Traditional Calculation Method Research Articles

Застосування CAD/CAE-системи Solidworks у задачах аналізу міцності деталей верстатних пристосувань

The strength of parts is a critically important characteristic of the design of Clamping Fixtures. Calculating the strength of parts of Clamping Fixtures is necessary both when designing new structures and when checking existing ones. Traditional calculation methods, such as nominal stress calculation, although easy to use, have low accuracy. This creates a need for more accurate and efficient strength analysis methods. Modern CAD/CAE systems, in particular SolidWorks with the Simulation application, provide powerful tools for engineering calculations and finite element modeling. The algorithm of static calculation presented in the article on the example of the "Grip" part demonstrates the sequence of actions during strength analysis in the SolidWorks environment. This algorithm includes creation of a 3D model, generation of a finite-element mesh, material selection, determination of anchoring and loading conditions, calculation and analysis of results. An important stage of the analysis is the choice of the material of the part. At the same time, it should be taken into account that the databases of foreign CAD/CAE systems may not contain some domestic materials, which requires the selection of analogues with similar characteristics. The system allows you to visualize the results of calculations in the form of colored diagrams of stress and strain distribution. This greatly facilitates the analysis and interpretation of the results, allowing the critical areas of the structure to be quickly identified. The use of CAD/CAE systems for strength analysis of machine tool parts has a number of advantages compared to traditional methods. It increases the accuracy of calculations, reduces the time for their implementation, allows you to easily make changes to the design and instantly assess their impact on the strength of the part. The proposed method allows you to carry out both design and verification calculations of details of Clamping Fixtures. This makes it a versatile tool for structural engineers. The implementation of such strength analysis methods is particularly relevant both for the educational process and for real production. In terms of training, it allows students to gain practical skills in working with modern engineering systems. In production, it helps to improve the quality of design, reduce the number of errors and optimize structures.

Quantum computing for several AGV scheduling models

Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.

Influence of theoretical and semi-empirical peak models on the efficiency calculation in chiral chromatography

Height equivalent to theoretical plate (H) equations, such as the van Deemter or Knox-Saleem equations, and other efficiency vs. linear velocity equations (u), provide kinetic insights into chromatographic separations phenomena and column performance. In enantioselective separations, the peak shape of the two enantiomers can differ significantly and are often asymmetric. The peak efficiency calculations heavily impact these efficiency-flow profiles, leading to erroneous estimations of eddy diffusion, longitudinal diffusion, and mass transfer terms. In this work, new asymmetric peak functions are employed for modeling enantiomer peaks based on the Haarhoff-Van der Linde function, its generalized variant (GHVL), once Generalized Asymmetric Gaussian (AGN), and Twice Generalized Gaussian (TGN). The new models (AGN, TGN, and GHVL) incorporate higher statistical moments besides the zeroth, first, and second moments to account for two-sided asymmetry (fronting or tailing). The fit results are compared with the traditional efficiency calculation methods endorsed by official pharmacopeia and numerical estimation of moments from the raw data. Enantiomeric separations of ibuprofen and dl-homophenylalanine were chosen as probe molecules. The results demonstrate that non-linear least squares fitted functions provide better estimations of peak efficiency data even in the presence of high noise. In particular, the generalized models consistently offered the best quality fits for various peak shapes in chiral separations. Conversely, the half-height Gaussian method greatly overpredicted skewed peak efficiencies. This investigation reveals that the commonly held assumptions of peak shape and numerical integration of raw data are highly insufficient for chiral chromatography. The impact of asymmetry on plate height should not be overlooked when accurate data from efficiency-flow rate curves is derived. We advocate for the broader adoption of these new generalized peak (AGN, TGN, GHVL) models because they provide robustness at various SNRs that account for right or left asymmetry while accurately representing peak geometry.

Benchmarking and contribution analysis of carbon emission reduction for renewable power systems considering multi-factor coupling

The renewable power system serves as a vital path towards achieving carbon peaking and neutrality goals. Accurately quantifying the carbon reduction emissions of renewable power systems and evaluating the contribution of various influencing factors to carbon reduction is quite important, which is related to the formulation of energy policies and regional power planning by the power grid or relevant departments. However, in the current research, there are few studies related to the quantification of carbon emission reduction, and in terms of optimal scheduling, the existing studies often only consider a single factor, ignoring the coupling effect between multiple factors, which makes the contribution of each optimization strategy to carbon emission reduction unclear. Therefore, based on the current power system scheduling mechanism and dual-carbon background, this paper optimizes the carbon emission reduction benchmark factor and its corresponding weight, proposes a new carbon emission reduction benchmark of renewable power system, and verifies the accuracy of this benchmark based on the classical scheduling model. On this basis, the mathematical models of thermal power deep peak shaving (DPS), electricity export (ELE) and green certificate trading (GCT) are established. Each factor is introduced into the classical scheduling model to simulate and verify the promoting effect of each influencing factor on carbon emission reduction. Finally, the above different influencing factors were constructed in different coupling scenarios to calculate the carbon emission reduction under each scenario, and the contribution degree of each influencing factor to carbon emission reduction was analyzed sharply according to the value of cooperation game instead of the traditional calculation method. The results show that the proposed benchmark can reduce the error by 2.6%–15.5 %. In the carbon emission reduction contribution simulation, the three factors have coupling effects, and deep peak shaving and electricity export are more sensitive to carbon emission reduction contribution. This study provides valuable insights for the development of carbon reduction strategies for renewable power systems.

Performance degradation model and life prediction method of spool pair caused by erosion wear

The erosion of solid particles in the fluid causes the wear of the metering edge of the spool pair, resulting in a decrease in system performance. This paper aims to develop a general method to evaluate performance degradation and life prediction of spool pairs induced by erosion. Considering the diversity of influencing factors and the complexity of operating conditions, traditional numerical calculation methods will consume huge computational costs. It is difficult to qualitatively and quantitatively analyze the evolution of metering edge by numerical calculation. The erosion prediction model (EPM) constructed by machine learning methods can be used to predict the wear profile discretized in time and space. Combined with the mathematical model of the valve-controlled cylinder, a performance degradation model (PDM) that can simulate real-time working conditions is established. Through the data exchange and iterative update between EPM and PDM, a complete digital platform for the calculation of slide valve durability is constructed. After comparing with the experimental results, it can be concluded that the proposed method can realize the performance degradation evaluation and life prediction of the spool pair caused by erosion wear under continuous working conditions.

A novel deep learning method based on 2-D CNNs and GRUs for permeability prediction of tight sandstone

The accurate calculation of tight sandstone reservoir permeability is crucial for optimizing production and maximizing natural gas recovery. Traditional physical model-based permeability calculation methods heavily rely on core experimental parameters. However, petrophysical experiments are costly, prompting the development of fast and reliable reservoir permeability prediction methods. In this paper, a novel deep learning model that combines two-dimensional convolutional neural network (2-D CNN) model and gated recurrent neural network is proposed to predict tight sandstone reservoir permeability. The 1-D conventional well logs data is converted into the 2-D geological feature map to fit the model's input dimension. The TimeDistributed layer wrapping technology is used to couple the 2-D CNN and GRU to extract comprehensive features of geological feature maps. By training the model, a depth nonlinear mapping between the spatial and temporal features in the well logs data and permeability is established. The model is divided into two subnetworks, 2-D CNNs and GRUs, which are used to compare and analyze the functions of different modules in the proposed model. Additionally, two traditional machine learning algorithms, SVM and XGBoost, are also introduced as comparison models. Four regression evaluation metrics are used to quantify the predictive performance of different models. The test results in a blind well demonstrate that the proposed method outperforms the comparison models in predicting permeability, with a coefficient of determination (R2) of 0.9305, a root mean square error (RMSE) of 0.0895, a mean squared error (MSE) of 0.0080, and a mean absolute error (MAE) of 0.0725. This model can provide insight into the petrophysical characteristics of reaction reservoirs based on conventional well logs data and can accurately estimate reservoir permeability under limited core data, which has great application potential in improving the exploration accuracy of unconventional oil and gas resources.

Biodiversity Conservation in Xishuangbanna, China: Diversity Analysis of Traditional Knowledge Related to Biodiversity and Conservation Progress and Achievement Evaluation

Biodiversity plays an important role in maintaining the ecological balance of the earth. The study of traditional knowledge related to biological resources is a hot issue in the field of international biodiversity conservation. Xishuangbanna is a key area of biodiversity and a cultural hotspot of international significance. According to the Technical Regulation for Classification, Investigation, and Inventory of Traditional Knowledge Relating to Biological Diversity issued by the Ministry of Ecology and Environment, we investigated and catalogued the traditional knowledge related to biodiversity of the Jino people who have lived in Xishuangbanna for generations, and collected 490 entries of traditional knowledge related to biodiversity of the Jino people. Drawing on the traditional knowledge diversity index calculation method proposed by Wang Guoping, the overall traditional knowledge α-diversity index of the Jino people is 0.63, indicating that the richness of the traditional knowledge of the Jinuo people is relatively high. The traditional culture related to biodiversity, the traditional knowledge related to agricultural genetic resources, and the traditional technology related to the sustainable utilization of biological resources are relatively rich and diverse. The diversity index is 0.86, 0.82 and 0.79, respectively. In addition, Xishuangbanna has invested a lot of energy in biodiversity protection, including the establishment of nature reserves, botanical gardens, zoos, ecological tea gardens and other species reserves, and the promulgation of laws and policies related to biodiversity protection, and has achieved remarkable results in in situ protection and ex situ protection. On the basis of analyzing the progress and achievements of biodiversity conservation in Xishuangbanna, this study points out that Xishuangbanna faces challenges such as the loss of traditional knowledge, insufficient conservation efforts, and great changes in land use, and puts forward corresponding suggestions.

A new method of fuel pebble temperature calculation based on neutronic and thermal coupling equivalent model

As one of the six reactor types in the fourth-generation nuclear energy systems, the Very High Temperature Gas-cooled Reactor (VHTR) possesses inherent safety features. One VHTR fuel pebble contains tens of thousands of randomly distributed TRISO fuel particles, displaying a double heterogeneity characteristic. Current research models employ the homogenized method for fuel pebble temperature calculation, which neglected the neutronic and thermal (N/T) coupling effect and double heterogeneity characteristic. This study proposes a fuel pebble coupled equivalent method (PCEM) for temperature calculation. The method is based on the effective thermal conductivity obtained by the fine structure fuel pebble N/T coupling method. The new method is evaluated on steady-state and transient VHTR multi-scale neutronic and thermal hydraulic (N/TH) coupling calculation. The results show that the traditional temperature calculation method is conservative. Under steady-state conditions, the maximum degree of conservatism of the fuel pebble average temperature and peak temperature are 0.6 K and 14.2 K respectively. For the sudden drop in coolant flow rate transient condition, the average temperature and peak temperature of the fuel pebble calculated by PCEM are smaller than the values calculated by traditional method at the same time. Due to the influence of the fuel Doppler effect, the transient response of the traditional calculation method is slower.

Design of an English vocabulary e-learning recommendation system based on word bag model and recurrent neural network algorithm

This article takes learning English vocabulary as the foundation, constructs precise classification strategies, and designs a recommendation system for English vocabulary learning. This article constructs a recommendation system for English vocabulary learning based on the word bag calculation model and recursive neural network calculation method, which has great significance, and also conducts in-depth research on this system. Thus, a classification calculation method based on the word bag calculation model was proposed. The calculation method in this paper is different from traditional calculation methods, and the classification accuracy in this paper is higher. Meanwhile, recurrent neural networks themselves have one or more feedback loops that can feed back information from the neural network to other neural networks, resulting in recurrent networks with different structures. After a series of research results, it has been found that the SIFT method takes longer to extract than the SURF method, and the number of extracted features is relatively large. Therefore, the recommendation system for English vocabulary learning proposed in this article can be improved according to the different needs of users, in order to better meet their individual requirements.

Stochastic Vibration-Driven Fatigue Analysis for Steel Structure Bridges Considering Track Irregularity

This study proposes a method for assessing the effect of stochastic vibrations resulting from track irregularities which are rarely studied but significant on bridge fatigue. First, the pseudo-excitation method is utilized to determine the power spectrum density of stress. Subsequently, the trigonometric series method is employed to acquire the stress time history set. Then, a traditional fatigue life calculation method is employed for analysis. By utilizing a post-sampling method, the need for costly repetitive solutions of the vehicle-bridge coupling system is eliminated. Numerical simulations are conducted on a steel truss bridge, revealing the non-negligible effect of track irregularity on bridge fatigue performance. The study investigates the underlying mechanism, highlighting the pronounced influence of long-wave track irregularity. Moreover, the research explores the correlation between track smoothness and bridge fatigue performance, demonstrating that a decrease in track smoothness leads to an increase in fatigue loss. These findings emphasize the necessity of considering stochastic vibrations when evaluating bridge fatigue, providing valuable insights for bridge design and maintenance.

Thickness-Dependent Gilbert Damping and Soft Magnetism in Metal/Co-Fe-B/Metal Sandwich Structure.

The achievement of the low Gilbert damping parameter in spin dynamic modulation is attractive for spintronic devices with low energy consumption and high speed. Metallic ferromagnetic alloy Co-Fe-B is a possible candidate due to its high compatibility with spintronic technologies. Here, we report thickness-dependent damping and soft magnetism in Co-Fe-B films sandwiched between two non-magnetic layers with Co-Fe-B films up to 50 nm thick. A non-monotonic variation of Co-Fe-B film damping with thickness is observed, which is in contrast to previously reported monotonic trends. The minimum damping and the corresponding Co-Fe-B thickness vary significantly among the different non-magnetic layer series, indicating that the structure selection significantly alters the relative contributions of various damping mechanisms. Thus, we developed a quantitative method to distinguish intrinsic from extrinsic damping via ferromagnetic resonance measurements of thickness-dependent damping rather than the traditional numerical calculation method. By separating extrinsic and intrinsic damping, each mechanism affecting the total damping of Co-Fe-B films in sandwich structures is analyzed in detail. Our findings have revealed that the thickness-dependent damping measurement is an effective tool for quantitatively investigating different damping mechanisms. This investigation provides an understanding of underlying mechanisms and opens up avenues for achieving low damping in Co-Fe-B alloy film, which is beneficial for the applications in spintronic devices design and optimization.

In Situ Gas Content Prediction Method for Shale.

Shale gas is a typical unconventional energy source and recently has received great attention around the world. Unlike conventional natural gas, shale gas mainly exists in two forms: free state and adsorbed state. Therefore, geologists have proposed the concept of gas content. The traditional calculation methods of gas content can be summarized as on-site gas desorption, logging interpretation, isothermal adsorption, and so on. However, all of the methods mentioned above have their shortcomings. In situ gas content is a new concept in the calculation of the gas content. In this paper, the in situ gas content is defined as the gas content obtained by direct measurement of core gas production through experimental or mathematical simulation of original reservoir conditions. In this work, a method to calculate the in situ gas content of shale is provided, which includes two parts: numerical simulation of the coring process and a gas content experiment. Compared with previous gas content prediction methods, this article considers the influence of the temperature field on gas content both in mathematical modeling and experiments. Then, the gas content of the Longmaxi Formation shale in the Sichuan Basin was calculated using both methods as an example. The results show that (1) the numerical model was considered to be reliable by analyzing the effects of coring speed and permeability on the loss of gas; (2) the total gas content predicted by numerical simulation of the coring process and the gas content experiment are approximately equal, with values of 5.08 m3/t and 4.95 m3/t, respectively; (3) the total gas content of the USBM method is only 4.28 m3/t, which is significantly lower than the above methods. In summary, this study provides an in situ gas content prediction method for shale from both mathematical modeling and experiments. The mutual verification of theory and experiment makes this method highly reliable.

Improvement and application of the ecological footprint calculation Method—A case study of a Chinese university

The Ecological Footprint (EF) model is an effective tool for determining the resource consumption demand of human activities and whether natural assets are overutilized. The EF calculation method has certain theoretical and methodological importance for low-carbon campus construction. With the development of science and technology, some parameters in traditional EF calculation methods need to be modified. Based on the definition of fossil energy land and the latest average grain yield, this paper revised the calculation parameters of the EF method in six aspects: absorbed gas, electricity, agricultural products, aquatic products, livestock products, and paper. This paper applied the calculation method before and after improvement to Henan Polytechnic University. The EF of the campus according to calculations was 28,358.41 gha, and the per capita EF was 0.657 gha/person using the improved calculation method. Compared with the unimproved method, the improved method resulted in a substantial decrease in EFs, which can be explained by three reasons. First, the fossil energy land was divided into forestland and pasture land. Second, the average yield was recalculated using the latest statistical database of the United Nations Food and Agriculture Organization. Finally, the utilization of waste pulp in the paper industry was considered. The calculated results were also analyzed and compared with those of other universities, Worldwide Fund for Nature and Enhancing Universities’Sustainability Teaching and Practices through Ecological Footprint. The improved calculation results can better reflect the actual situation of the campus, improve the rationality of EF calculation, and provide a reference for EF at the campus level. Accordingly, the current situation of campus resource consumption can be explained, further providing a basis for proposing an EF reduction scheme for green campus construction.

MicroPSCal: A MicroStation package for storage calculation of pumped storage power station during planning and designing

Pumped storage power plants can effectively guarantee the healthy development of energy and promote energy transformation and green development. The calculation accuracy is directly related to the flood control safety of the reservoir. The current storage calculation method of storage capacity is inefficient and complicated resulting in deviations between calculated values and actual storage capacity. The paper is devoted to the problem of efficiency and quality of capacity calculation in the planning and design stage of pumped storage power plants. A toolkit MicroPSCal is developed based on MicroStation software to simulate and calculate the corresponding storage capacity of different elevations and draw the storage capacity curve, which can be applied in the actual planning and design process of pumped storage power stations. The results of the calculation showed that this method can make full use of terrain accuracy to achieve high-precision calculation of reservoir capacity of different elevations and automatically draw high-quality storage curves. Compared with traditional storage capacity calculation methods, calculation time was reduced from 18 hours to 45 minutes. This method has obvious advantages and provides a new technical idea for storage capacity calculation during the planning and design of pumped storage power plants.

A Novel Spatial–Temporal Deep Learning Method for Metro Flow Prediction Considering External Factors and Periodicity

Predicting metro traffic flow is crucial for efficient urban planning and transit management. It enables cities to optimize resource allocation, reduce congestion, and enhance the overall commuter experience in rapidly urbanizing environments. Nevertheless, metro flow prediction is challenging due to the intricate spatial–temporal relationships inherent in the data and the varying influence of external factors. To model spatial–temporal correlations considering external factors, a novel spatial–temporal deep learning framework is proposed in this study. Firstly, mutual information is utilized to select the highly corrected stations of the examined station. Compared with the traditional correlation calculation methods, mutual information is particularly advantageous for analyzing nonlinear metro flow data. Secondly, metro flow data reflecting the historical trends from different time granularities are incorporated. Additionally, the external factor data that influence the metro flow are also considered. Finally, these multiple sources and dimensions of data are combined and fed into the deep neural network to capture the complex correlations of multi-dimensional data. Sufficient experiments are designed and conducted on the real dataset collected from Xi’an subway to verify the effectiveness of the proposed model. Experimental results are comprehensively analyzed according to the POI information around the subway station.

Spatial patterns of suicide in Texas: The potential confounding effect of race and ethnicity composition

In the United States, suicide rates are much higher among non-Hispanic Whites than any other racial or ethnic group. However, the impact of this pattern for spatial analysis remains unclear. This article explores the relationship among race and ethnicity, suicide mortality, and space in Texas. We compare traditional age-adjusted mortality rates with standardized mortality ratios created using indirect race or ethnicity rate adjustment. Both measurements are compared in three different contexts: choropleth maps, Getis–Ord Gi* hot spot analysis, and location-allocation modeling. Results show dramatic changes across all three methods after race or ethnicity adjustment. Regions of higher-than-expected suicide mortality shift from the non-Hispanic White-dominated north to the majority-Hispanic south. Accordingly, the p-median location-allocation model moves selected service sites closer to minority strongholds in large urban areas and along the Texas–Mexico border. A closer look at individual counties shows that minority-majority counties may have suicide rates two to three times the state average (given the population composition) yet remain hidden by traditional rate calculation methods. Failing to account for race and ethnicity effects will inevitably draw attention and resources to non-Hispanic White communities. At a time when governments are making investments in both mental health and diversity initiatives, this hidden bias must be addressed.

Physics-Informed Neural Network (PINN) for Solving Frictional Contact Temperature and Inversely Evaluating Relevant Input Parameters

Ensuring precise prediction, monitoring, and control of frictional contact temperature is imperative for the design and operation of advanced equipment. Currently, the measurement of frictional contact temperature remains a formidable challenge, while the accuracy of simulation results from conventional numerical methods remains uncertain. In this study, a PINN model that incorporates physical information, such as partial differential equation (PDE) and boundary conditions, into neural networks is proposed to solve forward and inverse problems of frictional contact temperature. Compared to the traditional numerical calculation method, the preprocessing of the PINN is more convenient. Another noteworthy characteristic of the PINN is that it can combine data to obtain a more accurate temperature field and solve inverse problems to identify some unknown parameters. The experimental results substantiate that the PINN effectively resolves the forward problems of frictional contact temperature when provided with known input conditions. Additionally, the PINN demonstrates its ability to accurately predict the friction temperature field with an unknown input parameter, which is achieved by incorporating a limited quantity of easily measurable actual temperature data. The PINN can also be employed for the inverse identification of unknown parameters. Finally, the PINN exhibits potential in solving inverse problems associated with frictional contact temperature, even when multiple input parameters are unknown.

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.

Full-field deformation measurement and cracks detection in speckle scene using the deep learning-aided digital image correlation method

An enhanced full-field deformation and crack measurement method for oblique optical-axis conditions was proposed. First, the thickness of the calibration plate causes an error in the homography conversion (H matrix) and the final displacement results. To solve this problem, a Perspective-n-Point (PnP) based thickness compensation method for H calibration was proposed. Second, for better measurement of the full-field deformation, the measured surface is mapped with random speckles and markers, and the complex background and covering problems create obstacles for crack skeleton detection. To overcome these interferences, a strain-guided two-phase detection method based on the deep learning model was innovatively designed. Third, the traditional crack width calculation method is susceptible to the binarization threshold and the connection to the speckles, and a crack width calculation method using the displacement of control points was suggested. During a bending test, the above methods were applied to a basalt fiber-reinforced polymer (BFRP) beam. The enhancement in the displacement measurement accuracy and the robustness of crack detection, particularly the potential of detecting hidden cracks, were verified by comparing the performance of the proposed method to that of traditional contact sensors.

Intelligent Processing of Power Operation Data Based on Improved Apriori Algorithm

This paper addresses several problems in the power system. Key challenges include low-power information integration, inappropriate system data management, inaccurate system data updating, and inefficient fault diagnosis. We focus on analyzing and diagnosing transmission line faults using the operation data of the power system. The study incorporates the quantitative identification of statements. This is done using the Apriori big data analysis and calculation method. Additionally, we utilize big data analysis and vast power operation data. We aim to achieve automatic analysis and pinpoint the causes of transmission line faults. Furthermore, we seek to optimize the traditional Apriori calculation method. This optimization results in a reduction of about 52% in the candidate item set calculation. The optimized M-Apriori calculation method can analyze the correlation between event index data and faults in real time, and realize automatic diagnosis and analysis of faults through operation data.