Table of Content

28 April 2026, Volume 36 Issue 4

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Safety Science Theories and Methods Safety Technology and Engineering Public Safety and Emergency Management Disaster Prevention and Mitigation Technology and Engineering Occupational Health Intelligent Safety Technology

Safety Science Theories and Methods

Research on primary responsibilities in prevention and handling of mine safety accidents

Yu Jun, Cai Minghong

2026, 36(4):  1-8.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1258

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In order to promote the implementation of the responsibilities of participating entities in the prevention and handling of mine safety accidents, improve the efficiency of safety governance and reduce the risk of mine safety accidents, the assumption of an enterprises' illegal liability was analyzed. The authority and responsibility system of regulatory authorities was sort out. The guarantee mechanisms for the professionalism and objectivity of work safety social service institutions were explored. Drawing on the provisions and practical experience of foreign jurisdictions regarding the primary responsibilities for work safety, and putting forward legal suggestions for improving the prevention and handling of mine safety accidents in China. The findings show that the addition of punitive damages and differentiated penalties for repeated illegal acts are important factors for enterprises to consider the costs and liabilities of illegal acts, which can raise enterprises' awareness of the importance of wore safety and firmly establish their sense of responsibility for work safety. Allocating the responsibilities of regulatory authorities by stages and constructing a regulatory model that attaches equal importance to inspections and unannounced investigations can straighten out the predicament of unclear division of powers and responsibilities of regulatory authorities and improve regulatory efficiency. Refining the access threshold for work safety social service institutions, establishing an information disclosure platform, and clarifying the provisions on accountability and the practice withdrawal system can ensure the professionalism, transparency and objectivity of the services provided by work safety social service institutions.

Risk zoo (X): a binary interpretation framework and resilience construction regarding butterfly effect

Tong Ruipeng, Hu Xiangyang, Ma Yuqing, Xin Le

2026, 36(4):  9-18.  doi:10.16265/j.cnki.issn1003-3033.2026.04.2147

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To prevent the occurrence of the butterfly effect in risk management, and in response to the scientific need to enhance the effectiveness of resilience governance in the face of risk amplification, a binary explanation framework for the butterfly effect was constructed based on the duality of risk. This framework encompassed social and cascading amplification of risk, clarifying the social amplification mechanism and cascading transmission pattern of the butterfly effect. On this basis, human-centered resilience construction mechanism in response to the butterfly effect was further explored. The results show that the duality of risk emphasizes the dialectical unity between the objective reality of risk its social constructiveness. Meanwhile, based on this duality, the uncertainty arising from the butterfly effect can be deconstructed, rooted in the complexity of modern socio-technical systems and the ambiguity resulting from the social construction of risk. On the one hand, modern socio-technical risks are interlinked and interdependent, forming an intricate risk network that exhibits endogenous complexity and provides impetus for risk cascade amplification. On the other hand, constrained by risk knowledge, the public's risk perception and behavior are influenced by social, institutional, and cultural factors, among others, manifesting as social ambiguity that creates conditions for the social amplification of risk. The duality of risk necessitates the establishment of a human-centered resilience governance system, that focus on improving risk monitoring and early warning system, refining risk communication mechanism, and promoting multi-stakeholder participation in governance, thereby enhancing the effectiveness of resilience management against the butterfly effect.

Recognition of construction workers' unsafe behaviors based on a multi-component topology graph convolutional network

Yu Yang, Jiang Lin, Hu Qijun, He Leping, Cai Qijie, Bai Yu

2026, 36(4):  19-27.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1106

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In order to effectively identify the unsafe behaviors of construction workers in high-altitude environments, a recognition model based on MCT-GCN was proposed. Firstly, a data preprocessing module was designed to convert video surveillance data into three-dimensional skeleton data. Secondly, a tri-component dynamic adjacency graph convolution was constructed. It integrated learnable structural prior topology, channel correlation adaptive topology, and relative position encoding topology to dynamically build adjacency matrices adapted to different actions. Furthermore, a multi-scale separable temporal convolution was proposed to decompose standard convolutions into deep temporal convolution and point-by-point convolution, thereby independently modeling the temporal characteristics and spatial distribution characteristics of construction workers' actions. Finally, experimental verification and comparative analysis were conducted on both public datasets and a self-built dataset of workers' unsafe behaviors. The results demonstrate that the proposed model outperforms existing methods in terms of recognition accuracy and cross-scene generalization. On the self-built dataset, the model achieved a peak recognition accuracy of 95.8%, significantly enhancing the detection of workers' unsafe behaviors in complex and dynamic construction environments, and making a significant contribution to the development of intelligent monitoring and control in the construction industry.

Multi-source fusion deep learning for electric vehicle charging station load forecasting and risk early warning

Li Fu, Lyu Wei, Cheng Wenyan

2026, 36(4):  28-37.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0114

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With the continuous expansion of electric vehicle charging stations, the power grid faces increasing risks such as power overload, load fluctuations, and uneven demand distribution. To address these issues, this paper proposes an Hybrid Deep Fusion(HDF)-Long Short-Term Memory(LSTM)-based method for load forecasting and graded early warning. The method integrates LSTM, Gated Recurrent Unit(GRU), and Transformer architectures with multi-source data, including historical load, meteorological conditions, and traffic flow. Pearson correlation analysis and a dynamic weight allocation mechanism are employed to improve nonlinear feature representation. Based on the transformer capacity and simultaneity factor specified in the Code for Design of Electric Vehicle Charging Stations, a three-level early warning mechanism is developed for rapid alerting near critical thresholds. Results show that the proposed model outperforms eXtreme Gradient Boosting(XGBoost), GRU, LSTM, and Transformer models, with an Mean Squared Error(MSE) of 0.185 2, an Mean Absolute Error(MAE) of 0.2682, and an R² of 0.985 7. The model also shows good computational efficiency and application potential in charging station load forecasting and operational risk warning.

Causation analysis of air traffic control operational unsafe event based on TM-HFACS-FRAM

Li Yike, Zhang Honghai, Shi Zongbei, Li Wenqing

2026, 36(4):  38-48.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0635

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In order to improve the systematicity and objectivity of safety risk identification in civil aviation ATC operations and to explore the causes and interaction relationships of ATC unsafe incidents, a causal analysis method for ATC unsafe incidents was proposed, integrating TM, HFACS, and FRAM. Firstly, TM technology was employed to perform text processing on unsafe incident reports, and high-frequency keywords related to ATC operation risks were extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Secondly, semantic attribution was conducted on these keywords, and an improved multi-level HFACS framework for ATC operations was constructed based on the characteristics of ATC work, and the corresponding risk factors were determined. Then, the interdependent relationships between various functional modules in the ATC operation system were analyzed by introducing FRAM to reveal the potential impact of interactions within the complex ATC system on incident occurrence. Finally, two cases of ATC unsafe incidents were investigated to analyze their risk causes. The results show that the proposed method effectively mines the interrelationships between multi-source causes and identifies multi-level risk causal factors, which verifies the effectiveness and scientific rigor of the method in multi-source data processing and complex causal structure mining.

Configurational paths and enhancement strategies for risk cognitive ability of enterprise safety professionals

Wang Qian, Tong Xinru, Yang Angbin, Tong Ruipeng

2026, 36(4):  49-56.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1224

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To improve the risk decision-making effectiveness of enterprise safety professionals and curb the occurrence of unsafe behaviors, the theoretical connotation and improvement paths of risk cognitive ability were explored in this study. Based on SRK cognitive theory, the antecedent conditions of risk cognitive ability were classified into skill-based, rule-based, and knowledge-based cognitive abilities. The analysis of risk cognitive dimensions was combined from the perspectives of management systems, cognitive load, and cognitive tasks. Subsequently, a cognitive theoretical model was constructed, covering hazard identification ability, equipment operation ability, hidden danger inspection ability, hidden danger management ability, scenario decision-making ability, and case reasoning ability. NCA and fsQCA were applied to conduct data calibration, necessity analysis, sufficiency analysis, and robustness analysis on the multi-dimensional risk cognitive abilities of 100 safety professionals. Furthermore, the configurational paths of the antecedent conditions were analyzed, and targeted strategies for improving cognitive abilities were proposed. The results show that there are four configurational paths for both high risk cognitive competence and non-high risk cognitive competence. Specifically, high risk cognitive competence encompasses three configurational types: the rule-knowledge dual-driven type, the multi-dimensional cognitive ability synergistic type, and the emergency management ability dominated type. Accordingly, a “precision empowerment-multidimensional synergy-dynamic adaptation” path for improving risk cognitive ability is put forward, thereby enhancing the level of enterprise safety management.

Risk assessment for overdue in-service pressure pipelines based on BOA-SAM-FTA

Tang Huabin, Xu Lei, Chen Siyu, Ma Songhua

2026, 36(4):  57-64.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0454

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To investigate the failure issues of overdue in-service pressure pipelines, this paper proposed a method based on integration of BOA and SAM for analyzing the failure probability of such pipelines, building upon the FTA framework. First, the causes of failure in overdue in-service pressure pipelines were analyzed, and a fault tree for failure was established. Then, experts were invited to evaluate the basic events of fault tree, and their opinions were aggregated using SAM method and an improved BOA-SAM approach. The accuracy of aggregation was assessed by comparing the uncertainty values of two methods(the lower the uncertainty value, the more precise the aggregation result). Finally, the failure probability of the top event was calculated, and an importance analysis was conducted. The results show that the uncertainty values of the aggregation results obtained using the improved BOA-SAM are significantly lower than those of SAM method. Notably, for the basic event X₆, the uncertainty value decreased by 7.6%. The failure probability of the top event is calculated to be 0.117 641. Stress corrosion cracking, third-party damage, weld cracking and excessive H₂S content in the medium are identified as the key factors contributing to the occurrence of accidents. This study provides a scientific and logical approach for risk assessment and failure analysis of overdue in-service pressure pipelines.

Safety Technology and Engineering

Analysis of CO₂ displacement CH₄ and storage effect in different stratified coals

Liang Bing, Zhang Shiyao, Sun Weiji, Zhang Xiaoyang, Nie Tao

2026, 36(4):  65-74.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1551

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To investigate the influence of coal bedding angle on enhancing CO₂ injection-enhanced coalbed methane recovery efficiency, coal samples from Mine 6 in Sijiazhuang, Jinzhong, Shanxi Province were studied. Using a flow displacement experimental system, CO₂ displacement of CH₄ experiments were conducted under different bedding angles. The variation patterns of displaced gas composition, gas flow rate, displacement efficiency, and storage capacity with bedding angle were analyzed during the displacement process. The results indicate: An increase in the angle between the bedding plane and the seepage direction significantly prolongs the breakthrough time of CO₂. The steeper the bedding angle, the slower the rate of change in outlet gas composition. The relationship between inlet and outlet flow rates follows the pattern 0 > 30 > 60 > 90°. The relationship for outlet CH₄ flow rate aligns with outlet flow rate in the early stage of the experiment, shifting to 90 > 60 > 30 > 0° in the later stage. At the end of the first displacement stage, CH₄ recovery rates followed 0 > 30 > 60 > 90°. By the end of the second stage, recovery rates shifted to 30 > 0 > 60 > 90°. Upon completion of the entire displacement process, the 30°-layered coal achieved the highest CH₄ recovery rate of 67%. The 30° bedded coal exhibited the lowest displacement ratio and the most effective replacement. Throughout the displacement process, CO₂ penetration consistently followed the pattern 0 > 30 > 60 > 90°, and non-0° bedded coal had not reached adsorption equilibrium for CO₂ by the end of displacement. Furthermore, the 90° bed coal achieved a storage rate of 81.59%, demonstrating the best storage effectiveness. These findings provide crucial insights for optimizing CO₂ injection strategies to enhance coalbed methane extraction.

Control and cause analysis for high-risk operations combined knowledge graph and Apriori algorithm

Guo Hanjun, Cui Huaying, Hu Zhenqi, Kang Rongxue, Zhao Jinlong

2026, 36(4):  75-84.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0702

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In order to improve the safety management level of high-risk operations, an intelligent management and control method was proposed based on the knowledge graph and the Apriori algorithm. First, some high-risk operation accidents were collected. Then, the accident causes were extracted and classified automatically by Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field(BERT-BiLSTM-CRF)model, which constructed a knowledge graph of high-risk operation accidents, storing relevant accident data. Subsequently, an index system of high-risk operation causative factors was established, combined with the corresponding operation standard specification. Furthermore, the correlation among causative factors was determined by the Apriori algorithm. Finally, some countermeasures were proposed for targeted control. In this paper, the application of confined space operations was used to demonstrate this method. The results showed that the main causes of confined space accidents are not wearing safety protective equipment", blind rescue", not being equipped with gas detection and other equipment", missing safety warning signs", insufficient safety education", and inadequate safety management". Moreover, the correlation between insufficient safety education" and not wearing safety protective equipment" is greater (80.7% support, 60.41% confidence), and the relationship between insufficient safety education" and blind rescue" also has a strong association (80.7% support, 55.88% confidence).

Prevention and control technologies for abnormal gas emission in tunnel construction through unstable coal-bearing strata

Wang Lichuan, Xiao Hongwu, An Fenghua, Liu Yanlong, Wang Zhaofeng, Zhou Zongqing

2026, 36(4):  85-93.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0133

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To prevent the risk of abnormal gas emissions during tunnel excavation through unstable coal-bearing strata, this study focused on the inclined shaft section of Baoanying No.1 Tunnel in the Chengdu-Kunming Railway Expansion Project. Through on-site monitoring, numerical simulation, and engineering application, gas emission patterns, risk identification methods, and prevention technologies were investigated. The results indicate that tunnel gas originates from unstable coal seams and carbonaceous rocks within the strata, and gas emissions correlate strongly with coal/carbon content in surrounding rocks and the degree of geological fracturing. Then an integrated geophysical-geological drilling approach was employed to establish an advanced identification method for abnormal gas emissions. This method utilizes multiple indicators: unfavorable geological bodies, lithological variations, borehole gas concentrations, and emission rates. A "borehole curtain" interception technique was developed for high-efficiency gas risk prevention and control. Boreholes arranged in curtain patterns were drilled from both side chambers to intercept and extract coal-rock gas ahead of the advancing tunnel face. Field applications demonstrate that this technology reduces construction interference and increases advance rates.

A driving risk and steering intention prediction model based on deep learning

Cheng Fangming, Hu Jiameng, Gou Rui

2026, 36(4):  94-102.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1070

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To address the limitations of traditional models in mining multi-dimensional temporal features and handling class imbalance, A multi-task driving risk prediction model based on Class Balance loss (CB)- Asymmetric Loss (ASL)-TimesNet and is proposed. A sliding time window was employed to extract multi-dimensional temporal features, improving the objectivity and granularity of risk level labeling. Based on these features, four types of labels were constructed: forward collision risk, rear collision risk, lateral collision risk, and steering intention. In terms of model design, the TimesNet architecture was incorporated to effectively capture periodic variations and dynamic evolution in multi-dimensional temporal features, thereby enhancing the modeling capability of temporal information in complex driving scenarios. Meanwhile, a hybrid loss function integrating CB and ASL was devised to improve prediction performance under class-imbalanced conditions. Experimental results demonstrate that the proposed CB-ASL-TimesNet model achieves an average accuracy of 0.908 6 in driving risk and steering intention prediction tasks. Compared with the traditional machine learning model CatBoost, the proposed approach yields a 16% improvement, and it outperforms the mainstream time-series model Gate Recurrent Unit(GRU) by 5.8%, verifying the significant effectiveness of the proposed model in enhancing prediction performance.

Safety analysis of aero-engine thrust reverser system based on STPA-MBSA

Xiao Guosong, Tang Hao, Dong Lei, Bai Jie

2026, 36(4):  103-113.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0636

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TRS is a safety-critical aero-engine system. In response to the inadequacies of conventional safety analysis techniques in addressing the complexities associated with multilevel coupling and cross-linking of multiple systems concerning system interaction and closed-loop design specifications, this study proposes a method that integrates STPA and MBSA. By establishing a whole-process analysis framework from system requirement capture to verification, an overall system model was constructed through the use of SysML to reveal the architectural principles. The STPA method was employed to define 4 types of system-level losses (accidents) and 8 types of system-level hazards, construct a TRS feedback control structure model, identify 11 unsafe control actions (UCAs), derive causal scenarios, and assign respective safety levels. Using the model checking tool new symbolic model verifier (NuSMV), fault and nominal models were constructed to verify critical system safety properties. The results demonstrate that the proposed model possesses logical integrity and correctness, and indicate that the probability of "Thrust Reverser Non-Command Open in Air," as determined from minimal cut set, is 1.95×10^-10 per flight hour, thereby meeting the safety requirement of a failure probability of less than 10^-9 per flight hour.

Settlement deformation analysis of fissured loess foundation based on centrifugal model test

Mei Yuan, Miao Mengna, Tian Xinyu, Le Yanan, Yu Yanan

2026, 36(4):  114-122.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0761

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To investigate the influence of pre-existing fissures on the settlement and deformation behavior of high-fill compacted loess foundations, geotechnical centrifuge model tests were conducted for a flat-terrain high-fill engineering scenario. Two foundation models, an intact model and a fissured model, were designed and comparatively examined during the loading process with respect to deformation development and failure characteristics. The results show that the failure mode is transformed by fissures from a single tensile-cracking pattern to a combined tensile-shear failure, in which fissure propagation, a branched crack network, and stepped surface dislocation are formed. The overall stiffness and stability of the foundation are reduced by fissures; settlement is initiated earlier than in the intact model, the total settlement is increased, and the settlement distribution is more non-uniform. A V-shaped concentration pattern of surface settlement is exhibited and is markedly intensified with increasing load. In addition, a distinct stratified effect on stress distribution is induced by the fissure structure: vertical stress is concentrated in the upper layer, weakened in the middle layer, and only slightly affected in the lower layer, whereas horizontal stress is concentrated near the bottom and released in the middle and upper layers. These changes reconstruct stress-transfer paths and increase the risk of local instability.

Resilience assessment and prediction of metro shield tunnels under explosive conditions

Wang Rui, Zhang Xun, Deng Xianghui, Wang Ping'an, Wang Xu, Zhang Wei

2026, 36(4):  123-131.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0085

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To ensure the operational and structural safety of the subway system, an assessment of the resilience of metro shield tunnels under explosive loading was conducted. A resilience assessment framework and grading criteria for shield tunnels under blast loading were established, and a resilience prediction model was developed based on a backpropagation (BP) neural network with a three-input, five-hidden-layer, single-output architecture. This model and evaluation approach were applied in a case study on the Xi'an Metro Line 1 to assess and predict the tunnel's resilience. The results indicate that a shorter standoff distance, a higher explosive yield, and a higher number of explosions each accelerate the decline in the tunnel's resilience. The resilience exhibited the most pronounced drop after the first explosion; the subsequent rate of decline was relatively gradual until the fifth explosion, after which it increased significantly. After the fifth explosion, the tunnel entered a low-resilience state requiring prompt repairs to meet operational requirements, and by the seventh explosion, the resilience had fallen to an extremely low level that could no longer ensure operational safety. The resilience assessment framework and prediction model developed in this study can be used to assess the safety status of metro shield tunnels under repeated external explosions.

Study on rock burst danger and double entry inward layout optimization for rock burst prevention under condition of inclined thick coal seam

Lyu Pengfei, Wang Yongliang, Liang Ye, Qi Yun, Geng Yijian, Sun Yuting

2026, 36(4):  132-141.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0878

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Based on the engineering geological conditions of a coal mine in western China, this research focuses on addressing the challenges associated with rock burst prevention in working face of inclined thick coal seams. Theoretical analysis, mechanical modeling, numerical simulation, and field monitoring were employed to analyze the influencing factors and hazardous areas of rock burst in working face of an inclined thick coal seam. Optimization strategies for double entry inward layout were proposed and subsequently validated through engineering application. It is found that the occurrence of rock burst is influenced by several factors including buried depth, thick-hard roof, coal seam dip angle, coal pillars, bottom coal. The belt and track roadways were identified as zones with strong rock burst danger, while the setup entry was categorized as a zone with moderate rock burst danger. Based on an analysis of roof structure evolution and floor stress calculations, the floor was divided into three low-stress zones: the semi-protected zone beneath key blocks (I), the protected zone beneath key blocks (Ⅱ), and the stress release zone of gangue (Ⅲ). Numerical simulations determined that the optimal positioning was within the protected zone beneath key blocks (Ⅱ), specifically at a horizontal distance of 5.15 m from the right of the track roadway and a vertical distance of 3 m from the goaf. Microseismic and pressure monitoring demonstrated that implementing the double entry inward layout transformed the energy release pattern of the surrounding rock from "low-frequency and high-energy" to "high-frequency and low-energy". Furthermore, the duration of continuous pressure was significantly reduced, thereby mitigating the rock burst danger in the working face.

Aircraft swarm improved velocity obstacle methods considering low carbon and deviation

Zhong Qingwei, Yu Yingxue, Liu Su, Wang Rui, Guo Jingwei, Pan Weijun

2026, 36(4):  142-151.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0144

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In order to address the requirements of low-carbon operation and safety in aircraft conflict resolution, an IVO method based on adaptive elliptical protected zones was proposed, taking into account aircraft carbon emissions and speed adjustment deviations. First, adaptive elliptical protected zones were constructed according to the velocity differences among aircraft. Second, aircraft flying on the same route with similar velocity vectors and close spatial proximity were grouped into clusters, and key characteristics such as cluster centroids, velocity vectors, and safety regions were defined. Then, a cluster control algorithm was applied to reasonably adjust the velocity vectors of aircraft within each cluster, ensuring the maintenance of safe separation during conflict resolution. Subsequently, velocity obstacle cones were established between clusters. By incorporating constraints on the maximum allowable adjustment per unit time and combining planar geometric analysis with a mathematical optimization model, conflict-free velocities and headings with minimal deviation were determined under low-carbon objectives. Finally, dynamic behaviour analysis was conducted through numerical simulations implemented in Python. The results show that, compared with the traditional velocity obstacle method, the proposed approach improves conflict resolution efficiency by 87.5%, reduces the average adjustment magnitude by 86.67%, and achieves fuel savings of up to 37.36%, demonstrating its effectiveness for aircraft conflict resolution and operational optimization in complex air traffic environments.

Performance mechanism of supported cobalt-copper catalyst for CO elimination

Teng Yifei, He Ziqi, Fu Yu, Quan Mingxu, Liu Chang, Cui Zheng

2026, 36(4):  152-159.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1002

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In order to realize the effective elimination of CO in the tail gas of mine trackless rubber-tyred vehicle, the development of high-performance CO elimination reaction catalyst was focused on in this study. Co₃O₄/CuO/Ni Foam(NF) supported catalyst was prepared by one-step solvothermal synthesis method. Characterization analyses, including X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and specific surface area measurement (BET), were conducted to investigate the effects of synthesis conditions such as synthesis time, synthesis temperature, and growth substrate, as well as physicochemical properties such as catalyst composition and morphological characteristics, on catalytic performance of catalyst. The optimal synthesis conditions and CO elimination performance of catalyst were determined. Hydrogen temperature-programmed reduction (H₂-TPR), oxygen temperature-programmed desorption (O₂-TPD), and in-situ infrared spectroscopy were employed to explore the intermediate processes of CO elimination catalyzed by the catalyst, revealing the catalytic mechanism of CO elimination by the catalyst. The results show that the optimal synthesis condition of Co₃O₄/CuO/NF supported catalyst is 140 ℃ for 4 h. Under this condition, the catalyst can completely eliminate CO at the elimination temperature of 150 ℃, and the elimination process of CO by the catalyst follows the Mars-van-Krevelen(MvK) mechanism.

Experiment on performance and thermal stability of ternary lithium-ion batteries after over-discharge cycles

Ma Mi'na, Zhang Chenyu, Zhang Yanhui, Chen Mingming, Liu Mengrao, Wang Qingsong

2026, 36(4):  160-167.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1484

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To enhance the long-term stability and safety of lithium-ion batteries, a study on potential safety hazards caused by over-discharge was conducted. Aiming at the over-discharge/recharge phenomenon that the lithium-ion batteries may encounter in practical applications such as new energy electric vehicles and electrochemical energy storage, ternary lithium-ion batteries were examined. Through cycle tests and Hybrid Pulse Power Characteristic(HPPC) tests, adiabatic accelerating rate calorimetry was employed to investigate thermal runaway. The effects of over-discharge/recharge cycles on electrical characteristics—such as discharge capacity, DC internal resistance, and incremental capacity—as well as on the thermal runaway characteristic parameters under adiabatic conditions, were analyzed. The results show that over-discharge cycling accelerates battery capacity decay and increases internal resistance, with a particularly notable rise in DC internal resistance in the low state-of-charge region. However, after short-term cycling, prolonged resting allows partial capacity recovery. Over-discharge cycles also reduce the thermal stability of batteries, lower the self-heating onset temperature, and shorten the thermal runaway time compared to fresh batteries and normally cycled batteries, although the maximum thermal runaway temperature is relatively lower.

Fault detection for chemical process based on memory-weighted kernel principal component analysis

Yuan Shuai, Yang Chunxi, Zhang Xiufeng, Wang Xian, Li Gengen

2026, 36(4):  168-175.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1571

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To enhance the detection ability of common faults in the chemical process and ensure the stable and reliable operation, a new fault detection method, termed M-WKPCA, was proposed. The method was based on historical fault data and KPCA, incorporating historical fault data through a weighting mechanism. Initially, kernel principal components(KPC) of normal data and historical fault data were calculated according to KPCA. KPC that can highlight the fault information were selected based on a comparison of the constructed indexes. A weighting matrix was then used to highlight the fault information, and a new statistic was constructed to establish an online fault detection model based on M-WKPCA method. Then, the M-WKPCA method was used to detect common faults in parallel. A parallel WKPCA fault detection strategy was proposed to achieve high precision detection of common faults. Finally, the proposed method was verified using simulated data from the Tennessee Eastman (TE) chemical process. The results show that the proposed method achieves an average accurate detection accuracy of 82.25%. This is much higher than that of the comparison methods, demonstrating its superiority in fault detection. At the same time, since fault information is incorporated during the selection of KPC, the detected fault data are significantly different from the normal data in terms of statistics.

Test study of sleeping bag's thermal insulation performance in low temperature composite environment

Gao Zhenbo, Liu Xiaoyong, Li Yayun, Wang Xuefan, Huang Mengyao, Cao Kai

2026, 36(4):  176-183.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0805

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To evaluate the insulation performance of sleeping bags during actual use in various low-temperatures, wind speeds and wind directions, and to improve the comfort of sleeping bag users, an experimental study on the thermal insulation performance of sleeping bags was conducted. The thermal insulation performance of mummy-style sleeping bags and envelope-style sleeping bags was evaluated using a low-temperature coupled environment chamber and "Newton" thermal manikin system. Thermal resistance parameters of the two types of sleeping bags under varying environmental temperatures (-20, -15, -10, and -5 ℃), wind speeds (1, 2, 3, 5, and 7 m/s), and wind directions (along the head, waist, and foot directions) were measured. The effects of a single low-temperature windless environment and a combined low-temperature windy environment on the thermal insulation performance of sleeping bags were studied. The experimental results show that in a single low-temperature windless environment, the thermal resistance value of the mummy sleeping bag changed more stably than that of the envelope sleeping bag, and the mummy sleeping bag has better thermal insulation performance. In a combined low-temperature windy environment, increasing the wind speed would reduce the thermal resistance of the air layer; and when the wind speed was 7 m/s, the thermal resistance of the air layer decreased by 50%. Wind direction along the head direction has the greatest impact on thermal resistance, with the total thermal resistance of the mummy sleeping bag decreasing by 42.8% and that of the envelope sleeping bag decreasing by 50.1%. Wind direction along the foot direction had the least impact on the thermal resistance value of the sleeping bag, with the total thermal resistance of the mummy sleeping bag decreasing by 23.1% and that of the envelope sleeping bag decreasing by 29.3%.When the wind speed along the head was 7 m/s, the air layer temperature at the feet of the mummy sleeping bag and the envelope sleeping bag dropped to the lowest, which were 21.83 and 26.54 ℃, respectively.

Public Safety and Emergency Management

Construction and analysis of a unitary scenario for cascading evolution of emergencies

Song Yinghua, Xu Hongqian, Sang Xiaoyan

2026, 36(4):  184-193.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0033

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To address problems such as fixed granularity in emergency scenario modelling and unclear descriptions of multi-scenario attributes and structures, a top-down unitary-scenario construction method was proposed from a microscopic perspective based on baseline thinking. Evolution relationships were established based on two types of knowledge elements, namely state elements and trigger elements. Combined with a hierarchical PN and refined layer by layer, the cascading evolution scenarios of emergencies were systematically characterized, and the evolution dynamics of scenarios were simulated. Taking cascading lifeline disaster events under heavy rainfall as an example, a hierarchical scenario model was constructed.. The characteristics of cascading failure, common cause failure and escalation failure features were characterized, and the evolution trends were analyzed. The interlocking dilemma between urban lifeline failure and emergency response was identified. The results indicate that the proposed scenario construction method can meet different levels of decision-making needs through a hierarchical scenario structure. The isomorphic Markov chain can identify the critical elements affecting the scenario evolution.

AGV path planning based on Voronoi skeleton for narrow roads

Wang Ping, Zhang Hao, Tang Youming, Zhang Yi

2026, 36(4):  194-203.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0245

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This paper addressed the issues of insufficient safety distance, unsmooth path, and low planning efficiency in AGV path planning in narrow road scenarios. A new path planning method based on Voronoi skeletons for narrow roads was proposed. Firstly, the key nodes of the Voronoi skeleton were extracted to generate a custom Voronoi graph layer, which was then combined with static, obstacle, and expansion layers to iteratively generate a four-layer network structure cost map, accurately distinguishing the influence range of obstacles. Secondly, the new cost map was used as a constraint in conjunction with an improved A^* algorithm for global path planning, guiding the AGV to drive along the center of the road to ensure its safety. Finally, the global path was optimized using B-spline smoothing to improve the AGV's efficiency and stability in navigating narrow roads and other complex scenarios. Experimental results show that, in narrow road scenarios, the safety of the AGV path planning is improved by 82%, the number of spatial corners is reduced by 55.85%, and the path planning time is shortened by 48.98%. The proposed algorithm effectively enhances the robustness, dynamic obstacle avoidance, and real-time performance of path planning in narrow road scenarios, enabling the AGV to move in the safest manner.

BP neural network-based personnel positioning-tracking-stationary alarm system for emergency rescue

Wang Li, Wang Zhe, Guan Wenling, Zhang Jiaqi, Liu Chaolin, Meng Yuying

2026, 36(4):  204-210.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0177

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To ensure timely rescue of trapped personnel in fire scenarios, a personnel positioning-tracking-stationary alarm system was proposed by combining BP neural networks and DS-TWR technology. The system was developed based on the LabVIEW platform, where the BP neural network learned the error patterns of multipath effects and non-line-of-sight propagation, corrected DS-TWR ranging errors, and achieved precise personnel positioning and trajectory tracking. By calculating the movement distance within a defined time, the system evaluated personnel motion status and established thresholds of displacement and time to trigger alarms, which automatically activated alerts when a person remained stationary beyond the safety threshold. Test results show that the system achieves centimeter-level static and dynamic positioning accuracy under both normal and metal/electromagnetic interference environments. It features high positioning precision and favorable stability, enables accurate trajectory generation, delivers reliable static personnel alarms, and maintains a low response latency.

Evacuation model considering panic and crowding avoidance behavior

Deng Sha, Zhang Jing, Li Ying, Tao Zhenxiang

2026, 36(4):  211-219.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1114

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To investigate the movement conflicts caused by pedestrians driven by panic and the avoidance of locally high-density areas,as well as their impact on crowd evacuation efficiency, the dynamic effects of the distance of pedestrians to the exit, surrounding crowd density and emotional infection on individual panic were considered. In addition, the detouring behavior of pedestrians to avoid the high-density areas and the mutual pushing behavior during evacuation are incorporated. A panic propagation model was constructed based on SFM, and crowding avoidance force and pushing force were introduced to develop a crowd evacuation model considering panic emotion and crowding avoidance behavior. The results indicate that moderate crowding avoidance behavior during evacuation can enhance evacuation efficiency to a certain extent, whereas excessively strong interactions amplify movement conflicts and the accumulation of pushing forces, thereby increasing the risk of stampedes. In low-density evacuation scenarios, individual panic levels are mainly influenced by the distance to exit, and the crowding avoidance force remains relatively weak. In contrast, under high-density situations, the dominant role of exit distance diminishes. The propagation of panic emotions interacts with crowd density to form a “density-emotion” coupled feedback, which significantly increases pedestrian crowding avoidance force and pushing intensity. During evacuation, the maximum crowding avoidance force of pedestrians first increases and then decreases. In the middle and late stages of evacuation, when pushing becomes intense, a larger ankle joint torque weight can significantly reduce the probability of falling.

Evaluation and empirical research on support capability of emergency resources for major disasters oriented to sustainable development

Zhao Chenyang

2026, 36(4):  220-227.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0991

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To address the unsustainable problems arising from significant demand gaps, inadequate support, and delayed supply of emergency resources in major disasters, and to tackle the limitation of traditional evaluation indicators that overlook social and environmental sustainability, this study was conducted. Firstly, based on the triple bottom line theory of sustainable development and literature mining method, a comprehensive evaluation index system was constructed from three dimensions: economic support capacity, social support capacity, and environmental support capacity for emergency resource support. Then, the original indicators were revised through expert evaluation to determine the final selected indicators. Finally, the grey relational analysis method was applied to construct an evaluation model for emergency resource support capacity in major disasters oriented towards sustainable development. Taking Henan Province as an example, the model was applied to verify its scientificity and effectiveness. The research shows that, when considering various factors such as economy, society, and environment, the correlation degree of social support is 0.933 90, having the strongest impact on emergency resource support capacity. In addition, indicators such as fixed assets investment in the logistics industry, the number of social organization units, the number of hospital beds in medical and health institutions, and regional GDP rank among the top, making them key influencing factors driving emergency resource support in Henan Province.

Analysis of vertical transport characteristics of foam fire extinguishing agent and influence of key parameters

Li Chunyi, Zhou Rui, Gu Yin, Liu Mengfan, Su Rongfang

2026, 36(4):  228-234.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0546

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In order to address the challenges associated with the long-distance vertical transport of fire suppressant agents delivered by tethered firefighting drones in super high-rise building fires, a vertical transport model for foam extinguishing agents was established. The simulation results were comparatively validated against both empirical model calculations and experimental data. Furthermore, the study conducted an in-depth investigation into the effects of flow rate, gas-liquid ratio, and pipe diameter on pressure loss and flow velocity during the vertical transport of foam extinguishing agents. The findings indicate that the numerical simulation errors remain stable and are consistently less than 3%, demonstrating superior accuracy compared to the empirical model. In terms of flow characteristics, as the flow rate increases, pressure loss gradually rises, with the rate of increase becoming more pronounced at higher flow rates; concurrently, the stabilization time of the flow velocity within the pipe is reduced. Under constant flow rate conditions, when the pipe diameters are 60 mm and 100 mm, respectively, pressure loss decreases with an increase in the gas-liquid ratio. Moreover, increasing the pipe diameter mitigates the influence of the gas-liquid ratio on both frictional pressure drop and flow velocity. The impact of pipe diameter on pressure loss is primarily realized through alterations in the internal flow velocity: as the pipe diameter increases, the flow velocity decreases, leading to a corresponding reduction in frictional pressure drop. Within the pipe diameter range of 40 mm to 60 mm, the effect of pipe diameter on pressure loss and velocity variation is significant; however, when the pipe diameter exceeds 80 mm, this influence gradually diminishes. The research outcomes provide theoretical guidance for the vertical.

Post-earthquake emergency supplies distribution route planning considering road conditions and demand urgency

Sun Qixuan, Liu Yang, Jia Shun, Lu Jifeng, Tian Jun

2026, 36(4):  235-243.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1651

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To enhance the efficiency of emergency supplies distribution after an earthquake, an emergency vehicle routing optimization problem was investigated under limited transportation capacity and the need to simultaneously deliver multiple categories of emergency supplies, while road conditions and demand urgency were jointly considered. Firstly, vehicle travel speeds were corrected based on road damage rates, and a demand urgency evaluation method is established by incorporating key characteristics of earthquake disasters. Then, a post-earthquake emergency supplies distribution optimization model was formulated to minimize the total delivery time and the total urgency-weighted demand cost, involving multiple depots, multiple affected sites, multiple types of emergency supplies, and heterogeneous vehicle fleets. Next, a HEA integrating the fast non-dominated sorting genetic algorithm (NSGA-II) with variable neighborhood search (VNS), is then designed to solve the model. Finally, a case study based on the 2008 Wenchuan (5.12) earthquake was constructed, and simulation experiments were conducted to validate the effectiveness and feasibility of the proposed model and algorithm. The results show that HEA outperforms three benchmark multi-objective optimization algorithms in both solution-set convergence and overall solution quality, and can provide emergency decision-makers with a diverse set of trade-off solutions within a short computation time (average 115.0 s).

Deep learning prediction for subway section passenger flow integrating physical information and snow geese optimization

Wan Jiahui, Yang Xiaoxia, Kang Yuanlei, Shao Chuang

2026, 36(4):  244-251.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0517

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The rapid growth of passenger volume of urban rail transit and the urgent need for intelligent operation have made accurate section passenger flow prediction a key technical challenge to improve the level of dynamic scheduling and safety control. To this end, this paper innovatively integrated the physical information constraint mechanism, data-driven method and SGA, and proposed a new deep learning framework. Firstly, a physical residual term was designed and embedded into memory cells as a regulation signal, forcing the model to learn the physical laws of passenger flow while retaining the temporal characteristics of passenger flow. Secondly, a dual-objective fitness function based on physical loss and data loss was innovatively proposed to achieve further optimization of model performance while establishing a constraint mechanism. Finally, SGA was used to balance the differentiated and synergistic effects of hyperparameters in the model. Experimental results show that the constructed model exhibits good predictive performance on both the training set and the validation set. The improved fitness function can narrow the error range of the model prediction results. In the two-stage ablation experiment, the mean square error range of the proposed deep learning framework is reduced by 71.03% compared with the long short-term memory model, which verifies the synergistic enhancement of the model prediction ability by the simultaneous introduction of physical constraint mechanism and intelligent optimization algorithm.

Disaster Prevention and Mitigation Technology and Engineering

Identification of rainstorm disaster chain in urban rail transit system based on event evolutionary graph

TANG Hongxia, WANG Xiaojie, LI Mengdi, SHAO Zhiguo

2026, 36(4):  252-261.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1718

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In order to scientifically identify the key disaster-causing factors and key disaster-causing paths in the urban rail transit system's flood disaster chain, and enhance the resilience of these systems, a study was conducted based on news report text data related to the urban rail transit flood disasters from 2010 to 2024. Natural language processing technology and a rule-based template matching method were used to extract causal event pairs. The t-Distributed Stochastic Neighbor Embedding (t-SNE) method was used to generalize the extracted event pairs, and the abstract event evolutionary graph of rainstorm disaster in the urban rail transit system was constructed, which was visualized as a disaster chain evolution network combined with Gephi. The complex network theory was introduced to quantitatively analyze the rainstorm disaster chain evolution network of the urban rail transit system, and identify the key disaster factors and key disaster paths in the disaster chain. The results show that the four types of disaster events, rainstorm, station water, subway shutdown and equipment failure, are of high importance and are the key disaster factors in the rainstorm disaster chain evolution network of the urban rail transit system. The three evolutionary paths of "road water → station water", "subway shutdown → economic loss" and "road water → rainwater backpouring" have high vulnerability, which are the key disaster paths in the rainstorm disaster chain evolution network of the urban rail transit system. The identified key disaster-causing factors and key disaster-causing paths can provide decision support for disaster prevention and mitigation.

Occupational Health

Joint simulation method of offshore platform noise based on sound ray tracing method and SEA

Zhang Yue, Liu Tao, Kang Zhongxu, Zhou Wei, Wu Rui, Chen Kunliang

2026, 36(4):  262-270.  doi:10.16265/j.cnki.issn1003-3033.2026.04.0187

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In order to accurately predict the noise distribution of offshore platforms to prevent occupational noise hazards, a joint simulation method combining the sound ray method and SEA method was proposed to solve the problem that the traditional single noise prediction method was difficult to take into account the structural sound and air sound propagation characteristics and could not describe the large-scale spatial sound field distribution in detail. In this method, SEA was used to simulate the acoustic propagation characteristics of the structure, and the acoustic propagation law of the air in the large space of the deck was simulated by the sound ray method. Taking an offshore central platform as the research object, a joint simulation model was constructed based on the measured vibration and noise source intensity data. The sound field distribution of each deck was simulated and calculated, and the effectiveness of the method was verified by a spatial grid noise test. The results show that the contribution of air acoustic energy to the total noise energy of each deck of the offshore platform is more than 50 %, which is higher than that of structural acoustic energy. Obstacles such as large-scale equipment, production area rooms and firewalls have a significant effect on noise shielding, and air sound propagation has a more prominent impact on the spatial sound field distribution of the deck, the joint simulation results are in good agreement with the measured results, and the overall average difference between the prediction and the test data is within 2 dB, which can realize the high-precision prediction of the spatial sound field distribution of the offshore platform deck.

Intelligent Safety Technology

Quality and safety risk analysis of intelligent consumer products based on LLMs and KG

Yang Yuexiang, Liu Xuewen, Tu Xinyu, Zheng Huaicheng, Xu Yingcheng

2026, 36(4):  271-280.  doi:10.16265/j.cnki.issn1003-3033.2026.04.1582

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To effectively identify and control quality and safety risk of intelligent consumer products, a KG for intelligent consumer product quality and safety risk was constructed based on LLMs. Complex network analysis methods were integrated to systematically analyze risk distribution characteristics and propagation mechanisms. Data on intelligent consumer products were collected through multiple channels; the quality and safety risk issues in product accident cases were sorted out. Based on safety theories and the system structure of intelligent products, a multi-level and extensible knowledge ontology for quality and safety risks was constructed. Under the constraints of this ontology, LLMs were utilized to achieve automated knowledge extraction, and a KG containing 16 611 nodes and 32 178 edges was constructed. The KG was mapped into a risk network. Based on complex network theory, centrality indicators of three key node types—hazard factors, accident injuries, and safety events—were calculated to identify critical nodes and propagation paths in risk transmission. The research results show that among various intelligent consumer products, intelligent home products account for the highest proportion of risk entities; physical hazards and information hazards are the main risk types; property damage and physical injury are the primary accident consequences. Typical direct propagation paths and cascading propagation paths can be identified by the risk network.