118 results about "Red tide" patented technology

The invention relates to the technical field of red tide anomaly detection, in particular to an ocean red tide anomaly detection method and system fusing multi-source remote sensing and a graph neural network. The method comprises the following steps: acquiring remote sensing image data, unmanned aerial vehicle image data and monitoring data of a monitoring point; performing data preprocessing on the acquired remote sensing image data and unmanned aerial vehicle image data; performing feature extraction and feature fusion on the remote sensing image and the unmanned aerial vehicle image to obtain remote sensing feature data; constructing a space-time diagram structure based on the monitoring data of the monitoring points to obtain diagram structure data; based on a cross-modal comparison self-supervised learning mechanism, carrying out consistency representation learning on a remote sensing feature mode and a graph structure feature mode; by introducing multi-source heterogeneous data and fusing a graph neural network modeling means, the limitation of a single data driving method in the aspects of coarse red tide recognition granularity, low space-time precision and the like is effectively broken through, and the meticulous property and global perception ability of red tide feature modeling are remarkably improved.

The invention discloses a marine environment dynamic monitoring system based on cooperation of artificial intelligence and multi-source remote sensing, relates to the technical field of marine environment monitoring, and realizes multi-dimensional monitoring of a sea surface state by receiving multi-source data of a plurality of monitoring ends. Chlorophyll concentration mutation is detected in real time by setting sliding window difference, a subsequent processing flow is dynamically triggered, and the response speed of sudden events (such as red tide) is increased; aI is utilized to predict clock offset, interpolation reconstruction, weighted fusion and ocean current compensation technologies are combined, and space-time asynchronous errors of multi-source data are remarkably reduced; and generating a data cube fusing the backscattering coefficient, the temperature field and the chlorophyll gradient, and calculating a diffusion velocity field, thereby providing high-precision decision support for marine disaster early warning. The limitation of a traditional single data source is broken through, efficient cooperation of multi-modal data is driven through artificial intelligence, and the real-time performance, the accuracy and the dynamic analysis capability of marine environment monitoring are improved.

The invention discloses an intelligent fishery comprehensive evaluation system based on multi-dimensional indexes, and belongs to the field of intelligent fishery. The intelligent fishery comprehensive evaluation system based on the multi-dimensional indexes comprises a water quality monitoring unit, a prediction and early warning unit, a breeding regulation and control unit, an ecological evaluation unit and a decision output unit. The method solves the problem of hysteresis of the detection result in the prior art, can advance the water quality change trend and the red tide occurrence probability in the future by monitoring the water temperature, the dissolved oxygen, the pH value and other key water quality parameters in real time and by means of the prediction model, provides a key basis for breeding decision making, and improves the breeding quality. Execution equipment is flexibly adjusted according to real-time data and early warning information to improve the breeding environment and improve breeding benefits, meanwhile, multi-source data is integrated to comprehensively evaluate the influence of breeding activities on the ecological environment, ecological balance maintenance is assisted, comprehensive evaluation results are integrated to generate ecological improvement suggestions, resource allocation is optimized, and the economic benefit is improved. And the intelligent and ecological level of fishery is integrally improved.

The invention discloses an offshore red tide multi-parameter real-time monitoring method and monitoring system based on a buoy array. The method comprises the following steps: S1, performing array deployment and topology configuration according to a sea area sensitivity degree, tide characteristics and a historical red tide trajectory; according to the invention, through risk differentiation buoy array deployment and redundancy topology, traditional isolated monitoring limitation is solved, and sea area full coverage and red tide diffusion tracking are realized; physical-chemical-biological multi-source sensing and in-situ detection are combined with data fusion, so that the problem of single parameter is solved, and the monitoring accuracy is improved; by means of liftable stratified sampling and in-situ algae species and toxicity analysis, the vertical monitoring blank is filled up, and the complete life cycle process of red tide from occurrence to regression is completely captured; through linkage of edge-cloud multi-model early warning and field equipment, a'monitoring-early warning-disposal 'closed loop is formed, and the red tide monitoring real-time performance and the emergency response capability are greatly improved.

The invention relates to the technical field of red tide anomaly detection, in particular to a red tide anomaly detection method and system based on an improved multi-mode Transform. The method comprises the following steps: acquiring a remote sensing image and text data; respectively carrying out data preprocessing according to the obtained remote sensing image and text data; performing visual positioning and text selection based on the preprocessed data; performing cross-modal feature learning on the basis of a hierarchical Transform of a multi-modal capsule mechanism; guiding an attention mechanism based on a semantic path to carry out image-semantic feature alignment optimization; and carrying out multi-modal knowledge distillation on the optimized features. According to the method, an image and text preprocessing module, a visual positioning module, a keyword extraction module and other modules are combined, multi-angle accurate perception of a complex red tide scene is achieved, and the bottleneck that a red tide area is difficult to accurately recognize under the condition that data are single and information dimensions are limited in a traditional method is broken through.

The invention provides an ocean red tide early warning method based on remote sensing inversion and a time-space map convolutional network. The ocean red tide early warning method comprises the following steps: collecting hyperspectral data and multispectral data; respectively carrying out data quality inspection on the collected hyperspectral data and multispectral data; preprocessing the hyperspectral data and the multispectral data after the quality inspection is qualified to respectively obtain a hyperspectral result image and a digital orthoimage; analyzing the hyperspectral result image and the digital orthoimage to obtain remote sensing inversion data of water temperature, salinity, total nitrogen, total phosphorus and chlorophyll a, and generating a gradient distribution thematic map of each index; according to water temperature, salinity, total nitrogen, total phosphorus, chlorophyll a and historical monitoring data, in combination with a change trend of key environmental parameters, introducing a space-time diagram convolutional network model, simulating a red tide diffusion situation, setting a risk threshold, and performing risk prediction on red tide occurrence through a red tide occurrence risk prediction model; and updating the red tide occurrence risk prediction model based on buoy monitoring and field monitoring data.

The invention relates to the technical field of environment monitoring, in particular to a seawall ecologicalization real-time monitoring method based on multi-source data fusion. The method comprises the following steps: acquiring structure monitoring data, ecological monitoring data and water quality monitoring data of a seawall through an Internet of Things sensor, and acquiring remote sensing data of a seawall area in combination with satellite remote sensing and an unmanned aerial vehicle image; performing space-time registration and fusion on different data sources to generate a unified multi-source fusion data set; identifying a red tide abnormal event based on the fused data set, and constructing a red tide influence conduction model for predicting the damage degree of the red tide to the ecological system and the influence of the red tide to the stability of the embankment body; establishing a coupling evaluation model of ecological indexes and structural indexes, calculating a coupling coefficient by using a fuzzy comprehensive evaluation method, dynamically adjusting ecological and structural weights according to a model output result, and calculating a seawall comprehensive health index; graded early warning information is automatically generated according to a preset threshold value, and collaborative monitoring and intelligent early warning of seawall ecology and structure safety are achieved.

The invention relates to the technical field of image recognition, in particular to a red tide algae recognition method based on a hyperspectral image, and the method comprises the following steps: calculating a gradient value of each pixel in a spatial dimension and a spectral dimension according to an original hyperspectral data cube, setting a diffusion coefficient for diffusion, and obtaining a three-dimensional fidelity hyperspectral data cube. According to the method, the gradient change trends of the spatial dimension and the spectral dimension are extracted at the same time pixel by pixel, and the regional differentiation diffusion operation is matched, so that the expression ability of the alga plaque boundary and the consistency of the internal spectral characteristics are enhanced, and the fidelity of the hyperspectral data in fine-grained region identification is improved; upper convex hull calculation of a spectral reflectivity curve and construction of a continuous spectrum background baseline are introduced, and characterization of pigment absorption morphological characteristics is enhanced; and in combination with the characteristic absorption wave bands of chlorophyll and phycobiliary, the synergistic weighted response to the distribution of the algae pigment is realized, and the sensitivity and selectivity of pigment recognition are improved.

The invention discloses a method for automatically detecting red tide of Paleocystis globosa based on video monitoring, which comprises the following steps of: S1, acquiring image data of a sea surface by using a high-definition monitoring camera on a buoy platform, transmitting the image data to a database in real time, and preprocessing the image data; s2, making a labeling scheme, labeling the preprocessed image data, and constructing a training sample library; s3, training and optimizing the YOLOv8 target detection model to obtain a globocystis globosa recognition model; s4, carrying out frame extraction processing on a video monitoring picture to be identified according to a certain time interval, inputting each frame of picture into the globocystis globocystis identification model for identification, and automatically generating globocystis globocystis red tide monitoring information; according to the method, a video monitoring technology and an artificial intelligence target detection algorithm are combined, so that efficient and accurate monitoring of capsules generated during red tide of the globocystis globocystis can be realized, and the real-time performance and the automation level of red tide monitoring of the globocystis globocystis can be remarkably improved.

The invention discloses a shrimp pond water quality prediction method based on a deep echo state network, and relates to the technical field of shrimp pond water quality prediction, and the method comprises the following steps: deploying an underwater fluorescence sensor in a shrimp pond culture water body to detect a fluorescence response signal of algae, and obtaining algae concentration data in the water body in real time based on the fluorescence signal intensity; dynamic changes of algae in the prawn pond water are monitored in real time; and performing time sequence alignment on the collected algae concentration data, and constructing an algae concentration time sequence data map based on the aligned time sequence data. According to the method, fluorescence sensor data and a short-time feature extraction mechanism are fused, a deep echo state network is combined to model an algae burst trend, and early warning can be realized before red tide outbreak; and the abnormal area monitoring precision is improved by enhancing a sampling mechanism, the problem of slow recognition of emergencies by a traditional model is effectively solved, and the aquaculture water quality risk perception and intelligent management capability is remarkably improved.

The invention provides a primer group for qualitative and quantitative detection of three types of fish toxicity karenia, a detection method and application, and belongs to the technical field of marine aquaculture guarantee. The primers as shown in SEQ ID NO.1-6 provided by the invention can be used for respectively identifying the species of the Karenia saddle, the Karenia longgroove and the Karenia butterfly; a plasmid standard curve and a cell standard curve are established in combination with an SYBR Green I fluorescent quantitative PCR technology, and specific identification and quantity determination of the three types of fish-toxic karenia are further realized. The method has the advantages of high specificity, high sensitivity, simplicity in operation, short time consumption and the like, can overcome the limitation of traditional morphological identification, can realize rapid and accurate qualitative identification and quantitative detection of a large number of samples, is time-saving and labor-saving, is relatively low in price, and can become an effective technical means for prediction, early warning and large-area monitoring of the Karenia red tide.

The invention relates to an inshore water quality day-by-day kilometer-level inversion method, system, device and medium, and belongs to the technical field of water quality monitoring, the method comprises the following steps: obtaining remote sensing image data, meteorological reanalysis data and ground water quality site measured data, preprocessing, carrying out space-time matching fusion, using the fusion data as a sample to carry out model training, and obtaining a model training model; obtaining a space model; a spatial model offshore water quality spatial distribution diagram; and constructing a CNN-BiLSTM mixed time sequence model, and training the CNN-BiLSTM mixed time sequence model by taking historical water quality data of a ground water quality station and the offshore water quality spatial distribution map as samples to obtain a prediction model. The method is suitable for red tide early warning, pollution tracing and marine ecological management, breaks through monthly monitoring limitation, and realizes day-by-day kilometer-level water quality dynamic tracking; remote sensing large-range coverage and site high-precision data are fused, and the problem of inshore environment heterogeneity is solved; the time-space double-model cascade architecture significantly improves the generalization ability under complex hydrological conditions.

The invention discloses a multi-element information acquisition and analysis system based on telemetering drifting buoys, and relates to the technical field of marine environment monitoring. A three-dimensional fluorescence spectrometer, a temperature-salinity-depth sensor, a positioning module and a miniature water sampler are carried on each telemetering drifting buoy; the system is used for collecting algae fluorescence spectrums, water quality parameters and position data in real time, inputting the data into a trained random forest model to output the red tide occurrence probability, triggering multi-level early warning in combination with a preset expert rule engine, linking air and shore-based equipment and a data storage system, and utilizing laboratory true value data obtained by a miniature water sampler to determine the red tide occurrence probability. And performing incremental training on the random forest model every day, thereby dynamically optimizing internal structure parameters of the model. The method effectively improves the prediction probability of the red tide.

The invention provides a fish stress behavior detection method in a red tide algae environment, and the method comprises the steps: collecting a plurality of groups of target images at a plurality of depth positions from a water surface to a pool bottom, and extracting the visual features of the target images based on a detection model; extracting a target fish school in the detection target image based on the detection model, identifying a motion trail of the target fish school, and extracting multiple behavior characteristics of the target fish school from the motion trail; the visual features, the multiple behavior features and the environment parameters in the red tide algae environment form a fusion sequence, and a fusion feature vector of the fusion sequence is extracted; and inputting the fusion feature vector into a classification model to obtain the stress level of the target fish school in the red tide algae environment. The invention further provides a fish stress behavior detection method, device and equipment in the red tide algae environment and a medium.

The invention discloses a marine ecological early warning management system and method based on multi-source data, and relates to the technical field of marine data monitoring. The method comprises the following steps: presetting sampling time and synchronously acquiring water body parameters and environmental parameters of each sampling layer at a sampling point; calculating a standard vertical gradient, and constructing an incidence matrix; calculating association strength to form an interlayer association vector; extracting a trend slope and a time difference degree of the inter-layer association vector, and splicing to obtain a space-time association vector; a sliding window method is adopted to calculate covariance between the space-time correlation vector and each environment parameter time sequence, a dynamic coupling coefficient is obtained, and a coupling coefficient matrix is constructed; and comparing the coupling coefficient matrix with the reference matrix element by element, and realizing accurate early warning of the red tide risk through a preset threshold value. According to the method, the time-space correlation characteristics of the multi-source data are fused, so that the early-stage and accurate early warning of the red tide is realized.

The invention relates to the technical field of red tide early warning, in particular to a red tide outbreak early warning method and system based on weak target detection. The method comprises the following steps: constructing a weak target detection enhancement model based on a Yov8 structure, and performing feature extraction on a red tide data set image by using the weak target detection enhancement model based on the Yov8 structure; carrying out feature fusion on the extracted features; target detection is carried out based on the fused features, and incremental training is carried out on a weak target detection enhancement model based on a target detection result; and performing red tide early warning by using the trained weak target detection enhancement model. According to the model provided by the invention, the recognition accuracy of the red tide outbreak period image reaches 91.1%, the recognition accuracy of the red tide early stage image reaches 84.3%, and red tide images in different stages are effectively recognized.

To solve such a problem that cultivation of seaweeds that generate blue carbon is committed to a natural environment which is very limited to shallows where sunlight reaches (=0.2% of the entire sea), the cultivation region may decrease but cannot increase because of installation of a port, tourism development such as a swimming beach, and expansion of commercial facilities, sunlight sometimes cannot sufficiently reach the cultivation region because of weather conditions and the ebb and flow of the tide, which makes the cultivation insufficient, and furthermore, an outbreak of red tides may inhibit algae from growing.SOLUTION: In a blue carbon system, a sealed wooden box is installed to each of the four corners of a wooden frame of a 12 m-sized wooden artificial island unit; a culture soil unit for cultivating algae is placed on the wooden frame; buoyancy of the four sealed wooden boxes and weight of the culture soil unit are controlled to be well balanced; and upper sides of the four sealed wooden boxes are set to be exposed from the water by approximately 30 cm while taking the water depth into account so as to provide a best growth condition of the algae.SELECTED DRAWING: Figure 1

The invention discloses a total factor monitoring method for a marine ranch, and belongs to the technical field of ocean engineering and information. The method comprises the following steps: collecting water quality parameters and biological population data in real time; the planktons and pollutants are identified through spectral analysis and chemical sensing, and environmental elements are classified; a sonar and an electronic tag are combined to track a fish trajectory, and dynamic monitoring data are obtained; predicting a red tide probability based on machine learning and outputting risk early warning; continuously updating the model to form multi-element integrated monitoring data; and finally, sorting the key indexes to generate marine ranch key element monitoring framework data. According to the invention, the real-time monitoring capability of the marine ranching environment and the biological state is obviously improved, the ecological risks such as red tide are reduced, and the sustainable development of the marine ranching is ensured.

The invention belongs to the technical field of microorganisms, and particularly relates to application of marine phycobacterium LZ-14T in production of indole-3-acetic acid. According to the present invention, the interphycobacterium LZ-14T is named as Maricatenella alexandrii gen. Nov., sp. Nov. The present invention discovers that the interphycobacterium LZ-14T can achieve the effect of significantly inhibiting the synthesis of the PSP toxin by releasing the IAA signal molecule under the condition of promoting the growth of the host algae. The new discovery can provide a new theoretical basis for analyzing the generation mechanism of paralytic shellfish poison of alexandrium chainchain and exploring scientific prevention and control of red tide. The invention also finds that the IAA produced by the phycobacterium LZ-14T can inhibit the growth of escherichia coli, and the result lays a foundation for the preparation of new antibacterial drugs.

The invention relates to the technical field of red tide disaster prevention and control, in particular to a method for improving red tide treatment efficiency of polyaluminum chloride modified clay. The specific method comprises the following steps: activating the polyaluminum chloride modified clay with acid, and then applying the activated polyaluminum chloride modified clay into a sea area to be treated, so that the red tide organisms in the sea area are removed. The method can synchronously improve the pH of the red tide water body and reduce aluminum compound residues, has high efficiency, low cost and ecological safety, and is suitable for large-scale application.

The invention relates to the technical field of red tide prediction, in particular to a red tide prediction method and system based on a time interval and a low-rank time frequency Traformer. The method comprises the following steps: acquiring ocean red tide multi-source data; performing data preprocessing on the acquired ocean red tide multi-source data; performing feature extraction and feature fusion on the ocean red tide multi-source data by using a multi-scale convolutional neural network; decomposing and reconstructing the fused multi-scale features based on a wavelet transform decoupling mechanism; performing time interval semantic extraction on the reconstructed features, and extracting a cross-channel dependency relationship by using a low-rank attention layer; through the time interval semantic extraction technology, the hidden ecological semantic features in the marine monitoring data are converted into the structured semantic vectors, the cross-domain feature learning ability of the low-rank time-frequency Transform model is combined, the generality law of red tide occurrence of different sea areas can be effectively captured, and dependence on data of a single area is reduced.

The invention discloses application of flagellimonas sp. SN16, and relates to the field of application of microorganisms. The strain is preserved in Guangdong Microbial Culture Collection Center, and the preservation number is GDMCC No. 65745. The culture supernatant has efficient algae-killing activity on free single cells and cysts of the globocystis globosa, and algae cells can be split after the culture supernatant is treated for 12 hours. In a red tide field experiment of the spherocystis globosa, the algae killing effect on the cysts is good, and the treated water body has no obvious biotoxicity. In addition, the algae-killing activity of the supernatant is kept stable in an environment with the temperature of-20 to 100 DEG C and the pH value of 2 to 12. The flagelmonas and the culture supernatant thereof provided by the invention have important application value in the field of biological treatment of the algal blooms of the globocystis globocystis, are expected to become an efficient and safe algicide, effectively treat the algal blooms of the globocystis globocystis and protect the marine ecological environment.

The invention discloses a three-dimensional red tide drift risk early warning method and system based on set disturbance, and relates to the technical field of physical oceanography, and the system comprises an ocean data summarization module, a red tide outbreak evaluation module, a red tide underwater three-dimensional drift module, and a red tide water surface multi-parameter disturbance drift module. A red tide risk early warning module and a red tide visual product manufacturing module. Performing dynamic evaluation by combining a biological model structure set to obtain a red tide outbreak early warning result, performing calculation by adopting a three-dimensional floating dynamic equation to obtain an upward floating speed of the red tide, performing calculation based on multi-parameter disturbance to obtain a red tide drift path, and performing spatial overlay analysis based on a red tide potential coverage area to obtain a red tide hazard risk grade result. And a visual product is generated. According to the method, the whole-process and three-dimensional early warning of the red tide from underwater growth and floating to water surface drifting is realized, and the accuracy and decision support value of red tide risk forecasting are remarkably improved through wave-tide-flow-ecology coupling analysis.

The invention belongs to the technical field of sewage treatment equipment, and particularly relates to a high-spinning-state sulfur-ferrihydrite adsorption material for a fixed bed, a preparation method and fixed bed adsorption equipment. According to the high-spin-state sulfur-ferrihydrite adsorption material for the fixed bed, in the preparation process, through the weak field ligand effect of sulfur, a FeO6 octahedron of ferrihydrite is triggered to generate remarkable Taylor (Jahn-Teller) distortion, Fe < 3 + > is induced to be converted into a high-spin state, spin polarization is regulated and controlled, part of low-spin-state Fe < 3 + > ions are promoted to be converted into the high-spin state, and the high-spin-state sulfur-ferrihydrite adsorption material for the fixed bed is obtained. The high-spin-state sulfur-ferrihydrite is obtained, the affinity with phosphate is enhanced, and the phosphate adsorption performance of the ferrihydrite is improved at present; the high-spinning-state sulfur-ferrihydrite is filled into fixed bed adsorption equipment, so that pollution of wastewater and sewage discharge to natural water bodies such as underground water, rivers and oceans can be effectively reduced, and the phenomena of algae bloom and red tide are avoided.

The invention discloses an ocean water pollutant monitoring device, belongs to the technical field of seawater detection, and aims to solve the problems that a maintainer needs to regularly and repeatedly calibrate and maintain detection probes on a buoy, the number of the detection probes on a traditional buoy is limited, only one sample can be detected each time, and the real-time sampling rate of data is low. A liquid storage tank is embedded in the bottom face of a buoy body, a mounting shell is fixedly arranged on the bottom face of the buoy body, a power assembly is fixedly arranged in the buoy body, a stirring assembly is fixedly connected to an output shaft of the power assembly, and a connecting assembly is arranged at the bottom of the stirring assembly; a sliding assembly, a compression assembly and a plugging assembly are separately arranged in the mounting shell, the high-frequency detection can be performed under the conditions of marine pollution accidents, red tide / algae bloom outbreak and typhoon / storm surge by changing the shape of the shifting assembly, and the automatic calibration can be realized after the detection probe is used every time.

The invention belongs to the technical field of marine environmental protection, and discloses a biological treatment method for efficiently removing red tide algae by utilizing ciliates, which comprises the following steps: culturing and treating ciliates; removing red tide algae; and data processing and analysis and red tide treatment. The method is high in removal efficiency, and experiments show that in a red-tide algae culture solution with the initial density of 3000cells / mL, the infusorians can reduce the abundance of the red-tide algae to be close to 0 (the removal rate reaches 99%) within 7 days. The method shows a remarkable removal effect on red tide algae with different initial concentrations, and is suitable for large-scale red tide treatment. The use of the ciliates is consistent with the natural ecological chain of the red tide algae, so that the problem of environmental pollution caused by a traditional treatment method is avoided.

The invention provides a probe primer composition, a method and a product for quantitative detection of Karenia saddle and Karenia mikimotoi, and belongs to the technical field of microbiological detection. According to the primer and the probe as shown in SEQ ID NO.1-6 provided by the invention, the Karenia saddle and the Karenia mikimotoi can be simultaneously and quantitatively detected in one system, so that the detection time is remarkably shortened, the reagent cost is reduced, and the detection method is simpler. The method has the advantages of high specificity, high sensitivity, simplicity in operation, short time consumption and the like, can overcome the limitation of traditional morphological identification, is time-saving, labor-saving and relatively low in price, can realize rapid and accurate qualitative identification and quantitative detection of a large number of samples, and can become an effective technical means for prediction, early warning and large-area monitoring of the Karenia red tide.

A water treatment system for the treatment of red tide contaminated water is provided. The system utilizes a cavitation system combined with the addition of compressed air or concentrated oxygen into the fluid flow. The system is able to very efficiently destroy red tide organisms as well as the toxins they create—brevetoxins.

The present invention relates to the field of red tide prediction technology, and in particular to a red tide prediction method and system based on time intervals and low-rank time-frequency Transformer. The method comprises acquiring multi-source marine red tide data; performing data preprocessing on the acquired multi-source marine red tide data; performing feature extraction and feature fusion on the multi-source marine red tide data using a multi-scale convolutional neural network; decomposing and reconstructing the fused multi-scale features based on a wavelet transform decoupling mechanism; performing time interval semantic extraction on the reconstructed features, and extracting cross-channel dependencies using a low-rank attention layer. The present invention transforms the implicit ecological semantic features in the marine monitoring data into structured semantic vectors through time interval semantic extraction technology, and combines the cross-domain feature learning capability of the low-rank time-frequency Transformer model to effectively capture the common laws of red tide occurrence in different sea areas and reduce dependence on data from a single region.