Monitoring quantity uncertainty prediction method and system based on variation auto-encoder

An uncertainty and autoencoder technology, applied in neural architecture, biological neural network models, etc., can solve problems such as low accuracy, lack of uncertainty estimation, and weak data modeling capabilities

Active Publication Date: 2020-04-17
BEIJING REALAI TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, these models have high model assumptions, and the model's ability to model data is not strong
[0006] On the whole, although some methods in the existing technic

Method used

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  • Monitoring quantity uncertainty prediction method and system based on variation auto-encoder
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  • Monitoring quantity uncertainty prediction method and system based on variation auto-encoder

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Experimental program
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Effect test

Embodiment 1

[0072] see figure 1 , provide a method for predicting the uncertainty of monitoring quantities based on variational autoencoders, the monitoring object adopted in embodiment 1 is a dam, including the following steps:

[0073] S1: Obtain the environmental sample data of the location of the dam body, use the environmental sample data as an independent variable to change the physical quantity of the dam body itself, and use the physical quantity of the dam body itself as the dependent variable;

[0074] S2: Construct a data set containing the environmental sample data and the physical quantity of the dam body itself, establish a time index corresponding to the physical quantity of the dam body itself according to the acquisition time of the environmental sample data, and use the acquisition time corresponding to the time index as the The time index point of the dataset;

[0075] S3: Construct the probability distribution model of the hidden variable that causes the physical quan...

Embodiment 2

[0095] see figure 2 , providing a monitoring quantity uncertainty prediction system based on a variational autoencoder, the monitoring object is a dam, including:

[0096] The data processing module 1 is used to obtain the environmental sample data of the location of the dam body, use the environmental sample data as an independent variable to change the physical quantity of the dam body itself, and use the physical quantity of the dam body itself as a dependent variable;

[0097] The data set construction module 2 is used to construct a data set comprising the environmental sample data and the physical quantity of the dam body itself;

[0098] The time index module 3 is used to establish a time index corresponding to the physical quantity of the dam itself according to the acquisition time of the environmental sample data, and use the acquisition time corresponding to the time index as the time index point of the data set;

[0099] The first model construction module 4 is use...

Embodiment 3

[0126] A computer-readable storage medium is provided, wherein the computer-readable storage medium stores program codes for predicting uncertainty of monitoring quantities based on variational autoencoders, and the program codes include the program codes used to implement Embodiment 1 or the Instructions for variational autoencoder-based supervisory quantity uncertainty prediction methods in any possible implementation.

[0127] The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server, a data center, etc. integrated with one or more available media. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, or a magnetic tape), an optical medium (for example, DVD), or a semiconductor medium (for example, a solid state disk (SolidStateDisk, SSD)) and the like.

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Abstract

The invention discloses a monitoring quantity uncertainty prediction method and system based on a variation auto-encoder, and the method comprises the steps: taking environment sample data as an independent variable for changing the physical quantity of a monitoring object, and taking the physical quantity of the monitoring object as a dependent variable; establishing a time index corresponding tothe physical quantity of the monitored object according to the acquisition time of the environmental sample data, and taking the acquisition time corresponding to the time index as a time index pointof the data set; constructing a probability distribution model of an implicit variable causing the change of the physical quantity of the monitored object; obtaining a prior probability of an implicit variable by using a prior distribution function; constructing a deep neural network model for the generation process of the prior probability, constructing an identification model, predicting the missing value of the missing physical quantity according to the identification model, obtaining the probability distribution of the physical quantity of the monitoring object, and sampling the probability distribution of the physical quantity of the monitoring object to obtain missing value uncertainty estimation. According to the method, the prediction result of the monitored object has good uncertainty estimation, and the performance of the monitored object can be better mastered.

Description

technical field [0001] The invention relates to the technical field of object monitoring, in particular to a method and system for predicting uncertainty of monitoring quantities based on variational autoencoders. Background technique [0002] At present, many objects in the real world need to be monitored continuously, so as to ensure that the monitored objects have the accuracy to meet specific performance. During the continuous monitoring of the monitoring object, a series of monitoring data with time attributes will be obtained, and people can judge whether the monitoring object meets the ideal state through the monitoring data. Since there is uncertainty in the monitoring data of the monitoring object, predicting this uncertainty will make the monitoring of the monitoring object more accurate and reliable. [0003] Time series is the numerical sequence formed by arranging the values ​​of certain statistical indicators in chronological order. By compiling and analyzing...

Claims

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Application Information

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IPC IPC(8): G06N3/04
CPCG06N3/049G06N3/045
Inventor 胡文波高嘉欣陈云天田天
Owner BEIJING REALAI TECH CO LTD
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