This invention relates to the field of
water supply supervision. By collecting water meter
pulse counting data and environmental parameters, it achieves data
standardization and temperature compensation, and generates metering
metadata with timestamps, cumulative values, and
checksum tags based on the ISO / IEC 14882 standard. On this basis, a sliding window LSTM model is used to identify
water use behavior categories and associate them with
knowledge graph nodes of regulatory intent, achieving a
semantic mapping between behavior and regulatory rules. Combined with
blockchain technology, verifiable semantic commitments and evidence certificates are generated through hashing, Merkle root
authentication, and aggregate signatures, supporting permission auditing and on-chain
traceability based on SNARK zero-knowledge proofs. The
system also incorporates feedback data from regulators and uses
graph neural networks to dynamically optimize the behavior
confidence threshold. This technical solution effectively ensures the
data integrity, compliance, and regulatory transparency of
water use behavior, enhancing the intelligence and
traceability of the water audit
system.