A positioning monitoring method and device for underwater culture

Abstract

This application provides a positioning and monitoring method and device for underwater aquaculture. The positioning and monitoring method includes: accurately locating a floating reference point; setting a connecting component at the floating reference point; one end of the connecting component is fixedly connected to the floating reference point, and the other end is fixed with a data acquisition component that can extend underwater; the data acquisition component includes a detector and a processor; the detector collects and detects the current environment and transmits the detection results to the processor for processing; the recognition model in the processor is used to identify biological features based on the detection results, and calculates the absolute coordinates of the detector based on the angle and parameters of the connecting component. This application can obtain accurate coordinates and their corresponding biological features, fundamentally avoiding error accumulation, while combining the flexibility of visual navigation with the high precision of physical calibration.

Description

Technical Field

[0001] This invention relates to the technical field of underwater detection, and in particular to a positioning and monitoring method and device for underwater aquaculture. Background Technology

[0002] In scenarios such as deep-sea aquaculture, marine ranching management, and underwater resource exploration, accurately grasping the spatial distribution information of underwater organisms (such as sea urchins, sea cucumbers, and abalone) or target objects is a core requirement for implementing scientific management, resource assessment, and ecological monitoring. However, the unique characteristics of the underwater environment (such as light attenuation, water turbidity, and GPS signal failure) make traditional monitoring methods relying on manual diving inefficient, costly, and risky. Although existing underwater robot autonomous monitoring technology has been developed, it still suffers from the bottleneck of "insufficient perception and positioning accuracy," making it difficult to meet the needs of large-scale, high-precision applications.

[0003] Current mainstream underwater positioning and monitoring technologies suffer from the following key shortcomings, which limit their application effectiveness: First, pure vision-based navigation technology based on SLAM (Simultaneous Localization and Mapping) suffers from the following core problems: a lack of absolute coordinate reference, leading to severe drift in positioning results. Existing technologies (such as the SLAM system disclosed in patent CN121616947A) construct local maps by real-time acquisition of images by the robot and estimate relative displacement based on visual odometry. However, this method only provides a "relative position" relative to the starting point and cannot directly output latitude and longitude coordinates in a geographic coordinate system. Furthermore, due to the sparse underwater environmental features, changes in lighting, or sensor noise, errors accumulate significantly over time, resulting in "distorted" or "lost" positioning results, failing to meet the rigid requirements for absolute coordinate accuracy in long-term, large-scale monitoring of aquaculture areas.

[0004] Second, positioning technologies based on physical sensor fusion suffer from the following core problems: reliance on rigid structures, poor environmental adaptability, and low feature utilization. Existing technologies (such as the sensor fusion system described in patent CN105629251A) connect surface base stations and underwater equipment via rigid rods, combining tilt sensors, pressure sensors, etc., to calculate position. However, this method has the following shortcomings: limited flexibility: the rigid structure restricts the robot's range of motion, making it unsuitable for complex terrain or large-area navigation; poor anti-interference: water flow disturbances, mechanical vibrations, or sensor errors can easily lead to calculation deviations; limited information: relying solely on physical parameters, it fails to fully utilize the rich environmental feature information in underwater images, and cannot achieve deep fusion of positioning and biometrics.

[0005] In summary, existing technologies either only provide relative displacement (SLAM) or rely on rigid structures with low feature utilization, making it difficult to balance the requirements of accuracy, flexibility, and intelligence. Summary of the Invention

[0006] This invention aims to at least partially solve one of the problems in related technologies. Therefore, one objective of this invention is to provide a positioning and monitoring method for underwater aquaculture that can obtain precise coordinates and their corresponding biological characteristics, fundamentally avoiding error accumulation. It also combines the flexibility of visual navigation with the high precision of physical calibration, thereby achieving precise positioning and monitoring of underwater aquaculture. Furthermore, the biological characteristics identified by the recognition model in this application can also form a distribution map, enabling dynamic observation of underwater target objects based on the distribution map.

[0007] A positioning monitoring method for underwater aquaculture, comprising: S1: Precisely locate the reference point of the floating body; S2: A connecting component is installed at the reference point of the floating body; one end of the connecting component is fixedly connected to the reference point of the floating body, and the other end is fixed with a collection component that can extend underwater. S3: The detector collects and detects the current environment and transmits the detection results to the processor for processing; the recognition model in the processor is used to identify biometric features based on the detection results; the processor calculates the absolute coordinates of the detector based on the angle and parameters of the connecting components, and then outputs the biometric features in the absolute coordinates.

[0008] Furthermore, the reference point of the floating body is located on the water surface, and the latitude and longitude coordinates of the reference point of the floating body are obtained by GPS.

[0009] Furthermore, the detector is at least one of an optical imaging device, an acoustic wave detection device, an infrared thermal imaging device, and a temperature sensor.

[0010] Furthermore, when the detector is an optical imaging device, the obtained detection result is a detection image; when the detector is an acoustic detection device, the obtained detection result is a biological distribution outline; when the detector is an infrared thermal imaging device or a temperature sensor, the obtained detection result is the metabolic heat of aquatic organisms and the temperature difference of the aquatic environment. The biological characteristics include at least one of the following: biological species, biomass density, biological fingerprint, and biological distribution map at the gene level.

[0011] Furthermore, the detector is an optical imaging device, and the biometrics include the number and category of target objects; the optical imaging device collects and detects the current environment to obtain detection images, and the recognition model outputs the number and category of target objects based on the detection images.

[0012] Furthermore, the processor extracts the planar coordinates, depth information, number and category of target objects corresponding to the calculated absolute coordinates of the detector, and generates a distribution map, heat map and gridded result map of the target objects, so as to realize an intuitive response to the distribution status of the target objects in the monitoring area. Based on the distribution of the target objects within the monitoring area, the processor outputs prompt or warning information.

[0013] Furthermore, this also includes establishing a priori database: The detector systematically scans the underwater target area to obtain biological features at various locations. At the same time, the processor calculates the absolute coordinates of the collected biological features based on the angle and parameters of the connecting components, and binds the biological features and their corresponding absolute coordinates to form a priori database.

[0014] Furthermore, the biological features in step S3 are compared with the biological features under the same absolute coordinates in the prior database to achieve dynamic monitoring of underwater organisms.

[0015] Furthermore, the detector is an optical imaging device, and the obtained detection result is a detection image; When the optical imaging device is unable to perform detection within a period shorter than a preset time threshold, it uses an optical flow algorithm to calculate the instantaneous displacement, or switches to a calculation and navigation mode based on an inertial measurement unit. At this time, the processor retrieves the detection images from the prior database as the detection images for that period.

[0016] The second objective of this application is to provide a positioning and monitoring device for underwater aquaculture, for performing the positioning and monitoring method for underwater aquaculture as described above.

[0017] Compared with the prior art, the technical solutions provided in this application have the following advantages: The positioning and monitoring method for underwater aquaculture provided in this application can obtain the precise absolute coordinates of the detector at the other end of the connecting component by accurately positioning the reference point of the floating body and the angle and parameters of the connecting component. Combined with the detection results obtained by the detector in real time, the precise coordinates and their corresponding biological features can be obtained, which fundamentally avoids error accumulation. At the same time, it has the flexibility of visual navigation and the high precision of physical calibration, thereby realizing precise positioning and monitoring of underwater aquaculture. The biological features identified by the recognition model in this application can also form a distribution map, and the dynamic observation of underwater target objects can be realized based on the distribution map. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other implementation schemes can be obtained based on these drawings without creative effort.

[0020] In the attached image: Figure 1 This is a flowchart illustrating the positioning and monitoring method in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of the connection component in an embodiment of this application; Figure 3 This is a density distribution diagram of sea urchins in Example 2 of this application.

[0021] Figure label: 1. Floating reference point; 2. Satellite positioning module; 3. Connecting components; 4. Detector. Detailed Implementation

[0022] To provide a clearer understanding of the technical features, objectives, and effects of this invention, specific embodiments are now described in detail with reference to the accompanying drawings. In the following description, it should be understood that the orientations or positional relationships indicated by terms such as "front," "rear," "upper," "lower," "left," "right," "longitudinal," "horizontal," "vertical," "horizontal," "top," "bottom," "inner," "outer," "head," and "tail" are based on the orientations or positional relationships shown in the accompanying drawings, and are constructed and operated in a specific orientation. They are only for the convenience of describing this technical solution and do not indicate that the referred mechanism or element must have a specific orientation; therefore, they should not be construed as limitations on this invention.

[0023] It should also be noted that, unless otherwise explicitly specified and limited, terms such as "installation," "connection," "linking," "fixing," and "setting" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. When an component is referred to as being "on" or "below" another component, the component can be located "directly" or "indirectly" on the other component, or there may be one or more intermediary components. The terms "first," "second," "third," etc., are only for the convenience of describing this technical solution and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, features defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0024] In the following description, specific details such as particular system structures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, mechanisms, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0025] Example 1

[0026] like Figure 1 As shown, this application discloses a positioning monitoring method for underwater aquaculture, comprising: S1: Precisely locate the reference point 1 of the floating body; S2: A connecting component is installed at the buoy reference point 1; one end of the connecting component is fixedly connected to the buoy reference point 1, and the other end is fixed with a collection component that can extend underwater; the collection component includes a detector 4 and a processor. S3: The detector 4 collects and detects the current environment and transmits the detection results to the processor for processing; the recognition model in the processor is used to identify biological features based on the detection results. The processor calculates the absolute latitude and longitude coordinates and underwater depth of the detector based on the angle and parameters of the connecting components and the latitude and longitude coordinates of the floating reference point, and outputs the absolute latitude and longitude coordinates and the underwater biological features.

[0027] This application provides a positioning and monitoring method for underwater aquaculture. By precisely positioning the floating reference point 1 and the angle and parameters of the connecting component, the precise absolute coordinates of the detector 4 at the other end of the connecting component can be obtained. Combined with the detection results acquired by the detector 4 in real time, the precise coordinates and their corresponding biological characteristics can be obtained, fundamentally avoiding error accumulation. At the same time, it combines the flexibility of visual navigation with the high precision of physical calibration, thereby realizing precise positioning and monitoring of underwater aquaculture. The biological characteristics identified by the recognition model in this application can also form a distribution map, and the dynamic observation of underwater target objects can be realized based on the distribution map.

[0028] In this application, the buoy reference point 1 is located on the water surface, and the latitude and longitude coordinates of the buoy reference point 1 are obtained by GPS.

[0029] Specifically, a satellite positioning module 2 can be installed on the floating body, using the floating body as the reference point 1. The satellite positioning module 2 is used to acquire the geographic coordinates of the floating body's position on the water surface. These geographic coordinates serve as the reference coordinates for the entire positioning system. Since the underwater detector 4 cannot directly receive satellite positioning signals, the real-time coordinates of the floating body reference point are used as a reference point to provide an absolute position reference for subsequent position calculations of the underwater detector 4. The satellite positioning module 2 has a positioning accuracy of up to six decimal places, providing meter-level planar positioning accuracy. The floating body, floating on the surface of the aquaculture area, serves as the spatial positioning reference point 1 for the entire system. Its real-time latitude and longitude coordinates are transmitted to a cloud server via a wireless communication module.

[0030] The connecting component in this application can be one or more connecting rods. Each connecting rod has attitude sensors at its front and rear ends for real-time measurement of three-dimensional spatial attitude data such as pitch and roll angles. The measured parameters are then transmitted to a processor for absolute position calculation. Each time the connecting component moves (lifting, rotating, or translating), the attitude sensors record the corresponding three-dimensional spatial attitude data and calculate the absolute coordinates of the detector 4 based on the absolute coordinates of the floating reference point 1.

[0031] When the floating reference point moves the detector to the target position through the connecting assembly, the hinge ends of each link in the connecting assembly will undergo displacement or angle change, and the attitude sensor is used to record the corresponding three-dimensional spatial attitude data.

[0032] In this application, the connecting components can be either rigid or flexible rods between the joints. When the rod is flexible, a torque sensor is built into it. The control center calculates the absolute coordinates of the detector based on the float reference point, three-dimensional spatial attitude data, and the torque value monitored by the torque sensor.

[0033] This application may also use a drone to replace the floating body as the floating reference point 1. The drone hovers or cruises in the air, suspends underwater detection equipment through connecting components, and uses an airborne satellite positioning system to provide reference coordinates.

[0034] This application may also install multiple fixed monitoring nodes as floating reference points 1 on the seabed or fixed facilities in the aquaculture area, and connect the detector 4 through the connecting components.

[0035] As an alternative technical solution, this application can also be based on electromagnetic signals for positioning. The relative position of the device can be determined by using an underwater electromagnetic signal generator and receiver, or the electromagnetic field generated by the pre-buried cable on the seabed can be used as an auxiliary positioning reference to locate the coordinates of the detector 4.

[0036] This application can also employ multiple small submersibles to work together, with one of the small submersibles serving as the floating reference point 1, its connecting components connected to the detector 4, and the remaining small submersibles performing relative positioning through a visual recognition reference machine or deployed optical / acoustic beacons, and sharing data to achieve precise positioning of the floating reference point 1.

[0037] This application also allows for the installation of a depth measurement device (pressure sensor) solely on detector 4 to estimate water depth through changes in water pressure. Although this approach can only acquire single-point water depth data and cannot independently determine planar position (due to ambiguity), using it as an auxiliary tool, combined with angle data from connecting components for comprehensive calculation, significantly improves positioning accuracy in complex sea conditions.

[0038] In this application, detector 4 is at least one of an optical imaging device, an acoustic detection device, an infrared thermal imaging device, and a temperature sensor. When detector 4 is an optical imaging device, the obtained detection result is a detection image; when detector 4 is an acoustic detection device, the obtained detection result is a biological distribution outline; when detector 4 is an infrared thermal imaging device or a temperature sensor, the obtained detection result is the difference between the metabolic heat of aquatic organisms and the temperature of the aquatic environment.

[0039] Specifically, the optical imaging device can be a camera module used to acquire underwater detection images or environmental videos. The recognition model can be the YOLOv8m best.pt model, which can identify the number, category, and other features of target objects based on the acquired detection images. Other features include detection time, depth information of detector 4, and identification information corresponding to the detection images. These other features facilitate subsequent retrieval, analysis, and visualization.

[0040] When detector 4 is an acoustic detection device, the detection result obtained is the biological distribution outline. Specifically, in turbid waters or low light environments, the biological distribution outline can be detected by using acoustic wave reflection signals, or by using an ultra-short baseline (USBL) / long baseline (LBL) acoustic system in conjunction with an underwater robot for calibration and positioning. The biological distribution outline, combined with its corresponding absolute coordinates, can form an acoustic biological distribution map.

[0041] When detector 4 is an infrared thermal imaging device or temperature sensor, the detection results obtained are the temperature difference between the metabolic heat of aquatic organisms and the water environment. Specifically, an infrared thermal imaging device or temperature sensor can be integrated at the end of detector 4 to perform basic thermal imaging analysis using the temperature difference between the metabolic heat of aquatic organisms and the water environment; or it can be combined with a temperature-depth sensing module (CTD) to correct the positioning parameters by utilizing the influence of water temperature and salinity on sound velocity.

[0042] The biometric features generated based on the detection results in this application include at least one of the following: biological species, biomass density, bio-fingerprint, and biological distribution map at the gene level. The biological species can be identified from the acquired detection images using a recognition model, and the number of target objects can be detected based on a convolutional neural network (CNN).

[0043] In addition, the recognition model in the processor of this application can also employ pixel-level semantic segmentation technology (such as DeepLabV3+ or SegNeXt) to calculate the pixel area or projected volume occupied by the organism in the image instead of counting individual organisms. Combined with the calibrated coordinates, a regression model of "pixel area - actual weight" is established to transform the visual data into "biomass density" and directly draw a "total weight distribution map of the aquaculture area".

[0044] The processor in this application can also be used as a biological verification alternative based on behavioral trajectory analysis: instead of relying on static image classification and recognition, it extracts the movement characteristics of organisms (such as swimming speed, trajectory shape, and cluster density) through time-series analysis. For example, it uses optical flow to extract the motion vector field of underwater organisms, uses the organism's movement pattern as a "biological fingerprint", matches it with a "motion trajectory feature library" to identify the species, and combines coordinates to draw a "biological activity distribution map".

[0045] The processor in this application can also be based on an alternative biometric fusion scheme using environmental DNA (eDNA): In the "application" stage, in-situ sampling technology is incorporated. While taking photographs, the submersible automatically filters the water and analyzes DNA fragments in the environment. "Visual images" are used as auxiliary verification, and "DNA detection results" are used as the primary basis for species identification. Visual coordinates are bound to species information detected by gene analysis to generate a "gene-level biological distribution map." This is a higher-order alternative to the "multi-source information fusion mapping" of this invention.

[0046] The processor in this application extracts the corresponding planar coordinates, depth information, and the quantity and category of target objects based on the calculated absolute coordinates of detector 4. It then generates a distribution map, heat map, and gridded result map of the target objects, providing a direct reflection of their distribution within the monitoring area. Based on the distribution of target objects within the monitoring area, the processor outputs prompts or warnings. For example, when target objects show abnormal decreases, abnormal aggregations, or migration trends at the edge of the monitoring area, corresponding prompts or warnings can be output according to preset rules to assist subsequent inspections, verifications, or management operations. The output of prompts or warnings is based on the aforementioned location calculation and detection image recognition results, thus reflecting the changes in target objects within a spatial range.

[0047] This application also includes the establishment of a priori database, the specific steps of which include: Detector 4 systematically scans the underwater target area to obtain biological features at various locations. At the same time, the processor calculates the absolute coordinates of the collected biological features based on the angle and parameters of the connecting components, and binds the biological features and their corresponding absolute coordinates to form a priori database.

[0048] This application compares the biological features collected in real time with the biological features under the same absolute coordinates in the prior database to achieve dynamic monitoring of underwater organisms.

[0049] Each systematic scan of the underwater target area by the detector 4 in this application creates a priori database, overwriting previous priori databases. Specifically, the priori database can be continuously updated through periodic systematic scans, or it can be updated according to actual needs or changes in environmental factors. The updated priori database serves as the latest priori database, and based on this, the biological characteristics detected in real-time are compared and analyzed.

[0050] When detector 4 in this application is unable to perform detection for a short period due to environmental factors, the detection results at the corresponding coordinates in the prior database can be used as the current detection results, thereby enabling continuous analysis of the detection results. Specifically, this application can use a hybrid mode of optical flow and inertial navigation for detection result analysis: when underwater environmental features are sparse (such as sedimentary bottom) or visual matching temporarily fails, an optical flow algorithm is introduced to calculate instantaneous displacement, or the system switches to an inertial measurement unit (IMU)-based calculation navigation mode (INS). After visual conditions recover, the database is re-matched to reset the error. This hybrid navigation logic of "vision-driven, inertial navigation / optical flow backup" relies on the prior database.

[0051] In this application, the systematic scanning of the detector can be achieved by controlling the movement of the detector and / or the movement of the floating reference point, so as to achieve a systematic scan of the target area without omission.

[0052] This application targets processors with limited computing power, eliminating the need for real-time matching calculations. Instead, it stores the detection results and uploads them to the cloud after the detection operation is completed for offline identification and processing using a recognition model, subsequently generating biometric features and corresponding distribution maps.

[0053] This application utilizes a connecting component to carry detector 4 to systematically scan the target sea area. Through physical geometric relationship calculation, the acquired detection images are precisely bound to the actual geographic latitude and longitude coordinates to construct a "visual prior database with coordinates." This database serves as an "absolute reference map" for the underwater environment, providing a high-precision reference system for subsequent real-time positioning and solving the problem of map drift over time in traditional SLAM technology.

[0054] Based on the precise coordinates obtained through matching, this application combines multi-source sensing data (images, videos, environmental samples) and uses a spatial interpolation algorithm to transform discrete sampling points into a continuous biological density distribution map. The spatial distribution and gradient changes of biological quantity, density, or biomass are displayed intuitively using color gradients or contour lines, providing a quantifiable situational awareness basis for aquaculture management.

[0055] This application also provides a positioning and monitoring device for underwater aquaculture, used to perform the positioning and monitoring method for underwater aquaculture as described above.

[0056] Example 2

[0057] This embodiment provides a positioning and monitoring method for underwater aquaculture. The method uses the geographical coordinates of a floating body as a reference point 1, and calculates the three-dimensional coordinates of the underwater detector 4 by combining the length parameters and angle or attitude parameters of the connecting component between the reference point 1 and the underwater detector 4. Based on these three-dimensional coordinates, images, videos, or other monitoring data collected by the detector 4 can be correlated with their corresponding spatial locations to support subsequent target identification, distribution analysis, or monitoring applications.

[0058] like Figure 1 As shown, in this embodiment, detector 4 is an optical imaging device; specifically, it includes the following steps: S1: Precise positioning of the floating reference point 1; specifically, a satellite positioning module 2 can be installed on the floating body, using the floating body as the reference point 1. The satellite positioning module 2 is used to obtain the geographic coordinate information of the floating body's position on the water surface. The geographic coordinate information can serve as the reference coordinate benchmark for the entire positioning system. Since the underwater detector 4 cannot directly receive satellite positioning signals, the real-time coordinates of the floating body are used as a reference point to provide an absolute position benchmark for the subsequent position calculation of the underwater detector 4. The positioning accuracy of the satellite positioning module 2 reaches 6 decimal places, providing meter-level planar positioning accuracy. The floating body floats on the surface of the aquaculture area, serving as the spatial positioning reference point 1 for the entire system. Its real-time latitude and longitude coordinate data is transmitted to the cloud server via a wireless communication module.

[0059] S2: Install a connecting component at the buoy reference point 1; such as Figure 2 As shown, one end of the connecting component is fixedly connected to the floating reference point 1, and the other end is fixed with a collection component that can extend underwater; the collection component includes a detector 4 and a processor.

[0060] In this embodiment, the surface float and the underwater detector 4 are connected by a connecting assembly. The connecting assembly is formed by connecting multiple connecting rods in sequence, the length of each connecting rod is a known parameter, and adjacent connecting rods are hinged sequentially. An angle measuring unit is provided at each connecting rod to obtain the connecting rod angle or attitude parameters between adjacent connecting rods.

[0061] By collecting the length parameters of each connecting rod and the angle or attitude parameters of each connecting rod, a spatial geometric constraint relationship extending from the surface float to the underwater detector 4 can be established. In some embodiments, the connecting assembly can be configured as a restricted deflection structure to keep the motion relationship of the connecting structure measurable and calculable, thereby improving the stability and reliability of the end-position calculation.

[0062] S3: The detector 4 collects and detects the current environment and transmits the detection results to the processor for processing; the recognition model in the processor is used to identify biological features based on the detection results, and the processor calculates the absolute coordinates of the detector 4 based on the angle and parameters of the connecting components, thereby outputting the biological features in the absolute coordinates.

[0063] Specifically, after receiving the coordinate information of the floating body, the length parameters of each section of the connecting components, and the angle or attitude parameters of each link, the processor calculates the position of the underwater detector 4.

[0064] Specifically, the location of the floating body on the water surface can be used as a reference point. Combining the length parameters of each connecting rod section with the corresponding angle or attitude parameters, the positional relationship of each node in space is calculated step-by-step along the transmission direction of the connecting assembly. Based on this, the spatial displacement of the underwater detector 4 relative to the reference point can be obtained. Based on this spatial displacement, the three-dimensional coordinate information of the underwater detector 4 can be further determined.

[0065] In the above calculation process, since the position of the underwater detector 4 is not obtained by simply accumulating historical trajectories, but is directly calculated based on reference coordinates and current connection structure parameters, a continuous position solution chain from the water surface reference point to the underwater detector 4 can be formed, thus providing a basis for determining the spatial position of the underwater detector 4.

[0066] In some embodiments, the three-dimensional coordinate information includes the planar position and depth information of the underwater detector 4. The depth information can be obtained from the spatial calculation results of the connecting component, or it can be determined with the assistance of the depth measurement unit configured on the detector 4.

[0067] In this embodiment, detector 4 is a camera module used to acquire underwater detection images or environmental videos. Based on the obtained three-dimensional coordinate information of underwater detector 4, the detection images or environmental videos can be associated with the corresponding acquisition location for storage or transmission, thereby forming monitoring data with spatial location information.

[0068] When acquiring detection images or environmental videos, associated information can also be obtained simultaneously. This associated information includes the acquisition time, the depth information of detector 4, and the identification information corresponding to the detection images or environmental videos, so as to facilitate subsequent retrieval, analysis, and visualization processing.

[0069] In addition to real-time detection, this application can also establish a priori database before detection, specifically including: Detector 4 systematically scans the underwater target area, acquiring images at various locations. The processor calculates the absolute coordinates of the acquired biofeatures based on the angles and parameters of the connecting components, binding the detection images and their corresponding absolute coordinates to form a priori database. This priori database is used for comparative analysis with the acquired detection images, enabling dynamic analysis and monitoring. It should be noted that in this application, the priori database can directly bind and store the detection images with their corresponding absolute coordinates, or it can bind and store the biofeatures identified from the detection images with their corresponding absolute coordinates.

[0070] In this application, the recognition module can be a YOLOv8m best.pt model, a convolutional neural network, etc. The recognition model is used to identify, count, classify, or analyze the state of target objects in the acquired detection images in order to extract target information corresponding to the spatial location. The target information can be further associated and stored with the absolute coordinates of the corresponding acquisition location to form a target detection result with spatial coordinates.

[0071] S4: Biometric Analysis and Monitoring: After obtaining target information from multiple collection locations, the target objects can be statistically analyzed, distributed, or graphically displayed based on their corresponding spatial coordinates to generate spatial distribution results of the target objects within the monitoring area.

[0072] In some implementations, multiple location-bound data records can be read, and their planar coordinates, depth information, and target quantity or feature information can be extracted. Corresponding distribution maps, heatmaps, gridded results, or other spatial representations can then be generated based on a preset spatial statistical method. This spatial distribution result can visually reflect the distribution status of target objects within the monitoring area.

[0073] In some implementations, prompts, warnings, or decision support information can be further generated based on the target spatial distribution results obtained in step six.

[0074] For example, when a target object shows an abnormal decrease, abnormal aggregation, or migration trend at the edge of the monitoring area, corresponding prompts or warnings can be output according to preset rules to assist subsequent inspections, verifications, or management operations. The prompts or warnings are based on the aforementioned location calculation and spatial distribution analysis results, thus reflecting the changes in the target object within a spatial range.

[0075] The corresponding diagram can be used to illustrate one implementation method for providing prompts or warnings based on spatial distribution results, but this implementation method does not constitute a limitation on the warning rules, analysis models, or application scenarios.

[0076] Tables 1-4 show the classification indicators, counting results, detection results, and inference speed for identifying the types and quantities of sea urchins, sea cucumbers, starfish, and scallops in the detected images using the YOLOv8m best.pt model.

[0077] Table 1. Categorical Indicators

[0078] Table 2 Count Results

[0079] Table 3 Test Results

[0080] Table 4 Reasoning Speed

[0081] Table 1 above- Figure 3 This demonstrates that the method in this application can accurately identify the type and quantity of target objects in environmental graphics and obtain biological characteristics. Table 4 shows that the identification time varies when the identification model is located in different positions, thus making it applicable to different underwater monitoring needs.

[0082] This embodiment can obtain a density distribution map of sea urchins based on the detected images, such as... Figure 3 As shown, the visualization is done in pixels, where the color of each pixel represents the relative density of sea urchins within the corresponding area. The color mapping uses a warm-to-cool color gradient: red represents high-density areas, followed by yellow and green, and blue represents low-density or sea urchin-free areas. This density distribution map supports interactive exploration, facilitating multi-scale observation of sea urchin aggregation patterns and spatial distribution.

[0083] This application employs a connecting component equipped with an optical shaping device to systematically scan the target sea area. Based on geometric relationships, each detected image is precisely bound to real geographic coordinates, establishing a "visual prior database with coordinates." This database serves as an "absolute coordinate anchor point," providing a reliable benchmark for subsequent real-time positioning and resolving the error accumulation problem caused by the reliance on relative displacement in SLAM technology.

[0084] During the detection process, the detector in this application directly calculates the absolute geographic coordinates (latitude and longitude) of the current location by combining a forward kinematics model. This method eliminates the need for integration calculations, fundamentally avoiding error accumulation, while combining the flexibility of visual navigation with the high precision of physical calibration.

[0085] After acquiring high-precision location information, this application further utilizes a recognition model to identify the species and quantity of organisms in the detected images, and overlays the biological information as semantic tags onto the geographic coordinates to generate a "spatiotemporally fused biological density distribution map." This map not only reflects the location of organisms but also reveals their spatial distribution patterns, providing an intuitive basis for scientific decision-making.

[0086] In summary, this application constructs an underwater intelligent monitoring system that integrates physical benchmark construction, visual positioning, density visualization, and biological semantic analysis by forming a closed loop of "calibration-drawing-recognition". This system provides a high-precision, verifiable, and intelligently cognitive technical solution for scenarios such as deep-sea aquaculture and marine ecological protection.

[0087] It is understood that the above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A positioning and monitoring method for underwater aquaculture, characterized in that, include: S1: Precisely locate the reference point of the floating body; S2: A connecting component is installed at the reference point of the floating body; one end of the connecting component is fixedly connected to the reference point of the floating body, and the other end is fixed with a data acquisition component that can extend underwater; the data acquisition component includes a detector and a processor. S3: The detector collects and detects the current underwater environment and transmits the detection results to the processor for processing; the recognition model in the processor is used to identify biological features based on the detection results. The processor calculates the absolute latitude and longitude coordinates and underwater depth of the detector based on the angle and parameters of the connecting components and the latitude and longitude coordinates of the floating reference point, and outputs the absolute latitude and longitude coordinates and the biological features below.

2. The positioning and monitoring method for underwater aquaculture according to claim 1, characterized in that, The reference point of the buoy is located on the water surface; the absolute coordinates of the reference point are obtained via GPS.

3. The positioning and monitoring method for underwater aquaculture according to claim 1, characterized in that, The detector is at least one of an optical imaging device, an acoustic wave detection device, an infrared thermal imaging device, and a temperature sensor.

4. The positioning and monitoring method for underwater aquaculture according to claim 3, characterized in that, When the detector is an optical imaging device, the detection result is a detection image; when the detector is an acoustic detection device, the detection result is a biological distribution outline; when the detector is an infrared thermal imaging device or a temperature sensor, the detection result is the metabolic heat of aquatic organisms and the temperature difference of the aquatic environment. The biological characteristics include at least one of the following: biological species, biomass density, biological fingerprint, and biological distribution map at the gene level.

5. The positioning and monitoring method for underwater aquaculture according to claim 3, characterized in that, The detector is an optical imaging device, and the biometrics include the number and category of target objects; the optical imaging device collects and detects the current environment to obtain detection images, and the recognition model outputs the number and category of target objects based on the detection images.

6. The positioning and monitoring method for underwater aquaculture according to claim 5, characterized in that, The processor extracts the planar coordinates, depth information, number and category of target objects corresponding to the calculated absolute coordinates of the detector, and generates a distribution map, heat map and gridded result map of the target objects, so as to realize an intuitive response to the distribution status of the target objects in the monitoring area. Based on the distribution of the target objects within the monitoring area, the processor outputs prompt or warning information.

7. The positioning and monitoring method for underwater aquaculture according to claim 1, characterized in that, This also includes establishing a priori database: The floating reference point drives the connecting components to move, enabling the detector to systematically scan the underwater target area and obtain biological features at various locations. At the same time, the processor calculates the absolute coordinates corresponding to the collected biological features based on the angle and parameters of the connecting components, and binds the biological features and their corresponding absolute coordinates to form a priori database.

8. The positioning and monitoring method for underwater aquaculture according to claim 7, characterized in that, In step S3, the biological features are compared with those in the prior database at the same absolute coordinates to achieve dynamic monitoring of underwater organisms.

9. A positioning and monitoring method for underwater aquaculture according to claim 7, characterized in that, The detector is an optical imaging device, and the detection result obtained is a detection image; When the optical imaging device is unable to perform detection within a period shorter than a preset time threshold, it uses an optical flow algorithm to calculate the instantaneous displacement, or switches to a calculation and navigation mode based on an inertial measurement unit. At this time, the processor retrieves the detection images from the prior database as the detection images for that period.

10. A positioning and monitoring device for underwater aquaculture, characterized in that, Used to perform the positioning and monitoring method for underwater aquaculture as described in any one of claims 1-9.

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