Yellow River basin pollution source monitoring and early warning method based on electric power-environmental protection data fusion analysis

Abstract

The invention provides a Yellow River basin pollution source monitoring and early warning method based on electric power-environmental protection data fusion analysis, and the method comprises the steps: carrying out the data fitting of the correlation degree between power utilization data and environmental protection index data, and obtaining the correlation mapping of the power utilization data and environmental protection index data of different enterprises; constructing an enterprise portrait of each enterprise in the sample set from two dimensions of environmental protection index data and power utilization characteristic data, and realizing enterprise sample pollution discharge monitoring data complementation based on the enterprise portraits in combination with own actual sampling point data and enterprise actual power utilization load data; and on the basis of the short-term power load prediction value of each enterprise, in combination with the association mapping of the power consumption data and the environmental protection index data of the enterprise, obtaining short-term pollution discharge prediction data of each enterprise in the region, and taking the short-term pollution discharge prediction data as the input of the dynamic monitoring and early warning model of the drainage basin, thereby predicting the short-term environmental index in the region. And environmental protection early warning monitoring is realized.

Description

technical field [0001] The invention relates to a pollution monitoring and early warning method, in particular to a method for monitoring and early warning of pollution sources in river basins to which rivers belong by integrating electric power and environmental protection data. Background technique [0002] At present, there is a lack of real-time sewage monitoring data for enterprises, and the early warning of regional environmental protection indicators is difficult to achieve. [0003] Due to the difficulties of corporate pollution prevention and control, such as many points, wide areas, and long lines, and industrial wastewater and waste gas treatment procedures are cumbersome and costly, some heavy polluting enterprises only consider their own interests, and there is a phenomenon of non-compliance discharge. Traditional pollution prevention and control work The implementation mainly relies on on-site inspections to carry out governance and prevention and control, with...

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Problems solved by technology

[0002] At present, there is a lack of real-time sewage monitoring data for enterprises, and it is difficult to realize the early warning of regional environmental protection indicators.

[0003] Due to the difficulties of corporate pollution prevention and control, such as many points, wide areas, and long lines, and industrial wastewater and waste gas treatment procedures are cumbersome and costly, some heavy polluting enterprises only consider their own interests, and there is a phenomenon of non-compliance discharge. Traditional pollution prevention and control work The implementation mainly relies on on-site inspections to carry out governance and prevention and control, with high manpower and material costs and low efficiency

[0004] Existing heavy polluting enterprises are often distributed in the central and eastern regions with developed economy and numerous industries, such as textiles, printing and dyeing, wine making, medicine, building materials, etc., often have geographical clustering; at the same time, based on the monitoring data of environmental pollution discharge monitoring stations, various It is difficult to effectively realize the traceability and tracking of pollution sources

[0005] With the advancement of national electric power coverage and ubiquitous power Internet of Things construction, electric power, as an indispensable energy source for enterprise production activities, can timely and accurately reflect the production status and equipment usage of enterprises. Electricity realizes enterprise pollution monitoring, but currently only some enterprises have real-time sewage monitoring data, and most enterprises (especially small and medium-sized enterprises) only have part of the sampling point monitoring data available. Early warning is difficult to achieve

[0006] At present, relevant institutions realize the monitoring of enterprise pollutant discharge by monitoring the electricity consumption of key enterprises, such as the literature [A method for monitoring illegal production of polluting enterprises based on the law of electricity consumption[J]. Electric Power Big Data, 2019, 22(08): 35-39. ], is based on the power consumption law of polluting enterprises to set the threshold value of electricity consumption to study and judge illegal production. This method can realize the monitoring and alarm of shutdown enterprises and the shutdown alarm of environmental protection equipment. However, due to the interference of production power consumption data in practical use, it leads to misjudgment The literature [Design of special action plan for pollution prevention and control of key enterprises based on power big data mining [J]. Power Supply and Utilization, 2021, 38(04): 28-36. The environmental impact prediction model, so as to realize the monitoring and early warning of air pollution prevention and control of key enterprises, but failed to dynamically evaluate the subsequent impact of enterprise production on air pollution

[0007] The above methods all show that electricity consumption data can be used as an effective basis for enterprise sewage monitoring. However, it depends on the linear data processing process. In practical application, the model has the problem of cross-enterprise offset, which leads to unsatisfactory monitoring and early warning effects of sewage discharge; on the other hand, none of the above methods have tapped the potential correlation of various factors in different time and space, and cannot be based on currently available data. Realize short-term pollution discharge data prediction, and lagging pollution monitoring results often lead to environmental governance that can only be repaired after a disaster

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