NAND flash memory requires a garbage collection strategy due to its physical constraints,which significantly degrades system efficiency.Precise hot and cold data separation is critical.In addition,NAND flash memory can only endure a limited number of erase operations.Therefore,improving wear leveling is crucial for extending its lifespan.Existing methods fail to learn data access patterns online and cannot accurately identify hot and cold data under dynamic workloads.To address these issues,a Reinforcement Learning-based Garbage Collection algorithm(RLGC)is proposed.It realizes accurate classification of hot and cold data through online Adaptive Learning and integrating wear leveling mechanism.Experimental results show that compared with the existing best performance of the baseline algorithm,the proposed algorithm reduces the block erase times by 16.4%,the effective page copy times by 50.0%,and improves the wear leveling degree by 33.3%,which significantly improves the garbage collection efficiency and prolongs the lifetime of flash memory.
This paper explores effective methods for predicting gold prices,and proposes three modeling strategies including standalone Long Short-Term Memory(LSTM)networks,Attention-based models,and LSTM-Attention combination model.Through empirical analysis,it systematically compares the performance of these three models in gold price prediction,with a focus on evaluating their predictive accuracy.The experimental results indicate that the LSTM-Attention combination model significantly outperforms the standalone LSTM and Attention models in terms of prediction accuracy,demonstrating a more comprehensive ability to capture the dynamic features of price fluctuations.This model not only showcases the potential of effective integration of LSTM and Attention Mechanism in time series prediction,but also provides a practical tool for financial decision-making,offering valuable references for investors in a volatile market environment.
Aiming at the problem that the prediction effect of the model is poor due to the large prediction space of the sequence annotation label,a multi-aspect extraction and sentiment analysis model based on Bert_Bilstm_Fusion is proposed.The method of span prediction is used for joint training of aspect words extraction and aspect words sentiment prediction and classification.The text embedding representation is obtained by using the Bert pre-trained language model.It uses the dependency relationship on the Bilstm learning observation sequence to enhance the learning position information to improve the model extraction effect.It conducts the feature fusion of output features of each layer for the Bert pre-trained language model,improves the sentiment polarity classification effect of the model,and designs comparison experiments and ablation experiments on three public data sets Laptop,Restaurant and Twitter.The experimental results show that the F1 values of the Bert_Bilstm_Fusion model reach 66.72%,78.44%and 62.10%,respectively,which are higher than those of the comparison model,indicating the effectiveness of the proposed model.
The Open University of China requires to improve the digitalization of teaching process.Grassroots open universities cannot achieve reform and innovation in the process of teaching support service without the support of information technology.This paper takes serving students as the starting point,based on standardizing the teaching and educational administration process and providing schools with decision-making analysis,and constructs a student-centered teaching and educational administration management information system of grassroots open university.The system realizes the"three integrations"of teaching management process and results,student management and service,refinement and informatization of educational administration,and has an important reference role for grassroots open universities to carry out teaching and educational administration management informatization construction.
Taking the steel reinforcement construction model of Zhangzhou"Hualong No.1"nuclear island civil engineering as the data interactive carrier,the two-dimensional design drawing information is transformed into a standardized steel reinforcement bill of materials through three-dimensional BIM software,which improves the problems of inconsistent and diverse forms of drawings'annotation in the traditional steel reinforcement in-depth design,integrates the automatic steel reinforcement processing equipment for BIM data integration,reduces manual input operations.In the distribution process of semi-finished steel reinforcement,develop a steel reinforcement positioning system that matches the steel reinforcement management process,and optimize the steel reinforcement distribution into a supervision mode with online as the main line and offline as the auxiliary,reducing the loss rate of steel reinforcement in the complex environment of nuclear island construction.At the same time,optimize the process,and connect the three key business processes of BIM cutting,automatic processing equipment,and positioning and distribution in the steel reinforcement management process with data.Using visualization technology to improve the efficiency of steel reinforcement transportation and achieve precise cost control of steel reinforcement main materials.
In order to facilitate the comprehensive development of campus waste separation and recycling work,through the investigation and analysis of the awareness and influencing factors of waste separation among students in colleges and universities in Ningxia,an intelligent waste separation and recycling bin is designed for the campus scene with STM32F103VET6 as the system control core,integrating hardware module,NB-IoT communication technology,IoT cloud platform and management software system to realize the functions of waste separation and disposal,user identification,weight weighing and points distribution.The recycling bin is conducive to cultivating and improving the awareness of environmental protection among teachers and students,and enhancing their enthusiasm for waste separation and recycling.It is also conducive to promoting the construction of ecological civilization in colleges and universities and building a"green campus".
The utilization rate of disks based on the meteorological downlink transmission system exhibits a certain periodicity over time.The analysis data is obtained by calling the indicator interface of the monitoring platform regarding disk utilization rate,and data preprocessing operations such as key indicator item extraction and missing data value correction are performed.The stability and periodicity of the time series are analyzed through the trend chart of disk utilization rate.ARIMA and triple exponential smoothing algorithms are used to predict the periodicity of time series of disk utilization rate,and reference indicators such as residuals and mean squared errors are compared between the two algorithms.The optimal algorithm model is obtained through the analysis of the results of the two prediction algorithms,and the alarm prediction time value is calculated by using the predicted results and the alarm threshold value.
In response to the bottleneck issues of the lack of business scenario training and insufficient relevance of students'practical training skills to the industry in the process of 5G talent cultivation in modern higher vocational education,it is proposed to introduce virtual simulation technology into the teaching process and restructure the course content module according to the current 5G network workflow to carry out blending learning.Taking the course"5G Network Planning and Optimization"as an example,this paper elaborates on the specific practice of a blending learning mode based on the 5G virtual simulation platform from the aspects of teaching process,teaching evaluation,and teaching effectiveness.This teaching mode meets the needs of scenarios,debugging operations,practical applications,and costs,is close to industry production standards,effectively promotes the integration of theory and practice,and promotes the improvement of the quality of vocational talent cultivation.It completes the best transition for students from school education to employment,and also provides reference for the implementation of 5G emerging discipline teaching.
In order to segment the image information more conveniently and quickly,reduce the background interference to the stamp image,and strengthen the information recognition technology of the civil aviation industry,a method of segmenting stamp image based on the RGB color model is proposed.This method uses the RGB color model to separate the R component of the RGB stamp image to obtain a grayscale image,which weakens the noise interference and highlights the key areas.Then it uses the OTSU to binarize the obtained grayscale image and select the optimal threshold.The algorithm test results show that a clearer and more detailed image can be obtained by separating the R component and introducing the OTSU algorithm,which improves the accuracy of the test.
To gain a deeper understanding of Sina Weibo users'information,from which to identify the most influential Weibo users,discover opinion leaders on current Sina Weibo,it is necessary to know the number of Weibo posts,followers and fans of every Weibo user so far.To this end,a Web crawler based on the Scrapy framework is designed and implemented in Python language.The crawler captures the number of Weibo posts,followers,fans published by the user according to the Weibo user ID entered.We have to visit Weibo website for many times in succession,to prevent Weibo website from denying access,the User Agent and IP Proxy are used in the designed crawler.Among them,the IP proxy selects the dynamic type of tunnel proxy.The experimental results show that it takes 6 hours and 22 minutes to download the information of more than 7 000 Weibo users.
The application of face recognition technology in smart campus is becoming increasingly widespread,but it also brings many security challenges during the application process.In order to deal with these challenges,firstly,from an application perspective,this paper classifies and summarizes the main application scenarios of face recognition technology on campus.Subsequently,it analyzes and sorts out the security hidden dangers that exist in the actual application process.Finally,a comprehensive research is conducted on how face recognition technology can be safely applied on campus from the perspectives of technical protection,legal compliance and operational management.And the corresponding security protection strategies are proposed.It is expected that the application of this technology in smart campus can be more extensive and in-depth,and at the same time safe and compliant.
Knowledge Graph technology can aggregate information,data and correlations and relationships in massive Big Data into a knowledge network to realize intelligent user retrieval and intent analysis and reasoning.The key technologies of ontology construction,entity extraction,relation extraction,graph updating and maintenance based on multi-source internet data are studied.And the military target professional field Knowledge Graph is constructed.It can be used in the visual query,intelligent question-and-answer,high-value relationship recommendations and other directions for application.It effectively improves the use efficiency of data and provides an effective tool and method for military intelligence analysis and knowledge application.
Stomatology medical equipment is an indispensable tool in stomatology diagnosis and treatment work,and achieving efficient management of stomatology medical equipment can help improve the quality and efficiency of clinical diagnosis and treatment work.Due to the wide variety of stomatology medical equipment and complex maintenance and repair types,equipment management is difficult.How to achieve unified and efficient management of equipment is a challenge for equipment management personnel.Based on the current situation of stomatology medical equipment management and the work needs of management personnel,this paper designs and develops a stomatology medical equipment management system using LabVIEW software and Microsoft Office Access database.The stomatology medical equipment management system has achieved safe,efficient and refined management of stomatology medical equipment.Through the system operation,the information management and statistical efficiency of stomatology medical equipment have been improved,which helps to improve equipment usage efficiency and provides reliable solutions for stomatology equipment management.
This paper explores the impact of the transformation from the information age to the digital age on talent cultivation in China's vocational and technical colleges,analyzes the differences in demand for information technology talents and digital technology talents,and draws on the cutting-edge digital talent training modes around the world.Through in-depth analysis of China's national conditions and policies,it is emphasized that vocational and technical colleges need to adapt to the rapidly changing digital environment and respond to challenges by cultivating digital technology and application talents.This paper provides a comprehensive framework for vocational and technical colleges to cultivate digital talents from aspects such as integration of industry and education,innovation of curriculum and teaching methods,construction of teaching staff,lifelong learning,and international cooperation.By implementing these strategies and drawing on international experience,China's vocational and technical colleges can effectively cultivate the digital technology and application talents needed to support the country's digital transformation,promote the development of the digital economy,and ensure competitiveness in the increasingly interconnected and technological integration environment of global digitization.
With the advent of the digital age,education is undergoing unprecedented changes.In this context,the construction of regional smart education,as an important part of the education construction in the digital age,has become a hot topic in academia and industry.In order to promote the better transformation and development of regional smart education,it is necessary to fully comply with national policy requirements and regional development trend,carry out good top-level design,provide reasonable policy support,and actively carry out practice and innovation.Combined with the current situation of education information in the digital age,the concept,characteristics and construction content of regional smart education are studied.It finds out that there are some problems in the construction of regional smart education in the aspects of technology,talents and policies,and it puts forward countermeasures from these aspects to provide theoretical suggestions for promoting the construction of regional smart education in the digital age.
With the popularization of the Internet,it has become increasingly important to showcase and promote a company's products through the internet.Designing a company website can facilitate customers to understand the basic situation of the company and its latest product information.This paper starts with the analysis of cosmetics website requirements,website homepage design,registration and login page design,and uses HTML5 technology combined with CSS3 animation technology to achieve image special effects.At the same time,PHP technology combined with MySQL database is used to achieve functions such as member registration and login.The website is clean and beautiful,simple and easy to use,with fast loading speed,and can meet the requirement for promoting company's products.
The optical fiber temperature sensing technology is applied to the temperature status monitoring of electromechanical equipment in expressway tunnel,which has the technical advantage of safety and real-time.In order to optimize the operating effect of the temperature monitoring system,the characteristics of the temperature field in the expressway tunnel are analyzed,and the signal analysis and abnormal event identification and judgment methods are designed accordingly.The results of several field simulated abnormal temperature rise tests and long-term operation verification show that the optical fiber temperature sensing system can timely and effectively monitor and locate the abnormal temperature rise and sudden fire of electromechanical equipment in the expressway tunnel,with high alarm accuracy and low false alarm rate,and it can effectively monitor the fire safety state of electromechanical equipment in the expressway tunnel.
In recent years,with the improvement of people's living standards,people have paid special attention to the quality of agricultural products,and the traceability problem of agricultural products has become a problem of special concern.Aiming at a series of problems such as easy data tampering and serious centralization of traditional agricultural products traceability system,a lightweight agricultural products big data security management model based on"Blockchain + IPFS"is proposed to reduce the load pressure on the chain.Secondly,this paper uses Kafka distributed message queues to solve the problem that Solo ordering service is prone to single points of failure.At the same time,the advantages of this system architecture and traditional system architecture in data storage and data management are compared,and it solves the problems of serious centralization and trust crisis of traditional traceability system,and ensures the security and authenticity of agricultural products traceability information.
With the increasing improvement of 5G network construction,telecommunications has basically achieved continuous outdoor coverage of 5G,but the 5G diversion ratio is low,the growth trend is slow,and 4G is still in a high utilization and high traffic state.How to quickly leverage the value of 5G networks,enhance 5G diversion capabilities,and alleviate the high load pressure of 4G has become one of the current key tasks.This paper uses 4G/5G diversion tools to accurately locate 5G community coverage,and then through"two state optimization"and"fast,accurate,and strong"three major measures,improves the 5G diversion ratio,improves 5G network quality,effectively enhances user experience,and accumulates experience for subsequent 5G network optimization.
In the context of the big data era,enterprise business data is not only structurally complex but also in large quantities.How to obtain effective data and present the information required for business management decisions in a visual form has become a focus of attention for enterprises.This paper studies the current status of financial analysis visualization for LONGi Green Energy,identifies the business and information technology issues presented in its financial analysis visualization process,and proposes ideas and suggestions for optimizing LONGi Green Energy's financial analysis visualization using Power BI,in order to provide reference for other enterprises.
