A data collection and human-machine collaborative labeling method based on edge computing
A technology of data acquisition and edge computing, which is applied in computing, other database retrieval, electronic digital data processing, etc., can solve the problems of low efficiency of data collection and labeling, troublesome data processing, and long data time The effect of comprehensive and simplification of tedious work
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2022-07-08
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Figure 1
Abstract
Description
technical field
[0001] The invention relates to the technical field of edge computing, in particular to a method for data collection and human-machine collaborative labeling based on edge computing. Background technique
[0002] Edge computing refers to the use of an open platform that integrates network, computing, storage, and application core capabilities on the side close to the source of objects or data to provide the most recent services nearby, and its applications are initiated on the edge side to generate faster network services Responding to meet the basic needs of the industry in real-time business, application intelligence, security and privacy protection, edge computing is between physical entities and industrial connections, or at the top of physical entities, while cloud computing can still access the history of edge computing Data, labeling the data can make the data more convenient to analyze and use;
[0003] However, the current data collection and labeli...
Examples
Embodiment
[0042] Example: as figure 1 As shown, the present invention provides a technical solution, a method for data collection and human-machine collaborative labeling based on edge computing, comprising the following steps:
[0043] S1. Select an edge device close to the data source, sort out the basic information of the device, and collect data;
[0044] S2. Use a computer to construct an annotation model according to the type of equipment;
[0045] S3. Preprocess the data of edge devices, and estimate the proportion of different data;
[0046] S4, and then sort the annotation models according to the type of data;
[0047] S5. Input the data into the labeling model for labeling;
[0048] S6. Extract the data that takes a long time to label and fail to label, and manually label, check and modify;
[0049] S7. Re-input the manually labeled data, and update the labeling model according to the reason for the labeling failure;
[0050] S8. Transmit the marked data to the data proce...