BNM-based (Bayesian network model-based) fishery forecasting method

A Bayesian network, Bayesian technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as whether environmental elements have conditional correlations without considering

Inactive Publication Date: 2013-08-07
EAST CHINA SEA FISHERIES RES INST CHINESE ACAD OF FISHERY SCI
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Problems solved by technology

At present, fishery forecasts mostly consider the relationship between one or two environmental elements and the location of the fishery, and seldom involve research on the relationship between multiple environmental elements and the location of the fishery, let alone whether there is a conditional relationship between environmental elements

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[0024] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0025] Embodiments of the present invention relate to a fishery forecasting method based on a Bayesian network model, such as figure 1 As shown, sea surface temperature (SST), chlorophyll concentration (chl-a), sea surface temperature anomaly (△SST), chlorophyll concentration anomaly (△chl-a) and sea surface temperature gradient intensity obtained by satellite remote sensing technology (Grad) and other five environmental facto...

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Abstract

The invention relates to a BNM-based (Bayesian network model-based) fishery forecasting method. The method includes the steps of discretizing historical ocean information datasets of fishery environment; establishing a table of conditional probability between Bayesian network structure charts and Bayesian network nodes; calculating a posterior probability distribution formula of fishery by the Bayesian network structure chart obtained by optimal learning algorithm; and forecasting the fishery by the obtained posterior probability distribution formula. The function of rapidly forecasting for the fishery can be realized by the BNM-based fishery forecasting method.

Description

technical field [0001] The invention relates to the technical field of fishing ground fish situation forecasting, in particular to a fishing ground forecasting method based on a Bayesian network model. Background technique [0002] The marine water environment is the basic space for the survival of marine organisms and marine fish. The growth and development, living habits, and temporal and spatial distribution of marine organisms and fish are inseparable from the marine environment. Therefore, information on the elements of the marine water environment can be obtained , Marine fish life habits to study the temporal and spatial dynamic evolution of fisheries, and then carry out analysis and forecasting of fishery conditions. In traditional fishing, the captain judges the possible fishing ground location based on simple methods such as visual seawater color, current flow direction, and fixed-point measurement of sea surface temperature. This method is relatively backward, and...

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Application Information

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IPC IPC(8): G06Q50/02G06Q10/04
Inventor 张衡崔雪森张胜茂樊伟周为峰唐峰华
Owner EAST CHINA SEA FISHERIES RES INST CHINESE ACAD OF FISHERY SCI
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