Atmospheric pollution source identification method and device, computer equipment and storage medium

An air pollution source and identification method technology, which is applied in the fields of devices, air pollution source identification methods, computer equipment and storage media, and can solve problems such as the inability to effectively predict the source of pollution.

Pending Publication Date: 2020-10-20
PINGAN INT SMART CITY TECH CO LTD
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Problems solved by technology

However, the neural network can only predict the degree of atmospheric pollution through the cu

Method used

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  • Atmospheric pollution source identification method and device, computer equipment and storage medium
  • Atmospheric pollution source identification method and device, computer equipment and storage medium
  • Atmospheric pollution source identification method and device, computer equipment and storage medium

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Embodiment Construction

[0052] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of the invention. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only, and are not intended to limit the present invention.

[0054] figure 1 It is a flow chart of the air pollution source identification method provided in Embodiment 1 of the present invention. The air pollution source identification method specifically includes the fo...

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Abstract

The invention relates to artificial intelligence, the invention provides an atmospheric pollution source identification method and a device, computer equipment and a storage medium, and the method comprises the steps: obtaining various atmospheric pollution components of a plurality of pollution sources, constructing a pollution component matrix, and initializing a first target function as follows: the pollution component matrix = a pollution component contribution matrix * a pollution component content matrix + a residual matrix; calculating a pollution component contribution matrix and a pollution component content matrix according to the residual matrix when the residual matrix is iteratively calculated through a gradient descent algorithm to be smaller than a threshold value; calculating the mass concentration contributed by each pollution source according to the pollution component matrix and the pollution component content matrix so as to determine the source spectrum of each pollution source; and identifying the pollution source type corresponding to the source spectrum of each pollution source according to the mapping relationship between the source spectrum of the pollution source and the pollution source type. According to the invention, each source of the atmospheric pollution components can be accurately identified. In addition, the invention also relates to the blockchain technology, and the mapping relationship is stored in the blockchain.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to an air pollution source identification method, device, computer equipment and storage medium. Background technique [0002] Air pollution has become a major bottleneck and potential risk that affects economic and social development. The effective prediction of air pollutants is of great significance for fighting the "blue sky defense war" and effectively coping with heavy pollution weather. [0003] The artificial neural network prediction method is the focus of research in the field of artificial intelligence in the world in recent years. The artificial neural network prediction method does not need a clear functional relationship between input and output. It mainly completes the simulation process by training and learning a large amount of data, and Use the trained network to make predictions on new input data. However, the neural network can only predict the ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2133G06F18/22G06F18/24
Inventor 林剑
Owner PINGAN INT SMART CITY TECH CO LTD
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