Dynamic data comprehensive value evaluation method

Abstract

The invention provides a dynamic data comprehensive value evaluation method. The method includes constructing a data source module, constructing a retrieval module, retrieving error value evaluation,performing training and value evaluation on retrieval data, meanwhile comparing a final evaluation result and the previous plurality of evaluation initial values, and performing analysis by combiningerror values of retrieval and assessment modules to update the optimized error weight and continuously improve the accuracy of the evaluation result. A dynamic data comprehensive evaluation and analysis system of the method adopts the modular design for a data analysis system. By using the retrieval module, users can retrieve relevant target data directly and conveniently, use multi-thread multi-interface modules, improve calling speed and respond to dynamic real-time data access flexibly. The data processing accuracy is improved as much as possible by real-time learning and comprehensive adjustment of the data error weight.

Description

technical field [0001] The invention relates to the field of dynamic big data analysis, in particular to a comprehensive value evaluation method for dynamic data. Background technique [0002] The 21st century is a highly information-based society. Information is a resource and an opportunity. If you can grasp and make good use of useful information, you can gain an advantage in the competition. At present, all kinds of data information are constantly emerging, and people are overwhelmed with the processing and absorption of data information. How to effectively use a lot of information to form information that has important reference value for users is increasingly attracting people's attention. [0003] For example, stock information, as one of them is very related to financial wealth, is destined to get people's attention from the beginning. In the stock market, there are many factors that can affect stock prices, including economic environment, national policies, market ...

AI Technical Summary

Problems solved by technology

At present, more and more companies are beginning to pay attention to the important role of intellectual property rights, especially patents, and pay special attention to the value of patents. However, in a sense, the value of patents is like stocks, and its value has many influence dimensions. The influencing factors are complex, not only in terms of timeliness, but also in many policy environments. How to find out the law of change and establish a reasonable model for evaluation is currently not a good solution

[0005] In the traditional sense, there are two extreme tendencies in people’s methods of processing information data. One is the static data evaluation method through association rules that has been more and more researched and applied, and the correlation calculation is performed through extremely complex association algorithms, but most of them just stay In the collation and analysis of historical static data, there is often no in-depth research on the necessity of the basis for the association between historical data and current data, resulting in unobjective analysis information or lagging analysis results; but the stock market or patent market needs timely And objective response, if you can’t adapt to the ever-changing free market, you can’t think that such an algorithm meets the requirements; the other is to make a quick judgment on historical data through relatively quick combing statistics, but such a statistical basis is not reliable enough First, because a large number of statistics will consume a lot of computing power and storage capacity, often resulting in unnecessary waste. Second, because this kind of statistics often rely on a small number of simple data for dynamic analysis, the accuracy of the results and Choices are greatly reduced

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