EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems is an open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2024, the journal started to publish on a monthly frequency (12 issues per year). INDEXING: ESCI-WoS (IF: 1.3), Compendex, DOAJ, ProQuest, EBSCO, Crossref, Dimensions eISSN: 2032-9407
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
Artificial intelligence-driven financial risk assessment: A deep learning-based credit scoring method for manufacturing enterprises
Financial risk assessment for manufacturing enterprises supports credit-risk screening and early warning. Existing methods often fuse multi-period financial and industry information through concatenation, shared representations, or unrestricted interactions, making it difficult to separate meaningful disturbance–sensitivity correspondences from irrelevant combinations. This study proposes an industry-disturbance-conditioned credit-scoring framework. Its originality lies in explicitly matching external disturbances with firm-level financial sensitivities rather than treating them as unrestricted features. The framework decomposes financial information into levels, intertemporal changes, and accounting divergences; constructs demand, cost, and production sensitivities; and estimates conditioned exposures through a correspondence matrix and conditional gates. A matching-consistency loss constrains disturbance–sensitivity relationships, while dual-path prediction retains exposure-related and firm-specific risk information. Using Moody’s Orbis and Eurostat Short-Term Business Statistics (STS), the method achieves an AUPRC of 0.512 in the full out-of-time test, exceeding TabPFN, the strongest AUPRC baseline, by 1.4 percentage points. Its AUROC is 0.879, and its FNR of 0.276 is lower than 0.289 for HGNN and 0.291 for TabPFN. Among highly sensitive firms, the proposed model achieves an AUPRC of 0.489 and an FNR of 0.288, improving on HGNN by 1.5 and 2.3 percentage points, respectively. Statistical tests, ablation studies, and repeated runs support the matching and prediction mechanisms. Independent calibration further reduces probability error and calibration bias. The framework supports relative risk ranking and distress screening during industry disturbances, although validation across additional databases and disturbance settings remains necessary.
Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
financial risk assessment of manufacturing enterprises
Copyright (c) 2026 Shiliang Chang https://creativecommons.org/licenses/by-nc-sa/4.0
A Transformer-Enhanced Multi-Agent Reinforcement Learning Model for Resilience Optimization in Educational Equipment Manufacturing Supply Chains
INTRODUCTION: Educational equipment manufacturing supply chains are vulnerable to demand fluctuations, equipment failures, logistics disruptions, and cross-node risk propagation, while conventional approaches often separate state prediction from recovery decision-making. OBJECTIVES: This study proposes TMARL-ESCR, a Transformer-enhanced multi-agent reinforcement learning framework for supply chain resilience optimization. METHODS: The model represents suppliers, manufacturers, logistics providers, and distribution centers as a dynamic network and uses a Transformer to capture long-range temporal dependencies and cross-node interactions. Multitask prediction heads estimate future demand, logistics lead time, available capacity, and disruption risk, and these predictions are fused with current states to support coordinated recovery decisions. Under a centralized training and decentralized execution framework, multiple agents jointly optimize procurement, production, transportation, and inventory reallocation through local-global rewards and explicit operational constraints. RESULTS:The prediction module achieves a demand WMAPE of 14.38% and a disruption-risk AUC of 0.941. The complete model reaches a 95.2% order fulfillment rate, a 7.1-day recovery time, a 0.5% constraint violation rate, and a resilience score of 0.892. Compared with Transformer-MAPPO, TMARL-ESCR reduces recovery time by 19.3% and improves the resilience score by 5.9%. CONCLUSION: Experiments integrating M5 Forecasting, AI4I 2020, LaDe, and an SCML-based simulation environment evaluate predictive accuracy and resilience optimization under multiple disruption scenarios, demonstrating improved proactive recovery and coordinated resilience under complex disruptions.
Copyright (c) 2026 Yiquan Kong https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent and secure Federated Learning for data elements circulation in heterogeneous edge computation
INTRODUCTION: Heterogeneous edge computing creates data islands due to privacy and environmental heterogeneity. Current solutions lack an overall approach. OBJECTIVES: This study constructs a federated learning framework for secure data element circulation, mitigating low-power node tailing, balancing communication with accuracy, and adapting to non-IID data, advancing intelligent systems and cybersecurity. METHODS: The framework fuses Dynamic Clustering, Adaptive Gradient Transmission, and Data Distribution Alignment. Validation uses simulations against existing technologies. RESULTS: The proposed method achieves 0.892±0.021 comprehensive performance, 11.1%–13.6% higher than existing technologies. Under 20% network interruption, attenuation is 3.3% vs. 8.6%–12.5%. Data flow reaches 18.6±0.7 MB/s; privacy leakage is 0.8±0.2 bit. CONCLUSION: This study provides reliable support for safe, efficient data element circulation, advancing intelligent systems and cybersecurity.
dynamic clustering technology for device performance
adaptive gradient transmission technology
Copyright (c) 2026 Geng Cheng, Jianbo Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Scalable Dynamic Trust Evolution Framework for Large-Scale IoT Information Systems with Asymmetric Evidence Weighting
INTRODUCTION: The growth of Internet-of-Things (IoT) infrastructure being built pushes the envelope to giant scale, heterogeneous systems where autonomous devices keep communicating amongst themselves with less and less human intervention. Static or transient security mechanisms based on pure authentication fall short in this case, as every device that has nominally been put in a non-trust domain can push bad from there. OBJECTIVES: To guard against, we present in this paper a Scalable Dynamic Trust Evolution (SDTE) framework for large-scale IoT information systems. METHODS: We model trust as a dynamically changing latent state dependent on multiple sources of evidence, such as direct interaction, neighbour recommendation, context condition, and historical behaviour. A temporal update mechanism with exponential forget rate (λ) allows fast adaptation to changing behaviour, while asynchronous weighting (β⁻ > β⁺) indicates negative actions have larger weighting on trust than positive ones. An uncertainty-aware fusion of heterogeneous evidence sources mitigates conflicting observations. Overall, the framework is fully distributed and operates in lightweight computation (O(d)) without any need for sufficient computing resources, and can thus be deployed on low-resource edge devices. RESULTS: Our experimental results on publicly available IoT datasets (IoT-23 and Edge-IIoTset) show that SDTE achieves high detection performance (96.8% accuracy, 95.7% F1-score) across malicious ratio scenarios, good robustness against intermittent on–off attacks, and scaling as the network grows in size. CONCLUSION: The proposed SDTE framework provides an effective, lightweight, and scalable trust management solution for large-scale IoT information systems.
Copyright (c) 2026 Tao Sun https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Interference Elimination-Based Energy Consumption of the Internet of Things
INTRODUCTION: During the uninterrupted operation of the Internet of Things, the continuous activity of a large number of terminal devices not only increases the energy consumption of the nodes, but also causes signal interference between devices, resulting in degraded channel conditions and Quality of Service of the Internet of Things, increased data transmission latency, and a further increase in energy consumption of the Internet of Things. OBJECTIVES: To mitigate the effects of communication conflict and signal interference on data transmission and reduce the energy consumption of the Internet of Things, an interference elimination-based energy consumption optimization algorithm for the Internet of Things is proposed. METHODS: According to the model of the Internet of Things, this paper divides the nodes of the Internet of Things into sink nodes and terminal nodes. The sink node dynamically allocates data transmission time-slots based on the Signal-to-Interference plus Noise Ratios of the terminal nodes. Meanwhile, the terminal node adaptively adjusts its power on the basis of its activity status while guaranteeing data transmission performance. RESULTS: The simulation results showed that the data transmission latency was reduced, signal interference mitigated, channel environments improved, and energy consumption of the Internet of Things lowered. CONCLUSION: The proposed algorithm can effectively reduce the impact of channel interference on the data transmission of terminal nodes, improve data transmission performance and Quality of Service of the Internet of Things, and reduce energy consumption of the Internet of Things.
Copyright (c) 2026 Lianlian Song, Chuanhong Song, Yicheng Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Reference-Assisted Learner-Aligned Distributed Stacking for Edge Intrusion Detection in the Industrial Internet of Things
Networked manufacturing couples sensors, programmable logic controllers, and industrial gateways to production that cannot simply be paused. An intrusion detector in this setting must control false alarms and edge-resource use as well as detect attacks. Industrial Internet of Things (IIoT) edge nodes add three practical constraints: training traffic cannot be centralized, local statistical distributions differ, and the deployed models may be structurally heterogeneous. We address this setting with Reference-Assisted Learner-Aligned Distributed Stacking (RA-LADS). It evaluates node models on mutually exclusive reference pools, groups LightGBM and XGBoost responses by learner family, and represents each family by its mean, standard deviation, and five quantiles in a fixed, permutation-invariant 7M-dimensional vector. Across 20 matched partitioning and training seeds, the full 14-dimensional summary attained a mean F1 of 0.984514. Its gain over a global 7-dimensional summary without family semantics was 0.000756 (95% CI: 0.000559–0.000962; Holm-adjusted p = 1.08 × 10−5). By comparison, the difference from node-wise raw prediction concatenation was −0.000009, with an interval spanning zero. Random balanced grouping was not significantly different from true learner-family grouping after multiplicity correction; family identity is therefore a useful grouping prior, but not the only one. Mean F1 scores for a compact reference-pool LightGBM, a 41-parameter Tiny Deep Sets model, and a heterogeneous one-model-per-node summary were 0.983887, 0.983659, and 0.983865. The full method delivered small yet reproducible paired gains over all three controls. At fixed false-alarm-rate (FAR) budgets, no stable advantage over a single LightGBM appeared between 0.1% and 2% FAR; at 5% FAR, F1 increased by 0.000417 (Holm-adjusted p = 0.017). Five-seed retraining on two external NetFlow datasets did not show a general advantage. The claim is therefore limited to settings in which node responses carry exploitable distributional structure. Within that boundary, RA-LADS offers a lightweight interface for heterogeneous tree collaboration when labeled reference data and explicit alert operating points are available, although system cost still grows linearly.
Copyright (c) 2026 Jing Li, Shuhao Shen, Kangrui Xu, Lin Cui https://creativecommons.org/licenses/by-nc-sa/4.0
Correction: Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection
Correction to: Kong S et al. (2026) Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection. https://doi.org/10.4108/eetsis.11781 This paper was inadvertently published without the funding information. The following information was added to the “Funding” section of the paper: This paper is supported by Dezhou Engineering Research Center for Big Data and Intelligent Perception Technology (Platform No. PT2025KJT002). The original article has been updated.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-sa/4.0
Human-Centered Maintenance Decision-Making in Smart Manufacturing Enhanced by English Technical Documents and Human-in-the-Loop Collaboration
INTRODUCTION: Predictive maintenance is essential in human-centered smart manufacturing, yet existing methods often convert sensor data into fault diagnosis or RUL prediction without effectively transforming English technical documents into actionable maintenance constraints. OBJECTIVES: This study aims to develop an interpretable human-in-the-loop maintenance decision-making framework that integrates equipment states, English technical clauses, and human feedback to support constraint-aware maintenance actions. METHODS: An English technical document-enhanced framework is proposed, consisting of the English Document-Induced Constraint Learning (ELIC) module and the Human Feedback Loss Optimization (HFLO) module. ELIC models maintenance clauses from manuals, SOPs, and safety documents as state–document–action feasibility constraints. HFLO converts preference and risk feedback into optimization signals. A constraint-aware inference strategy combines task prediction scores, document feasibility scores, and feedback consistency scores to rank candidate actions. RESULTS: Experiments on MetroPT-3 and XJTU-SY under the controlled protocol show that the proposed method achieves the lowest SP-CVR on both datasets and the highest F1 on MetroPT-3. On XJTU-SY, TCN–Transformer achieves a lower RMSE, while the human-in-the-loop baseline achieves a higher SP-FAS. Ablation results show that ELIC, HFLO, and constraint-aware inference affect different task and protocol-consistency metrics. CONCLUSION: The proposed framework provides a traceable approach for jointly incorporating English technical clauses and feedback into maintenance action ranking under the constructed controlled protocol.
Copyright (c) 2026 Yanxia Quan https://creativecommons.org/licenses/by-nc-sa/4.0
Data-Model Hybrid-Driven Multi-Timescale Low-Carbon Economic Dispatch for Manufacturing-Park Virtual Power Plants
INTRODUCTION: Manufacturing parks combine energy-intensive production equipment, auxiliary systems, distributed renewables, and flexible loads, creating coupled fluctuations between production schedules and electricity demand. OBJECTIVES: This study develops an auditable data-model hybrid method that links empirically updated uncertainty scenarios with a production-constrained, low-carbon, three-stage dispatch model for manufacturing-park virtual power plants. METHODS: The data module estimates wind-speed, irradiance, manufacturing-load, and price distributions from rolling historical records, generates joint scenarios, and updates operating states. The model module solves a coupled day-ahead-intraday-real-time stochastic program with process-feasibility, carbon-flow, storage, and inter-park constraints; reduced scenarios and measured deviations form the interface between the two modules. RESULTS: Case studies show a 22.6% reduction in operating cost, an 18.0% reduction in carbon emissions, a renewable-energy absorption rate of 94.8%, and a 48.8% reduction in power-fluctuation standard deviation compared with single-time-scale dispatch. CONCLUSION: The proposed framework coordinates energy and production decisions while maintaining production feasibility and improves economic, low-carbon, and operational performance under high renewable penetration.
Copyright (c) 2026 Ruosong Hou, Wei Guo, Ziheng Zhao, Xiaolin Tan, Yuan Cao https://creativecommons.org/licenses/by-nc-sa/4.0
Playbook-Guided Executable SOC Automation for Privacy-Preserving Response in Distributed Networked Systems
INTRODUCTION: Distributed networks require security operations center (SOC) automation that connects data security monitoring, privacy-aware evidence handling, controlled execution, and measurable evidence. Static playbooks cannot fully handle ambiguous cross-domain incident context. OBJECTIVES: This paper presents an executable multi-agent framework for data security monitoring and response in distributed networks. METHODS: LLM-based roles generate event analysis, tasks, actions, commands, execution records, and summaries. Security orchestration, automation, and response (SOAR) playbooks and a virtual security capability layer provide controlled execution and repeatable evaluation. RESULTS: On 83 labeled incidents, the framework achieved 0.9684 precision, 0.4742 recall, 0.6367 F1-score, and 76.45 s average handling time for tool-call evaluation. CONCLUSION: The framework makes distributed data-security response auditable and quantitatively evaluable. The main improvement direction is stronger planning verification for complex multi-step incidents.
Data Security and Privacy Protection in New Distributed Networks and System
Copyright (c) 2026 Jie Zhang, Haizhuang Liu, Le Ren, Yuxiang Zhao, Zekai Song https://creativecommons.org/licenses/by-nc-sa/4.0
Correction: A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
Correction to: Hongcai Chen et al. (2026) A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar. https://doi.org/10.4108/eetsis.13388 Due to typesetting errors, some of the formulas in sections 2.2.2 and 2.3.1 of this article did not display correctly. These errors were corrected and the original article has been updated.
Digital Twin and Constraint-Guided Generative Decision Support for Human-Machine Collaborative Manufacturing under Industry 5.0
INTRODUCTION: In Industry 5.0 manufacturing systems, adaptive decision support is required to handle demand fluctuations, machine degradation, and operational feasibility constraints under human supervision. However, existing digital twin and data-driven approaches remain primarily focused on monitoring or forecasting, with limited integration into decision-oriented production control and feedback adaptation. OBJECTIVES: This study aims to develop a human-in-the-loop adaptive production decision-support framework that transforms external demand signals into feasible, risk-aware, and reviewable production actions by integrating demand-production state fusion, digital twin-based constraints, structured generative decision decoding, and feedback-driven updates. METHODS: A constrained decision-making system is constructed using Online Retail II and AI4I 2020 datasets. A unified state representation is learned by fusing demand indicators with manufacturing digital twin variables. A constraint-guided policy decoder is designed to generate discrete production actions under feasibility constraints rather than performing demand forecasting. Human feedback is encoded into subsequent decision states to enable iterative adaptation. The model is trained via a unified optimization objective balancing demand alignment, risk control, and explanation consistency. RESULTS: Experimental results on two datasets show that the proposed framework achieves improved demand-production alignment while consistently reducing manufacturing risk exposure and human override rates compared with forecasting-based and decision-oriented baselines. CONCLUSION: The proposed framework demonstrates that integrating digital twin constraints, constrained generative decision-making, and human feedback enables a shift from demand prediction to feasibility-aware production decision optimization, improving safety, interpretability, and human acceptability in Industry 5.0 manufacturing systems.
Copyright (c) 2026 Chen Qian https://creativecommons.org/licenses/by-nc-sa/4.0
Reinforcement Learning Method for Collaborative Multi-Objective Path Planning of IIoT Drones and Unmanned Vehicles in Random Demand and Dynamic Delivery Environments
INTRODUCTION: Dynamic Industrial Internet of Things (IIoT) delivery is characterized by stochastic order arrivals, heterogeneous UAV–UGV capabilities, and real-time demand updates, making conventional static routing methods insufficient for collaborative delivery planning. Existing UAV trajectory-planning and truck–drone routing studies usually focus on either single-platform trajectory control or offline vehicle routing, and they rarely integrate stochastic IIoT demand updating, UAV–UGV rendezvous coordination, operational constraints, and multi-objective decision-making into a unified reinforcement-learning framework. OBJECTIVES: To address this limitation, this study proposes a guidance-factor-enhanced MADDPG framework for collaborative multi-objective path planning of UAVs and UGVs in random-demand and dynamic delivery environments. METHODS: Order arrivals are modeled by a Poisson process, while order location, payload, priority, and time-window attributes are generated through multivariate random distributions and updated by IIoT terminals every 5 s. Payload, endurance, speed, rendezvous, and task-sequencing constraints are incorporated into a normalized multi-objective function considering delivery time, delivery cost, demand satisfaction, and UAV endurance loss. A distance–demand–priority guidance factor and a reconstructed reward function are further introduced to alleviate sparse-reward effects and guide agents toward high-value demand regions. RESULTS: Simulation and ablation results show that the reconstructed reward is essential for stable learning, while the guidance factor significantly accelerates convergence. Under 40 IIoT nodes, the proposed method converges after approximately 2,000 episodes, whereas the model without guidance information requires about 5,000 episodes. Comparative experiments further show that the proposed method improves system throughput and collaborative delivery efficiency while maintaining feasible UAV trajectories. CONCLUSION: The proposed framework provides an adaptive reinforcement-learning solution for UAV–UGV cooperative logistics in dynamic IIoT environments.
Copyright (c) 2026 Li Qiao, Changkai Xu, Bin Ni https://creativecommons.org/licenses/by-nc-sa/4.0
Optimization of Load Balancing and Real-Time Scheduling in Distributed Systems for Cross-Language Text Processing Tasks
INTRODUCTION: With the rapid expansion of cross-language text processing applications, distributed systems must support large-scale multilingual workloads with high efficiency and responsiveness. Effective load balancing and real-time scheduling are therefore essential for maintaining system performance. However, many existing approaches rely on static or general-purpose scheduling strategies, which fail to capture language-dependent workload variations, resulting in limited scalability and inefficient resource utilization. OBJECTIVES: This study aims to improve load balancing efficiency and real-time scheduling performance in distributed systems for cross-language text processing. The focus is on addressing dynamic task variations, heterogeneous resource demands, and robustness challenges in multilingual, noisy, and fluctuating computing environments. METHODS: A cross-language-aware dynamic load balancing and scheduling optimization framework based on reinforcement learning is proposed. The framework incorporates language pair, task type, priority, input length, estimated resource demand, and real-time node status into an adaptive scheduling strategy. Denoising mechanisms and feature fusion modules are further introduced to enhance scheduling stability and decision robustness. RESULTS: Experimental results show that the proposed method outperforms existing approaches in response time, resource utilization, and task completion rate. The framework achieves a task completion rate of 96.5%. Under high-noise conditions, the reduction in task completion rate is approximately 50% lower than that of baseline methods, demonstrating improved robustness. CONCLUSION: The proposed framework provides an effective solution for dynamic scheduling and load balancing in cross-language text processing systems. By coupling multilingual task characteristics with distributed resource states, it offers scalable support for multilingual distributed computing and new insights into reinforcement learning-based scheduling optimization.
Scheduling optimization and load balancing in scalable distributed systems
Copyright (c) 2026 Hongying Pu, Jixiang Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Bearing Cross-Domain Fault Diagnosis Based on Wavelet Time-Frequency Map and Resnet Transfer Learning in Industrial IoT-Enabled Environments
To overcome the difficulties faced by current bearing fault diagnosis methods in industrial applications, such as the difficulty in obtaining sufficient labeled real fault training samples and the subpar performance of cross-domain fault diagnosis due to data distribution deviation, a new bearing cross-domain fault diagnosis method based on wavelet time-frequency maps and Resnet transfer learning (CDF-WTI-RTL) is proposed.In the context of the Industrial Internet of Things (IIoT) and Industry 5.0, real-time condition monitoring and intelligent predictive maintenance have become critical for ensuring manufacturing system resilience and sustainability. First, raw vibration signals are processed using continuous wavelet transform to obtain two-dimensional time-frequency maps. Second, a Resnet network adapted for bearing fault diagnosis is constructed to autonomously mine deep features from source and target domains. A random forest classifier is then employed to build a cross-domain fault pattern recognition classifier. Experimental analysis of training samples using two different bearing fault datasets shows that the accuracy of the CDF-WTI-RTL model reaches 98.38% and 91.5% with unbalanced training samples respectively, notably surpassing the other 5 comparative models. The results demonstrate that the proposed method can be effectively deployed in IIoT architectures for reliable, cross-condition bearing fault diagnosis.
Copyright (c) 2026 Wanchuan Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on digital workshop dynamic scheduling technology based on blockchain
INTRODUCTION: As a critical process in high-end equipment manufacturing, dynamic scheduling in digital workshops plays a vital role in order delivery and project cost control. However, traditional scheduling frameworks suffer from weak data security, opaque multi-agent collaboration, and slow constraint checking. OBJECTIVES: To address these limitations, this study proposes an efficient and trustworthy dynamic scheduling solution that balances operational efficiency with data reliability. METHODS: A three-layer collaborative framework comprising cloud, edge, and blockchain layers is designed. The cloud layer employs a multi-strategy improved multi-objective genetic algorithm with three-stage encoding targeting processes, equipment, and job teams. This algorithm integrates tournament selection, adaptive repair, and particle swarm optimization to globally minimize maximum delivery time, total energy consumption, and equipment load fluctuation. The blockchain layer leverages consortium blockchain and smart contracts for tamper-proof data storage and automatic constraint verification, while the edge layer handles real-time on-site scheduling. RESULTS: The optimization algorithm converges to an optimal value of 55.1 in an average of only 3.9 iterations, outperforming three mainstream heuristic algorithms. In a real deployment across 57 workstations in a cruise ship laser sheet metal workshop, scheduling tasks are completed within 25 seconds. Blockchain technology achieves a 100% data tampering detection rate, improves data consistency from 96.2% to 99.8%, and reduces scheduling deviation from 4.39% to 3.82%, with only a 3-second processing overhead. CONCLUSION: The integrated architecture enhances both scheduling efficiency and data reliability, supporting the digital and intelligent transformation of shipbuilding enterprises.
Copyright (c) 2026 Ganlong Wang, Jun Zhu, Guoyin Zhang, Jingjing Sun, Wei Zheng, Bin Xie, Xiang Li https://creativecommons.org/licenses/by-nc-sa/4.0
Optimization of Circular-Rail RGV Scheduling in a Tobacco Warehouse
Continuous operation in tobacco warehouses places high requirements on circular-rail rail guided vehicle (RGV) dispatching, because task concentration, track interference, and equipment abnormalities may quickly cause vehicle waiting and material-flow delay. To improve the response capability of this system, this study develops a hybrid scheduling method based on an improved genetic algorithm and simulated annealing. According to the operating characteristics of the circular rail, a scheduling model is formulated to minimize the overall task completion time while considering rail operation rules and composite task requirements. In the solution process, the simulated annealing temperature is used to adjust parent selection and mutation behavior, so that the algorithm can maintain search diversity in the early stage and improve convergence in the later stage. Case results show that the proposed method reduces the average maximum completion time from 150.2 s to 143.4 s and improves the best result from 145.7 s to 139.6 s compared with the conventional genetic algorithm. These results indicate that the proposed method can improve RGV response efficiency and provide decision support for stable warehouse logistics under disturbances such as task fluctuation and path conflict.
Copyright (c) 2026 Dingke Shi; Zhihui Ye, Nanzhe Ding, Jie Gao, Chao Cheng, Wenwen Lin https://creativecommons.org/licenses/by-nc-sa/4.0
A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
INTRODUCTION: Roadside perception systems in intelligent transportation have stringent requirements for all-weather, high-precision, and real-time vehicle target tracking. However, there are inherent technical bottlenecks in such systems, including sparse millimeter-wave radar point clouds, severe noise interference, and the poor adaptability of traditional algorithms to distance-dependent density variations. OBJECTIVES: This study aims to propose a robust vehicle target tracking method based on roadside millimeter-wave radar to address the aforementioned technical bottlenecks and meet the stringent requirements of roadside perception systems for vehicle target tracking. METHODS: The research employs three key technical methods: Firstly, a point cloud distribution histogram is constructed to accurately localize the road area, and radar cross-section (RCS) characteristics are synergistically integrated with motion attributes to eliminate static clutter, multipath false plots, and electromagnetic noise. Secondly, a Distance-Zoned Multi-Frame DBSCAN (DZM-DBSCAN) clustering algorithm is proposed, which partitions the radar’s effective detection range into near-distance and far-distance intervals based on the physical principle of point cloud density attenuation with increasing detection distance, dynamically optimizes clustering parameters (Eps, MinPts) for each interval using local density statistics from k-nearest neighbor (KNN) analysis, and suppresses single-frame isolated false clusters through inter-frame motion continuity constraints. Finally, a multi-target tracking framework is established by combining a dynamically threshold-adjusted Kalman Filter (KF) with multi-feature weighted Hungarian matching. RESULTS: The main results obtained in this paper are the following: Experimental validation on the public RadarData dataset shows that the DZM-DBSCAN algorithm achieves a Silhouette Coefficient (SC) of 0.8456, representing a 10.09% improvement over the traditional DBSCAN (0.7681), and a Davies-Bouldin Index (DBI) of 0.1936, which is 10.66% lower than that of the traditional DBSCAN (0.2167). This effectively resolves the long-distance target missing detection issue prevalent in conventional methods. Additionally, the proposed tracking method achieves an optimal balance between tracking accuracy and continuity, with an average single-frame processing time of only 1.08 ms, fully meeting the real-time requirements of roadside perception systems. CONCLUSION: The proposed robust vehicle target tracking method based on roadside millimeter-wave radar provides a reliable all-weather, high-precision, and real-time roadside vehicle perception solution for intelligent transportation systems. Thus, this work holds significant engineering application potential and theoretical value.
Copyright (c) 2026 Hongcai Chen, Yu Cheng, Yaheng Ren, Yaoxing Kang https://creativecommons.org/licenses/by-nc-sa/4.0
A Communication-Reduced Privacy-Preserving ViT Inference Framework for Distributed Edge Intelligence
To address privacy leakage from user images, intermediate representations, and outputs during Vision Transformer (ViT) inference in distributed edge services, this paper presents SViT, a two-server secret-sharing framework with offline correlated randomness. The revised design specifies fixed-point arithmetic over the ring Z_(2^64) with 16 fractional bits, fresh one-time masks, probabilistic truncation, numerical ranges, and complete input-output procedures for SExp, SDiv, SSqrt, SVar, SLayerNorm, SSoftmax, and SGeLU. The protocols use fixed-depth range reduction, lookup-assisted initialization, and a constant number of Newton updates, so their online depth is independent of numerical convergence tolerances. Under the semi-honest, non-colluding-server model, the revised security analysis defines approximate ideal functionalities, public leakage, simulator inputs, and sequential composition. Existing microbenchmarks show 2.28-6.50 times lower runtime and 4.00-14.20 times lower online communication than CrypTen for the reported core operators; for SDiv, runtime decreases from 9.1 ms to 1.4 ms and communication from 10.8337 MB to 0.7629 MB. Synthetic Q16 numerical checks report maximum absolute errors of 5.10×10−5 for SExp and 4.73×10−4 for SGeLU, maximum relative errors of 1.27×10−5 for reciprocal and 3.72×10−5 for square root, and 100% top-1 consistency over 10,000 randomly generated Softmax vectors. The reported end-to-end result remains 3799.744 ms and 2.85 GB per inference; therefore, SViT is described as communication-reduced relative to the evaluated MPC baselines rather than universally lightweight, and its present practical scope is primarily high-bandwidth LAN or provider-edge deployments.
Copyright (c) 2026 Tingting Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Human-Centric Smart Manufacturing under Industry 5.0:IIoT-Enabled Machine Learning for Real-Time Fault Identification and Adaptive Workforce Scheduling
This study addresses Industry 5.0's demand for human-machine collaboration and manufacturing resilience by developing machine learning models for rapid fault identification and adaptive personnel scheduling. When a production-line fault occurs, the proposed approach uses IIoT data to identify the fault category quickly, support targeted intervention, and facilitate the restoration of normal production. XGBoost achieved near-perfect fault identification accuracy in model evaluation and accuracy and recall of 1.0 in validation, significantly outperforming the Random Forest baseline. Analysis revealed that experienced operators increased product qualification rates by 15% but reduced output by 10% due to physical factors. Decision tree regression minimized scheduling errors with an MSE of 0.49 and an R2 of 0.83. These results demonstrate that integrating IIoT-driven rapid fault identification with intelligent staffing provides a practical basis for shortening the response cycle, improving recovery capability after disruptions, and strengthening the resilience of human-centric manufacturing systems.
Copyright (c) 2026 Yaocong Xie, Ning Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Collaborative Resource Allocation for Mechanical Manufacturing Edge Networks: A Deep Reinforcement Learning Approach
Industry 4.0 is transforming mechanical manufacturing systems into edge-enabled, networked, and intelligent environments, where concurrent task execution, heterogeneous resource coordination, and dependency-aware scheduling have become critical requirements. In such scenarios, resource allocation must jointly consider computational demand, storage demand, business priority, deadline urgency, and inter-task dependencies, while enabling coordinated decisions among distributed edge agents. However, existing single-agent reinforcement learning methods have limited capability to model complex dependency relationships and heterogeneous resource collaboration under concurrent workloads, whereas conventional multi-agent systems often rely on coarse-grained task modeling and simplified cooperation mechanisms. To address these limitations, this paper proposes MIRA, a multi-agent deep reinforcement learning-based method for resource allocation in mechanical manufacturing edge networks. MIRA first decomposes tasks into fine-grained dependent subtasks, constructs a deadline-aware multi-metric priority function, and introduces a dynamic weight adjustment mechanism to balance computational demand, storage demand, normalized business priority, and deadline urgency. It then employs an adjacency matrix to characterize topology-aware agent interactions, enabling coordinated decision-making between computing agents and storage agents. Furthermore, MIRA incorporates an event-triggered state-exchange mechanism that updates subtask priorities and agent policies under changing workload, deadline, resource, and topology conditions. Experimental results on a PCB-derived simulated scheduling workload show that MIRA outperforms the selected baselines across multiple scheduling metrics.
Copyright (c) 2026 Kaile Xiao, Xiajing Wang, Jing Wang https://creativecommons.org/licenses/by-nc-sa/4.0
A Cross-Domain Data Flow Security Governance and Privacy Protection Mechanism for Distributed Digital Art Platforms
INTRODUCTION: Distributed digital art platforms face ownership ambiguity, privacy leakage, unauthorized access, and audit difficulties in cross-domain data flows. OBJECTIVES: To enhance trust, privacy, controllability, and auditability through a dedicated data security governance mechanism. METHODS: A closed-loop governance mechanism jointly models art data, AIGC models, copyrights, and transactions, integrating five coordinated modules: damage-tolerant ownership verification, privacy-preserving cross-chain auditing, dynamic access control, secure delivery with rollback, and efficient traceability. RESULTS: Ownership confirmation reaches 97.9%–98.7% (mild perturbations) and 85.9% (combined); authorization accuracy: 95.6%; illegal access interception: 94.9%; normal transaction audit pass rate: 99.3%; anomaly detection: up to 100%; on-chain storage compression: 99.99% (50 MB); traceability success: 94.8%. Access latency remains below 20 ms with 180+ transactions/s under concurrency. CONCLUSION: The mechanism effectively secures cross-domain data flows with high trustworthiness and low overhead, suitable for distributed digital art ecosystems.
Copyright (c) 2026 Meixuan Lv https://creativecommons.org/licenses/by-nc-sa/4.0
Research on a Deep Learning-Based Hybrid Attention and Regularised Supervised Detection Model for Surface Defects in Precision Components
To solve the challenges in detecting hundreds of minute surface defects (scratches, pits, material shortages) and their combinations on precision components (connecting rods, gears, bearings, etc.) ranging from 6 to 30 mm in size—notably identification difficulty and high misdetection rates—this research proposed a deep learning convolutional neural network detection model integrating a with dropout regularization supervision. This model aims to achieve rapid identification and precise judgment of micro-defects, combined defects, and other issues in precision components. This model extracted global fragmented features from precision component images through convolutional layers. Pooling layers incorporated a hybrid attention mechanism integrating maximum and average pooling. Fully-connected layers concurrently assembled hybrid attention dual-pooling filtered features, creating hybrid attention weights via hyperbolic tangent activation functions to obtain precise fusion of location feature and defect type extraction. The dropout regularization mechanism randomly deactivates relevant neurons during the feature fusion stage of the fully connected layer. Experimental results demonstrated that this model reduced false detection rate for similar defects (e.g., dents of varying shapes and scratches with different contours) to obtaining an average defect detection accuracy of 99.8%. with an average detection speed of 300 pcs/min. This represented improvements of 9.2% and 7.6% in detection accuracy and 15% and 10% in detection speed compared to traditional CNN and single-attention models, respectively. It also presented excellent robustness in random defect detection under field conditions, providing a novel solution for efficient and precise surface defect detection on precision components.
Copyright (c) 2026 Honggang Duan https://creativecommons.org/licenses/by-nc-sa/4.0
Physics-Prior and Multi-Scale Attention-Based Three-Dimensional Quality Field Reconstruction for Sparse Industrial Manufacturing Inspection
INTRODUCTION: Three-dimensional quality field reconstruction from sparse industrial measurements is critical for intelligent manufacturing yet remains challenging due to sensor accessibility limitations, high inspection costs, and component occlusion. OBJECTIVES: This study develops a reconstruction framework that recovers full-domain three-dimensional fields from sparse observations by integrating physics-based constraints with data-driven deep learning, and validates it on simulated three-dimensional optical scattering fields that serve as a controlled, physically rigorous proxy for industrial quality fields. METHODS: A physics-guided loss function embedding Helmholtz equation residuals and boundary conditions is constructed, combined with a multi-scale three-dimensional convolutional network incorporating serial channel-spatial attention to capture both global structural and local high-frequency defect features. The benchmark fields are solved on a 128 × 128 × 128 grid, and 5% of voxels are retained as labeled samples. RESULTS: Under 5% sparse sampling of the simulated optical scattering fields, the proposed framework achieved the best reconstruction performance among all compared methods. On 40 independent test samples, it reached a PSNR of 34.6 ± 1.3 dB, an SSIM of 0.932 ± 0.011, and a phase RMSE of 0.183 ± 0.025 rad, outperforming MS-CNN by 4.5 dB, 0.049, and 33.2%, respectively. Visual comparisons showed improved recovery of strong scattering regions, boundary variations, diffraction-focus positions, and high-frequency defect-related structures, with an average peak-intensity error of about 4.2% and centroid deviation of about 0.3 μm. Under noisy inputs, the method retained PSNR above 29 dB and SSIM above 0.88 at SNR = 10 dB, indicating stronger robustness than 3D U-Net and standard PINN. Ablation experiments (reported as mean ± standard deviation over the 40 test fields) further confirmed that physical constraints, multi-scale convolution, channel attention, and spatial attention each contributed to the overall performance gain, with paired statistical tests confirming significant improvements across the main comparisons. CONCLUSION: The framework offers a robust, physically consistent route for sparse quality-field reconstruction in intelligent manufacturing. Validation currently relies on high-fidelity numerical simulation; extension to real industrial measurements is identified as the primary direction of future work.
Copyright (c) 2026 Liangyu Chen https://creativecommons.org/licenses/by-nc-sa/4.0
ARaNet: Attention and Residual Aware Network for Resilient Digital Twins in Rail Transit Equipment Manufacturing
INTRODUCTION: In rail transit equipment manufacturing, which encompasses locomotive body welding, metro vehicle assembly, and high-speed rail carriage production, high-fidelity 3D point cloud data acquired by industrial sensors serves as the foundation for digital twin modeling, automated quality inspection, and robotic guidance. However, harsh production environments characterized by metallic dust, welding spatter, mechanical vibration, and frequent occlusions by fixtures and tooling inevitably introduce severe data corruption, including missing regions and non-uniform point density. Such degraded sensor data undermines the reliability of downstream manufacturing processes that depend on accurate 3D representations. OBJECTIVES: This paper presents an effective 3D sensor data recovery method that reconstructs complete geometric representations from corrupted partial scans, specifically targeting the data integrity challenges encountered in rail transit equipment manufacturing. The proposed approach aims to simultaneously restore global structural completeness and local geometric precision, thereby enabling resilient digital twin systems that maintain operational continuity despite sensor-induced data loss. METHODS:We propose an Attention and Residual Aware Network (ARaNet) featuring a Multi-Scale Channel-Aware Convolution encoder and a Hierarchical Residual-Aware Decoder. The encoder performs Farthest Point Sampling at multiple resolutions (2048, 1024, 512 points) and applies a channel attention mechanism to dynamically weight feature channels, emphasizing geometrically salient structures such as sharp edges, curved surfaces, and mechanical joints that are critical in rail transit component geometries, including bogie frames, car body shells, and coupler assemblies. The decoder progressively generates point clouds from coarse resolution (64 points) to medium resolution (128 points) to high resolution (2048 points), incorporating a residual refinement module that predicts coordinate offsets to rectify local geometric errors. This capability is particularly valuable for precision metrology in component manufacturing. RESULTS: Experimental evaluation on the ShapeNet and ModelNet40 datasets demonstrates that ARaNet achieves substantial improvements over benchmark models PCN and PF-Net. Quantitative assessment shows average reductions of 12.9% and 23.8% in the Gt-to-Pre and Pre-to-Gt metrics, respectively. The generated point clouds exhibit more uniform distributions and superior detail restoration, particularly for complex mechanical geometries with curved surfaces and structural joints characteristic of rail transit equipment components. Conclusion:The effectiveness of integrating channel attention with residual refinement mechanisms for industrial 3D sensor data recovery is validated. ARaNet provides a robust data recovery foundation for resilient digital twin systems in rail transit equipment manufacturing, enabling sustained operational capability and rapid geometric reconstruction even when sensor data is compromised by production environment disruptions. However, it should be noted that ARaNet is currently validated exclusively on synthetic training data, and its performance under extremely high missing-region ratios or in real industrial deployment scenarios without domain adaptation warrants further investigation.
Copyright (c) 2026 Xi Chen, Xiaolong Gao, Hanyue Zhan, Wanting Liu https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Based Optimization Method for Coverage of IoT Upload Link Communication
To address the degradation in uplink communication quality of the Internet of Things (IoT) caused by long-distance signal attenuation, multipath interference, and dynamic environmental changes, this paper proposes an artificial intelligence (AI)-based optimization method for IoT uplink communication coverage. First, service sampling points, base stations, and antenna parameters are defined within a target area, and a signal propagation model is constructed. The Sigmoid function is then used to smoothly evaluate service quality, and the coverage optimization problem is formulated as an objective function that maximizes coverage. The Q-learning algorithm is employed to achieve dynamic optimization by designing the state space (e.g., coverage rate, number of devices), action space (e.g., antenna parameter adjustments), and reward function (e.g., coverage improvement). Through iterative Q-value updates, the base station controller adaptively adjusts antenna configurations to balance coverage and interference, thereby improving long-term communication performance. To further enhance optimization efficiency, prioritized experience replay and an adaptive exploration mechanism are introduced to dynamically adjust the exploration–exploitation trade-off during learning, accelerating convergence and improving adaptability in dynamic IoT environments. Experimental results show that the proposed method significantly improves the communication performance of the uplink: the number of complete sensor data upload packets increases by 20%–25%, the number of transmission interruptions decreases by 70%–80%, and the average communication duration is reduced by 30%–40%, verifying the effectiveness and robustness of the method.
Copyright (c) 2026 Sheng Ye https://creativecommons.org/licenses/by-nc-sa/4.0
Security Risk Modeling and Protection Resilience Assessment of Smart Tourism Distributed Graph Database Based on Hybrid Random Graph Model
INTRODUCTION: Distributed graph database for smart tourism scenarios is becoming a key target of network attacks because it stores a large amount of sensitive data, such as user trajectories and consumer preferences. The network security and operational resilience of such databases directly determine the stability of the upper application ecosystem. OBJECTIVES: To address the lack of coordination between topology-aware risk modeling, active protection, and resilience assessment, this study develops an HRG–structural framework for distributed graph databases in smart tourism. The framework explicitly defines the threat model, including external attackers with partial graph knowledge and legitimate insiders who may abuse authorized traversal privileges. METHODS: First, a closed-loop mechanism of “risk identification–quantitative assessment–dynamic protection” is constructed. The HRG topology and the structural resilience model are coupled bidirectionally: topology-derived risk is transferred to the resilience evaluator, while quarantine and policy decisions feed back to edge probabilities and access paths. A time-decayed attack-exposure term, dynamic access control, traversal-depth restriction, sensitive-edge masking, node quarantine, and audit logging are integrated. In addition to the controlled security simulations, a real Neo4j Community 5.26.0 property-graph prototype is evaluated at 1,000–25,000 nodes. Fixed seeds, attack schedules, configuration files, raw results, and source code are supplied as Supplementary Material S1. RESULTS: In the original controlled comparison, the HRG–structural model achieved a comprehensive performance score of 92.6±1.5, an attack interception rate of 95.8%±0.8%, and a sensitive-data leakage suppression rate of 68.9%±2.5%. In the separate reproducibility stress test, removing both time decay and topology feedback reduced interception from 87.8% to 51.6% and increased trusted-state restoration time from 1.99 h to 3.54 h. The Neo4j prototype loaded a 25,000-node/100,000-edge graph in 1.51 s with a 60.59 MB store; policy-filtered traversal and quarantine-update mean latencies were 0.038 ms and 0.110 ms, respectively. These prototype values are single-host, in-process measurements rather than distributed-cluster results. CONCLUSION: The combined evidence supports the internal effectiveness of topology–risk–protection feedback and demonstrates technically feasible property-graph integration under the evaluated single-host and short-horizon conditions. The evidence does not constitute production-cluster validation against zero-day exploits, long-term advanced persistent threats, side channels, or multi-node collusion; those scenarios remain deployment-stage priorities.
Copyright (c) 2026 Xiang Li, Siying Li https://creativecommons.org/licenses/by-nc-sa/4.0
Privacy-Preserving SACAA-Net: A Scale-Aware Consistency and Anomaly-Attentive Network for Fine-Grained Industrial Anomaly Detection in Secure Distributed Edge-IIoT Systems
Fine-grained industrial anomaly detection has become a core component of intelligent inspection in Industrial Internet of Things (IIoT) scenarios, particularly when visual data are continuously acquired and processed through distributed sensors together with edge devices. Yet once industrial visual data and model updates are distributed in this way, a series of security risks becomes difficult to avoid, including data leakage, unauthorized access, gradient inversion attacks, model poisoning, and privacy inference. What may be exposed through these risks is not only raw data itself, but also sensitive information related to equipment structure, production status, and enterprise operations. Under such conditions, privacy preservation is no longer optional; it is an essential requirement for real-world industrial deployment. Existing methods still concentrate mainly on representation learning under heterogeneous data distributions, while security constraints in distributed environments are often insufficiently considered. This can easily result in inconsistent feature representations and inadequate protection of privacy-sensitive information during both training and transmission. To address these issues, we propose Privacy-Preserving SACAA-Net for fine-grained industrial anomaly detection in secure distributed IIoT systems. The proposed model brings together cross-scale semantic consistency learning, anomaly-aware attention, and structural dependency modeling, with the goal of improving detection accuracy while retaining fine-grained details and overall structural information. For privacy-preserving deployment, SACAA-Net supports local training on edge devices, reduces the need for raw data sharing, and remains compatible with secure aggregation, differential privacy, and encrypted transmission, thereby helping mitigate the risks of information leakage and model inversion attacks.Extensive experiments on six industrial datasets demonstrate that SACAA-Net achieves superior performance in both accuracy and robustness, achieving average image-level and pixel-level AUROC scores of 96.4\% and 92.3\%, respectively, and outperforming the strongest baseline by 1.5\% and 1.8\% on these two metrics, providing an effective and secure solution for distributed industrial anomaly detection.
Copyright (c) 2026 Jiani Zeng, Xinyu He, Weixian Wang, Chenyu Jin, Hui Xu, Siyue Lu https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Detection Method for In-the-Wild Exploit Attacks in Distributed IoT Networks
INTRODUCTION: In distributed IoT networks, massive devices, edge nodes and cloud platforms exchange data via open protocols, making device exploit attacks a severe threat to business continuity, data security and cross-domain propagation. Existing rule-based methods fail to balance real-time performance, accuracy and attack localization against rapidly mutating, obfuscated and large-scale in-the-wild exploits. OBJECTIVES: To address the above limitations, this paper aims to propose an intelligent detection method integrating hybrid deep learning and open-source intelligence correlation for distributed IoT systems. METHODS: First, HTTP packet preprocessing extracts suspected attack samples to mitigate extreme class imbalance. Second, a BERT-CNN coupled model classifies suspicious packets automatically. Finally, attack vector regression correlation with public vulnerabilities, PoCs and threat intelligence realizes accurate 1Day/NDay attack identification. RESULTS: Experiments show the method achieves over 99.99% accuracy and 99.98% F1-score on real datasets. The IoT_Exploits_Founder system discovered 13 new in-the-wild exploits within one month in real environment. This study also supplements quantitative comparisons with representative methods, online deployment measurements of latency, memory overhead and throughput, and ablation studies for the attack-vector regression threshold and feature weights. CONCLUSION: The proposed method provides effective support for AI-driven security monitoring in distributed IoT systems, with strong real-time edge applicability and robustness.
Copyright (c) 2026 Xiaohu Wu, Mingyuan Zhang, Xiaolei Liu, Xiaojian Zhang, Ke Jing https://creativecommons.org/licenses/by-nc-sa/4.0
Data Security and Privacy Protection in Distributed Digital Economy: Economic Impacts and Governance Mechanisms
INTRODUCTION:The distributed digital economy, characterized by decentralization and cross-entity data flow, improves factor allocation efficiency but increasingly raises concerns over data security and privacy abuse. OBJECTIVES: Unlike the conventional digital economy, which often centers on centralized platforms (e.g., e-commerce, cloud computing), the distributed digital economy in this paper specifically refers to an economic system where data—as a production factor—is stored, computed, and circulated across multiple independent nodes without a central coordinating authority, relying on technologies such as blockchain, distributed ledger, edge computing, and peer-to-peer networks. Its core governance features include decentralized data control, consensus-based verification, and peer-to-peer economic activities. METHODS: This paper studies data security and privacy protection in the distributed digital economy from two aspects: economic impact and governance mechanism. Based on panel data from 30 provinces in China from 2018 to 2023, this paper uses the entropy weight-TOPSIS method, a two-way fixed effects model, a mediation effect model, and a spatiotemporal heterogeneity model to empirically test the economic impact and transmission mechanism of data security and privacy protection on the distributed digital economy. RESULTS: The empirical analysis results show that the level of data security and privacy protection significantly and positively promotes the development of the distributed digital economy, with each unit increase leading to a 0.412 unit increase in the development index. Blockchain smart contracts, privacy computing standards, and cross-border data flow rules play significant mediating roles, accounting for 93.7% of the total mediating effect. This positive economic effect exhibits significant spatiotemporal differences, increasing year by year, and is significantly higher in the eastern region than in the central and western regions. CONCLUSION: Based on empirical analysis results, optimization paths are proposed from four levels: collaborative governance, technology empowerment, regional balance, and institutional improvement, in order to improve the level of data security and privacy protection in the distributed digital economy.
Copyright (c) 2026 Cuilan Wang https://creativecommons.org/licenses/by-nc-sa/4.0