Study on the Application of Artificial Intelligence Technology in Decision Support System of Energy Enterprises

Transcription

1 Abstract Study on the Application of Artificial Intelligence Technology in Decision Support System of Energy Enterprises Changtong Song Zhenjiang College, Zhenjiang , China Currently, given the increasingly larger database of energy enterprises and frequent storage and transfer of realtime information across regions, their demand for system integration and sharing of knowledge and information increases. To upgrade energy enterprises decision support system and further improve their decision-making efficiency, this paper designs the decision the support system of energy enterprise based on artificial intelligence technology, probes into the overall system architecture and functions, looks at knowledge representation, construction and storage, and finally puts forward the concrete realization of energy enterprises decision support system. The results show that energy enterprises decision support system based on artificial intelligence technology can greatly reduce the workload of database server, accelerate its response and improve the overall efficiency of energy companies. A flexible and suitable enterprise decision-making system will not only meet the needs of enterprises, but also save the costs of design, reduce the types of decision-making models and system complexity. In its specific operation, artificial intelligence technology as a design basis can facilitate the realization and optimization of such system by building a good network and software environment, upgrading the office system, and integrating multi-systems. The research results in this paper are of certain reference values for analyzing the application of artificial intelligence in energy enterprise decision support system. Keywords: Artificial Intelligence Technology, Energy Enterprises; Decision Support System, Application Research. 1. RESEARCH BACKGROUND 1.1 Literature Review Scholars at home and abroad have conducted considerable research on the application of enterprise decision support system and artificial intelligence technology in such system. CG Şen et al. have proposed a new decision support system that uses heuristic algorithms, fuzzy multi-criteria decision-making and multi-objective programming models for final selection decisions (Şen et al., 2009). J Ma et al. have developed a fuzzy decision support system that can process information expressed in linguistic terms and help enterprises make business decisions (Ma et al., 2010). Ma Qiuju and Zhang Shuqi applied the B / S (browser / server model) threehierarchical architecture to build SME decision support system to meet the needs of these enterprises and reduce the complexity of the system (Ma and Zhang, 2010). R Misdolea analyzed the structure of decision support systems and customer relationship management, holding that decision support system is a powerful command and control tool in the Internet age (Misdolea, 2010). Liu Wenchang and Li Tongyu studied the enterprise emergency management decision support system, proposed the work content of specific emergency management decision support system, and designed major functions and workflow of the system (Liu and Li, 2011). AF Salam put forwards a semantic-intelligent agent-based architecture and decision support system that helped companies choose reliable suppliers (Salam, 2011). F Shafiei et al. developed a support system that facilitates collaborative decision-making across enterprises, the decision-making components of which could be flexibly used by decision makers from various supply networks to explore hypothetical scenarios and make the best decisions based on the results for their organization, customers, and partners (Shafiei et al., 2012). JA Alalwan studied whether enterprise content management system (ECMs) had the functions of four types of decision support system, namely, DSS, EIS, ES and GDSS, and found that ECMs had all the decision support functions of DSS, EIS and ES, but only part of the GDSS functions (Alalwan, 2013). B Shirazi proposed a service-oriented decision support system framework that sought for service-oriented architecture (SOA) governance, and analyzed the agility and optimality of the original architecture (Shirazi, 2013). As for the application of artificial intelligence technology in enterprise decision support system, Zhao Yu analyzed the application of expert system (ES), machine learning 801

2 and agent in such system, and elaborated on its development (Zhao, 2009). Wu Lihao constructed an example of market decision-making on the prototype of IADSS, and initially evaluated its operation effect through model analysis. It is believed that only when the decision support system is equipped with rich knowledge and strong knowledge processing ability will it be more effective in decision-making support (Wu, 2009). From the above studies, most scholars mainly study the enterprise decision support system, while some analyze the application of artificial intelligence technology in the enterprise decision support system. However, the literature is rather general in scope and not specific to energy companies. Based on artificial intelligence technology, this paper designs a decision support system for energy enterprises and proposes specific ways to implement decision support system for energy enterprises. It is of strong theoretical and practical significance for the development of energy enterprises. 1.2 Purpose of Research Artificial intelligence, also known as machine intelligence, is an intelligence that combines that of human and nature. Artificial intelligence was first proposed in the 1950s. Artificial Intelligence preconditioning techniques about inaccurate and fuzzy knowledge expands the scope of problems handled by decision support systems and improves their processing power (Shu, 2009). Also, this technology is an important alternative to conventional technologies or as a component of integrated systems (Kalogirou, 2010). In recent years, it has been widely used in robotics, economic and political decision-making, control system and simulation system, but little used in energy enterprises, the competition between which is extremely fierce. Given the constantly changing business environment, the key for them to possess competitive advantage in the market is to give quick response and formulate coping strategies. Moreover, with the rapid development of information technology, the soaring information and data, quick and effective analysis and decision-making of energy enterprises depend more and more on the design and development of their information systems. Therefore, major energy companies now attach great importance to how to excavate the law of production activities and market trends from massive business data and make important decisions to participate in market competition. While in practice, they inevitably encounter such problems as low technology content and slow response to demand. In view of this, this paper focuses on how to rationally apply artificial intelligence technology to decision support system of energy enterprises to meet their decision-making demand and enhance their competitiveness. 2. DESIGN OF DECISION SUPPORT SYSTEM OF ENERGY ENTERPRISES BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY 2.1 Overall System Structure Artificial intelligence mainly includes three hot spots, namely, intelligent interface, data mining, the main system. The design of energy enterprise decision support system mainly involves data mining technology and the extraction of potentially useful information through technical means. The decision data in such system come from the system database, and the decision support system analyzes and integrates various data of energy enterprises to the database. It then uses online analytical processing to classify the data for such processing as slicing, dicing, drilling and rotating, so that users can extract data from different angles. In this process, system data update must be considered to keep up with the pace of development of energy enterprises and better meet their future demands. In view of this, the overall functional design of decision support system is divided into four parts: user management, database management, decision-making base management and decision analysis. Moreover, the system adopts B / S three hierarchies to manage the data, decision analysis layer and web user application respectively, which is conducive to maintaining and upgrading the energy enterprise decision support system. When the system needs to increase or decrease functions, only part of these three layers needs to be modified. The decision analysis layer is responsible for the connection between the database and the user. The application server component program is used to process various data information and enterprise logic, transmitting the request of the user to the database server on the one hand, and return the query result of the server to the user. The database server only needs to focus on responding to user request. In this way, the workload of the database server reduces, the response gets faster, and the overall efficiency of the system improves. The overall system architecture is shown in Figure System Function The energy enterprise decision support system based on artificial intelligence technology has its own unique 802

3 functions in user management, database management, knowledge base, algorithm base management as well as decision analysis. See Table 1 for details. Table 1 System Function Overview Function module User management Database management Knowledge base and algorithm base management Decision analysis Description Strictly manage system users name and password for the security of energy businesses decision support system. Also, user management module can add or delete users. Efficiently manage the database of energy businesses decision support system, mainly include database update, and import, export of processed data. It should be noted that the data in such database must be updated in time so as to ensure the authenticity and reliability of the data required for these enterprises to make correct decisions. Manage and maintain the knowledge base and algorithm base on a daily basis. The main function of the knowledge base module is to create, modify and delete knowledge. In energy enterprises decision support system, based on different user demands, it generates independent models and establish the algorithm for each of them. The energy enterprises decision support system is realized via decision analysis function, where these enterprises can choose their own decision-making algorithm to make a detailed analysis of specific decision-making goals. As is shown in Table 1, the energy enterprise decision support system based on artificial intelligence technology contains a variety of functions. It stresses multi-functional coordination in setting a single function to form a harmonious system. Data transmission of all the sections and windows should be guaranteed in a series of operations, such as deletion and modification of data information on a certain system section, so as to ensure existence of data in the entire system and the connection between them. 2.3 Knowledge Base Design Knowledge base is the core of energy enterprise decision support system based on artificial intelligence technology. Energy enterprises can manage the knowledge base to improve various of its functions, such as modification and invocation. Therefore, artificial intelligence technology should be fully leveraged in the design of the knowledge base Knowledge Representation The knowledge representation of energy enterprise decision support system based on artificial intelligence technology is mainly divided into the following three steps: First, determine the target population for the application of energy enterprise decision support system, and based on the relationship of inheritance and AHP, organize them to form a Binary Tree representation; second, reasonably transform all the paths from the root node to the leaf object node in line with the IF-THEN rules to obtain the rule set; third, make each object a subtask, and divide all the rules that can accomplish the subtasks into rule sets. The entire knowledge base ultimately constitutes a graphical hierarchy of smart objects and rules Knowledge Building and Storage Based on the artificial intelligence technology, the construction of energy enterprise decision support system should take into account the different requirements of the whole system to the knowledge base and its three layers, i.e. knowledge, mode and architectural design. According to the categories and internal structures of the designed objects, each layer is decomposed into upper-lower objects with inheritance relationship so that the knowledge is more modularized. In the knowledge base where energy enterprises are objects of intelligence, all the problems involved are listed as the key objects and expressed in the frameworks. Relevant operational methods and rules of use are clearly included in the framework. In addition, the system is presented in static and dynamic models corresponding to its daily operation. The static model is mainly a relational energy enterprise decision support system database set up with SOLserve2007. Each of its record is regarded as an instance, and each field reflects the real property of the object of intelligence. In such a model, energy enterprises only need to input the condition 803

4 option, and the decision support system will automatically call out the data information to help enterprises make decisions. Based on the static model and in line with different needs of energy enterprises in different periods, dynamic model chooses targeted and effective conditions, and gives flexible decision-making results. 3. TEST OF ENERGY ENTERPRISES ARTIFICIAL INTELLIGENCE TECHNOLOGY BASED DECISION SUPPORT SYSTEM 3.1 Create Test Database When creating the test database, create a database user dbt. Modify the script to create the test database. Set database parameters meantime: param _ addvolume. Define the path and file name for storing system files: param_addvolume 1 SYS $SYS_DIR/$SID/SYS_001 F Defined data and log space size of test database: param_addvolume 1 DATA $SYS_DIR/$SID/DBT33_DATA_001 F param_addvolume 1 LOG $SYS_DIR/$SID/DBT33_LOG_001 F Create Test Data Table Invoke the script create_tables.sh for remote creation of eight data tables to be tested: Suppliers, Part, Partsupp, Customer, Orders, LineItem, Nation, Region, and a table used to count test results: time_statistics. 3.3 Generate Updated Data and Load Use the tool dbgen to generate updated data /dbgen[<options>] Option-U indicates the number of data set of the specified size created to update the function. Option-s, the size of test database is the same as the one specified above when generating the data file. The process of loading updated data is essentially the same as loading database data. 3.4 Power Test Perform appropriate power test script for power test. The result will be used to calculate the ability of the decision support evaluation system to execute query at database of selected size. The comprehensive hourly query power indicators of the decision support evaluation system are the number of queries per hour multiplied by the database size, which is accurate to 0.1, as is calculated below: power=3600*scale_factor/(q1*q2* Q22*RF1*RF2)**(1/24) (Q1 * Q2 --- Q22 * RF1 * RF2) ** (1/24) = average time required by per query 3600 / average time required by per query = number of queries per hour 804

5 Number of queries per hour * database Size = hourly query power indicators 3.5 Throughput Test With database size specified, the result of the throughput test will be used to calculate the throughput of the decision support system. throughput=22*3600*stream*scale_factor/ 22 * STREAM / (test end time - start time) = number of queries per second Number of queries per hour * database size = comprehensive query power of each hour Measurement Results: Database Scaling (SF/Size) =0.2 Query Streams for Throughput Test = 4 TPC H Power Metric (QppH@0.1GB) = 6.45 TPC-H Throughput Metric (QthH@0.1GB) = 1.51 Composite Query -per -Hour Rating (QphH@0.1GB) =3.15 The test results prove excellent. When the business direction or personnel should change, or in time of business problems, this system can provide corresponding data based on past policies to analyze the method and automatically predict the further investment, performance benefits and labor turnover, so as to fully reflect the multiple influences on energy enterprises and minimize corporate losses. Moreover, the system fully leverages the hierarchical relationship method, one for system visualization by multiple divisions. Sub-images thus obtained then decompose to the number of boxes on each dimension until the system is completed. Users can complete the entire system only with the browser and the Internet, thus saving enterprises a lot of time and cost and greatly enhancing their decision-making efficiency. 4. CONCLUSION With the rapid development of science and technology, especially the constant progress of computer technology and communication technology, most energy companies can use their own information system to manage their production activities. However, their traditional data analysis methods for production decision-making can hardly meet the demand of the society. Therefore, energy enterprise managers urgently need a set of decision support system suitable for the development of their own businesses, keeping abreast of relevant information and making decisions conducive to their growth. The rise of artificial intelligence technology provides an opportunity for the construction of such system. To explore the application of artificial intelligence technology in decision support system of energy enterprises, this paper uses artificial intelligence technology to design such a system for energy enterprises. It also discusses in detail the knowledge representation, construction and storage based on artificial intelligence, and proposes the realization of such system. The research finds that the application of artificial intelligence technology to energy enterprise decision support system can not only accelerate server response, but also enable these enterprises to extract data from different perspectives. In its specific operation, artificial intelligence technology as a design basis can facilitate the realization and optimization of such system by building a good network and software environment, upgrading the office system, and integrating multi-systems. Although this paper designs the decision support system for energy enterprises based on artificial intelligence technology and puts forward specific ways to realize it, it has some limitations in the evaluation of multi-system integration and system functions. It is suggested that a good network and software environment be built in the future and more in-depth and meticulous research conducted with such methods as application cases and empirical analysis. REFERENCES 805

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