An artificial intelligence-based engineering cost intelligent agent method and system

By employing an AI-based intelligent agent approach for engineering cost estimation, which utilizes engineering knowledge graphs and intelligent reasoning models to generate budget plans and perform anomaly detection, the problem of traditional engineering budgets relying on human experience is solved, thus achieving efficient and accurate engineering cost management.

CN122415009APending Publication Date: 2026-07-17SHENZHEN SHENPENGDA POWER GRID TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN SHENPENGDA POWER GRID TECH CO LTD
Filing Date
2026-04-10
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Traditional engineering budgeting relies on manual experience, resulting in poor consistency of budget results, a large workload, and a long review cycle, making it difficult to meet the needs of large-scale projects.

Method used

An AI-based intelligent agent approach for engineering cost estimation is adopted. Multimodal data is processed through an engineering knowledge graph model to extract entity labels and relationships. A cost estimation intelligent reasoning model is then used to generate a budget plan, and an anomaly detection model is used to perform discrepancy analysis and automatically adjust the budget plan.

Benefits of technology

It enables intelligent processing of project budgets, reduces omissions and errors, improves review efficiency, shortens the review cycle, and is suitable for large-scale projects.

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Abstract

The application provides an artificial intelligence-based engineering cost intelligent agent method and system, relates to the technical field of engineering budget, and comprises the following steps: collecting multi-modal data and performing a pretreatment operation to obtain pretreated multi-modal data; an engineering knowledge graph model performs entity extraction on the pretreated multi-modal data, analyzes entity relationships through a relationship extraction submodel, matches attribute values, and constructs an engineering cost knowledge graph; a cost intelligent reasoning model analyzes input engineering project files, extracts project key information, matches budget items based on the engineering cost knowledge graph, and generates an engineering budget scheme; and an anomaly detection model detects whether the engineering budget scheme is abnormal, intelligently analyzes different budget items, and obtains an engineering budget scheme that meets a normal difference range. The above technical scheme builds an engineering cost intelligent agent, realizes the processing of engineering project files, obtains an engineering budget scheme, and effectively improves the engineering cost budget efficiency.
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