This PDF file contains the front matter associated with SPIE Proceedings Volume 12128, including the Title Page, Copyright information, and Table of Contents.

Fatigue damage is one of the main reasons for the failure of aircraft and its structural parts. According to the statistical analysis of practical application, it is found that more than 80% of the strength problems of aircraft in the application process are caused by fatigue damage. Compared with uniaxial fatigue loading, multiple fatigue refers to the fatigue caused by the direction and value of the force changing with the time. Therefore, based on the understanding of various fatigue life analysis methods and the idea of critical surface method, this paper makes a comprehensive analysis of the mechanical structure of aircraft.

In this paper, the stochastic user equilibrium model and its optimization conditions are studied. At the same time, the first and second order differential information of the objective function is reasonably used to deduce the corresponding sensitive influence equilibrium evaluation model according to the nonlinear programming theory. In this paper, we study mainly derived from the road, traffic demand, free travel time and link capacity three variables, and then on a small network model to implement sensitivity analysis of numerical experiments, the final results show that the sensitivity calculation can be integrated into the network model in the solving process, does not need to increase the number of actual calculation, plays a positive role on the various elements of the network model.

With the rapid development of China's bond market, credit risk and the systemic financial risk derived from it are increasingly exposed. The market shock brought by the normalization of bond default events reflects the role of bond mispricing in fueling the financial crisis. Scientific prediction of bond yield is of great significance for financial product pricing and financial risk control. Based on the perspective of public fund manager, this paper mainly studies the prediction problem of cross-section excess returns of credit bonds in China. In the aspect of forecasting factors, this paper creatively selects micro influencing factors of credit spread and term spread to forecast the annual excess return of credit bonds. In terms of research model, besides the traditional logistic regression model, this paper also adopts six machine learning algorithms to construct the bond yield prediction model, including three single classifiers (neural network, support vector machine and k-nearest neighbor) and three ensemble algorithms (random forest, XGBoost and Adaboost).The results show that, compared with the traditional logistic regression model, machine learning algorithms significantly improve the out-of-sample prediction ability of bond returns. The prediction performance difference between the ensemble algorithm and the single classifier is mainly reflected in the recall rate. Among them, random forest algorithm is the ensemble algorithm with the best performance and the strongest stability. At the same time, the predictor system constructed in this paper effectively improves the situation that the explanatory power of prediction factors is not strong in previous studies, and shows a good degree of differentiation in the prediction of bond yield.

At present, there is an increasing demand for diversified fitness in the Internet Information Age. By using modern science and technology, especially the Internet of things technology, we study the design and application of a new generation of Internet of things digital fitness equipment, taking full advantage of meeting the diverse needs of fitness groups for fitness-oriented. Using various research methods, this paper explores the functional design and application of Internet of things fitness equipment and designs a more intelligent and three-dimensional Internet of Things Fitness Service mode. The results show that constructing an internet-of-things fitness cloud service platform and data management system makes the internet-of-things fitness service supply remote, real-time, diversified. It can be seen that this study not only provides convenient and value-added fitness services for fitness people, but also brings sustainable development space for the health service industry.

With the wide application of Radio Frequency Identification Devices (RFID) technology, the security and privacy threat of this transmission mode is becoming more and more prominent. This paper summarizes the security problems faced by RFID, such as eavesdropping, man-in-the-middle attack, physical cracking, falsifying, and so on. Based on these problems, three solutions are proposed: 1. Using sleeping or killing to change RFID relevance. 2. Using renaming or hash lock cryptography to change the uniqueness of RFID. 3. Hiding the real information according to the distance. So as to provide a theoretical basis for RFID applications to solve security problems.

Internet of Things (IoT) industry is exploding quickly. The application of 5G network makes the interaction between IoT devices and outside network more frequently. The function of cloud server is storing the data sent by the IoT devices via 5G network. Fog nodes are adopted to help conduct the complex tasks for cloud server and IoT devices. However, the lack of supervision of IoT devices become the hidden risk because the illegal or sensitive information may be uploaded to the cloud and influence the normal operation of IoT network. To solve the problems mentioned above, we propose an encryption scheme with supervision of IoT devices in 5G cloud and fog environment (SID-ABE). Firstly, our SID-ABE scheme combines cloud and fog to realize secure data sharing and storing in 5G cloud and fog environment. Secondly, our SID-ABE scheme realizes attribute revocation of fog nodes and users. Thirdly, our SID-ABE scheme realizes supervision of IoT devices by fog nodes. Fog nodes are responsible to check the data received or sent by IoT devices for security. Fourthly, our SID-ABE scheme can withstand collusion attacks. Finally, we compare the security performance with some representative schemes.

The body size parameters of sheep are very important for breeding and exploring intelligent breeding mode of sheep. In the measurement of sheep size parameters based on machine vision, the accurate acquisition of sheep contour is the key point of sheep size parameter estimation. In this paper, a self-developed contour extraction algorithm is proposed. The algorithm detects the noise, occlusion wire rope and edge points in the image, and evaluates the segmentation results by calculating the indexes of segmentation accuracy, over-segmentation rate and under-segmentation based on GT (ground truth) image. Experiments results show that the accuracy of the algorithm can reach 95% - 99%.

The Internet of Things for Substation Equipment is the integrated application of the Internet of Things technology in the field of power transmission and transformation equipment. It has the characteristics of intelligence, diversification, and ecology. It is an important part of the ubiquitous power Internet of Things strategy. Taking the pilot construction of a 500kV substation intelligent Internet of Things comprehensive application system as an example, the overall plan for the construction of a substation ubiquitous power network system was explored and studied. Various types of bottom-level sensing equipment were used in the pilot, using wired private networks and wireless private networks. The combined method ensures the safe communication of the power Internet of Things system, and uses the LoRa wireless technology to solve the problem of low power consumption and transmission distance of the sensor. With the continuous application of the Internet of Things, smart gateway technology has also become an important content of many scholars' research. It can play a good role in application services, information processing, and gateway versatility. Moreover, OSGI middleware technology is used as the basis to strengthen functions such as traffic scheduling and transmission. The application of gateways based on the application of the Internet of Things in various industries at this stage is mainly responsible for the transmission of information and the provision of related services, and has been widely recognized for its own advantages. The article mainly analyzes the hardware and application scenarios of the intelligent gateway system and the intelligent gateway technology from the perspective of software design.

Through in-depth study of the current vulnerability discovery technology, this paper analyzes the advantages and disadvantages of common vulnerability discovery technology, as well as the application field. Based on the CVE vulnerability library, the existing OpenVAS source code design was improved, and its scanning process was improved to enhance the ability of scanning known vulnerabilities. For the unknown vulnerability mining, optimize the design of the vulnerability mining test generating algorithm and vulnerability mining algorithm, carry out network vulnerability mining on the target industrial control test system, so as to obtain the known or unknown vulnerability analysis report of the industrial control system, and form the security assessment report and security response strategy of the industrial control system.

Autonomous driving is the key technology of intelligent logistics in Industrial Internet of Things (IIoT). 3D vision using Light Detection And Ranging (LiDAR) under vehicle industrial standard is the rigid demand in autonomous driving due to its lower cost, more robust, richer information, and meeting the mass-production standards. However, the appearance of incomplete point clouds losing geometric and semantic information is inevitable owing to limitations of occlusion, sensor resolution, and viewing angle when the LiDAR is applied. The emergence of incomplete point clouds, especially incomplete vehicle point clouds, would lead to the reduction of the accuracy of autonomous driving vehicles in object detection, traffic alert, and collision avoidance. Therefore, the point fractal network (PF-Net), a precise and high-fidelity 3D point cloud repair network based on Generative Adversarial Network (GAN), is first applied to repair incomplete vehicle point clouds in autonomous driving. To evaluate the performance of the GAN-based point cloud repair network, an autonomous driving scene is created, where three incomplete vehicle point clouds are set for different autonomous driving situations. Experimental results demonstrate the effectiveness of the PF-Net for challenging vehicle point cloud completion tasks in autonomous driving.

At the beginning of the 2020s, computing is moving into a new phase from a centralized model to a decentralized one. The first shift from centralized computing to decentralized computing in 1980 was due to personal computing, which formed a foundation for the decentralization method. Since mid-2000, the centralized cloud computing has begun its rise to the outstanding position. Driven by the flourishing of IoT, many new issues have arisen, such as unprecedented data volume, latency control, bandwidth efficiency, reliability of service, and sustainability. These issues limit the development of latency-sensitive IoT-based applications such as unmanned autonomous vehicles (UAV), Machine to Machine (M2M) communications. Hence, various emerging edge-based computing models have been proposed to address these issues related to the post-cloud. This paper first reviews the concepts and challenges of cloud computing. It then explores the driving force from IoT technologies and reveals the relationship between the flourish of IoT and the emerging of post-cloud computing. It also compares several fundamental post-cloud paradigms and propose a new method to meet the challenges using simulations methods. Finally, it concludes the paper and highlights prospects for future research.

Artificial intelligence, block chain technology, and the application of modern technology such as cloud computing within the scope of development will inevitably to the financial impact and interest rates, credit systems will be big data algorithm is applied to the enterprise or individual financial credit system, can on the basis of fully display algorithm advantages, more feasible countermeasures are put forward. Therefore, this paper, on the basis of understanding the algorithm framework of financial credit investigation system, combined with the existing application cases, puts forward effective solutions.

Social impact investment, as the latest form of investment that integrates business investment into the field of social governance, can effectively integrate financial investment and charity in practical development, so it is an effective tool in line with social innovation and development. More benefits can be achieved by addressing the problem by integrating the market capital needed to develop the social sectors where there are shortages. Based on the practice and development cases of comprehensive value theory research, it can be seen that social impact investment can obtain more value by investing in social enterprises, which involves three aspects: enterprise, society and environment. Therefore, on the basis of understanding the relevant basic concepts and based on the investment cooperation relationship between an information company and a technology company in a certain place, this paper makes an in-depth discussion on the specific ways to realize the comprehensive value of social impact investment.

In the process of accelerating urbanization, the Internet technology has been fully promoted, at this time using the algorithm based on big data technology to identify high-quality customers has become the focus of marketing attention. Therefore, on the basis of understanding how to identify high-quality passenger sources based on big data, this paper analyzes how to carry out marketing services in view of taxi track data mining.

In the context of the era of big data, with the wide use of electronic network and smart phones and other high-tech equipment, virtual space has been rapid development. Because its internal contains a large amount of data information, so in the development of practice has been gradually applied to various fields. Especially for the electronic evidence collection of duty crime investigation, it can not only solve the more secret and advanced crime mode, but also efficiently integrate the investigation resources. Therefore, on the basis of understanding the process of electronic evidence forensics, based on BP neural network model and its algorithm, this paper studies the application advantages of electronic evidence forensics in duty crime investigation.

In the rapid development of social economy, information technology has been widely used. Especially in the context of the era of big data, with the attention and recognition of various high-tech concepts by all enterprises, the technical performance and economic development level has been steadily improved. Therefore, on the basis of understanding the changes in economic statistics thinking under the background of big data, this paper analyzes the application effect of neural network in economic forecasting according to practical application technology concepts, thus proving the unique advantages of big data analysis in economic statistics.

In the continuous innovation of cloud computing technology, the financial market is facing more and more risks. Especially on the basis of the large-scale promotion of big data technology concept, financial institutions should strengthen the analysis of financial market willingness and behavior after integrating their own development experience. With the increasing number and demand of system users, the remote sensing cloud service platform has been unable to provide the required services for users in the financial market. Therefore, it is necessary to provide them with diversified and customized high-quality services on the basis of a comprehensive understanding of users' personalized behaviors. Therefore, after clarifying user behavior methods and related technical concepts in the cloud computing environment, this paper conducts prediction and analysis of user behavior according to the optimized K-means clustering algorithm, and finally verifies the validity and accuracy of this model.

The development of big data in education breaks through the traditional teaching management mode, and gradually changes the existing teaching methods. Under the influence of technological innovation in the information age, the rational use of big data information for comprehensive perception, analysis and sharing, so as to provide an effective basis for the development of education. Therefore, on the basis of understanding the development of big data education information management, this paper analyzes how data mining algorithm based on Spark platform can provide users with effective information according to the design form of practical education platform, and compares the application performance of K-means clustering algorithm and collaborative filtering recommendation algorithm.

The time-domain mathematical model of land combat multi-platform concealed guerrilla warfare system is established, and the situation vector analytical solution of land combat multi-platform concealed guerrilla warfare through Laplace transform is presented, which shows that the situation vector at any time can be obtained by the transfer of the initial situation vector. On this basis, the number of remaining platforms and the combat time at the end of land combat multiplatform concealed guerrilla warfare are given. Finally, an example is given to analyze the situation change of the proposed land war multi-platform concealed guerrilla warfare, and the effectiveness of the proposed conclusion is verified.

In recent years, with the rapid increase of image data in the Internet, the requirements of image data storage and retrieval are increasingly high. Tra-ditional image retrieval is based on high-dimensional features of the image to carry out similarity (Euclidean distance or cosine distance) calculation for retrieval. Storage and retrieval cost is high, although there are some methods to achieve hash retrieval, but the accuracy is generally not very high. In this paper, a deep hashing coding method based on supervised learning is proposed. This method uses deep neural network to obtain approximate binary codes. After quantifying these approximate binary codes, it can simply and quickly search similar images from massive image data, thus realizing large-scale image retrieval technology. We are in general MNIST dataset, CIFAR-10 dataset, SUN397 dataset and large-scale visual dataset ILSVCR2012 commonly used evaluation test data sets, such as the experimental results showed that the proposed method can achieve good retrieval accuracy, the MAP value of CIFAR-10 compared with existing methods improved a lot, to prove the effectiveness of the proposed method.

This paper presents an adaptive method to search best number of trees (Ntree) in complete random forest (CRF). Ada- CRF can automatically determine whether the forest has reached a stable state during the establishment of the forest, thereby avoiding inaccurate results caused by too small Ntree, or low efficiency caused by too large Ntree. As a general sampling method, Ada-CRF can not only effectively compress the amount of data, but also filter label noise to improve data quality. Ada-CRF can identify the noise points in the data by automatically searching for the results of the complete random tree division of the data. To improve the data quality, it filters the noise points of the label and retains valid data.

This article mainly introduces the advantages of lake water quality monitoring based on remote sensing technology, the remote sensing data and monitoring methods commonly used to monitor lake water quality by remote sensing, and focuses on the remote sensing monitoring methods of several main lake water quality parameters. The new technology of remote sensing has injected new vitality into the water quality monitoring business and traditional water quality monitoring scientific research with its macroscopic, objective, fast, dynamic, and economic advantages, and has played an irreplaceable role in the development of the water quality monitoring industry.

In order to improve the effective and accurate prediction of financial time series, an intelligent hybrid prediction model of financial time series is established on the basis of big data analysis. Firstly, the big data analysis and prediction model of financial time series is established, and then the parameters of the model are optimized by using the big data analysis method, the residual of the prediction model is analyzed, and the prediction results are compensated. Finally, the simulation experiment is carried out, and the experimental results show that the financial time series prediction model based on big data analysis has high effectiveness and fully meets the research requirements.

Neighborhood rough set (NRS) is an important extension of rough set theory, which can process continuous data directly without any prior knowledge. As far as we know, all present neighborhood rough set models are defined on distance metric (usually Euclidean distance), which makes neighborhood rough set model invalid in high-dimensional space due to "Curse of Dimensionality". Even in low-dimensional space, the performance of this model will be degraded due to the neglect of attribute weight by distance metric. This paper proposes a novel neighborhood rough set model based on space partition, and designs an attribute reduction algorithm based on this model. Experimental results on UCI benchmark datasets show that our algorithm performs better than the state-of-the-art NRS algorithms.

More and more regions are stimulating the development speed and direction of livestreaming e-commerce through relevant policies. While there is little literature focus on exploration the role and consequence of these policies in livestreaming e-commerce. Therefore, based on the perspective of policy instruments, this paper uses quantitative analysis of policy instruments classification, content coding and frequency statistics to analyze the texts of livestreaming e-commerce policies in China. Eleven policy instruments are evaluated and 205 coded records are analyzed. It finds that there exists uneven use and uncoordinated internal structure of those policy instruments. Furthermore, the structure of policy instruments in different regions tends to be similarity. In the light of these results, this paper argues that the state should draw attention to the balance application of supply-side, environment-side, and demand-side policy instruments, optimize their internal structure, and strengthen the cross regional cooperation of livestreaming e-commerce policies to highlight the effect of industrial cluster in the future.

Different aircraft leasing methods will have a certain impact on the financial statements of air transport companies, thus affecting the tax revenue of air transport companies. Therefore, in order to minimize the tax revenue of air transport companies, this paper proposes an intelligent tax planning model of aircraft leasing methods based on big data mining algorithm. Based on the detailed analysis of aircraft leasing methods, this paper explores the influence details of different aircraft leasing methods on financial statements. This paper introduces the widely used tax planning technology, uses big data mining algorithm to deeply mine and obtain the balance sheet information of air transportation companies under different leasing methods, and constructs the tax intelligent planning model, so as to minimize the tax, so as to realize the intelligent tax planning of aircraft leasing. The experimental results show that: under the condition of different aircraft rental quantity, the tax saving amount of the model is greater than the existing model, which fully confirms that the model has better application performance.

The birth of social networking has opened up a new world for human access to information. It not only provides people with a new platform for communication, but also changes the ecology and evolution of network marketing. Based on the development of popular networks, the fan market emerges as the times require. Starting from the characteristics of fans on social networks, this paper explains that the marketing mode of fan economy in social networks has changed from the traditional single track chain mode to the three track circular network mode, and puts forward the marketing strategies for creating the communication single element of detonating the circle from communication thinking to social thinking, and managing the fans community from fans marketing to fans circle marketing. Based on this, combined with the random forest algorithm, the marketing effect model of fan economy is constructed and analyzed, and the actual investigation into the marketing effect of fan economy is carried out. The results show that the marketing effect model of an economy based on random forest algorithm has high practicability in the practical application process, and fully meets the research requirements.

Artificial Intelligence is extensively applied and evolving in every field with emerging new techniques and approaches. Similarly, in the health care sector, robotic surgeries are expanding too. The significant leap from the fourth generation to the fifth generation of robots in the medical sector involves crucial decision making, robust infrastructure, and addressing ethical and legal obligations. This research addresses the ethical concern that may arise if surgeons are replaced with autonomous Robots. In this research, we performed a mixed-method approach involving quantitative (various literature reviews) and a qualitative survey which involved 60 participants and was conducted online. 52% of the respondents were not ready for complete automation of surgeries, and 77% were opposed to the possibility of the robot replacing surgeons. 75% of respondents recommended that surgeons monitor the interaction and that robots are aided than being entirely autonomous. Although surgeon substitution is not an ethical choice, these skills should be included in anesthetic and surgical preparation curricula and improved in a simulation environment. The future of this area requires exposure to continuous technological advancement and costing models and healthcare benefit networks for the next wave of robotic systems to achieve a foothold in the new healthcare industry.

This paper examines the impact of investor’s sentiment on non-life insurance demand during economic impairment period. We used initial dataset of thirty-three (33) OECD countries over the period from 2007 to 2016 and employ biascorrected bootstrapping technique to create a big dataset with 10,220 observations. We argue that big dataset derived from bias-corrected bootstrapping technique will generate unbiased and efficient regression estimates. Our results showed that during economic impairment period risk-averse individuals will buy insurance policies to safeguard their wealth resulting in an increase in the demand for non-life insurance. Our findings are robust to different estimation techniques and also control for the potential endogeneities.

We present a label noise self-filtering based learning method called ”NSFL” for improving generalizability of a classifier in label-noisy data. In this method, label noise is identified from normal samples by iteratively implementing the 2- means on loss values according to their different effects on loss values; and then, the label noise are filtered in a validation process. The NSFL does not rely on a specific loss function,resulting in a good performance in generalizability. Besides,it does not require to optimize any extra parameters of a specific measurement or noise estimation, so it is adaptive. In addition, it is proven that the learning process has the same convergence speed as the used loss function and is consistent with the optimal solution of the noise-free samples. To the best of authors knowledge, this is the first general and adaptive label noise-filtering method. The experimental results on synthetic and real datasets confirm that in comparison with the state-of-the-art methods, the proposed method is more effective in label noisy classification.

A visualization system with source code vulnerability detection is constructed based on the deep learning BLSTM model. The system firstly has the static analysis and slicing capabilities of the vulnerability statement, and then based on the deep learning model of the recurrent neural network and the attention mechanism technology, it can learn from the vulnerability data set the characteristics of the vulnerability and the realization of vulnerability detection. Firstly, the system analyzes the source code through the deployed open-source static analysis tool Joern according to the code to be tested, and stores the generated code attribute graph in the Neo4J graph database, then slices the program code according to the API call. The sliced code is treated as a text, which is mapped into a vector using word embedding technology, and finally input into the BLSTM model to determine whether there is vulnerability in the code under test. Through the visual interface, the system can interact with the user, and the user can independently select and train the model. Through experimental analysis and testing, the system has achieved good results.

The advent of the data age has brought about a big data environment, where big data will affect people’s daily life, study, and work status.Companies have optimized their e-commerce links through big data. More and more companies need to use the power of big data to integrate e-commerce marketing links into traditional marketing models in their work.This can not only clarify the company's marketing direction, but also reduce the company's marketing costs. However, some companies do not fully understand the relevant content of e-commerce marketing, so their own e-commerce marketing activities have not produced ideal results.

This article analyzes the legally collected real data and explains the analysis process and results of the unregulated third - party traceable agricultural product sales data in the current environment. We use a hybrid method that combines qualitative and quantitative analyses to process the regional data and quantitative analysis to process the count and proportion of data. To the greatest extent, it helps businesses obtain more information. At the same time, it discusses the development status and ethical issues of traceable agricultural products in China based on the literature and analysis process.

This work explores how to efficiently incorporate both the multi-scale features and attention mechanism into blind image quality assessment modules and proposes an end-to-end multi-scale attention guided deep neural network for perceptual quality assessment. Our method is established on a hierarchical learning framework in which two learning stages including coarse learning upon single-scale and quality refinement upon multi-scale, by which the quality-aware features could effectively extracted and aggregated into quality prediction scores. The proposed MSANet is based on the observation that multi-scale features could provide more flexible and robust features for BIQA whilst attention mechanism are beneficial for quality-aware feature aggregating. Through performance comparison with the state-of-theart approaches, our proposed mothed shows promising potential for blindly measuring the perceptual quality of distorted images.

TCAS can provide drivers with two kinds of information: traffic warning and decision warning. Among them, the relative height, distance, azimuth ascending/descending state and threat level of adjacent aircraft can be displayed on the electronic flight instrument by symbols with different colors and shapes. When the adjacent aircraft poses or may pose a threat to the aircraft, the traffic monitor will give a warning to the pilot and provide the relative orientation, which is helpful for the pilot to observe the adjacent aircraft before responding to the decision warning. When the adjacent aircraft is at the TA/VSI (Traffic Warning/Vertical Speed Indicator) boundary, a decision warning command will be issued.

With the proposal of "One Belt and One Road", the foreign trade of Gansu Province has become the main factor affecting the economic growth. As the intermediate link of "One Belt and One Road", since the proposal was put forward, Gansu's foreign trade has entered a stage of rapid development, and the trade volume has increased rapidly. This paper analyzes the development status of Gansu's economy and foreign trade, and selects the residents' consumption, total foreign trade, and GDP of Gansu from 1995 to 2017 as the research objects to study the impact of Gansu's foreign trade on economic growth. The results show that the increase in foreign trade will promote the economic growth of Gansu. Based on the results of empirical analysis, the paper puts forward some feasible suggestions for the development of foreign trade in Gansu.

With the development of the economy, the tradition of internal operating procedures and the change of external structure have brought great bottlenecks to the banking industry. “21st-century economic report” the banking regulatory commission data shows that the non-performing loan rate of commercial banks continued to increase. In addition, foreign banks are flocking into the Chinese market, making them more competitive. In this case, we must start from the bank itself to carry on the effective computation and the appraisal of the performance. In this way can form a certain incentive mechanism for improvement. In the past, the study found that most Banks to create real value is not high as traditional indicators, Banks have inflated the actual benefit, is not conducive to the overall performance evaluation, this article explains how to EVA method to examine the operating performance of listed commercial banks in our country, such ability more can find their own development problems of real and effective and the real situation, Only by improving the performance appraisal system can the banking industry develop better.

Corporate brand image, as consumers' overall cognition of the brand, is the main influencing factor of brand equity. Brand identity refers to the expected state of the brand that an enterprise wants to achieve in its development, and it is also the basis for the construction of brand image. In the past, the model of brand image is mainly studied from the two aspects of assets and image, but from the practical point of view, it can also be constructed and analyzed from the identification system. Therefore, on the basis of a simple understanding of the existing corporate brand image model, this paper takes Bell model as an example to conduct an in-depth analysis of the composition of the brand image of an enterprise, and finally obtains a clear application result.

This paper uses time series to study the impact of industrial structure upgrading and unemployed poor population on China's economic development. The final results show that the main reason for the slow growth rate of China's social and economic development is that the number of unemployed and poor population continues to rise. Through literature reading and data analysis, it is found that the reason for the continuous rise of poor population in China is that the "Lewis turning point" has been crossed, and the quantity of labor supply continues to decline, which leads to the lack of comprehensive improvement in the quality of practical supply.

Financial distress theory, as the main research field of enterprise financial management, is an important branch of modern capital structure theory. In the current increasingly competitive living environment, the phenomenon of financial difficulties of enterprises is more and more frequent. Market participants begin to make in-depth research on the prediction of the difficulties of enterprises while paying attention to the financial situation of enterprises. Therefore, on the basis of understanding the current research and application of financial distress theory, I built the corresponding prediction model based on Kalman filter, and made an empirical analysis of the application effect of the model.

Micro, small and medium-sized enterprises are the key to the stability of market entities in China's economy, and credit business is the most important asset business for banks. Based on the time series optimal model and multiple linear regression model, this paper solved the concrete quantification problem of the enterprise credit risk and combines the multi-classification linear discrimination and the strategy tree to formulate the concrete strategy of the bank credit under various circumstances, hope for the relevant practitioners for reference.

China's emergency logistics has the characteristics of bursting in time and space, diversified demands, temporary materials, government dominance and social welfare. Although China's emergency logistics has made some achievements, it is still in the early stage of development. There are problems such as insufficient prevention, imperfect system, inadequate technology and inadequate leading role of the government. Therefore, it is necessary to establish risk management, strengthen node construction and technology application, and reforce the leading role of the government, so as to provide theoretical support and practical reference for the emergency logistics management system.

Smart logistics is an inevitable trend of China's logistics industry, which can bring many opportunities for logistics enterprises. To vigorously promote the development of smart logistics will help the logistics industry to reduce costs and increase efficiency, meet the requirements of China's economic transformation and upgrading, and drive the economic efficiency of the whole industry. Based on the current situation of the implementation of intelligent logistics in the logistics industry, this paper discusses the problems existing in the development of intelligent logistics in enterprises, and puts forward targeted countermeasures and suggestions for the logistics industry.

At present, the world is in an era of economic globalization and information globalization. If China's logistics industry wants to meet the needs of logistics development in the era of Internet of Things, it must accelerate logistics transformation and develop wisdom logistics. This paper sorts out the evolution stage of wisdom logistics, analyzes the opportunities and challenges for Chinese enterprises to develop wisdom logistics, analyzes the typical model of supply chain wisdom logistics -- crowdsourcing logistics, and finally puts forward countermeasures and suggestions for the development of wisdom logistics.

At present, the supplier management of small and medium-sized enterprises mostly lacks detailed classification and rigorous evaluation. In the face of this phenomenon, on the basis of understanding the specific work of supplier management, this paper proposes the K-means algorithm with adaptive attribute optimization as the core by rectifying the traditional K-means algorithm. By integrating the design and analysis of the actual enterprise management system, it can be seen that the improved K-means algorithm has a higher accuracy rate than the previous application.

Under the background of the wide application of the Internet, it is more and more difficult for the traditional supply chain to realize the coordination and unity among members. Especially under the condition of the sustainable development of intelligent manufacturing, the proposed modern technologies such as artificial intelligence and big data provide new opportunities for the intelligent innovation of supply chain. Today, more and more manufacturers are automating and digitizing their production processes, and traditional supply chains are beginning to evolve into diversified and personalized new formats. Therefore, on the basis of understanding the intelligent development trend of intelligent manufacturing supply chain, this paper analyzes how to build a systematic intelligent big data solution framework of intelligent manufacturing supply chain according to the application of existing big data algorithms.

The company's operation and management in the complicated and changeable environment, deal with various problems caused by itself or in the market environment, the financial risk as the main factors influencing the operation of the company, in the development of practice must be using neural network to build systematic risk early warning model, and thus to obtain a comprehensive analysis. On the basis of understanding the financial risk early warning indicators of listed companies, this paper builds the early warning model of the impact according to BP neural network, and carries on the practical verification analysis from this.

In recent years, with the development of Internet technology, the Internet has been transformed from fragmented concept to practical application. It is no exaggeration to say that Internet of things technology will be used in all industries in future. In view of the application of Internet in supply chain finance, we explore the corresponding risks and effects and puts forward relevant countermeasures.

Many investors have begun to pay attention to the business environment, and the business environment has also become a key factor in attracting foreign investment. After improving human resources and optimizing the ecological environment, the inflow of foreign direct investment has been accelerated. Therefore, the reform of the business environment needs to be promoted at different levels to attract more foreign investment.

Under the background of the comprehensive popularization of computer network technology, with the continuous improvement of scientific research technology and the level of social and economic development, human beings have gradually entered the information society. According to the analysis of the requirements of education innovation in recent years, in order to use the concept of modern network information technology to build a high-quality online education system, it is necessary to conduct an in-depth discussion from the needs of real-time classroom teaching. Therefore, on the basis of understanding the operation of the online ideological and political education system in recent years, this paper analyzes how to use the Web to design a high-quality online education platform according to the requirements of practical system design, and thus conducts tests and studies to analyze the operation effect of the system.

Conventional teaching mode of English reading has failed to meet the needs of current college students, thus college English teaching is facing new challenges. With the arrival of the information age, information technology has developed rapidly and the teaching mode of personalizing reading is becoming more and more important. This paper briefly describes the current situation of the teaching of English reading under the background of “Internet +” and explores the teaching mode of personalizing reading from the perspective of teaching objectives, students’ self-learning, the guidance of teachers and multi-mode of evaluation, so as to construct path for the practice of the teaching mode of personalizing reading and promote the personalized development of students in reading.

With the rapid development of Internet, online shopping has become the mainstream choice in people's life. For the busy college students, online shopping greatly reduces the shopping time. However, due to the large area of most university campuses and unreasonable planning of express delivery points, it is also difficult for students to get express delivery, so how to make it convenient for students to get express delivery has become the next urgent problem to be solved. Therefore, based on Android studio development platform and with the help of java and other languages, we developed an APP to solve the problem of college students' difficulty in getting express delivery-taking delivery instead of taking delivery. This article will introduce how to solve this problem through the functional description, design concept and module functions of the APP.

This paper discusses the optimization process of higher education, which is of great help to the overall ideal and sustainable development stage of higher education. First, several countries have developed an indicator that is more consistent with the level of health than the ideal level of school education. The adaptability of higher education can be compared. Then, the applicability of human ANFCE model to a wider educational structure in seven countries is discussed China is on the US list. This is our goal. In addition, it also puts forward coverage suggestions and improvement analysis. Finally, I simulate China's transformation from a modern country to the proposed one, and draw the corresponding conclusions.

In the information age, knowledge is power, information is resource, has become the consensus of people. Information has penetrated into all fields and corners of human society's production and life, profoundly affecting and restricting the production and life of human society. In order to create wealth, enterprises need to use information as a tool to guide "strategy", and take correct business actions according to information. From the perspective of information, this paper studies the emergence of the view of information wealth and the tactics and strategy of information creating wealth, in order to enlighten the healthy and rapid development of Chinese enterprises in the information age.

In a paper published in Optics Communications, Liu et al. presented a flexible ultra-wideband microwave metamaterial absorber with multiple perfect absorption peaks based on the split square ring. Unfortunately, the cross-polarization reflection was omitted, and then the proposed absorber’s absorp-tion rate was not calculated in a correct way. In this paper, we reveal the problem of the paper. Our results show that the proposed design owns a structure with poor absorptive performance.

The article establishes a practical teaching quality evaluation index system from five aspects: practical teaching goal, practical teaching content, practical teaching process, practical teaching implementation guarantee, and practical teaching effect. Through large sample surveys and big data mining, combined with factor analysis to build a complete index system for practical teaching quality evaluation. On this basis, the rough set reduction method is adopted to reduce the complete index system and establish a scientific practical teaching quality evaluation system consisting of 5 first-level indicators, 21 second-level indicators, and 72 observation indicators.