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Service index abnormity detection method and device based on time sequence and electronic device

A technology of time series and business indicators, applied in error detection/correction, electrical digital data processing, instruments, etc., it can solve the problems of large magnitude difference, shrinking sample spatial statistics, low reliability of abnormal detection, etc., to improve accuracy The effect of improving statistical accuracy

Active Publication Date: 2019-07-12
ADVANCED NEW TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since the values ​​of some core business indicators vary greatly in different stages, directly using the residual values ​​in the residual sequence to detect the abnormality of the business indicators will shrink the statistical value of the sample space of the current residual sequence, resulting in abnormal Detection reliability is not high

Method used

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  • Service index abnormity detection method and device based on time sequence and electronic device
  • Service index abnormity detection method and device based on time sequence and electronic device
  • Service index abnormity detection method and device based on time sequence and electronic device

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Experimental program
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Embodiment 1

[0039] refer to figure 1 As shown, it is a schematic diagram of the steps of the time-series-based business indicator anomaly detection method provided by the embodiment of this specification. The execution subject of the anomaly detection method can be an electronic device with computing and processing functions, such as a computer, a smart phone, a smart wearable device, etc. Smart terminals, and various servers, etc. The abnormal detection method may include the following steps:

[0040] Step 102: Determine a residual sequence corresponding to the target business indicator, wherein the residual sequence is a sequence based on a time sequence.

[0041] It should be understood that the business indicators involved in the embodiments of this specification may be the types of indicators covered by various businesses. For example, for network operation products such as websites and APPs, the business indicators may include: user visits, user downloads, etc. traffic, user acces...

Embodiment 2

[0112] Figure 5 It is a schematic structural diagram of an electronic device according to an embodiment of this specification. Please refer to Figure 5 , at the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Wherein, the memory may include a memory, such as a high-speed random-access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. Of course, the electronic device may also include hardware required by other services.

[0113] The processor, the network interface and the memory can be connected to each other through an internal bus, which can be an ISA (Industry Standard Architecture, industry standard architecture) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnection standard) bus or an EISA (Extended Industry StandardArchitecture, extended industry standard archi...

Embodiment 3

[0124] The embodiment of this specification also proposes a computer-readable storage medium, the computer-readable storage medium stores one or more programs, and the one or more programs include instructions, and the instructions are used when a portable electronic device including multiple application programs When executed, enables the portable electronic device to execute figure 1 The method of the illustrated embodiment, and specifically for performing the following methods:

[0125] Determining a residual sequence corresponding to the target business indicator, wherein the residual sequence is a sequence based on a time series;

[0126] Based on at least one preset time sliding window, the residual value in the residual sequence segment corresponding to the preset time sliding window is normalized to obtain at least one relative residual sequence segment, wherein the residual sequence The segment is a residual sequence segment corresponding to the time period represent...

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Abstract

The embodiment of the invention discloses a service index abnormity detection method and device based on a time sequence and an electronic device, and the method comprises the steps: determining a residual error sequence corresponding to a target service index, the residual error sequence being a sequence formed based on time sequence arrangement; based on at least one preset time sliding window,performing normalization processing on the residual value in a residual sequence segment corresponding to a preset time sliding window to obtain at least one relative residual sequence segment, the residual sequence segment being a residual sequence segment corresponding to a time period represented by the preset time sliding window in the residual sequence; and performing anomaly detection on thetarget service index based on the at least one relative residual sequence segment.

Description

technical field [0001] This description relates to the technical field of computer software, in particular to a method, device and electronic equipment for abnormal detection of business indicators based on time series. Background technique [0002] With the rapid development of business, the open operation platform can provide various intelligent analysis capabilities, such as: anomaly detection, which is used to monitor and analyze abnormal fluctuations of business indicators. Now, anomaly detection can be applied to core scenarios such as mini-program search and application recommendation, providing more reliable data support for the operation of these network products. [0003] At present, abnormal detection of business indicators is mainly based on the residual sequence corresponding to the time series of business indicators. However, since the values ​​of some core business indicators vary greatly in different stages, directly using the residual values ​​in the residu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/30G06F11/34
CPCG06F11/3079G06F11/3452
Inventor 余芳张多坤
Owner ADVANCED NEW TECH CO LTD
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