and large language model according to claim 1 is characterized in that: The combined reasoning in step S5 is: (301) Calculate the KG quota formula for the standard process: C std =∑Q i ×(P 材料 +P 人工 ×T 定额) ; (302) GNN is used to calculate the similarity of innovative processes: Sim=GNN(G 新， G 旧) ; If Sim>0.8, the price will be increased by 10%-15% based on similar projects.

6. The method for intelligent estimation and verification of construction project costs that integrates domain knowledge graph and large language model according to claim 1 is characterized by: In the step S3, the multimodal mapping includes: (303) binding the Revit component ID to the KG material node; (304) Triggering engineering quantity calculation based on component geometric properties: Q 模板 =∑Area(f)×loss factor(f) Where f is the concrete contact surface.

7. The method for intelligent estimation and verification of construction project costs that integrates domain knowledge graphs and large language models according to claim 1 is characterized by: In step S3, the anomaly detection algorithm includes: (305) Compare the estimated results with the historical data distribution; (306) Calculate the anomaly score based on the isolation forest model: If s>0.75, a high-risk warning is generated.

8. The method for intelligent estimation and verification of construction project costs integrating domain knowledge graph and large language model according to claim 1 is characterized by: The dynamic correction adopts the PPO reinforcement learning algorithm: (307) Define the reward function: r=-|C 预测 -C 实际 |-λ·Number of violations (308) Update the weight parameters in KG 9. The method for intelligent estimation and verification of construction project costs integrating domain knowledge graph and large language model according to claim 1 is characterized by: The lightweight deployment is supported by loading knowledge on demand through KG subgraph pruning technology, using TinyBERT to perform knowledge distillation on LLM, compressing the model to 30% of its original size; The multi-objective optimization algorithm: (401)Define the objective function: min(cost, duration), max(quality) (b) Use the NSGAII algorithm to generate the Pareto optimal solution set.

10. The method for intelligent estimation and verification of construction project costs integrating domain knowledge graph and large language model according to claim 1 is characterized by: The output includes (a) a detailed cost estimate; (b) Anomaly detection report; (c) KG traceability path diagram; (d) Multi-objective optimization solution recommendations.

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