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Carbon emission inversion system and method based on deep learning

A technology of carbon emissions and deep learning, applied in the field of carbon emissions inversion system based on deep learning, can solve problems such as the inability to display corporate carbon emissions data

Pending Publication Date: 2022-01-28
重庆东煌高新科技有限公司
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Problems solved by technology

[0004] The technical problem solved by the present invention is to provide a carbon emission inversion system and method based on deep learning to solve the problem that the existing technology cannot display enterprise carbon emission data in three dimensions, and to detect abnormal carbon emission data and reason

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  • Carbon emission inversion system and method based on deep learning
  • Carbon emission inversion system and method based on deep learning
  • Carbon emission inversion system and method based on deep learning

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Embodiment Construction

[0058] The following is further described in detail through specific implementation methods:

[0059] The embodiment is basically as attached figure 1 , figure 2 Shown: The carbon emission inversion system based on deep learning, including server and client, the server includes real-time data acquisition module, carbon emission calculation module, carbon emission prediction module, data anomaly detection module and push module, among which , the real-time data acquisition module is used to collect real-time data on carbon emissions and electricity consumption of enterprises. In this embodiment, the real-time data acquisition module mainly collects data from thermal power enterprises, and the sources of carbon emissions from thermal power enterprises mainly consist of three parts. Including fossil fuel combustion, desulfurization process, and carbon emissions generated by purchased electricity. Among them, fossil fuel combustion and desulfurized processes can be monitored and...

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Abstract

The invention belongs to the field of carbon emission inversion, and particularly relates to a carbon emission inversion system and method based on deep learning. The system comprises a server and a client, wherein the server firstly collects enterprise real-time carbon emission data and real-time power utilization data; then a real-time emission condition is calculated through a carbon emission calculation module; meanwhile, a carbon emission prediction module is used for predicting a future emission condition; a data abnormity monitoring module is used for detecting and analyzing abnormal conditions in a prediction result; and finally, the client is used for displaying the data collected by the server, a carbon emission real-time calculation result and a abnormal condition analysis result in real time. The system and the method have the advantages that the problem that enterprise carbon emission data cannot be displayed in real time through a three-dimensional BIM model in the prior art can be solved, abnormal data of carbon emission can be detected, and generation reasons can be analyzed.

Description

technical field [0001] The invention belongs to the field of carbon emission inversion, in particular to a carbon emission inversion system and method based on deep learning. Background technique [0002] Calculation methods for carbon emissions include theoretical accounting methods and direct measurement methods. At present, when calculating carbon emissions in my country, theoretical accounting methods are usually used, although some power generation companies use direct measurement methods to calculate carbon emissions from power plants. , but has not yet established a systematic mining analysis for carbon emissions. The premise of calculating carbon emissions needs to be applied to the online monitoring technology of carbon emissions. [0003] The online monitoring technology of carbon emissions in the prior art mainly adopts the continuous flue gas emission monitoring system CEMS method, which arranges online monitoring equipment at the outlets of fixed pollution sourc...

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Application Information

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IPC IPC(8): G06F16/26G06F16/2458G06Q50/26
CPCG06F16/26G06F16/2462G06Q50/26Y02P90/84
Inventor 冯磊周后飞王华平卢呈远雷雨马东陈艺尹邓佑超
Owner 重庆东煌高新科技有限公司
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