Early Warning Analysis Research Articles

Asset Management Early Warning Analysis in Higher Education Institutions from an Audit Perspective

Asset Management Early Warning Analysis in Higher Education Institutions from an Audit Perspective

A MENTAL ILLNESS EARLY WARNING ANALYSIS MODEL USING AN IMPROVED SVM ALGORITHM AND EEG SIGNALS

The fast-paced lifestyle, intense competition, and constantly changing environment of modern society are important factors contributing to the emergence of psychological disorders. Factors such as work pressure, family conflicts, and interpersonal relationship difficulties pose challenges to individual mental health. Prolonged use of electronic devices and the widespread use of social media further exacerbate mental health issues, as the constant influx of information makes it difficult for people to relax and focus. The harm of psychological disorders to individuals and society cannot be ignored. It not only diminishes the quality of life for patients but also affects work efficiency and social participation. In extreme cases, psychological disorders can lead to serious consequences like suicide, causing immense pain to families and society. Therefore, prioritizing mental health, raising awareness of mental well-being, and timely intervention and treatment of psychological disorders are crucial for individual happiness and social stability. Based on this, this study employs an improved Support Vector Machine (SVM) and Electroencephalogram (EEG) signals for the diagnosis of psychological disorders, aiming for more effective treatment of mental health conditions. Specifically, the EEG signals of the experimental subjects are first collected as training data for the model. The dataset undergoes preprocessing operations such as cleaning and normalization. Due to the limited availability of published information about individuals with psychological disorders, an ensemble SVM algorithm is used as a warning model to improve the accuracy of psychological disorder diagnosis. This algorithm utilizes a differential measurement method to adjust the selection of SVM parameters. The training sample size remains constant, with several iterations of training performed. By combining the differential measurement method, a specific parameter combination is obtained, maximizing the differentiation between base classifiers. This ultimately addresses issues such as small sample size and insufficient sample information in the dataset. Experimental results demonstrate that the model used in this study exhibits good performance in diagnosing psychological disorders.

Early warning analysis of mountain flood disaster based on Copula function risk combination

Mountain torrent disaster prevention is the focus of flood control and disaster reduction in China. Critical rainfall is an important indicator to determine the success or failure of mountain torrent disaster early warning. In this paper, the M-Copula function is introduced, the multi-dimensional joint distribution of critical rainfall is constructed, and the joint distribution of rainfall and peak rainfall intensity is analyzed. Taking A village in Xinxian County as an example. The critical rainfall of the combined probability is calculated, and the critical rainfall of the flash flood disaster water level, the pre-shift warning and the sharp-shift warning is warned and analyzed. The results show that the flood peak modulus calculated by Yishangfan group is 8.89, which has certain rules for the flood peak modulus of rivers in hilly areas. The larger the basin area is, the smaller the flood peak modulus is, the smaller the area is, and the larger the flood peak modulus is. The calculation result of the design flow of 533 m3/s is reasonable. It is reasonable and reliable to select the M-Copula function as the connection function to fit the joint distribution of rainfall and peak rainfall intensity, which can provide theoretical support for flash flood disaster warning in other regions.

Early Warning Analysis of Grid Ferromagnetic Resonance Overvoltage Risk Based on Multi-source Data

In the impartial factor ungrounded system, ferromagnetic resonance overvoltage is a frequent fault that lasts for a lengthy time and is hazardous to the grid. In this paper, the mechanism of grid ferromagnetic resonance overvoltage is first explored in depth. The precept of impartial voltage shift and ferromagnetic resonance brought about through PT saturation is analyzed with the aid of graphical and mathematical analysis. Then, the characteristics of fault current information are extracted by wavelet transform, and indicators such as wavelet fault degree, wavelet singularity and wavelet energy measurement are obtained respectively. D-S evidence theory is used to fuse multi-source information of electrical volume and switching quantity, so as to obtain comprehensive fault results of power grid more accurately. Finally, based on the time series risk assessment, the distribution network time series risk index is calculated, the risk level and risk area of each period are determined, and the early warning results are issued. Finally, an example is given to verify the effectiveness of the proposed method.

Design and implementation of financial data statistics and risk early warning analysis system in the era of big data

In the era of big data, the financial field is facing more and more data statistical risk, in order to ensure the continuous improvement of the financial economic development level, Chinese scholars put forward the transformation of the traditional non-empirical analysis method and artificial subjective evaluation method, reasonable use of experts system and information technology as the core management mode. From the perspective of overall development, traditional assessment methods still play an important role in financial data statistics and risk early warning analysis, while the application level of relevant information systems and expert systems is not high. Therefore, in the future innovation and development of the financial field, it is necessary to continue to discuss the design and implementation of financial data statistics and risk early warning analysis system in the era of big data. After understanding the statistical characteristics of financial data and the design principles of risk early warning system in the era of big data, this paper mainly studies the design structure and implementation of financial data statistics and risk early warning analysis system, which will play a positive role in enhancing the competitiveness of financial institutions in the new era.

Prefabricated acoustic emission array system for landslide monitoring

The quality of landslide monitoring data is crucial to the reliability of early warning systems. One effective approach to improving data quality is through innovation in monitoring techniques. In the case of existing acoustic emission (AE) monitoring methods, the achievable deformation quantification accuracy is limited by the variability of internal and external factors that affect the generated AE signal. To overcome these limitations and further improve monitoring effectiveness, this study developed a novel monitoring equipment consisting of an AE array, which facilitates the standardisation of equipment manufacturing and improves the consistency of data interpretation, thus providing a system that is simple and practical for field application. The developed AE monitoring system includes modules for on-site monitoring, data transmission, information management, and early warning analysis. The AE monitoring system was installed on different soil landslides in China and is continuously operating. Because the AE array provides a standardised and simple tool for landslide subsurface deformation monitoring, it is well-suited for low-income mountainous areas with a high risk of landslides.

Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees

The issues of low accuracy, poor generality, high cost of transformer fault early warning, and the subjective nature of empirical judgments made by field maintenance personnel are difficult to solve with the traditional measurement methods used during the development of the transformer. To construct a transformer fault early warning analysis, this study recommends a data-fusion-based decision tree approach for merging electrical quantity signals with a non-electrical amount of vibration signals. By merging a decision tree inference with actual operation data, a clustering center, and an early warning model, this method creates a transformer fault early warning model with self-learning ability and adaptive capabilities. After reasonable verification, the method becomes more universal and interpretable, and it can successfully conduct an early warning of transformer faults.

Energy financial risk early warning model based on Bayesian network

Oil is a global, non-renewable energy source, which plays a pivotal role in the development of the global economy and the strategic reserve system. With the expansion of crude oil futures trading scale, crude oil is no longer a pure energy commodity, but is increasingly controlled by the financial market. Based on Bayesian network, this paper studies and analyzes the financial risks of non renewable energy such as oil. Finally, the prediction models of BP neural network and Bayesian network are given. Neural network is a mathematical model based on neurons, a nonlinear adaptive dynamic system formed by the connection between neurons, and based on these two models, discusses the financial risk early warning method of petroleum in non-renewable energy. The test results of this paper show that the Bayesian network and BP neural network are used for early warning analysis based on the comprehensive oil financial risk data from 1987 to 2015. The prediction results of neural network show that there is 65% correlation between the two. From the fitting results of the prediction results and real data, the fitting effect of Bayesian network is better than that of BP neural network, and the fitting success rate is as high as 80%. The Bayesian network prediction model is analyzed by using the autoregressive movement of the model itself and the time series measurement results of the data itself. The results show that the prediction model is highly reliable. Therefore, it is very necessary to adopt Bayesian network prediction model in the risk early warning of petroleum finance.

Research on rapid construction technology of three-dimensional safety control area for the complex maintenance work environment of substation

According to the actual needs of the substation operation and maintenance team, this paper studies the defect video intelligent analysis technology in realizing three-dimensional cross-border early warning, analyzes the principle and early warning application of narrowband millimeter-wave technology and develops a set of 3D cross-border early warning system for substation maintenance operation. The system adopts wideband network technology to realize the remote operation and stable data transmission between the system and the warning and analysis service software of mobile terminal. In this early warning analysis system, this study uses the combination of intelligent video analysis and millimeter-wave radar dynamic target capture to solve many deficiencies in early warning accuracy by relying solely on intelligent video analysis and early warning. Through the on-site verification of the test system, the system’s overall function is consistent with the substation maintenance and maintenance requirements. It is simple to use, and the accuracy of the crossover warning reaches more than 85%.

Determinations of the early warning threshold for creep landslides and their applications

<p indent="0mm">The accuracy of the threshold is very important for early warning of slopes. However, the existing slope failure threshold criteria are insufficiently accurate and have limited applicability. This paper proposes three dimensionless parameters based on slope deformation displacement, velocity, and acceleration and statistically analyzes 80 slope deformation monitoring examples. The second-order dimensionless failure threshold is obtained using probability and statistics theory. The dimensionless threshold is applied in the early warning analysis of landslide #2 in Jiangxi Province’s copper mine. The results show that (1) the “two-sample heteroscedasticity t-test” method shows that the dimensionless parameters in the failure case are significantly greater than the dimensionless parameters in the non-failure case; (2) a dimensionless threshold can eliminate the influence of vector parameters, which are physical in nature; (3) the “ROC curve” approach is used to validate the reliability and accuracy of the dimensionless threshold model, and it is theoretically explained that the prediction accuracy of the dimensionless threshold of acceleration is relatively the highest; (4) in this situation, the corresponding warning time of the three dimensionless thresholds is not the same, with the secondary dimensionless threshold of acceleration having the earliest time.

High voltage transmission line live working risk early warning method based on big data frequent itemset mining

The complexity of power monitoring data is high, and the efficiency of early warning analysis is low due to the relevance of live line operation risk information. Therefore, a risk early warning method of live line operation of high voltage transmission line based on large data frequent itemset mining is proposed. According to the main contents of live line operation, the risk factors of live line operation are analyzed from static and dynamic aspects. Collect the defect and abnormal data of equipment related to live line operation, and mine the characteristic information of line operation state. The early warning level is divided into five levels, the early warning rule set is created, and the minimum threshold of support and confidence is designed. Using the iterative method of layer by layer search, the association rules are extracted according to the frequent itemsets, the matching historical records are found in the live working database, and the risk is warned. The analysis results of engineering examples show that the high-voltage transmission line live line operation risk early warning method based on big data frequent itemset mining can improve the accuracy and real-time of early warning, and is more applicable in engineering application.

Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk.

Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel’s efficiency, whereas the panel enhances the model’s reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.

Wearable Sensor-Based Motion Data Analysis and Dance Performance Using Images and Cloud Computing

China’s cultural performance career has been developed in recent times, resulting in a large number of students embarking on the professional path of cultural performance. To reduce the teaching pressure on school teachers, dance training gradually introduces the idea of “digital dance”, using sensors to monitor posture. The nonwearable monitoring method does not require contact with the subject and typically relies on images or signal waves for positioning, but it has stringent requirements for the measurement scene. On the other hand, the wearable monitoring method requires the sensor to be worn on the subject’s body all the time, so it needs to be light and energy-efficient. Therefore, this paper designs and implements a set of application platforms, which parametrically analyze the dance movements, consume low energy, are lightweight, and are simple to use. For this purpose, a wearable digital dance training system based on microelectro-mechanical system (MEMS) sensors has been developed, and a solution to the challenges of teaching movement precision, scientific teaching methodologies, and early warning analysis of joint is also proposed. Firstly, the basic knowledge of human posture measurement and human model is merged to build a new modifiable human model. Secondly, the overall design of the posture data acquisition device is presented, which includes a base station and several nodes. Finally, the overall framework of the software platform is designed, which consists of client and server. The experimental results show that the system can achieve the purpose of providing teaching programs for dance students.

Analysis of Safety Behavior of Prefabricated Building Workers’ Hoisting Operation Based on Computer Vision

Safety accidents occur frequently in the construction industry, and unsafe behavior of workers is one of the main reasons for the occurrence of safety accidents. In recent years, the rapid development of emerging technologies has provided effective support for the automatic identification of unsafe behaviors. In particular, computer vision technology does not require equipment to be attached to workers. It has little impact on its operation and can process a large amount of image data in a timely and fast manner. Therefore, it is more suitable for the construction site environment. This study starts from the hoisting operation on the construction site of prefabricated building projects and introduces relevant machine learning methods. Early warning analysis on the safety risk of the tower crane is carried out to complete the hoisting operation of prefabricated components. To suspend dangerous tasks in time according to the warning results, relevant measures are taken and potential risks are eliminated to prevent accidents.

Force and deformation response analysis of dual structure slope excavation and support

Aiming at the problem of fuzzy selection basis of dual-structure slope simulation calculation parameters and the complicated force and deformation of the slope body in the supporting process. The study relies on the treatment project of the right side slope of the K5 + 220-K5 + 770 section of the TJ1A section of the Jiangwen Expressway. The slope is a gravel soil-weathered bedrock dual structure slope, which is referred to as Hetangba slope in the study. Use Midas/GTS NX to build a trial calculation model. A new method for checking simulation parameters of deep displacement monitoring data is proposed. The superposition calculation method of pore water pressure is proposed to realize the slope stress-seepage coupling. The analysis method of slope excavation and support process is put forward. The analysis shows that the most dangerous working condition in the slope excavation and support process is the second-level excavation. When there is a local displacement and plastic strain concentration area on the slope, the reference significance of the extreme value and the safety factor is reduced. Compared with the application of pore water pressure and no pore water pressure, there is no obvious change in the position of the extreme value of horizontal displacement and plastic strain in working conditions 2–8, but the extreme value changes are different. The slope safety factor of working conditions 2–8 is reduced. The continuous distribution zone of plastic strain inside the slope disappeared in condition 7. There is a very small unstable area on the top of the third-level slope of working conditions 9–11. The research results have great theoretical guiding significance for the measurement and control simulation and early warning analysis of the dual structure slope construction process.

Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing.

The purpose of this study is to analyze the molecular epidemiological characteristics and resistance mechanisms of Escherichia coli. The study established a big data cloud computing prediction model for the epidemic mechanism of the pathogen. The study establishes the early warning, control parameters, and mathematical model of Escherichia coli infectious disease and monitors the molecular sequence of the pathogen based on discrete indicators. A nonlinear mathematical model equation was used to establish the epidemic trend model of Escherichia coli. The study shows that the use of the model can control the relative error at about 5%. The experiment proves the effectiveness of the combined model.

Research on Engineering Geomechanics Characteristics and CFRP Reinforcement Technology Based on Machine Learning Algorithms

We have completed the design of an early warning and evaluation analysis module based on machine learning algorithms. Aiming at the prestressed CFRP-strengthened reinforced concrete bridges under natural exposure, we developed a theoretical model to analyze the long-term prestress loss of reinforced parts and the adhesion behavior of the CFRP-concrete interface under natural exposure conditions. The analysis deeply reveals the technical and engineering geomechanics characteristics of the D bridge. At the same time, through a series of experimental studies on the D bridge condition monitoring system, the data acquisition and transmission, processing and control of the D bridge condition monitoring system, and the bridge condition monitoring and evaluation software are provided. Regarding how to repair the engineering geomechanical characteristics of D bridge, we mentioned the prestressed CFRP reinforcement technology. The prestressed carbon fiber reinforced composite (CFRP) structure made of reinforced concrete (RC) makes better use of the high-strength characteristics of CFRP and changes. It strengthens the stress distribution of the components and improves the overall strength of the components. It is more supported by engineers in the civil engineering and transportation departments. However, most prestressed CFRP-reinforced RC structures are located in natural exposure environments, and the effect of natural exposure environments on the long-term mechanical properties of prestressed C FRP-reinforced RC components is still unclear. This article mainly uses the research on the engineering geomechanics characteristics and reinforcement technology of the bridge body, so that people have a deep understanding of its concept, and provides reasonable use methods and measures for the maintenance and protection of the bridge body in the future. This paper studies the characteristics of engineering geomechanics based on machine learning algorithms and applies them to the research of CFRP reinforcement technology, aiming to promote its better development.

Analysis of Local Macroeconomic Early‐Warning Model Based on Competitive Neural Network

At present, the commonly used index selection methods for macroeconomic early‐warning research include K‐L information volume, time difference correlation analysis, and horse farm methods. These traditional statistical methods cannot cope with the continuous changes of economic indicators, and due to the existence of statistical errors, these methods are difficult to perform. Therefore, this paper proposes to use a self‐organizing competitive neural network to select early warning indicators. Its self‐learning and adaptive characteristics and fault tolerance overcome the limitations of the above statistical methods. This article proposes a method of selecting macroeconomic early‐warning indicators using self‐organizing competitive neural networks and designs a macroeconomic nonlinear early warning model of self‐organizing competitive neural networks; using fuzzy logic reasoning to introduce economic experts’ experience into macroeconomic early warning analysis, the system has the ability to deal with nonlinear and uncertain problems and realizes the intelligence of the early‐warning process, uses the national macroeconomic indicator data from January 1997 to March 2008 for empirical analysis, and compares the self‐organizing competitive neural network method with the traditional KL information method. From the experimental results, compared with the KL information method, the self‐organizing competitive neural network method selects more comprehensive indicators and has greater advantages in seismic resistance and stability.

Financial Risks Classification Early Warning Analysis of Data Mining Technology

To further improve the informatization level of financial risk early warning, a financial risk classification early warning method based on neural network quantile regression algorithm is proposed. Among them, macro and micro indicators are selected as the index input of early warning, and then the neural network quantile regression algorithm is used to classify and warn the financial risks, finally the specific risk level is output. Simulation results show that the MAE and RMSE of neural network quantile regression algorithm are 7.12e-09 and 1.301e-08, which are lower than those of BP neural network and generalized neural network. Thus the superiority of neural network quantile regression model is verified.

The Construction and Empirical Analysis of the Company’s Financial Early Warning Model Based on Data Mining Algorithms

With the rapid advancement of the informatization process, enterprise informatization management has received more and more attention. Facing the increasingly complex and changeable social and economic environment, the difficulty of enterprise risk management has gradually increased. How to establish an efficient risk management mechanism for early warning of corporate risks is the goal that companies seek. Traditional statistical analysis can no longer satisfy the processing of massive financial data. Therefore, how to find useful information for the financial risk early warning management of the enterprise from the large amount of financial data information generated by the business activities of the enterprise is a problem that enterprises urgently need to solve at present. The continuous improvement and innovation of data mining technology and the good performance of research and analysis of massive data have made the two closely linked. First, this study introduces the theories of financial risk early warning and data mining technology; second, it introduces the research process of financial risk early warning model and elaborates the three data mining techniques used in this study; then combined with the actual situation of listed companies in my country, it constructs financial risk early warning index system; and finally, 77 listed manufacturing companies and their matching companies that were first processed by ST in 2005‐2007 were used as research samples, based on the financial data of the 2.4 years before being processed by ST and CXISP. It is found that the financial risk early warning model established by data mining technology has strong early warning capabilities. From the perspective of the prediction capabilities of the three models, the closer the time to ST, the higher the accuracy of the prediction; from the perspective of short‐term early warning, the three models have better prediction effects, but from the perspective of long‐term early warning, the prediction effects of neural networks and decision trees are better than logistic regression of statistical analysis; data mining techniques based on knowledge discovery are not only suitable for short‐term early warning but also for longer‐term early warning. Therefore, data mining can be applied to financial risk early warning analysis to achieve the purpose of using data mining technology for decision support.