College Assistant Information Decision Support System Based on Smart Card

With the continuous deepening of information construction in China's colleges and universities, various information systems based on smart cards (known as campus cards in colleges and universities) are gradually being applied to various aspects of university management, such as catering management systems, entry barrier systems, and books. Borrowing systems, etc., these systems have played a huge role in the auxiliary management of colleges and universities. However, these systems can only meet a certain application, and do not form a comprehensive auxiliary information system. They have not yet reached the level of comprehensive data processing and management. In addition, as these single systems run longer, they accumulate. A large amount of data, which in fact has formed an information island, can not be comprehensively analyzed and utilized.

Since its first introduction in the early 1970s, the decision support system has matured over the past 40 years. It has been a long and complicated evolution of information technology. The main purpose of constructing a decision support system is to transform simple data into useful information. This goal is fully consistent with the needs of colleges and universities in auxiliary management. Therefore, a smart card-based auxiliary decision support system is constructed to comprehensively utilize data resources. It has become an urgent problem to be solved in the auxiliary management of colleges and universities. Based on the above reasons, we have studied several key issues in the model construction, architecture and system design of the intelligent information-based decision support system for intelligent information management.

1 Construction of auxiliary information decision support system

1. 1 Logical model design of decision support system

At present, various management systems based on smart cards are run in isolation. In order to eliminate information silos, the purpose of comprehensive management and utilization of these data is to first collect and organize data, build a smart card auxiliary information data warehouse, and secondly, based on data warehouse. Establish a decision analysis and forecasting system for different subject domains. According to this situation, combined with the requirements of data collection and data utilization, the logical model of the system can be designed, as shown in Figure 1.

Smart Card Auxiliary Management Information System Logical Model

It can be seen from Figure 1 that the data is obtained from the existing smart card-based information system, and after being cleaned, collected and filtered, it is saved to the data warehouse, and the data is processed by the data mining engine. The decision information formed after the data is processed It can provide improved guidance for smart card-based information systems, standardize the interface for sharing data, and use visual technology to provide decision-making basis to school decision makers. The results of the analysis are evaluated by the user and the decision information is identified based on objective measures. The value of constantly improving the mining engine.

1. 2 Architecture of Decision Support System

According to the system logic model, processing process and the categories involved in the current smart card auxiliary information system in colleges and universities, combined with the classification and layering technology in information processing, the data processing of the smart card auxiliary information management decision support system can be divided into 3 Levels: data acquisition layer, information processing layer and predictive analysis layer, the architecture is shown in Figure 2.

Smart card auxiliary information management system structure

As can be seen from the architecture shown in Figure 2, the data acquisition layer is the basis for the implementation of the entire decision support system, and the collection and integration of all data resources is completed. These multi-channel, long-term accumulated data resources constitute the entire decision. Supports the underlying data sample of the system, which is derived from all existing information systems that use smart cards, such as access control data, medical data, and book borrowing data. In addition, the data acquisition layer provides standard shared data for information processing systems using smart cards. Interface, consider the problem of reserved expansion interface, to facilitate access to the new system when demand increases.

The information processing layer, as a bridge between the data layer and the predictive analysis layer, is responsible for providing intermediate layer data that is initially classified and associated for the mining model. In the information processing layer, the underlying data samples need to be structured to increase different levels of information. Processing granularity, extracting preliminary comprehensive data as the basis for decision analysis, and providing management tools to manage data in the data warehouse.

The predictive analysis layer undertakes the dual work of data mining and result display, extracts the middle layer data, and forms the subject-oriented instantiation integrated data. These instantiated comprehensive data are specific to the subject domain and are further standardized. The data mining algorithm can be directly applied to the decision analysis based on the instantiated comprehensive data. After the processing of the decision analysis layer, valuable and hidden rules in the original data sample are found and presented to the user.

2 Several key issues affecting system implementation

According to the 3-layer architecture shown in Figure 2, it can be seen that there is no essential difference between the information processing layer and the predictive analysis layer of the decision support system and other types of decision support systems in terms of design and implementation methods. The data acquisition layer is the working basis of the entire smart card auxiliary information management decision support system. Therefore, several key points in the implementation of this layer are described below.

2. 1 Metadata design

The design of metadata is an important part of the construction of decision support system. It plays different roles in all levels of data processing. Although in the decision support system, it is necessary to define the description data structure, data mapping relationship, and alias and status. The type of metadata, but the specific content is determined according to the characteristics of the decision support system built.

In the smart card auxiliary information management decision support system, the system is characterized by a wide range of systems, numerous entities, complex relationships, unstable requirements, etc. The most critical is the design of entity information metadata, which can be derived based on entity information metadata. Other important metadata, such as subject domain partitioning, granularity of data, etc., directly affect the degree of information refinement of the decision support system, the scope of the subject domain, the application efficiency of the mining algorithm, and the scalability of the system.

The entity information metadata mainly describes the entities in the system and their dimensions, granularity, aliases and unique identifiers. Table 1 shows some of the entity information metadata of the smart card auxiliary information management decision support system.

Table 1 Example of partial entity information metadata

In Table 1, the level information reflects different levels and different angles of data processing. The level of atomic data in the system is defined as 0, the data classification information level is 1, the application information level of the entity is 2, and the granularity information level is 3; In the information, the primary classification statistical information level is lower, the analysis information level according to the granularity extension is higher, and so on. By applying the entity information metadata, the data can be converted into information in the process of specific topics, from dimensions, The granularity and other angles facilitate the degree of control information refinement.

2. 2 Implementation of system data acquisition

Although the data used in existing smart card-based information systems differ greatly in terms of storage modes and data formats, they are essentially composed of two major categories, namely, record files and character stream files, which are commonly used. Recorded and non-recorded files (for the convenience of description, the following is a database file, and the character stream file is a non-database file). Therefore, data collection can be handled separately according to these two categories. The implementation process mainly includes the data source. The three main steps of identification and reading, data conversion and data preservation. The algorithm of the client procedure Imp Procedure of data acquisition is described as follows:

/ / Input Enter the character to be processed, file type File Type
/ / Output Feedback information ReturnInfo s
Algorithm Db_Import
Step1 : / / determine the file type, connect to the target database if filetype = Databasefile
t hen go step2
Else go step3
Step2 : / / Process database files Open Databaselink (DNS) SourceDbLink / / Connect to the source database Open Databaselink (DNS) AimDbLink / / Connect to the target database / / Process the records in the data set one by one for each SourceDbLink. Table. record do
Begin
Filedst r = "/ / Initialize / / Read the fields in the record one by one for each SourceDbLink. Table. filed do
Filedst r ←Filedst r + SourceDbLink. Table.
Fieldvalue +','
/ / Submit the record to the target database side stored procedure ReturnInfo ←AimDbLink. Procedure ( Filedst r)
If ReturnInfo = errorinfo t hen show(ReturnInfo)
End
Close SourceDbLink
Close AimDbLink
Step3 : / / Handle non-database files Open Databaselink (DNS) AimDbLink / / Connect to the target database Open File ( FilePat h) SouFile / / Open the file / / Process the records in the file one by one for each AimDbLink. record do
Begin
SouFile. read ( Filedst r)
/ / Submit records to the target database side process ReturnInfo ←AimDbLink. Procedure ( Filedst r)
/ / If an error occurs, an error message is displayed if ReturnInfo = errinfo t hen
Show(ReturnInfo)
End
Close SouFile
Close AimDbLink
As can be seen from the above algorithm, the ODBC technology is used for the database file to access the database through the DNS data source; when the non-database file is read, the read function adapted to the specific development language is used to operate through the file name and the absolute path. Access files, have a certain format requirements for non-database files. Data conversion and save work is handled by a unified stored procedure according to the rules defined in the metadata.

2. 3 system data feedback interface implementation

In addition to the functions of the general decision support system, the smart card-based auxiliary information decision support system can also provide guidance information for the existing smart card-based information system. In view of the different development time of the existing smart card-based information system, the decision of the construction The support system can adopt two kinds of data feedback modes: export and synchronization. The export mode only needs to save the feedback data to a specified file according to a fixed format, mainly for adapting to the information system developed in the early stage; the synchronous mode is for the system currently developed by adopting new technology. Provide a unified data access interface, the process mainly includes three main steps: user authority determination, request data query and result return. The process is also easy to implement. For example, the server-side process implemented by the Webser2vice technology under the Framewrok2.0 framework The algorithm of FeedbackProcedure is described as follows:

/ / Input user code Userid, user password PassWord, synchronization type Info Flag,
Start time Begin Time, end time EndTime, data table name TableName
/ / Output dataset DateSet
Algorithm Db_Feedback
Step1 : / / Determine if the user is legal if (Userid not Read TableName) or ( Password = false) t hen
Begin
Show errorinfo / / prompt error message exit
End
Step2 : / / Determine the synchronization type if InfoFlag = t rue t hen / / Synchronize all information sSt rSql ←select 3 f rom sTableName
Else / / synchronization part information sSt rSql ←select 3 f rom TableName where
Change Time > = Begin Time and Change Time < = EndTime
Step3 : / / Query information and feedback Open Databaselink (DNS) AimDbLink
/ / Execute the Sql statement and output DateSet = AimDbLink. Exec ( sSt rSql)
Close AimDbLink

The above algorithm describes a common data synchronization feedback processing process. The process can determine whether to have access to the specified data table according to the access level defined in the input parameter and the metadata; after the authentication is passed, the query statement is dynamically combined and executed according to the feedback mode. When incremental data sharing, use the timestamp mechanism for control. However, the implementation of multiple security mechanisms should be considered in the implementation process. The handling of security mechanisms will not be repeated here.

3 Conclusion

The smart card auxiliary information management decision support system mainly utilizes the massive data accumulated by the current smart card-based information system in colleges and universities, and provides multi-faceted decision-making basis for the auxiliary management of colleges and universities. The article discusses the model and architecture of its system, and describes several important links involved in system design and implementation. At present, the system is still in the prototype design stage. Although it can solve some problems existing in the current college auxiliary management, it still needs further research on model evaluation, interest measurement and improvement of mining algorithms.

(Author/1. School of Information Science and Engineering, Lanzhou University; 2. Lanzhou Lanxiao Elf New Technology Co., Ltd.; 3. Communication Network Center of Lanzhou University)

Zipper Bag

Stand Up Zipper Pouch,Zipper Bag,Plastic Packaging Pouch,Food Pouch With Zipper

Guangdong Danqing Printing Co., Ltd. , https://www.spoutpouchfactory.com