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Water resource efficiency detection and prediction method

A prediction method and efficiency detection technology, applied in prediction, neural learning methods, instruments, etc., can solve problems such as the efficiency of water resource use that has not been paid attention to

Active Publication Date: 2020-03-24
SICHUAN UNIV
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  • Application Information

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Problems solved by technology

[0003] However, the existing water resources monitoring and use planning basically avoids the waste of water resources by controlling the amount of water resources used throughout the year. This method does not pay attention to the efficiency of water resources use, nor can it provide corresponding improvement suggestions to relevant users.

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  • Water resource efficiency detection and prediction method

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Embodiment Construction

[0048] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0049] Such as figure 1 As shown, the water resource efficiency detection and prediction method includes the following steps:

[0050] S1. Obtain the input data of the water generation system, the output data of the water generation system, the input data of the water use system, the output data of the water use system, the input data of the sewage treatment system and the output data of the sewage treatment system;

[005...

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Abstract

The invention discloses a water resource efficiency detection and prediction method. The method comprises the following steps: S1, obtaining input data of a water generation system, a water use systemand a sewage treatment system and data of the sewage treatment system; S2, constructing a three-stage data envelope analysis model; S3, inputting the data of the same year in the water production system, the water use system and the sewage treatment system into a three-stage data envelope analysis model, and respectively obtaining the water resource efficiency of the water production system, thewater use system and the sewage treatment system; S4, training a neural network model according to the data of the water production system, the water use system and the sewage treatment system and thecorresponding water resource efficiency; and S5, predicting the water resource efficiency by adopting the trained neural network model. The water resource efficiency of the target area can be detected, the link with low water resource efficiency is given, and the targeted improvement of related departments is facilitated.

Description

technical field [0001] The invention relates to the field of water resource monitoring, in particular to a water resource efficiency detection and prediction method. Background technique [0002] Water resources are a limited and special natural resource closely related to economic, social and ecological development, and play an important role in human civilization and technological progress. The total amount of water resources in China ranks fifth in the world, but the per capita water resources are only a quarter of the world's average; and the distribution of water resources between the north and the south is obviously unbalanced. With the rapid economic development and the acceleration of urbanization, water consumption The water consumption problem is also getting more and more attention. [0003] However, the existing water resources monitoring and use planning basically avoids the waste of water resources by controlling the amount of water resources used throughout t...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06N3/08G06N3/045
Inventor 姚黎明颜诗雨陈旭东徐忠雯
Owner SICHUAN UNIV