69 results about "Chlorophyll a" patented technology

The invention discloses a Chla monitoring method based on visible-near infrared spectrum and machine learning, which is a modeling method for performing chlorophyll a concentration parameter inversion by using visible-near infrared hyperspectral data, and combines primary screening of spectral characteristic wave bands, training sample expansion based on GAN, spectral characteristic wave band fine screening based on CARS and a regression modeling technology. The problems of high dimension of hyperspectral data and insufficient samples are solved, the overall Chl-a modeling precision is improved, and the method is suitable for water eutrophication monitoring, marine ecological assessment and environment remote sensing application.

The invention belongs to the technical field of remote sensing image data processing, and relates to a multi-feature fusion coastal zone culture pond extraction method, which comprises the following steps: obtaining spectral features and polarization features based on an obtained Sentinel-1 image and an obtained Sentinel-2 image; calculating and evaluating an NDWI time sequence based on the NDWI to generate a time sequence synthesis NDWI image; a water body main body is obtained through the hierarchical feature fusion decision tree; obtaining morphological characteristics based on the water body object; obtaining the chlorophyll a concentration and the dynamic characteristic factor of the chlorophyll a concentration based on the Sentinel-2 image; and extracting a culture pond through a random forest classifier, and generating a culture pond spatial distribution diagram. According to the method, the spectral features, the polarization features, the morphological features, the chlorophyll a concentration and the chlorophyll a concentration dynamic feature factors are fused, and the decision tree and the random forest classifier are fused through the hierarchical features, so that the problems of low accuracy and poor stability of existing culture pond extraction are solved.

The invention belongs to the technical field of water ecology risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, and the method comprises the steps: obtaining remote sensing image data, meteorological data and water quality data of a target region, and carrying out the preprocessing; performing frequency domain feature extraction to generate a three-dimensional frequency domain feature vector; fusing the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector by using a multi-task deep learning network to obtain a feature map; obtaining an algae bloom binary segmentation probability graph and a continuous value distribution graph of chlorophyll a concentration based on the characteristic graph; generating an image semantic embedding vector by utilizing a semantic embedding head, and performing semantic alignment on the image semantic embedding vector by adopting a pre-trained knowledge graph to generate a research and judgment information text; and performing cross validation on the study and judgment information text, the algae bloom binary segmentation probability graph and the continuous value distribution graph of the chlorophyll a concentration to obtain a final algae bloom risk judgment result. According to the invention, the algal bloom risk remote sensing intelligent identification is realized.

The invention discloses an XGBoost chlorophyll a concentration aerial remote sensing inversion method based on characteristic wave band selection, and relates to the technical field of environmental monitoring. By constructing an integrated input characteristic vector, an optimal characteristic wave band most relevant to the chlorophyll a concentration, an enhanced chlorophyll a index and specially designed correction characteristics are fused; and more targeted information is provided for the model. Moreover, correction features obtained through calculation of spectral difference values of adjacent shadow regions and non-shadow regions are introduced, and training data containing samples of the two regions are utilized to train an XGBoost model, so that the XGBoost model can autonomously learn and quantify a composite interference effect brought by illumination and suspended matter concentration change. Therefore, according to the technology, inversion noise caused by complex environmental factors can be effectively inhibited, high-precision and full-coverage inversion of the chlorophyll a concentration under different illumination and turbidity conditions is realized, and the robustness and the practical application value of the method are remarkably improved.

The invention belongs to the technical field of water environment monitoring and early warning, and discloses a lake and reservoir chlorophyll a concentration prediction method based on an SO-KNN model. According to the invention, multi-time scale meteorological cumulative effect features are introduced to enrich information representation, and an SO-KNN intelligent prediction model is constructed. According to the method, under the conditions of data scarcity and non-equilibrium, the chlorophyll a concentration, especially the high-precision and strong-generalization prediction capability of the water bloom risk critical point, is remarkably improved. The model is simple in structure and efficient in calculation, the common defects of overfitting, insufficient generalization ability and the like of a complex machine learning model in the scene are effectively overcome, and a reliable and practical innovative technical solution is provided for early water bloom warning of northern reservoirs and water areas with similar data conditions.

The invention discloses an intelligent water bloom prediction and early warning system and method based on an AI large model. The system is mainly composed of a data acquisition module, a data preprocessing module, an AI large model module, an early warning module and a visualization module. The data acquisition module is responsible for widely collecting multi-source data of a target water area. A water quality sensor, an underwater robot and other equipment go deep into a water body to accurately collect physical and chemical parameters of the water body, a meteorological station is utilized to obtain comprehensive meteorological data in real time, hydrological data is obtained from a water conservancy department database or on-site hydrological monitoring equipment, and satellite remote sensing data is obtained by means of a satellite receiving station or a third-party remote sensing data platform. Macroscopic information such as water color, chlorophyll a concentration distribution and water area change is covered. The invention belongs to the field of water environment monitoring and early warning, and particularly relates to an intelligent water bloom predicting and early warning system and method based on an AI large model.

The present application relates to the technical field of water environment treatment and ecological restoration, and discloses a water transparency improving device and method based on an intelligent linkage dosing system, the water transparency improving device based on the intelligent linkage dosing system comprising a water transparency sensor, a suspended substance concentration sensor, a chlorophyll a sensor, a data acquisition and discrimination module, a medicament dosing control module, a medicament storage and dosing module and a medicament spraying module; the water transparency sensor, the suspended substance concentration sensor and the chlorophyll a sensor are arranged in a target water body; the data acquisition and discrimination module is connected with the water transparency sensor, the suspended substance concentration sensor and the chlorophyll a sensor respectively; the medicament dosing control module is connected with the data acquisition and discrimination module and the medicament storage and dosing module respectively; and the medicament storage and dosing module is connected with the medicament spraying module. The present application realizes intelligent and automatic improvement of water transparency.

The present application belongs to the technical field of water ecological risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, remote sensing image data, meteorological data and water quality data of a target area are acquired and preprocessed; frequency domain feature extraction is performed to generate a three-dimensional frequency domain feature vector; a multi-task deep learning network is used to fuse the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector to obtain a feature map; based on the feature map, an algal bloom binary segmentation probability map and a continuous value distribution map of chlorophyll a concentration are obtained; a semantic embedding head is used to generate an image semantic embedding vector, a pre-trained knowledge graph is used to perform semantic alignment on the image semantic embedding vector to generate research and judgment information text; the research and judgment information text, the algal bloom binary segmentation probability map and the continuous value distribution map of chlorophyll a concentration are cross-validated to obtain a final algal bloom risk judgment result. The present application realizes algal bloom risk remote sensing intelligent identification.

The invention provides a method for distinguishing chlorophyll a concentration of an upwelling region influenced by different types of cyclones, and relates to the technical field of ocean remote sensing and environment monitoring. Preliminarily judging the influence of vortex on chlorophyll a concentration distribution; dividing the research area into a near-shore area and a far-shore area according to the water depth, respectively drawing vortex center-chlorophyll a concentration fusion maps for the two areas, and comparing the influence of cyclone and anti-cyclone of the two areas on chlorophyll a concentration distribution; dividing the vortex into a near-shore vortex and a far-shore vortex according to the ratio of the distance from the vortex center to the coastline to the vortex radius, respectively drawing vortex center-chlorophyll a concentration fusion graphs, and comparing the influence of cyclone and anti-cyclone on the chlorophyll a concentration distribution; and matching the temperature and salt data of the Argo buoy with the vortex position, analyzing the correlation between the potential density abnormity and the chlorophyll a concentration, and distinguishing the influence mechanism of vertical nutrition input and horizontal advection on the chlorophyll a concentration.

This invention presents a method for retrieving chlorophyll a concentration in surface water based on satellite remote sensing. The method acquires remote sensing images from a satellite that match the acquisition time of the water quality data. Based on the spectral information extracted from these images, a new grayscale image is generated. A grayscale co-occurrence matrix (HCEM) is calculated from this new image. The HCEM is then used to mine the texture features contained in the remote sensing images, and each texture feature is quantified. This results in a significantly improved model performance compared to models trained solely using spectral information. Furthermore, with the same amount of raw data, this invention achieves higher accuracy, which is beneficial for the precise measurement of surface water eutrophication.

The invention belongs to the technical field of water quality detection, and discloses a chlorophyll a concentration inversion model construction method and system based on regional adaptation. A quality control method; a Chl-a estimation model is established; and an error analysis method. According to the method, two satellite systems of Sentine-3 OLCI and Sentine-2 MSI of the European Spatial Administration are adopted to carry out modeling and verification of Chl-a remote sensing estimation on two lakes, namely the nebula lake and the Fuxian lake which are located in the same region and have great difference in nutrition level, and research of business remote sensing inversion.

The invention relates to the technical field of water environment monitoring, and discloses a lake water quality multi-parameter deep learning inversion framework based on hyperspectral data, and the framework comprises the following steps: S1, data preparation and preprocessing: obtaining the hyperspectral data of lake water quality and corresponding water quality in-situ data, and preprocessing the hyperspectral data and the water quality in-situ data; s2, constructing a feature extraction and parameter inversion model which sequentially comprises a one-dimensional convolutional neural network module, a bidirectional long-short-term memory network module and a three-dimensional attention module, and inputting the preprocessed hyperspectral data into the model. According to the method, loss distribution can be dynamically optimized according to inversion requirements of different water quality parameters, the accuracy and generalization ability of simultaneous inversion of multiple parameters such as chlorophyll a, total suspended solids and transparency are remarkably improved, dependence of a traditional model on specific water body types is broken through, and the method can adapt to lake water bodies with different hydrological and optical characteristics.

The invention relates to the field of water purification, and discloses a targeted multi-dimensional water quality purification device and an intelligent operation method thereof.The targeted multi-dimensional water quality purification device comprises a water purification unit, an ecological floating island main body and aeration equipment, an aquatic plant planting area is arranged at the top of the ecological floating island main body, an artificial filler biological membrane is hung at the bottom, and a multi-cavity microbial agent storage tank is arranged in the ecological floating island main body for targeted addition of microbial agents according to water quality data; the energy supply unit is composed of a solar photovoltaic panel at the top of the floating island and a storage battery; the propelling unit controls the floating island to move; the monitoring unit collects pollutant concentration, dissolved oxygen DO and chlorophyll a data in real time through a bottom water quality sensor group and wirelessly transmits the data to the cloud platform; the cloud platform calculates a pollution index based on the pollutant concentration, and remotely regulates and controls the operation of the aeration equipment and the microbial inoculum adding and propelling unit in combination with analysis results of DO and chlorophyll a; according to the invention, dynamic identification and precise treatment of the polluted area are realized, and the water quality purification efficiency is improved.

The invention provides a water chlorophyll a prediction model generation method and device based on a space-time transmission mechanism and an agent model and electronic equipment, and relates to the field of water component analysis and prediction.The method comprises the steps that the optimal time lag transmitted from the upstream to the downstream and the upstream optimal time lag chlorophyll a concentration are determined; a flow gating function is constructed based on a set flow threshold value, and flow gating characteristics are generated in combination with the upstream optimal time-delay chlorophyll a concentration; determining a time-space coupling feature set based on the optimal time lag and upstream optimal time lag chlorophyll a concentration and flow gating features; calculating the importance degree of each feature in the full feature set based on a time sequence prediction agent model, recursively eliminating the feature with the lowest importance degree until the number of the features in the full feature set is reduced to a preset value, and obtaining an optimal feature subset; and training based on the optimal feature subset to obtain a water chlorophyll a prediction model. According to the method, the prediction precision is improved, and meanwhile, technical support is provided for eutrophication early warning.

The chlorophyll a prediction method based on spatial heterogeneity perception graph timing confrontation network relates to the technical field of chlorophyll a prediction, and is used for solving the problems that the subjectivity is strong in response to spatial heterogeneity by artificial partition, the partition boundary is not fine enough, and the statistical characteristic difference in the region is large, etc.The reconstructed daily scale chlorophyll a concentration remote sensing data and numerical simulation sea surface temperature data SST are taken as inputs, through spatial heterogeneity partition based on the time evolution behavior of chlorophyll a, graph convolution network GCN spatial coding, time convolution network TCN time coding and regional discriminator constraint, short-term prediction of the spatial distribution of chlorophyll a concentration in the future several days is realized, so that the representation ability of the model to the inhomogeneous change process of chlorophyll a in the complex offshore sea area is improved.

The application discloses a kind of multicomponent plankton algae concentration measurement method based on fluorescence spectrum stratification partitioning.The application belongs to the field of marine aquatic ecological environment monitoring technology.The algorithm will unknown to be analyzed mixed three-dimensional fluorescence spectrum according to the following stratification partitioning analysis idea to be analyzed.First layer: full spectrum is involved in analysis, and the purpose is to analyze cyanophyta concentration and cryptophyta concentration, and obtain the difference spectrum in full spectrum region.Second layer: partitioning analysis, take the region containing effective spectral feature to participate in analysis, and the purpose is to analyze chlorophyll a concentration of green algae, diatom, dinoflagellate and yellow algae.The algorithm is significantly superior to common analysis algorithm in reducing misidentification, especially in processing such as diatom, dinoflagellate and yellow algae with high similarity living body fluorescence spectrum.

This invention relates to the field of optical monitoring technology for water environment, and discloses a method for analyzing and characterizing eutrophication components of water bodies based on hyperspectral feature inversion. The method includes: retrieving the intrinsic absorption spectrum sequence of pure water as a physical constraint benchmark; calculating the ratio of the hyperspectral reflectance to be measured to the benchmark to generate a modulation vector; determining the fractional-order differential sequence related to the sampling wavelength; extracting trough features through morphological baseline correction; completing the fractional-order differential transformation by combining the order sequence; extracting the characteristic trough depth and skewness parameters; and outputting the concentrations of chlorophyll a and colored soluble organic matter using an unmixing model. This invention utilizes the benchmark to drive the dynamic evolution of the differential operator, achieving topological separation of the scattering background and trace component absorption characteristics in different bands, suppressing local waveform distortion caused by background scattering heterogeneity, and improving the accuracy of component inversion under complex matrices.

The present application provides a satellite remote sensing monitoring method for methane diffusion emission of eutrophic algal water bodies, obtains satellite remote sensing image data, photosynthetically active radiation data, ground temperature data and measured methane diffusion flux data of a research area; an estimation model of diffuse attenuation coefficient and chlorophyll a concentration is established by using the satellite remote sensing image data; a prediction model is established by using a random forest algorithm and precision verification is carried out by taking the combination of the diffuse attenuation coefficient, the chlorophyll a concentration, the photosynthetically active radiation data, the ground temperature data and / or different mathematical transformation forms thereof as independent variables and the measured methane diffusion flux data as a dependent variable, and the methane diffusion emission prediction model is established by selecting the independent variables with the best model precision for water body methane diffusion emission monitoring. The method of the present application can realize large-area, synchronous, accurate and continuous water body methane emission monitoring of the target water area.

The invention belongs to the technical field of water environment detection, and relates to a method for identifying algae in a water body and measuring chlorophyll content, which comprises the following steps: constructing a fluorescence fingerprint spectrum of each algae according to the fluorescence intensity ratio of each algae under different excitation wavelengths, and establishing an algae fluorescence fingerprint database; taking the fluorescence intensity, temperature and photosynthetically active radiation of all experimental water samples of each alga under various excitation wavelengths as input parameters, taking the chlorophyll a concentration as an output parameter, and training a multilayer perceptron model to obtain a chlorophyll a concentration prediction model of each alga; the method comprises the following steps: measuring the fluorescence intensity, water body temperature and photosynthetically active radiation of mixed algae in a water body under various excitation wavelengths on site, obtaining the proportion of each algae in the water body and the fluorescence intensity of each algae under each excitation wavelength by utilizing an algae fluorescence fingerprint database, and further obtaining the chlorophyll a concentration of each algae by utilizing a chlorophyll a concentration prediction model. According to the invention, rapid identification of different algae in a water body and accurate measurement of chlorophyll content are realized.

The invention relates to a modified covalent organic framework (COF) photocatalyst (SNW-1 (at) CuS-D and SNW-1 (at) CuS-S. SNW-1 is loaded on the surface of the CuS photocatalyst through an electrostatic adsorption method, so that the SNW-1 (at) CuS-D photocatalyst is synthesized; sNW-1 is loaded on the surface of the CuS photocatalyst through a hydrothermal method, so that the SNW-1 coated CuS-S photocatalyst is synthesized. Meanwhile, the two composite materials are used for inhibition experiment research on green tide algae-enteromorpha under visible light. When the doping amount of the two composite material photocatalysts is 0.15 g / L, the composite material photocatalysts have an effective inhibition effect on enteromorpha. After 120 hours of experimental treatment, the inactivation rate of the enteromorpha microcosmic propagules reaches 70%; after 144 hours of treatment, the relative growth rate of the three-week-old enteromorpha seedlings is reduced to 0.011. Meanwhile, chlorophyll a is reduced to the minimum and is 257.84 mu g / g FW, the content of MDA is increased to 206.2 nmol / g FW, meanwhile, the content of various antioxidant enzymes is increased firstly and then reduced in the photocatalysis process, and an antioxidant system is damaged. The material provides a reference idea for COF modification and enteromorpha green tide treatment.

The invention belongs to the technical field of image completion, and discloses a chlorophyll a completion method and system based on time decomposition and multistage frequency domain enhancement, and the method comprises the steps: obtaining chlorophyll a concentration original data at different time scales, including monthly average data, weekly average data and daily average data; decomposing the daily average data into a trend component, a seasonal component and a residual component; using the expanded monthly average data and weekly average data to supplement the trend component; and finally, dynamically integrating multi-time scale characteristics through channel attention and a VMama module, and outputting chlorophyll a concentration complementation data with global consistency and local details through cross-path merging and residual connection collaborative optimization. According to the invention, the complementation precision and robustness of the chlorophyll a concentration data are improved.

The invention relates to the technical field of water environment treatment and ecological restoration, and discloses a water transparency improving device and method based on an intelligent linkage chemical dosing system. The water transparency improving device based on the intelligent linkage chemical dosing system comprises a water transparency sensor, a suspended matter concentration sensor, a chlorophyll a sensor, a data acquisition and judgment module, a chemical dosing control module, a chemical storage and dosing module and a chemical spraying module. The water body transparency sensor, the suspended matter concentration sensor and the chlorophyll a sensor are arranged in a target water body; the data acquisition and judgment module is respectively connected with the water transparency sensor, the suspended matter concentration sensor and the chlorophyll a sensor; the medicament adding control module is respectively connected with the data acquisition and judgment module and the medicament storage and adding module; and the medicament storage and addition module is connected with the medicament spraying module. The intelligent and automatic improvement of the transparency of the water body is realized.

A method for early warning of algal bloom levels based on an Ordinal Forests model includes the following steps: S1, preprocessing water quality data from a system for online monitoring of water quality and water ecology; S2, determining an algal bloom level according to a chlorophyll a value of the pre-processed water quality data; S3, using a resampling method to solve the problem of imbalanced algal bloom level data, and synthesizing a dataset of balanced algal bloom levels; and S4, taking the newly synthesized dataset in the S3 as an input variable, constructing a model for early warning of algal bloom levels based on the Ordinal Forests model, and performing early warning of algal bloom levels through the trained model for early warning of algal bloom levels.

The invention relates to the technical field of lake eutrophication monitoring, in particular to a lake pigment concentration remote sensing estimation method, which comprises the following steps: firstly, acquiring remote sensing reflectivity data of a target water body, extracting reflectivity values of at least four characteristic wavebands 665nm, 710nm, 620nm and 750nm related to pigment absorption and fluorescence characteristics, and estimating water body absorption coefficients of at least two pigment sensitive wavebands; on the basis, a four-waveband combination index UMEP is constructed and is transformed according to a water body radiation transmission biological optical model, and the influence of environmental parameters is eliminated. By introducing a dynamic decomposition factor beta and an absorption coefficient difference epsilon, a mixed pigment absorption signal is decomposed into independent contributions of chlorophyll a and phycocyanin, and finally the concentrations of chlorophyll a and phycocyanin are calculated by using respective specific absorption coefficients. The method is clear in physical mechanism, can adapt to changes of optical characteristics of the water body, can be directly applied to multi-source satellite remote sensing images, and achieves synchronous and high-precision business inversion of the concentrations of the two key pigments in the lake water body in a large range.

The application discloses a water bloom prediction method based on timing causal knowledge guidance, and belongs to the fields of deep learning and environmental monitoring. The method collects multi-source data through water quality monitoring stations and weather stations, extracts a multi-dimensional causal adjacency matrix by using convergent cross-mapping and PCMCI methods, and constructs an encoder composed of parallel variable channels, CCM channels and PCMCI channels. Each channel hard-codes the causal structure to the feature space through a graph convolutional causal coding module, then adaptively adjusts the causal constraint strength through a soft constraint causal attention module, and finally dynamically fuses the channel features through a multi-channel adaptive fusion mechanism, and outputs a prediction value of chlorophyll a concentration through a projection layer. The application significantly improves the mechanism interpretability of the model while ensuring the prediction accuracy, and provides a scientific and effective means for water bloom disaster warning and water environment management.

The invention provides a model construction method, a water body grade determination method and device, equipment and a medium. The construction method of the correlation model comprises the following steps: acquiring first water quality data and second water quality data of a to-be-estimated regional water body; respectively constructing fitting curves of correlations between the chlorophyll a and the turbidity, the chemical oxygen demand, the total phosphorus and the total nitrogen; respectively determining a fitting parameter and a correlation coefficient of each correlation fitting curve; constructing a correlation model based on the fitting parameters and the correlation coefficients; sD and CODMn are replaced by turbidity and COD parameters so as to meet the requirement of on-site rapid detection, and the detection efficiency is improved; a correlation model constructed based on fitting parameters and correlation coefficients meets the technical requirements of different nutrition state assessment of different types of lakes in different time periods, and the assessment application scene of a water body in a to-be-assessed region is expanded; the second water quality data corresponding to the correlation coefficient is screened and weighted, and an accurate data basis is provided for subsequent evaluation of the nutrition level of the water body of the to-be-evaluated region.

The application discloses a transcription factor AbHY5 for regulating the formation of colorful leaf color of Guzmania lingulata and application thereof. The application clones a light signal transcription factor AbHY5 from Guzmania lingulata, and the expression of the transcription factor has obvious tissue specificity and is specifically expressed in petals and red leaves. Research shows that the AbHY5 promoter responds to light induction, the expression level of AbHY5 significantly decreases under dark conditions, and the expression of AbHY5 has extremely significant positive correlation with the content of anthocyanin, flavonoids, sucrose and starch. Further research by transgenic technology shows that overexpression of AbHY5 significantly promotes the synthesis of pigments and flavonoids of the transgenic plant, the accumulation of endogenous sucrose and starch and the balance of ROS in the leaves, can increase the content of flavonoids, chlorophyll a, anthocyanin and carotenoids, and improve the photosynthetic rate of the plant leaves, thereby providing support for cultivating new Guzmania varieties with colorful leaves.

This invention discloses an intelligent monitoring and early warning method for small urban water bodies based on visible light drones, belonging to the field of water quality prediction and management technology. The method acquires water body images using a drone equipped with a visible light sensor, automatically stitches them together to generate orthophotos, automatically segments water body areas and outputs boundary vector layers, extracts band DN values ​​and constructs a water quality parameter feature dataset through normalization, band combination, and chromaticity angle calculation, uses a machine learning model to invert chlorophyll a concentration, turbidity, and phycocyanin concentration, and optimizes the model accuracy by combining measured data, performs statistical analysis and future trend prediction on the inversion results, and generates thematic maps, completes water quality evaluation according to standards, and automatically outputs reports and pushes them to relevant units when early warning conditions are met. This invention uses low-cost visible light equipment to achieve fully automated intelligent monitoring, effectively improving inversion accuracy and regulatory efficiency, and adapting to the needs of large-scale, routine monitoring and early warning of small urban water bodies.

The embodiment of the application discloses a kind of chlorophyll a and suspended matter water surface spectrum synchronous iteration inversion methods, the simulated spectrum of Hydrolight radiation transfer model is verified by water surface spectrum;Two combination factors that normalized reflectance data are simultaneously significantly strong correlation with chlorophyll a concentration and suspended matter concentration are selected as method factor;With chlorophyll a and suspended matter as initial variable, the iteration relationship is constructed to obtain the synchronous iteration inversion model of water surface spectrum chlorophyll a and suspended matter;Synchronous iteration model is applied to water surface spectrum data, and the synchronous iteration inversion result of the chlorophyll a and suspended matter of water body is obtained, can provide theoretical reference for water quality monitoring and water environment protection, it has important significance and value.

This application discloses a method and detection device for determining the chlorophyll a content and spatial distribution in a single microalgal cell, relating to the field of bio-optical detection and spectral analysis technology. The method includes: acquiring the Raman spectrum and average Raman spectrum of each pixel in a preset pixel set within a preset wavenumber range for the single microalgal cell to be tested; using the average Raman spectrum or the Raman spectrum of any pixel as the current Raman spectrum; and extracting the chlorophyll a spectrum from 1521 cm⁻¹. ‑1 and 1156cm ‑1 The method involves analyzing the response value and determining the characteristic response vector of chlorophyll a; obtaining a normalized Raman feature vector based on the characteristic response vector; determining the chlorophyll a content based on the normalized Raman feature vector corresponding to the average Raman spectrum; and generating a spatial distribution image of chlorophyll a content based on the normalized Raman feature vector corresponding to the Raman spectrum of any pixel. This application achieves in-situ, non-destructive, automated, and accurate quantitative detection of chlorophyll a content in single microalgal cells.