T/WEA 011-2026 Specification for data acquisition technology of intelligent safety risk early warning systems for building engineering
T/WEA 011-2026 Specification for data acquisition technology of intelligent safety risk early warning systems for building engineering
Basic Information
Scope
This document specifies the general requirements for data collection of the intelligent early warning system for safety risks in construction projects, including the collection objects and elements, equipment selection and deployment requirements, data format and object coding requirements, data transmission and security requirements, data quality control and exception handling requirements, data storage and traceability requirements, operation and maintenance requirements, and acceptance evaluation requirements. This document applies to the design of data collection for intelligent early warning systems for safety risks in housing construction projects and municipal infrastructure projects, as well as their construction sites. For standalone monitoring devices used in a single scenario or data collection for statistical reports, the relevant provisions of this document can be referenced. Contents involving statutory inspections of special equipment or occupational health monitoring, which are regulated by dedicated laws or industry standards, must also meet the requirements of relevant laws and standards. This document targets the data collection phase of intelligent early warning systems for safety risks in construction projects and focuses on the entire process of "object coding - collection elements - collection methods - data quality - time-space alignment - interface and security - evidence chain and traceability". It specifies technical requirements and implementation rules for data collection, aiming to: a) Unify data collection objects, elements, and minimum available data sets to enhance the engineering feasibility and cross-project reusability of the early warning system; b) Standardize equipment selection and deployment, sampling frequency, and triggering mechanisms, as well as data format and metadata requirements to support multi-source data integration and model reasoning; c) Establish data quality control and exception handling requiremen