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Time-space domain model modeling method and system for ecological environment monitoring

A technology for environmental monitoring and model modeling, applied in character and pattern recognition, instrumentation, computing, etc., can solve problems such as inability to become, discrete and sparse ecological environment monitoring data, and achieve good classification results.

Pending Publication Date: 2020-03-27
谢国宇 +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] Aiming at the defects in the prior art, the present invention provides a time-space domain model and modeling method for ecological environment monitoring, through which the monitoring data is converted into orderly and dense data, which can be provided to deep learning for training, In order to solve the technical problem that the existing technology in the field of environmental protection, the ecological environment monitoring data is discrete and sparse, and cannot be applied to the input training set of deep learning

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  • Time-space domain model modeling method and system for ecological environment monitoring

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Embodiment 1

[0034] Such as figure 1 As shown, the present invention provides a kind of time-space domain model modeling method for ecological environment monitoring, comprising the following steps:

[0035] S1. Collect pollutant data regularly to obtain multiple data frames;

[0036] S2. In chronological order, multiple data frames are superimposed using a spatial superposition method to obtain a space-time domain data structure;

[0037] S3. Using the change of the time-space domain data as a feature quantity to perform feature extraction on the time-space domain data structure, and obtain a data structure without null values;

[0038] S4. Use the data dimensionality reduction method to perform dimensionality reduction processing on the data structure that does not contain null values, and obtain the space-time domain model;

[0039] S5. Input the sparse monitoring data into the time-space domain model, and convert the monitoring data into dense data through the time-space domain model...

Embodiment 2

[0060] The effect of PCA algorithm on classification is not obvious in data dimension reduction processing, which is not particularly ideal for the input of building deep learning. Because the data of deep learning needs to be divided into training set and verification set.

[0061] In order to solve the above technical problems, the Laplacian feature mapping algorithm is used in data dimensionality reduction.

[0062] The working principle is explained below: the Laplacian algorithm uses the local angle of the data vector to construct the relationship between the data. If the two data frames are very similar, they should be as close as possible in the target subspace after dimensionality reduction. The specific steps of the algorithm during use are: first, use the KNN algorithm to construct all points into a graph, and connect each point to the nearest K points. In this embodiment, the requirement is to reduce the dimensionality of the data from 4 dimensions, and select 4 as...

Embodiment 3

[0064] The present invention also provides the present invention also provides a time-space domain model modeling system for ecological environment monitoring, such as figure 2 As shown, it includes: data input unit, data superposition unit, data feature extraction unit, data dimensionality reduction processing unit;

[0065] The data input unit is used to input multiple data frames obtained according to the collected pollutant information;

[0066] The data superposition unit is used to superimpose multiple data frames using a spatial superposition method to obtain a time-space domain data structure;

[0067] The data feature extraction unit is used to extract the features of the space-time domain data structure using the change of the time-space domain data as a feature quantity, and obtain a data structure without null values;

[0068] The data dimensionality reduction processing unit is used to perform dimensionality reduction processing on a data structure without null ...

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Abstract

The invention provides a time-space domain model modeling method for ecological environment monitoring, and the method comprises the following steps: S1, collecting pollutant data at regular time, andobtaining a plurality of data frames; S2, superposing the plurality of data frames in a spatial superposition mode according to a time sequence to obtain a time-space domain data structure; S3, performing feature extraction on the time-space domain data structure by taking the change of the time-space domain data as a feature quantity to obtain a data structure without a null value; S4, performing dimension reduction processing on the data structure without the null value by using a data dimension reduction method to obtain a time-space domain model; and S5, inputting the sparse monitoring data into the time-space domain model, and converting the sparse monitoring data into dense data as input of deep learning training. The invention further provides a time-space domain model modeling system for ecological environment monitoring. The time-space domain model modeling system comprises a data input unit, a data superposition unit, a data feature extraction unit and a data dimension reduction processing unit.

Description

technical field [0001] The invention relates to a data processing method and a data processing system for prediction purposes, in particular to a time-space domain model modeling method and system for ecological environment monitoring. Background technique [0002] By monitoring the ecological environment, a series of monitoring data can be obtained, which can be used for analysis, prediction and governance of environmental protection. At present, for the ecological environment monitoring data in the field of environmental protection, deep learning methods have been used to analyze the data, but deep learning needs big data as support, and its training model is difficult to converge when encountering sparse data. In the field of environmental protection, a considerable part of the monitoring data on the ecological environment is discrete and sparse; for example, for air pollutants, they may appear randomly, and may disappear soon after they appear. In this way, these discre...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2136G06F18/2135G06F18/214G06F18/24
Inventor 谢国锦谢国宇刘仲阳
Owner 谢国宇
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