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Data detection method and device

A technology of data sub and traffic data, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of inability to detect abnormal data, unable to truly reflect road traffic conditions, inaccurate traffic information, etc.

Active Publication Date: 2015-06-03
ALIBABA (CHINA) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In practical applications, due to various factors (such as weather, traffic accidents, etc.), some data in the city's historical traffic data will be abnormal data, and this part of abnormal data may not actually reflect the traffic conditions of the road. The current technology The scheme cannot detect these abnormal data. Therefore, it is not accurate to directly conduct typical analysis on all historical traffic data of the city to obtain the traffic information of each road in each statistical period corresponding to each type of typical day.

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0089] refer to figure 1 , which is a flow chart of Embodiment 1 of a data detection method provided by this application, the method may include the following steps:

[0090] Step 101: Obtain target data.

[0091] Wherein, the target data includes traffic history data of a target road every day within a preset statistical period, that is to say, the target data is the data that needs to be detected abnormally.

[0092] Wherein, the preset statistical period can be set by the testing personnel, for example, the preset statistical period can be set to one year or one month according to experience values.

[0093] It should be noted that the current data processing system that outputs historical traffic data usually releases a traffic data (such as the vehicle's driving speed on the target road, The travel time required for the target road, the occupancy rate of the road surface or the number of vehicles on the road surface, etc.). The target data may be all traffic data accum...

Embodiment 2

[0117] Compared with the data detection method provided by Embodiment 1, the data detection method provided by Embodiment 2 of the present invention has a greater impact on the aforementioned figure 1 Step 103 in the flow chart shown is refined. refer to image 3 , is a flowchart of step 103, wherein said step 103 may include the following steps:

[0118] Step 301: Determine the U statistic and the rejection domain critical value of each historical traffic data of the same typical day type and within the same preset statistical period.

[0119] For example, the determination of the U statistic and the critical value of the rejection domain is carried out for each historical traffic data of the target road within the statistical period of 7:00-8:00 on all Mondays within the statistical period.

[0120] Wherein, when determining the U statistic of each historical traffic data in the step 301, it can be realized in the following manner:

[0121] use Get the U statistic for e...

Embodiment 3

[0129] Compared with the foregoing embodiment one and embodiment two, the difference between this embodiment three is that figure 1 Step 104 in the flow shown performs refinement. refer to Figure 4 , is a flow chart of step 104, wherein, in said step 104, the following steps are performed on the historical traffic data of each day whose release date conforms to the typical day type (if the typical day type is Monday, then for each Monday in the statistical period Historical traffic data performs the following steps):

[0130] Step 401: Divide the historical traffic data of the same day according to release time to obtain a sequence of historical traffic data.

[0131] Wherein, during specific implementation, the step 401 may be implemented in the following manner:

[0132] First, among the historical traffic data of the same day, the historical traffic data whose release time is in the same release time period is divided into the same historical traffic data subsequence. ...

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Abstract

The application discloses a data detection method and a data detection device. The data detection method includes: obtaining target data which includes history traffic data of a target road in each day of a preset statistic period; screening out the history traffic data of release dates which conform to preset typical day types according to the preset typical day types; performing first abnormal detection on the history traffic data of the same preset typical day type and in the same preset statistic time segment so as to obtain a first abnormal detection result; performing second abnormal detection on the history traffic data of the release dates which conform to the typical day types by the day so as to obtain a second abnormal detection result; confirming the first abnormal detection result and the second abnormal detection result as an abnormal data detection result of the target data. The data detection method and the data detection device can detect abnormal data of the history traffic data of the target road so as to guarantee that the history traffic data for analyzing typicality is the data which can truly reflect traffic conditions of the road, and thereby improve accuracy of an analysis result.

Description

technical field [0001] The present application relates to the technical field of data detection, in particular to a data detection method and device. Background technique [0002] With the continuous development and wide application of intelligent transportation systems, its urban traffic guidance applications are gradually becoming intelligent and dynamic. Traffic data, and timely release, so that users can timely understand the current traffic information of their city. Due to the frequent release of traffic data and the accumulation of a large amount of historical traffic data, the traffic operation rules of urban roads can be obtained by analyzing the historical traffic data in different granularities and dimensions, so as to fill in and predict urban traffic information. provide important evidence. [0003] At present, the typical analysis is directly performed on all historical traffic data of the city, and the traffic information of each road in each statistical per...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06F17/30G08G1/00
Inventor 杨承继
Owner ALIBABA (CHINA) CO LTD
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