Ensure that every crane is “fully under control.”

System Overview and Technical Background

Port and industrial lifting equipment (such as gantry cranes, portal cranes, and Quay Cranes) are numerous, highly robust, and operate under complex conditions, yet they have long been plagued by four persistent challenges: difficult condition monitoring, low operational efficiency, slow fault resolution, and fragmented data management. The traditional model of manual inspections, paper-based records, and reactive repairs suffers from limited coverage, inconsistent standards, and reliance on the experience of senior technicians for fault diagnosis. Moreover, operational, energy consumption, and alarm data remain siloed, failing to support informed decision-making and readily leading to unplanned downtime, soaring maintenance costs, and shortened equipment lifespans.

 

With the Intelligent and IoT‑enabled upgrade of lifting equipment, the industry urgently needs an integrated management platform that combines comprehensive monitoring, standardized operations and maintenance, intelligent diagnostics, data‑driven decision‑making, and mobile collaboration, enabling digital control throughout the entire lifecycle of equipment and shifting from “manual, passive management” to “Intelligent, proactive control.”

Definition of the Intelligent IoT platform

 

The Intelligent IoT platform for lifting equipment developed by HIRI FUTURE is a comprehensive lifecycle management system for lifting gear, built on the foundations of the Internet of Things, big data, AI intelligent algorithms, and mobile internet technologies. The platform connects various sensors, controllers, and video terminals of lifting equipment, collecting real-time data on operating parameters, mechanism status, alarm information, and energy consumption. It integrates a standardized maintenance system with AI-based fault diagnosis capabilities and, paired with a mobile app, enables collaborative operations across all domains.

 

The platform covers mainstream models such as gantry cranes, portal cranes, and harbor cranes, seamlessly integrating equipment monitoring, inspection and maintenance, fault diagnosis, data analysis, and on-site operations into a unified Procedure. It enables visualization of equipment status, standardization of operation and maintenance Procedures, Intelligent fault diagnosis, and data-driven management decision-making, thereby comprehensively empowering the safe, efficient, and cost-effective operation of lifting equipment.

 

Core value of the platform

 

Full‑area visibility: Centralized monitoring of multi‑type, multi‑region devices, enabling remote control of the entire system.

Standard Operations: Unified job templates, end-to-end closed-loop traceability, and auditability.

AI Quick Diagnosis: Based on a knowledge base and an Intelligent matching engine, it quickly identifies the root cause of the fault and recommends repair solutions.

Data-driven: Automated statistical analysis of energy consumption, alarms, and operational metrics, providing a solid foundation for scheduling and cost optimization.

Mobile Collaboration: On-site QR‑code scanning, remote query access, and one‑click diagnostics—breaking free from site‑based constraints.

 

Technical Pipeline: From Data Collection to End-to-End Governance

 

2.1 Industry Challenges

Managing lifting equipment is not merely about data collection and visualization; it is a comprehensive, end-to-end management system that spans equipment operation, on-site work, fault handling, and data analysis. The equipment encompasses a wide variety of brands and models, with diverse interface protocols; on-site data is voluminous and requires precise categorization and early warning; the maintenance Procedure involves personnel, tasks, materials, and record-keeping, making coordination highly complex; fault mechanisms are varied, making it difficult to codify accumulated experience; and the equipment is widely dispersed, posing challenges for remote collaboration.

 

2.2 Technical Approach:

Integrated Full-Stack Digital Governance

Traditional lifting equipment management employs a fragmented “manual inspection – paper-based recording – post‑incident fault repair – sporadic data collection” model, in which each stage is disjointed, data remains siloed, and Procedures are disconnected, leading to frequent vulnerabilities in large‑scale equipment fleet management.

This Intelligent IoT platform adopts an integrated edge‑cloud‑end design: the front end uniformly interfaces with PLCs, sensors, inverters, and video devices; the mid‑end performs data cleansing, parsing, and alert analysis; and the cloud deploys business modules and an AI engine, integrating operations and maintenance rules, a fault knowledge base, and big‑data models. The platform is not merely a data visualization tool; it deeply integrates five core capabilities—monitoring, operations and maintenance, AI diagnostics, data analysis, and mobile collaboration—while retaining essential industrial‑system features such as fixed Procedures, log traceability, and access control, thus balancing Intelligence with security.

 

2.3 Design of the Five Core Modules

 

To achieve full-lifecycle Intelligent management and control, the platform adopts a five-core-module design in project implementation, complemented by a three-tier technical architecture of underlying access, data processing, and terminal adaptation, as follows:

 

Module One: Comprehensive Intelligent Monitoring Module: Multi-dimensional Status Awareness

 

Adopting a three-tier monitoring architecture—equipment overview, organizational visualization, and real-time alerts—enables precise control from the global level down to individual points.

• Home Dashboard: Presents macro-level metrics such as total device count, online status, fault count, and fault type distribution via heatmaps and data dashboards, with one-click drill-down to the detailed page of any individual device, providing a clear, at-a-glance overview of the overall system status.

• Equipment Monitoring Overview: The individual device page provides real-time display of operating parameters, mechanism status, and daily alarm information, enabling intuitive assessment of the equipment’s health condition.

• Equipment IoT Hub: Provides 2D visualization of core mechanisms such as the trolley, hoist, luffing, and slewing; enables real-time monitoring of critical parameters including limit switches, power supply, inverters, and communication; and features dual displays—numeric values and indicator lights—for more intuitive status identification.

• Video Integration: Compatible with video devices from multiple brands, supporting real-time preview, video playback, and remote access to live on-site footage, ensuring comprehensive, blind-spot-free monitoring.

 

Module Two: Standardized Operations and Maintenance: “Guided by standards and traceable processes”

 

Establish a templated, automated, and traceable operations and maintenance system that covers all scenarios, including inspections, patrols, maintenance, and spare parts management.

 

• Standard template configuration: Supports customizing inspection, patrol, and maintenance standard templates, automatically generating exclusive QR codes; on-site personnel can simply scan the code to perform tasks and record information.

• Automated planning and control: The system automatically dispatches operations and maintenance tasks according to a predefined schedule, triggering automatic alerts and push notifications for overdue tasks, thereby preventing missed inspections and unaddressed maintenance.

• End-to-end closed-loop management: Establishes a complete workflow—“standard formulation → plan issuance → mobile execution → result upload → ledger archiving”—with operation durations, on-site photos, and inspection records fully documented and permanently traceable.

• Integrated Materials Management: Enables end-to-end lifecycle management of parts—covering receipt, issuance, inventory, and transaction history—and links maintenance materials to work orders for more granular control.

 

Based on a knowledge base, RAG retrieval, and AI analysis technologies, an Intelligent fault diagnosis system has been developed to replace traditional manual, experience-based troubleshooting.

 

• Enterprise-specific knowledge base: Supports uploading and categorizing maintenance manuals, technical documents, and fault cases, accumulating internal technical expertise and providing a data foundation for Intelligent diagnosis.

•AI Intelligent Analysis: Combining the knowledge base with AI algorithms, it automatically parses device alarms and abnormal data, identifies the root cause of faults, and simultaneously pushes standardized repair procedures and solutions. The system also supports daily scheduled automatic inspections and diagnostics.

• Diagnostic Iteration and Optimization: Retains all historical diagnostic records, supports human evaluation of model effectiveness, and enables continuous iterative refinement of the AI model to improve diagnostic accuracy.

 

• Intelligent Q&A Assistant: Equipped with AI-powered Q&A capabilities on both PC and mobile platforms, it provides real-time answers to operations and maintenance technical queries, reducing the learning curve for new users.

 

Module Four: Data-Driven Decision Making: Big Data Statistical Analysis

 

Automated collection, statistical analysis, and visual analytics of equipment operation, energy consumption, alarms, and other data provide a basis for management decision-making.

 

• Energy Consumption Management: Automatically collects daily electricity usage and operating hours, generates energy consumption dashboards, calculates energy efficiency metrics, and supports energy-saving management.

• Alarm Statistics: Aggregate alarm counts, false alarm rates, and alarm types across multiple dimensions; identify high‑frequency fault locations; and implement targeted optimizations to equipment and operations & maintenance strategies.

 

• Operation Report: AI automatically generates daily equipment operation reports, summarizing operating hours, fault occurrences, and job data, while providing optimization recommendations. The reports can be exported with a single click.

 

Module 5: Mobile Collaboration – Mobile-Device Optimization for On-Site Operations

 

Comes with a mobile app that bridges the gap between remote management and on-site operations, enabling efficient collaboration across the entire workforce.

•Portable Device Monitoring:

Access equipment inventories, real-time status, and historical data anytime, anywhere; receive highlighted alerts for fault locations; and remotely monitor equipment conditions.

• Scan the code for quick assignment submission:

On-site inspections, maintenance, and patrol tasks are processed via QR code scanning, with results recorded, attachments uploaded, and status updates synchronized to the cloud in real time.

•Mobile Fault Diagnosis:

When a fault is detected on-site, users can initiate real-time diagnostics with a single click, quickly obtain root-cause analysis and repair recommendations, and resolve the issue right where it occurs.

• Full Work Order Traceability: On the mobile app, you can view all historical maintenance, inspection, and patrol work orders, with work order details and task records readily available for verification at any time.

 

Platform Capacity Analysis: Beyond Monitoring, It’s About Boosting Efficiency

Compatible with all device models, with unified access and management.

The platform is compatible with mainstream lifting equipment such as portal cranes, gantry cranes, and harbor cranes, and supports controllers, sensors, and video terminals from multiple brands. It breaks down data silos across different devices and systems, enabling centralized and unified management of multiple plants and diverse types of equipment, and is well-suited for cluster-based equipment control scenarios.

 

Standard Procedure implementation, eliminating management loopholes.

Leveraging configurable operational standards and an automation scheduling mechanism, it standardizes maintenance procedures across the entire plant, mitigating the arbitrariness of manual management. Coupled with overdue alerts and end-to-end record-keeping and traceability, it ensures that every inspection and maintenance task is carried out as planned, meeting industrial equipment safety management and compliance audit requirements.

 

Multi-dimensional validation enhances the accuracy of fault diagnosis.

Integrate equipment sensor data, operational logs, historical fault cases, and a knowledge base of expert expertise for joint analysis, distinguishing between normal parameter fluctuations and genuine equipment malfunctions to reduce false alarms. For complex faults, systematically deconstruct the root causes layer by layer and provide tiered maintenance recommendations, significantly improving fault-handling efficiency.

 

Self‑diagnosis of equipment health, solidifying the safety baseline.

The platform monitors in real time the device communication status, data acquisition status, point anomalies, offline faults, and other issues. Once it detects data interruptions, point distortions, or devices going offline, it immediately triggers an alert to notify management personnel to intervene and troubleshoot, thereby preventing regulatory blind spots caused by data failures and ensuring the safe operation of equipment.

 

Getting Started with Lightweight Design: Building the “Minimum Closed Loop” in Four Steps

 

1. Legacy Integration: Directly interface with the crane’s existing PLC, sensors, and cameras, adding auxiliary data‑acquisition terminals as needed without installing unnecessary hardware.

2. Tier layer: Launch a cloud-based management backend, enter device information, and configure monitoring and alerting rules.

3. Terminal tier: All personnel are equipped with a mobile app, and each device is affixed with a unique QR code; scanning the code initiates work.

4. Define clear permissions: configure basic alert thresholds, maintenance cycles, and user access rights.

Our key optimization priorities

 

The knowledge base continues to grow: it accumulates complex faults, maintenance procedures, and parts standards, enhancing the capacity for identifying composite faults.

Enhanced operational adaptability: Optimized data Stability and anomaly alerting strategies under network fluctuations, electromagnetic interference, and extreme temperature and humidity conditions.

Deep data value extraction: expanding health assessment, residual life prediction, and intelligent optimization of maintenance cycles, shifting from “post-event statistics” to “predictive management.”

Cross‑scenario rapid replication: By accumulating data and expertise across different plants, brands, and tonnage levels, we optimize universal adaptation modules, thereby reducing deployment and replication costs.

System Interoperability: Open interfaces with enterprise ERP, materials, and human resources systems to build an integrated digital management framework encompassing equipment, personnel, materials, and finance.