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Process incapability index is a useful tool for measuring the performance of a process to maintain the quality of products. In this article, we discuss some statistical properties of the estimator of process incapability index when process observations are autocorrelated and contaminated by measurement errors. In many industry, it has been proven that observed quality characteristics for consecutive items are autocorrelated. Moreover, some amount of measurement errors is always present in the observed data. Therefore, it is important to analyze the statistical properties of the estimator under the combined effect of autocorrelation and measurement error for commenting approximately on the ability of a manufacturing process. [ABSTRACT FROM AUTHOR]

Fiber orientation tensors (FOT) are widely used to approximate statistical orientation distributions of fibers within fiber-reinforced polymers. The design process of components made of such fiber-reinforced composites is usually accompanied by a virtual process chain. In this virtual process chain, process-induced FOT are computed in a flow simulation and transferred to the structural simulation. Within the structural simulation, effective macroscopic properties are identified based on the averaged information contained in the FOT. Solving the field equations in flow simulations as well as homogenization of effective stiffnesses necessitates the application of a closure scheme, computing higher-order statistical moments based on assumptions. Additionally, non-congruent spatial discretizations require an intermediate mapping operation. This mapping operation is required, if the discretization, i.e., mesh, of the flow simulation differs from the discretization of the structural simulation. The main objective of this work is to give an answer to the question: Does the sequence of closure and mapping influence the achieved results? It will turn out, that the order influences the result, raising the consecutive question: Which order is beneficial? Both questions are addressed by deriving a quantification of the closure-related uncertainty. The two possible sequences, mapping followed by closure and closure followed by mapping, yield strongly different results, with the magnitude of the deviation even exceeding the magnitude of a reference result. Graphical consideration reveals that for both transversely isotropic and planar FOT-input, invalid results occur if the mapping takes place prior to closure. This issue is retrieved by orientation averaging stiffness tensors. As a by-product, we explicitly define for the first time the admissible parameter space of orthotropic fourth-order fiber orientation tensors and define a distance measure in this parameter space. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence applications based on machine learning is creating not only many opportunities but also risks. The recent regulatory and political debate, at the international level, emphasizes the urgent need to develop appropriate statistical methods that can measure the safety and the risks of AI applications. In line with this emerging need, the aim of this work is to launch a call for discussion papers in the Statistics journal on safe machine learning methods. To this aim, we define which metrics to develop and an example of a recent proposal, along with its advantages and disadvantages [ABSTRACT FROM AUTHOR]

Indoor positioning has been extensively studied for at least the past twenty years. In the list of the most common solutions, those based on the Received Strength Signal Indicator (RSSI) have gained importance due to the simplicity of RSSI as well as the fact that it is available in several wireless sensor networks. In this work, we propose SeALS (Selection Strategy of Access Points with Least Squares Estimation), a new RSSI-based indoor positioning system using Bluetooth Low-Energy (BLE) access points, whose accuracy is improved by a new selection strategy of collected RSSI combined with the Ordinary Least Squares (OLS) estimation method. The main advantage of the proposed solution is the fact that it requires less time in the training phase allied with better system accuracy if compared to traditional methods. The proposed system is validated in a large-scale, real-world scenario, and the obtained results for the positioning error are reduced by up to 13% concerning the pure OLS method, and by up to 30% concerning the widely deployed K-Nearest Neighbors technique. [ABSTRACT FROM AUTHOR]

This research focuses on estimating the stress-strength reliability in a system characterized by the influence of two random stresses on its strength, employing both frequentist and Bayesian approaches. The reliability of such systems is represented by the function δ = P (U < V < W) , where V denotes the system's strength, and U and W represent the stresses. The analysis is performed under a progressive Type-II censoring scheme, considering the random variables U, V, and W as independent and following the Burr-XII distribution. In a frequentist setup, both the maximum likelihood estimator and the maximum product spacings estimator of δ have been obtained. In the Bayesian paradigm, the Bayes estimator of δ under the squared error loss function is derived utilizing the Markov chain Monte Carlo method, considering independent gamma priors for the unknown parameters. In addition, asymptotic confidence intervals and highest probability density credible intervals for δ are also formulated. An extensive simulation experiment is carried out to compare the performances of the different developed estimators. Finally, a real-life application is presented to demonstrate the practical applicability of the proposed theory. [ABSTRACT FROM AUTHOR]

This article deals with the problem of classifying Fréchet populations by proposing a non-linear fixed effects model. Various properties of the proposed model are investigated in detail. Estimations of the effects are achieved in explicit expressions and stochastic properties of the estimators are discussed. Two procedures for testing the homogeneity of populations are proposed. Illustrative examples are also given. The proposed model is an alternative method for situations in which the existing parametric methods such as generalized linear models provide poor efficiency and factor effects are on the response non-linearly. The proposed model is also applicable in situations where the mean response does not exist. [ABSTRACT FROM AUTHOR]

Distribution-free or nonparametric control charts are used for monitoring the process parameters when there is a lack of knowledge about the underlying distribution. In this paper, we investigate a single distribution-free triple exponentially weighted moving average control chart based on the Lepage statistic (referred as TL chart) for simultaneously monitoring shifts in the unknown location and scale parameters of a univariate continuous distribution. The design and implementation of the proposed chart are discussed using time-varying and steady-state control limits for the zero-state case. The run-length distribution of the TL chart is evaluated by performing Monte Carlo simulations. The performance of the proposed chart is compared to those of the existing EWMA-Lepage (EL) and DEWMA-Lepage (DL) charts. It is observed that the TL chart with a time-varying control limit is superior to its competitors, especially for small to moderate shifts in the process parameters. We also provide a real example from a manufacturing process to illustrate the application of the proposed chart. [ABSTRACT FROM AUTHOR]

To solve underdetermined box-constrained integer least squares (UBILS) problems, we propose an integer-constrained alternating direction method of multipliers (IADMM), which can be much more accurate than the ADMM method. To guarantee to find the optimal solution, then we incorporate IADMM to DTS, a tree search method, to make the latter more efficient. Numerical tests show that the combined method IADMM-DTS can be much faster than the original DTS method. Finally, we apply the combined method to a practical communication problem. Numerical results indicate that IADMM-DTS typically performs better than the commercial solvers CPLEX and MOSEK in terms of both efficiency and accuracy, and it can be used as an alternative to the commercial solver Gurobi for UBILS problems. [ABSTRACT FROM AUTHOR]

Joint monitoring of time and magnitude is an important issue in many industrial and non-industrial fields and several proposals exist in the literature. The aim of this article is to propose a new maximum exponentially weighted moving average (Max-EWMA) chart assuming generalized exponential distribution for time as well as magnitude. As the test-statistic of Max-EWMA chart consists of two EWMA statistics, it is easy to identify the out-of-control signal raised by the chart. The performance of the chart is evaluated using different run length characteristics including, average run length (ARL), standard deviation of run length (SDRL), and quantiles of the run length distribution. Besides extensive simulation studies, a real data set is also used to show the practicality of the proposed chart. The results show that the proposed chart is efficient in detecting different size of small to moderate shifts. [ABSTRACT FROM AUTHOR]

Monitoring profiles with count responses is a common situation in industrial processes and for a count distributed process, the Conway–Maxwell–Poisson (COM-Poisson) regression model yields better outcomes for under- and overdispersed count variables. In this study, we propose CUSUM and EWMA charts based on the deviance residuals obtained from the COM-Poisson model, which are fitted by the PCR and r–k class estimators. We conducted a simulation study to evaluate the effect of additive and multiplicative types shifts in various shift sizes, the number of predictor, and several dispersion levels and to compare the performance of the proposed control charts with control charts in the literature in terms of average run length and standard deviation of run length. Moreover, a real data set is also analyzed to see the performance of the newly proposed control charts. The results show the superiority of the newly proposed control charts against some competitors, including CUSUM and EWMA control charts based on ML, PCR, and ridge deviance residuals in the presence of multicollinearity. [ABSTRACT FROM AUTHOR]

In the quality management system, skip-lot sampling is a prominent beneficial sampling plan concerning the economic aspect since it requires minimum expenses for inspection. This study attempts to propose an innovative sampling plan by assimilating the concept of a resampling scheme in a two-level skip-lot sampling plan of type 3, that is, SkSP-2L.3, and the resultant plan is designated as SkSP-2L.3-R with a single sampling plan as the reference plan. Using Markov chain formulation, the performance measures are derived. The designing methodology is provided to determine the plan parameters of the SkSP-2L.3-R plan to minimize the average sample number by using two points on the operating characteristic curve approach. Tables are constructed to select the plan parameters under binomial distribution and Poisson distribution by considering various combinations of producer and consumer quality levels along with respective producer and consumer risks. An illustrative example is provided to depict the execution of the proposed SkSP-2L.3-R plan. The comparative study substantiates that the proposed SkSP-2L.3-R plan with a single sampling plan as the reference plan has outstanding performance than some other existing plans under binomial and Poisson distributions. [ABSTRACT FROM AUTHOR]

In this article, three control charts including MEWMA, MEWMA/ χ 2 and DMEWMA equipped with proposed runs rules schemes, are developed to improve the performance of existing control charts in monitoring multivariate simple linear profiles. Using five criteria of average run length, percentile of the run length, standard deviation of run length, relative mean index and relative standard deviation index the behavior of the suggested control schemes has been assessed. The results of our study demonstrate that the suggested schemes have a better performance compared to current charts for detecting shifts. Ultimately, using an actual calibration application case in the automotive industry, the suggested control charts applicability is depicted. [ABSTRACT FROM AUTHOR]

Consider a class of discrete probability distributions with a limited support. A typical example of such support is some variant of a Likert scale, with a response mapped to either the { 1 , 2 , ... , 5 } or { - 3 , - 2 , ... , 2 , 3 } set. Such type of data is common for Multimedia Quality Assessment but can also be found in many other research fields. For modelling such data a latent variable approach is usually used (e.g., Ordered Probit). In many cases it is convenient or even necessary to avoid latent variable approach (e.g., when dealing with too small sample size). To avoid it the proper class of discrete distributions is needed. The main idea of this paper is to propose a family of discrete probability distributions with only two parameters that play the same role as the parameters of the normal distribution. We call the new class the Generalised Score Distribution (GSD). The proposed GSD class covers the entire set of possible means and variances, for any fixed and finite support. Furthermore, the GSD class can be treated as an underdispersed continuation of a reparametrized beta-binomial distribution. The GSD class parameters are intuitive and can be easily estimated by the method of moments. We also offer a Maximum Likelihood Estimation (MLE) algorithm for the GSD class and evidence that the class properly describes response distributions coming from 24 Multimedia Quality Assessment experiments. At last, we show that the GSD class can be represented as a sum of dichotomous zero–one random variables, which points to an interesting interpretation of the class. [ABSTRACT FROM AUTHOR]

In recent years, several probability distributions have been introduced in the literature to analyze the data exhibiting an upside-down bathtub–shaped failure rate; an inverse Pareto distribution (IPD) is an appropriate choice among them. For stress–strength reliability models, estimation of parameters is an interesting area of research. In this article, we estimate the stress–strength reliability parameter R = P (X > Y) (where X and Y are strength and stress variables, respectively) of the IPD, whereby we focus on the problem of fixed-accuracy confidence interval estimation of R. It is established that the proposed interval estimation problem cannot be solved with the help of any fixed sample technique. As a result, we propose a two-stage sequential sampling strategy (which reduces the sample size significantly) to solve the given estimation problem. We obtain the expressions of several exact operating characteristics associated with our two-stage sampling technique. We also establish that the proposed two-stage procedure enjoys interesting first-order asymptotic properties. The detailed simulation analyses support our theoretical findings, and two real data sets based on insurance claims reinforce the practical utility of the proposed technique. [ABSTRACT FROM AUTHOR]

Multivariate statistical process control (MSPC) was developed for the monitoring of variables that are either all numerical or all categorical. In the present paper, we describe a nonparametric control scheme that can be used to monitor a mixture of numerical and categorical variables simultaneously. It integrates Principal Component Analysis Mix (PCA Mix), a multivariate statistical tool, with the conventional Hotelling T2 chart. To estimate the control limit for the PCA Mix-based T2 statistic, two nonparametric approaches – kernel density estimation (KDE) and bootstrap – are employed, because of the unknown nature of the underlying distribution. The simulation results demonstrate that with an appropriate number of principal components, both bootstrap and KDE exhibit convincing performance in terms of generating the same, or nearly the same, number of false alarms (ARL0) as expected, and being able to detect process shifts efficiently (ARL1). Compared with bootstrap, KDE is shown to work better with small sample sizes (n < 800) and to be slightly more sensitive to small shifts. However, the results also show the instability of the estimated nonparametric control limit when highly imbalanced categorical variables are included, which indicates the need for further research on this topic. [ABSTRACT FROM AUTHOR]

In this paper, the two parameters Weibull distribution lifetime model has been used to improve the reliability of a system in radar structure. This model is flexible to any lifetime data analysis and describing all stages of failure rate. Redundant and reduction methods of improving the system reliability are provided, including cold, hot, and imperfect methods. The lifetimes of units are assumed to be independent and non-identical components. To distinguish between different methods, we computed the mean time to failure and γ -fractiles. Reliability equivalence factors are also derived. Simulation examples were given to differentiate between different improving methods.

Many quality characteristics in manufacturing industry are of one sided specifications. The well-known process capability indices C P ⁢ U and C P ⁢ L are often used to measure the performance of such type of production process. It is usually assumed that process observations are independent and measurement system is free of errors. But actually in many industry it has been proven that auto-correlation is an inherent nature of the production process, especially for chemical processes. Moreover, even with the use of highly sophisticated advanced measuring instruments some amount of measurement error is always present in the observed data. Hence gauge measurement error also needs to be considered. In this paper we discuss some inferential properties of one-sided process capability indices for a stationary Gaussian process in the presence of measurement errors. As a particular case of a stationary Gaussian process, we discuss the case of a stationary AR ⁡ (1) process where measurement error follows an independent Gaussian distribution. [ABSTRACT FROM AUTHOR]

Artificial neural networks (NNs) have been extensively studied for their application to quality control (QC) to monitor the conformity of processes to quality specifications. However, the requirement of at least five QC measurements increases the associated costs. This study explores the potential of using NNs on samples of QC measurements of very small size. To achieve this, three one-dimensional (1-D) convolutional NNs (CNNs) were designed, trained, and tested on datasets of n-tuples of simulated, standardized, normally distributed QC measurements, where 2 ≤ n ≤ 4 . The performance of the designed CNNs was compared to that of statistical QC functions applied to samples of equal sizes, maintaining equal probabilities for false rejection. The results demonstrated that for n-tuples of QC measurements distributed as 풩 ⁢ (0 , s 2) , where 1.2 < s ≤ 9.0 , the designed CNNs outperformed their statistical QC functions counterparts. Therefore, the use of 1-D CNNs applied to samples of two to four QC measurements can effectively enhance the detection of nonconformity of a process to quality specifications. This approach has the potential to significantly reduce the costs of QC measurements and improve the overall efficiency of the QC process. [ABSTRACT FROM AUTHOR]

In this paper, a c-control chart using a triple sampling scheme (TS-c) is studied. The chart design, procedure, and a bi-objective optimization model are given to optimize the TS-c-chart. The Average Run Length for in-control and out-of-control ( ARL 1 ), and Average Sample Number metrics are calculated. A Comparison among TS-c, Fixed parameters c (FP-c), VSS-c, EWMA-c, and Double Sampling c (DS-c) control charts are carried out in terms of ARL 1 . The proposed TS-c-chart has lower ARL 1 values for detecting small and moderate shifts in the mean number of non-conformities in control compared with FP-c, VSS-c, EWMA-c, and DS-c. [ABSTRACT FROM AUTHOR]

A modelling framework suitable for detecting shape shifts in functional profiles combining the notion of the Fréchet mean and the concept of deformation models is developed and proposed. The generalized mean sense offered by the Fréchet mean notion is employed to capture the typical pattern of the profiles under study, while the concept of deformation models, and in particular of the shape-invariant model, allows for interpretable parameterizations of the profile's deviations from the typical shape. The EWMA-type control charts compatible with the functional nature of data and the employed deformation model are built and proposed, exploiting certain shape characteristics of the profiles under study with respect to the generalized mean sense, allowing for the identification of potential shifts concerning the shape and/or the deformation process. Potential shifts in the shape deformation process are further distinguished into significant shifts with respect to amplitude and/or the phase of the profile under study. The proposed modeling and shift detection framework is implemented to a real-world case study, where daily concentration profiles concerning air pollutants from an area in the city of Athens are modeled, while profiles indicating hazardous concentration levels are successfully identified in most cases. [ABSTRACT FROM AUTHOR]

In this study, a variance control chart is proposed with some modifications in terms of the rapid detection of process shifts by using the homogenously weighted moving average ( HWMA) statistic by using the information from an auxiliary variable. The proposed HWMA chart is a new memory type variance control chart with a three-parameter log transformation that operates by assigning the specified weights to the most recent value. All previous samples are allotted with an equal proportion of the remaining smoothing parameter weight. The remaining weights put their effect as a counterpart within the smoothing parameter and the current observations take the maximum of it. To study the performance of the proposed chart the respective run length properties are determined by Monte Carlo simulations and extensively presented in the tables. The comparison has been made with the existing exponentially weighted moving average ( EWMA) variance control chart. It is figured out that the proposed control chart is sensitive to process shift by using the auxiliary information with a high degree of correlation. The real-life dataset is selected from an industrial production environment to demonstrate the implementation of the proposed chart. [ABSTRACT FROM AUTHOR]

The emergence of intelligent tourism as a new trend in the travel industry has raised the bar for the excellent growth of the travel and cultural sectors. The merger of the artistic and tourist industries significantly advances tourism’s modernization and evolution. Finding a high-quality growth path for the tourist and culture industries within the context of intelligent tourism is a significant problem that must be overcome in order to achieve the two sectors’ in-depth integration and synergistic growth. A scientific assessment system and model analysis is necessary to uncover practical ways to support industry integration and upgrading based on the current industrial development status quo. A strong connection between this province’s cultural and tourism sectors is indicated by the coupling degrees generally higher than 0.85. Although the degree of coupling coordination has improved annually, many cities are still unable to achieve a higher level of coordination, according to an analysis. This study utilizes the degree of coupling coordination model as a basis for creating a coupling index for the culture and tourist industries and examining the growth situation of an area from 2015 to 2022. In a particular province, the median annual growth rates for the tourism and cultural sectors between 2015 and 2022 are 19.18% and 32.11%, respectively. This suggests that there is still more room for growth and integration between the two sectors in the context of intelligent tourism. The tourism industry is developing more slowly than the cultural industry. The research suggests ways to aid the integration and superior growth of the tourist and cultural sectors, including boosting infrastructure construction and enhancing employee knowledge.

This study addresses the problem of predicting student performance by proposing a heterogeneous transfer learning method based on FMBCC-FT, which aims to accurately predict students’ Civics knowledge mastery. The study achieves effective knowledge transfer by defining university Civics teaching as the source domain and considering the university’s demand for students’ Civics dynamics as the target domain. After collecting and standardizing students’ Civics education data, this study constructed a knowledge base model of Civics teaching based on the source domain data and migrated it to the target domain. To compensate for the bias in the migration, the study designed a three-stage iterative optimization migration weight learning method, which significantly improved the quality of Civics teaching by updating the parameter weights of the source domain model. The process was verified through the experimental control group pre and post-tests. The results showed that the total score of critical thinking ability pre and post-tests was −3.39. The students’ innovative consciousness score in the experimental group was 3.96, which was significantly higher than that of the control group, which was 0.18. In addition, the scores of the quality dimensions of the students in the control group and the experimental group ranged from 3.22 to 3.40 and 3.43 to 3.68, respectively. This empirical result effectively confirms that the Civics teaching model proposed in this paper can effectively improve students’ critical thinking ability, innovation consciousness and overall quality, which is of great guiding significance to the current practice of Civics teaching in colleges and universities.

To further analyze the content of “dual-teacher” teacher team construction in higher vocational colleges and universities, this paper focuses on teacher moral construction, teacher training, assessment and evaluation, and incentives for in-depth investigation. It is clear that higher vocational colleges and universities should distinguish between “dual-teacher” teacher quality assessment and ordinary teacher assessment, and put forward the gradient “dual-teacher” teacher quality team construction. Improve the support vector machine, use the binary tree to propose an evaluation model based on incomplete DBT-SVM, and combine multiple binary classifiers to solve the multi-classification problem of “dual-teacher” teacher quality evaluation. The optimal parameter combinations, i.e., C =28 and γ = 0.0534, are obtained using the kernel function and parameter tuning experiments, and the accuracy of the model prediction results reaches 94.325%, taking the “dual-teacher” teachers in a university in Y province as the specific evaluation object. This shows that the accuracy of this DBT-SVM-based evaluation method of “dual-teacher” teacher quality is good.

Myth influences the thinking and imagination of engineering activities with its rich narrative and symbolic meaning, and engineering transforms technological evolution into reality, providing new possibilities for the inheritance and reshaping of myth. From the perspective of technological culture, we analyze the engineering myths that are intertwined and fused with mythological narratives and engineering practices, breaking the “sacred” limitations in the process, exploring new engineering fields and creating a sustainable future with a broader, more open, and more flexible vision. To examine the social influence of engineering myths, a knowledge map of traditional myths and technological culture was created. Taking the Chang’e moon exploration project as an example, academic qualifications have significant correlations with political influence, military influence, psychosocial influence, and educational influence. After analyzing the technological culture exhibition of the engineering myth, the technological culture of engineering myth purports to strengthen the interaction between culture and engineering, encourage engineering practice to transcend itself and guide the realization of technological ethics in engineering practice.

In this paper, we use Caffe framework to implement the improved Faster R-CNN recognition technique for building images in civil engineering under Linux system and add feature pyramid network and regional feature aggregation into the ResNet-50 network and ResNet-101 network, respectively, to strengthen the training effect, and establish ResNet-101+FPN+ROI Align image recognition technique. Simulated crack experiments and concrete surface quality defect detection experiments confirm that the ResNet-101 FPN ROI Align method is accurate and detects defects at a high rate. The method established in this paper has a minimum error of only 0.4% in the simulated crack experiment, and the detection rate is much higher than that of other detection methods when detecting quality defects on the concrete surface, and the accuracy can reach up to 94% at the same time. In civil engineering, the image recognition technology established in this paper has practical significance and high application value, as demonstrated by the experiment.

This paper takes universities and enterprises as the basic game subjects of collaborative innovation of live e-commerce talent cultivation and defines the involved interest subjects as government, school, enterprise and industry to construct a multi-party evolutionary game model. The model’s evolutionary stable equilibrium point can be calculated using the replicated dynamic equations of multiple parties. MATLAB was used to analyze the numerical simulation to explore the sensitivity of the relevant parameters for the three parties. Finally, according to the game results, a four-point practical path to optimize the live e-commerce talent training program is proposed. The results show that under the satisfaction of the equilibrium point (0, 0, 0), the critical value of the active participation willingness of the four parties is between 0.4 and 0.5, and the final equilibrium point tends to (1, 1, 1). In this paper, the cooperation between schools and enterprises is prompted to change from limited rationality to full rationality in order to realize the Pareto optimal equilibrium of the final game.

The rich traditional culture is an inexhaustible treasure trove of resources for the brand operation of the modern tourism industry. This paper proposes an improved method for the LDA model of text big data mining, optimizing the topic representation of LDA by introducing the Topic2Vec model, optimizing the probability calculation of generated words by combining the original Dilley distribution and Bernoulli distribution and optimizing the assignment of topic words in the Gibbs adoption process of LDA model with the help of likelihood function. Then, the big data of tourism brand design text, promotion, and cultural products are mined and analyzed based on the improved LFV-LDA model. In the design of tourism brands focusing on traditional culture, the frequency of the keyword “pattern” was the highest, accounting for 8.20%. In brand promotion, the most distributed channel from 2017 to 2021 was a live broadcast, and the growth rate in 2020 and 2021 reached 22.71% and 36.29%. Regarding traditional cultural and creative products, tourism cultural and creative products have maintained a high growth momentum from 2012 to 2021, with an average annual growth rate of 154.25%. Based on the analysis of big data, it can guide the operation of tourism industry brands in all aspects, such as design, promotion, and landing, and give full play to the value connotation of traditional culture.

Bank credit financing is an effective method to promote the development of enterprises, and this paper discusses the use of enterprises in the credit financing advantage supported by supply chain financial networks and industrial policy in the new era. In the paper, the strategic choice of enterprises using bank credit financing is analyzed, the Nash equilibrium point of the bank and the enterprise is modeled by calculus, and the game evolution model of enterprise use of credit financing advantage is constructed to explore the equilibrium distribution of bank credit financing. On this basis, the regression model of bank credit financing and enterprise reputation is established, and it is combined with relevant data for empirical analysis. The results show that under the initial conditions, there are two evolutionary stable strategies of (0,0,0) and (1,1,1) in the enterprise bank credit financing game system, which verifies the analysis for the stability of equilibrium point under the dual mechanism of supply chain finance and industrial policy. The regression coefficient of bank credit financing of enterprises with greater corporate reputation is 1.26, and when the explanatory variable is the indicator of short-term lending and long-term investment, the coefficient of long-term borrowing ratio is -0.003, with a significance level of 1%, and the increase of long-term borrowing ratio can significantly reduce the behavior of short-term lending and long-term investment of enterprises.