9 results about "Deep sea creature" patented technology

The present invention provides an online purification and sampling and visualized automatic enrichment apparatus and method for deep-sea microorganisms. Through online sample pretreatment during microorganism sampling, automatic biological concentration and purification, online monitoring of environmental indicators during biological enrichment, online observation of biomass, and automatic enrichment and transfer of high-purity enriched materials, the present invention overcomes the shortcomings of the prior art, such as difficulties in purifying deep-sea microorganism samples under high-pressure environments, complex manual operations for enrichment and observation, low timeliness of biomass observation, and difficulties in real-time cell counting of the enriched materials. This effectively increases the enrichment rate and acquisition efficiency of difficult-to-culture deep-sea microorganisms, providing a fundamental way for enhancing deep-sea biological research and bioresource development efficiency.

The application discloses a deep-sea biological image processing method based on a neural network, belongs to the technical field of image processing, and is used for deep-sea biological image processing and comprises the following steps: preparing a target data set, constructing a neural network, inputting a processing result of a Backbone layer into an improved Neck layer, obtaining a deep-sea biological image processed and optimized after being processed by the improved Neck layer, and inputting the deep-sea biological image processed and optimized into a Detect layer; the improved Neck layer comprises a dynamic self-adaptive up-sampling module, a multi-level feature fusion module, an HT_C2f module based on HyperTransformer, and a down-sampling convolution module. The application realizes accurate focusing of target features under a low signal-to-noise ratio condition by improving the Neck layer, improves the feature consistency of multi-scale target processing, reduces the loss of key information in the dimension reduction process while improving the small target feature retention rate.

The invention provides application of ciliates in heavy metal toxicity detection and a heavy metal toxicity detection method, and relates to the technical field of heavy metal toxicity evaluation. Specifically, the invention provides application of the Nalklukella marrimilis as a detection organism in heavy metal toxicity detection. The research finds that compared with other marine organisms, the nanokallukella marrimilis has the advantages that the acute toxicity response time of the nanokallukella marrimilis to heavy metals is shorter, and the threshold value is more sensitive. Therefore, the nanokallukella margaritae can be used for detecting the toxicity of the heavy metals, a reference is provided for evaluating the toxicity effect of deep-sea organisms on the heavy metals, and a scientific basis is also provided for evaluating the marine pollution risk. The method is of great significance to environmental protection.

The application discloses a deep-sea rare biological target detection method based on a stable diffusion model and belongs to the technical field of deep-sea biological target detection. Rare biological categories are determined from a deep-sea benthic biological real scene data set, and a foreground region image is extracted to construct a real target data set, a foreground image generation model is trained, and diversified synthetic foreground images are generated. A conditional control network is used to generate an external drawing of the foreground image according to a layout mask, the synthetic foreground image is fused with a deep-sea background, and a target position label is automatically generated according to the layout mask to obtain a synthetic scene image. After the synthetic quality is screened and the label frame is refined, the synthetic scene image is combined with the real scene data set to construct an enhanced training data set, and a target detection model is trained. The application solves the problem that the target detection performance is limited due to the long-tail distribution effect of the deep-sea exploration data set, and improves the detection capability of the target detection model for the rare biological categories.

The application provides a device for in-situ capturing and pressure-keeping sampling of deep-sea animals and a control method thereof, which mainly comprises a sampler and a pressure compensator, and the sampler is connected with the pressure compensator through a fluid pipeline. The sampler is provided with opposite biological capturing openings, and the inner sides of the openings are respectively provided with a capturing opening sealing cover which can make reciprocating motion. The pressure compensator is externally provided with a liquid inlet, and is internally provided with a pressure-keeping liquid cabin and a pressure balance cabin; the pressure-keeping liquid cabin is connected with the pressure balance cabin and the liquid inlet. The application has simple structure and low cost, and through the combined use of the sampler and the pressure compensator, automatic pressure compensation and pressure keeping work can be carried out after the deep-sea animals are captured, and the opposite biological capturing openings can realize rapid and continuous capturing activities, so that the efficiency of overall task execution and the stability of the preservation environment after biological capturing are improved.

The invention discloses a dispatching management system and method applied to a deep-sea organism monitoring buoy, and relates to the field of deep-sea organism monitoring, and the system comprises a data collection subsystem which collects the data of the deep-sea organism monitoring buoy; the data processing subsystem is used for determining the electric quantity state according to the electric quantity of the battery, screening underwater sound signals based on the sound signal state, screening water surface images based on the water surface object state and then uploading the electric quantity state and the water surface image to the intelligent scheduling subsystem; the meteorological data, the water quality data and the position data are analyzed and uploaded to the intelligent scheduling subsystem; and the intelligent scheduling subsystem selects one mode from a watching mode, a monitoring mode, an ending mode and a sleep mode as a current mode by combining a preset start-stop strategy, generates an instruction and respectively issues the instruction to the data acquisition subsystem and the data processing subsystem so as to control start-stop of data acquisition and start-stop of data reporting. The long-term stable operation capability and the energy use efficiency of the buoy are improved.

The invention discloses a deep-sea organism image processing method based on a neural network, which belongs to the technical field of image processing, is used for deep-sea organism image processing, and comprises the following steps: preparing a target data set, constructing the neural network, inputting a processing result into an improved Neck layer by a Backbone layer, and obtaining an optimized deep-sea organism image after processing by the improved Neck layer. The improved Neck layer inputs the optimized deep-sea biological image into a Deect layer; the improved neck layer Neck comprises a dynamic adaptive up-sampling module, a multi-level feature fusion module, an HTC2f module based on a HyperTransform, and a down-sampling convolution module. According to the method, accurate focusing of target features under the condition of a low signal-to-noise ratio is realized by improving the Neck layer, the feature consistency of multi-scale target processing is improved, and the loss of key information in a dimension reduction process is reduced while the retention rate of small target features is improved.

This invention discloses a method and system for multi-stage processing and reverse transcription stabilization of deep-sea in-situ metatranscriptomic RNA, relating to the fields of deep-sea biotechnology and marine instrumentation. Within a continuous, sealed fluid channel, the following components are connected in series: a filtration and enrichment chamber, a controlled decompression unit, a programmable control unit, and at least one multifunctional reaction chamber. The multifunctional reaction chamber integrates a temperature control system. At least one set of bidirectional opposing electromagnets is disposed on the outer wall of the multifunctional reaction chamber. The programmable control unit controls the on / off state and current of the bidirectional opposing electromagnets, generating a dynamically switchable spatial magnetic field gradient within the multifunctional reaction chamber. This drives magnetic microspheres within the chamber to perform at least one of the following operations without centrifugation: adsorption and aggregation, suspension and mixing, in-situ washing, inter-chamber transfer, and elution / desorption. All biochemical operations are completed within a continuous, sealed fluid channel, eliminating cross-contamination between the sample and the external environment and achieving fully automated sample processing in deep-sea environments.

The aim is to provide a device that can alleviate the symptoms of decompression sickness in deep-sea organisms, while also being small, lightweight, and easily portable. [Solution] A simple pressurized container 1 for housing deep-sea organisms, comprising a container body 2 having a housing chamber 3 for housing deep-sea organisms, capable of pressurizing the housing chamber 3 to a level that can alleviate the symptoms of decompression sickness in deep-sea organisms, and being small and light enough to be carried by hand, and a manual pressurized pump 25 that is detachably attached to the container body 2 and capable of pressurizing the inside of the container body 2 to a level that can alleviate the symptoms of decompression sickness in deep-sea organisms.