Ecological mechanism and causal enhancement integrated blue algae community succession prediction method and device

By constructing a causal relationship aggregation structure and a species ecological strategy embedding mechanism, and combining a multi-species interaction prediction model with a multi-head self-attention mechanism, the problems of unclear causal relationships and unintegrated species interaction mechanisms in existing cyanobacteria prediction methods are solved, and high-precision simulation and intelligent early warning of cyanobacteria community succession are achieved.

CN122047524BActive Publication Date: 2026-06-23CHANGJIANG RIVER SCI RES INST CHANGJIANG WATER RESOURCES COMMISSION
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Patent Information

Application Number
CN202610519253.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-20
Publication Date
2026-06-23
Estimated Expiration
2046-04-20

AI Technical Summary

Technical Problem

Existing cyanobacteria prediction methods lack a clear characterization of the causal relationship between environmental factors and different species, fail to effectively integrate interspecific interaction mechanisms among cyanobacteria species, ignore the differences in ecological strategies of species in nutrient use, light adaptation and temperature response, time series prediction models are difficult to take into account trend, seasonality and random perturbation components, and deep learning models have insufficient interpretability.

Method used

We construct a causal relationship aggregation structure between environmental factors and dominant cyanobacterial species, introduce a species ecological strategy embedding mechanism, combine time series multi-scale analysis, and use a multi-head self-attention mechanism to fuse a multi-species interaction prediction model to accurately characterize the competition, substitution, and synergy relationships among species. We also extract trend, periodic, and random perturbation components to achieve high-precision simulation and prediction.

Benefits of technology

It improves the interpretability and prediction accuracy of the model, can accurately depict the dynamics of cyanobacterial community succession, realize end-to-end intelligent early warning and risk assessment, support high-frequency data input for various water types, and has good algorithm scalability and ecological applicability.

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Abstract

The application discloses a cyanobacterial community succession prediction method and device fusing ecological mechanism and causal enhancement. The method comprises the following steps: collecting multi-source high-frequency monitoring data of a target water area, and constructing an environmental driving factor characteristic matrix; constructing a causal weight matrix through causal relationship aggregation analysis, and outputting a causal enhancement characteristic matrix; constructing a species ecological strategy embedding matrix; performing multi-scale decomposition on algal density time series data; constructing a multi-species interaction prediction model based on a multi-head self-attention mechanism, fusing environmental characteristics, causal enhancement characteristics, ecological strategy embedding and multi-scale time series characteristics, and predicting the biomass and relative abundance of each dominant cyanobacterial species; and outputting a cyanobacterial community succession process and a water bloom risk grade. Through deep fusion of causal enhancement, ecological strategy embedding and multi-head attention mechanism, the precision, stability and ecological interpretability of cyanobacterial community succession prediction are significantly improved, and the application is suitable for water bloom early warning and ecological management of lakes, reservoirs and rivers.
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