Kelude Technical Middle Platform: One Control for 10+ Industries

The Rise of Technology Middle Platforms in Industrial Equipment

Driven by Industry 4.0 and the Made in China 2025 initiative, traditional industrial equipment manufacturers are confronting unprecedented challenges: customer requirements are growing more diverse, delivery windows are tightening, and R&D resources remain constrained. It is against this backdrop that the concept of a Technology Middle Platform has been introduced to the industrial equipment sector. Unlike the data middle platforms common in the internet industry, the Technology Middle Platform in industrial equipment focuses on decoupling hardware control capabilities, algorithm capabilities, and industry-specific business logic into distinct layers — realizing the engineering vision of "one platform, multi-industry adaptation."

Kelude's Technology Middle Platform is the tangible engineering outcome of this philosophy. Built around the control system of smart cranes, it employs 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 chemical processing to ports and automotive — significantly shortening project delivery cycles and reducing development and maintenance costs.

The Three-Layer Architecture of the Kelude Technology Middle Platform

The Kelude Technology Middle Platform adopts 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 in close coordination while remaining decoupled from one another, providing the architectural foundation for "develop once, reuse everywhere."

Layer 1: Hardware Abstraction Layer (HAL) — Bridging Diverse Hardware Differences

One of the most persistent headaches in crane control systems is hardware diversity. Different motor brands (Siemens, ABB, SEW-Eurodrive), encoders with different protocols (absolute encoders, incremental encoders, SSI interfaces), various 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 has meant re-adapting the hardware from scratch.

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

To date, the HAL has accumulated support for 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 design follows a plug-and-play principle for device drivers: a standard Device Description File (DDF) automatically identifies the hardware type and loads the appropriate driver, transforming hardware adaptation from code-centric development into configuration-based management.

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

The Control Algorithm Layer is where the Kelude Technology Middle Platform's core competitive advantage lies. This layer consolidates the essential algorithm capabilities Kelude has accumulated over years of crane control expertise, modularizing and standardizing them into a configurable, composable algorithm library. Each algorithm module undergoes rigorous unit testing and operating-condition validation to ensure stability and reliability throughout the 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 lifting requirements across industries. The algorithm includes built-in auto-calibration that automatically completes position calibration during initial equipment startup.
• Anti-Sway Control Algorithm: A closed-loop, active anti-sway technology combining optimal control with adaptive control strategies, capable of eliminating sway within two oscillation cycles (reference: RL anti-sway algorithm). This algorithm has been validated through thousands of real-world operating-condition tests, delivering sway elimination efficiency more than 60% higher than traditional open-loop control.
• Safety Protection Algorithm: Real-time monitoring of multi-dimensional parameters including load, speed, position, and torque, with support for multi-level alarms and progressive shutdown strategies. The algorithm design strictly follows international and domestic 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, leveraging graph theory and operations research optimization for path planning and task allocation. The algorithm responds to dynamic task changes in real time, maximizing overall operational efficiency while maintaining safety. In port application tests, multi-machine coordination efficiency improved by 35%.
• Energy Consumption Optimization Algorithm: Analyzes equipment operating conditions to intelligently adjust drive parameters and operating curves, reducing energy consumption. This algorithm delivers particularly notable energy-saving effects in heavy-load hoisting and frequent start-stop scenarios, with measured average energy savings of 18%.

The Control Algorithm Layer uses a "building block" approach: engineers select the required algorithm modules from the library based on project needs, 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 issue diagnosis and optimization during the on-site commissioning stage.

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

The Industry Application Layer serves as the bridge between the Technology Middle Platform and end users. Drawing on years of project implementation experience, the Kelude team has abstracted industry-specific business logic, operational practices, safety regulations, and process requirements into industry configuration templates. Each template contains not only parameter configurations but also encapsulates the unique operational workflows, interlock logic, and alarm systems of that industry.

Standardized templates have been developed for the following industries:

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

These templates are by no means rigid or static — they are configurable, extensible "living templates." When a project engineer takes on a new project, they simply select the corresponding industry template and fine-tune parameters based on site conditions to complete most of the system configuration work. Templates can also be combined: for example, if a project requires features from both the metallurgy and chemical templates, the template overlay function handles it seamlessly.

New Industry Adaptation: From 3 Months to 2 Weeks

Before the Technology Middle Platform was established, every new industry Kelude entered required starting from scratch — requirements analysis, hardware selection, driver development, algorithm adaptation, interface customization, and integrated testing — with the full cycle typically taking over 3 months. With the Technology Middle Platform, the adaptation process for new industries has been restructured into four standardized steps, dramatically compressing project delivery time.

1. Requirements Analysis (2–3 days): In-depth discussions with the customer to clarify the operating scenario, 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 any new HAL driver adapters need to be developed.
2. Parameter Configuration (1–2 days): Using the graphical configuration center of the Technology Middle Platform, hardware parameters, algorithm parameters, and business logic parameters are configured. The configuration center supports parameter version management and one-click rollback; once configuration is complete, a full project configuration file is automatically generated.
3. Simulation Verification (1–3 days): The configuration is loaded into a Digital Twin environment for simulated operation and verification. The simulation system automatically generates typical operating-condition test cases based on historical data and automatically reports risk points and performance bottlenecks. Simulation verification coverage can reach over 95%.
4. On-Site Commissioning (3–5 days): The configuration is deployed to the actual equipment for on-site integrated testing. Since more than 80% of verification 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 adaptation cycle has been reduced from 3 months to under 2 weeks — an efficiency improvement of more than 6 times. To date, Kelude has successfully completed fast delivery of over 30 new industry projects through this process, with the first-pass project acceptance rate rising from 47% to 91%.

The Closed-Loop Value: Delivery Efficiency Gains and After-Sales Data Feedback

The Kelude Technology Middle Platform delivers significant value on two fronts: quantifiable improvements in project delivery efficiency, and a data-driven continuous improvement loop.

Delivery Efficiency: The technical middle platform has significantly increased project delivery parallelism. Previously, an engineering team could only handle 2-3 projects simultaneously because the code and hardware adaptation work for each project was independent and could not be reused. Now, with the configuration-based development model, a single team can manage 8-10 projects in parallel. According to internal Kelude statistics, since the platform went live, average project delivery time has been reduced by 65%, R&D labor costs have dropped by 40%, cross-project code reuse has increased from under 15% to 82%, and R&D personnel have been freed from repetitive work to focus on more innovative algorithm optimization and expansion into new industries.

Data Closed Loop: The technical middle platform is not just a development platform—it also serves as the vanguard of the data middle platform. Every device connected to the platform automatically transmits its operational data, fault records, operation logs, and environmental parameters to the cloud data platform via edge gateways. The R&D team performs deep analysis on this data to identify algorithm bottlenecks, hardware risks, and optimization opportunities, then feeds 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 improvements in the reliability and intelligence of Kelude products. Data shows that since the full rollout of the technical middle platform, the equipment after-sales failure rate has dropped by 37%, customer satisfaction scores have risen from 4.2 to 4.8 (out of 5), and the average trouble-free operation time (MTBF) has increased by 42%.

Technical Architecture and Technology Selection of the Platform

From a technical implementation standpoint, the Kelude technical middle platform adopts a modular microservice 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 the real-time kernel, while non-time-critical algorithms run on the Linux application layer. The industry application layer is built on web technologies, delivering a cross-platform HMI with a consistent experience across PCs, tablets, and mobile terminals.

On the data side, the platform uses a unified data bus architecture. All device operational data is reported in real time to a cloud time-series database via the MQTT protocol. The cloud data analytics platform processes streaming data in real time using Spark Streaming and synchronizes the results back to the edge, enabling collaborative edge-cloud intelligent decision-making. The entire architecture supports horizontal scaling, with a single platform instance capable of managing more than 1,000 devices simultaneously.

For version management, the platform has a robust CI/CD pipeline. Every update to the algorithm library and industry templates goes through automated testing and canary release processes to ensure system stability. The platform also maintains a complete configuration change audit log, making every configuration modification traceable to a specific operator and timestamp.

Future Outlook and Development Roadmap

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

• Deep AI Integration: Deploying deep learning models directly onto edge controllers to enable anomaly detection, Predictive Maintenance, and Adaptive Control. A fault prediction model based on time-series data has already achieved 92% accuracy in internal testing.
• Expanded Industry Coverage: Growing from the current 10+ industries to 20+ industries, with a focus on new energy, semiconductors, and aerospace—high-end manufacturing sectors.
• Open Ecosystem Strategy: Opening the HAL layer interface specifications and algorithm SDK to partners, allowing more industrial automation equipment manufacturers and application developers to connect to the Kelude technical middle platform and jointly build an open ecosystem for industrial control.

The technical middle platform is not an end in itself—it is a bridge to Smart Manufacturing. Kelude aims to use the platform as a vehicle to share the experience and capabilities accumulated over years in crane control with more industry partners, driving the digital transformation of industrial equipment. As the platform continues to evolve, Kelude remains committed to its founding principle of "solving industry fragmentation through platform thinking," continuously improving product capabilities and delivery efficiency to create greater value for customers.

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