Cloud system structure pre-warning system and method for riverway type reservoir tributary bay water bloom

Abstract

The invention relates to a cloud system structure pre-warning system and method for riverway type reservoir tributary bay water bloom. The cloud system structure pre-warning system comprises a monitoring cloud module, an information cloud module, an application supporting module, a water bloom pre-warning module and a discussion decision-making module. The monitoring cloud module is composed of a field information collecting unit and an operation communication unit, storage media of the information cloud module are composed of movable distributed storage station nets combined fixedly, the application supporting module is composed of a basic supporting unit, an application customization unit, a data cooperation unit and an emergency response module, the water bloom pre-warning module is composed of a risk analysis unit and a pre-warning and forecasting unit, and the discussion decision-making module is used for achieving decision-making discussion and information sharing communication in a cloud environment. The cloud system structure pre-warning system and method for riverway type reservoir tributary bay water bloom achieve seamless linkage and operation control over the modules based on a cloud system structure, carry out all-weather monitoring, cloud storage and water bloom risk pre-warning on the inducing factors of the riverway type reservoir tributary bay water bloom and water bloom biochemical properties, and provide a new measure based on the cloud system structure for preventing and reducing water bloom events.

Description

technical field [0001] The invention relates to the technical fields of water conservancy and hydropower engineering and water environment protection, in particular to a cloud system framework early warning system and method for algal blooms in a tributary of a river-type reservoir. Background technique [0002] In eutrophic water bodies with slow water flow, if the environmental conditions are suitable, algae are easy to reproduce rapidly and gather to form algae blooms. The self-purification capacity of river-type reservoirs decreases significantly after impoundment, and algae blooms are prone to occur in sensitive waters (such as tributary bays) when environmental conditions are suitable. For example, after the Three Gorges Reservoir was impounded, most of the secondary rivers formed reservoir bays, with slowed flow, increased transparency, and seasonal stratification of water temperature, which is very conducive to the formation of algal blooms in spring and summer. Alg...

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Problems solved by technology

[0004] (1) The water bloom early warning method based on manual sampling needs to collect water samples manually on site, and then take them to the laboratory for off-line water chemical analysis to obtain water quality information. Problems such as high labor intensity, poor pertinence, and slow data collection cannot reflect the dynamic changes in the water environment, and it is difficult to detect pollution sources early and call the police;

[0005] (2) The water bloom early warning method based on automatic monitoring stations generally consists of a monitoring center and several fixed remote automatic monitoring stations to form an automatic water quality monitoring system. However, due to the wide geographical range of the tributary bays of river-type reservoirs, it is difficult for fixed monitoring stations to comprehensively monitor The target water area, while remote monitoring will generate massive data, data storage and transmission and data mining are relatively difficult, and the computing power, storage capacity and application service capacity of current hardware equipment are generally not enough to support the demand;

[0006] (3) Water bloom early warning method based on remote sensing images. This method mainly uses satellite remote sensing images or radar technology. Although it solves the defect of insufficient spatial resolution of measurement data in the traditional method of point distribution, it will be difficult due to the long measurement period of remote sensing satellites. This leads to a new problem of low temporal resolution of the measurement images. At the same time, remote sensing technology also requires on-site measurement data to be calibrated to improve the accuracy of information extraction such as water quality and algal blooms.

However, the existing cloud computing theory does not address the actual characteristics of large-scale, strong human influence, randomness, and nonlinearity of algal bloom disasters in the tributary bays of river-type reservoirs. It can not only get rid of the expensive hardware cost required for big data monitoring, realize high-performance, fast and effective information mining, but also overcome the obstacles caused by the temporal and spatial randomness of algal blooms in the tributaries of river-type reservoirs to the early warning and forecasting

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