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A method, system and device for obtaining anomaly index based on time node
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A time node and anomaly index technology, applied in the transmission system, electrical components, etc., can solve problems such as poor accuracy, false alarms, and loss of express companies, and achieve the effects of improving safety, simple operation, and high accuracy
Active Publication Date: 2019-12-27
SF TECH
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Abstract
Description
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Problems solved by technology
[0004] The accuracy of the above-mentioned user anomaly check list / login inspection method is poor, and there will be a large number of false alarms, resulting in investigators being unable to cover and investigate abnormal events one by one, resulting in real abnormal events not being discovered by investigation, which may bring irreparable damage to express companies. loss
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Embodiment 1
[0061] like figure 1 As shown, this embodiment provides a method for obtaining an abnormality index based on time nodes, including:
[0062] 1) Obtain checklist / login data for the past six months;
[0063] 2) Screen the data that has been successfully checked / logged in;
[0064] 3) Aggregate the number of times each job number successfully checks / logs in at each hour according to the job number and checklist / login time node (hourly);
[0065] 4) Calculate the average (mean) and standard deviation (sd) of checklist / login times in the past six months according to the job number;
[0066] 5) Start traversing each hour from 0:00:
[0067] Define the minimum checklist / login times of each user Count=mean-1×sd;
[0069] If the number of checklists / logins in this hour is greater than or equal to Count, the flag is 1;
[0070] If the checklist / login times of this hour is less than Count, but the hour point next to one hour is greater than or equal to Count, t...
Embodiment 2
[0093] The features of this embodiment that are the same as those of Embodiment 1 will not be described in detail. The features of this embodiment that are different from Embodiment 1 are:
[0094] In the method for obtaining the abnormality index based on the time node in this embodiment,
[0095] 4) Calculate the average (mean) and standard deviation (sd) of checklist / login times in the past six months according to the job number;
[0096] 5) Start traversing each hour from 0:00:
[0097] Define the minimum checklist / login times of each user Count=mean-2×sd;
[0098] 7) When processing the user's new checklist / login data:
[0099] If the checklist / login point is 1 to 2 hours away from the nearest 1 or 2 label, the outlier value is set to 60%;
[0100] The distance is 3 hours, and the outlier is set to 85%;
[0101] Set to 100% for more than 4 hours away.
Embodiment 3
[0103] The features of this embodiment that are the same as those of Embodiment 1 will not be described in detail. The features of this embodiment that are different from Embodiment 1 are:
[0104] In the method for obtaining the abnormality index based on the time node in this embodiment,
[0105] 4) Calculate the average (mean) and standard deviation (sd) of checklist / login times in the past six months according to the job number;
[0106] 5) Start traversing each hour from 0:00:
[0107] Define the minimum checklist / login times Count=mean for each user.
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Abstract
The invention relates to a method, system and equipment for acquiring an abnormality index based on time nodes. The method includes the following steps: obtaining the user's successful checklist / login data within the historical preset time period; according to the successful checklist / login data, creating a checklist / login habit table in the order of time nodes; obtaining the latest checklist / login data. The time node of the login data is compared with the checklist / login habit table; if the time interval between the time node where the latest checklist / login data is located and the normal value of the time node on the checklist / login habit table is less than or equal to the preset threshold, It is a normal checklist / login; if the time interval between the time node where the latest checklist / login data is located and the normal value of the time node on the checklist / login habit table is greater than the preset threshold, it is an abnormal checklist / login. The time interval is generated to take the abnormal index. The operation is simple and fast, and the accuracy of the check list / login is high because it is judged by the time node data.
Description
technical field [0001] The invention relates to a data exchange network, in particular to a method, system and equipment for acquiring an abnormal index based on a time node. Background technique [0002] At present, abnormal checklists are usually sorted based on the number of checklists, and then an alarm is issued, or abnormal events are judged by detecting whether the checklist user has machine checklists and other characteristics. [0003] Abnormal logins often judge abnormal events through common dimensions such as early morning login, remote login, and multiple access failures. [0004] The accuracy of the above-mentioned user anomaly check list / login inspection method is poor, and there will be a large number of false alarms, resulting in investigators being unable to cover and investigate abnormal events one by one, resulting in real abnormal events not being discovered by investigation, which may bring irreparable damage to express companies. loss. Contents of t...
Claims
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Application Information
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