Intelligent Management Research Articles

Integrity management system of hydrogen pipeline based on big data mining model optimized by improved whale algorithm

ABSTRACT In order to improve the integrity management level of the hydrogen pipeline, the intelligent information management system of the hydrogen pipeline based on big data intelligent mining model is proposed. The improved whale optimization algorithm is proposed by applying the adaptive weight update formula and quantum revolving door operation. Finally, a hydrogen pipeline is selected to carry out abnormal data detection, and the running time with different nodes and different clustering algorithms optimized by different intelligent algorithms is obtained, and the results show that the proposed Gaussian clustering optimized by an improved whale optimization algorithm can effectively find out abnormal data of integrity management of hydrogen pipeline. Monitoring precision of the proposed model is improved effectively, and the running time of the proposed model is also shortened; therefore, the proposed model can be an effective means for integrity management of the hydrogen pipeline.

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations

In this work, we introduce a novel method to develop whole system digital twins (DTs) for advanced nuclear reactors. This method treats a complex reactor system as a heterogeneous graph: with the system components as different types of graph nodes and their physical interconnections as edges. Based on the heterogeneous graph, a graph neural network combining graph convolution and temporal node attention is developed as the DT, facilitating a comprehensive understanding of the system’s dynamic behavior. By utilizing the System Analysis Module (SAM) code for simulating various operational transients, we develop a graph-based database that trains the DT. This DT is characterized by two primary functions: It can infer the entire system’s status using sparse node information, and it can predict the progress of transients based on current and historical system information. Our approach is validated through case studies on the Experimental Breeder Reactor II (EBR-II) system and a generic Fluoride-salt-cooled High-temperature Reactor (gFHR), demonstrating the DT’s accuracy in forecasting operational transients. The DT’s rapid computation capabilities enhance its potential for supporting advanced reactor operations, offering benefits in intelligent simulation, autonomous control, and anomaly detection, paving the way for improved safety analysis and intelligent component health management for advanced reactor systems and reducing their operations and maintenance cost.

Optimizing the Agricultural Supply Chain Through the Internet + Model: Enhancing Efficiency with a Case Study of TikTok

This study uses the TikTok platform as a case study to explore the application of the Internet+ model in the agricultural supply chain and analyses the impact of Internet+ model on improving supply chain efficiency and reducing costs. With the development of science and technology, Internet+ has become an important trend in modern industry, which deeply integrates traditional agriculture with information technology and promotes the innovation and efficiency of supply chain management. This study not only discusses the practical application of the Internet + model in agriculture but also proposes to solve the bottlenecks in the traditional agricultural supply chain using logistics informatization, intelligent scheduling system, cold chain monitoring and intelligent warehouse management. Through case studies, the effectiveness of Internet technology in optimizing the supply chain, improving the circulation efficiency of agricultural products and reducing costs are verified, providing theoretical and case study support for promoting the development of agricultural modernization.

How intelligence organisations innovate

ABSTRACT Practitioners and academics recognize the importance of innovative intelligence organisations. However, we know little about how intelligence organisations innovate and what challenges they are being confronted with. Drawing upon intelligence studies as well as innovation and organisational studies this paper forwards a conceptual framework to answer the question: how do intelligence organisations innovate? To address this, we introduce the concept of innovation capability, an organisation’s potential to innovate. The paper identifies seven attributes of innovation capability and explores these attributes within intelligence organisations. The attributes are: (1) vision and strategy, (2) competence base, (3) organisational intelligence, (4) creativity and idea management, (5) organisational structure, (6) culture and climate and (7) management of technology. The paper concludes with an agenda for further research.

Research on Multi-Sensor Fusion Fault Diagnosis Method Based on Spatiotemporal Attention Mechanism

As industrial systems become increasingly complex, real-time monitoring and intelligent management of industrial equipment have become imperative. However, the limitations in coverage and accuracy of single sensors make it challenging to comprehensively characterize the operational state of equipment, leading to reduced system reliability and increased pressures on data transmission and storage. To address these challenges, this study presents a novel fault diagnosis method based on multi-sensor fusion using a spatio-temporal attention mechanism. Initially, one-dimensional convolutional neural networks (1D-CNN) are employed to extract features from raw signals, effectively capturing local characteristics and ensuring the integrity and validity of fault signals. Subsequently, the spatiotemporal attention mechanism adjusts the feature weights based on the temporal and spatial correlations of different sensors, as well as their respective importance, thereby capturing the spatio-temporal dependencies across multiple sensors and enhancing the efficacy of information fusion. Finally, the proposed method is validated through experiments on a nickel flash smelting furnace system. The results demonstrate that the method achieves a fault diagnosis accuracy exceeding 97.78%, significantly enhancing fault detection and decision-making performance.

Flow regime discrimination and methodology for calculating discharge in trapezoidal sluice gates

This study introduces a novel trapezoidal sluice gate designed for water division and flow control within irrigation networks featuring canals with trapezoidal cross-sections. Experiments were conducted on trapezoidal channels with side slopes of m = 1.5, 1.75, and 2.0, using trapezoidal sluice gates at various gate openings, upstream water depths, and downstream depths. A total of 411 experimental sets were conducted to study methodologies for discerning between free flow and submerged flow conditions and for determining discharge rates. A method was proposed to distinguish flow patterns of trapezoidal sluice gates: submerged flow occurs when the downstream channel depth (ht) exceeds the submergence threshold (Ht'), while free flow is indicated when ht is less than Ht'. The formula for calculating Ht' was derived, along with formulas for computing discharge rates under free flow and submerged flow conditions for trapezoidal sluice gates. The results revealed that the average relative errors of the formulas obtained through the partition method and the submergence coefficient correction method were 2.24 % and 5.37 %, respectively, demonstrating high accuracy and reliability. The scale effects on the flow regimes and formulas of discharge rates are also discussed. Findings from this study enhance the understanding of the hydraulic characteristics of trapezoidal sluice gates, which hold significant implications for the adoption and intelligent management of such gates in irrigation areas, offering a viable solution for selecting appropriate water gate configurations in irrigation systems.

Reverse logistics for electric vehicles under uncertainty: An intelligent emergency management approach

The frequency and intensity of global disasters, including the COVID-19 pandemic, and natural disasters such as earthquakes, floods, and wildfires, are increasing, necessitating effective emergency logistics management. Climate change significantly contributes to these events, emphasizing the importance of limiting human and environmental impacts. The transportation sector, particularly the automobile industry, ranks second in global carbon emissions, highlighting the need to adopt electric vehicles (EVs) to reduce emissions and minimize the impact of climate change. However, this has led to an increase in demand for lithium-ion batteries. During emergencies, end-of-life (EOL) battery management through reverse logistics is essential because recycling EOL batteries can recover valuable raw materials, decrease landfill waste and costs, and support environmental sustainability. This study proposed a two-phase method for intelligent emergency EV battery reverse logistics management. The first phase employed machine learning to address unpredictable battery demands, whereas the second phase proposed a multi-objective model to minimize carbon emissions through efficient order allocation during uncertain emergencies. The model considers carbon emissions and defect rates as sources of uncertainty, current regulations, and customer environmental awareness. The model is solved using the weighted sum and ε-constraint methods, resulting in non-dominant solutions. The findings indicate that combining the selection of third-party reverse logistics providers (3PRLPs) with optimal order allocation for recycling old batteries during emergencies effectively minimizes environmental impacts and combats climate change.

Inovasi Teknologi Digital sebagai Penggerak Reformasi dalam Sistem Pendidikan

Digital technology innovation has become the main driver in reforming the education system in the modern era. The use of digital technologies, such as online learning platforms, artificial intelligence, and data-based education management systems, has changed the way education is learned, taught, and managed. These innovations not only increase the accessibility and flexibility of learning, but also enable the personalization of the learning experience for students. This article aims to examine the role of digital technological innovation in driving education system reform, as well as highlighting the opportunities and challenges faced in the transformation process. The results of this research conclude that digital technology innovation has great potential to encourage education system reform, as long as it is accompanied by supportive policies, increasing the competency of teaching staff, and equitable development of technological infrastructure. It is hoped that this reform can create an education system that is more inclusive, adaptive and relevant to future challenges.

Retraction Note: Optimization of book information search in intelligent library system management based on cellular network

Retraction Note: Optimization of book information search in intelligent library system management based on cellular network

Maximising Synergy: The Benefits of a Joint Implementation of Knowledge Management and Artificial Intelligence System Standards

Implementing management systems in organisations of all types and sizes often raises the following question: “What benefits will this bring?” Initial resistance and criticism are common as potential challenges are identified during the implementation process. To address this, it is essential to highlight the advantages of these systems and engage stakeholders in supporting management efforts. While the planning, implementation, use, maintenance, auditing, and improvement of management systems are generally voluntary, certification is frequently driven by external factors, particularly customer demands. Employees also stand to gain significantly, with knowledge and information serving as valuable resources, especially for leveraging artificial intelligence. This article explores the management’s readiness to adopt and fully utilise two management systems based on international standards: the ISO 30401 Knowledge management system (KMS) and the ISO/IEC 42001 Artificial intelligence management system (AIMS). Through interviews, we assess the challenges and solutions associated with implementing these systems, whether planned or partially adopted. The findings illustrate the synergistic benefits of integrating the KMS and AIMS, demonstrating how their combined use can enhance Integrated Management Systems (IMSs). Such integration supports comprehensive planning, operation, and performance evaluation of processes and services while also promoting continuous improvement.

Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

The intelligent operation and maintenance method and experimental research of CNC machine tool bearings

As the core components of high-precision rotary machine tools, machine tool bearings are prone to failure or damage, and their running state directly affects the safety and reliability of the entire equipment. Therefore, the paper proposes an intelligent operation and health management method for machine tool bearings based on Bayes-VMD-KPCA, Bayes-CNN-SVM, and health status evaluation index. Aiming at the complex working environment and high nonlinear degree of fault features of machine tool bearings, the Bayes-VMD-KPCA algorithm is used to perform variational mode decomposition of the original signal of the bearing fault, which avoids the influence of background noise and solves the problem of high dimension of multi-domain fault feature set. Second, the CNN-SVM fault diagnosis model uses a convolutional neural network (CNN) to deeply mine the extracted multi-domain fault features, and a support vector machine (SVM) can be used as a fault classifier to complete the diagnosis of various fault types of machine tool bearings. At the same time, the Bayes algorithm is introduced to tune the hyperparameters in the CNN-SVM model to further improve the prediction performance of the CNC machine tool-bearing fault diagnosis model. The results show that the intelligent operation and maintenance and health management method of CNC machine tool bearings proposed in this paper can effectively complete the fault type diagnosis and health status assessment of bearings, and its accuracy index reaches 99.33%. Compared with the single SVM model, Bayes-CNN model, Bayes-SVM model, and CNN-SVM model, the health effect is evaluated. The accuracy is increased by 6.33%, 4.33%, 2.66%, and 1%, respectively. It can be seen that the proposed method can more effectively realize the monitoring and health management of bearing fault.

A lightweight video anomaly detection model with weak supervision and adaptive instance selection

Video anomaly detection is to determine whether there are any abnormal events, behaviors or objects in a given video, which enables effective and intelligent public safety management. As video anomaly labeling is both time-consuming and expensive, most existing works employ unsupervised or weakly supervised learning methods. This paper focuses on weakly supervised video anomaly detection, in which the training videos are labeled whether or not they contain any anomalies, but lack information about the specific frames and quantities of anomalies. However, the uncertainty of weakly labeled data and the large model size prevent existing methods from wide deployment in real scenarios, especially the resource-limit situations such as edge-computing. In this paper, we develop a lightweight video anomaly detection model. On the one hand, we propose an adaptive instance selection strategy, which is based on the model’s current status to select confident instances, thereby mitigating the uncertainty of weakly labeled data and subsequently promoting the model’s performance. On the other hand, we design a lightweight multi-level temporal correlation attention module and an hourglass-shaped fully connected layer to construct the model, which can reduce the model parameters to only 0.56% of the existing methods (e.g. RTFM). Extensive experiments on three public datasets UCF-Crime, ShanghaiTech and XD-Violence show that our model performs better than or equally to the existing lightweight methods, while with a significantly reduced number of model parameters. Furthermore, by integrating the improved module designed in this paper with the VadCLIP method proposed by Wu et al., we achieve the state-of-the-art performance of non-lightweight models on the UCF-Crime and XD-Violence datasets.

Using Support Vector Machines (SVM) for Intelligent Traffic Management in Microservices Environments

Using Support Vector Machines (SVM) for Intelligent Traffic Management in Microservices Environments

Intelligent Energy Management for Electric Vehicle Charging

Intelligent Energy Management for Electric Vehicle Charging

Design and stability performance optimization of a novel hybrid inspection robot walking device for smart grid applications

Inspecting in high-altitude windy environments requires maintaining a stable attitude to avoid significant oscillations, which is a crucial factor in advancing the use of hybrid power transmission line inspection robots (PTLIRs) in real-world scenarios for the electrical power and energy industry. We developed and optimized a novel walking device to improve the safety of hybrid PTLIRs operating in strong wind conditions. First, we investigate the operational conditions of transmission line inspection and design a walking device capable of transporting a flight device to be suspended under the line for inspection purposes. Second, a force equilibrium analysis is performed to explore the relationship between the key structural parameters of the walking device and the wind-induced deflection of the robot. A multiobjective optimization model is formulated to minimize the maximum deflection angle of the robot along three axes under wind loads. Finally, an optimization algorithm, using an improved non-dominated sorting particle swarm optimization (NSPSO) approach, is proposed to solve the model with good global search ability and fast convergence speed. A test platform is constructed to collect the wind deflection angle of a self-developed hybrid PTLIR under the transmission line. The test results demonstrate that the walking device can carry 30 kg to move along the transmission line. The average deflection angle of the robot around the Z axis is the highest under the influence of wind force level 7 (wind speed of 15.5 m/s) perpendicular to the windward surface of the robot. After optimization, the maximum deflection angle is limited to 10.38°, aligning within safety thresholds. The optimization algorithm effectively reduces the maximum deflection angles around the X, Z, and Y axes by 33.19 %, 39.21 %, and 13.23 %, with minimal average errors of 2.40 %, 3.87 %, and 2.13 %, respectively. The research in this paper can solve the bottleneck problem of robot practicality, which has important theoretical significance and practical application value for the safety, stability, and intelligent operation and maintenance management of power transmission lines.

Application of digital twin technology in monitoring system of pump turbine

The advent of advanced productive forces has catalyzed the ongoing evolution of intelligent and digital technologies within the pump-turbine sector. The integration of digital technologies has not only enhanced production efficiency but also facilitated intelligent operation management, offering novel solutions for pump-turbine design and operation, thereby accelerating the digital transformation in fluid machinery. This study first established the theoretical framework of the digital twin system for pump-turbines, followed by the development of a mathematical model and the construction of a twin virtual model utilizing Proper Orthogonal Decomposition (POD) reduction theory. The model’s accuracy was validated through real-time pressure pulsation data, and exploratory investigations into cavitation prediction were subsequently carried out. To enable the visualization of the digital twin system, this study incorporated Open3D (Three-dimensional computer graphics tools) point cloud technology, effectively rendering the state cloud map of the pump-turbine. Finally, by synthesizing the proposed theories and technologies, the digital twin system’s visualization and monitoring for pump-turbines were successfully implemented through the Unity3D simulation platform. This study offers novel insights into intelligent monitoring and prediction of pump-turbines, holding significant implications for the modernization and intelligent advancement of the hydropower industry.

Research and Design of Intelligent Parking Management System based on the YOLO Algorithm

Given the difficulty in managing parking lots and inefficient utilization of parking spaces at university and college campuses, this paper designs an intelligent campus parking management system with functions such as license plate recognition (LPR), online parking space reservations, campus parking navigation, and mobile app payment through the parking space detection technology based on You Only Look Once (YOLO) algorithm, Internet of Things (IoT) technology, and cloud platform technology. This system obtains information relating to license plates, parking spaces, etc. from sensing nodes with the IoT technology and transmits the collected information through NB-IoT technology to a cloud platform for storage and management to facilitate information exchange between hardware and software. This paper describes the architecture of the intelligent parking management system, parking space detection technology based on the YOLOv4 algorithm, and hardware and software design of the system. The paper proposes an effective solution to parking problems facing colleges and universities. The paper concludes that the intelligent campus parking management system based on the YOLOv4 algorithm can prevent the haphazard parking of vehicles and traffic congestion at open campuses and therefore has important value for application and popularization.

Intelligent Financial Management Systems Innovations and Applications in the Digital Era

With the advancement of digital transformation, intelligent financial management systems have played a significant role in improving the efficiency, accuracy, and decision-making capabilities of corporate financial management. This study explores the performance and impact of three intelligent financial management systems — Digital Processing Methods and Financial Reimbursement Document System (System A), Industrial Digitalization Industry Enterprise Growth Evaluation System (System B), and Cloud-based Intelligent Financial Management Tools (System C) — in practical applications through case study methods. The results show that these systems have significantly improved the efficiency and accuracy of financial management, enhanced decision-making capabilities, and reduced operational costs through automated processes, data mining, and machine learning techniques. The study also points out challenges in the implementation process, such as data quality, user acceptance, and system integration issues, and predicts future development trends. Finally, the paper discusses the limitations of the research and proposes directions for future research.

Integrating Semantic Web, Artificial Intelligence, and Knowledge Management for Enhanced Accounting in SMEs

This study explores the integration of Semantic Web (SW), Artificial Intelligence (AI), and Knowledge Management (KM) to improve accounting practices in small and medium-sized enterprises (SMEs). Recognizing the challenges SMEs face in managing accounting knowledge due to insufficient expertise and fragmented data sources, this research proposes a conceptual model that utilizes SW technologies, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), to create a unified ontology for diverse accounting information. By leveraging AI techniques, including machine learning and natural language processing, the model aims to enhance knowledge retrieval, ontology enrichment, and case-based reasoning, facilitating more efficient and accurate accounting knowledge management. The findings highlight the potential of this integrated approach to support online training, decision-making, and overall competitiveness of SMEs, addressing the limitations of traditional accounting practices. The study concludes with recommendations for further research to refine the integration of SW, AI, and KM in accounting.