1043 results about "Salinity" patented technology

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 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 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 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.

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 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 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 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 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 utility model relates to a biological fluidized bed tower-type water treatment system, which belongs to the technical field of sewage treatment, and comprises a tower body, the interior of the tower body is divided into an aerobic zone, an anoxic zone and a settling zone through an inner cylinder wall, a flow guide wall and a mud bucket wall, and a gas lifting device is arranged to communicate the interior of the aerobic zone with the interior of the anoxic zone. According to the present invention, the anoxic tank, the aerobic tank and the secondary sedimentation tank are organically combined to form the highly integrated tower reactor, and the occupied area is small compared with the traditional biochemical process; the biological fluidization system is higher in sludge concentration, can tolerate higher salinity, higher COD (Chemical Oxygen Demand) and higher nitrogen-containing wastewater, and is higher in load impact resistance; the oxygen utilization rate of the biological fluidized bed system is improved by 50% compared with that of traditional aeration, the sludge backflow and nitrification liquid circulation of the biological fluidized bed system synchronously adopt the gas stripping technology, a sludge backflow pump and a nitrification liquid backflow pump are reduced, and the operation energy consumption of the system is greatly reduced.

Systems and methods for crude oil desalting technology are provided. More specifically, systems and methods for the usage of waste heat from compression to treat high salinity water are provided. The systems and methods generate recycled water streams for desalter treatment. The systems and methods can treat high salinity waters from desalters and dehydrators. The high salinity streams are introduced to the compressed gas streams, where the waste heat causes the water portion of the water streams to evaporate, resulting in a fully or near-saturated gas stream and a concentrated waste water stream. The saturated gas streams are cooled and condensed, and low TDS water is separated from the gas stream in knockout drums. The low TDS water can be reintroduced to the desalting system as wash water, generating a circular water reuse.

The invention discloses a Janus super-wettability gradient pore structure cellulose aerogel material for efficient seawater desalination and oil-water separation as well as a preparation method and application of the Janus super-wettability gradient pore structure cellulose aerogel material. According to the aerogel material, a self-assembled gradient pore structure is matched with a high-strength bio-based skeleton formed by chemical crosslinking, an enhanced chemical crosslinking-physical strengthening network structure is formed, and finally solution replacement and normal-pressure drying are performed in absolute ethyl alcohol to obtain the aerogel material. Under the support of a chemical crosslinking effect and a gradient pore physical structure formed by self-assembly of the material, the integrity and mechanical strength of the pore wall of the obtained material are ensured, and meanwhile, in the aerogel preparation and modification process of PVA, PEI and MTMS, the material has excellent upper and lower double-layer super-hydrophobic and super-hydrophilic asymmetric wettability; therefore, the composite material has efficient oil-water emulsion separation performance, lasting super-hydrophilic / underwater super-oleophobic stability, excellent mechanical performance and seawater desalination treatment performance in a high-salinity environment.

The invention discloses a multifunctional geotechnical mechanical testing system for controlling temperature, humidity and salinity in an extreme environment, and relates to the technical field of ocean geotechnical engineering and geotechnical engineering. The system integrates a high-precision temperature and humidity and salinity control system, can accurately simulate the influence of complex environments such as extreme temperature and humidity, salt spray corrosion and the like on the mechanical properties of geotechnical materials, supports a direct shear test, a pull-out test, a soil body single-particle crushing test and a geosynthetic material stretching and puncturing test, can distinguish static, dynamic and long-term creep test modes, and can be used for testing the mechanical properties of the geotechnical materials. Comprising creep mechanical property testing under cyclic direct shear and continuous load. A visual monitoring assembly is additionally arranged, so that the microscopic deformation and damage process of the sample can be recorded in real time. Through multifunctional integration, temperature, humidity and salinity joint control, visual monitoring and dynamic and static coupling test devices, the technical bottlenecks of single function, environment simulation limitation and microcosmic observation deficiency of traditional equipment are solved, and high engineering application value and popularization potential are achieved.

The invention belongs to the field of near-inertia internal wave modeling, and particularly relates to a near-inertia internal wave modeling method based on a reverse echo observation array, which comprises the following steps: arranging a reverse echo measurement device array in an observation area, and acquiring echo signals from the seabed to the sea surface; performing data cleaning and band-pass filtering on the signals, and extracting near-inertia internal wave signals; calculating the depth and the relative vorticity of a mixed layer by combining historical temperature-salinity data; calculating a wind input energy flux by using the wind stress data; calculating a net level energy flux by adopting a boundary integration method; judging whether the boundary energy flux influence is smaller than a threshold value or not, and if not, adjusting array layout; and finally establishing a multivariable linear model containing wind energy input, mixed layer depth, relative vorticity and energy flux. According to the method, the temporal-spatial resolution and prediction precision of near-inertia internal wave modeling are effectively improved, the problems that in a traditional method, the boundary effect is remarkable, and multi-factor comprehensive modeling is insufficient are solved, and the method is suitable for long-term efficient monitoring of a large-range marine environment.

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 relates to the technical field of ocean current data analysis, in particular to a multi-scale modeling and verification method and system based on ocean current spring layer structure analysis. And obtaining the thermocline and halocline parameters of the target sea area and carrying out abnormal value elimination and spatial interpolation to form a thermocline data set. A convolutional neural network is used to extract spring layer space structure features, and based on Gaussian curvature quantification boundary complexity, a thermocline and halocline equivalent geometric model is constructed. On the basis, taking the equivalent geometric model as a boundary condition, and performing multi-scale decomposition on the Navier-Stokes equation set to generate a dynamic approximate equation; and then inputting the approximate equation and the equivalent geometric model into a physical information neural network for constraint training to obtain a multi-scale flow field prediction model, and comparing the multi-scale flow field prediction model with actually measured flow field data to complete verification. According to the method, automation, refinement and multi-scale coupling of spring layer structure modeling are achieved, and scientificity and engineering applicability of ocean current modeling are improved.

The invention relates to a surfactant oil displacement composition suitable for a high-temperature and high-salt oil reservoir and application of the surfactant oil displacement composition. The surfactant oil displacement composition comprises the following components in percentage by mass: 0.10-0.50 wt% of a nitrogen-containing surfactant, less than 0.12 wt% of a sulfur-containing surfactant or hydrophobic salt and the balance of water. The surfactant oil displacement composition stably exists under the conditions that the temperature is 70-130 DEG C, the total mineralization degree of formation water is (10-25) * 10 < 4 > mg / L, and the concentration of calcium and magnesium ions is (1-2.5) * 10 < 4 > mg / L, and is kept clear without forming precipitates. The interfacial tension of the surfactant oil displacement composition can reach 10 <-2 > mN / m order of magnitude under high-temperature and high-salt conditions, the oil washing efficiency can reach 80% or above, and the surfactant oil displacement composition is suitable for improving the recovery efficiency of a high-temperature and high-salt oil reservoir in tertiary oil recovery.

The invention provides a laying tool, a laying method and a recovery method for a sailing type temperature-salinity-depth detector, and the tool comprises a tow fish which is provided with an attitude sensor on the surface of a housing and is used for detecting the pitch angle of the tow fish; the throwing frame is installed on the ship body and used for guiding the laying path of the tow fish; the winch is installed on a ship afterdeck or in a cabin, and the tow fish is hung through a mooring rope. Wherein an executing mechanism of the winch is in communication connection with the attitude sensor and is configured to respond to an attitude signal generated when the tow fish enters water and drive the tow fish to be adjusted to be in a vertical state; and driving the tow fish to be adjusted to be in a horizontal state in response to a posture signal when the tow fish is out of water. The laying tool provided by the invention is suitable for various laying scenes, realizes automatic posture adjustment of the tow fish when the tow fish enters and exits water, improves the automation degree and safety of operation, avoids human interference, and is beneficial to improving the measurement precision of the temperature-salinity-depth sensor.

The invention provides a three-dimensional multi-dimensional oceanic nekton monitoring method based on artificial intelligence, and relates to the technical field of oceanic nekton monitoring, and the method comprises the steps: deploying a temperature-salinity-depth section plotter and an acoustic Doppler flow velocity section plotter, collecting data, configuring an environment parameter data set, obtaining a three-dimensional environment parameter data set, and obtaining a three-dimensional environment parameter data set; and a multi-dimensional environment parameter database is generated. Various ocean fishery resource investigation means are fused, an advanced artificial intelligence method is applied, three-dimensional multi-dimensional dynamic monitoring of the ocean swimming animals is achieved, on one hand, the number composition and space-time distribution of the ocean swimming animals, especially the vertical distribution characteristics can be monitored, the population dynamics of the ocean swimming animals and the relation between the population dynamics and environmental factors can be revealed, and on the other hand, the population dynamics of the ocean swimming animals can be monitored. By monitoring the swimming animals, basic data can be provided for population resource evaluation, and the method serves formulation of fishing quota of ocean economic swimming animals, delimitation of ocean protection areas and the like.

The invention provides a thickening agent adding control system based on online viscosity and salt double parameter feedback, and relates to the technical field of industrial automation, the method comprises the following steps: collecting a slurry conductivity signal in real time, establishing a complex nonlinear mapping relation between the conductivity signal and preset mineral ion interference, dynamically taking a current conductivity value as an input, and calculating the viscosity and salt content of the slurry; outputting the real-time corrected salinity concentration; using real-time correction salinity concentration as a trigger condition, and activating an online rotary viscometer to collect a viscosity original time sequence signal; kalman filtering processing of mechanical damping lag errors is carried out on the time sequence signals, three characteristic measurement points are dynamically calibrated in a continuous time sequence window to construct a covariance matrix, characteristic values of the covariance matrix are extracted to generate viscosity compensation factors, and after original viscosity data are corrected, a real-time compensation viscosity value is output. According to the invention, dynamic and accurate regulation and control of addition of the thickening agent are realized.

The invention discloses an XBT-T5 observation data deviation analysis and temperature correction method, and relates to the technical field of ocean observation, and the method comprises the steps: obtaining the original temperature observation data of a target sea area based on an XBT-T5 front-end temperature sensor, calculating the depth, generating a temperature-depth profile, and constructing a temperature-depth database in combination with historical depth observation archive data; the method comprises the following steps: synchronously acquiring temperature and depth data by using a CTD temperature-salinity-depth instrument, and carrying out association pairing with a temperature-depth database to generate an XBT-T5 observation data deviation field under the same spatial-temporal scale; based on generation of an XBT-T5 observation data deviation field under the same spatial-temporal scale, designing a dual-channel adaptive encoder, and constructing a temperature-depth collaborative correction model; and according to the temperature-depth collaborative correction model, coupling the temperature-depth database and generating an XBT-T5 observation data deviation field under the same spatial-temporal scale, thereby realizing XBT-T5 observation data deviation analysis and temperature correction. The method improves the quality and application value of observation data.