21 results about "Benthos" patented technology

The utility model discloses a river benthos sampling and analyzing device, and relates to the technical field of sampling devices. The sampling and analyzing assembly is used for sampling excreted sediments of river benthos, and the sampling and analyzing assembly comprises a sampling cylinder and a balancing weight fixedly arranged at the bottom end of the sampling cylinder; and the lifting assembly is used for automatically lifting the sampling analysis assembly. According to the river benthic organism sampling and analyzing device disclosed by the utility model, the sampling and analyzing component is arranged, so that when the device is used for sampling excrement of river benthic organisms, the aim of automatically sampling the excrement of the river benthic organisms is fulfilled, and in the sampling process, the excrement of the river benthic organisms can be automatically sampled. The rotation of the small motor can drive three L-shaped rods and bristles to stir the river bottom, so that excrement around the bottom end of the sampling barrel can be raised and is sucked into the sampling barrel, the purpose of sampling the periphery of the sampling barrel in a large range is achieved, and the sampling sufficiency is improved.

The utility model discloses a ship submarine organism cleaning robot, including main body frame, mobile system, cleaning execution system, adsorption balance system and identification system. Mobile system is by multi -freedom degree mechanical leg and the constitution of the Mecanum wheel, realizes the omni -directional movement on complex surface. The cleaning execution system is by rough cleaning wheel and fine cleaning wheel composition, through the gear set and transmission chain synchronous drive, utilizes the speed difference and spring pressure adjusting assembly to realize grading high -efficient cleaning. Adsorption balance system is by electromagnet, counter -thrust rudder and compressed air bag constitute, and cooperatively provide stable adhesion and attitude control. The identification system includes camera, LED lamp and main control unit, can intelligent identification ship body attachment and dynamic adjustment cleaning strategy. The utility model can be stable operation in underwater complex environment, realizes the efficient, accurate cleaning of the attachment such as acorn barnacle, algae, improves the ship operation efficiency and prolongs the service life.

The invention discloses a benthic organism distribution measurement method and device influencing the conductivity of a water body, and relates to the technical field of aquaculture, and the method comprises the steps: dividing a target water area into measuring point grids; setting background conductivity in the non-target organism area; synchronously measuring the conductivity, the temperature and the water depth of the water body at the measuring point grid, and collecting the conductivity gradient in a vertical set range; correcting the original conductivity into normalized conductivity by using the temperature coefficient and the water depth coefficient; calculating a biological conductivity difference value; when the biological conductivity difference value exceeds a preset threshold value and lasts for a certain time, biological negative anomaly is judged; inverting the position of the target organism by taking the biological conductivity difference value as input; after all the positioning points in the grid are clustered, estimating a target biomass quantity; splicing each grid result, and outputting a target biological density distribution diagram and a total amount; according to the method, the accuracy of benthic organism distribution measurement is improved through conductivity space-time mapping and a spherical intersection algorithm.

The application belongs to the technical field of marine benthic organism detection, and provides a Faster R-CNN marine benthic organism detection method based on an improved attention mechanism. In view of the problems of complex environmental noise, diverse biological scales, insufficient target detection frame positioning and classification accuracy and the like of marine benthic organisms, an improved multi-scale channel attention mechanism is used to optimize a ResNet50 network, and the ResNet50 network is used as a backbone network of a Faster R-CNN model to enhance the feature extraction capability. Then, a RoIAlign layer with a bilinear interpolation technology is used to replace a traditional RoIPooling layer, so that the position accuracy of the detection frame is improved. In the final classification stage, an extreme learning machine (ELM) feedforward classification network is introduced to replace a full connection layer, so that the classification accuracy is improved. The average precision mean (mAP) of the model is as high as 82.5% and 81.36% respectively, and these results fully prove the effectiveness of the algorithm.

The invention belongs to the field of ecological risk assessment, and particularly relates to an ecological risk assessment method for heavy metal in sediment based on benthic organism heavy metal tissue residues. The method comprises the following steps: (1) extracting and testing the total amount of heavy metals and the occurrence form of the heavy metals in a surface sediment sample of a target drainage basin, and measuring the content Ci of the ith heavy metal in an effective indication organism; (2) constructing a biological residue safety threshold Pi of the ith heavy metal in the sediment of the target drainage basin for the effective indicator organism; and (3) based on the improved single-factor tissue residual index TI and the comprehensive tissue residual index NTI, dividing the ecological risk of a single heavy metal element to the target drainage basin and the comprehensive ecological risk level of multiple heavy metal elements to the target drainage basin in the sediment. Important reference and basis are provided for evaluating the implementation effect of developed treatment measures and formulating scientific sediment heavy metal pollution treatment plans in the later period.

The utility model provides a sampling support and a sampling device for collecting large benthonic animals, which are used for solving the problem that a net opening of a triangular trawl net does not sink to the bottom of a river when the large benthonic animals are collected in the prior art, and comprise a sleeve type sampling rod and a triangular net opening support enclosed by three connecting rods, a dragging rope fixing ring is arranged at the joint of every two connecting rods of three connecting rods of a triangular net opening support, a fixing piece used for being connected with a sleeve type sampling rod is arranged in the center of the bottom connecting rod of the triangular net opening support, and the sleeve type sampling rod is arranged. According to the sampling device for the large benthonic animals, the sleeve type sampling rod can be pressed to ensure that all the sampling devices sink to the bottoms of rivers which are different in water depth and cannot wade, and then it is ensured that ideal samples can be collected when the large benthonic animals are collected.

The invention discloses a tidal flat benthonic animal distribution prediction method based on remote sensing satellite data, and relates to the technical field of tidal flat benthonic animal distribution research, and the method comprises the steps: obtaining habitat feature data through inversion according to the remote sensing satellite data, and carrying out the preprocessing; sea surface temperature data are obtained according to remote sensing satellite data and preprocessed; sampling a target area to obtain samples and benthonic animal sampling data, and preprocessing the samples and the benthonic animal sampling data; dividing the preprocessed habitat feature data, sea surface temperature data and benthonic animal sampling data into a training set and a verification set, establishing a machine learning model, and pre-training the machine learning model; selecting an optimal hyper-parameter of the machine learning model based on a particle swarm optimization algorithm; and verifying the fitting degree of the machine learning model based on the verification set to screen out an optimal model, and obtaining a benthonic animal prediction distribution diagram by using the optimal model. According to the method, the benthonic organism data is fused with the optimization algorithm and the machine learning model, so that the prediction precision of the benthonic animal distribution of the tidal flat is improved.

The invention relates to the technical field of ecological monitoring, and discloses a key habitat element identification method and device for benthonic animals in a diversion lake, and the method comprises the steps: normalizing the multi-source environment and biological data of a target diversion lake, and carrying out the quality control fusion according to ecological monitoring specifications, and forming a standardized environment-biological data set; constructing a data network topological structure by taking a preset ecological mechanism library as a base, taking habitat elements as a middle layer and taking benthonic animal states as a top layer; fitting topological structure nodes and key edge parameters to obtain a parameterized causal relationship network; a habitat element node intervention value is set, anti-fact intervention simulation is carried out, and a benthonic animal state continuous response value is obtained; discretizing the response value according to a preset ecological grade standard to obtain benthonic animal state simulation distribution; quantitatively simulating the distribution causal effect intensity, and sorting the key habitat elements to form a key habitat element list; according to the invention, the key habitat element identification efficiency can be improved.

The application discloses a kind of based on improved YOLO11n's marine ranching benthos detection method, specifically includes the following steps: (1) establishes marine ranching benthos image dataset;(2) add annotation information for the image in dataset;(3) construct based on improved YOLO11n's marine ranching benthos detection model;(4) training set and verification set are used to train model and save the model trained;(5) test set is used to test model, meet the requirement of accuracy and generalization, that is, obtain the final benthos detection model.Compared with prior art, the marine ranching benthos detection method based on improved YOLO11n disclosed in the application can enhance the detection capability of the occluded target, enhance the discrimination degree of benthos and background, and improve the detection capability of small target benthos.

The present invention proposes a device and method for inducing isolation and protection of seabed organisms, which belongs to the technical field of equipment for underwater operations, and includes: two bases, wherein the upper surface of the base is provided with a socket, and the opposite side of the two bases is provided with a notch; a reel motor, which is arranged at the bottom of the socket, and the motor shaft of the reel motor is coaxially arranged with the socket; a reel, one end of which is inserted into the socket, and the reel is connected to the motor shaft of the reel motor; an isolation net, the two ends of which are respectively connected to the reels on the two bases; an acoustic wave generator, which is fixed on the base; a propeller, a propeller is respectively provided at the four corners of the base, for adjusting the posture and position of the base. The seabed biological induction isolation and protection device of the present invention can isolate the target work area and the isolation protection area according to the set area. By setting the acoustic wave generator to guide migration, it is ensured that the organisms will not be affected by mining and die, and it can also prevent the expelled organisms from being easily affected by natural enemies or the environment and die.

The utility model discloses a collecting device suitable for river benthos, and relates to the technical field of biological monitoring. Comprising a shell, the bottom of the shell is an opening, and a hanging plate is arranged at the top; the two grab buckets are symmetrically arranged on the side walls of the two ends of the shell, and the grab buckets are used for opening or closing the opening; one end of the rotating shaft is fixedly connected with the grab bucket, and the other end is rotationally connected with the shell; the torsional spring sleeves the rotating shaft and is used for driving the bottoms of the two grab buckets to get close to each other; the first electromagnet is arranged on the side wall, away from the other grab bucket, of the grab bucket; the second electromagnet is arranged at the upper end of the side wall of the shell and corresponds to the first electromagnet; and the rope is arranged on the lifting plate. The device has a simple structure, is convenient to operate and carry, and is convenient for working personnel to use under various conditions.

The invention discloses a modular ecological protection and scour prevention integrated device of an offshore wind power foundation and a construction and maintenance method. The device is arranged around an offshore wind power foundation and comprises an annular support, an anti-scour assembly and an ecological protection module assembly. The annular supporting and anti-impact assembly comprises a rigid inner ring plate, a rigid outer ring frame, a rigid supporting plate and an interlocking block soft mattress. The rigid inner ring plate is fixedly connected with the circumferential outer wall of the offshore wind power foundation; the rigid outer ring frame is arranged on the circumferential outer side of the rigid inner ring plate in a surrounding mode. The opposite sides of the rigid inner ring plate and the rigid outer ring frame are connected through a rigid supporting plate. An interlocking block soft body row is arranged on the upper surface of the rigid outer ring frame in a surrounding manner; the ecological protection module assembly comprises a bionic aquatic plant frame module; and the bionic aquatic plant lattice module is arranged between the rigid inner ring plate and the rigid outer ring frame. According to the offshore wind power foundation, scouring of the seabed around the offshore wind power foundation can be slowed down, meanwhile, an inhabiting space is provided for benthos, and unitized replacement and long-term monitoring are facilitated.

The present application belongs to the field of environmental pollution and environmental chemistry, and particularly relates to a model and method for simulating the marine environmental behavior and food web migration of typical persistent organic pollutants. The model comprises atmosphere, seawater (including suspended particles), sediment (including pore water), and a food web unit composed of multi-trophic level organisms (including plankton, benthos and fish of different diets). The present application integrates biological energy science and input parameters of aquaculture activities (such as feed delivery, residual feed loss, and fecal excretion), and constructs a multi-medium non-steady-state fate model of the aquaculture marine ecosystem. The model can accurately quantify the cross-medium flux of organic pollutants (such as PFAS, PCBs, DDTs, pesticides, etc.) between the "aquaculture source-environment-organism", realizes the quantitative tracing of the environmental contribution rate of aquaculture activities, and can predict the temporal and spatial distribution and enrichment level of pollutants in wild fish, thereby providing a scientific basis for the ecological risk management of the marine aquaculture area and the overall marine environment.

The utility model relates to the technical field of underwater sampling robots, and particularly discloses a squid bionics-based shallow sea organism sampling robot which comprises a bionic main body, prefabricated connecting pieces are arranged on the front side and the rear side of the bionic main body, and a detachable picking storage bin and a detachable floating collection bin are respectively arranged on the prefabricated connecting pieces on the two sides. A mechanical arm connected with the bionic body is further arranged on the side adjacent to the picking storage bin, a bin door plate capable of being automatically opened and closed is arranged on the picking storage bin and connected with the mechanical arm in a matched mode, and the floating collecting bin and the picking storage bin are symmetrical in weight. According to the shallow sea organism sampling robot based on cuttlefish bionics, the motion mode of bionic benthos is adopted, so that disturbance of the robot to surrounding water bodies in the seabed operation process is remarkably reduced. Through the synergistic effect of the bionic appearance and the flexible driving mechanism, the robot can carry out fine operation under the condition that the stable posture is kept.

The utility model relates to a kind of intelligent portable deep layer collection benthos device, the top of hemispherical cover is fixed with arc handle, and the bottom plate of hemispherical cover is opened with hanging hole;Maintenance door is arranged on the opening of the side wall of hemispherical cover;Visual digital display module is embedded in the side wall of hemispherical cover;DC motor is fixed in the inside of hemispherical cover, and its output shaft is connected with winding wheel disc, winding wheel disc is wound with wire rope, and the lower end of wire rope is movably arranged in hanging hole;Internal control component is fixed in the inside of hemispherical cover;The DC motor is electrically connected with internal control component, and internal control component is connected with visual digital display module.Utilize arc handle and corresponding frame pole, through buckle interaction, increase the overall stability when the device works and facilitate staff to carry.

This invention belongs to the field of ecological risk assessment, specifically relating to a method for assessing the ecological risk of heavy metals in sediments based on the heavy metal tissue residues of benthic organisms. The method includes the following steps: (1) extracting and testing the total amount and occurrence forms of heavy metals in surface sediment samples from the target watershed, and effectively indicative of their presence in organisms. i Heavy metal content C i (2) Construct the first sediment in the target watershed. i Biosafety thresholds for heavy metals as effective indicator organisms P i (3) Based on the improved single-factor tissue residue index (TI) and the comprehensive tissue residue index N TI This invention classifies the ecological risks of a single heavy metal element in sediments to a target watershed, as well as the combined ecological risks of multiple heavy metal elements to the target watershed. This invention will provide important reference and basis for assessing the effectiveness of existing remediation measures and for developing scientific plans for the remediation of heavy metal pollution in sediments.

The invention discloses a deep learning integrated RVFL-based benthic organism image classification method and system, and belongs to the field of computer vision and deep learning. The method comprises the following steps: uniformly scaling an input benthic organism image, and extracting initial features through a preprocessing module; the tail end of a middle-level feature extraction module outputs a feature map L1 through space attention weighting, the tail end of a deep-layer feature extraction module outputs a feature map L3 through channel attention weighting, and a high-level module outputs a feature map L2; performing global pooling and normalization on the L1, the L2 and the L3, and splicing the L1, the L2 and the L3 to form a fusion feature vector; and finally, predicting biological category probability distribution by a TRVFL classifier according to the fusion features. The method is suitable for marine organism recognition, underwater robot navigation, underwater monitoring and other tasks, and the classification accuracy is remarkably improved under the conditions of limited samples and complex backgrounds.

This invention provides a hollow reef assembly with a seaweed-like structure. The assembly comprises multiple stacked reef blocks, each a rectangular concrete block. A rectangular channel is located in the center of each reef block, with its opening on the upper surface. Four grooves connect the opening to the four sides of the rectangular channel and are communicatively connected to it. A metal mesh is installed inside the rectangular channel, and a seaweed-like structure is inserted through it, with its top extending above the upper surface of the reef block. The grooves divide the upper surface of the reef block into four areas, each covered with seaweed. This invention, by incorporating the seaweed-like structure on the reef surface, allows marine life to gather and cling, compensating for the lack of shelter during certain seasons when seaweed is absent. The metal mesh not only secures the seaweed-like structure but also provides additional support.

The invention discloses a multi-scale marine benthos detection method based on global-local cross-attention fusion, and the method comprises the steps: collecting and preprocessing submarine data information through an underwater robot, and building a multi-scale marine benthos data set; preprocessing the multi-scale marine benthic organism data set and dividing the multi-scale marine benthic organism data set into a training set and a test set; building a multi-scale benthic organism detection framework based on global-local cross-attention fusion; adopting the training set to train the multi-scale benthos detection framework to obtain a multi-scale marine benthos detection model; and performing deployment and hyper-parameter adjustment on the trained multi-scale marine benthos detection model, and using the obtained model to detect marine benthos. According to the method, histogram equalization is introduced to pre-process the image, so that the image contrast is effectively enhanced, and the problem of low contrast of the underwater image caused by uneven illumination, color degradation or blurring is solved.

The application discloses a method for defining a critical hydrodynamic environment of passive migration of benthos, and belongs to the technical field of hydraulic engineering. The method comprises the following steps: burying a wide-mouth slot sampler in a preselected research area where passive migration of benthos occurs; determining a test time based on the average distribution width and the average migration speed of the benthos population in the research area; taking out the wide-mouth slot sampler when the test time is up, and weighing and measuring the volume of the collected benthos to calculate the migration rate and individual density of the benthos; repeating the test at different positions of the preselected research area where passive migration of benthos occurs to obtain a plurality of test data; calculating the undetermined parameters in a pre-constructed parameter determination model according to the plurality of test data, and calculating the critical hydrodynamic environment of passive migration of benthos due to exposure according to the undetermined parameters. The method can accurately define the critical hydrodynamic environment of passive migration of benthos due to exposure.

The application discloses a seabed biological cementation state identification method based on TCN analysis of CPT in-situ high-frequency data and belongs to the technical field of marine engineering geological exploration. The method comprises the following steps: acquiring cone tip resistance, sidewall friction resistance and pore water pressure high-frequency time series data in the CPT penetration process; performing time series alignment, background trend stripping, high-frequency residual extraction and Z-score standardization processing on the original data, and adopting a sliding window to construct a 64*4 standardized feature matrix; taking the feature matrix as input, automatically extracting multi-channel time series features by using a time convolution network, and mapping the multi-channel time series features into a biological cementation quantitative index BCI between 0 and 1; and according to the BCI numerical interval, dividing seabed sediments into four grades of non-cementation, weak cementation, medium cementation and strong cementation. The application can realize in-situ, continuous and high-resolution identification of seabed sediment biological cementation states, effectively avoids sampling disturbance, and provides reliable technical support for marine engineering geological exploration.