182 results about "Sea temperature" patented technology

The invention discloses a sea surface temperature prediction method based on an adaptive space-time diagram neural network, and the method comprises the following steps: (1), employing an optimal interpolation sea temperature OISST data set provided by an NOAA official website, and selecting two-dimensional sea surface temperature data of sea areas of the Bohai sea and the East China Sea; (2) carrying out data preprocessing on the collected two-dimensional data set to obtain spatio-temporal data with time coding and an initial adjacency matrix; dividing the data into a training set, a verification set and a test set; (3) constructing an SST prediction model based on the adaptive space-time diagram convolutional network; wherein the SST prediction model comprises a space-time prediction module, a graph learning module and an adjacent matrix updating module; (4) inputting a test set into the trained model for prediction, performing anti-standardization processing on a result, and calculating with an observation value to obtain a prediction error; the prediction capability is improved.

The invention relates to a marine fishery breeding work ship deep water taking device and method, the marine fishery breeding work ship deep water taking device comprises a water taking cantilever, a submersible pump, a guide roller, a rolling part and a lifting device, the water taking cantilever is connected with a ship body through a rotation pair, and the water taking cantilever can stably descend underwater and keep the accurate water taking depth; the water taking method comprises the steps that the position and angle of a water taking cantilever are adjusted through a winding and unwinding winch, a temperature sensor at the water taking cantilever end monitors the seawater temperature in real time, and when the temperature reaches a preset value, a submersible pump is started to extract deep seawater; compared with a traditional method, the device has the characteristics of compact structure, simple operation, high efficiency and reliability; the problems that underwater butt joint is difficult, a hose is prone to damage and the like are solved, and operation efficiency and safety are remarkably improved; through the connection of the flexible short pipe, the flexibility and the stability in the water taking process are ensured, and meanwhile, the labor intensity of operators is reduced; the technology can be widely applied to marine fishery breeding, especially extraction of deep cold water resources, and has remarkable energy-saving, environment-friendly and economic benefits.

The invention provides a marine environment-oriented integrated monitoring and early warning method, medium and system, and belongs to the technical field of electrical digital data processing.The marine environment-oriented integrated monitoring and early warning method comprises the steps that firstly, remote sensing monitoring data such as seawater temperature, salinity, pollutant concentration and flow velocity are collected, and environmental stability and variation components are obtained through multi-element mixed decomposition; a diffusion kinetic model of N time scales is established based on the flow velocity data, and a diffusion matrix group is obtained through calculation. And carrying out cascade operation and time sequence weighted fusion on the environment change component and the diffusion matrix to obtain a comprehensive environment change trend matrix, and constructing a risk vector containing temperature, salinity and pollutant components according to the comprehensive environment change trend matrix. And finally, performing matching operation with an early warning threshold matrix set established by historical data to obtain an early warning index and outputting an early warning level. According to the method, mathematical and physical models are utilized to overcome the defects of strong empirical and subjectivity in a traditional method, and the technical problem that effective conversion from static remote sensing data to dynamic environment risk early warning is difficult to realize in the prior art is solved.

The invention belongs to the technical field of sea temperature complementation, and discloses a sea temperature complementation method and system based on an asynchronous diffusion Schrodinger bridge, and the method comprises the steps: firstly, generating a weight anomal which reflects the abnormal degree of a pixel through a preprocessing step S1, so as to guide a subsequent diffusion process; s2, establishing a bidirectional diffusion path between the initial complementation image and the real image based on a diffusion Schrodinger bridge theory, and dynamically adjusting a diffusion coefficient according to anomay to generate an intermediate state xt; predicting a score function pred through a U-Net network S3, and reconstructing a current image x0 for updating a state or calculating loss; model parameters are optimized through iteration during training, multi-round denoising reconstruction is carried out during inference, and finally a complete high-quality sea surface temperature image SSTrecon is output. According to the invention, local details are fully reserved, and the accuracy of image completion is improved.

The invention belongs to the technical field of image processing, and particularly relates to a sea temperature complementation method and system based on recursive double-flow Mama, and the method comprises the following steps: splicing a damaged SST image and a weekly average SST image as input, and outputting a predicted weekly average SST image and a predicted abnormal SST image through N times of recursive iteration of N same recursive hierarchical Mama blocks, and the two output images are added to obtain a final complemented SST image. According to the method, a recursive hierarchical Mama block for a sea surface temperature completion task is set, two parallel layers, namely a stable information representation module and an abnormal information representation module, are integrated in each block, features related to stability and features related to anomalies are extracted respectively, long-range dependency relationship modeling under large-area deficiency is achieved, and completion accuracy is improved.

The invention belongs to the technical field of ocean remote sensing detection, and relates to a sea temperature inversion method based on K-Ka band microwave radiometer weak signal enhancement deep learning. Parameters of measured data of the microwave radiometer and reanalysis sea gas parameter data are selected; determining the size of a space-time window based on the brightness temperature characteristics of K and Ka wavebands, and carrying out space-time matching to establish a modeling data set; performing quality control on the modeling data set based on indexes such as quality identification, offshore distance, brightness temperature abnormity and air-sea parameter abnormity; normalizing the modeling data set and splitting the modeling data set into a training set, a verification set and a test set; and constructing a weak signal enhancement DNN model based on the K-Ka band microwave radiometer by using the training set and the verification set, and performing inversion by using the test set to obtain a sea surface temperature product. According to the method, the precision of performing sea surface temperature inversion by using the K and Ka wave band brightness temperatures of the microwave radiometer is improved, the problem of insufficient precision is solved, and a new possibility is provided for inversion of SST on a finer scale.

The invention belongs to the technical field of image processing, and discloses a sea surface temperature complementation method and system based on missing correlation and physical constraint, and the system comprises a sea surface temperature data acquisition module, a preprocessing module, a generator, a discriminator, and a loss calculation module. The generator comprises a multi-scale self-adaptive missing association module and a physical constraint space-time interaction guiding module, the multi-scale self-adaptive missing association module comprises a space-time feature extractor and a dynamic multi-level up-down sampler, and the space-time feature extractor is used for extracting space-time correlation features; then the image is output and sent to a dynamic multi-level up-down sampler for image reconstruction completion; the physical constraint space-time interaction guiding module is used for performing correction and physical constraint embedding on the features reconstructed and complemented by the multi-scale adaptive missing association module through numerical mode sea surface temperature data, and outputting a complete reconstructed image; according to the invention, the accuracy and reliability of sea temperature image completion are improved.

The invention discloses a corrosion prevention and control method and system for a seawater pipeline, and relates to the technical field of seawater corrosion prevention. The method comprises the following steps: acquiring basic parameters of an electronic transmission module of each seawater pipeline section, seawater temperature of each seawater pipeline section and basic parameters of each seawater pipeline section; determining a target protection potential of each seawater pipeline section according to the basic parameter of the electron transmission module of each seawater pipeline section, the seawater temperature of each seawater pipeline section, the basic parameter of each seawater pipeline section and a preset potential adjustment algorithm model; and adjusting the potential of each seawater pipeline section according to the target protection potential of each seawater pipeline section. According to the method, the protection potential of each seawater pipeline section of the seawater pipeline can be dynamically adjusted according to the seawater temperature of each seawater pipeline section of the seawater pipeline and the preset potential adjustment algorithm, intelligent anti-corrosion control is achieved, and the method is suitable for ship seawater pipeline application under the polar region environment condition and is wide in application range and high in flexibility.

The invention relates to an ocean internal temperature inversion method fusing PIES and sea surface multi-source parameters, and belongs to the technical field of ocean environment parameter inversion and ocean dynamics observation. Comprising the following steps: constructing a nonlinear empirical model (Multi-RF) of seawater temperature inversion by using a random forest algorithm and fusing data such as a historical observation temperature-salinity profile observed by a temperature-salinity-depth meter (CTD) and sea surface multi-source parameters; and inverting the seawater temperature through a Multi-RF model by using the sound propagation time and multi-source sea surface data calculated by the PIES. Compared with a traditional empirical mode mapping (GEM) model, the Mull-RF model provided by the invention has the advantages that on one hand, the Mull-RF model utilizes fewer samples to establish a mapping relation between the seawater temperature and multiple parameters, and the problem of sample shortage in construction of a seawater temperature inversion model fusing sea surface multiple parameters and PIES observation sound propagation time is solved; on the other hand, compared with polynomial fitting in a GEM model, the random forest adopted by the Multi-RF model can achieve nonlinear fitting from multiple parameters to the seawater temperature, and therefore the temperature inversion precision is remarkably improved.

The invention discloses a testing method of an underwater optical connector plugging testing device, which comprises the following steps of: injecting turbid seawater, pressurizing, adjusting the temperature of the turbid seawater, detecting the minimum plugging pressure of horizontal forward rotation, detecting the minimum plugging pressure of horizontal reverse rotation, detecting the minimum plugging pressure of forward inclination and forward rotation, and detecting the minimum plugging pressure of forward inclination and reverse rotation. And detecting the minimum plugging pressure of the reverse tilt forward rotation, and detecting the minimum plugging pressure of the reverse tilt reverse rotation. The invention relates to a test method of an underwater optical connector plugging test device, which can simulate a complex underwater environment and perform cable connection test.

The invention discloses a method for researching the cross-seasonal relation of rainfall abnormity in spring and summer in the middle and America, and the method comprises the steps: downloading a data set, and processing the data set to retain interannual signals; calculating Chinese American rainfall indexes in spring and summer by using the data; calculating a precipitation level phase transition index to obtain a transition type event and a persistent type event; further dividing the transition event and the persistent event into a positive event and a negative event; the physical mechanism of rainfall anomaly in the middle and American regions in spring and summer during the occurrence period of transformation and persistent events is studied; identifying and exploring the physical process of the sea temperature key area influencing rainfall anomaly in spring and summer; and exploring precursor factors influencing the cross-seasonal relationship of rainfall anomalies in spring and summer. The method solves the problem that in the prior art, cross-season continuous rainfall abnormity in the middle and American regions is not involved. According to the method, the precursor factors of the persistent events and the transformed events are identified, and the method has the potential to be applied to rainfall forecast in the middle and American regions, and provides reference for disaster prevention and reduction and agricultural production.

The invention discloses a seawater temperature, salinity and sound velocity inversion method based on a Brillouin laser radar, and belongs to the technical field of marine environment monitoring. According to the method, the temperature, salinity and sound velocity of seawater are inverted by measuring the Brillouin frequency shift and the Brillouin line width and combining the MLP neural network; the method specifically comprises the steps of collecting seawater parameter data of different sea areas, different seasons and different depths and corresponding frequency shifts and line widths; carrying out data preprocessing and standardization on the collected data; and establishing an inversion model by using an MLP neural network, and outputting temperature, salinity and sound velocity. The multi-layer perceptron MLP neural network processes frequency shift and line width data measured by the Brillouin laser radar, high-precision and real-time inversion of seawater temperature, salinity and sound velocity is achieved, the parameter range is remarkably expanded, the inversion precision is improved, and efficient and reliable marine environment monitoring is provided.

The invention provides a seabed data center double-loop cooling system suitable for a shallow sea area, and the system comprises a heat exchanger which is respectively communicated with a pressure reducing valve, a cooling working medium storage box and a compressor through a first circulation loop; the heat exchanger is provided with an open-loop cooling channel, seawater circulates in the open-loop cooling channel, and the cooling working medium in the circulation loop is cooled through the seawater. Seawater can also directly cool the subsea data center cooling housing through the second circulation loop via valve control. According to the invention, the cooling requirements of the seabed data center can be met at different seawater temperatures through the two circulation loops, and meanwhile, the energy consumption and the PUE value of the cooling system are reduced.

The invention relates to a three-dimensional sea temperature monitoring method, and the method comprises the steps: predicting a residual error through a sea surface temperature reconstruction model based on low-resolution sea temperature background data, meteorological data and geographic feature data of a to-be-monitored sea area, superposing the residual error with the low-resolution sea temperature background data, and obtaining a high-resolution sea temperature parameter of each region of a sea surface; and the precision and the physical consistency of the sea surface temperature parameters are improved. In deep sea temperature inversion, based on biochemical environment data and high-resolution surface sea temperature parameters, sea temperature parameters of all depth areas of a sea area to be monitored are predicted through a deep sea temperature prediction model, and a heat exchange rule is reflected more accurately. According to the method, surface reconstruction and deep inversion are combined, an association connection mechanism is established, information collaborative integration is achieved, complete and coherent three-dimensional sea temperature data are output, and reliable support is provided for applications such as ocean heat transfer calculation.

The invention discloses a method, system and device for constructing a sea surface temperature multi-source data completion fusion model based on SwinGAN, and belongs to the technical field of data processing, and the method achieves the high-precision downscaling of sea surface temperature through the integration of field measured data, numerical reanalysis data and satellite data. An improved Swindow-UNet is adopted as a generator, and a window attention mechanism of the improved Swindow-UNet is utilized to effectively capture multi-scale spatial-temporal characteristics of the sea temperature field; and the discriminator introduces conditional adversarial learning and gradient penalty strategies to ensure the physical consistency of the generated data and the high-resolution target. According to the method, the RMSE of the generated data and the real data in the 2.5-time downscaling multi-source data fusion task in the Atlantic Ocean sea area reaches 1.1, and the spatial-temporal characteristics of the ocean temperature can be accurately reproduced. According to the method, the multi-source sea surface temperature data are fused, the physical rationality and time-space continuity of the data are ensured, and higher-quality data support is provided for the fields of marine environment monitoring, climate prediction, fishery resource management and the like.

The invention relates to the technical field of circulating water system optimization control, in particular to a circulating water system energy-saving optimization control method based on pipeline characteristic identification, and the method comprises the steps: building a lumped parameter model of a pipeline according to a momentum equation of fluid in the pipeline; constructing an agent model of the thermodynamic system mechanism model; identifying at least one pipeline characteristic of the circulating water system; predicting the seawater temperature and the unit load in the target period; establishing a pipeline characteristic model for energy-saving optimization control of the circulating water system; and on the basis of a closed-loop adaptive online correction and model iteration mechanism, optimizing the weights of the long and short-term memory neural network model and the regression model of the circulating water system by using the new operation data. Based on mechanism model and data model hybrid modeling and measuring point data verification, high-precision pipeline characteristic identification is realized, and by predicting future working conditions such as seawater temperature and the like and combining real-time pipeline characteristics, prospective optimization is carried out, and closed-loop frequency conversion optimization control is realized.

The invention relates to the technical field of ocean dynamic environment intelligent forecasting, and provides a three-dimensional ocean dynamic environment intelligent forecasting method and device and electronic equipment, and the forecasting method comprises the steps: S1000, preparing 1 / 30-degree high-resolution ocean dynamic environment element data (three-dimensional temperature and salt flow and two-dimensional water level), and carrying out the normalization; s2000, selecting a corresponding model input sequence from the training set according to a forecasting strategy of the model; s3000, training a hierarchical feature projection network; and S4000: performing reverse normalization and generating future prediction data: performing reverse normalization on the feature-enhanced output data according to the maximum value and the minimum value of the ocean data, and finally generating a day-by-day prediction product of the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity with 1 / 30-degree high resolution in the future day, namely, the product is obtained by predicting the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity.

The invention relates to the technical field of sea water desalination impurity treatment, and particularly discloses a sea water desalination impurity pretreatment system and technology.The pretreatment technology comprises rough filtration, flocculating settling, filter filtration and ultrafiltration, and the flocculating settling comprises the following steps that S100, the temperature of sea water obtained after rough filtration is detected, and when the temperature is lower than a set temperature interval beneficial to a flocculation reaction, the flocculation reaction is stopped; heating the roughly filtered seawater by using waste heat recovered by evaporating the seawater; s200, conveying the heated seawater to a mixing tank, detecting the temperature of the seawater, and adding a flocculating agent according to the dosage corresponding to the temperature; s300, conveying the seawater mixed with the flocculating agent to a flocculation reaction tank; and S400, conveying the seawater subjected to the flocculation reaction to a flocculation sedimentation tank, measuring concentration data of the flocculation sedimentation tank at different depths, and correcting front-end reaction regulation and control parameters according to the measured data. According to the invention, the temperature influence is regulated in a targeted manner, and meanwhile, a dynamic feedback regulation mechanism based on the flocculating settling effect is established.

The invention provides a Bohai sea temperature prediction model based on a fusion model, the previous research mainly carries out prediction through single models, and the single models have corresponding limitations more or less. In order to solve the problem, a Bohai sea temperature prediction model based on a fusion model is provided. According to the method, through fusion of an Informer model and an LSTM model, and through feature optimization and XGBoost correction, the model obtains better prediction performance. According to the model, the prediction precision of the sea surface temperature of the semi-closed sea area is improved, and high-reliability data support is provided for ocean decisions such as storm surge early warning, fishing ground scheduling and ecological management.

The invention provides an automatic compensation method for underwater buoyancy of an AUV (Autonomous Underwater Vehicle), which meets the navigation depth control requirement caused by the change of seawater density in the navigation process of the underwater vehicle. Comprising the following steps: step 1, acquiring seawater temperature, salinity and navigation depth data of a current sea area; 2, according to the obtained seawater temperature, salinity and navigation depth data, the seawater density rho1 of the position where the AUV is located is calculated, and meanwhile the current liquid level of an AUV buoyancy adjusting device is collected through a liquid level sensor; 3, calculating the weight M1 of the AUV according to the seawater density rho1 and the volume V of the AUV, and comparing the weight M1 with the initial weight M0 to obtain weight change; 4, after the water amount of the AUV is adjusted, a proper liquid level value H1 is obtained, and the AUV stores the liquid level value to serve as the balance liquid level for depth control during underwater navigation of the AUV.

This invention relates to the field of meteorological services and provides a method and system for correcting subseasonal model precipitation based on climate zoning and sea surface temperature (SST) background constraints. The method includes: climate zoning, adding SST background constraints, reanalysis data modeling, model historical return data modeling, real-time prediction model matching, and prediction result correction. The system includes: a data preprocessing unit, a climate zoning unit, an SST background constraint unit, a reanalysis data modeling unit, a model historical return data modeling unit, and a real-time model matching and prediction result correction unit. This invention considers the importance of regional differences in precipitation and SST background constraints, solving the problems of significant regional differences in daily summer precipitation in my country, the tendency for overfitting in single-point modeling, and the lack of physical constraints that make correction methods unsuitable. It effectively utilizes physically meaningful predictability sources to modify the cumulative probability density correction method based on percentile mapping, thereby improving my country's summer subseasonal precipitation prediction capabilities.

The invention discloses a sea surface temperature prediction method based on a depth time index attention model, and the method comprises the following steps: selecting sea surface temperature SST data of five observation points in a South China Sea region through employing an interpolation sea temperature OISST data set provided by an open source website; the collected one-dimensional sea temperature data and the corresponding time index data are preprocessed, and a data set is divided into a training set, a verification set and a test set; an SST prediction model based on depth time index attention is constructed and trained, wherein the model comprises a Fourier feature transform layer, an implicit neural representation INR layer and a double attention layer; inputting a test set into the trained model for prediction, carrying out anti-standardization processing on an SST prediction result, and finally comparing the SST prediction result with an observation value; according to the method, the model has better generalization ability, and the prediction precision is further improved by adding the attention module.

The invention discloses a three-dimensional sea temperature space downscaling method and device and electronic equipment, and relates to the field of image processing, and the method comprises the steps: obtaining a target data set which comprises wind field data, ocean current data, first sea temperature data and second sea temperature data, and inputting the wind field data, the ocean current data, the first sea temperature data and the second sea temperature data in the target data set into a three-dimensional sea temperature generation model for spatial downscaling processing, and outputting three-dimensional sea temperature data. A target data set is obtained, wind field data, ocean current data and second sea temperature data are further introduced in the target data set except first sea temperature data, and the wind field data and the ocean current data contain information capable of representing the sea temperature data; and the first sea temperature data, the wind field data, the ocean current data and the second sea temperature data are sent to the three-dimensional sea temperature generation model for downscaling processing, so that the finally output sea temperature data is more accurate.

Along with the development of marine ecological environment protection and aquaculture planning, the invention provides a sea temperature prediction method based on a TCN neural network for the change of an important factor marine surface temperature (SST) and the prediction problem thereof. According to the method, a TCN neural network, an improved attention module and a convolutional bidirectional gated recurrent unit network (CNN-Bi-GRU) are combined. The method comprises the following steps: firstly, extracting local features of SST through TCN; then, according to the time sequence characteristics of the SST, weights are distributed for temperature changes of different areas in the network by utilizing a self-attention mechanism; then, modeling the input sequence from the front direction and the back direction by means of the Bi-GRU; according to the method, the future SST value is predicted by using the multi-dimensional information of the historical data, so that the prediction accuracy is improved.

The invention relates to an East India Ocean sea level height positive anomaly next year ENSO event forecasting method, and the method comprises the steps: employing the mark parameters of the sea abnormally descending Kelvin wave of the tropical East India Ocean during the India Ocean negative dipole event, such as the sea level height anomaly or the subsurface sea temperature anomaly; comprising the following steps: using near equatorial sea level height or subsurface sea temperature abnormity of Indonesian sea or Western Pacific Ocean, or Kelvin wave of East India Ocean obtained through India Ocean wind field, using a regression forecasting model, and using path change of old flow of Pacific Ocean cotton lancea in winter and spring or Western Pacific Ocean west wind outbreak index as a logic switch, and the starting process and the explosion intensity of the ENSO event can be forecasted. According to the method, spring obstacles can be broken through, and the outbreak and the intensity of the El Nino and La Nina events can be successfully forecasted.

The invention discloses an improved offshore oil spill identification and prediction method, and belongs to the technical field of deep learning methods and neural network models.The improved method comprises the steps that a neural network model is constructed to carry out oil spill area identification, and an image processing method is adopted to divide an oil spill area boundary contour; future environmental data of the oil spill area are obtained, and a diffusion simulation result is obtained through a Lagrange model and drawn. According to the improved method for identifying and predicting the oil spill on the sea, the oil spill area can be automatically identified and segmented from the satellite image, the Lagrange particles are sown on the boundary contour of the oil spill area, the initial state of oil spill is truly simulated, data of a wind field, a flow field and a wave field are corrected, and sea temperature data are predicted; and inputting the data into a Lagrange model to obtain a more accurate oil spill diffusion prediction result.

The invention relates to a nuclear power unit power control method and system based on seawater temperature and tide level prediction, and the method comprises the steps: obtaining seawater temperature data of a sea area where a nuclear power unit is located in past first set time, and predicting the seawater temperature after second set time according to the seawater temperature data, so as to obtain a predicted temperature; obtaining tide data of a sea area where the nuclear power unit is located within a third set time, and predicting a tide level after the second set time according to the tide data to obtain a predicted tide level; and obtaining historical operation data of the nuclear power unit, and determining an electric power set value of the nuclear power unit according to the historical operation data, the predicted temperature and the predicted tide level. The power generation power and stability of the nuclear power unit can be improved, and the over-power risk of the unit is reduced.

The invention discloses a sea temperature prediction method and system based on long-time modeling and cross-branch fusion, and belongs to the technical field of marine meteorological intelligent prediction and deep learning modeling. The method comprises the following steps: carrying out spatial feature coding on a standardized sea temperature time sequence image sequence by using a deep convolution encoder to obtain multi-level spatial features; performing two-dimensional discrete wavelet decomposition on the multilevel spatial features to obtain low-frequency approximate representation, horizontal singular features, vertical singular features and diagonal edge features, and inputting the features into a wavelet attention module to obtain short-term time sequence enhancement features; inputting the multi-level spatial features and the low-frequency approximate representation into a ConvLSTM module to obtain high-frequency dynamic enhancement features; and inputting the short-term time sequence enhancement feature and the high-frequency dynamic enhancement feature into a cross-branch attention fusion module to obtain a fusion feature, and sequentially inputting the fusion feature into an Informer time sequence encoder and a decoder to obtain a prediction result.

The invention provides an early warning method for sea surface temperature anomaly detection, which belongs to the technical field of ocean forecasting, and comprises the following steps of: constructing a multi-source sea temperature data fusion system and a multi-scale adaptive grid, generating a high-resolution temperature field by adopting ensemble Kalman filtering data assimilation, extracting an abnormal component by utilizing ensemble empirical mode decomposition, and carrying out early warning on the abnormal component. Multi-level anomaly discrimination is performed based on a sparse coding recognition model and fractal dimension mutation detection, anomaly types are distinguished in combination with an atmospheric compulsive event feature library and a random forest classifier, and partial differential equation inverse problem reverse deduction is performed on ocean endogenous anomaly to reconstruct a three-dimensional anomaly structure. And finally, the early warning level is determined through the early warning decision function and the multi-dimensional indexes, and the technical problem that the real-time performance and the accuracy of sea surface temperature anomaly detection are difficult to guarantee at the same time is solved.

The invention relates to the technical field of satellite remote sensing, and discloses a method for evaluating the ecological health of human activities on coral reefs based on a noctilucent remote sensing sequence, which comprises the following steps: acquiring a noctilucent remote sensing image of a research sea area acquired by a satellite in a research time sequence, and taking the noctilucent remote sensing image as human activity data; obtaining a corrected sea area live coral coverage degree corresponding to the research time sequence after the sea temperature influence is eliminated; and carrying out partial correlation analysis on the corrected sea area live coral coverage and the luminous remote sensing image corresponding to the research time sequence to determine the influence of human activities on the live coral coverage. The technical problem that the influence of human activities on coral reef ecological health is difficult to evaluate in the prior art is solved.