Multi-source heterogeneous data unified aggregation method and system
A multi-source heterogeneous data and data technology, applied in the field of multi-source heterogeneous data unified aggregation method and system, can solve the problems of huge amount of multi-source heterogeneous data, low degree of data fusion, unfavorable data use, etc. Classified storage, high degree of fusion, and less invalid data
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Embodiment 1
[0029] Such as figure 1 As shown, a multi-source heterogeneous data unified aggregation system proposed by the present invention, the system includes a client, a cloud server and a storage; the client, the cloud server and the storage are connected through a network; the client is provided with a data acquisition module; The cloud server is equipped with a data processing module and a data fusion module; the data collection module collects multi-source data information through the client, including a text collection unit, a picture collection unit and an audio-visual collection unit; the data processing module is connected with the data collection module, including data classification Unit, data processing unit and data matching unit, the data processing module groups the data in the data acquisition module, eliminates heterogeneity, and extracts the corresponding data feature groups according to the categories, and then compares the data feature groups with the existing data f...
Embodiment 2
[0038] The present invention also provides a unified aggregation method for multi-source heterogeneous data, including the above-mentioned system, and the steps of the method are as follows:
[0039] S1. The user inputs all multi-source heterogeneous data through the client;
[0040] S2. Classify multi-source heterogeneous data, eliminate heterogeneity, extract corresponding data features, and form new data feature groups according to categories;
[0041] S3. Carry out a correlation degree query on the new data feature group and the original data feature group, and find out the original data feature group whose correlation degree reaches the set value;
[0042] S4. Putting the new data characteristic group and the original data characteristic group into a data pool;
[0043] S5. The data decision-making model inspects and adjusts the data in the data pool until the estimated fusion degree is reached, and then performs fusion;
[0044] S6, the fused data is returned to the memo...
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