Industrial Platform Research Articles

Teaching Reform and Practice Based on Four Dimensions and One Penetration for Sensing and Detection Technology

Sensing and Detection Technology is a core course in engineering specialties. Traditional sensor teaching methods have obvious deficiencies in cultivating students’ ability. To better foster students’ comprehensive qualities, this study explored a 4D1P (Four Dimensions and One Penetration) teaching mode. We independently developed an industrial sensor teaching platform with intellectual property rights, integrating classroom and sensor experiments to address the disconnection between traditional sensor teaching and practical application. This mode combined the teaching platform with SPOC (small private online courses) and Rain Classroom teaching software, enriching classroom teaching and stimulating students’ interest. By applying industry-academia-research integration to sensor teaching, students’ horizons were broadened and their creative thinking enriched. The mode set up discussion-based learning in the classroom, making the class atmosphere lively. Throughout the teaching process, data-driven learning and teaching evaluation were consistently applied, allowing teachers to promptly understand students’ learning situations. Data shows that under the backdrop of the COVID-19 pandemic, students’ grades improved and they were satisfied with this teaching mode. This mode solves most current problems in university classroom teaching and significantly enhances students’ practical abilities. It also has certain significance for education in other disciplines.

Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.

Different roles, different strokes: Disseminating e‐WOM in industrial Internet platform through multi‐actor value co‐creation

Different roles, different strokes: Disseminating e‐WOM in industrial Internet platform through multi‐actor value co‐creation

Knock-out of the major regulator Flo8 in Komagataella phaffii results in unique host strain performance for methanol-free recombinant protein production

Flo8 is a main transcriptional regulator of flocculation and pseudohyphal growth in yeast. Disruption of FLO8 in the popular recombinant protein production host Komagataella phaffii (Pichia pastoris) prevents pseudohyphal growth and reduces cell-to-surface adherence, making the mutant an interesting platform for research and industry. However, knowledge of the physiological impact of the mutation remained scarce. In-depth analysis of transcriptome data from FLO8-deficient K. phaffii revealed that Flo8 affects genes involved in cell cycle, mating, respiration, and catabolite repression additionally to flocculation targets. One gene with considerably increased expression in flo8 was GTH1, encoding a high-affinity glucose transporter in K. phaffii. Its promoter (PG1) was previously established as a strong, glucose-regulatable alternative to methanol-induced promoters. PG1 and its improved derivatives PG1-3, D-PGS4 and D-PGS5, proved to be promising candidates for controlling recombinant protein production in the FLO8-deficient background. In small-scale screenings, PG13-controlled intracellular EGFP levels were 2.8-fold higher, and yields of different secreted recombinant proteins were up to 4.8-fold increased. The enhanced productivity of the flo8 mutant in combination with the PG1 variants was transferrable to glucose-limited fed-batch processes and could largely be attributed to higher transcriptional activity of the promoter, leading to a much higher productivity per chromosomally integrated gene copy. K. phaffii flo8 has many advantageous characteristics, such as reduced surface growth and increased transcriptional strength of glucose-regulatable promoters. These features turn the flo8 strain into a valuable new base strain for various experimental designs and establish flo8 as an excellent strain background for methanol-free recombinant protein production processes.

Operational long-term management of a salt cavern for industry targeted green H2 production

Hydrogen and its derivatives such as ammonia are increasingly important for reducing carbon emissions in sectors with limited alternatives. Large production systems are generally preferred to reduce costs and for those, advanced management is a cornerstone to the project economic viability. The present work explores the development of an Energy Management System for an industrial low-emission ammonia production platform. The latter is connected to the electrical grid and consists of 1.5 GW of renewable production units, around 500 MW of electrolyzers and storage facilities including especially a salt cavern for H2 storage. The long-term management of the salt cavern is decisive to enhance the profitability while satisfying industry related constraints such as the carbon content of the produced H2. The literature lacks in addressing both aspects simultaneously. Methodological approaches to tackle the long-term management and industrial constraints are proposed and compared. For the considered platform, it is shown that a strategy based on precalculated cost functions in the long term horizon allows relative gains of 2.5% with respect to a state-of-the-art short term management leading to an annual cost saving of approximately $6.2 million in the platform operational expenses. This translates to a reduction of the gap between the state-of-the-art and the optimal a posteriori solution by about 82%.

Application Research of Industrial Internet Networked Operation Control Platform Based on RFID and Blockchain Traceability

With the development of Industry 4.0, the demand for data accuracy, transparency, and traceability in the manufacturing industry is increasing day by day. This paper proposes an industrial networking operation management and control platform based on radio frequency identification (RFID) and blockchain technology. Through the deep integration with the national industrial Internet identification analysis system, the platform has realized the full life cycle traceability of products from raw materials to finished products, ensuring the data tamper proof and cross validation of multi-dimensional data. The research results show that after adopting this platform, the accuracy of data collection in manufacturing enterprises is significantly improved, production efficiency is optimized, and operating costs are significantly reduced. In addition, the introduction of blockchain technology effectively prevents data tampering, enhances system transparency and security. This paper provides a new technological path for the digital transformation of the manufacturing industry, and verifies the application potential of RFID and blockchain technology in the industrial Internet environment.

Application and practice of industrial IoT cloud platform in oilfield intelligent transformation

Abstract Under the context of global energy transformation and low-carbon development, the traditional petroleum industry is facing a series of challenges such as technological innovation, market competition, environment protection, etc. Therefore, there is an urgent demand to realize green, safe, economical, and efficient development of oil and gas through oilfield intelligent transformation. This study develops a novel cloud platform covering the whole business based on the technical architecture of the industrial Internet of Things (IoT) for assisting the intelligent transformation of oilfields. Since the application of the IoT-based cloud platform in Qingcheng Oilfield, functions such as automatic collection of whole-domain data, remote monitoring, early warning, as well as intelligent analysis and decision-making have been realized. Furthermore, compared with the traditional platform, the new platform has improved eight indicators, such as data processing efficiency and sharing capacity, by more than 20%. Consequently, the developed IoT-based cloud platform can significantly improve the production and management level of the oilfield, and efficiently support oilfield intelligent transformation.

Information technologies in agriculture of Northern Kazakhstan: advantages, reserves

Ensuring sustainable development of the agrarian sector of the economy requires the integrated use of digitalization technologies. The aim is to show the current trends in the application of information technologies in agriculture in Northern Kazakhstan and to determine the conditions necessary for the introduction of modern digital infrastructure. Methods - system-structural, economicstatistical, comparative analysis, mathematical and system-functional approaches to the study of the use of information and communication technologies by agribusiness of agricultural profile. Results - the authors analyzed the scope of application of digital processes in the region. The factors influencing the use of modern digital technologies in the cultivation of agricultural land have been outlined. The dependence of the application of precision farming elements on the farm size is revealed. The calculation of economic efficiency of reference and information systems of production management is presented. Measures to overcome obstacles and mitigate the limitations of digital transformation in agriculture in the northern territories of the republic are proposed. Conclusions - based on the study it is noted that the spread of digital innovations is from 18 to 55% of the total sown area. When comparing the average land size of economic entities that implement information technologies with other farms, it was found that digital solutions in crop production are primarily available to large and medium-sized agroformations that have the financial capacity and qualified specialists for digitalization. The survey of respondents allowed us to express an opinion on the influence of the size, technical equipment and economic stability of an agro-industrial enterprise on the use of modern farming systems, as well as the level of awareness of agro-entrepreneurs about new technologies. The use of digital platforms in the crop production industry is a key element that contributes to increasing the economic efficiency of the agro-industrial complex.

Blockchain implementation for food safety in supply chain: A review.

Food safety has emerged as the topmost priority in the current fast-paced food industry era. According to the World Health Organization, around 600 million people, approximately 1 in 10 individuals worldwide, experience illness due to contaminated food consumption, resulting in nearly 0.42 million fatalities annually. The recent development in software and hardware sectors has created opportunities to improve the safety concerns in the food supply chain. The objective of this review is to explain the fundamentals of blockchain and its integration into the supply chain of various food commodities to enhance food safety. This paper presents the analysis of 31 conceptual works, 10 implementation works, 39 case studies, and other investigations in blockchain-based food supply chain from a total of 80 published papers. In this paper, the significance of adapting conceptual ideas into practical applications for effectively tracing food commodities throughout the supply chain has been discussed. This paper also describes the transformative role of blockchain platforms in the food industry, providing a decentralized and transparent ledger to access real-time and immutable records of a product's journey. In addition, both the positive impacts and challenges associated with implementing blockchain technology in the food supply chain have been evaluated. In summary, the blockchain-based food supply chains offer greater transparency, traceability, and trust, ultimately resulting in higher standards of food safety and quality.

RENTALIN APPS: Development of a Motorbike Rental Application Using Design Thinking for a Digital Rental Solution

The motorbike rental industry in Indonesia is characterized by significant challenges, including security risks, inefficient operations, and difficulty for customers in finding reliable rental services. This study aims to develop a comprehensive digital solution through the RENTALIN application, which addresses these challenges using a Design Thinking approach. The objective of this research was to create an application that enhances both the operational efficiency for rental owners and the user experience for customers. Data was collected from 13 motorbike rental owners, reported issues such as theft, fraud, and difficulties in tracking motorbikes. Additionally, surveyed customers indicated challenges in finding trustworthy rental services. Based on these findings, the RENTALIN application was designed with features such as GPS-based motorbike tracking, secure data verification systems, and an intuitive user interface. These solutions aim to improve security, expand market reach, and simplify the rental process. Testing of the prototype showed that the application effectively increases the efficiency and safety of the rental process, providing a more seamless experience for both rental owners and customers. RENTALIN demonstrates significant potential to become a leading platform in the Indonesian motorbike rental industry, offering targeted solutions to the sector’s critical issues.

Smart industrial information integration: a lightweight privacy protection model in an intelligent manufacturing architecture

In the industrial ecosystem, both the manufacturing and edge clouds collaborate via virtualisation technology, playing a crucial role in the efficient aggregation and processing of vast data from endpoints, offering computational and analytical support for enhanced decision-making in all industrial operations. Consequently, ensuring the reliability of virtualised manufacturing environments is paramount. Remote attestation offers a viable solution, however, current schemes are plagued by inefficiencies and privacy leakage issues. To address these concerns, we propose an efficient remote attestation approach. The approach actively conceals sensitive industrial information in measurement logs by using a hash algorithm, thereby preventing privacy breaches. Furthermore, it streamlines log entries with a log compression algorithm, enhancing remote verification efficiency in the industrial information architecture. Extensive experiments were conducted in an authentic cloud environment by applying this approach across both mesh and hierarchical remote verification structures, reducing time overhead by approximately 81.32%. Additionally, it marginally impacts server performance with only a 1.49% reduction. The results affirm the proposed approach's exceptional efficacy in decreasing verification durations and mitigating remote verification attacks, demonstrating its promising utility and feasibility in the intelligent industrial platform.

Research on Enhancement Strategies for the Integration of Industry-Education Information Technology Platforms in Vocational Colleges

During this pivotal period in which China's vocational education is transitioning from a scale-oriented to a connotation-oriented development, efforts to reform vocational education are continuously intensifying, with the integration of industry and education consistently serving as the key logical thread in the reform of the modern vocational education system. Educational informatization stands as a crucial indicator of educational modernization, and the comprehensive promotion of educational modernization through educational informatization is a significant strategic decision for China's educational reform and development. However, integration of industry and education has not formed mutual integration and commonality between the entire school and enterprise levels in aspects such as talent cultivation, faculty construction, and scientific research cooperation, leading to a lack of a scientific evaluation system for assessing the effectiveness of this integration. Therefore, vocational colleges should actively construct an integration of industry and education information management platform based on the thinking of integration of industry and education and the ISO 25010 software quality model. This will further improve and enhance the service capabilities of the integration of industry and education information technology platform, facilitate multi-party interactive cooperation among governments, industries, enterprises, and schools, better meet the requirements of talent cultivation model transformation and innovative development in the construction of higher vocational colleges, and standardize the process of integration of industry and education, making it more transparent, visualizing data analysis, and comprehensively evaluating effectiveness.

ORBIT SELECTION OF THE SPACE INDUSTRIAL PLATFORM WITH DISTRIBUTED ELECTRICAL-POWER SYSTEM MODULES

Space industrialization is one of the prospective directions in modern aerospace science and engineering for space exploration of new resources and habitats. The key issue is to provide industrial space modules with the required amount of electricity needed. One type of power supply for such modules is the use of distributed power systems, which consist of constellations of spacecraft with contactless power transmission. Given this, the problem of rational orbit selection for their dislocation arises. Considering these problems, the methodology for orbits selection of the space industrial platform with distributed electrical-power system modules is proposed in the paper. This methodology includes orbital translation, attitude, relative dynamics estimation for each power satellite, and its corresponding orbit optimization algorithm. The orbit optimization algorithm includes statistical processing and elements of gradient and coordinate descent methods, allowing us to determine the most significant parameter influencing the duration of the contactless power transmission session. Also, quaternion mathematics is used to estimate the dynamics in the program parameters for targeting the transmitter spacecraft antenna to the receiver spacecraft rectenna. With the approaches mentioned above, the methodology proposed in this paper allows us to form the requirements for the power satellites’ attitude and orbit control system to improve the process of selecting corresponding design parameters of such systems. Thus, the usage of the proposed methodology can allow the designing of the power satellites’ attitude and orbit control system in the conceptual stages of designing.

TSDRL: A three-stage deep reinforcement learning method for reliable collaboration of manufacturing service towards mass personalized production

With the development of intelligent manufacturing driven by new-generation information technology, manufacturing service collaborative chains (MSCC) based on Industrial Internet Platforms have become the main way to cope with mass personalized production. To adapt the dynamics and variability of personalized demands, manufacturing services are required to be frequently reorganized. This leads to dynamic changes in manufacturing service availability and ambiguous reliable collaboration mechanisms, making it challenging for existing methods to consistently maintain stable MSCC. Therefore, this paper proposes a three-stage deep reinforcement learning method (TSDRL) to improve the reliability of MSCC while guaranteeing the satisfaction of personalized demands. The method comprehensively considers the dynamic changes of manufacturing services and service reliability, and solves the reliable MSCC recommendation problem. Specifically, in stage one, high-quality services are selected through DRL state. In stage two, the improved NSGA-III algorithm is used to optimize the MSCC. In stage three, the BP+GA algorithm is used to mine user preferences and recommend high-satisfaction MSCC. By leveraging the exploratory process of DRL, cyclic optimization of MSCC is performed. Through comprehensive analysis and experimental research, it is shown that the method of this paper is robust and practical in dynamic environment.

Research on Fault Diagnosis and Predictive Maintenance Technology for Construction Crane Equipment

Construction lifting machinery equipment plays an important role in construction operations, but due to complex environments and high-intensity use, it is prone to malfunctions, causing economic losses to enterprises and users. This research aims to realize intelligent monitoring, fault diagnosis and predictive maintenance of construction machinery equipment based on industrial Internet platform, Internet of Things and artificial intelligence technology, so as to reduce maintenance costs, improve equipment efficiency, and promote digital transformation of the industry.

Manufacturing enterprises digital collaboration empowered by Industrial Internet Platform: A multi-agent stochastic evolutionary game

Digital transformation is a critical way to improve the resilience of the manufacturing industry chain. However, the current digitalization of manufacturing enterprises lacks upstream and downstream linkage. If the level of digitization of upstream and downstream enterprises differs significantly, it will increase transaction costs. Digital transformation with the help of industrial Internet platform has become a trend. For this reason, this study deeply explores the influence mechanism of digitalization collaboration of manufacturing enterprises under the empowerment of industrial Internet platform. This paper constructs a stochastic evolutionary game model with core manufacturing enterprise, supplier and local government as the main players. Consider stochastic differential equations to analyze the stability conditions of game subjects, and explore different incentive mechanisms on enterprise strategy. The results show that the data security of the platform and the network effect coefficients significantly affect the strategy selection results of the players. Additionally, the change of environmental noise affects the strategy evolution process of the subject, and the supplier strategy is more affected by environmental noise compared to the core manufacturing enterprises. Furthermore, the dynamic incentive effect of the government is better than the static incentive, which can better promote the digital collaboration of manufacturing enterprises. Dynamic penalties are less effective than static penalties. The study’s conclusions can provide a reference for the government to implement the gradient digitalization incentive policy and provide a theoretical basis for promoting digital collaboration in manufacturing enterprises.

Spatial Distribution Characteristics and Driving Factors of Little Giant Enterprises in China’s Megacity Clusters Based on Random Forest and MGWR

As a representative of potential “hidden champions”, a concept originating in Germany, specialized and innovative Little Giant Enterprises (LGEs) have become exemplary models for small and medium-sized enterprises (SMEs) in China. These enterprises are regarded as crucial support for realizing the strategy of building a strong manufacturing country and addressing the weaknesses in key industrial areas. This paper begins by examining urban agglomerations, which serve as the main spatial carriers for industrial restructuring and high-quality development in manufacturing. Based on data from LGEs in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations from 2019 to 2023, the study employs the Random Forest (RF) and Multi-scale Geographically Weighted Regression (MGWR) methods to conduct a comparative analysis of their spatial patterns and influencing factors. The results are as follows: (1) LGEs exhibit spatial clustering in both the YRD and PRD regions. Enterprises in the YRD form a “one-axis-three-core” pattern within a distance of 65 km, while enterprises in the PRD present a “single-axis” pattern within a distance of 30 km, with overall high clustering intensity. (2) The YRD is dominated by traditional manufacturing and supplemented by high-tech services. In contrast, the PRD has a balanced development of high-tech manufacturing and services. Enterprises in different industries are generally characterized by a “multi-point clustering” characteristic, of which the YRD displays a multi-patch distribution and the PRD a point–pole distribution. (3) Factors such as industrial structure, industrial platforms, and logistics levels significantly affect enterprise clustering and exhibit scale effects differences between the two urban clusters. Factors such as industrial platforms, logistics levels, and dependence on foreign trade show positive impacts, while government fiscal expenditure shows a negative impact. Natural geographical location factors exhibit opposite effects in the two regions but are not the primary determinants of enterprise distribution. Each region should leverage its own strengths, improve urban coordination and communication mechanisms within the urban cluster, strengthen the coordination and linkage of the manufacturing industry chain upstream and downstream, and promote high-tech industries, thereby enhancing economic resilience and regional competitiveness.

A systematic solution of distributed and trusted chain-network integration

Blockchain, with its characteristics of decentralization, transparency, openness, and intangibility, has become the preferred choice for enhancing the credibility of the industrial cluster network platform. Industrial clusters are an important organizational form for developing small and medium-sized enterprises, and the information service platform plays a key role. This paper constructs a trusted paradigm model and a new trusted framework for the industrial cluster network platform and proposes an adaptive distributed network scheme. Specifically, it includes a decentralized and non-repudiable solution for the autonomous scenario of the industrial cluster Industrial Internet, promoting the transformation of the network into a self-adjusting and self-managing distributed network, enhancing the credibility of the platform, and reducing the negative impact of distrust and information asymmetry; it proposes three models (private chain, alliance chain, and public chain) for the integration of blockchain and the Industrial Internet, providing a secure and reliable support platform for the development of industrial clusters, solving the problem of information asymmetry, promoting trust and synergy, and maximizing the synergistic effect; from the perspective of security and reliability, it deeply analyzes the industrial cluster network platform, proposes a trusted framework, and realizes the data layer and application layer of the network with the help of blockchain technology. The experimental results show that these models meet the requirements of the industrial cluster in terms of data privacy, control, and credibility and have a positive significance for promoting the development and digital transformation of the industrial cluster.

Smart product service resources composition optimization for smart product service system in context of industrial IoT platform

Smart product service resources composition (SPS-RC) is a core issue of smart product service system (PSS) development in the emerging context of industrial internet of things (IIoT) platform, aiming to organize distributed resources for smart product service operation tasks. Existing research mainly focuses on the traditional physical PSS, while contains scant exploration of the new features emerging in context of IIoT platform, leading to an urgent updating need for SPS-RC approaches. Therefore, this study proposes a systematic framework for SPS-RC in context of IIoT platform based on cyber physical system (CPS)-based SPS blueprint, graph theory and adaptive genetic algorithm. In the proposed framework, the SPS-RC problem is formulated in accordance with the realistic smart product service operation process using the CPS-based SPS blueprint. The graph theory and depth-first search algorithm are employed to calculate the composition performance under the environment of heterogenous connection structures between SPS tasks. In addition, an optimization model considering heterogenous composition structures is established with aggregating various quality of service into one comprehensive objective and solved by an adaptive genetic algorithm. Finally, an application of this systematic framework for smart port container crane service demonstrates the feasibility and effectiveness of the proposed approach.

Technological innovation, industry platforms or financialization? A comparative institutional perspective on Nokia, Apple, and Samsung

The puzzle of how Nokia lost the smartphone wars has intrigued recent scholarship. Despite Nokia’s dominant position in the mobile phone industry and its technological capabilities and reputation for strategic agility, it was completely wiped out from the market, only a few years after the launch of Apple’s iPhone. The article provides a comparative, historical and institutional account on the smartphone industry by focusing on three key players: Nokia, Apple, and Samsung. This perspective enriches earlier accounts that were overly focused on explaining Nokia’s decline by looking at internal organisational design and conflicts. We propose a two-pronged explanation focused on the reconfiguration of industry platforms and financialisation. The article suggests that single company histories could be enriched by integrating a comparative perspective that examines additional cases. We discuss opportunities for further research to understand how success or failure in technological innovation is embedded in a wider societal and institutional context.