Evaluation method, device, and terminal based on multi-source and multi-dimensional data

Abstract

The invention discloses a multi-source multidimensional data based evaluation method, an evaluation device and a terminal. The method includes the steps: acquiring an evaluation objective comprising an evaluation object, evaluation dimensions and condition requirements; screening an evaluation database to acquire identification data corresponding to the evaluation object; screening dimension values meeting the condition requirements from dimension values corresponding to the acquired identification data according to the evaluation dimensions; performing statistical evaluation on the screened dimension values to acquire evaluation results. The evaluation database comprises evaluation data from at least one data source, and each evaluation datum has at least one identification type of the evaluation object, corresponding identification data and the dimension values of at least one evaluation dimension. The evaluation data of different data sources and different data dimensions can be combined based on the standard evaluation database, more comprehensive and accurate evaluation results are acquired, and diversified evaluation requirements of users are met.

Description

technical field [0001] The present invention relates to the technical field of big data applications, in particular to an evaluation method, device and terminal based on multi-source and multi-dimensional data. Background technique [0002] With the introduction of the concept of smart city, more and more people began to use the concept of smart city to measure and plan the development of the city. For example, business districts can be evaluated and scored based on big data, so as to comprehensively grasp the pros and cons of different business districts, which is conducive to more reasonable planning of urban construction. [0003] The existing evaluation system generally determines the data indicators that need to be evaluated by the user. For each determined data indicator, find the corresponding data provider to make statistics on the required data, calculate and process the statistical results according to their own needs, and obtain The evaluation results of each dat...

AI Technical Summary

Problems solved by technology

However, since each data indicator is statistically obtained from a single data source, it is very likely that the statistical results will be incomplete and inaccurate

On the other hand, the existing evaluation schemes are still unable to achieve multi-dimensional data stitching, resulting in extremely limited data indicators that can be evaluated, and cannot form complex observations.

[0005] In addition, the weight distribution of each data indicator still needs to be done manually by the user, and complex observations cannot be formed. Each data indicator is determined by the user, which is not standardized enough and has a low reuse rate

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