Crane Control Platform for 10+ Industries

Technology Platform Strategy for Industrial Equipment

The core objective of the crane technology platform is to run one control system across multiple industries. Driven by Industry 4.0 and the Made in China 2025 initiative, traditional industrial equipment manufacturers are at a critical inflection point—shifting from selling standalone machines to delivering platform-based services. Kelude Heavy Industry's technology platform was built specifically to solve this challenge.

The Kelude technology platform is the practical result of this vision. Built around the control system of smart cranes, it uses a three-layer architecture to standardize and platformize control capabilities that were previously scattered across individual projects. This enables rapid adaptation across more than 10 industries—from metallurgy and chemicals to ports and automotive—dramatically shortening project delivery times and reducing both development and maintenance costs.

Three-Layer Architecture of the Kelude Technology Platform

The Kelude technology platform follows a classic three-layer architecture, from bottom to top: the Hardware Abstraction Layer (HAL), the Control Algorithm Layer, and the Industry Application Layer. Each layer has a distinct role—they work closely together while remaining decoupled from one another, providing the architectural foundation for "develop once, reuse everywhere."

Layer 1: Hardware Abstraction Layer (HAL)—Eliminating Hardware Diversity

One of the most persistent headaches in crane control systems is hardware diversity. Different motor brands (Siemens, ABB, SEW-Eurodrive), different encoder protocols (absolute encoders, incremental encoders, SSI interfaces), different VFD models (Inovance, Delta Electronics, Danfoss), and a wide range of sensors (laser distance sensors, ultrasonic sensors, force sensors, limit switches)—nearly every new project means re-adapting the hardware from scratch.

Kelude's hardware abstraction layer defines a unified hardware interface specification that encapsulates all underlying hardware into standardized "driver objects." Upper-level algorithms and business logic no longer need to concern themselves with specific hardware models or communication protocols—they simply call the unified API interface to control equipment. The HAL layer supports a hot-swappable driver loading mechanism, so when new hardware is integrated, only the corresponding driver adapter needs to be written, with no modifications required to upper-level code.

To date, the HAL layer supports over 50 mainstream motor drives, 30 encoder protocols, 20 VFD communication protocols, and 15 sensor interface types, covering more than 90% of industrial crane application scenarios. The HAL layer is designed around a plug-and-play driver principle: it automatically identifies hardware types and loads the appropriate drivers via standard Device Description Files (DDF), shifting hardware adaptation from code-based development to configuration-based management.

Layer 2: Control Algorithm Layer — A Modular Intelligent Algorithm Library

The control algorithm layer is the core competitive advantage of the Kelude technology platform. This layer consolidates the essential algorithm capabilities Kelude has accumulated over years of crane control experience, modularizing and standardizing them into a configurable, composable algorithm library. Each algorithm module undergoes rigorous unit testing and operational validation to ensure stability and reliability throughout its entire lifecycle.

Key algorithm modules include:

• High-Precision Positioning Algorithm: Supports absolute positioning, relative positioning, multi-speed positioning, and electronic cam positioning, achieving positioning accuracy of ±3 mm to meet precision hoisting requirements across industries. The algorithm includes built-in auto-calibration that completes position calibration automatically during initial equipment startup.
• Anti-Sway Control Algorithm: Uses closed-loop feedback-based active anti-sway technology, combining optimal control with adaptive control strategies to eliminate sway within two oscillation cycles (reference: RL anti-sway algorithm). This algorithm has been validated through thousands of real-world operational tests, improving sway elimination efficiency by over 60% compared to traditional open-loop control.
• Safety Protection Algorithm: Monitors multi-dimensional parameters in real time, including load, speed, position, and torque, with support for multi-level alarms and progressive shutdown strategies. The algorithm design strictly follows international safety standards including ISO 4301, GB/T 36464-2018, and ISO 13849, meeting SIL3 functional safety requirements (custom solutions for extreme operating conditions).
• Intelligent Scheduling Algorithm: Designed for multi-crane, multi-machine coordination scenarios, this algorithm leverages graph theory and operations research optimization for path planning and task allocation. It responds to dynamic task changes in real time, maximizing overall operational efficiency while ensuring safety. In port application testing, multi-machine coordination efficiency improved by 35%.
• Energy Optimization Algorithm: Analyzes equipment operating conditions to intelligently adjust drive parameters and operating curves, reducing energy consumption. This algorithm delivers particularly significant energy savings in heavy-load hoisting and frequent start-stop scenarios, with an average measured energy savings rate of 18%.

The control algorithm layer uses a "building block" composition approach: engineers can select the required algorithm modules from the library based on project requirements, then adjust parameters and combine rules in configuration files—no C++ or Python coding required for customization. Each algorithm module also provides detailed debug logs and performance monitoring interfaces, facilitating problem identification and optimization during on-site commissioning.

Layer 3: Industry Application Layer — Ready-to-Use Industry Templates

The industry application layer serves as the bridge between the technology platform and end users. Based on years of project implementation experience, the Kelude team has abstracted business logic, operational practices, safety specifications, and process requirements from different industries into industry configuration templates. Each template includes not only parameter configurations but also industry-specific operational workflows, interlock logic, and alarm systems.

Standardized templates have been developed for the following industries:

• Metallurgy: Safety logic for molten metal hoisting, multi-level interlock protection, precise positioning control, and high-temperature-resistant protection configurations
• Chemical: Explosion-proof environment adaptation (Ex d/Ex e explosion protection class), hazardous material hoisting process control, emergency shutdown logic, and anti-static grounding detection
• Port: Automated container stacking, ship loading/unloading process management, terminal TOS system data integration, and wind anchor control
• Automotive: Automated assembly line material handling, AGV collaborative scheduling, and flexible production line rapid changeover adaptation
• Food & Beverage: Hygienic-grade material requirements, CIP cleaning process modes, and batch tracking with traceable management
• Paper & Pulp: Large-span structure control, heavy-load smooth hoisting, and automated roll handling and stacking
• Logistics & Warehousing: High-density automated warehouse integration, intelligent sorting coordination, and WMS/WCS system integration

These templates are not rigid, one-size-fits-all solutions but rather configurable, extensible "living templates." When project engineers take on a new project, they simply select the relevant industry template and fine-tune parameters to match site conditions, completing most of the system configuration work. Templates also support hybrid usage—for example, if a project requires features from both the metallurgy and chemical templates, this can be achieved through template layering.

New Industry Onboarding: From 3 Months to 2 Weeks

Before the technology platform was established, Kelude had to start from scratch for every new industry it entered—requirements analysis, hardware selection, driver development, algorithm adaptation, interface customization, and integration testing—with the full cycle typically taking over 3 months. With the technology platform in place, the new industry onboarding process has been restructured into four standardized steps, dramatically compressing project delivery timelines.

1. Requirements Analysis (2–3 days): In-depth discussions with the customer to clarify operating scenarios, accuracy requirements, safety standards, and environmental conditions, and to determine which algorithm modules and industry templates need to be invoked. This phase also assesses whether new HAL driver adapters need to be developed.
2. Parameter Configuration (1–2 days): Using the graphical interface in the platform's configuration center, hardware parameters, algorithm parameters, and business logic parameters are configured. The configuration center supports parameter version management and one-click rollback, and automatically generates a complete project configuration file once configuration is complete.
3. Simulation Validation (1–3 days): The configuration is loaded into a digital twin environment for simulated operational validation. The simulation system automatically generates typical operating condition test cases based on historical data and automatically reports risk points and performance bottlenecks. Simulation validation coverage can reach over 95%.
4. On-Site Commissioning (3–5 days): The configuration is deployed to actual equipment for on-site integration testing. Since over 80% of validation work has already been completed during the simulation phase, on-site commissioning time is significantly reduced. Any abnormal data generated during commissioning is automatically recorded and transmitted back to the R&D platform.

Through this process, the new industry onboarding cycle has been compressed from the original 3 months to under 2 weeks—a more than 6x improvement in efficiency. To date, Kelude has successfully delivered over 30 new industry projects through this streamlined process, with the first-pass project acceptance rate rising from 47% to 91%.

The Value of a Closed Loop: Faster Delivery and Data-Driven After-Sales Improvement

The Kelude technology platform delivers significant value on two fronts: measurable improvements in project delivery efficiency and a data-driven continuous improvement loop.

Delivery Efficiency: The technology platform has greatly increased project delivery parallelism. Previously, a team of engineers could only handle 2–3 projects simultaneously because each project's code and hardware adaptation work was independent and non-reusable. Now, through the configuration-based development model, a single team can advance 8–10 projects in parallel. According to internal Kelude statistics, since the technology platform went live, average project delivery time has been reduced by 65%, R&D labor costs have decreased by 40%, and cross-project code reuse has jumped from under 15% to 82%. R&D personnel have been freed from repetitive work and can focus on more innovative algorithm optimization and new industry expansion.

Data Closed Loop: The technology platform is not just a development platform—it is also the vanguard of the data platform. Every device connected to the technology platform automatically transmits operational data, fault records, operation logs, and environmental parameters to the cloud data platform via edge gateways. By conducting in-depth analysis of this data, the R&D team can identify algorithm bottlenecks, hardware vulnerabilities, and operational optimization opportunities, then feed those improvements back into the algorithm library and industry templates—creating a virtuous closed loop of "after-sales data → R&D iteration → delivery upgrade."

This closed loop has driven continuous improvement in the reliability and intelligence of Kelude products. Data shows that since the full rollout of the technology platform, equipment after-sales failure rates have dropped by 37%, customer satisfaction scores have risen from 4.2 to 4.8 (out of 5), and mean time between failures (MTBF) has improved by 42%.

Technology Platform Architecture and Technology Selection

From a technical implementation standpoint, the Kelude technology platform adopts a modular microservices architecture. The HAL layer runs on edge controllers and is implemented in C++ to ensure real-time performance. The control algorithm layer uses a hybrid architecture: time-critical algorithms (such as anti-sway and safety protection) run on a real-time kernel, while non-time-critical algorithms run on the Linux application layer. The industry application layer is built on web technologies, providing a cross-platform HMI interface with a consistent experience across PC, tablet, and mobile devices.

On the data side, the platform uses a unified data bus architecture, with all equipment operational data reported in real time to a cloud time-series database via the MQTT protocol. The cloud data analytics platform uses Spark Streaming for real-time stream processing while simultaneously synchronizing processing results back to the edge, enabling edge-cloud collaborative intelligent decision-making. The entire architecture supports horizontal scaling, with a single platform instance capable of managing over 1,000 devices simultaneously.

In terms of version management, the technology platform has established a comprehensive CI/CD pipeline. Every update to the algorithm library and industry templates goes through automated testing and canary release processes to ensure stable operation of production systems. Additionally, the platform provides a complete configuration change audit log, allowing any configuration modification to be traced back to the specific operator and timestamp.

Future Outlook

Development of the Kelude technology platform continues to advance. Key focus areas for the future include:

• Deep AI Integration: Deep learning models are deployed directly onto edge controllers, enabling anomaly detection, predictive maintenance, and adaptive control. A time-series-based fault prediction model has already achieved 92% accuracy in internal testing.
• Expanded Industry Coverage: Moving from the current 10+ industries to 20+ sectors, with a focus on high-end manufacturing fields such as new energy, semiconductors, and aerospace.
• Open Ecosystem Strategy: HAL-layer interface specifications and algorithm SDKs are opened to partners, allowing more industrial automation equipment manufacturers and application developers to connect to the Kelude technology platform and jointly build an open ecosystem for industrial control.

The technology platform is not an end goal but a bridge to smart manufacturing. Kelude aims to leverage this platform to share the expertise and capabilities accumulated over years in crane control with more industry partners, driving digitalization across industrial equipment. As the platform continues to evolve, Kelude remains committed to its founding principle of "solving industry fragmentation through platform thinking," consistently improving product capabilities and delivery efficiency to create greater value for customers.

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