CN-121980496-A - Calculation method, equipment and storage medium for early trigger index of physically-constrained algal bloom

CN121980496ACN 121980496 ACN121980496 ACN 121980496ACN-121980496-A

Abstract

The application discloses a calculation method, equipment and a storage medium of a physical constraint algal bloom early trigger index, which comprise the steps of detecting multi-source monitoring data of a target water body in real time, extracting core features of the water body from the multi-source monitoring data, carrying out standardization processing and mapping to a unified numerical interval to obtain a standardized feature value, carrying out monotone increasing function mapping conversion on the standardized feature value to obtain an algal bloom contribution degree response value, carrying out linear weighting synthesis on the algal bloom contribution degree response value by using a symbol constraint weight to obtain a comprehensive index initial value, carrying out normalization processing on the comprehensive index initial value to obtain an algal bloom early trigger index, obtaining algal bloom trigger criterion conditions based on the target water body, comparing the algal bloom early trigger index with the algal bloom trigger criterion conditions, and judging the algal bloom state of the target water body. According to the application, by constructing the standardized trigger index fused with the physical constraint and establishing the weighted synthesis mechanism with symbol constraint and monotonicity guarantee, the technical effect of improving the early warning precision is realized.

Inventors

TANG DINGDING
GUO ERWEI
LI WEILONG
WANG AIJIE
Zhan de
TAO YU
LIU XUEJIN
XIAO PENG
Xie Luyang
ZHANG LINA
ZHOU YAN

Assignees

中建三局绿色产业投资有限公司
哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院)

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (10)

1. The method for calculating the early-stage trigger index of the physically-restrained algal bloom is characterized by comprising the following steps of: Detecting multi-source monitoring data of a target water body in real time through a plurality of sensors deployed in advance; Extracting water body core features from the multi-source monitoring data, carrying out standardized processing on each water body core feature, and mapping standardized processing results to uniform numerical intervals to obtain standardized feature values, wherein the standardized processing needs to set corresponding numerical intervals based on physical effective ranges corresponding to each water body core feature, and the physical effective ranges are predefined according to the water environment mechanism of the target water body; applying monotonically increasing function mapping to each standardized characteristic value, and converting each standardized characteristic value into a response value of the algal bloom occurrence contribution degree; Performing linear weighted synthesis on the response value of each algae bloom contribution degree by using a preset symbol constraint weight to obtain an initial value of a comprehensive index, wherein the symbol constraint weight assigns a value to the promotion or inhibition effect of algae growth based on the water body core characteristics; normalizing the initial value of the comprehensive index to obtain an early-stage algal bloom trigger index; And acquiring algal bloom trigger criterion conditions based on the type of the water body of the target water body, and judging the algal bloom state of the target water body according to the comparison result by comparing the algal bloom early trigger index with the algal bloom trigger criterion conditions.
2. The method of claim 1, wherein the water core features include algae growth features, water stability features, mixing strength features, nutrient limitation features, light sufficiency features, dissolved oxygen supersaturation features, and re-suspension features, and the step of extracting the water core features from the multi-source monitoring data comprises: splitting the multi-source monitoring data based on data types, and acquiring a data calculation strategy corresponding to each data type, wherein each data type is predefined with a corresponding data processing strategy; And taking the splitting result as input of a corresponding data calculation strategy, and outputting the water body core characteristics through the data calculation strategy.
3. The method for calculating the early-stage trigger index of the physically-constrained algal bloom according to claim 1, wherein the step of performing standardization processing on each water body core feature and mapping the standardization processing result to a unified numerical interval to obtain a standardization feature value comprises the following steps: Acquiring a corresponding physical effective range according to the data type of the water body core characteristics; Constructing a standardized mapping function based on the physical effective range, wherein the standardized mapping function linearly or nonlinearly converts the water core characteristics to a preset closed interval; and processing each water body core feature through the standardized mapping function to obtain the standardized feature value.
4. The method for calculating an early-stage trigger index of a physically constrained algal bloom as claimed in claim 1, wherein the step of converting each normalized feature value into an algal bloom occurrence contribution degree response value by applying a monotonically increasing function map to each normalized feature value includes: And inputting each standardized characteristic value into a corresponding monotonically increasing response function, and mapping the standardized characteristic value into a response value of the contribution degree of the algal bloom through the monotonically increasing response function, wherein the monotonically increasing response function is configured to continuously and monotonically increase in a defined domain so that the algal bloom early trigger index is enhanced along with any promotion characteristic.
5. The method for calculating early-stage trigger indexes of physically-constrained algal bloom according to claim 1, wherein the step of linearly weighting and synthesizing each of the algal bloom contribution degree response values by using a preset symbol constraint weight to obtain an initial value of a composite index comprises the steps of: Weighting each algal bloom contribution degree response value and a corresponding symbol constraint weight, wherein the algal bloom contribution degree response value representing the algae growth promotion effect is endowed with a non-negative weight, and the algal bloom contribution degree response value representing the algae growth inhibition effect is endowed with a non-positive weight; algebraic summation is carried out on the response value of the contribution degree of the algal bloom after the weighting treatment, and the basic offset is added to the algebraic summation result to obtain the initial value of the comprehensive index.
6. The method for calculating the early-stage trigger index of physically-constrained algal bloom as set forth in claim 5, wherein the step of algebraically summing the weighted response values of the contribution degree of the algal bloom and superposing the algebraic summation result with the basic offset to obtain the initial value of the comprehensive index comprises the steps of: Acquiring historical detection data of a target water body, and counting typical algal bloom early-stage trigger index background values of the target water body in a non-algal bloom period based on the historical detection data; And taking the background value of the typical algal bloom early trigger index as the basic offset.
7. The method for calculating an early-stage trigger index of a physically-constrained algal bloom as set forth in claim 5, wherein before the step of weighting each of the algal bloom occurrence contribution degree response values with a corresponding symbol constraint weight, the method further includes: acquiring historical multi-source monitoring data and unified algal bloom occurrence state labels, and constructing a training sample set; Initializing a linear model, and presetting a weight initial symbol based on a physical action mechanism based on the initialized linear model; training the linear model through the initial symbols of the weights and the training sample set, and obtaining a linear model based on a training result of the linear model; and extracting model parameters from the linear model to generate symbol constraint weights.
8. The method for calculating the early-stage algal bloom trigger index according to claim 1, wherein the step of acquiring algal bloom trigger criteria based on the type of the water body to which the target water body belongs, and determining the algal bloom state of the target water body according to the comparison result by comparing the early-stage algal bloom trigger index with the algal bloom trigger criteria comprises: Comparing the early-stage algal bloom trigger index with a static threshold value corresponding to the algal bloom trigger criterion condition, and calculating a dynamic change trend of the early-stage algal bloom trigger index in a preset sliding time window; if the value of the early-stage algal bloom trigger index and the dynamic change trend simultaneously meet the preset duration requirement, determining an early-warning algal bloom state; And if the early trigger index of the algal bloom is determined to fall back to a preset cancelling threshold value based on the dynamic change trend and the preset stable duration is continued, the early warning state of the algal bloom is relieved.
9. A computing device for a physically constrained algal bloom early-triggering index, characterized in that the computing device for a physically constrained algal bloom early-triggering index stores a computer program, which when executed by a processor, implements the method for computing a physically constrained algal bloom early-triggering index according to any one of claims 1-8.
10. A storage medium storing a computer program which, when executed by a processor, implements the method of calculating a physically constrained algal bloom early trigger index according to any one of claims 1-8.

Description

Calculation method, equipment and storage medium for early trigger index of physically-constrained algal bloom Technical Field The application relates to the technical field of lake and reservoir water ring management prevention and control, in particular to a calculation method, equipment and storage medium of an early-stage trigger index of physically restrained algal bloom. Background Currently, the monitoring and early warning of algal bloom in freshwater lakes and reservoirs mainly adopts the technical means of a single-index threshold method, a multi-index scoring method, a machine learning prediction method, a remote sensing and shore station fusion method and the like. The single index threshold method has a simple structure, but depends on only single indexes such as chlorophyll and the like, so that the real proliferation of algae cannot be distinguished from the concentration false increase caused by physical processes such as wind and wave mixing and the like, and the false alarm rate is high. The multi-index scoring method fuses a plurality of parameters, but the weight of the multi-index scoring method depends on experience assignment, the standardized mode is extensive, and the consideration of physical relevance among the parameters is lacked, so that the mobility of the model in different lake areas and among different monitoring devices is poor, and the prediction error is obviously increased. Although the more advanced machine learning prediction method has stronger data fitting capability, the 'black box' characteristic causes that the model is difficult to embed into the physical mechanism of the algal bloom, and when key physical conditions such as thermocline is not established, cause and error judgment often occurs, so that report missing is caused. Meanwhile, the method generally faces the dilemma of weak noise immunity, and sensor drift or short-time disturbance is extremely easy to cause false alarm. The definition of the algal bloom occurrence tag is not uniform when the algal bloom occurrence tag is applied across a lake region, so that the complexity of model training and acceptance is further increased, and the practical application effect is restricted. The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art. Disclosure of Invention The application mainly aims to provide a calculation method, equipment and a storage medium for an early-stage trigger index of a physically restrained algal bloom, and aims to solve the technical problem that an existing algal bloom early-warning method lacks physical consistency and mobility. In order to achieve the above purpose, the application provides a method for calculating an early trigger index of a physically restrained algal bloom, which comprises the following steps: Detecting multi-source monitoring data of a target water body in real time through a plurality of sensors deployed in advance; Extracting water body core features from the multi-source monitoring data, carrying out standardized processing on each water body core feature, and mapping standardized processing results to uniform numerical intervals to obtain standardized feature values, wherein the standardized processing needs to set corresponding numerical intervals based on physical effective ranges corresponding to each water body core feature, and the physical effective ranges are predefined according to the water environment mechanism of the target water body; applying monotonically increasing function mapping to each standardized characteristic value, and converting each standardized characteristic value into a response value of the algal bloom occurrence contribution degree; Performing linear weighted synthesis on the response value of each algae bloom contribution degree by using a preset symbol constraint weight to obtain an initial value of a comprehensive index, wherein the symbol constraint weight assigns a value to the promotion or inhibition effect of algae growth based on the water body core characteristics; normalizing the initial value of the comprehensive index to obtain an early-stage algal bloom trigger index; And acquiring algal bloom trigger criterion conditions based on the type of the water body of the target water body, and judging the algal bloom state of the target water body according to the comparison result by comparing the algal bloom early trigger index with the algal bloom trigger criterion conditions. In one embodiment, the water core features include algae growth features, water stability features, mixing intensity features, nutrient limitation features, light sufficiency features, dissolved oxygen supersaturation features, and re-suspension features, and the step of extracting the water core features from the multi-source monitoring data comprises: splitting the multi-source monitoring d