Real-time Matching Research Articles

Application of Sensor Technology in Wheelchair Sports for Real-Time Data Collection during Training and Competition and for Assessment of Performance Parameters: A Systematic Review and Future Directions

This review reports on the use of sensors in wheelchair sports to monitor and analyze performance during match and training time. With rapid advancements in electronics and related technologies, understanding performance metrics in wheelchair sports is essential. We reviewed nine studies using various sensor types, including electric motors, inertial measurement units, miniaturized data loggers with magnetic reed switches, and smartphones with inbuilt accelerometers and gyroscopes, operating at frequencies from 8 Hz to 1200 Hz. These studies measured parameters such as angular and translational velocities, distance, number of starts/pushes, and other performance indicators in sports such as basketball, rugby, tennis, and racing. Despite differences in sport types and methodologies, most studies found sensor-derived data effective for assessment of performance. Future developments and research in this field should focus on multi-sensor systems that could provide real-time match analysis and deeper insights into performance metrics. Overall, sensor technologies show significant potential for improving wheelchair sport performance diagnostics, contributing to better athlete training and future wheelchair design, and enhancing competitive outcomes. This review emphasizes the need for continued innovation and standardization in applying sensor technologies in wheelchair sports.

Optimal capacity configuration and operation strategy of typical industry load with energy storage in fast frequency regulation

As the potential and competent load-side resources for frequency response and control in modern power grids, typical industrial load can compensate for the deficiency of frequency response capability for new-type power systems. However, the operational flexibility is seriously enforced by the operation conditions uncertainties of industrial load. With “Online Calculation, and Real-time Matching” as the core, based on fuzzy mathematical theory, the coordinated operation strategy of typical industrial loads and energy storage systems (ESS) is proposed to finish fast frequency regulation (FFR) tasks. And an optimal capacity configuration model of industrial loads with ESSs is established to evaluate the whole economic profitability in FFR. Specially, in terms of algorithms, a novel Two-layer Parallel Particle Swarm Optimization and Genetic Algorithm (TPPSGA) is innovatively designed in this manuscript. Comparing with traditional heuristic algorithms, TPPSOGA improves computation speed by 6.84 times. Simulation results show that our proposed strategy can reasonably estimate the frequency capability of industrial load by membership degree function, and prove that the industrial load participating in FFR with the support of ESSs is able to get more revenue and stability than industrial load or ESSs separately is in regulation of FFR. The economic analyze in market price and ESS parameters can help the load agent to screen the suitable-type ESS and its capacity to serve for FFR and enhance the flexibility in load-side frequency regulation.

Enhanced Ridesharing Using Directions API and Geolocation

Abstract: In the ever-evolving realm of urban transportation, the fusion of technological advancements and ubiquitous mobile connectivity has sparked a paradigm shift in how cities are navi- gated. This shift has led to innovative approaches in ridesharing and crowdsourcing, offering promising solutions to tackle traffic congestion, reduce emissions, and prioritize travel needs in urbanareas. Unlike traditional carpooling methods, where arrange-ments are made in advance, ridesharing in smart cities operates on real-time matching of drivers and riders, with priority given to factors such as destination and purpose.This study delves into the intricate dynamics of ridesharing and crowdsourcing for smart city transportation, investigating the complex interplay between technology and social factors.Additionally, it considers aspects of security, policy, and pricing strategies to ensure a seamlessand equitable experience for all stakeholders.Drawing insights from comprehensive case studies and practical implementations, this exploration highlights the advantages of ridesharing and crowdsourcing, including the reduction of carbon dioxide emis- sions and the prioritization of travel needs based on purpose. It offers guidance for stakeholders navigating the complexities and embracing the transformative potential of these innovative solutions in shaping the future of urban mobility.

Bluetooth Enabled LCD Cricket Scoreboard

This abstract centres on designing and developing a Bluetooth-enabled LCD cricket scoreboard with a primary focus on reducing delay time during score display, aiming to significantly enhance the cricket match experience for players, spectators, and officials. The scoreboard seamlessly integrates Bluetooth technology with LCDs to ensure real-time transmission and instant display of match statistics, scores, player information, and game progress. The system incorporates Arduino-based hardware for LCD control and Bluetooth communication, complemented by a dedicated mobile application functioning as an intuitive interface for scoreboard management. Through the mobile app, users can wirelessly interact with the scoreboard, thereby reducing delays by enabling swift updates of match data, configuring display preferences, and ensuring rapid data exchange between the app and the LCD scoreboard. The emphasis on reducing delay time in score presentation underscores the commitment to delivering timely and accurate information, contributing to an enriched viewing experience for cricket enthusiasts, sports venues, educational institutions, and community cricket events. This represents an innovative approach, leveraging Bluetooth connectivity and LCD technology to minimize delays and provide comprehensive, real-time match information, thereby modernizing and augmenting traditional cricket scoreboards.

AI-based betting anomaly detection system to ensure fairness in sports and prevent illegal gambling

This study develops a solution to sports match-fixing using various machine-learning models to detect match-fixing anomalies, based on betting odds. We use five models to distinguish between normal and abnormal matches: logistic regression (LR), random forest (RF), support vector machine (SVM), the k-nearest neighbor (KNN) classification, and the ensemble model—a model optimized from the previous four. The models classify normal and abnormal matches by learning their patterns using sports betting odds data. The database was developed based on the world football league match betting data of 12 betting companies, which offered a vast collection of data on players, teams, game schedules, and league rankings for football matches. We develop an abnormal match detection model based on the data analysis results of each model, using the match result dividend data. We then use data from real-time matches and apply the five models to construct a system capable of detecting match-fixing in real time. The RF, KNN, and ensemble models recorded a high accuracy, over 92%, whereas the LR and SVM models were approximately 80% accurate. In comparison, previous studies have used a single model to examine football match betting odds data, with an accuracy of 70–80%.

Bluetooth Enabled LCD Cricket Scoreboard

This abstract centres on designing and developing a Bluetooth-enabled LCD cricket scoreboard with a primary focus on reducing delay time during score display, aiming to significantly enhance the cricket match experience for players, spectators, and officials. The scoreboard seamlessly integrates Bluetooth technology with LCDs to ensure real-time transmission and instant display of match statistics, scores, player information, and game progress. The system incorporates Arduino-based hardware for LCD control and Bluetooth communication, complemented by a dedicated mobile application functioning as an intuitive interface for scoreboard management. Through the mobile app, users can wirelessly interact with the scoreboard, thereby reducing delays by enabling swift updates of match data, configuring display preferences, and ensuring rapid data exchange between the app and the LCD scoreboard. The emphasis on reducing delay time in score presentation underscores the commitment to delivering timely and accurate information, contributing to an enriched viewing experience for cricket enthusiasts, sports venues, educational institutions, and community cricket events. This represents an innovative approach, leveraging Bluetooth connectivity and LCD technology to minimize delays and provide comprehensive, real-time match information, thereby modernizing and augmenting traditional cricket scoreboards.

BDIS-SLAM: a lightweight CPU-based dense stereo SLAM for surgery.

Common dense stereo simultaneous localization and mapping (SLAM) approaches in minimally invasive surgery (MIS) require high-end parallel computational resources for real-time implementation. Yet, it is not always feasible since the computational resources should be allocated to other tasks like segmentation, detection, and tracking. To solve the problem of limited parallel computational power, this research aims at a lightweight dense stereo SLAM system that works on a single-core CPU and achieves real-time performance (more than 30Hz in typical scenarios). A new dense stereo mapping module is integrated with the ORB-SLAM2 system and named BDIS-SLAM. Our new dense stereo mapping module includes stereo matching and 3D dense depth mosaic methods. Stereo matching is achieved with the recently proposed CPU-level real-time matching algorithm Bayesian Dense Inverse Searching (BDIS). A BDIS-based shape recovery and a depth mosaic strategy are integrated as a new thread and coupled with the backbone ORB-SLAM2 system for real-time stereo shape recovery. Experiments on in vivo data sets show that BDIS-SLAM runs at over 30Hz speed on modern single-core CPU in typical endoscopy/colonoscopy scenarios. BDIS-SLAM only consumes around an additional time compared with the backbone ORB-SLAM2. Although our lightweight BDIS-SLAM simplifies the process by ignoring deformation and fusion procedures, it can provide a usable dense mapping for modern MIS on computationally constrained devices. The proposed BDIS-SLAM is a lightweight stereo dense SLAM system for MIS. It achieves 30Hz on a modern single-core CPU in typical endoscopy/colonoscopy scenarios (image size around ). BDIS-SLAM provides a low-cost solution for dense mapping in MIS and has the potential to be applied in surgical robots and AR systems. Code is available at https://github.com/JingweiSong/BDIS-SLAM .

Control strategies and performance analysis for a novel nuclear heating system with heat storage

A nuclear heating system integrated with a heat storage tank (HST-NHS) is a promising technical solution for countries with large-scale heating demand. HST-NHS can greatly curtail carbon dioxide emissions and contribute to sustainable development. Appropriate control strategies are not only crucial to realize the real-time matching between HST-NHS heat supply and heat demand, but also ensure the stable and safe operation of HST-NHS. Until now, control strategies for HST-NHS have received limited attention. A refined dynamic simulation model for HST-NHS is established based on APROS software. The verification of the built model in steady state and transient state is completed. Two basic control strategies for the primary heating network (PHN) of HST-NHS, constant supply water temperature and variable flow rate (CT-VF) and variable supply water temperature and constant flow rate (VT-CF) are presented in detail. Based on various typical working conditions of a central heating region in Liaoyuan City, the control effects of two control strategies are investigated. Additionally, a comprehensive performance comparison between the two strategies is explored. Simulation results indicated that under the two control strategies, the indoor temperature can be effectively controlled at a high comfort level. During the same typical day, the total energy consumption cost under the VT-CF control strategy is 0.5 × 105 yuan lower than that under the CT-VF control strategy.

Capacity and operation optimization of a low-temperature nuclear heating system considering heat storage

Integrating heat storage devices with the pool-type low-temperature heating reactor (PLTHR) can improve the flexibility of PLTHR for load following. A novel low-temperature nuclear heating system (HST-NHS) is proposed, which combines the DHR-400 heating reactor system, a heat storage tank (HST), and a gas boiler (GB) to achieve real-time matching between heating supply and demand. A mixed integer nonlinear programming (MINLP) optimization model with the annual cost (AC) as the optimization objective is established. An operation strategy that considers the device priority is proposed. The capacity and operation of HST-NHS are simultaneously optimized using the CPLEX solver. The optimization results indicate that an HST with a rated capacity of 1324.14 MWh and a GB with a rated capacity of 201.69 MW can provide the operational flexibility needed for the HST-NHS throughout the heating period. When compared with comparison schemes 1 (DHR-400 and HST), 2 (DHR-400 and GB), and 3 (pure gas boiler heating system), HST-NHS achieves significant annual cost savings. Finally, a sensitivity analysis of device price and fuel price is conducted, revealing that the unit price of the DHR-400 heating reactor system and natural gas price are the main factors affecting the AC and dynamic payback period (DPP) of HST-NHS. This work offers theoretical guidance for the subsequent engineering application of the DHR-400 heating reactor system.

Diuresis inducida frente a hidratación combinada en pacientes sometidos a procedimientos cardiovasculares percutáneos: metanálisis de ensayos clínicos aleatorizados

Introduction and objectivesContrast-associated acute kidney injury (CA-AKI) is a potential complication of procedures requiring administration of iodinated contrast medium. RenalGuard, which provides real-time matching of intravenous hydration with furosemide-induced diuresis, is an alternative to standard periprocedural hydration strategies. The evidence on RenalGuard in patients undergoing percutaneous cardiovascular procedures is sparse. We used a Bayesian framework to perform a meta-analysis of RenalGuard as a CA-AKI preventive strategy. MethodsWe searched Medline, Cochrane Library and Web of Science for randomized trials of RenalGuard vs standard periprocedural hydration strategies. The primary outcome was CA-AKI. Secondary outcomes were all-cause death, cardiogenic shock, acute pulmonary edema, and renal failure requiring renal replacement therapy. A Bayesian random-effect risk ratio (RR) with corresponding 95% credibility interval (95%CrI) was calculated for each outcome. PROSPERO database number CRD42022378489. ResultsSix studies were included. RenalGuard was associated with a significant relative reduction in CA-AKI (median RR, 0.54; 95%CrI, 0.31-0.86) and acute pulmonary edema (median RR, 0.35; 95%CrI, 0.12-0.87). No significant differences were observed for the other secondary endpoints [all-cause death (RR, 0.49; 95%CrI, 0.13-1.08), cardiogenic shock (RR, 0.06; 95%CrI, 0.00-1.91), and renal replacement therapy (RR, 0.52; 95%CrI, 0.18-1.18)]. The Bayesian analysis also showed that RenalGuard had a high probability of ranking first for all the secondary outcomes. These results were consistent in multiple sensitivity analyses. ConclusionsIn patients undergoing percutaneous cardiovascular procedures, RenalGuard was associated with a reduced risk of CA-AKI and acute pulmonary edema compared with standard periprocedural hydration strategies.

Real-Time Cost Optimization Approach Based on Deep Reinforcement Learning in Software-Defined Security Middle Platform

In today’s business environment, reducing costs is crucial due to the variety of Internet of Things (IoT) devices and security infrastructure. However, applying security measures to complex business scenarios can lead to performance degradation, making it a challenging task. To overcome this problem, we propose a novel algorithm based on deep reinforcement learning (DRL) for optimizing cost in multi-party computation software-defined security middle platforms (MPC-SDSmp) in real-time. To accomplish this, we first integrate fragmented security requirements and infrastructure into the MPC-SDSmp cloud model with privacy protection capabilities to reduce deployment costs. By leveraging the power of DRL and cloud computing technology, we enhance the real-time matching and dynamic adaptation capabilities of the security middle platform (Smp). This enables us to generate a real-time scheduling strategy for Smp resources that meet low-cost goals to reduce operating costs. Our experimental results demonstrate that the proposed method not only reduces the costs by 13.6% but also ensures load balancing, improves the quality-of-service (QoS) satisfaction by 18.7%, and reduces the average response time by 34.2%. Moreover, our solution is highly robust and better suited for real-time environments compared to the existing methods.

Diuresis-matched versus standard hydration in patients undergoing percutaneous cardiovascular procedures: meta-analysis of randomized clinical trials

Introduction and objectivesContrast-associated acute kidney injury (CA-AKI) is a potential complication of procedures requiring administration of iodinated contrast medium. RenalGuard, which provides real-time matching of intravenous hydration with furosemide-induced diuresis, is an alternative to standard periprocedural hydration strategies. The evidence on RenalGuard in patients undergoing percutaneous cardiovascular procedures is sparse. We used a Bayesian framework to perform a meta-analysis of RenalGuard as a CA-AKI preventive strategy. MethodsWe searched Medline, Cochrane Library and Web of Science for randomized trials of RenalGuard vs standard periprocedural hydration strategies. The primary outcome was CA-AKI. Secondary outcomes were all-cause death, cardiogenic shock, acute pulmonary edema, and renal failure requiring renal replacement therapy. A Bayesian random-effect risk ratio (RR) with corresponding 95% credibility interval (95%CrI) was calculated for each outcome. PROSPERO database number CRD42022378489. ResultsSix studies were included. RenalGuard was associated with a significant relative reduction in CA-AKI (median RR, 0.54; 95%CrI, 0.31-0.86) and acute pulmonary edema (median RR, 0.35; 95%CrI, 0.12-0.87). No significant differences were observed for the other secondary endpoints [all-cause death (RR, 0.49; 95%CrI, 0.13-1.08), cardiogenic shock (RR, 0.06; 95%CrI, 0.00-1.91), and renal replacement therapy (RR, 0.52; 95%CrI, 0.18-1.18)]. The Bayesian analysis also showed that RenalGuard had a high probability of ranking first for all the secondary outcomes. These results were consistent in multiple sensitivity analyses. ConclusionsIn patients undergoing percutaneous cardiovascular procedures, RenalGuard was associated with a reduced risk of CA-AKI and acute pulmonary edema compared with standard periprocedural hydration strategies.

A hybrid ensemble learning framework for zero-energy potential prediction of photovoltaic direct-driven air conditioners

Integrating renewable energy is a promising solution for buildings to achieve the net-zero-energy goal. Expanding real-time matching between renewable energy generation and building energy demand can help realize more enormous zero-energy potential in practice. However, there are few studies to investigate the real-time energy matching in renewable energy building design. Therefore, in this study, a hybrid ensemble learning framework is proposed for analyzing and predicting zero-energy potential in the real-time matching of photovoltaic direct-driven air conditioner (PVAC) systems. First, the datasets of zero-energy probability (ZEP) are generated under the three main climate regions in China, which are with consideration of the load flexibility of air conditioners and based on six important design variables. Second, a novel ensemble learning method named Extreme Gradient Boosting (XGBoost) is selected to predict ZEP and the Bayesian Optimization (BO) is adopted to identify the optimal hyperparameters and further improve the prediction performance. The statistical analysis shows that ZEP distributions are very different from one region to another one and the PVAC systems in Beijing are the easiest to achieve the zero-energy goal. Among all the variables, PV capacity is the most significant and positively related to ZEP. The prediction results show BO-XGBoost achieves more than 99% accuracy and outperforms other benchmark models in the ZEP prediction of three cities. In a word, this paper reveals BO-XGBoost is the most effective model for ZEP prediction and provides the framework for designers to utilize zero-energy potential analysis and prediction for the first time.

Remaining Useful Life Prediction of Rolling Element Bearings Based on Hybrid Drive of Data and Model

For remaining useful life (RUL) prediction of machinery, model-driven methods often use a single model to process individual data, which is difficult to adapt to the diversity of degradation behaviors. Data-driven methods are more dependent on training data, and in practice a large amount of run-to-failure data is difficult to obtain. In this paper, a new hybrid drive of data and model method is proposed. In the model-driven path, a new scalable two-stage linear/nonlinear composite model is constructed to represent various degradation behaviors, and to clarify the evolution law of individual degradation. In the data-driven path, the long short-term memory prediction network is trained to track the degradation process and learn knowledge of multi-sample degradation behavior. The newly established dynamic matching index integrates the model-driven and data-driven paths, and realizes the interactive fusion of information and RUL prediction through real-time matching of hidden layer states. The whole life cycle performance degradation data of two sets of different experimental rigs are used for analysis, and compared with some state-of-art RUL prediction methods, the results show that the proposed method has higher prediction accuracy.

Synergistic development of heating system decarbonization transition and large-scale renewable energy penetration: A case study of Beijing

In response to climate change, China has set the target to achieve carbon neutrality by 2060. The large-scale development of renewable energy and the decarbonization transition of the heating system are crucial for achieving this target. Is there a synergistic effect between the heating system's decarbonization transition and the use of large-scale renewable energy sources? What effect does this synergistic development have on the CO₂ emissions and the economic costs of an energy system? This paper conducts a case study of Beijing, China, and designs three technical path scenarios for heating system decarbonization transition in the future, namely, business as usual scenario where existing policies and regulations are applied, the electricity substitution scenario where the traditional fossil fuel heating system is replaced by electric heat pump, and the synergistic development scenario where the existing thermal power units are retrofitted to replace the consumption of fossil fuels with renewable energy sources. Using the EnergyPLAN model, the hourly operation of Beijing's power and heating systems under the three scenarios are simulated. The research results are: Firstly, the proportion of local renewable energy will be over 70% in all three scenarios, and in the synergistic development scenario, the electricity and heating demand can be met by 100% renewable energy, reducing CO₂ emissions resulted from fossil fuel combustion; secondly, in view of the real-time match of power supply and demand in the power system, at times renewable energy supply would exceed demand. The rational use of the surplus renewable energy can promote further the synergistic development of decarbonization transition of heating system and renewable energy, reducing the primary energy consumption and CO₂ emissions by over 16% and 28% respectively in the future heating system; Thirdly to improve the economic benefit of a synergistic electric power and heating system, focus should be on the reduction of infrastructure construction cost rather than the reduction of fuel consumption. The proposed method can be extended to regional energy systems with more energy sectors, and can be used to formulate decarbonization transition schemes for industry, agriculture, etc.

Deep-Reinforcement-Learning-Based Optimal Transmission Policies for Opportunistic UAV-Aided Wireless Sensor Network

When there are unmanned aerial vehicles (UAVs) performing their specifically assigned tasks in the air, some of them still have available resources to access different ground communication networks to improve their communication performance, especially for the wireless sensor network. Technically, when they execute their own given missions with predetermined trajectories, they can also provide opportunistic assistance for terrestrial networks at the same time. In this article, we solve an opportunistic UAV-assisted data transmission problem in a wireless sensor network from a novel perspective. In consideration of UAVs dynamic behaviors, varying transmission tasks, and real-time matching between UAVs and sensor clusters, we propose to jointly optimize UAV scheduling and power control aiming to obtain optimal policies to maximize the network data transmission in a long run under the opportunistic access mode. We reformulate this optimization problem as a Markov decision process (MDP) and take deep reinforcement learning (DRL) as our tool to obtain solutions. We develop a DQN-based and a deep deterministic policy gradient (DDPG)-based optimization approaches to adjust the power allocation of cluster heads, and the scheduling and bandwidth allocation of UAVs during their missions over the covered area to improve the whole network data transmission performance. Simulation results demonstrate the validity and superiority of our proposed approaches compared with other benchmark policies in different perspectives.

TAPv2: An Approach Towards Sub-Microsecond Level Timing Accuracy Over Air Interface

Absolute time synchronization is a basic issue in wireless communication. With the advent of 5G, many emerging vertical industry applications have put forward demands for timing accuracy over air interface. High Precision Timing method over Air Interface based on PHY Layer Signals (TAP) is proposed for mobile networks. It is designed to provide microsecond level timing accuracy at a relatively low cost compared to the existing timing methods, such as GNSS, NTP, PTP, etc. The purpose of this paper is to comprehensively improve TAP, including timing accuracy, stability and compatibility, so as to promote its practical application. This paper proposes improvements to TAP from four aspects: timing algorithm, timing pilot signal, terminal timing process and timing parameters determination. We improve the TAP’s deficiencies in delay estimation, delay compensation accuracy and real-time matching of timing information. We implement the workflow of TAPv2 for UE based on FPGA. Finally, we settle the problem of TAP parameters determination by applying a DQN method. The simulations and tests not only show that the DQN model can effectively determine the TAP parameters, but also show TAPv2 can achieve higher accuracy and stability than TAP in multiple scenarios based on the interaction with baseband.

MmWave Beam Tracking for V2I Communication Systems Based on Spectrum Environment Awareness

Real-time matching of millimeter wave (mmWave) narrow beams is a great challenge in dynamic vehicle to infrastructure (V2I) environments due to the mobility of vehicles. In this paper, a novel beam search strategy based on spectrum-environment awareness is proposed. Combining the technique of label iterative optimization with three-dimensional (3D) grid encoding, the strategy treats the optimal beam pair indexes (BPIs) as labels and encodes the environments as features. Three-dimensional grid encoding is a symmetry-based environmental coding technology. A new Convolutional Neural Network (CNN) model is also constructed, which is trained by the features. The situational beam search of actual vehicles is performed under the trained CNN model. As a result, real-time mmWave narrow beam matching can be achieved. Simulation results demonstrate that the proposed strategy can effectively reduce the beam search overhead and improve the efficiency while guaranteeing the matching accuracy.

A Fine-Grain Batching-Based Task Allocation Algorithm for Spatial Crowdsourcing

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing.

A Modified Rainbow-Based Deep Reinforcement Learning Method for Optimal Scheduling of Charging Station

To improve the operating efficiency and economic benefits, this article proposes a modified rainbow-based deep reinforcement learning (DRL) strategy to realize the charging station (CS) optimal scheduling. As the charging process is a real-time matching between electric vehicles ‘(EVs) charging demand and CS equipment resources, the CS charging scheduling problem is duly formulated as a finite Markov decision process (FMDP). Considering the multi-stakeholder interaction among EVs, CSs, and distribution networks (DNs), a comprehensive information perception model was constructed to extract the environmental state required by the agent. According to the random behavior characteristics of the EV charging arrival and departure times, the startup of the charging pile control module was regarded as the agent’s action space. To tackle this issue, the modified rainbow approach was utilized to develop a time-scale-based CS scheme to compensate for the resource requirements mismatch on the energy scale. Case studies were conducted within a CS integrated with the photovoltaic and energy storage system. The results reveal that the proposed method effectively reduces the CS operating cost and improves the new energy consumption.