1. System Architecture Optimization Design
The traditional silo architecture in which each ELV subsystem operates independently can no longer meet the needs of smart hotels. The optimization direction is a converged architecture of bus + networking + cloud platform. Specific plan: eliminate independent RS485/CAN buses and use a unified TCP/IP network to carry BAS, access control, RCU, etc., achieving one network to the end; introduce an edge computing architecture to complete real-time control at the DDC/RCU end and reduce cloud dependence; dual-link redundancy design—the BAS backbone network adopts an RSTP ring network to ensure that a single point of failure does not affect the whole.
2. In-depth Optimization of Energy-saving Control Strategies
Comprehensive energy-saving optimization plan for ELV systems: the cooling source side adopts a deep optimization strategy of cooling water temperature reset + variable flow control + machine learning prediction, predicting load change trends in advance based on the previous 7 days of operating data and weather forecasts; AHU terminals adopt demand-controlled ventilation (DCV) technology to adjust fresh air volume according to CO2 concentration; the lighting system integrates three functions: area sensing control + daylight harvesting + time schedule management; the guest room system provides occupancy-status linked control—lights off, air conditioning off, and curtains closed when the guest leaves.
3. Optimization of System Reliability and Redundancy Design
Optimization measures to improve ELV system reliability: critical controller redundancy—the chiller group control controller adopts 1+1 hot standby; network redundancy—core switches use dual-machine hot standby plus dual-link redundancy; power redundancy—DDC controllers use PoE power supply plus local UPS dual-path power supply; sensor redundancy strategy—critical parameters adopt a 3-out-of-2 voting mode; data storage redundancy—dual backup locally and in the cloud.
4. Intelligent Optimization of Operation and Maintenance Management
O&M management optimization direction: a digital twin platform maps the physical system into a 3D virtual space to display equipment status and energy consumption data in real time; AI predictive maintenance predicts faults and issues early warnings based on equipment vibration, current, and temperature data; a mobile O&M APP enables engineering personnel to conduct inspections, report repairs, and accept completion via mobile phone; an automatic work order system automatically generates repair work orders for equipment faults and dispatches them to the corresponding engineers.