Collaborative Operation Research Articles

Directional Adaptive Triboelectric Nanogenerator with Wind-Wave Synergy.

To enhance the adaptability and synergy of the triboelectric nanogenerator (TENG) in a wave environment, this paper introduces a directional adaptive triboelectric nanogenerator (DA-TENG) with wind-water synergistic action for wave energy collection. An innovative design combining a wind vane on the top and fan-shaped blade electrodes internally allows the DA-TENG to adjust its swinging direction adaptively to align with the direction of wave motion. The internal multiple power generation units work in coordination, effectively addressing the issues of low efficiency associated with spherical TENGs in capturing multidirectional wave energy. The DA-TENG demonstrates superior performance under various wind speed conditions, showcasing its practical application potential. Experimental results show that the DA-TENG, equipped with a single tail wind vane and a 700 g mass block, can achieve an output voltage, current, and charge of 374.97 V, 84.77 μA, and 622.69 nC under a mild wind environment. Its peak power density reaches 7.51 W m-3, enabling successful data transmission for a wireless temperature and humidity sensor and powering 248 light-emitting diodes (LEDs). This research expands the possibilities of omnidirectional wave energy collection and the collaborative operation of multiple power generation units, offering an effective method for powering low-power maritime devices.

Collaborative task decision-making of multi-UUV in dynamic environments based on deep reinforcement learning

ABSTRACT The problem of collaborative task decision-making for unmanned underwater vehicles (UUVs) in an unknown dynamic ocean environment is investigated. In view of the unknown marine environment, a partially observable Markov decision process is designed to achieve partially observable path planning for multi-UUV. Aiming at the problems of long planning time, large amount of data, and insufficient multi-task decision-making capability in the process of multi-UUV underwater collaborative operation, a multi-UUV collaborative task decision-making model based on the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm for the dynamic environment is constructed. The implementation of the centralised training with distributed execution (CT-DE) training framework enables each UUV to possess autonomous decision-making capability and ensures task safety under weak communication conditions. The pre-training phase shows that the MATD3 algorithm outperforms the multi-agent deep deterministic policy gradient (MADDPG) algorithm for training multi-UUV autonomous decision-making scenarios in dynamic environments. The simulation experiment results verify that the autonomous decision-making method based on the MATD3 algorithm can effectively solve the multi-UUV collaborative task decision-making problem in unknown dynamic environments, and the task process satisfies the requirements of real-time, safety and economy.

Collaborative operation strategy of multiple microenergy grids considering demand‐side energy‐sharing behavior

AbstractIn recent years, as the energy market continues to change, an emerging business model is gradually emerging as an energy provider (EP). The participation of EP as an independent entity in the distribution network investment model not only helps realize a win–win situation for multiple stakeholders but also promotes the innovation and sustainable development of the energy industry. However, in the energy system, EPs, grids, microenergy grids (MEGs), and shared energy storage service providers usually belong to different stakeholders, and their respective pursuit of profit maximization can easily lead to uncontrolled competition, which greatly reduces market efficiency. To address the varying ownership structures within the energy system, a refined bilevel coordinated optimization operation model is introduced, rooted in the master–slave game theory. In this model, the EP assumes the role of the master, while the MEG operator and the shared energy storage operator (SESO) act as slaves. A peer‐to‐peer energy‐sharing mechanism between MEGs and an energy trading mechanism between MEGs and shared energy storage is used to further increase the local consumption rate of new energy. Simulation results demonstrate that the proposed method not only boosts the revenues of an EP, SESO, and MEG operators but also effectively accounts for the MEG's energy storage needs and SESOs' profitability.

Shape optimization of cast steel tubular joints based on subdivision surface and genetic algorithm

Cast steel tubular (CST) joints are extensively employed in steel structures for their excellent integrity and mechanical performance. However, the diverse configurations of CST joints result in the absence of standard verification formulas, leading to inefficient trial-and-error design approaches. To tackle this issue, this paper introduces a novel shape optimization method for CST joints that integrates subdivision surface techniques with genetic algorithms. The method comprises three key components: geometric modeling, structural analysis, and optimization algorithm, with shape optimization achieved through their collaborative operation. Applied to two types of CST joints, this approach resulted in significant reductions in joint volume by 39.2 % and structural stress by 49.0 %, respectively. The shape-optimized joints were compared with topology-optimized joints from published literatures, and demonstrated enhanced mechanical performance while presenting superior manufacturability. The proposed method offers considerable advantages over traditional trial-and-error approaches and topology optimization methods. It generates designs with continuous and smooth boundaries, better suited for the conventional casting process, and overcomes manufacturability issues of topology optimization. Additionally, the genetic algorithm allows for flexible selections of optimization objectives, addressing the restrictions of topology optimization methods. Furthermore, the methodʼs high level of automation is anticipated to expedite the joint design process, significantly reducing the requirement for manual intervention.

Intelligent smelting process, management system: Efficient and intelligent management strategy by incorporating large language model

In the steelmaking industry, enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities. Thus, effectively integrating various production modules is crucial to enable collaborative operations throughout the entire production chain, reducing management costs and complexities. This paper proposes, for the first time, the integration of Vision-Language Model (VLM) and Large Language Model (LLM) technologies in the steel manufacturing domain, creating a novel steelmaking process management system. The system facilitates data collection, analysis, visualization, and intelligent dialogue for the steelmaking process. The VLM module provides textual descriptions for slab defect detection, while LLM technology supports the analysis of production data and intelligent question-answering. The feasibility, superiority, and effectiveness of the system are demonstrated through production data and comparative experiments. The system has significantly lowered costs and enhanced operational understanding, marking a critical step toward intelligent and cost-effective management in the steelmaking domain.

A diverse, unbiased group decision-making framework for assessing drivers of the circular economy and resilience in an agri-food supply chain

The circular economy (CE) and supply chain resilience have received significant scientific attention in the recent supply chain literature. Incorporating CE practices and resilience practices into a supply chain enables companies to address their sustainability goals. This research explores the joint impact of implementing CE practices and resilience practices in an agri-food supply chain in Peru. The synergetic assessment of CE practices and resilience practices in a supply chain constitutes a multi-criteria decision-making challenge, requiring the involvement of subject-matter experts. Most studies have selected subject-matter experts based on the researchers’ convenience, which may lead to biased evaluations by some experts, resulting in unreliable study results. To mitigate potential bias, this research introduces a new framework named Diverse and Unbiased Group Decision-Making (DUGDM). Subsequently, the study examines the interaction between the drivers of CE and resilience using the Grey-DEMATEL method. The results indicate that drivers, such as the economic growth level, agricultural contamination, and collaborative operation of multiple firms are most influential in the successful integration of circular economy and resilience into a Peruvian agri-food supply chain system.

Shared Energy Storage Capacity Configuration of a Distribution Network System with Multiple Microgrids Based on a Stackelberg Game

With the ongoing development of new power systems, the integration of new energy sources is facing increasingly daunting challenges. The collaborative operation of shared energy storage systems with distribution networks and microgrids can effectively leverage the complementary nature of various energy sources and loads, enhancing energy absorption capacity. To address this, a shared energy storage capacity allocation method based on a Stackelberg game is proposed, considering the integration of wind and solar energy into distribution networks and microgrids. In this approach, a third-party shared energy storage investor acts as the leader, while distribution networks and microgrids serve as followers. The shared energy storage operator aims to maximize annual revenue, plan shared energy storage capacity, and set unit capacity leasing fees. Upon receiving pricing, distribution networks and microgrids aim to minimize annual operating costs, determine leased energy storage capacity, and develop operational plans based on typical daily scenarios. Distribution networks and microgrids report leasing capacity, and shared energy storage adjusts leasing prices, accordingly, forming a Stackelberg game. In the case study results, the annual cost of MGs decreased by 29.63%, the annual operating cost of the ADN decreased by 11.25%, the cost of abandoned light decreased by 60.77%, and the cost of abandoned wind decreased by 27.79% to achieve the collaborative optimization of operations. It is proven that this strategy can improve the economic benefits of all parties and has a positive impact on the integration of new energy.

Research on a multiobjective cooperative operation scheduling method for agricultural machinery across regions with time windows

This article aims to solve the problems of high cost, low efficiency, and task delay caused by the lack of scientific scheduling strategies during the agricultural harvest season. Considering the constraints of operation time windows, a multimachine multiobjective cross-regional collaborative operation scheduling model is constructed with the goal of minimizing the transfer distance of agricultural machinery and nonoperational scheduling costs. A multiobjective genetic algorithm (HTSMOGA) based on a hybrid time window priority rule and a tabu search strategy is designed. The initial population is generated by utilizing time window priority rules, genes with earlier operation start times are preferentially retained, and a mixed search strategy is introduced to avoid local optimal solutions. Experiments were conducted among 24 farms in a region of Hebei Province, and the results showed that, compared with other algorithms, the HTSMOGA achieved an average reduction of 17.18 % in the transfer distance of agricultural machine operations and 19.36 % in the nonoperational waiting time. Therefore, this study provides a rational and feasible solution for the cross-regional operation scheduling of agricultural machinery cooperatives and provides theoretical support for ensuring the timely completion of harvesting tasks and the cost savings and efficiency of agricultural machinery operations.

Design and Simulation of Multi Unmanned Boat Cooperative Obstacle Avoidance System Based on 5G Edge Computing

Background:: Compared to single unmanned boat, multi unmanned boats have more flexible mobility and efficient task completion capabilities, which can effectively expand the types of tasks. However, the traditional independent path planning and obstacle avoidance methods of unmanned boats are difficult to meet the requirements of collaborative operation among multiple unmanned boats due to the lack of information exchange. Objective:: According to the actual demand of multi unmanned boats' cooperative operation, a method of multi unmanned boats cooperative obstacle avoidance based on 5G edge computing is proposed to realize the unified planning and scheduling of multi unmanned boats. Methods:: Firstly, 5G technology and Kubeedge edge computing tools are used to build a multi unmanned boat collaborative obstacle avoidance system based on cloud, edge and end collaboration, and the Kubeedge edge computing platform was optimized by optimizing communication strategies, building a highly available Kubeedge cluster, building a Harbor image center, and using Web management interfaces further to improve the reliability and stability of the system. Secondly, the YOLOR-Deepport multi-target recognition and tracking algorithm based on cloud, edge and end collaborative network is used to complete the recognition and tracking tasks of obstacle targets, and a set of EECBS path planning methods based on the Kubedge centralized control platform is designed to plan collision-free and efficient paths for each unmanned boat in realtime. Finally, the effectiveness of the system was verified through simulation experiments. Results:: The experimental results show that compared to the traditional autonomous planning obstacle avoidance method for unmanned boats, the collaborative planning obstacle avoidance method proposed in this paper can exhibit excellent performance in dense and narrow scenarios, with a more reasonable navigation path, a range reduction of 20% - 50%, and higher safety. Conclusion:: The results show that the cooperative obstacle avoidance system based on 5G edge computing designed in the paper is feasible, and it can effectively realize the cooperative path planning and obstacle avoidance of multi unmanned boats.

A clock synchronization and trigger distribution scheme for China Spallation Neutron Source

China Spallation Neutron Source (CSNS) is a complex system engineering composed of particle accelerators, a target station, and neutron spectrometers with various equipment types and distribution. The collaborative operation of the equipment requires accurate clock synchronization and trigger distribution. Currently, to meet the requirements of different system devices, clock synchronization and trigger distribution in CSNS are implemented based on different methods, resulting in complex clock and trigger networks, demanding upgrades, and a lack of unified timestamps. This paper proposes a clock synchronization and trigger distribution scheme based on the field-programmable gate array (FPGA) and single-mode dual-core fiber link. The decoupling of clock phase synchronization and time base synchronization improves the frequency of phase compensation. Benefiting from the phase interpolator (PI) in the FPGA transceivers, high-precision phase compensation and phase deterministic control are realized, achieving picoseconds synchronization precision and stability in the four-level network. Based on the custom lightweight link layer protocol, by optimizing the routing method of data frames and adopting priority control, the transmission of multiple data is realized, and the hardware delay of the critical data, such as interlock, is guaranteed to be less than 1.5 μs in the four-level system. The scheme provides a general precise clock synchronization and distributed trigger method for CSNS devices, simplifies the system complexity, and is conducive to the system upgrade in the second stage.

Application and prospects of spatial information technology in CO2 sequestration monitoring

This paper systematically reviews the current applications of various spatial information technologies in CO2 sequestration monitoring, analyzes the challenges faced by spatial information technologies in CO2 sequestration monitoring, and prospects the development of spatial information technologies in CO2 sequestration monitoring. Currently, the spatial information technologies applied in CO2 sequestration monitoring mainly include five categories: eddy covariance method, remote sensing technology, geographic information system, Internet of Things technology, and global navigation satellite system. These technologies are involved in three aspects: monitoring data acquisition, positioning and data transmission, and data management and decision support. Challenges faced by the spatial information technologies in CO2 sequestration monitoring include: selecting spatial information technologies that match different monitoring purposes, different platforms, and different monitoring sites; establishing effective data storage and computing capabilities to cope with the broad sources and large volumes of monitoring data; and promoting collaborative operations by interacting and validating spatial information technologies with mature monitoring technologies. In the future, it is necessary to establish methods and standards for designing spatial information technology monitoring schemes, develop collaborative application methods for cross-scale monitoring technologies, integrate spatial information technologies with artificial intelligence and high-performance computing technologies, and accelerate the application of spatial information technologies in carbon sequestration projects in China.

Research on collaborative inspection key technologies for multi-UAVs and multi-nests

The single individual capability and insufficient endurance of unmanned aerial vehicles (UAVs) have always been problems in power inspection operations. This article is based on the new development needs of UAV inspection and proposes a new model of multi-UAVs collaborative inspection. On this basis, this article studies the decoupling and intelligent matching technology between airports and UAVs, task merging and splitting technology in complex multi-task situations, and proposes a safety inspection strategy for multi-UAVs collaborative operations to ensure the safety of operations in the multi-UAVs multi-nests collaborative inspection mode. Finally, a complete multi-UAV, multi-nests collaborative inspection strategy is constructed.

A Local-and-Global Attention Reinforcement Learning Algorithm for Multiagent Cooperative Navigation.

The cooperative navigation algorithm is the crucial technology for multirobot systems to accomplish autonomous collaborative operations, and it is still a challenge for researchers. In this work, we propose a new multiagent reinforcement learning algorithm called multiagent local-and-global attention actor-critic (MLGA2C) for multiagent cooperative navigation. Inspired by the attention mechanism, we design the local-and-global attention module to dynamically extract and encode critical environmental features. Meanwhile, based on the centralized training and decentralized execution (CTDE) paradigm, we extend a new actor-critic method to handle feature encoding and make navigation decisions. We also evaluate the proposed algorithm in two cooperative navigation scenarios: static target navigation and dynamic pedestrian target tracking. The multiple experimental results show that our algorithm performs well in cooperative navigation tasks with increasing agents.

Multi UAV Cooperative Reconnaissance based on Dynamic Programming VDN Algorithm

This paper proposes a multi agent value decomposition network (VDN) based multi UAV collaborative reconnaissance and control method to address the issue of insufficient strategies for multi UAV collaborative reconnaissance and control. By designing corresponding algorithm networks and training processes, the goal of autonomy, collaboration, and intelligence among multiple unmanned aerial vehicle systems has been achieved, assisting unmanned aerial vehicle combat forces in achieving collaborative operations and decision-making. This article uses AirSim as the simulation verification environment to verify the effectiveness of the proposed algorithm. The experimental results show that the algorithm proposed in this paper can achieve multi UAV collaborative reconnaissance tasks in complex environments, providing an intelligent solution for UAV collaborative control.

Innate Orientating Behavior of a Multi-Legged Robot Driven by the Neural Circuits of C. elegans.

The objective of this research is to achieve biologically autonomous control by utilizing a whole-brain network model, drawing inspiration from biological neural networks to enhance the development of bionic intelligence. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of the cellular synapses. The neural network simulation integrates computational programming and the visualization of the neurons and synapse connections of C. elegans, containing the specific controllable circuits and their dynamic characteristics. To illustrate the biological neural network (BNN)'s particular intelligent control capability, we introduced an innovative methodology for applying the BNN model to a 12-legged robot's movement control. Two methods were designed, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of the BNN. Both the simulation and experimental results indicate that the robot exhibits more autonomy and a more intelligent movement performance under BNN control. The systematic approach of employing the whole-brain BNN for robot control provides biomimetic research with a framework that has been substantiated by innovative methodologies and validated through the observed positive outcomes. This method is established as follows: (1) two integrated dynamic models of the C. elegans' whole-brain network and the robot moving dynamics are built, and all of the controllable circuits are discovered and verified; (2) real-time communication is achieved between the BNN model and the robot's dynamical model, both in the simulation and the experiments, including applicable encoding and decoding algorithms, facilitating their collaborative operation; (3) the designed mechanisms using the BNN model to control the robot are shown to be effective through numerical and experimental tests, focusing on 'foraging' behavior control and locomotion control.

Research on the spatio-temporal coupling relationship between agricultural green development efficiency and food security system in China

Food security and agricultural green development are fundamental issues concerning the survival of mankind. To realize the coordinated development of them is of great significance to promote the green transformation of agriculture and ensure national food security. However, few studies have analyzed the coupling relationship between agricultural green development and food security. Therefore, this study complements the research on the coupling relationship between them. First, we use the agricultural panel data of 31 provinces in China from 2010 to 2021 to measure the agricultural green development efficiency and food security level by Super-SBM model and entropy weight method. Then, the coupling relationship and spatiotemporal evolution of the two are analyzed by coupling coordination degree model. The results show that, first of all, China's agricultural green development efficiency and food security level have improved overall, but there are regional differences. Secondly, the degree of coupling and coordination between the two is significantly improved, and most provinces develop from the break-in stage to the coordination stage, and the regional differences are relatively reduced. Finally, Beijing, Shanghai and other developed areas are mostly of the lagging food security type, Liaoning, Shandong and other major grain producing areas have reached a high degree of coordination, while Tibet, Qinghai and other western regions are still in the break-in stage. According to the development situation of different regions, corresponding suggestions are put forward. For regions with lagging food security type, the government should give full play to the application of science and technology in agriculture and promote green and low-carbon planting technologies. For regions with low coupling and coordination degree, the government should improve support policies and build a collaborative operation system, adjust planting structure to improve land utilization rate and food security level. Finally, the government should work together to build agricultural industrial parks and give full play to the leading role of competitive provinces to achieve common development.

Collaborative Operation Optimization Scheduling Strategy of Electric Vehicle and Steel Plant Considering V2G

With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared with traditional steel production methods, but the production steps have fixed electricity consumption behavior, and impact loads are easily generated in the production process, which has an impact on the stability of the power system. EV has the characteristics of a mobile energy storage unit. When a large number of EVs are connected to the power grid, they can be regarded as distributed energy storage units with scheduling flexibility. Through the orderly scheduling of EVs, the spatial–temporal transfer of EV charging and discharging load can be realized. Therefore, the EV situated in the steel plant’s distribution network node has the capacity to be utilized by providing peak shaving and valley filling services for the steel production load. This study proposes an operation optimization scheduling method for EVs and steel plants. Taking the lowest overall operating cost as the objective, an optimal scheduling model considering EVs operation, steel plant, and distributed generator is established. Based on the IEEE-33 node distribution network model considering distributed generators, the proposed model is simulated and analyzed, and the effectiveness of the EV steel plant operation optimization scheduling strategy is investigated.

Study on multi-timescale operation of hydrogen-containing energy coupled with transportation system

Abstract With the widespread attention on hydrogen energy, the operation optimization of the coupling system of hydrogen energy and transportation has become a critical issue. Therefore, this study develops a hydrogen-containing energy transportation coupling system for the system collaborative operation framework, operation mode and equipment modelling of the system. Furthermore, a medium- to long-term operation optimization model and a short-term operation optimization model were constructed considering the differences in the operation of coupled systems at different timescales and operating costs as the objective function and power balance and system equipment as constraints. In the medium- and long-term operation optimization model, the planning scenario reduction method is used to reduce the wind power generation scenario. In the short-term operation optimization model, a multivariate uncertainty model is constructed to represent the uncertainty in the coupling system. Subsequently, the solution method of the model is proposed. Finally, a coupled system is simulated to verify the effectiveness of the model. (i) When the initial scene set is 600, the typical scene reduction method using Latin hypercube sampling and the Wasserstein distance can reduce operating costs by 7.60% and 9.49% compared with K-means reduction and K-media reduction methods. (ii) The sensitivity coefficients of hydrogen sales price, electricity sales price and maintenance rate to operating costs are –0.031%, –1.009% and 0.0105%, respectively. (iii) Considering multiple uncertainties can help make optimal decisions based on the overall consideration of disadvantage scenarios, thereby reducing system operating costs.

Non-Iterative Coordinated Optimisation of Power–Traffic Networks Based on Equivalent Projection

The exchange of sensitive information between power distribution networks (PDNs) and urban transport networks (UTNs) presents a difficulty in ensuring privacy protection. This research proposes a new collaborative operation method for a coupled system. The scheme takes into account the schedulable capacity of electric vehicle charging stations (EVCSs) and locational marginal prices (LMPs) to handle the difficulty at hand. The EVCS hosting capacity model is built and expressed as the feasible area of charging power, based on AC power flow. This model is then used to offer information on the real schedulable capacity. By incorporating the charging loads into the coupling nodes between PDNs and UTNs, the issue of coordinated operation is separated and becomes equal to the optimal problem involving charging loads. Based on this premise, the most efficient operational cost of PDNs is transformed into a comparable representation of cost information in PDNs. This representation incorporates LMP information that guides charging decisions in UTNs. The suggested collaborative scheduling methodology in UTNs utilises the collected projection information from the static traffic assignment (STA) to ensure data privacy protection and achieve non-iterative calculation. Numerical experiments are conducted to illustrate that the proposed method, which uses a smaller amount of data, achieves the same level of optimality as the coordinated optimisation.

Evaluation of force pain thresholds to ensure collision safety in worker-robot collaborative operations.

With the growing demand for robots in the industrial field, robot-related technologies with various functions have been introduced. One notable development is the implementation of robots that operate in collaboration with human workers to share tasks, without the need of any physical barriers such as safety fences. The realization of such collaborative operations in practice necessitates the assurance of safety if humans and robots collide. Thus, it is important to establish criteria for such collision scenarios to ensure robot safety and prevent injuries. Collision safety must be ensured in both pinching (quasi-static contact) and impact (transient contact) situations. To this end, we measured the force pain thresholds associated with impacts and evaluated the biomechanical limitations. This measurements were obtained through clinical trials involving physical collisions between human subjects and a device designed for generating impacts, and the force pain thresholds associated with transient collisions between humans and robots were analyzed. Specifically, the force pain threshold was measured at two different locations on the bodies of 37 adults aged 19-32 years, using two impactors with different shapes. The force pain threshold was compared with the results of other relevant studies. The results can help identify biomechanical limitations in a precise and reliable manner to ensure the safety of robots in collaborative applications.