1292 results about "Salinity" patented technology

The invention provides a comprehensive and intelligent mariculture net cage management and control system. The system integrates a multi-parameter sensor module, an underwater robot module, an underwater camera module, a central control module, a data storage and analysis module, an early warning module, a remote communication module and an automatic execution module. By monitoring multiple environmental parameters such as water temperature, salinity, dissolved oxygen and the like in real time and utilizing the underwater robot and the underwater camera to carry out autonomous inspection and fish behavior observation, the system can accurately master the culture environment condition. The AI algorithm and the intelligent model are combined, the system dynamically adjusts the bait casting amount, the oxygenation frequency and the net cage position, and the breeding benefits are effectively improved. Meanwhile, the early warning module carries out graded early warning on risk events such as typhoon and diseases, and the breeding safety is ensured. The remote communication and visual human-computer interface further enhances the remote monitoring and management capability of the system.

The invention provides a multi-temperature-salinity-depth sensor cooperative detection task planning method, and aims to improve the detection precision and resource utilization efficiency of a sensor network. The method comprises the following steps: firstly, acquiring initial detection parameters of a plurality of temperature-salinity-depth sensors, and calculating a detection capability matrix of each sensor based on the information; then, a target detection area is partitioned through a regional environment model, and a target information entropy model is constructed based on dynamic changes of environment parameters; and combining the target information entropy model and the detection capability matrix to construct a task allocation optimization model, and generating a preliminary sensor task allocation scheme. Furthermore, on the basis of considering environment change, detection data quality and sensor resource constraints, the preliminary task scheme is optimized, and finally a sensor collaborative detection task planning scheme is formed. Through the information entropy model and the optimization task allocation method, the detection efficiency and data quality of the sensor are improved, and the method can be widely applied to marine environment monitoring and underwater detection tasks.

The invention relates to the technical field of marine environment monitoring, in particular to a marine physical data intelligent fusion and optimization processing method, which comprises the following steps: collecting marine physical data in real time through various sensors, including temperature, salinity, flow velocity, tide and sound wave characteristic data; and spatial-temporal feature extraction and spatial correlation analysis are carried out by using a depth residual-graph convolution hybrid model. And performing parameter optimization on the multi-source fusion data set by using a quantum annealing optimization algorithm to generate an optimal parameter set so as to improve the fusion precision of the data. And abnormal data nodes are identified through a self-adaptive threshold detection algorithm, so that the accuracy and reliability of the data are further ensured. And finally, constructing a three-dimensional visual marine environment model according to the optimized parameter set, and realizing a dynamic mapping relationship between model parameters and physical characteristics so as to realize real-time updating and visual display. According to the method, the processing precision of the marine environment data is effectively improved, accurate marine environment prediction is provided, and the method has a wide application prospect.

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

The invention discloses an integrated measurement method and system for hydrological elements in a high-salinity and high-sand environment, and relates to the technical field of hydrological monitoring, and the method comprises the steps: correcting the theoretical signal receiving strength of each sonar through an established sound attenuation model, and then calculating an effective signal-to-noise ratio, thereby achieving the measurement of the underwater topography in the high-salinity and high-sand environment. According to the method, a dynamic weight fusion algorithm is adopted to perform data fusion on the water depth data of each sonar to obtain fused water depth data, and an inversion method based on residual error minimization is adopted to perform iterative optimization on model parameters, so that the accuracy is relatively high. When the water velocity and the flow rate are measured in the high-salt and high-sand environment, iterative decoupling is carried out based on the sediment particle motion comprehensive model to obtain the sediment particle velocity, then the water velocity is updated based on the measured apparent velocity and the difference value, and after the updated water velocity is converged, the flow rate is calculated. And the accurate water flow rate can be calculated by combining the flow cross section area. The measurement precision of the unmanned ship on the hydrological elements in the saline-alkali area can be improved.

The invention provides an ocean subsurface thermohaline reconstruction method based on multi-scale spatial-temporal feature fusion, and solves the technical problem of poor thermohaline reconstruction precision caused by incapability of capturing deep-level spatial-temporal dependence in ocean data in the prior art. The method comprises the steps of obtaining an ocean temperature-salinity anomaly feature data set, performing preprocessing to obtain time sequence data and space sequence data, inputting a multi-scale spatial-temporal feature fusion network model to perform feature fusion to obtain a temperature-salinity anomaly value, performing reverse normalization, and superposing a climate average value to obtain a temperature-salinity reconstruction result; the multi-scale spatio-temporal feature fusion network model comprises a spatial feature extraction branch, a temporal feature extraction branch and a spatio-temporal feature fusion module. The method can be widely applied to the technical field of marine science.

The invention discloses an ocean three-dimensional temperature and salt field prediction method based on remote sensing data driving, and the method comprises the steps: firstly constructing a satellite remote sensing sea surface temperature data set, a sea surface height anomaly data set and an ocean three-dimensional reanalysis data set which are needed by an experiment; then, on the basis of a convolutional neural network and a Transform module, a basic framework of the model is constructed according to the temporal and spatial variation characteristics of the remote sensing data and the temporal and spatial variation characteristics of the three-dimensional temperature and salt field; the model adopts an end-to-end design mode, and adapts to planar spatial-temporal variation of historical remote sensing data through a two-dimensional convolutional coding structure; the three-dimensional spatial-temporal change of a future three-dimensional temperature and salt field is concerned through a three-dimensional convolution decoding structure; an attention mechanism and a feature evolution module are designed according to the spatial-temporal features of the two kinds of data, and the change relation of the remote sensing data and the temperature and salt field data under different spatial-temporal dimensions is learned. And finally, inputting a mode of combining satellite remote sensing temperature of historical n days and sea surface height anomaly into the trained model to obtain three-dimensional temperature and salt field data of future n days.

The invention discloses a scouring pile foundation reinforcement method based on an MICP curing technology, which comprises the following steps: acquiring initial environment information such as scouring pit parameters, water flow parameters, tide period and seawater salinity of a delimited area around a pile, and determining grouting parameters such as a grouting window period and a safe flow velocity threshold value and a matching numerical value of a bacterial liquid concentration, a urea concentration and a calcium ion concentration; a grouting assembly composed of a first grouting pipe and a second grouting pipe which are spliced up and down is arranged, and grouting holes are formed in the side wall of the second grouting pipe which is pre-buried in a soil layer; after MICP slurry is prepared according to blending parameters, the slurry is injected into a soil layer in combination with grouting parameters, and meanwhile, the grouting process is dynamically adjusted by monitoring the resistivity of a soil body in real time; and after grouting is completed, the assembly is dismantled, and supplementary grouting is conducted on the local area according to the detection result. According to the method, the accuracy and reliability of pile foundation reinforcement are achieved through environmental parameter adaptation, grout dynamic regulation and control and layered detection and reinjection.

The invention belongs to the technical field of crossing of environmental engineering and ocean dynamics, and particularly relates to a tidal dynamics prediction method and control device for a seawater desulfurization system, and the method comprises the steps: constructing a time-space coupling prediction model fusing multi-source hydrological observation data, and introducing a nonlinear dynamic weight distribution mechanism; the influence of terrain constraint, wind stress disturbance and upstream runoff on tidal propagation is quantified in real time, and a rolling prediction sequence of tide level phase, flow velocity gradient and salinity disturbance in the next three hours is output; the prediction result drives the scheduling of a desulfurization pump set, the adjustment of spraying density and the matching of aeration intensity, and the model weight is corrected on line based on the actually measured feedback of desulfurization efficiency to form closed-loop control. By means of the technical scheme, accurate cooperation of the operation parameters of the desulfurization system and tidal dynamics is achieved, meaningless energy consumption is reduced while the desulfurization efficiency is guaranteed, and the utilization rate of the desulfurization agent and the stability of the system are improved.

The invention discloses a soil water and salt dynamic monitoring and quality evaluation method based on multispectral remote sensing, and relates to the field of geological monitoring, and the method comprises the steps: carrying out the scattering deviation correction and roughness correction of an observation spectrum through a satellite remote sensing image, an unmanned aerial vehicle multispectral image, a ground sample and meteorological driving data; the soil intrinsic reflectivity, the vegetation coverage component and the salt crusting component are obtained; a water-salt inversion model is established by combining thermal infrared and red edge wave band information, and multi-temporal soil volumetric moisture content and surface salinity index are inverted; by introducing meteorological conditions and vegetation dynamic characteristics, a water-salt coupling partial differential equation and a graph space-time constraint model are constructed, and continuous space-time distribution of soil water content and salinity is obtained; extracting salinity, moisture, vegetation response and soil health indexes, establishing a soil quality comprehensive evaluation model, and outputting high-risk plaques and a treatment priority sequence. The method realizes dynamic monitoring and quality grading evaluation of soil water and salt, and is suitable for saline-alkali soil treatment and ecological restoration.

The invention belongs to the technical field of ocean three-dimensional temperature and salt field data processing, and discloses a method and a system for reconstructing an ocean three-dimensional temperature and salt field by utilizing satellite remote sensing data. The method comprises the following steps: acquiring daily average sea surface satellite remote sensing data of an experimental area, and performing interpolation processing and standardization processing; inputting the processed sea surface satellite remote sensing data into the constructed ocean three-dimensional temperature and salinity field reconstruction model based on the graph attention network and the time convolution network, and outputting an ocean three-dimensional temperature field or salinity field through time convolution, graph learning and graph attention processing; and carrying out spatial distribution consistency verification on the output ocean three-dimensional temperature field or salinity field reconstruction result and ocean reanalysis data serving as real data. According to the method, a sea area is taken as an experimental area, a sea three-dimensional temperature and salt field reconstruction experiment based on sea surface satellite remote sensing data is carried out, and the effectiveness and feasibility of the provided model method are evaluated.

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 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 crossing field of artificial intelligence and process control, particularly relates to an intelligent coupling control strategy for chlorine production through seawater electrolysis based on digital twinning, and aims to solve the problems of response lag, weak disturbance suppression and lack of electrode aging early warning in a traditional control method. According to the strategy, a high-fidelity digital twin fusing electrochemistry, fluid and thermodynamics is constructed, characteristics of current, temperature, impedance spectroscopy and the like are collected and fused at high frequency through a multi-source sensor, a state space model driven by a deep recurrent neural network is established, and current efficiency and electrode aging coupling evolution recognition is achieved; when salinization or flow mutation is detected, the twinborn body predicts and dynamically calibrates the current density and the voltage set value in advance, and the safety process window is synchronously scaled. According to the strategy, the chlorine gas yield fluctuation is reduced by 60%, the energy consumption standard deviation is reduced by 45%, the fault early warning is advanced by 2-5 hours, the fault-free operation time of the system is prolonged by 50%, and high-precision, self-adaptive and preventive intelligent control of the electrolysis process is realized.

The invention discloses a fishery sea area monitoring system based on satellite remote sensing, and belongs to the technical field of fishery monitoring, and the system specifically comprises a remote sensing data acquisition module which obtains and preprocesses sea surface temperature, chlorophyll concentration and sea surface height abnormal data; the buoy data acquisition module synchronously acquires temperature-salinity profile data of the Argo buoy array, and the layout density of the Argo buoy array is dynamically adjusted along with historical fish catch and ocean frontal surface intensity; the space-time alignment module divides a time window through an orbital period, corrects space offset in combination with a buoy drift trajectory, sets a time buffer area based on a georotation flow rate and a satellite image resolution, and generates a space-time unified three-dimensional marine environment data set; the fishery suitability calculation module constructs a biophysical coupling model, correlates surface chlorophyll and subsurface thermohaline structures, and calculates the suitability index of each water layer; the fishery division module intelligently defines a core area and an edge area according to the index three-dimensional distribution characteristics; according to the invention, accurate fishery identification based on multi-source data fusion is realized.

The invention relates to the technical field of environment monitoring, and discloses a radio-based environment monitoring method and system, and the method comprises the steps: deploying an ocean buoy integrated with a plurality of parameter sensors, and detecting the water temperature, salinity, dissolved oxygen, pH value and illumination intensity in real time; transmitting the data detected in real time to a shore-based receiving station by using a radio communication module; performing drift correction and noise filtering on data transmission detected in real time; performing data fusion analysis according to the mutual relation among different data, and generating a marine environment assessment report; and abnormal early warning information is generated according to the marine environment assessment report and is fed back to a monitoring center in real time through radio. According to the invention, through integration of various parameter sensors, comprehensive monitoring of multiple parameters of the marine environment is realized, and the accuracy and reliability of monitoring are improved.

The invention provides a space-time intelligent ocean satellite internet of things perception information screening method and device, and belongs to the technical field of data processing. Marine environment data space-time prediction is carried out based on a block time sequence converter, marine hot spot area mining is carried out based on a density clustering algorithm of mean shift, and marine environment data comprises sensing data of sea surface temperature, salinity, wave height and the like; marine environment risk assessment is carried out based on a BP neural network, information screening feedback control is carried out based on multi-agent reinforcement learning, the efficient information processing requirement of a complex marine environment detection scene is met, and the low-redundancy and high-precision data acquisition and transmission adaptive feedback control effect is achieved. The method can be directly applied to an ocean observation and detection facility networking system in the ocean Internet of Things, can provide powerful support in the aspects of military application and civil application, and has wide and important application prospects and value.

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.

A lidar system and an inversion method for simultaneously detecting carbon sequestration efficiency of the oceanic biological pump and ocean environmental dynamic parameters. The information of Mie scattering and Brillouin scattering intensity and the information of frequency shift and linewidth of Brillouin scattering spectra are combined, and for the detection requirements of the current ocean carbon cycle and dynamic environmental parameters, the vertical profile distribution of the carbon cycle mechanism and environmental dynamic parameters in the euphotic layer is mainly detected; at the same time, the present disclosure constructs an inversion model of the carbon sequestration efficiency of the oceanic biological pump and the temperature and salinity of the environmental dynamic parameters and realizes the synchronous detection of the carbon sequestration efficiency of the oceanic biological pump and the vertical profile distribution of the ocean environmental dynamic parameters in the euphotic layer.

The invention belongs to the technical field of automatic sailing of unmanned ships, and particularly relates to an automatic sailing temperature-salinity-depth profile observation system and method for mesoscale eddy prediction, the system is arranged on an unmanned ship and connected with an automatic retractable winch, and the system comprises a temperature-salinity-depth probe device arranged at the tail end of an A frame of the automatic retractable winch and used for retracting through the automatic retractable winch; lifting is realized, so that temperature, salinity and depth parameters of seawater with different profiles are measured, and key ocean data are provided for mesoscale eddy prediction; the data is transmitted to a mother ship / shore base end through a remote intelligent navigation control system; and the remote intelligent navigation control system is used for receiving various data and sending the data to the mother ship / shore base end, and controlling the driving process or task process of the automatic winding and unwinding winch, the temperature-salinity-depth probe device and the unmanned ship according to the instruction of the mother ship / shore base end. According to the invention, the laying and recovery of the temperature-salinity-depth profile measuring equipment can be controlled when the ship is sailing, and the laying and recovery of the sailing type temperature-salinity-depth profile measuring equipment can be carried out on the unmanned ship after upgrading and reconstruction.

The invention belongs to the field of ocean remote sensing detection, and particularly relates to an ocean surface salinity inversion method, system and device and a storage medium, and the method comprises the steps: obtaining training day brightness temperature data and training month salinity data of an ocean surface; preprocessing the brightness temperature data of the training day and then carrying out regional screening to obtain regional brightness temperature data; performing interpolation processing on the training month salinity data to obtain interpolation salinity data; performing time alignment on the regional brightness data and the interpolation salinity data to obtain a brightness temperature data set and a salinity data set; extracting features according to the brightness temperature data set to obtain brightness temperature features, inputting the brightness temperature features into the salinity intelligent inversion network model to obtain an output salinity value, calculating the output salinity value and an output loss value of the salinity data set through a loss function, and adjusting model parameters of the salinity intelligent inversion network model according to the output loss value to obtain an optimization model; and inputting the actual brightness temperature data into the optimization model to obtain a salinity value. The method has the effect of improving the inversion precision.

The invention provides an ocean mesoscale vortex identification method and device, and the method comprises the steps: obtaining the input data of an ocean environment numerical forecasting product, and the input data at least comprise the sea surface height abnormality, the horizontal flow velocity field, the temperature and the salinity; preprocessing the input data to obtain processed marine environment data; recognizing vortexes in the processed marine environment data according to a plurality of preset vortex recognition algorithms to obtain a plurality of vortexes; wherein the preset multiple vortex recognition algorithms at least comprise an Okubo-Wei ss parameter method, a vector geometry method, a rotation invariance method and an SSH isoline method; performing clustering analysis according to the vortex centers of the plurality of vortexes to obtain a target vortex corresponding to the input data; performing feature extraction on the target vortex to obtain feature parameters corresponding to the target vortex; and the feature parameters are displayed according to a preset display mode, so that the recognition accuracy and comprehensiveness are improved.

The invention relates to the field of ocean observation data processing, and discloses an ocean salinity data correction method based on space-time gradient adaptive filtering, which comprises the following steps of: calculating a motion compensation salinity gradient and a motion compensation temperature gradient; constructing a double-gradient driving filtering window, introducing a gradient sensitive weight distribution strategy, and carrying out adaptive dynamic filtering on the salinity data; generating an initial hyper-parameter combination of the GBDT model; inputting salinity data subjected to adaptive dynamic filtering into the model, optimizing an initial hyper-parameter combination of the model by using a grey wolf optimization algorithm, obtaining an optimal hyper-parameter combination through a dynamic search strategy, and obtaining a final salinity correction value at the same time; and carrying out index verification on the final salinity correction value, and when the salinity does not reach the standard, correcting a model hyper-parameter or a self-adaptive dynamic filtering parameter until the salinity reaches the standard. According to the method disclosed by the invention, the physical abrupt change signal of the spring layer region is reserved to the greatest extent while the noise is suppressed, and the accuracy of salinity profile correction is remarkably improved.

The invention provides a floating type marine hydrology real-time monitoring device, and belongs to the technical field of marine hydrology monitoring. The floating type marine hydrology real-time monitoring device comprises a buoy, a bracket, a monitoring mechanism, a cleaning mechanism and an equipment box, wherein the bracket is fixedly mounted on the surface of the buoy; a sampling mechanism is mounted in an inner cavity of the equipment box and is used for replacing a water sample in the equipment box. The monitoring mechanism is arranged, the sea surface condition and the sea surface wind speed can be monitored through a camera and a wind speed sensor, the seawater salinity and the seawater solubility are monitored in real time through a conductivity sensor and an optical dissolved oxygen sensor, and the conductivity sensor and the optical dissolved oxygen sensor are arranged in the inner cavity of the equipment box, so that the sea surface condition and the sea surface wind speed can be monitored in real time. A filter plate is arranged at the bottom of the equipment box, so that impurities and marine organisms in seawater are prevented from being attached to the surfaces of the conductivity sensor and the optical dissolved oxygen sensor, and the measurement precision is ensured.

The invention discloses a system for saline-alkali land farmland soil water and salt detection and drought and flood disaster early warning. According to the method, spectral information is extracted through a remote sensing image or an unmanned aerial vehicle image, and a salinity spectral index and a moisture spectral index are constructed. Meanwhile, the water content and the salt content of the soil at different depths are collected. Key spectrum variables are screened through an RXGBoost model, a salinity-moisture coupling relationship is synchronously calculated through a single model, and an independent calibration link is reduced for a calibration process. According to the method, a comprehensive and modular technical method and a solution are applied for the first time, maximum application of limited information is realized, and the technical requirements of efficient utilization, monitoring and management of irrigation water are met; and difficult points and key points coexist. Through deep coupling of theoretical innovation and system design, the system is realized in the field of saline-alkali soil irrigation: single data monitoring is transited to a multidisciplinary collaborative ecological hydrological theoretical system, a recessive water-salt interaction process is converted into modelable and controllable dominant decision parameters, and a systematic solution is provided for efficient utilization of saline-alkali soil agricultural resources.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention discloses an underwater LED fish gathering lamp control method based on a light field scattering model, and the method comprises the following steps: obtaining water optical characteristic parameters and environmental parameters of a target water area in real time, and building an underwater light field scattering model through employing a radiation transfer equation in combination with a Monte Carlo simulation method; with improvement of target fish school gathering efficiency and reduction of energy consumption as optimization objectives, environment parameters are taken as constraint conditions, illumination effects under different LED parameter combinations are simulated through a light field scattering model, and LED control parameters with optimal comprehensive performance are determined; a control instruction is generated according to the LED control parameters, and the working state of each LED unit is adjusted; meanwhile, fish school gathering data and current change values of water temperature, salinity and turbidity are monitored in real time and fed back to the light field scattering model for parameter correction, and closed-loop control is formed. The underwater LED fish gathering lamp control method solves the technical problems of low light field distribution prediction precision, insufficient fish school visual matching performance, poor environmental adaptability and unbalanced energy consumption optimization in the existing underwater LED fish gathering lamp control method.

The invention relates to the technical field of the Internet of Things, in particular to a landscaping intelligent management method and system based on the Internet of Things, and the method comprises the following steps: obtaining humidity, temperature, pH value and salinity sensors deployed in a landscaping area, collecting the monitoring data of an integral point, synchronizing the values of the four types of sensors, and eliminating change rate abnormal points. And a distributed soil sensing data set is generated after integration and archiving. According to the method, the time consistency is ensured and the monitoring precision is improved through hourly data synchronization and abnormal value elimination, the microenvironment change identification capability is enhanced through comprehensive analysis of salinity, humidity and temperature, the abnormal region discrimination accuracy is improved, and after a plant database is matched, the actual guidance value of plant adaptability assessment is improved, the health risk direction is defined, and the health risk assessment efficiency is improved. Historical irrigation and salinity analysis reveal the structural deviation of irrigation behaviors, the irrigation scheme is adjusted in a partitioned mode, the pertinence of resource allocation and the water-saving efficiency are enhanced, and efficient, accurate and self-adaptive development of landscaping management is promoted.

The invention provides a pore-scale underground natural hydrogen migration and accumulation behavior prediction method, and belongs to the field of underground natural hydrogen exploration and exploitation. Comprising the following steps: reconstructing an underground porous medium according to a real geological structure image; establishing an empirical formula of fluid attributes such as wettability, interfacial tension and viscosity of the hydrogen-water-rock system and temperature, pressure, salinity and rock type; establishing a flow equation of the hydrogen-water two-phase system in the porous medium; after initial conditions and boundary conditions are determined, the initial distribution, migration and aggregation process of natural hydrogen in the porous medium is simulated; and after the flow is stable, a migration and accumulation image of the hydrogen-water-rock system and a hydrogen recovery rate change curve are obtained. The hydrogen-water two-phase migration-accumulation model constructed by the method is suitable for the characteristics of high density ratio and high viscosity ratio of hydrogen and water, and is good at treating a porous medium complex structure and identifying a hydrogen-water interface; and the method is oriented to a real underground reservoir, and is suitable for predicting the hydrogen-water-rock migration and accumulation process under no environmental factors.

The invention discloses a water environment treatment method and platform based on a water environment mathematical model and artificial intelligence, and belongs to the technical field of water environment treatment, and the method comprises the following steps: S1, obtaining water quality data including water depth, water level, flow velocity, flow direction, temperature, salinity, dissolved oxygen, pH value, chemical oxygen demand, ammonia nitrogen, total phosphorus and turbidity; s2, constructing a water environment mathematical model and an artificial intelligence model; the method specifically comprises the following steps: constructing a water environment mathematical model by using water quality data; the water environment mathematical model is used for simulating a hydrodynamic process and spatial and temporal distribution of water quality parameters; the method comprises the following steps: firstly, selecting a basic artificial intelligence model, then carrying out training learning on the artificial intelligence model by using water quality data, and finally obtaining a trained artificial intelligence model; s3, analyzing a water environment by using the artificial intelligence model to obtain spatial and temporal distribution characteristics and dominant factors of water quality parameters; and S4, making a water environment treatment scheme according to the spatial and temporal distribution characteristics of the water quality parameters and the dominant factors.