Construction and Optimization of a Financial Early Warning System Based on Big Data and Deep Learning Technology

New technologies such as big data,artificial intelligence,mobile internet,cloud computing,Internet of Things,and blockchain have brought about significant changes and development in the financial industry.Predicting the financial situation of enterprises,reducing the probability of uncertainty risks,and reducing the likelihood of financial crises have become important issues in enterprise financial crisis warning.In view of the issues in enterprise financial early warning systems such as lag,low accuracy,and high warning costs in data analysis,a financial early warning system based on big data and deep learning technology has been established,taking into account the different situations of listed and non-listed companies.This carries significance in improving the accuracy of enterprise financial early warning and promoting timely and effective decision-making.

Research on Technology Early-Warning System Based on Dynamic Information Monitoring

Relying on the advanced information technologies, such as information monitoring, data mining, natural language processing etc., the dynamic technology early-warning system is constructed. The system consists of technology information automatic retrieval, technology information monitoring, technology threat evaluation, and crisis response and management subsystem, which implements uninterrupted dynamic monitoring, trace and crisis early-warning to the specific technology. Empirical study testifies that the system improves the accuracy, timeliness and reliability of technology early-warning.

Financial crisis early-warning model of listed companies based on predicted value

To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without information loss. Based on the index system, the early-warning model is constructed according to the Fisher rules. And then the GM(1,1) is adopted to predict financial ratios in 2004, according to 40 testing samples from 2000 to 2003. Finally, two different methods, a self-validated and a forecasting-validated, are used to test the validity of the financial crisis warning model. The empirical results show that the model has better predictability and feasibility, and GM(1,1) contributes to the ability to make long-term predictions.

Mechanism and monitoring and early warning technology for rockburst in coal mines

On the basis of the massive amount of published literature and the long-term practice of our research group in the field of prevention and control of rockburst,the research progress and shortcomings in understanding the rockburst phenomenon have been comprehensively in-vestigated.This study focuses on the occurrence mechanism and monitoring and early warning technology for rockburst in coal mines.Results showed that the prevention and control of rockburst had made significant progress.However,with the increasing mining depth,several unre-solved concerns remain challenging.From the in-depth research and analysis,it can be inferred that rockburst disasters involve three main problems,i.e.,the induction factors are complicated,the mechanism is still unclear,and the accuracy of the monitoring equipment and multi-source stereo monitoring technology is insufficient.The monitoring and warning standards of rockburst need to be further clarified and im-proved.Combined with the Internet of Things,cloud computing,and big data,a study of the trend of rockburst needs to be conducted.Further-more,the mechanism of multiphase and multi-field coupling induced by rockburst on a large scale needs to be explored.A multisystem and multiparameter integrated monitoring and early warning system and remote monitoring cloud platform for rockburst should be explored and developed.High-reliability sensing technology and equipment and perfect monitoring and early warning standards are considered to be the de-velopment direction of rockburst in the future.This research will help experts and technicians adopt effective measures for controlling rock-burst disasters.

Design and Construction of Automatic Monitoring System for Open-pit Coal Mine Slopes

[Objectives]To monitor the stability of open-pit coal mine slopes in real time and ensure the safety of coal mine production.[Methods]The automatic monitoring system of coal mine slope was explored in depth,and the core functions of the system were designed comprehensively.According to the design function of the automatic monitoring system,the slope automatic monitoring system was constructed.Besides,in accordance with the actual situation of the slope,the monitoring frequency of slopes was set scientifically,and the key indicators such as rainfall,deep displacement and surface displacement of the slopes were monitored in an all-round and multi-angle way.[Results]During the monitoring period,the overall condition of the slope remained good,and no landslides or other geological disasters occurred.At the same time,the overall rainfall in the slope area remained low.In terms of monitoring data,the horizontal displacement and settlement of the slopes increased first and then tended to be stable.Specifically,the maximum horizontal displacement during the monitoring period was 22.74 mm,while the maximum settlement was 18.65 mm.[Conclusions]The automatic slope monitoring system has obtained remarkable achievements in practical application.It not only improves the accuracy and efficiency of slope stability monitoring,but also provides valuable reference experience for similar projects.

Research on the corporate crisis early warning factors based on the GRA

By decomposing complex issues into factors at all levels, Grey Relativity Analysis （GRA） can effectively calculate the degree the main factors affect the goal value, and overcome the problems that the traditional mathematical statistics have such as big samples, heavy computation, and inconsistency between quantitative results and qualitative analysis. So it becomes one kind of simple, convenient and distinctive systematic analysis method. GRA is applied to corporate crisis early warning analysis. The paper through the GRA the half of this quantitative approach, explores the correlation degree among the corporate crisis early warning factors, in order to find the importance sequence of instance enterprise early warning crisis factors by calculation so as to provide a theoretical foundation for the prevention and decrease of the instance enterprise crisis.

Designing a Teaching Website for Early Warning Technology Support Specialty Based on Information Architecture

With the advancement of education informatization,learning through the internet has become a very important approach.Existing teaching websites generally have problems such as low accuracy of information grouping and obvious disconnection between the navigation system and content.Based on information architecture,a teaching website for early warning technical support specialty is designed in this paper from four aspects:content organization,identification,navigation,and interaction.The unification of information processing and information requirements is achieved using this method,which improves the quality of professional course construction for early warning technology support specialty.

Research on Heterogeneous Data Sharing in Early Warning System for Grain Security

The data nodes with heterogeneous database in early warning system for grain security seriously hampered the effective data collection in this system. In this article,the existing middleware technologies was analyzed,the problem-solution approach of heterogeneous data sharing was discussed through middleware technologies. Based on this method,and according to the characteristics of early warning system for grain security,the technology of data sharing in this system were researched and explored to solve the issues of collection of heterogeneous data sharing.

An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pro-blems associated with existing sticking-identification technologies.The method is tested against a practical case study(Southern Sichuan shale gas drilling operations).It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state;furthermore,the results from four groups of verification samples are also consistent with the actual downhole state.This shows that the pro-posed training-based model can effectively be applied to practical situations.

Development of Crisis Management Models Combined with Cloud Computing

Three monitor models of enterprise crisis were introduced,i.e.,the monitoring model of enterprise crisis based on intelligent Meta search,the enterprise crisis management model based on artificial neural network and the combined early-warning model.Combined with the advantages of cloud computing,the prominent crisis management models are improved and more efficient,comprehensive and accurate in enterprise crisis management.Through the empirical study of the models,cloud computing makes the early warning structures of enterprise crisis tend to be more simple and efficient,cloud computing can effectively enhance the recognition ability and learning ability of the crisis management,and cloud computing can keep data information updating and realize the dynamic management of enterprise joint early-warning.At the same time,according to the comparative analysis and the experimental result,the crisis management models based on cloud computing also need some improvements.

Early warning research on salt-alkalization desertification in western Songnen Plain

Desertification is an environmental issue in the world. The salt-alkalization desertification land area formed by both primary and secondary salt-alkalization has extended in a large scale, which has become a significant eco-environmental problem. Based on the characteristics of eco-environment and the situation of desertification in westem Songnen plain, this paper reports the analyzes of its formation in background and cause. An early waming system on the salt-alkalization desertification is established and the GIS technology is used to abstract the information of desertification evaluation index. Supported by the integrated technology of the GIS and ANN, the orientation and quantitative result of desertification are gained, which is helpful to the eco-environment protection and resource development in westem Songnen Plain.

基于激光脉冲ToF技术的带电作业安全监测预警装置

由于高强度电场、暂态电击以及作业过程中存在不稳定因素,带电作业工作存在危险,因此,本文设计一种基于激光脉冲ToF技术的带电作业实时安全监测预警装置。该装置通过ToF摄像机对工作人员周围的空间进行三维重建,以准确获取物体的坐标,实时监测物体与工作人员之间的距离,当工作人员身体与带电导线之间的距离超过限定值时,该装置将作出预警,提醒工作人员。

基于云计算的地震网络数据安全态势感知仿真

地震发生前具有特殊波动响应特征,通过地震台网灾害信息可确定震灾范围,但是网络个人用户无法通过终端服务器及时查询地震数据,导致对地震的安全感知出现延迟问题。针对地震数据处理的任务密集、流程复杂的计算需求,提出基于云计算的地震网络数据安全态势感知方法。在地震节点布局中融入贝叶斯结构,构成混合结构化安全态势采集模型。应用数据分类方法估计出地震态势数据,提取地震发生的隶属度特征,构建地震网络数据安全态势感知模型。按照区域影响因子算出地震信息量,利用相似原则区分灾害程度,检测地震发生范围的安全态势。实验结果证明:研究方法感知获取的地震层间位移与震中国方位角和实际情况一致,偏差较小,能够明确当前地震区域的安全态势。

基于PS-InSAR技术的晋城矿区地表形变监测及地质灾害风险预警

地表形变是一种严重的地质灾害现象,不仅严重影响灾害区居民的日常生活,而且会造成巨大的社会经济危害,尤其在采煤区。针对传统地表沉陷监测方法费时费力、无法获取地表沉降面状信息、难以进行地表沉陷灾害评估的不足,基于高分辨率SAR卫星影像,利用永久散射体合成孔径雷达干涉测量(PS-InSAR)技术对山西省晋城市晋城矿区2018年1月至2018年12月期间地表沉陷进行监测,分析获取了该地区地表连续形变情况,并利用该技术获取的海量PS点建立支持向量机(SVM)地质灾害风险评估预警模型,对晋城矿区周边居民点地质灾害风险进行了识别和预测。结果表明:晋城矿区10个煤矿及其周边区域存在较大的地表形变;晋城矿区平均LOS向年平均地表形变速率范围为-37~30.3 mm/a;PS-InSAR技术在晋城矿区地表形变监测中具有可行性,且可以实现矿区地质灾害风险综合识别和预警。

基于大数据的高校学生心理危机智能预警模型构建

目的本研究基于大学生心理健康测评数据,综合学生基本信息及日常行为数据,利用大数据、人工智能技术,探索建立大学生心理危机智能预警模型。方法采用整群抽样法,选取某高校部分在校大学生作为测试样本;采取大数据技术,分析提取影响大学生心理健康问题的特征量;采取神经网络技术,构建大学生心理危机预警模型。结果1)成功提取大学生心理健康问题的有效特征量,其中心理健康症状特征因子4个,分别是强迫、人际关系敏感、抑郁等3个单症状因子,1个多症状因子;学生基本信息特征量4个,分别是母亲教养方式、父亲教养方式、家庭经济条件、有无心理治疗(咨询)史;学生日常行为特征量2个,分别是学生学业情况和出勤情况。2)实现特征量归一化处理,通过数据对比分析及特征量的影响大小,分别对3个方面的10个特征量赋予权重并进行归一化处理。3)完成大学生心理危机预警模型构建。结论本研究提取了影响大学生心理健康问题的主要特征,建立了大学生心理危机预警指标体系,并结合心理危机预警等级,利用神经网络技术,搭建了大学生心理危机预警模型,为及时有效干预大学生心理危机提供了新的解决方案。

金融危机预警情报的失灵与治理

[研究目的]金融安全是国家安全的重要构成部分。金融危机预警作为金融监管的前哨和第一道防线,可将危机扼杀在摇篮中或将灾害损失降到最少。因此,探究金融危机预警情报服务框架,不仅为完善金融风险治理提供新的思路,还有助于提升情报服务金融安全的能力和水平。[研究方法]基于情报学视角,采用访谈和文献调查方法,对金融危机预警情报失灵的根源进行探析,构建了金融危机预警情报框架,并从预警原则、情报感知、分析策略和预警机制等方面进行阐释。[研究结论]研究表明,实施分级分解的分析战略、转变预警方式和强化危机预警机制等手段,有助于在不确定风险中提高危机管理响应效能,减少危机预警情报失灵的发生。

CCUS-EOR监测预警技术进展及发展趋势

碳捕集、利用与封存—提高油藏采收率(Carbon Capture, Utilization and Storage-Enhanced Oil Recovery, CCUS-EOR)技术是目前最现实、有效、可行的碳减排技术,但在项目实际运行过程中存在CO_(2)泄漏的风险。为保障CO_(2)驱油效果和系统安全,需要对包括CO_(2)注入、驱油、埋存、采出、集输、回注等全流程进行监测、评价、预警。通过对国内外百余个CCUS-EOR项目的文献调研,系统分析了各种监测技术的特点、应用场景、应用问题,从监测体系和关键技术两方面综述了国内外CCUS监测预警技术进展。同时,指出了CCUS-EOR监测预警技术的发展趋势,最后针对推动CCUS-EOR发展提出了健全完善监测体系、研制关键装备、深化数智技术研究、打造工业互联网平台的建议。

油气行业作业人员不安全行为智能预警方法探讨

油气行业涉及领域广,从业人员在作业过程中通常面临复杂的生产工艺、恶劣的工作环境及高强度的工作状态等,油气行业事故发生率常年居高不下,其中人为失误等事故原因所占比例超过70%。实现油气行业作业人员不安全行为的智能预警,对于减少油气行业事故的发生具有重要意义。油气行业作业人员的不安全行为主要包括错误行为与安全隐患行为。针对作业人员的不安全行为识别和预警,目前主要采用人员检查、事后分析及视频监控等方法,但是上述方法分别存在无法实时识别作业人员的错误行为,无法识别和预警作业人员的安全隐患行为等。视线追踪技术是通过捕捉作业人员的注视点与注视时间的变化过程,从而判断作业人员当前工作状态的一项技术。该技术具有反应灵敏、准确度高、无死角与可穿戴等特点,在医疗健康、交通安全、心理学等领域均已有较好的应用。利用视线追踪技术在人员状态监测等方面的优势,将其引入到油气行业作业人员不安全行为的识别或预警方法研究,用以探讨在该领域实现作业人员不安全行为智能预警的可行性。

智慧课堂下在线开放课程学习危机预警与干预研究

在线开放课程学习者的学习参与度偏低、学习成效不高等问题是业界学者较为关注的热点。精准的学习危机预警与个性化的教学干预是破解上述难题、提高教学质量的重要措施。针对现有学习预警与干预存在的不足,本研究构建了智慧课堂学习环境下在线开放课程学习预警与干预研究框架。框架从学习者的学习行为数据、知识点掌握情况数据和学习情绪数据三个维度对其学习状态进行诊断预警,并根据诊断预警报告精细设计对应的干预策略,应用学习分析与数据挖掘等方法精准匹配干预策略以确保干预效果,最终达到提高学习成效的目的。

基于BP神经网络模型的矿井生产预警技术研究

矿井生产具有高动态、快时变、混沌和随机特点,生产过程中的人、设备、环境、管理等海量数据存在多源异构特征使得安全风险评估不确定性增加。使用智能算法对空间生产场景进行模拟建模,对影响矿井安全的因素建立预警指标体系并对指标量化与预处理,通过BP神经网络模型对预处理后的指标数据模型训练与测试,测试结果表明,随着迭代次数的增加,模型的收敛性与准确率极大提高,可以最大限度地提供矿井安全风险评估决策所用信息、为提高煤矿安全监测的效率和监测深度提供技术支持。