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

Active Publication Date: 2020-05-19
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a data analysis method, device and system that separates the debugging environment and the operating environment, aiming to solve the problem of how to ensure that data privacy is not leaked and use real data for data analysis

Method used

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  • Debugging environment and running environment separated data analysis method, device and system
  • Debugging environment and running environment separated data analysis method, device and system
  • Debugging environment and running environment separated data analysis method, device and system

Examples

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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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Abstract

The invention is applicable to the technical field of computers, and provides a debugging environment and running environment separated data analysis method, device and system, and the method comprises the steps: extracting part of data from real data of a running environment, desensitizing the part of data, and transmitting the desensitized part of data to a debugging environment to serve as sample data; in the debugging environment, connecting the sample data and the machine learning components to form a machine learning workflow, and obtaining component parameters set by each machine learning component; debugging the machine learning workflow in the debugging environment, and after debugging is completed, migrating the machine learning workflow to the running environment for running; importing real data of the operating environment to execute a machine learning workflow to obtain a machine learning model; and importing the machine learning model obtained by training in the running environment into the debugging environment after white list examination for a user to check and download. According to the method, data privacy can be prevented from being leaked, and real data can beused for data analysis.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a data analysis method, device and system for separating a debugging environment and an operating environment. Background technique [0002] Data analysis refers to the use of appropriate statistical analysis methods to analyze a large amount of collected data, summarize them, understand and digest them, in order to maximize the development of data functions and play the role of data. [0003] Intelligence is driven by big data as fuel. However, at present, there is a huge contradiction between data privacy protection and data mining utility. The former focuses on data desensitization to prevent privacy leakage and secondary distribution; the latter focuses on comprehensive open sharing to fully exploit the value of data. Strengthening data security and privacy protection, and realizing data sharing and exchange under the premise of data security, is the general tr...

Claims

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Application Information

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Patent Type & AuthorityApplications(China)
IPC IPC(8): G06F11/36G06F21/62G06N20/00
CPCG06F11/3664G06F21/6245G06N20/00Y02D10/00
Inventor方滨兴刘川意韩培义段少明
OwnerHARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL