A data abnormal point detection method, device, system, and storage medium
A data anomaly and point detection technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of single definition, inaccurate detection results, and single prediction algorithm, so as to improve accuracy and increase flexibility Effect
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0052] see figure 1 , is a flow chart of the data outlier detection method provided by Embodiment 1 of the present invention. In this embodiment, according to different requirements, figure 1 The order of execution of the steps in the flowcharts shown may be changed, and certain steps may be omitted.
[0053] Step S1: Receive the data to be detected, and perform preprocessing on the data to be detected. In some of these embodiments, the data to be detected is satellite telemetry time series data or other types of time series data.
[0054] Further, in some of the embodiments, the step S1 includes the following steps.
[0055] Step S1.1: Carry out initialization settings of the program, such as reading configuration files, etc.
[0056] Step S1.2: Perform an operation of eliminating wild points on the received data to be detected, for example, eliminate data points that exceed the specified range (such as 20%) of the normal working range.
[0057] Step S1.3: Use the mean v...
Embodiment 2
[0122] see image 3 , is a schematic structural diagram of the device provided by Embodiment 2 of the present invention.
[0123] In some of these embodiments, the device 2 may include, but not limited to, a memory 21 and a processor 22 coupled to the memory 21, and the memory 21 and the processor 22 may be communicatively connected to each other through a system bus. It should be pointed out that, image 3 Only device 2 is shown with components 21 and 22, but it is to be understood that embodiment 2 does not show all components of device 2 and that device 2 has more or fewer components that may alternatively be implemented. Wherein, the device 2 may be a computing device such as a rack server, a blade server, a tower server, or a cabinet server, and the device 2 may be an independent server or a server cluster composed of multiple servers.
[0124] The memory 21 stores program instructions for implementing the above-mentioned data anomaly detection method. The memory 21 in...
Embodiment 3
[0128] see Figure 4 , is a schematic structural diagram of the data outlier detection system provided by Embodiment 3 of the present invention.
[0129] In this embodiment, the data anomaly detection system 3 can be divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (such as the processor 22) to complete the present invention. For example, in Figure 4 In the above, the data outlier detection system 3 can be divided into a data preprocessing module 31 , an algorithm prediction module 32 , and an outlier detection module 33 . The program module referred to in the present invention refers to a series of computer program instruction segments capable of completing specific functions, which are more suitable than programs for describing the execution process of the data anomaly detection system 3 in the device 2 . The functions of each program module 31-33 will be described in detail b...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More