Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590769
Tianshu Li, Chunyan Huo
Modern manufacturing system is a highly decentralized manufacturing system, which consists of many intelligent processing machines, material transportation equipment, robots and various manufacturing resources. With the penetration of internet plus, the deepening of cloud services and the popularization of industrial Internet of Things, PLC (Programmable Logic Controller) should adapt to the demand of intelligent manufacturing from hardware and software. In the intelligent manufacturing process, the production is fully automated and unmanned. Once unreasonable processing technology arrangement occurs, the machine may collide and cause safety accidents. The finite state machine model commonly used in modeling and simulation of manufacturing systems can't deal with complexity and distributed problems, and the functional hierarchy model can only represent data flow, but can't represent control flow, and the description of the system is not accurate enough. In this paper, the digital, networked and intelligent system architecture operation model of intelligent manufacturing system is constructed based on PLC, and the functions of state perception, real-time analysis, independent decision-making and precise execution of intelligent manufacturing system are realized by using the role of big data in intelligent manufacturing system.
{"title":"Research on Intelligent Manufacturing System Model Based on Programmable Logic Controller","authors":"Tianshu Li, Chunyan Huo","doi":"10.1109/ICISCAE52414.2021.9590769","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590769","url":null,"abstract":"Modern manufacturing system is a highly decentralized manufacturing system, which consists of many intelligent processing machines, material transportation equipment, robots and various manufacturing resources. With the penetration of internet plus, the deepening of cloud services and the popularization of industrial Internet of Things, PLC (Programmable Logic Controller) should adapt to the demand of intelligent manufacturing from hardware and software. In the intelligent manufacturing process, the production is fully automated and unmanned. Once unreasonable processing technology arrangement occurs, the machine may collide and cause safety accidents. The finite state machine model commonly used in modeling and simulation of manufacturing systems can't deal with complexity and distributed problems, and the functional hierarchy model can only represent data flow, but can't represent control flow, and the description of the system is not accurate enough. In this paper, the digital, networked and intelligent system architecture operation model of intelligent manufacturing system is constructed based on PLC, and the functions of state perception, real-time analysis, independent decision-making and precise execution of intelligent manufacturing system are realized by using the role of big data in intelligent manufacturing system.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124518196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590809
Xiaozheng Zhang, Zhe Jiang, W. Guo, X. Ren
The YOLOv3 target recognition algorithm has a wide range of applications in various fields. Under the haze conditions, the recognition effect of the YOLOv3 algorithm is affected, and its mAP value decreases significantly. To solve this problem, it can be improved by adding a dehazing algorithm before the recognition algorithm. In this paper, three commonly used dehazing algorithms are studied, and they are combined with YOLOv3 algorithm to perform target recognition experiments on hazing images. Solve their mAP values separately and compare them. The results show that all the three dehazing algorithms can improve the target recognition ability, and the Retinex algorithm works best.
{"title":"Research on the Influence of Dehazing Algorithm on YOLOv3 Target Recognition","authors":"Xiaozheng Zhang, Zhe Jiang, W. Guo, X. Ren","doi":"10.1109/ICISCAE52414.2021.9590809","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590809","url":null,"abstract":"The YOLOv3 target recognition algorithm has a wide range of applications in various fields. Under the haze conditions, the recognition effect of the YOLOv3 algorithm is affected, and its mAP value decreases significantly. To solve this problem, it can be improved by adding a dehazing algorithm before the recognition algorithm. In this paper, three commonly used dehazing algorithms are studied, and they are combined with YOLOv3 algorithm to perform target recognition experiments on hazing images. Solve their mAP values separately and compare them. The results show that all the three dehazing algorithms can improve the target recognition ability, and the Retinex algorithm works best.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123028829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590814
Rongjian Zhang
Data mining technology can accurately extract useful information from a large amount of information data, and also has the function of removing falsehood and preserving truth, so it has a very important application in intelligent system set. The essential condition of intelligent building is building intelligence, and the core of intelligent building system design is system integration. The main goal of intelligent building system integration is to realize information integration, and data warehouse technology is an effective solution to solve the problem of intelligent building information integration. Data mining technology is used to analyze and process various kinds of massive data and information, so as to realize the conversion from data to information and provide effective support for leaders' decision-making. The new model of building intelligent system integration based on data mining technology proposed in this paper has solved the compatibility and openness problems in the process of building intelligent system integration, and achieved the goal of safe and efficient building intelligent system.
{"title":"Research on Application of Intelligent System Integration Based on Data Mining Technology","authors":"Rongjian Zhang","doi":"10.1109/ICISCAE52414.2021.9590814","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590814","url":null,"abstract":"Data mining technology can accurately extract useful information from a large amount of information data, and also has the function of removing falsehood and preserving truth, so it has a very important application in intelligent system set. The essential condition of intelligent building is building intelligence, and the core of intelligent building system design is system integration. The main goal of intelligent building system integration is to realize information integration, and data warehouse technology is an effective solution to solve the problem of intelligent building information integration. Data mining technology is used to analyze and process various kinds of massive data and information, so as to realize the conversion from data to information and provide effective support for leaders' decision-making. The new model of building intelligent system integration based on data mining technology proposed in this paper has solved the compatibility and openness problems in the process of building intelligent system integration, and achieved the goal of safe and efficient building intelligent system.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129425690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590787
Shu Xu, Jiayu Liu
As the representative of many information technologies, navigation technology is quietly entering the details of human life. How to adapt to complex environment and fuse multi-sensor information to achieve more accurate positioning has become the key of navigation technology. Facing the requirements of high-precision and reliable positioning technology, aiming at the problems of low positioning accuracy and poor robustness caused by incomplete information of single source positioning technology, this paper focuses on the fusion positioning method of wireless signal, pedestrian dead reckoning (PDR) and map information based on particle filter (PF), and proposes an adaptive vector particle filter algorithm, A personal navigation (PND) system based on adaptive vector particle filter is designed, which can effectively improve the positioning performance. The experimental results show that the navigation results can correctly reflect the changing process of the attitude, speed and position of the pedestrian's foot. The position error of the navigation algorithm is positively correlated with the walking distance and the number of walking steps, and the relative error between the position error and the traveling distance is about 5%.
{"title":"Research on algorithm of personal navigation system based on adaptive vector particle filter","authors":"Shu Xu, Jiayu Liu","doi":"10.1109/ICISCAE52414.2021.9590787","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590787","url":null,"abstract":"As the representative of many information technologies, navigation technology is quietly entering the details of human life. How to adapt to complex environment and fuse multi-sensor information to achieve more accurate positioning has become the key of navigation technology. Facing the requirements of high-precision and reliable positioning technology, aiming at the problems of low positioning accuracy and poor robustness caused by incomplete information of single source positioning technology, this paper focuses on the fusion positioning method of wireless signal, pedestrian dead reckoning (PDR) and map information based on particle filter (PF), and proposes an adaptive vector particle filter algorithm, A personal navigation (PND) system based on adaptive vector particle filter is designed, which can effectively improve the positioning performance. The experimental results show that the navigation results can correctly reflect the changing process of the attitude, speed and position of the pedestrian's foot. The position error of the navigation algorithm is positively correlated with the walking distance and the number of walking steps, and the relative error between the position error and the traveling distance is about 5%.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133681552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590700
Jinming Du
With the development of Internet and information technology, data has become an important asset related to the development prospects of society and all walks of life. At present, there are many quality problems in the use of educational data, which has brought great obstacles to exerting the value of educational data. Only by using scientific statistical methods, obtaining real, objective, comprehensive, scientific and effective basic data, and carrying out systematic and comprehensive analysis on the obtained data, can we give full play to its command and decision-making role. The development of big data and artificial intelligence technology provides new ideas for the analysis and evaluation of educational data quality, and is committed to restoring the overall picture of the education system and promoting the change of regional educational ecology. In this paper, an educational data quality analysis model based on multiple constraint model is proposed, which classifies the data in the database, divides the information in the database into several different categories according to the data characteristics, and establishes a quality management system for educational data in universities, so as to effectively improve the quality of educational data.
{"title":"Research on the Construction of Educational Data Quality Model Based on Multiple Constraints Model","authors":"Jinming Du","doi":"10.1109/ICISCAE52414.2021.9590700","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590700","url":null,"abstract":"With the development of Internet and information technology, data has become an important asset related to the development prospects of society and all walks of life. At present, there are many quality problems in the use of educational data, which has brought great obstacles to exerting the value of educational data. Only by using scientific statistical methods, obtaining real, objective, comprehensive, scientific and effective basic data, and carrying out systematic and comprehensive analysis on the obtained data, can we give full play to its command and decision-making role. The development of big data and artificial intelligence technology provides new ideas for the analysis and evaluation of educational data quality, and is committed to restoring the overall picture of the education system and promoting the change of regional educational ecology. In this paper, an educational data quality analysis model based on multiple constraint model is proposed, which classifies the data in the database, divides the information in the database into several different categories according to the data characteristics, and establishes a quality management system for educational data in universities, so as to effectively improve the quality of educational data.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133727800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590794
Yufei Song, M. Chu
Nowadays, in the network age, computer security issues have attracted much attention. In order to ensure computer network security, it is necessary to pay attention to improving data encryption technology. Data encryption technology is mainly divided into two types: asymmetric key and symmetric key. Generally, the sender and receiver use different password settings to realize network security. In this paper, a scheme of applying machine learning classification algorithm to homomorphic encrypted data sets is proposed: firstly, the plaintext is preprocessed to ensure that it meets the requirements of homomorphic encryption of data; Then, compare and sort the encrypted data set by protocol. Finally, the classification results are obtained. Combined with machine learning algorithm, the text information hiding convergence is controlled, and the text information hiding algorithm is optimized. Simulation results show that this method can hide text information in a higher depth, and has stronger anti-attack ability, thus improving the security of text information storage.
{"title":"Research on the Application of Data Encryption Technology in Computer Network Security Based on Machine Learning","authors":"Yufei Song, M. Chu","doi":"10.1109/ICISCAE52414.2021.9590794","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590794","url":null,"abstract":"Nowadays, in the network age, computer security issues have attracted much attention. In order to ensure computer network security, it is necessary to pay attention to improving data encryption technology. Data encryption technology is mainly divided into two types: asymmetric key and symmetric key. Generally, the sender and receiver use different password settings to realize network security. In this paper, a scheme of applying machine learning classification algorithm to homomorphic encrypted data sets is proposed: firstly, the plaintext is preprocessed to ensure that it meets the requirements of homomorphic encryption of data; Then, compare and sort the encrypted data set by protocol. Finally, the classification results are obtained. Combined with machine learning algorithm, the text information hiding convergence is controlled, and the text information hiding algorithm is optimized. Simulation results show that this method can hide text information in a higher depth, and has stronger anti-attack ability, thus improving the security of text information storage.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124661486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590763
Shih Pan, Zhixuan Xiao
In the context of the accelerating process of information technology in the international society, information security has become the focus of attention of the whole society. How to ensure communication security in the application of information communication has become the main direction of research and exploration. Therefore, on the basis of understanding the good cryptographic system, this paper analyzes how to design the practical information security system for the two commonly used cryptographic algorithms.
{"title":"Design of the Information Security System Based on the Encryption Mechanism","authors":"Shih Pan, Zhixuan Xiao","doi":"10.1109/ICISCAE52414.2021.9590763","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590763","url":null,"abstract":"In the context of the accelerating process of information technology in the international society, information security has become the focus of attention of the whole society. How to ensure communication security in the application of information communication has become the main direction of research and exploration. Therefore, on the basis of understanding the good cryptographic system, this paper analyzes how to design the practical information security system for the two commonly used cryptographic algorithms.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127421301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590666
Xiao Bao
How to use computer vision technology to automatically identify and analyze human behavior in video has become a research hotspot. In traditional behavior recognition methods, features need to be extracted manually, and the recognition effect of features largely depends on the experience of feature designers. This paper takes the dual-stream convolutional neural network as the basic theory, and uses the TSN (Temporal Segment Networks) model as the basic framework to analyze the shortcomings and shortcomings of the single-stream network and the original dual-stream network. A multi-modal human behavior recognition model based on dual-stream network is proposed. In order to extract video-level features effectively, this model adopts two attention mechanisms, which are used to learn image frame features and video-level feature transfer. Then, CNN is used to extract global motion features, and finally, it is fused with spatio-temporal features. The fusion feature is evaluated on the public data set, and the results show that the two features are complementary, and their fusion makes the features more expressive, and the recognition result on the public data set is greatly improved compared with the single spatio-temporal feature.
{"title":"Research on Multimodal Human Behavior Recognition Based on Double Flow Network","authors":"Xiao Bao","doi":"10.1109/ICISCAE52414.2021.9590666","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590666","url":null,"abstract":"How to use computer vision technology to automatically identify and analyze human behavior in video has become a research hotspot. In traditional behavior recognition methods, features need to be extracted manually, and the recognition effect of features largely depends on the experience of feature designers. This paper takes the dual-stream convolutional neural network as the basic theory, and uses the TSN (Temporal Segment Networks) model as the basic framework to analyze the shortcomings and shortcomings of the single-stream network and the original dual-stream network. A multi-modal human behavior recognition model based on dual-stream network is proposed. In order to extract video-level features effectively, this model adopts two attention mechanisms, which are used to learn image frame features and video-level feature transfer. Then, CNN is used to extract global motion features, and finally, it is fused with spatio-temporal features. The fusion feature is evaluated on the public data set, and the results show that the two features are complementary, and their fusion makes the features more expressive, and the recognition result on the public data set is greatly improved compared with the single spatio-temporal feature.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115616492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590813
Gang Chen
In communication network mega data, unstructured data is characterized by large scale, diversity and timeliness. Traditional unstructured processing methods have been difficult to meet the data processing needs. Complex data sets and large data orders in modern mega data require professional analysis tools to realize analysis. Information fusion is a multi-source information processing technology, which can optimize and synthesize redundant information from multiple sensors in space and time, and obtain more accurate and complete values than single information source, and obtain the consistent description of the measured object. In order to effectively solve the problem of unstructured data model of communication network mega data, this paper proposes an algorithm for unstructured data analysis of communication network based on feature fusion, and analyzes the key problems in the process of unstructured data feature modeling, such as the storage of original data and feature data, the selection of feature space, information query and data visualization.
{"title":"Research on Unstructured Mega Data Analysis Algorithm of Communication Network Based on Feature Fusion","authors":"Gang Chen","doi":"10.1109/ICISCAE52414.2021.9590813","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590813","url":null,"abstract":"In communication network mega data, unstructured data is characterized by large scale, diversity and timeliness. Traditional unstructured processing methods have been difficult to meet the data processing needs. Complex data sets and large data orders in modern mega data require professional analysis tools to realize analysis. Information fusion is a multi-source information processing technology, which can optimize and synthesize redundant information from multiple sensors in space and time, and obtain more accurate and complete values than single information source, and obtain the consistent description of the measured object. In order to effectively solve the problem of unstructured data model of communication network mega data, this paper proposes an algorithm for unstructured data analysis of communication network based on feature fusion, and analyzes the key problems in the process of unstructured data feature modeling, such as the storage of original data and feature data, the selection of feature space, information query and data visualization.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123578565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590784
WeiSheng Wen, Jun Ma, Shuqin Liu
The logistics network is composed of lines and nodes. The traditional logistics is too centralized, so it is necessary to try “decentralization” to reduce the management difficulty and operational risk. This paper proposes a security data management method based on blockchain to manage distributed logistics network system. Through IOT technology, the warehouse will be connected by tagging individual items and operating hardware to improve the transparency and localization of all assets. The client of IOT node encrypts the logistics transaction data and uploads it to the blockchain to ensure the privacy and unforgeability of the logistics information, so that the encrypted data can be decrypted by the system nodes and uploaded to the blockchain through the logistics network. According to the experiment of school enterprise cooperation enterprise, this is a sharing framework with high security and high availability.
{"title":"Data security management of logistics network based on blockchain technology","authors":"WeiSheng Wen, Jun Ma, Shuqin Liu","doi":"10.1109/ICISCAE52414.2021.9590784","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590784","url":null,"abstract":"The logistics network is composed of lines and nodes. The traditional logistics is too centralized, so it is necessary to try “decentralization” to reduce the management difficulty and operational risk. This paper proposes a security data management method based on blockchain to manage distributed logistics network system. Through IOT technology, the warehouse will be connected by tagging individual items and operating hardware to improve the transparency and localization of all assets. The client of IOT node encrypts the logistics transaction data and uploads it to the blockchain to ensure the privacy and unforgeability of the logistics information, so that the encrypted data can be decrypted by the system nodes and uploaded to the blockchain through the logistics network. According to the experiment of school enterprise cooperation enterprise, this is a sharing framework with high security and high availability.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123085465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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