This is a management platform designed specifically for ship and marine engineering operation scenarios, based on a ship-to-shore collaborative technology architecture. It focuses on every aspect of voyages and operations, enabling shore-based teams and onboard personnel to share data and resources, optimizing navigation and operational efficiency, and making decisions faster and more accurately.

The system provides comprehensive voyage and operation workflow management functions for ships, including but not limited to route planning, equipment deployment plans, and sampling operations. It can optimize all stages of planning, execution, and feedback.

Our platform provides the ship management team with real- time monitoring of the ship's operating status through system monitoring. This includes the output of the power system, the operating status of operational equipment, and key operational parameters, ensuring dynamic synchronization of planning and progress tracking.

Our E series is an intelligent energy efficiency management system integrated with operational workflows. By leveraging real-time data, the teams can make decisions that ensure offshore tasks are performed under the most energy-efficient conditions, allowing every drop of fuel and every kilowatt-hour of electricity to deliver maximum value to marine research endeavors.

Our energy optimization system provides specific energy-saving solutions through deep analysis, such as speed adjustment, equipment optimization, and route planning. The system comprehensively monitors energy consumption, helping ship management personnel adjust operational strategies in real-time to respond to dynamic ocean and weather conditions, ensuring efficient operations, cost savings, and reduced environmental impact.

Our system synchronizes operational processes with energy efficiency management using real-time condition recognition technology. It accurately analyzes the ship's daily energy consumption status, monitors energy sources and usage in real-time, ensuring optimal energy efficiency for navigation and operations across various voyage segments and task types.

Our M series aims to provide highly specialized equipment management solutions for vessels engaged in engineering operations, enhancing the digitalization level of equipment management, ensuring efficient performance while minimizing energy consumption and operational costs, and delivering all-encompassing insights into the health status of marine equipment.

Our system integrates industrial big data analysis and machine learning algorithms to instantly diagnose equipment status, identify potential faults and anomalies, and predict equipment lifespan and maintenance needs. This enables the ship management team to perform preventive maintenance, reducing the risk of unexpected failures and ensuring safe and reliable navigation.

Our system provides real-time equipment performance monitoring, capturing key indicators such as vibration, noise, and temperature, and building a comprehensive equipment big data platform based on different conditions and tasks. Paperless maintenance management improves efficiency, offering necessary data and analysis through an intuitive user interface, enabling easy access to equipment logs, maintenance records, and performance reports for the ship's maintenance team.

This employs cutting-edge SCADA data and Piezoelectric Wafer Active Sensor (PWAS) technology to deliver real-time monitoring and precise assessment of turbine cluster efficiency, drivetrain health, and structural components. This product offers cost-effective, comprehensive coverage, ensuring optimal performance and reliability of wind power equipment, helping you minimize operational risks and maintenance costs.

Utilizing SCADA data, this module provides real-time monitoring of the power generation capacity of wind farms. It accurately evaluates the energy loss due to health degradation, enabling comprehensive control over the performance status and operational risks of the wind turbine fleet. This ensures optimal performance and proactive maintenance strategies.

Leveraging SCADA data and CMS vibration signal analysis, this module precisely models, diagnoses, locates, and predicts the trends of potential failures in critical components such as the drivetrain, pitch system, yaw system, and anemometers. This enables accurate health assessments and preemptive fault identification, thereby reducing downtime and maintenance costs.

Employing Piezoelectric Wafer Active Sensor (PWAS) technology, this module offers low-cost, comprehensive coverage for real-time online health monitoring of wind turbine structural components. It provides high-precision (mm level), high-sensitivity, and detailed damage assessment for various failure modes, including gear wear, tower collapse, blade damage, bolt loosening, and foundation erosion. This ensures early detection and precise maintenance planning, enhancing the reliability and longevity of wind turbine structures.

This product leverages big data for intelligent decision-making, ensuring safe, stable, efficient, and economical offshore wind farm operations. By utilizing advanced wind power forecasting technology, intelligently dispatching wind farm power, and optimizing maintenance task scheduling, this product significantly enhances wind farm operational efficiency and economic benefits.

This product leverages SCADA data to accurately forecast wind power over short-term (next 0-4 hours), medium-term (next 1-3 days), and long-term periods, achieving an accuracy rate of over 90%. Wind speed predictions incorporate meteorological data and turbine location information through spatial correlation analysis. Wind power predictions are modeled using adaptive fuzzy neural networks based on SCADA data. This model significantly outperforms traditional meteorological forecasts in both short-term and medium-term wind power predictions, meeting national standards.

We offer a wind farm maintenance optimization solution based on genetic algorithms and dynamic programming. This system utilizes real-time wind farm data and wind power forecasts to generate daily maintenance plans and schedules, minimizing maintenance costs and power generation losses. The optimization model combines turbine health status, availability of maintenance resources, short-term wind power forecasts, and maintenance costs. It calculates the sequence, start time, and resource allocation for maintenance tasks, providing decision support. The goal is to minimize the overall operational cost of the wind farm, considering real-time wind speed, availability and costs of maintenance teams and vessels, and outputs vessel route planning and maintenance cost analysis to ensure minimal task risk.