The process of attack identification of data-driven complex oil and gas production systems is often mixed with fault data, which leads to the failure of timely attack defense or fault relief, resulting in the occurrence of attacks and oil and gas production accidents. Most of the current attack detection focuses on the detection of external attacks, ignoring the impact of the possible fault data of the oil and gas system itself on the attack detection. To distinguish abnormal events such as system faults from information attacks in complex oil and gas production systems and improve the accuracy of information physical attack detection in complex oil and gas production systems, an SVM-based undirected graph joint detection method is proposed. Firstly, the key sensors in the complex system of oil and gas production are topologized to form an undirected graph. Secondly, SVM is used to detect the anomalies of the undirected graph sensor system. Finally, the receiving station low-pressure pump system is taken as an example to verify. The results show that the accuracy rate, precision rate, recall rate, and F1 of the proposed attack detection method are above 99
The determination of the critical sliding surface is a crucial aspect of slope stability analysis. In reality, the environment in which slopes are located is often very complex, and the sliding surfaces have different shapes. Therefore, a three-dimensional stability study is proposed, using irregular ellipsoidal shapes as the sliding surface. By constructing the equation of the irregular ellipsoid and applying spatial geometric transformations, an irregular ellipsoid controlled by six parameters, (semi-axis length (a), width (b), height (c), rotation angle (θ), and spatial displacements (tx and tz)), is obtained. The interpolation method is employed to apply the irregular ellipsoid to 3D slopes, thereby generating irregular ellipsoidal sliding surfaces. The slope stability coefficient is calculated by using the residual thrust method and genetic algorithm to determine the critical sliding surface. A comparative stability analysis is conducted between the standard ellipsoidal sliding surface and the irregular ellipsoidal sliding surface through a classic case study. The results show that the Type II irregular ellipsoidal sliding surface aligns well with the mining area. Finally, the research findings are applied to the 3D slope stability analysis of the Shengli West No.2 Open-Pit Coal Mine in China, validating the feasibility of the proposed method.
Auto-ignition temperature (AIT) is one of the crucial exponents in the design of fire and explosion safety measures. Therefore, in this study, quantitative structure-property relationship approach was used to predict the AIT of ternary hybrid liquids based on molecular structure information. The optimal molecular descriptors were calculated and filtered using Mordred software. Twelve mixing rules were proposed for calculating molecular descriptors of mixtures. A prediction model for the AIT value of binary liquid mixtures was developed, validated and evaluated using a back propagation neural network (BPNN) and a one-dimensional convolutional neural network (1DCNN). The relative contribution and positive and negative correlations between individual molecular descriptors and AIT in the model were interpreted using the shapley additive explanations method. The results show that BPNN and 1DCNN models using mixing rule 1 have the best fitting ability, stability and prediction ability. The determination coefficient of the BPNN and 1DCNN models in the training set were 0.996 and 0.992, the root mean square errors were 3.613 °C and 5.284 °C, the mean absolute errors were 2.483 °C and 4.144 °C, the nash efficiency coefficient was 0.996 and 0.992, respectively, the willmott index was 0.999 and 0.998. and the values of the top three molecular descriptors of relative contribution, SssCH2, SsOH and SsCH3, were negatively correlated with the AIT values. The BPNN and 1DCNN models provide an accurate and reliable method for predicting ternary mixing liquid AIT.
The occurrence of safety incidents for existing glass curtain walls (EGCWs) pronounced menace to the security of both lives and property. Undertaking safety assessment for EGCWs carries essential practical significance. However, current fuzzy evaluation methods overlook the uncertainty of indicator weights and the intricacies of rank attribution. In response, this paper proposes a novel approach to the safety assessment of EGCWs. This research establishes a framework of evaluation indicators for EGCWs and divides the safety ranks of each indicator into four tiers: Safe, Mild risk, Moderate risk, and High risk. Quantitative and qualitative indicators are quantified via the variable fuzzy cloud algorithm and cloud model. The information cloud combination weighting method is introduced to determine the weight clouds of indicators. Finally, a two-dimensional assessment result is derived using an improved fuzzy comprehensive evaluation method and fuzzy entropy. The exemplified outcomes demonstrate that this approach captures the safety status of evaluation subjects based on risk ranks, and fuzzy entropy addresses two issues: inconsistent level attribution and the comparison of identical risk ranks. The appraisal method further unveils the safety details of EGCWs, with findings that align consistently with the actual situation.
It is of great significance to study the influence of obstacles on the sound field distribution in the comprehensive mining face system and the noise accumulation law in the roadway, which is of great significance to the prevention and control of mine noise and to improving the occupational health protection level of miners. Based on the multi-physical field coupling analysis software COMSOL Multiphysics, the finite element method and ray acoustic method were used to establish the sound field simulation model of the comprehensive mining face system, which was combined with the field test of the measured weighted sound pressure level of the roadway noise for comparison, and the distribution characteristics of the sound field under the influence of different obstacle obstruction rates, obstacle shapes, quantities, spacings, etc. were further analyzed. The results show that: the simulated values of the sound field in the low and high frequency ranges are basically in agreement with the experimental values, which proves the validity of the simulation model; the overall level of the sound pressure level in the roadway is less affected by the shape, spacing, and number of obstacles, which can be disregarded for the simplification of the roadway model; the obstacle obstruction rate and the attenuation amount of the sound pressure level are positively correlated, and the attenuation amount is within 3 dB if the obstacle obstruction rate is less than 50%; When the obstruction rate is less than 50%, the sound pressure level attenuation is within 3 dB.
