Intelligent financial data analysis system and method

[0023] Example 1

[0024] A intelligent financial data analysis system includes: data acquisition unit, data pre-processing unit, data analysis unit, and data presentation unit, where the data acquisition unit is used to collect the original data of the financial website and store To the original database; the data pre-processing unit is used to perform data pretreatment on the raw data in the original database; the data analysis unit is configured to receive the data pre-processing unit for the formation of the original data. The data analysis input stream, and analyzes the data analysis input stream to establish index and statistics, and synchronize the data analysis input stream with the corresponding data in the data acquisition unit, forming data analysis output stream, and outputs To the post-analysis database; the data presentation unit is used to receive the data analysis output stream in the database, and exhibit the display data to the user in the interface according to the different type, and receive user input Focus instructions.

[0025] Example 2

[0026] On the basis of the previous embodiment, the original data of the financial website includes: allocation data, the front and rear desk type association data, the minimum product information under the minimum category; where the payment instruction includes the name of the product, The data display unit is also used to generate an exemplary system interaction data according to the payment instruction, and transmit the display system interaction data to the analysis database; the data analysis unit is further configured to receive the database in the database. The display data feedback input stream is displayed in the system interaction data, and the presentation data feedback input stream is analyzed to obtain the name of the payment of the product, and the payment of the product is established, and the payment of the product information and the data presentation unit The corresponding data is synchronized to form a data feedback output stream, and the presentation data feedback output stream is output to the original database; the data acquisition unit is further configured to receive the presentation data feedback output stream in the original database, and according to The presentation data feedback output stream preferentially collects information of the product, and is displayed to the user through the data analysis unit and the data display unit.

[0027] Specifically, the purpose of data analysis is to focus on a large number of information in data in data that makes messy, so that the inherent law of the research object is found. In practical applications, data analysis can help people make judgments in order to take appropriate action. Data analysis is organized with destination to collect data, analyzing data, making it a process of information. This process is the support process of the quality management system. In the entire life cycle of the product, including the appropriate use of data analysis processes to enhance the effectiveness of each process of the product's overall life cycle, including from market research to after-sales service and final disposal. For example, designers have started a wide design survey before starting a new design survey, analyzing the design direction, so data analysis has an extremely important position in industrial design.

[0028]Specifically, online data analysis is also called online analysis processing, which is used to process users' online requests, which are relatively high in response time (usually no more than several seconds). Compared to offline data analysis, online data analysis can process user requests in real time, allowing users to change the analysis and restrictions at any time. Compared to offline data analysis, online data analysis can handle more data, but with the development of technology, current online analysis systems have been able to handle millions of or even billions of records in real time. The traditional online data analysis system is built on the data warehouse in the relational database as the core, and the online large data analysis system constructs on the NOSQL system of the cloud computing platform. If there is no online analysis and processing of big data, you can not store and index the number of large-scale Internet pages. There will be no today's high-efficiency search engine, and there will be weapons, blogs, social networks that build on big data processing. The booming development.

[0029] Example 3

[0030] On the basis of the above, the data pre-processing order sequentially removes unique attributes, processing abnormal values ​​and abnormal detection processing; the data protocol unit, for processing data pre-processed data, so that The data after the statute processed is not coheed, but the original information can be maintained as much as possible; the data standardization unit is scaled to scale the statute processed data, so that it falls into a small specific interval.