68 results about "Arbitrary Fluorescence Unit" patented technology

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention discloses a regional GPP assimilation inversion system fusing foundation-multi-source satellite chlorophyll fluorescence, and belongs to the technical field of terrestrial ecosystem carbon cycle monitoring. The signal unmixing and simulation generation module is used for unmixing the total fluorescence of the mixed pixels into pure chlorophyll fluorescence (SIF) and driving an ecological model to generate SIF and GPP simulation values based on environmental stress data; the state component decoupling module is used for decoupling the total GPP analog value into a light response fast component, a light response slow component and a light response memory component; the state evolution prediction module is used for predicting dynamic evolution of the slow component and the memory component; and the self-adaptive assimilation inversion module is used for performing inversion on the region GPP by taking the slow component and the memory component as state variables, taking the SIF observation value as observation data and combining a prediction result of the state evolution prediction module. According to the method, systematic deviation caused by model simplification or data singleness in a traditional method is overcome.

The invention discloses a sky-ground integrated sunlight-induced vegetation chlorophyll fluorescence monitoring system, and belongs to the technical field of remote sensing monitoring. The system provides functions, parameter constraints and cooperation relations of space-based, empty-based and foundation chlorophyll fluorescence information acquisition and data analysis processing and application modules. Spatial scale matching, spectral scale matching, observation angle matching and atmospheric element matching are performed on the acquired sky-ground multi-element data with different scales, so that reflection characteristics, heat dissipation, photochemical indexes, fluorescence emission and atmospheric transmission characteristics during vegetation photosynthesis can be completely acquired; the invention provides a parameter relation and a method for extracting a fluorescence full-spectrum radiation information curve based on oxygen absorption and Fraunhofer dark line. On the basis of sky and landmark calibration and model verification, chlorophyll fluorescence detection of different fineness degrees of global large-range, key areas and ground crown planting scales can be carried out, various stress effects of vegetation can be monitored in real time, and the primary total productivity of the vegetation can be accurately estimated.

The invention relates to the technical field of remote sensing monitoring, and discloses a lake cyanobacterial bloom detection method and system fused with a remote sensing image. The method comprises the following steps: acquiring a multispectral remote sensing image and a synthetic aperture radar image of a target lake; calculating a phycocyanobilin characteristic index and a chlorophyll fluorescence peak index according to the characteristic wave band reflectivity, and screening pixels meeting discrimination conditions to obtain an optical water bloom distribution mask; calculating a radar inversion phycocyanobilin index through a nonlinear regression model according to the dual-polarization backscattering coefficient to obtain a radar water bloom distribution mask; and fusing the double masks to obtain a cyanobacterial bloom monitoring result. The method solves the problems that an existing cyanobacterial bloom detection method cannot realize all-weather monitoring, lacks cyanobacterial specific recognition capability and is insufficient in reliability of a single data source, and improves the timeliness, accuracy and reliability of cyanobacterial bloom detection.

The invention provides an automatic illumination adjusting method and system based on seed and seedling cultivation, and relates to the technical field of illumination control and intelligent agriculture, the method comprises the following steps: firstly, collecting seedling chlorophyll fluorescence parameters to construct a light stress index, and accurately identifying a light inhibition risk; then calculating a physiologically permissible light intensity threshold adaptive to the dark response capability of the plant by combining the environment temperature and the CO2 concentration; positioning a shielded weak light area by using three-dimensional point cloud voxelization and a ray tracing technology, and calculating a shadow compensation gain coefficient; and finally, generating a control matrix in combination with a dynamic left-rotation light strategy, and driving an independent addressing LED array and a variable-angle lens to execute local precise light supplement and light vector adjustment. Through physiological feedback and spatial light field reconstruction, the problem that traditional illumination control neglects individual tolerance difference and canopy shielding is solved, photooxidation damage is effectively prevented, and the growth uniformity and the light energy utilization rate of seedling groups are remarkably improved.

The embodiment of the invention discloses a time-space prediction method and device for sun-induced chlorophyll fluorescence. The method comprises the following steps: acquiring a time-space data set; based on the spatio-temporal data set, taking a county administrative unit as a reference unit to perform area aggregation, dividing a large-scale area into a plurality of homogenized sub-areas, and selecting a representative spatial point from each sub-area; extracting the SIF of the corresponding representative spatial point and variable feature information of the key covariable, and constructing a four-dimensional space-time cube based on the variable feature information; sTL decomposition is carried out on the four-dimensional space-time cube to obtain a trend term, a seasonal term and a residual term, and flattening processing is carried out on the trend term, the seasonal term and the residual term to obtain a time sequence feature vector; the time sequence feature vector is input into a trained LSTM-Transform model for prediction, and a trend-season point value prediction result is obtained; and jointly interpolating the point value prediction result and a preset covariable to a preset target prediction area by using an ANUSPLIN thin plate smooth spline interpolation method, and generating an SIF large-scale space-time prediction map.

The invention provides a crop functional chlorophyll fluorescence inversion method, system and device and a readable medium, and relates to the technical field of ecological environment monitoring, and the method comprises the steps: obtaining a multi-source data cube; acquiring a crop type distribution diagram based on the multi-source data cube; constructing a sun-induced chlorophyll fluorescence SIF inversion basic model according to the crop type distribution diagram; and outputting a crop solar-induced chlorophyll fluorescence time sequence product according to the solar-induced chlorophyll fluorescence SIF inversion basic model. According to the crop functional chlorophyll fluorescence inversion method provided by the invention, the multi-source data cube is used as input, and two special processing links which are completely independent in physical mechanism and dynamic correction are constructed; high-precision and high-physical-reliability SIF time sequence product production for crops can be realized, and a reliable data basis is provided for precise monitoring of photosynthesis of an agricultural ecosystem.

The application discloses a water-carbon flux optimization method based on carbonyl sulfide (COS) and sun / solar-induced chlorophyll fluorescence (SIF), and belongs to the technical field of land ecosystem model optimization and application. The application combines COS and SIF observation information, adopts a four-dimensional variation algorithm to perform data assimilation on observation data and simulation data, simultaneously restrains plant photosynthesis processes and gas transmission processes, and then realizes joint optimization of carbon flux and water flux. Meanwhile, the application compares and verifies simulation data before and after optimization and observation data, and proves effectiveness and reliability of the method.

The application relates to the technical field of intelligent agriculture and plant protection, and particularly discloses a broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion, which comprises a portable collection platform, a mobile experiment terminal and a server. The portable collection platform is integrated with a hyperspectral imaging camera, a chlorophyll fluorescence imager, a dark-adapted leaf clamp and a camera for sample barcode recognition, and is provided with an adjustable height support and a controllable light source to ensure the consistency of data collection conditions. The mobile experiment terminal is internally provided with a physicochemical index rapid detection module, including a portable PCR instrument for on-site nucleic acid rapid extraction and amplification and a miniature ultraviolet spectrophotometer for rapid determination of pigment content. The broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion are highly integrated, portable and automated, are suitable for a field environment and provide an efficient disease diagnosis tool for non-expert users.

Implementations of a data acquisition unit for a ground vehicle may include an enclosure; a single board computer included in the enclosure; and a base board; operatively coupled with the single board computer. The data acquisition unit may include an optocoupler operatively coupled with the single board computer; at least one chlorophyll fluorescence sensor coupled with the optocoupler; at least one camera coupled to the base board; and a global positioning sensor coupled with the base board. The data acquisition unit may include a flow meter coupled with the single board computer; and a power source operatively coupled with the single board computer and with the base board.

The application discloses a water body chlorophyll concentration profile inversion method and a laser radar detection method, wherein the inversion method comprises the following steps: obtaining laser radar detection echo signals at a specific depth in a target area, which comprises a fluorescence channel and a Raman channel; expressing a backscattering signal profile of the fluorescence channel as a function comprising a fluorescence backscattering coefficient; expressing a backscattering signal profile of the Raman channel as a function comprising a Raman backscattering coefficient; normalizing the backscattering signal profile function of the fluorescence channel with the backscattering signal profile function of the Raman channel to obtain a normalized signal; inverting the fluorescence backscattering coefficient based on a perturbation method; inverting a chlorophyll fluorescence absorption coefficient at a specific laser wavelength based on the inverted fluorescence backscattering coefficient; and inverting a chlorophyll concentration profile in the water body based on the inverted chlorophyll fluorescence absorption coefficient. The inversion method can invert the water body chlorophyll concentration profile from one measurement.

The present invention provides a method and system for reconstructing sunlight-induced chlorophyll fluorescence based on satellite observation data, comprising: collecting and processing ground site observation data, temperature data, leaf area index data, MODIS bidirectional reflectance distribution function nadir-corrected reflectance data, enhanced vegetation index data, normalized difference moisture index data, global land cover classification data, OCO-2 and TROPOMI satellite observation SIF data; then using a machine learning method to reconstruct data using OCO-2 SIF and TROPOMI SIF as original data respectively; then, constructing an empirical formula between the two reconstructed SIF data and the site observation SIF, and optimizing to obtain estimated parameters at the site; then, calculating the estimated parameters of the empirical formula for each land cover type; finally, substituting the two reconstructed SIF data into the empirical formula to obtain a reconstructed SIF with site SIF information.

The application discloses a kind of plant chlorophyll fluorescence three-dimensional imaging method, belong to plant chlorophyll fluorescence three-dimensional field.The plant chlorophyll fluorescence three-dimensional imaging method of present application is complicated, needs multi-angle shooting, synthesis image needs a lot of data and so on for the problems existing in prior art, the present application provides a kind of plant chlorophyll fluorescence three-dimensional imaging device and method, and plant single-view image is obtained under different position light source, and plant is three-dimensionally reconstructed by plant neural radiation field, generates plant fine three-dimensional model, simultaneously, plant fine three-dimensional model is optimized in combination with rendering, obtains plant three-dimensional image and plant chlorophyll fluorescence three-dimensional image, by which the precision and speed of plant chlorophyll fluorescence three-dimensional imaging under single view angle can be improved, data processing time is reduced, more accurate, comprehensive information is provided for plant growth, disease and adversity response and the like research.

The invention discloses a method, system and medium for machine learning inversion of sunlight-induced chlorophyll fluorescence, and relates to the technical field of vegetation sunlight-induced chlorophyll fluorescence remote sensing inversion, and the method comprises the steps: obtaining an actual measurement data set, and generating a synthetic data set; introducing principal component analysis to carry out dimension reduction preprocessing on features in the synthetic data set to obtain a preprocessed synthetic data set, and taking the preprocessed synthetic data set as a training set; introducing noise of different levels into the training set to train each machine learning model, and obtaining each trained machine learning model; pre-processing the incident irradiance and the reflected radiance in the measured data set, taking the pre-processed incident irradiance and reflected radiance as input of each trained machine learning model, and comparing performance indexes of output results of each model to obtain an optimal machine learning model; executing an SIF inversion task based on the optimal machine learning model; the method is used for high-resolution and rapid inversion of the SIF.

Implementations of a data acquisition unit for a ground vehicle may include an enclosure; a single board computer included in the enclosure; and a base board; operatively coupled with the single board computer. The data acquisition unit may include an optocoupler operatively coupled with the single board computer; at least one chlorophyll fluorescence sensor coupled with the optocoupler; at least one camera coupled to the base board; and a global positioning sensor coupled with the base board. The data acquisition unit may include a flow meter coupled with the single board computer; and a power source operatively coupled with the single board computer and with the base board.

The application relates to the technical field of remote sensing monitoring, and discloses a lake cyanobacterial bloom detection method and system fusing remote sensing images. The method comprises the following steps: acquiring multispectral remote sensing images and synthetic aperture radar images of a target lake; calculating phycocyanobilin characteristic indexes and chlorophyll fluorescence peak indexes according to characteristic band reflectivity, screening pixels meeting a discrimination condition to obtain an optical water bloom distribution mask; calculating a radar-retrieved phycocyanobilin index according to a dual-polarization backscattering coefficient through a nonlinear regression model to obtain a radar water bloom distribution mask; and fusing the two masks to obtain a cyanobacterial bloom monitoring result. The application solves the problems that the existing cyanobacterial bloom detection method cannot realize all-weather monitoring, lacks cyanobacterial specificity recognition capability, and has insufficient reliability of a single data source, and improves the timeliness, accuracy and reliability of cyanobacterial bloom detection.

The application discloses a method and device for classifying drought stress degrees of plants based on chlorophyll fluorescence images, measures canopy gray images of each plant to be measured, carries out threshold segmentation on the canopy gray images excited by saturated light to obtain mask mask images, calculates chlorophyll fluorescence parameters based on the mask mask images, takes the chlorophyll fluorescence parameters of each plant to be measured and the proportion of abnormal points corresponding to the chlorophyll fluorescence parameters as characteristic parameters, inputs a plant drought stress degree classification model, and determines the drought stress degree of the plant to be measured. The application improves the accuracy of drought stress degree classification by introducing the proportion of abnormal points.

This invention discloses a rapid detection method and system for antibiotic residues in vegetables based on chlorophyll fluorescence imaging, belonging to the field of non-destructive testing technology for agricultural product quality and safety. The invention applies saturated pulsed light to dark-adapted vegetable leaves and acquires multi-temporal resolution fluorescence imaging sequences at a logarithmic scale within the transient time window from phase J to phase I of the rapid chlorophyll fluorescence induction kinetics. From these sequences, a temporal feature tensor composed of transient inflection point time shifts and a spatial feature tensor composed of leaf vein-mesophyll boundary heterogeneity textures are extracted. These two tensors are then fused using a Tucker core tensor cross-external product to obtain an antibiotic type fingerprint tensor. The antibiotic type and confidence level are output through CNN classification. Finally, a category-conditional gradient boosting regression is used to output concentration prediction values, and a reverse closed-loop adjustment is applied to adjust the sampling time window and region of interest. This invention achieves integrated rapid detection of antibiotic residues at the level of tens of micrograms per kilogram in vegetables, combining type identification and concentration quantification.

The invention relates to an imaging method for measuring sunlight-induced chlorophyll fluorescence (CF) of an object to be measured (30), in which the object to be measured is irradiated with sunlight (40), and light (41) reflected off the object to be measured, after being filtered to form filtered light (42) by means of an interference filter (15) that is tiltable vis-à-vis an optical axis, is recorded to form an image (20) by means of an image sensor (11) having several light-sensitive sensor elements (12) and is made available to a calculation and control device (16) by said image sensor, wherein an image series (21) of several recorded images of the object to be measured is created by means of the calculation and control device, said images being recorded at different tilt angles (δ, ε) of the interference filter in order to change, for a portion of the sensor elements within the image series and by means of the different tilt angles per image, an effective transmission wavelength (λ') of the interference filter at which said interference filter has maximum transmission, said effective transmission wavelength being related to the individual sensor elements, wherein the sunlight-induced chlorophyll fluorescence detected per sensor element is determined for this portion of the sensor elements by means of the calculation and control device on the basis of intensity values (IP, Px,y) of the filtered light (42) recorded by the respective sensor element per image of the image series; the invention furthermore relates to a device (10) for measuring sunlight-induced chlorophyll fluorescence of an object to be measured.

This invention provides a method for monitoring marine red tides based on chlorophyll fluorescence remote sensing, belonging to the field of remote sensing monitoring technology for large floating algae in nearshore areas. The steps of this invention include: acquiring sunlight-induced chlorophyll fluorescence data, chlorophyll concentration data, and fluorescence index data of the target area; performing gridding and data quality control on the sunlight-induced chlorophyll fluorescence data to obtain effective sunlight-induced chlorophyll fluorescence data; screening the effective sunlight-induced chlorophyll fluorescence data according to chlorophyll concentration, correcting the screened sunlight-induced chlorophyll fluorescence data for solar zenith angle to obtain corrected sunlight-induced chlorophyll fluorescence data; constructing a relationship model based on the corrected sunlight-induced chlorophyll fluorescence data and fluorescence index data, performing linear threshold conversion to determine the sunlight-induced chlorophyll fluorescence threshold for red tide identification, identifying red tides in the target area, and obtaining red tide monitoring results. This method improves the accuracy and reliability of red tide monitoring.

A three-dimensional imaging device based on line-laser-induced chlorophyll fluorescence, and a method. A line-shaped laser light source device (7) emits line laser light, the line laser light is refracted by a triangular prism (2) and irradiates a leaf to be measured, a CMOS camera (1) captures an image at the same time, and as a stepping motor (3) drives the triangular prism (2) to rotate, scanning of the leaf to be measured is completed; an embedded computer (5) performs multi-threaded processing on the image to form three-dimensional point cloud data comprising fluorescence kinetic parameters. Rapid measurement of real-time fluorescence parameters is achieved by means of the line laser light and the long exposure of the CMOS camera (1), and the embedded computer (5) performs multi-threaded processing on the image to obtain the real-time fluorescence parameters. Using line laser scanning to synchronously perform chlorophyll fluorescence detection and three-dimensional point cloud generation alleviates the problems of high cost and large size of area excitation light sources. Moreover, by means of the line laser scanning in light of the long exposure of the camera, large-area chlorophyll fluorescence-based rapid imaging can also be achieved, achieving the purpose of real-time detection.

The invention provides a crop yield remote sensing inversion method based on a chlorophyll fluorescence mechanism, and belongs to the technical field of agricultural crop growth monitoring, and the method comprises the steps: obtaining sunlight-induced chlorophyll fluorescence remote sensing data, photosynthetically active radiation data and crop type distribution data in the whole growth period of crops; and distinguishing the photosynthetic path according to the crop type distribution data, calculating by adopting a corresponding photoreaction mechanism model based on the obtained data to obtain the total primary productivity of the crop, obtaining the net primary productivity by combining the historical net primary productivity and the total primary productivity ratio, and obtaining the crop yield based on carbon content inversion. By utilizing coupling association between sunlight-induced chlorophyll fluorescence and a photosynthesis process, the problem that photosynthetic physiology in vegetation is difficult to effectively capture due to dependence on a static vegetation index in an existing crop technology is solved; the problem of how to accurately associate instantaneous sunlight-induced chlorophyll fluorescence remote sensing data with photosynthesis of crops in the whole growth period is solved.

1. Name of the product in this design: Sunlight-induced chlorophyll fluorescence measuring device. 2. Purpose of this design: It is used in a sunlight-induced chlorophyll fluorescence measurement device to achieve non-destructive real-time monitoring by accurately capturing fluorescence signals from plant photosynthesis. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The present invention provides a method and device for measuring phytoplankton primary productivity, comprising the following steps: obtaining spectra of calibration samples at different temperatures; establishing a calibration plane based on the spectra, using water environment temperature and spectral baseline intensity as independent variables and fluorescence peak intensity as a dependent variable; obtaining the corrected fluorescence peak intensity of the calibration sample based on the calibration plane; establishing a calibration curve based on the corrected fluorescence peak intensity of the calibration sample and primary productivity; obtaining a fluorescence spectrum of a sample to be measured; obtaining the corrected fluorescence peak intensity of the sample to be measured based on the calibration plane; and obtaining the phytoplankton primary productivity of the sample to be measured from the corrected fluorescence peak intensity of the sample to be measured based on the calibration curve. The present invention can improve the accuracy of chlorophyll fluorescence measurements and further improve the measurement accuracy of phytoplankton primary productivity observed using laser-induced chlorophyll fluorescence.

The invention discloses a high-light-resistant algal strain high-speed screening method and system based on micro-fluidic single cell sorting, and relates to the field of bioengineering.The high-light-resistant algal strain high-speed screening method comprises the steps that haematococcus free cells in the logarithmic phase are taken and added into a double-fluorescence probe mixed solution, and incubation is conducted in a dark place to obtain a haematococcus solution; bBM sheath fluid and haematococcus fluid are introduced into a sample introduction area of the micro-fluidic chip, the BBM sheath fluid and the haematococcus fluid are focused by the sample introduction area and then enter a highlight stress area, highlight treatment is carried out to induce motile cells to be transformed into spores, and the transformed cells enter a detection area. Through in-situ high light stress and single cell continuous sorting of the micro-fluidic chip, the sorting efficiency is high, and the screening period is greatly shortened; three parameters of chlorophyll fluorescence, astaxanthin fluorescence and forward scattering light are adopted for cooperative detection, and a threshold value which is quantitatively set based on a control group is combined, so that the proportion of a false positive strain is reduced to be less than 5%, and the screening accuracy of a double-superior strain is greatly improved; the microfluidic chip is extremely low in single reagent consumption, greatly reduces the screening cost of single strains, and is easy for large-scale popularization and application.

The invention provides a laser-induced observation system and method, and the system comprises a laser-induced light source which is used for outputting laser, exciting chlorophyll fluorescence of a detected sample, and forming a detectable fluorescence signal; wherein the detected sample is a living plant; the optical receiving system is used for collecting fluorescence signals excited by the detected sample; the spectral information acquisition system is connected with the optical receiving system and is used for carrying out light splitting and detection on the fluorescence signal to obtain spectral data; the spectral information processing system is connected with the spectral information acquisition system and is used for preprocessing the spectral data acquired by the spectral information acquisition system to obtain preprocessed spectral data; and the quantitative analysis algorithm system is connected with the spectral information processing system and is used for carrying out quantitative analysis on the preprocessed spectral data to calculate chlorophyll fluorescence parameters. The problem that laser-induced chlorophyll fluorescence of living plants cannot be remotely observed or cannot be detected in an existing laser-induced chlorophyll fluorescence technology is solved.

The invention relates to the field of agricultural detection, discloses a lossless seed maturity detection system, and solves the problems that the existing seed maturity detection technology depends on a single optical mode, so that the detection precision and robustness are insufficient, the misjudgment rate is high, and accurate planting decision support cannot be quickly provided on site. The system comprises a fusion sensing module which comprises a resonance acoustic microscopic unit and a chlorophyll dynamic fluorescence unit and is used for collecting an internal structure acoustic signal and a chlorophyll fluorescence dynamic signal of a seed and constructing multi-dimensional seed characteristic data; the intelligent edge calculation module is integrated with a processor of a preset lightweight artificial intelligence model, and is used for performing feature extraction and data fusion on the acoustic signal and the fluorescence dynamics signal, and reasoning and outputting a seed maturity score; and the interaction module is used for receiving agronomic parameters input by a user, so that the processor generates planting decision suggestions based on the maturity score and the agronomic parameters, and displays the maturity score and the planting decision suggestions.

The application discloses a SIF spectrum inversion method based on singular value decomposition and machine learning regression. The method comprises the following steps: step 1, acquiring sample radiance spectrum data, solar-induced chlorophyll fluorescence spectrum data corresponding to the radiance spectrum data and the like; step 2, performing singular value decomposition on a feature matrix, selecting a plurality of main singular vectors to construct a reconstruction base matrix, and calculating low-dimensional expansion coefficients of the fluorescence spectrum; step 3, performing singular value decomposition on the feature matrix; step 4, taking the low-dimensional feature coefficients of the radiance spectrum, preset key band radiance features, band ratio features and observation geometry features as input variables of a machine learning regression model; and step 5, inputting a sample to be inverted into the trained machine learning regression model. The application converts the high-dimensional solar-induced chlorophyll fluorescence spectrum inversion problem into a low-dimensional expansion coefficient prediction problem, and has the advantages of high inversion efficiency, strong model stability and suitability for popularization and application.

The invention discloses a green plant flowering phase synchronous regulation and control method and system based on dynamic perception, and the method comprises the steps: obtaining a green plant canopy multispectral image sequence and ambient light spectrum distribution data through the scanning of a visible light-near infrared dual-channel imaging device; plant physiological status vectors are generated by extracting chlorophyll fluorescence intensity values, anthocyanin reflectivity and other characteristics, and accurate data are provided for subsequent regulation and control; with the help of a flowering phase convolution prediction model optimized by transfer learning, an ambient light spectrum and historical phenological data are fused, and a flowering time window of each green plant is accurately predicted; green plants with abnormal flowering phases are screened out through space-time overlay analysis, compensation spectrum parameters are calculated to generate a spectrum regulation and control instruction, and finally directional irradiation is carried out through a rotatable array type spectrum emitter. According to the method, the accuracy of synchronous regulation and control of the flowering phase of the green plants can be improved.