With the rapid development of the electric power industry in recent years, the strengthening of the power construction market and the diversification of the main body of power investment, there appears a prominent question in front of the project owners——How to control and reduce construction costs? There are many methods to estimate the cost quickly and accurately. Among the common methods and some new ways which have appeared in recent years, people can find about seven types out of them, in which, neural network model is known for its versatility and adaptability. It does not exclude new sample. On the contrary, it improves its ability to generalize and forecast with the increasing number of samples. Therefore this paper establish a cost estimation model by introducing neural network which is based on the optimization of genetic algorithm, and expresses the relationship implied in the interior of data by using the network topology and parameters by studying a large number of samples so as to fit the conventional non-linear mapping relationship between the amount and cost of a transmission line project. The results show that the artificial neural network model has a significant effect on the project cost estimation. The introduction of neural network model will certainly promote the development of informatization of power project costs management.
In recent years, with the slowdown in power generation, the reform of transmission and distribution prices has continued to deepen, and objectively requires more precise management of projects invested by power grid companies. This paper takes the asset group as the evaluation unit of grid engineering asset management, starts with the characteristics of the asset group, expounds the scope and division criteria of the asset group, and determines the asset group classification. The cost and benefit sharing method of the asset group is proposed. The actual cost method is used to collect the components of each asset. The electricity and cash flow based methods are used to separate the asset groups of the transmission and distribution networks. The proceeds are apportioned. The asset group input-output evaluation system was constructed. Through the multi-dimensional selection of indicators reflecting the input and output of the asset group, a comprehensive evaluation of the operation of the asset group was made. The cost-benefit sharing method and input-output evaluation system proposed in this paper can effectively guide the asset management of power grid projects and assist the power grid companies in making decisions.
In Web 3.0 times, Internet and Electronic Commerce develop rapidly, it is necessary to solve the problem which is how to recommend the personalized information to the user when the user faces numerous of information. But now, it is only studied from three aspects: collaborative filtering, content analysis, associated rules, which belong to the two sides based on the user and the goods. All of them dig the information from the individual records of history, or the user who similar to the record of history. After analyzing the content of web 3.0, this paper point out how to mining information from the perspective of semantic-formal concept analysis based on the user, and then draw the personalized information recommendation model. After analyzing the user’s information behavior, we can find the the user’s preferences, finally recommend the proper information about commodity to the user and improve the user's satisfaction.
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