Debugging environment and running environment separated data analysis method, device and system
A technology of debugging environment and running environment, applied in the field of computer, to ensure the correctness and guarantee the effect of the code
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Embodiment 1
[0036] figure 1 It is the implementation flow chart of the data analysis method that separates the debugging environment and the operating environment provided by Embodiment 1 of the present invention. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:
[0037] In step S101, part of the data is extracted from the real data of the operating environment, and the part of the data is desensitized and sent to the debugging environment as sample data;
[0038] Among them, the present invention is oriented to the typical scenarios of data analysis and AI training, and innovatively proposes a machine learning platform based on the premise of privacy protection based on program floating. Sent to the data consumer and out of control.
[0039] In step S102, in the debugging environment, connect the sample data and machine learning components to form a machine learning workflow, and obtain compo...
Embodiment 2
[0072] The embodiment of the present invention describes the implementation process of forming a machine learning workflow. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:
[0073] In the debugging environment, connect the sample data and machine learning components to form a machine learning workflow, and obtain component parameters set by each machine learning component, specifically:
[0074] In the debugging environment, obtain sample data and machine learning components selected by the user by dragging and dropping, connect the sample data and machine learning components to form a machine learning workflow, and obtain component parameters set by each of the machine learning components, The machine learning components include custom programming components and system preset components.
[0075] Among them, the system preset components include one or a combination of data preproc...
Embodiment 3
[0091] Figure 5 It is the application flowchart of the self-defined programming component code alarm provided by the third embodiment of the present invention. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown. Specifically, the steps of the self-defined programming component code alarm are described in detail as follows:
[0092] Obtain the machine learning code written by the user in the custom programming component, extract the machine learning code in real time to obtain a custom program, perform program analysis on the custom program to obtain the abstract syntax tree AST corresponding to the custom program, and traverse all The AST searches for the data reference code, and if the data reference code is found, the warning information and prompt information of the data reference code are displayed in the custom component.
[0093] It should be noted that the step of user-defined programming component code warn...
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