46 results about "Benthos" patented technology

An environment multi-parameter monitoring base station capable of being used for long-term seafloor work is laid to the seafloor through a mother ship in a cable mode, energy is supplied through a dissolved oxygen seawater battery, long-term seafloor environment in-situ monitoring operation is carried out through carried universal and special instruments and equipment, a shore-based instruction is received to adjust a monitoring strategy, and the monitoring base station is used for monitoring the seafloor environment. Monitoring data is transmitted back to the shore-based laboratory in real time; the monitoring base station comprises a rack system, a monitoring system, a control system, a communication system, an energy supply system and a laying and recycling system; the monitoring system is used for completing in-situ monitoring operation on the influence of a seabed biochemical environment and benthos by adopting a general monitoring instrument and an extended special instrument; and the communication system is used as a signal transmission medium to perform parallel and uplink and downlink data transmission and instruction issuing so as to realize networking between the base station and each node around the base station and interaction and communication between the base station and a water surface buoy or a ship.

The utility model belongs to the technical field of biological trapping devices, and particularly relates to a lake benthos trapping device which comprises a device body, the device body comprises a trapping box and shunting assemblies, the shunting assemblies are arranged on the two sides of the trapping box and comprise arc-shaped plates and shunting pipelines arranged in the arc-shaped plates, and the shunting pipelines are of hollow structures. A supporting rod is arranged in the base, and a plurality of fan blades are rotationally connected to the supporting rod; the trapping box comprises a first fixing frame, a second fixing frame, a fixing rod, a trapping outer net and a trapping inner net, the trapping inner net is arranged on one side of the first fixing frame and communicated with the trapping outer net, and the trapping outer net and the fixing rod are both arranged between the first fixing frame and the second fixing frame; a counterweight assembly is arranged on one side of the first fixed frame; through the shunting assembly and the counterweight assembly in the device, the water flow impact force in the advancing process can be effectively reduced, shaking caused by non-uniform filtered water quantity is avoided, and the stability of the trapping device is effectively improved.

The invention relates to the field of underwater benthos detection, in particular to an underwater benthos detection method based on an improved DETR model. The method comprises the following steps: constructing an improved DETR model, and training by using a marked underwater image set to obtain a trained improved DETR model; using the trained improved DETR model to detect benthic organisms in the underwater image; wherein the improved DETR model comprises a backbone network, an encoder and a decoder with an auxiliary prediction head; in the backbone network, extracting image features by adopting a convolution gating linear unit which introduces a compact inverted bottleneck module and a channel shuffling module; in an encoder, a HIFI module combining a HiLo high and low frequency attention mechanism and multi-scale expansion convolution is adopted to enhance local detail preservation and global environment understanding, and an EDCM module is adopted to enhance feature representation and calculation efficiency of an underwater scene. According to the invention, underwater benthos detection can be realized with high precision and high efficiency, and the method is suitable for an underwater environment with limited resources.

The invention discloses a physical simulation method and device for the transportation effect of submarine organisms in a nuclear power plant, and the method comprises the steps: obtaining the motion parameters of target submarine organisms in the nuclear power plant, the motion parameters comprise one or more of individual mass, volume, body length, settling velocity, critical starting friction flow velocity, critical starting vertical line average flow velocity, starting wave height and critical starting flow velocity of target benthos of the nuclear power plant; establishing a physical model for simulating the transportation effect of the submarine organisms of the nuclear power plant; determining a simulant of the target benthos based on the motion parameters of the target benthos and a physical model; and simulating the amount of motion of the target benthos of the nuclear power plant by using the simulant and the physical model. In this way, it can be guaranteed that the motion process of the simulant should be kept similar to the actual motion process of the target benthic organism, the scale effect is avoided, and the premise is provided for accurately evaluating the entrainment and entrainment risk of the subbottom organism.

A marine benthic organism detection, identification and tracking method comprises the following steps: collecting, screening and constructing a marine organism benthic data set, performing small target data enhancement, and performing detection and preliminary classification on a target by using a P2-YOLOv11 deep learning model; then, a lightweight ShuffleDeepsort tracking algorithm is used for carrying out tracking; and finally, performing dynamic frame skipping identification in combination with track information obtained by tracking and preliminary classification information, and realizing fine classification under different order element systems by using a multi-level classification network to obtain a multi-level identification result of marine organisms. According to the invention, the processing effect of the small-target marine benthic organism image and the submarine multi-target tracking image actually shot by the underwater vehicle can be obviously improved.

The invention discloses a rapid calculation method for multi-type heavy metal distribution in a dredged soil bottom throwing process, which comprises the following steps: measuring the mass ratio of various heavy metals to a dredged soil sample to obtain the solid content ratio of various heavy metals in dredged soil; collecting the temperature of a water body, performing particle size grade screening on the dredged soil, and calculating balance distribution coefficients of a heavy metal dissolved state and a particle state; establishing a rapid calculation model of the concentration of the dissolved heavy metals and the concentration of the suspended particulate heavy metals in the overlying water body; according to the deposition thickness, the mixing thickness and the dry volume weight of the sediments, calculating the heavy metal-containing sediment concentration and the total sediment concentration after the bottom bed sediments are mixed; and establishing a rapid calculation model for the concentrations of the dissolved heavy metals and the bottom bed granular heavy metals in the interstitial water body. According to the method, plankton and benthos environments are considered, toxic heavy metal environment monitoring, management and treatment in the bottom throwing operation are realized, and rapid, efficient, total-space and multi-type heavy metal distribution evaluation requirements in the bottom throwing operation process are met.

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 belongs to the technical field of marine benthos detection, and provides a Faster R-CNN marine benthos detection method based on an improved attention mechanism. In order to solve the problems of complex noise, various biological scales, insufficient target detection frame positioning and classification precision and the like of an environment where marine benthic organisms are located, 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. And then a RoIAlign layer with a bilinear interpolation method technology is used for replacing 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 as to improve the classification precision. The average precision mean value mAP of the model reaches up to 82.5% and 81.36% respectively, and the results fully prove the effectiveness of the algorithm.

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 invention provides a channel-space fuzzy logic attention-guided marine benthos progressive detection enhancement method under detail loss. The method comprises the following steps: constructing a data set based on pictures of one or more marine benthos of sea urchins, scallops, starfishes and sea cucumbers; dividing the data set into a training set and a test set; constructing an ocean benthos progressive detection enhancement model including a detail loss lower channel and spatial fuzzy logic attention guidance; training the channel-space fuzzy logic attention guided marine benthos progressive detection enhancement model based on the training set data to obtain a trained channel-space fuzzy logic attention guided marine benthos progressive detection enhancement model; and inputting test set data into the trained channel-space fuzzy logic attention-guided marine benthos progressive detection enhancement model to realize detection and identification of sea urchin, scallop, starfish and sea cucumber marine benthos.

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 relates to the technical field of benthos ecological research, in particular to a large benthos inhabiting device which comprises a floating ring, the bottom of the floating ring is fixedly connected with a net bag with an upward opening, the bottom of the inner side of the net bag is connected with a plurality of sets of bottom nets distributed in the vertical direction, and the lower end of the net bag is fixedly connected with a fixing ring; when the amount of feed in the feeding box reaches a certain amount, balls are separated from extrusion rods by means of the weight of the feeding box, when the feeding box moves downwards to a certain position, a piston plate and a piston rod are driven to move downwards by means of pressure generated by water, and when the piston rod moves downwards, a swing plate is extruded, so that a magnet on the swing plate is not magnetically attracted to the feeding box any more; meanwhile, when the swing plate swings, the feed in the feeding box is discharged into the water body, so that the benthic organisms in the area can be conveniently fed, and the feeding convenience is improved.

The invention provides a benthic organism induction isolation protection device and method, and belongs to the technical field of equipment for underwater operation, the benthic organism induction isolation protection device comprises two seat bodies, the upper surfaces of the seat bodies are provided with insertion holes, and the opposite sides of the two seat bodies are provided with notches; the reel motor is arranged at the bottom of the insertion hole, and a motor shaft of the reel motor and the insertion hole are coaxially arranged; one end of the reel is inserted into the insertion hole, and the reel is connected with a motor shaft of the reel motor; two ends of the separation net are respectively connected with the reels on the two seat bodies; the sound wave generator is fixed on the seat body; the propellers are arranged at the four corners of the seat body respectively and used for adjusting the posture and the position of the seat body. According to the benthos induction isolation protection device, a target work area and an isolation protection area can be isolated according to a set area. The sound wave generator is arranged to guide migration, it is guaranteed that the organisms cannot be affected by mining and die, and it can be avoided that the repelled organisms are prone to being affected by natural enemies or the environment and die.

The present invention discloses a method for detecting seabed biological targets based on yolov5 optimization, and the method comprises the following steps: S1: collecting images containing various seabed biological targets in a public database, and processing to obtain an original data set; S2: performing light processing and ocean background object occlusion data enhancement processing on the original data set; S3: optimizing the yolov5 model to obtain a detection model for seabed biological target detection; S4: using the data set after the data enhancement processing in step S2 to train and learn the detection model in step S3 to obtain a trained detection model; S5: using the trained detection model to detect seabed biological targets. The present invention realizes rapid and accurate identification of various seabed biological targets, is applied to automatic fishing of seabed biological targets, improves automatic fishing of seabed biological targets, improves fishing efficiency, reduces manpower and reduces fishing operation risks.

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 present invention discloses a clamping type biological sampler for a deep - sea submersible manipulator, comprising: a sampler support body; an electrical control cabin, the limit pin assembly includes a limit pin, and the controller can control and drive the limit pin to perform telescopic actions; a handle; a net bag, which has an opening at the bottom; a self - shrinking closing part, which is a closed structure; an outer traction part, one end of which is connected to the self - shrinking closing part, and the other end is a free end for detachably connecting with the limit pin; an inner traction part, one end of the inner traction part is connected to the self - shrinking closing part, and the other end is fixed on the inner wall of the sampler support body. The clamping type biological sampler for a deep - sea submersible manipulator of the present invention solves the problem that it is difficult to directly capture organisms with strong activity abilities such as fish, shrimps, and crabs at present. It has a high capture rate, convenient sampling, and can greatly increase the types and quantities of seabed biological samples.

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 invention belongs to the field of computer vision, and particularly relates to an ocean particle image velocity measurement method based on unsupervised learning and multi-scale deep convolution. Based on a classical optical flow network LiteFlowNet, in combination with a multi-scale Inception deep convolution module, the ability of the network to extract complex flow field features is enhanced. Meanwhile, a sub-pixel up-sampling layer is adopted to replace a traditional bilinear interpolation method, so that the reconstructed flow field is finer. Besides, the network is trained through unsupervised loss, dependence on a large-scale annotation data set is eliminated, structural similarity loss is fused in an unsupervised loss function, so that image structure information is more effectively reserved, and the precision of optical flow field estimation is further improved. The device can be used for detecting a seabed flow field, and plays a key role in ocean development and benthic organism research.

The invention belongs to the field of ecological environment evaluation and treatment, and particularly relates to a salt marsh wetland benthic organism environment DNA monitoring method giving consideration to biological invasion early warning. Comprising the following steps: 1) collecting a sample; 2) indoor analysis; 3) environment DNA extraction, PCR amplification and sequencing; and 4) data analysis. Wherein in the step (3), sediment DNA is extracted by adopting a CTAB (cetyltrimethyl ammonium bromide) method, then the purity and the concentration of the DNA are detected by utilizing agarose gel electrophoresis, a purified environment DNA sample is subjected to PCR (Polymerase Chain Reaction) amplification by using a Barcode specific primer pair environment DNA template, and the sequences of the four primers are shown as SEQ ID NO.1-8. According to the invention, salt marsh wetland benthos species lists detected by the four primers are compared, and in combination with synchronously collected morphological identification results, the method can be applied to salt marsh wetland benthos diversity monitoring and biological invasion molecule detection in various coastal provinces and cities.

The invention relates to a domain self-adaptive marine benthos detection method based on light field and color distribution consistency, which comprises the following steps of: obtaining a pair of degradation-reference marine benthos images with any one or more of sea urchins, scallops, starfishes and sea cucumbers, and constructing a data set; dividing the data set to obtain a training set and a test set; constructing a domain adaptive detection model based on light field and color distribution consistency; training the domain adaptive detection model based on the light field and color distribution consistency based on the training set data to obtain a trained domain adaptive detection model based on the light field and color distribution consistency; and inputting the data of the test set into the trained domain adaptive detection model based on the consistent light field and color distribution to realize detection of any one or more of sea urchins, scallops, starfishes and sea cucumbers based on the consistent light field and color distribution.

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 invention relates to the technical field of biological collection, in particular to a three-dimensional reef body hanging plate device capable of attaching benthos in a reef cluster, which comprises a floating block and a collection assembly, and further comprises a plurality of support frames movably connected with the floating block; the horizontal adjusting assembly comprises a rotating plate fixedly installed at the top end of the supporting frame, and the middle sections of the two adjusting ropes are wound on a first rotating frame and a second rotating frame in a one-to-one correspondence mode; and the resistance adjusting assembly comprises a guide plate which is mounted on the rotating plate in a sliding manner. The supporting frame is arranged at the bottom of the floating block, a plurality of sampling plates are placed in the supporting frame, the sampling plates are periodically fished out for biological data acquisition, the acquisition efficiency and quality are improved, the acquisition difficulty is reduced, the supporting frame is kept in a horizontal state in water by controlling the length of the adjusting rope, and the sampling efficiency is improved. The orientation of the support frame is changed by utilizing the guide plate, so that the impact effect of undercurrent on the sampling plate is reduced, and the biological collection quality is further improved.