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Sliding window type multivariate time sequence missing value filling method based on 5G network

A multivariate time series and sliding window technology, applied in the direction of services based on specific environments, complex mathematical operations, special data processing applications, etc., can solve the problems of increasing the cost of industrial data collection, ignoring correlation, etc., and achieve high accuracy and realization Simple, highly portable effects

Inactive Publication Date: 2021-04-23
朗坤智慧科技股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this solution increases the cost of industrial data collection, and at the same time ignores the correlation between different time series data when filling data

Method used

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  • Sliding window type multivariate time sequence missing value filling method based on 5G network
  • Sliding window type multivariate time sequence missing value filling method based on 5G network
  • Sliding window type multivariate time sequence missing value filling method based on 5G network

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Embodiment 1

[0045] see figure 1 , the present invention provides a 5G network-based sliding window multivariate time series missing value filling method. It solves the problem of low network bandwidth and time extension. The ratio uses the alternating matrix algorithm and the sliding window method to make full use of the relationship between the data of each measuring point in the historical operation of the equipment. Compared with the mean filling, maximum filling, mode filling, Data missing value filling is more accurate than model prediction filling. At the same time, the method proposed by the invention is simple to realize and has high portability, and is applicable to most equipments.

[0046] Specifically, the 5G network-based sliding window multivariate time series missing value filling method includes the following steps:

[0047] S110: Collect the sensor data of each measuring point of the device, and transmit it through the 5G network.

[0048] Specifically, the historical ...

Embodiment 2

[0093] see figure 2 , this embodiment provides a sliding window multivariate time series missing value filling device based on a 5G network, the device comprising:

[0094] The data acquisition module is suitable for collecting sensor data of each measuring point of the equipment and transmitting them through the 5G network;

[0095] The data processing module is suitable for merging the sensor data of each measuring point to form multivariate time series data.

[0096] Among them, the sensor data of each measuring point collected by the data acquisition module is unary time series data.

[0097] The fixed window missing value supplementary module is suitable for filling missing values ​​based on the reconstruction error based on the matrix iterative optimization method for time series data fragments with a fixed window size according to the preset window value;

[0098] The sliding window missing value supplement module is suitable for filling missing values ​​of the entir...

Embodiment 3

[0100] The embodiment of the present invention also provides a computer-readable storage medium, one or more instructions are stored in the computer-readable storage medium, and the processor of the risk analysis device in the one or more instructions executes It is the 5G network-based sliding window multivariate time series missing value filling method provided in Example 1.

[0101] In this embodiment, the sliding window multivariate time series missing value filling method based on the 5G network collects the sensor data of each measuring point of the device and transmits them through the 5G network; merges the sensor data of each measuring point to form the multivariate time series data; Preset window value, for fixed window size time series data fragments, use matrix iterative optimization method to fill missing values ​​based on reconstruction error; according to preset stride value, use sliding window to fill missing values ​​for the entire time series data. It solves ...

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Abstract

The invention provides a sliding window type multivariate time sequence missing value filling method and device based on a 5G network, and the method comprises the steps: collecting the sensor data of all measurement points of equipment; combining the sensor data of each measuring point; according to a preset window value, for the time series data segment with the fixed window size, filling a missing value by using a matrix iterative optimization mode based on the reconstruction error; and performing missing value filling on the whole time series data by using a sliding window according to a preset stride value. The problems of low network bandwidth and time delay are solved, the relation between data of all measuring points of historical operation of equipment is fully utilized by using an alternating matrix algorithm and a sliding window method in proportion, and compared with mean value filling, maximum value filling, mode filling and model prediction filling, the data missing value filling accuracy is higher. Meanwhile, the method provided by the invention is simple to implement, high in portability and suitable for most equipment.

Description

technical field [0001] The invention belongs to the technical field of data filling, in particular to a 5G network-based sliding window multivariate time series missing value filling method and device. Background technique [0002] With the rapid development of industrial Internet of Things technology, more and more devices are connected to the Internet of Things platform through various sensors. Due to abnormal sensor communication caused by sensor damage, low traditional network bandwidth, and time extension, the problem of data loss and loss is prone to occur during the collection process of industrial data. The existence of missing values ​​will bring difficulties to the subsequent analysis of industrial data. Therefore, it is of great significance to solve the problem of missing industrial data sets caused by the low bandwidth and time extension of traditional networks. [0003] The commonly used solution at present is to increase the traditional network bandwidth, an...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F17/16H04W4/38G06F119/12
CPCG06F30/20G06F17/16H04W4/38G06F2119/12
Inventor 武爱斌魏小庆毛旭初张翔卞志刚王磊张宽阔
Owner 朗坤智慧科技股份有限公司
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