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Automatic data labeling method and device

A label and data technology, applied in the field of data processing, can solve problems such as no good solutions, achieve rapid analysis and improve accuracy

Pending Publication Date: 2020-05-08
SHENZHEN ZTE NETVIEW TECH +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, data labeling mainly focuses on irregular data such as face recognition and automatic driving, but there is no good solution for data with certain rules and trends such as IDC computer room data.

Method used

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  • Automatic data labeling method and device
  • Automatic data labeling method and device
  • Automatic data labeling method and device

Examples

Experimental program
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Effect test

Embodiment 1

[0042] Please refer to image 3 , this embodiment provides a method for labeling temperature data, including the following steps:

[0043] S10, such as figure 2, build a prediction model 40, according to the characteristics of the temperature data of the IDC computer room, because the computer room is in a constant temperature environment, the differential integrated moving average autoregressive model is used for prediction, and the temperature outside the computer room is affected by seasonal changes, and the seasonal prediction model is used for prediction.

[0044] S101, collect the temperature data outside and inside the computer room for multiple periods through the temperature acquisition device (such as a temperature sensor). Since the temperature data mostly show the law that the temperature is lower in the morning and evening and the temperature is higher at noon, the day is used as the cycle of the temperature data. The batch temperature data acquired in this embo...

Embodiment 2

[0089] Please refer to Figure 4 , on the basis of Embodiment 1, this embodiment provides a method for finding the optimal prediction model in the seasonal prediction model label and the differential integrated moving average autoregressive model, including:

[0090] S50, the abnormal label probability statistics step, counting the number of abnormal labels and the total number of labels of the seasonal prediction model of the current temperature data, and the number of abnormal labels and the total number of labels of the differential integration moving average autoregressive model of the current temperature data;

[0091] According to the above statistical results, according to the formula (3), calculate the probability that the current temperature data is predicted by the seasonal prediction model to obtain an abnormal label and the probability that the abnormal label is predicted by the differential integration moving average autoregressive model;

[0092]

[0093] Amon...

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Abstract

The invention discloses an automatic data tagging method and device, and the method comprises the steps: carrying out prediction through employing a plurality of prediction models, tagging data according to a prediction value such that each data contains a plurality of types of tags, making comparative analysis of tags at the same time point, and combining with the manual correction of tags, thereby improving the accuracy of data tags. The optimal prediction model can be obtained by calculating and comparing the abnormal rates of the tags, so that information such as normality, abnormality andnoise of the data can be accurately and quickly analyzed, and a data basis is provided for subsequent data training and testing.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a method and device for automatically labeling data. Background technique [0002] With the rapid development of computer technology and communication technology, people can obtain more and more data, but at the same time, they also need to spend more time organizing and sorting the data. The premise of organizing and sorting the data is the need to Labeling is done so that data with different labels can be organized, collated and applied later. Due to different data attributes, some data are irregular, such as face recognition, voice, automatic driving and other fields, while some data itself has certain rules or trends, such as Internet data center (IDC) computer room. Data, including temperature, humidity, electricity, cooling capacity and other data, these data have certain rules and trends according to seasons or collection cycles. [0003] At present, data labeling mainly f...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/907G06F16/901G06K9/62G16Y20/10G16Y40/10
CPCG06F16/907G06F16/901G06F18/10
Inventor 向洁董维张磊黄如
Owner SHENZHEN ZTE NETVIEW TECH
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