Neural network training method for intelligent comprehensive overall quality evaluation

A quality evaluation and neural network technology, which is applied in the training field of neural network for intelligent comprehensive overall quality evaluation, can solve the problem of inaccurate sampling results and achieve the effect of improving accuracy

Inactive Publication Date: 2021-05-07
广州蕊生网络科技有限公司
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Problems solved by technology

[0003] However, for complex water bodies, such as rivers and lakes with large water volume, the quality of water resources will change with time and detection location, especially the sampling depth during detection, resulting in inaccurate sampling results

Method used

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  • Neural network training method for intelligent comprehensive overall quality evaluation
  • Neural network training method for intelligent comprehensive overall quality evaluation
  • Neural network training method for intelligent comprehensive overall quality evaluation

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Embodiment Construction

[0055] Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described here.

[0056] Scenario overview

[0057] As mentioned above, when the quality of water resources is inspected, the method of sampling inspection is usually used. However, for complex water bodies, the quality of water resources will vary with time and detection location, especially the sampling depth during detection. Therefore, it is expected to provide a method that can accurately assess the overall water quality considering the sampling time and sampling depth factors.

[0058] Based on this, the inventors of the present application consider using a deep neura...

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Abstract

The invention relates to intelligent comprehensive overall quality evaluation in the field of intelligent environmental protection, and particularly discloses a training method of a neural network for intelligent comprehensive overall quality evaluation, which adopts an encoder-decoder architecture to evaluate the overall water quality, and particularly, in the training process, detection result information of a water quality sample and information of sampling time and sampling depth are fully utilized and fused through a multi-layer encoder structure to carry out encoding so as to obtain an encoded feature vector, and a triple loss function value of the encoded feature vector is calculated and is used for training the classifier so as to obtain a classification result of the water quality sample. The accuracy of comprehensive overall evaluation of the water quality is improved.

Description

technical field [0001] The present invention relates to intelligent comprehensive overall quality evaluation in the field of intelligent environmental protection, and more specifically, to a neural network training method for intelligent comprehensive overall quality evaluation, an intelligent evaluation method for water quality based on a deep neural network, A neural network training system for intelligent comprehensive overall quality assessment, a water quality intelligent assessment system and electronic equipment based on a deep neural network. Background technique [0002] Environmental quality monitoring is a kind of environmental monitoring content. It mainly monitors the distribution and concentration of pollutants in the environment to determine the state of environmental quality. The historical data of environmental quality monitoring at regular and fixed points provides basic data for scientific research on the laws of pollutant migration and transformation. . ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/26G06K9/62G06N3/04G06N3/08
CPCG06Q10/06395G06Q50/26G06N3/04G06N3/08G06F18/241Y02A20/152
Inventor 郑伦彬
Owner 广州蕊生网络科技有限公司
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