Intelligent investigation system and method for deep-sea middle-layer fish resources

Abstract

The invention discloses an intelligent survey system and method for deep-sea middle-layer fish resources. According to the method, terrain detection signal data of a target water area and fish resource detection signal data of a target water layer are obtained through multi-beam sonar equipment, and the fish school distribution condition of the target water layer is determined accordingly. A target water area three-dimensional terrain model is constructed by using terrain detection signal data, and an underwater robot cruising strategy is determined by combining fish school distribution. And the underwater robot obtains fish school image data according to the strategy, identifies fish types and numbers to obtain fish resource data, and finally transmits the data to a water surface control center through an underwater sound wave communication technology. Intelligent and efficient investigation of deep-sea middle-layer fish resources is achieved, powerful support is provided for deep-sea fishery resource research and development, and wide application prospects and important practical significance are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource survey, and in particular to an intelligent survey system and method for deep-sea mesopelagic fish resources. Background Art

[0002] In the field of deep-sea resource research and development, the investigation of deep-sea mesopelagic fish resources is of great significance. The deep-sea mesopelagic area is below the ocean's photic zone, with high water pressure, weak light, and complex environment, and is rich in fish resources. These fish play a key role in the material cycle and energy flow of the marine ecosystem, and also have potential economic value.

[0003] Traditional methods for investigating deep-sea mid-layer fish resources have many limitations. On the one hand, the equipment and technology used are relatively backward, making it difficult to obtain high-precision and comprehensive terrain and fish resource detection signal data. For example, early sonar equipment had low resolution and could not accurately detect the distribution of fish schools, resulting in large errors in the judgment of the number and species of fish schools. On the other hand, when determining the cruising strategy of underwater robots, there is a lack of comprehensive consideration of terrain and fish distribution, which makes the work efficiency of underwater robots low and unable to comprehensively and efficiently obtain fish image data. In addition, in terms of fish species and quantity identification, relying on manual identification or simple image analysis methods is not only time-consuming and labor-intensive, but also difficult to guarantee accuracy. In terms of data transmission, traditional communication methods have problems such as severe signal attenuation and low transmission rate, resulting in the inability to transmit the acquired fish resource data to the surface control center in a timely and accurate manner.

[0004] With the deepening of marine scientific research and the growing demand for marine resource development, there is an urgent need for an efficient, accurate and intelligent deep-sea mesopelagic fish resource survey method and system to overcome the shortcomings of traditional methods, achieve a comprehensive and in-depth understanding of deep-sea mesopelagic fish resources, and provide strong support for marine ecological protection and sustainable utilization of fishery resources. The present invention is based on this background and aims to solve the above problems in the prior art and provide a new deep-sea mesopelagic fish resource intelligent survey method and system. Summary of the invention

[0005] In order to solve at least one of the above technical problems, the present invention proposes an intelligent survey system and method for deep-sea mesopelagic fish resources.

[0006] The first aspect of the present invention provides an intelligent survey method for deep-sea mesopelagic fish resources, comprising: Acquire terrain detection signal data of the target water area and fish resource detection signal data of the target water layer based on the multi-beam sonar equipment, and determine the distribution of fish schools in the target water layer according to the fish resource detection signal data; Constructing a three-dimensional terrain model of the target waters according to the terrain detection signal data, and determining a cruising strategy of the underwater robot according to the three-dimensional terrain model and the distribution of the fish school; The underwater robot acquires fish school image data of the target water layer according to the cruising strategy, identifies the fish species and quantity of the target water layer according to the image data, and obtains fish resource data; The fish resource data is transmitted to a surface control center according to underwater acoustic wave communication technology.

[0007] In this solution, the terrain detection signal data of the target water area and the fish resource detection signal data of the target water layer are obtained based on the multi-beam sonar equipment, and the fish distribution of the target water layer is determined according to the fish resource detection signal data, specifically: Acquire terrain detection data of target waters and fish resource detection signal data of target water layers in target waters based on multi-beam sonar equipment; Based on the dynamic threshold segmentation algorithm, an adaptive filtering operation is performed on the fish resource detection signal data to obtain the fish resource detection signal data with noise removed, a variational mode decomposition algorithm is introduced, and initial values ​​of the variational mode decomposition algorithm parameters are set; Performing signal decomposition on the fish resource detection signal data after noise removal according to the variational mode decomposition algorithm to obtain k intrinsic mode functions; Performing Hilbert transform on the intrinsic mode function, constructing an analytical signal of each intrinsic mode function, calculating the instantaneous amplitude and instantaneous frequency of each intrinsic mode function according to the analytical signal, and constructing a time-frequency distribution matrix of the instantaneous amplitude and instantaneous frequency; Calculate the energy distribution, amplitude mean, energy sum, and amplitude peak of each frequency band emitted by the multi-beam sonar device according to the time-frequency distribution matrix to obtain a fish detection characteristic spectrum; Acquire the measurement point position information of the fish detection characteristic spectrum according to the fish resource detection signal data, perform position mapping of the fish detection characteristic spectrum of each measurement point according to the measurement point position information, and construct a three-dimensional fish detection characteristic spectrum; The fish density at each measuring point is determined according to the three-dimensional fish detection characteristic spectrum, a fish distribution thermodynamic map and a fish density gradient field of the target water layer are constructed according to the fish density at each measuring point, and the fish distribution situation of the target water layer is determined according to the fish distribution thermodynamic map and the fish density gradient field.

[0008] In this solution, the three-dimensional terrain model of the target waters is constructed according to the terrain detection signal data, and the cruising strategy of the underwater robot is determined according to the three-dimensional terrain model and the distribution of fish schools, specifically: Performing multi-scale curvature analysis on the terrain detection signal data, constructing a terrain feature tensor through gridding processing, identifying the water bottom type of the echo signal of the terrain detection signal data based on a support vector machine classifier, and generating a three-dimensional terrain model of the target water area including terrain elevation, curvature characteristics and geological type labels; Performing a position mapping operation on the fish distribution situation and the three-dimensional terrain model to construct a three-dimensional spatial distribution model of fish resources, and dividing the three-dimensional spatial distribution model of fish resources according to a preset grid size to construct N subspace areas; According to the fish distribution, fish density information of each subspace area is obtained, and subspace areas with similar fish density in the three-dimensional spatial distribution model of fish resources are clustered based on the k-means clustering algorithm to obtain a clustering result; Determine the distribution probability of fish schools in each subspace area according to the clustering result, obtain the corresponding position information of each subspace area in the target water area, and evaluate the importance of fish school detection for each position of the target water layer in the target water area according to the distribution probability and the corresponding position information to obtain the detection importance score of each position in the target water layer; Determining detection requirement information of each position in the target water layer according to the detection importance score of each position, wherein the detection requirement information includes whether to perform detection and detection accuracy requirement information; Obtaining image acquisition clarity data of the underwater robot for fish schools at different distances, determining image acquisition distance information of the underwater robot for each position of the target water layer according to the image acquisition clarity data and detection requirement information, and determining an image acquisition point set of the underwater robot according to the image acquisition distance information; The cruising strategy of the underwater robot is determined according to the image acquisition point set.

[0009] In this solution, the cruise strategy of the underwater robot is determined according to the image acquisition point set, specifically: Obtaining the three-dimensional coordinate information of each image acquisition point and the initial position information of the underwater robot, and determining the terrain obstacles in the target waters according to the three-dimensional terrain model; Perform path planning on the initial position information and the three-dimensional coordinate information of each image acquisition point based on the A* algorithm, and use the terrain obstacles as the path planning restriction area to output the shortest initial cruising path of the underwater robot; Acquire in real time the underwater robot's attitude change data during the underwater robot's cruising according to the shortest initial cruising path, and determine the water flow direction and water flow speed at the underwater robot's real-time cruising position according to the attitude change data; Acquiring driving stability data of the underwater robot, wherein the driving stability data includes data on the underwater robot's resistance to water currents of different directions and different speeds; Acquire recognition reaction time data of the underwater robot to the water flow direction and water flow speed, and determine the cruise path deviation correction lag of the underwater robot according to the recognition reaction time data; Analyze the water flow direction and water flow speed at the real-time cruising position of the underwater robot according to the resistance data and the cruising path deviation correction hysteresis, and determine the accumulated amount of the cruising path deviation of the underwater robot within the recognition reaction time; If the accumulated amount of the cruise path deviation is less than a preset value, determining a cruise deviation direction and a cruise deviation distance of the underwater robot according to the shortest initial cruise path according to the accumulated amount of the cruise path deviation, determining a deviation correction direction and a deviation correction distance of the underwater robot according to the cruise deviation direction and the cruise deviation distance, and obtaining deviation correction data; According to the deviation correction data, a deviation correction operation is performed on the shortest initial cruising path of the underwater robot during real-time driving to obtain a first cruising strategy; If the accumulated amount of the cruise path deviation is greater than a preset value, the water flow velocity and water flow direction data of the real-time driving path of the underwater robot are obtained, a water flow change diagram is constructed according to the water flow velocity and water flow direction data of the real-time driving path, and the water flow change trend of the target water layer in the target water area is determined according to the water flow change diagram; An interpolation operation is performed on the water flow change trend based on radial basis function interpolation to determine the water flow information within the preset range of the shortest initial cruising path, and the driving stability of the underwater robot within the preset range of the shortest initial cruising path is determined according to the water flow information and resistance capacity data. According to the driving stability, the shortest initial cruising path section with a cumulative amount of cruise path deviation greater than a preset value is adjusted to obtain an updated cruise path, and the underwater robot performs a cruise operation according to the updated cruise path to obtain a second cruise strategy.

[0010] In this solution, the underwater robot obtains fish image data of the target water layer according to the cruising strategy, identifies the fish species and quantity of the target water layer according to the image data, and obtains fish resource data, specifically: Acquire standard image data of different types of fish in the target waters, and label the standard image data with fish names to obtain labeled image data; Constructing a fish recognition model based on a convolutional neural network, and importing the labeled image data into the fish recognition model for training; The underwater robot obtains fish school image data of the target water layer according to the cruising strategy, imports the fish school image data into the trained fish recognition model to identify the fish species, and counts the number of each fish species to obtain fish resource data.

[0011] In this solution, the fish resource data is transmitted to the surface control center according to the underwater acoustic wave communication technology, specifically: Building a layered underwater acoustic communication protocol stack based on underwater acoustic wave communication technology, modulating the fish resource data, and building a fish resource acoustic wave transmission signal; The fish resource acoustic wave transmission signal is acoustically transmitted to a water surface control center according to the layered underwater acoustic communication protocol stack, and the fish resource acoustic wave transmission signal is decoded to obtain fish resource survey data of the target water layer.

[0012] The second aspect of the present invention further provides a deep-sea mesopelagic fish resource intelligent survey system, the system comprising: a memory, a processor, the memory comprising a deep-sea mesopelagic fish resource intelligent survey method program, the deep-sea mesopelagic fish resource intelligent survey method program when executed by the processor, implements the following steps: Acquire terrain detection signal data of the target water area and fish resource detection signal data of the target water layer based on the multi-beam sonar equipment, and determine the distribution of fish schools in the target water layer according to the fish resource detection signal data; Constructing a three-dimensional terrain model of the target waters according to the terrain detection signal data, and determining a cruising strategy of the underwater robot according to the three-dimensional terrain model and the distribution of the fish school; The underwater robot acquires fish school image data of the target water layer according to the cruising strategy, identifies the fish species and quantity of the target water layer according to the image data, and obtains fish resource data; The fish resource data is transmitted to a surface control center according to underwater acoustic wave communication technology.

[0013] The present invention discloses an intelligent survey system and method for deep-sea mid-layer fish resources. The method obtains terrain detection signal data of the target water area and fish resource detection signal data of the target water layer through a multi-beam sonar device, and determines the distribution of fish schools in the target water layer based on the data. A three-dimensional terrain model of the target water area is constructed using the terrain detection signal data, and the underwater robot cruising strategy is determined in combination with the fish school distribution. The underwater robot obtains fish school image data based on this strategy, identifies the fish species and quantity to obtain fish resource data, and finally transmits the data to the surface control center through underwater acoustic wave communication technology. The present invention realizes the intelligent and efficient survey of deep-sea mid-layer fish resources, provides strong support for the research and development of deep-sea fishery resources, and has broad application prospects and important practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart showing an intelligent investigation method for deep-sea mesopelagic fish resources according to the present invention is shown; Figure 2 The flow chart of obtaining fish resource data of the present invention is shown; Figure 3 A flow chart showing the method of transmitting fish resource data to a water surface control center according to the present invention is shown; Figure 4 A block diagram of an intelligent deep-sea mesopelagic fish resource survey system according to the present invention is shown. DETAILED DESCRIPTION

[0015] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0017] Figure 1 A flow chart of an intelligent investigation method for deep-sea mesopelagic fish resources according to the present invention is shown.

[0018] like Figure 1 As shown, the first aspect of the present invention provides an intelligent survey method for deep-sea mesopelagic fish resources, comprising: S102, acquiring terrain detection signal data of the target water area and fish resource detection signal data of the target water layer based on the multi-beam sonar device, and determining the distribution of fish schools in the target water layer according to the fish resource detection signal data; S104, constructing a three-dimensional terrain model of the target waters according to the terrain detection signal data, and determining a cruising strategy of the underwater robot according to the three-dimensional terrain model and the distribution of the fish school; S106, the underwater robot acquires fish school image data of the target water layer according to the cruising strategy, identifies the fish species and quantity of the target water layer according to the image data, and obtains fish resource data; S108, transmitting the fish resource data to a surface control center according to underwater acoustic wave communication technology.

[0019] It should be noted that the multi-beam sonar equipment can simultaneously transmit and receive multiple beams, so as to quickly and comprehensively obtain the terrain detection signal data of the target water area and the fish resource detection signal data of the target water layer, thereby improving the efficiency and coverage of data collection; by determining the distribution of fish schools in the middle layer of the deep sea through the fish resource detection signal data, the main distribution area of ​​the fish school can be determined in advance, providing targets for the subsequent underwater robot to identify resources, reducing resource waste and improving investigation efficiency; combining the three-dimensional terrain model and the distribution of fish schools to determine the cruising strategy of the underwater robot can enable the underwater robot to avoid terrain obstacles and more specifically detect fish-dense areas, thereby improving investigation efficiency and accuracy; according to the optimized cruising strategy, the underwater robot can obtain high-quality fish school image data of the target water layer at the appropriate position and angle, providing clear image basis for the identification of fish species and quantity; using advanced image recognition technology to analyze the fish school image data, it can accurately identify the fish species and quantity of the target water layer and obtain detailed fish resource data; underwater acoustic wave communication technology can realize the reliable transmission of fish resource data in the underwater environment, and transmit the data collected underwater to the surface control center in a timely manner to avoid data loss or delay.

[0020] According to an embodiment of the present invention, the terrain detection signal data of the target water area and the fish resource detection signal data of the target water layer are obtained based on the multi-beam sonar device, and the fish distribution of the target water layer is determined according to the fish resource detection signal data, specifically: Acquire terrain detection data of target waters and fish resource detection signal data of target water layers in target waters based on multi-beam sonar equipment; Based on the dynamic threshold segmentation algorithm, an adaptive filtering operation is performed on the fish resource detection signal data to obtain the fish resource detection signal data with noise removed, a variational mode decomposition algorithm is introduced, and initial values ​​of the variational mode decomposition algorithm parameters are set; Performing signal decomposition on the fish resource detection signal data after noise removal according to the variational mode decomposition algorithm to obtain k intrinsic mode functions; Performing Hilbert transform on the intrinsic mode function, constructing an analytical signal of each intrinsic mode function, calculating the instantaneous amplitude and instantaneous frequency of each intrinsic mode function according to the analytical signal, and constructing a time-frequency distribution matrix of the instantaneous amplitude and instantaneous frequency; Calculate the energy distribution, amplitude mean, energy sum, and amplitude peak of each frequency band emitted by the multi-beam sonar device according to the time-frequency distribution matrix to obtain a fish detection characteristic spectrum; Acquire the measurement point position information of the fish detection characteristic spectrum according to the fish resource detection signal data, perform position mapping of the fish detection characteristic spectrum of each measurement point according to the measurement point position information, and construct a three-dimensional fish detection characteristic spectrum; The fish density at each measuring point is determined according to the three-dimensional fish detection characteristic spectrum, a fish distribution thermodynamic map and a fish density gradient field of the target water layer are constructed according to the fish density at each measuring point, and the fish distribution situation of the target water layer is determined according to the fish distribution thermodynamic map and the fish density gradient field.

[0021] It should be noted that the variational mode decomposition algorithm (VMD) is an adaptive signal processing method that can decompose complex signals into multiple intrinsic mode functions (IMFs) with different center frequencies and bandwidths. In deep-sea environments, the fish resource detection signal data received by multi-beam sonar equipment contains various noises and interferences. VMD can adaptively decompose the signal according to the inherent characteristics of the signal, thereby effectively extracting the characteristic information related to the fish school. By setting different decomposition layers k, IMF components of different scales can be obtained. Each IMF component contains the characteristic information of the signal in different frequency bands, so that the fish detection signal can be analyzed from multiple angles to improve the accuracy and comprehensiveness of feature extraction. For example, the IMF component in the low-frequency band may reflect the overall distribution trend of the fish school, while the IMF component in the high-frequency band may contain the detailed characteristics of the fish school. The three-dimensional fish detection characteristic spectrum is constructed by position mapping the parameters such as the energy distribution, amplitude mean, energy sum, and amplitude peak of each frequency band emitted by the multi-beam sonar equipment. These parameters are closely related to the density of fish schools. For example, areas with large energy distribution and amplitude peaks usually indicate that fish schools are denser. Because the presence of fish schools will scatter and reflect sonar signals, causing changes in the energy and amplitude of the signals, the density of fish schools can be inferred by analyzing these changes. The dynamic threshold segmentation algorithm is used for adaptive filtering, combined with the variational mode decomposition algorithm, which can effectively remove complex noise and interference in deep-sea environments, deeply mine and analyze fish resource detection signal data, and extract accurate and comprehensive fish detection feature spectra. Based on the three-dimensional fish detection feature spectrum, the correlation between features and density, spatial information, statistical analysis and model verification can be used to accurately determine the fish density at each measurement point, and then construct a fish distribution heat map and fish density gradient field of the target water layer, clearly and intuitively showing the distribution of fish schools in the target water layer. The fish distribution includes the fish distribution boundary and the approximate distribution density of fish schools at each location. The variational mode decomposition algorithm parameters include the number of modal components, the maximum number of iterations, the convergence accuracy, the modal component value, the center frequency value, and the Lagrange multiplier value.

[0022] According to an embodiment of the present invention, the three-dimensional terrain model of the target waters is constructed according to the terrain detection signal data, and the cruising strategy of the underwater robot is determined according to the three-dimensional terrain model and the distribution of fish schools, specifically: Performing multi-scale curvature analysis on the terrain detection signal data, constructing a terrain feature tensor through gridding processing, identifying the water bottom type of the echo signal of the terrain detection signal data based on a support vector machine classifier, and generating a three-dimensional terrain model of the target water area including terrain elevation, curvature characteristics and geological type labels; Performing a position mapping operation on the fish distribution situation and the three-dimensional terrain model to construct a three-dimensional spatial distribution model of fish resources, and dividing the three-dimensional spatial distribution model of fish resources according to a preset grid size to construct N subspace areas; According to the fish distribution, fish density information of each subspace area is obtained, and subspace areas with similar fish density in the three-dimensional spatial distribution model of fish resources are clustered based on the k-means clustering algorithm to obtain a clustering result; Determine the distribution probability of fish schools in each subspace area according to the clustering result, obtain the corresponding position information of each subspace area in the target water area, and evaluate the importance of fish school detection for each position of the target water layer in the target water area according to the distribution probability and the corresponding position information to obtain the detection importance score of each position in the target water layer; Determining detection requirement information of each position in the target water layer according to the detection importance score of each position, wherein the detection requirement information includes whether to perform detection and detection accuracy requirement information; Obtaining image acquisition clarity data of the underwater robot for fish schools at different distances, determining image acquisition distance information of the underwater robot for each position of the target water layer according to the image acquisition clarity data and detection requirement information, and determining an image acquisition point set of the underwater robot according to the image acquisition distance information; The cruising strategy of the underwater robot is determined according to the image acquisition point set.

[0023] It should be noted that the k-means clustering algorithm can effectively cluster the subspace areas in the three-dimensional spatial distribution model of fish resources based on the fish density. The distribution probability of fish in each subspace area is determined by the clustering results, and the dense and sparse fish areas can be clearly distinguished. This enables the survey work to focus on areas with high probability of fish distribution, avoid wasting too much resources and time in areas with sparse fish, and significantly improve the pertinence and efficiency of resource detection. According to the distribution probability of fish in the subspace area and the corresponding position information, the importance of fish detection is evaluated for each position in the target water layer and the detection importance score is obtained. This process can quantify the detection value of different positions, determine the detection demand information based on the score, and clarify which areas need to be focused on detection and which areas can appropriately reduce the detection accuracy or ignore. In this way, the detection resources of the underwater robot can be reasonably allocated to ensure that the most valuable fish information is obtained under limited energy and time conditions. Combined with the detection importance score and the image acquisition clarity data of the underwater robot for fish at different distances, the image acquisition distance information and the image acquisition point set are determined. This ensures that the underwater robot can shoot the fish area of ​​focus at the most appropriate distance and position during the image acquisition process. On the one hand, it avoids blurry images due to too long a distance, which makes it impossible to accurately identify the type and quantity of fish; on the other hand, it prevents other important areas from being missed due to too close a distance. Through precise image acquisition point planning, the quality of the collected image data is improved, providing more reliable data support for subsequent fish resource identification and analysis. The higher the detection importance score, the higher the detection accuracy requirement.

[0024] According to an embodiment of the present invention, the cruise strategy of the underwater robot is determined according to the image acquisition point set, specifically: Obtaining the three-dimensional coordinate information of each image acquisition point and the initial position information of the underwater robot, and determining the terrain obstacles in the target waters according to the three-dimensional terrain model; Perform path planning on the initial position information and the three-dimensional coordinate information of each image acquisition point based on the A* algorithm, and use the terrain obstacles as the path planning restriction area to output the shortest initial cruising path of the underwater robot; It should be noted that after determining the distribution of fish schools, it is necessary to use underwater robots equipped with cameras to obtain images of fish schools in the fish distribution area and then conduct a survey on the types and numbers of fish schools. By analyzing each image acquisition point through the A* algorithm and planning the shortest cruising path, the energy consumption of underwater robots can be greatly reduced and the survey efficiency can be improved.

[0025] Acquire in real time the underwater robot's attitude change data during the underwater robot's cruising according to the shortest initial cruising path, and determine the water flow direction and water flow speed at the underwater robot's real-time cruising position according to the attitude change data; Acquiring driving stability data of the underwater robot, wherein the driving stability data includes data on the underwater robot's resistance to water currents of different directions and different speeds; Acquire recognition reaction time data of the underwater robot to the water flow direction and water flow speed, and determine the cruise path deviation correction lag of the underwater robot according to the recognition reaction time data; Analyze the water flow direction and water flow speed at the real-time cruising position of the underwater robot according to the resistance data and the cruising path deviation correction hysteresis, and determine the accumulated amount of the cruising path deviation of the underwater robot within the recognition reaction time; If the accumulated amount of the cruise path deviation is less than a preset value, determining a cruise deviation direction and a cruise deviation distance of the underwater robot according to the shortest initial cruise path according to the accumulated amount of the cruise path deviation, determining a deviation correction direction and a deviation correction distance of the underwater robot according to the cruise deviation direction and the cruise deviation distance, and obtaining deviation correction data; According to the deviation correction data, a deviation correction operation is performed on the shortest initial cruising path of the underwater robot during real-time driving to obtain a first cruising strategy; It should be noted that in order to efficiently complete the task of collecting images of school of fish, the underwater robot needs to quickly reach each image collection point according to the planned path. However, in the actual navigation process, due to the influence of factors such as water flow, if the path cannot be adjusted in time, it may not be able to reach the point on time, or the quality of the image collected when arriving at the point will be affected, which cannot meet the needs of fish resource identification and analysis. When determining the amount of correction of the cruise path, the underwater robot needs to obtain its own posture change data, water flow direction and speed data in real time. The collection, transmission and processing of these data all take time. In a complex underwater environment, data transmission may be affected by signal interference, attenuation, etc., resulting in data transmission delays. In the data processing process, such as the solution of posture data, the analysis of water flow data, and the calculation of correction amount based on these data, a certain amount of calculation time is required. This series of data-related operations together cause correction lag. When the accumulated amount of cruise path deviation is less than the preset value, the water flow direction and speed are determined by obtaining the underwater robot posture change data, and the cruise deviation direction and distance can be accurately calculated by combining the driving stability data and the cruise path correction lag, and then the correction direction and distance can be determined. This allows the underwater robot to make timely fine-tuning of the shortest initial cruising path with small deviations, and always keep on a trajectory close to the predetermined path.

[0026] If the accumulated amount of the cruise path deviation is greater than a preset value, the water flow velocity and water flow direction data of the real-time driving path of the underwater robot are obtained, a water flow change diagram is constructed according to the water flow velocity and water flow direction data of the real-time driving path, and the water flow change trend of the target water layer in the target water area is determined according to the water flow change diagram; An interpolation operation is performed on the water flow change trend based on radial basis function interpolation to determine the water flow information within the preset range of the shortest initial cruising path, and the driving stability of the underwater robot within the preset range of the shortest initial cruising path is determined according to the water flow information and resistance capacity data. According to the driving stability, the shortest initial cruising path section with a cumulative amount of cruise path deviation greater than a preset value is adjusted to obtain an updated cruise path, and the underwater robot performs a cruise operation according to the updated cruise path to obtain a second cruise strategy.

[0027] It should be noted that when the accumulated amount of cruise path deviation is greater than the preset value, it indicates that the underwater robot encountered strong water flow interference during navigation. The complex and changeable water flow direction and high-speed water flow cause the robot to be continuously acted upon by external forces, and it is impossible to maintain the predetermined cruise path. Therefore, by obtaining the water flow velocity and water flow direction data of the real-time driving path of the underwater robot and constructing a water flow change diagram, the water flow change of the target water layer in the target water area can be presented intuitively and comprehensively. The radial basis function interpolation is used to interpolate the water flow change trend, and more detailed and accurate water flow information can be obtained within the preset range of the shortest initial cruise path. This refined processing can fill in the data gaps, optimize the continuity and integrity of the water flow information, and thus more accurately evaluate the impact of the water flow on the driving stability of the underwater robot. According to the driving stability, the shortest initial cruise path section with the accumulated amount of cruise path deviation greater than the preset value is adjusted, and the unreasonable path planning can be corrected in time, so that the updated cruise path can better adapt to the complex and changeable water flow environment. The water flow information includes water flow velocity and water flow direction.

[0028] Figure 2 A flow chart of obtaining fish resource data according to the present invention is shown.

[0029] According to an embodiment of the present invention, the underwater robot obtains fish school image data of the target water layer according to the cruising strategy, identifies the fish species and quantity of the target water layer according to the image data, and obtains fish resource data, specifically: S202, obtaining standard image data of different types of fish in the target waters, and labeling the standard image data with fish names to obtain labeled image data; S204, constructing a fish recognition model based on a convolutional neural network, and importing the labeled image data into the fish recognition model for training; S206, the underwater robot obtains fish school image data of the target water layer according to the cruising strategy, imports the fish school image data into the trained fish recognition model to identify the fish species, and counts the number of each fish species to obtain fish resource data.

[0030] It should be noted that the underwater robot works according to the established cruising strategy, and can conduct a comprehensive scan of the target water layer in a targeted manner and accurately obtain fish image data. This avoids blind collection and greatly improves the efficiency of data collection. At the same time, it ensures that the collected data is representative. The fish recognition model built based on the convolutional neural network can accurately identify different types of fish through learning and training a large amount of labeled image data. The powerful feature extraction ability of the convolutional neural network enables it to capture the subtle features of fish, thereby effectively distinguishing similar types of fish, reducing recognition errors, and improving the accuracy of fish species identification. By counting the number of each fish species, the distribution of fish resources in the target water layer can be fully grasped. Not only can the population size of different fish species be understood, but also the proportional relationship between them can be analyzed, providing key data for fishery resource assessment.

[0031] Figure 3 A flow chart showing the method of transmitting fish resource data to a surface control center according to the present invention is shown.

[0032] According to an embodiment of the present invention, the fish resource data is transmitted to the surface control center according to the underwater acoustic wave communication technology, specifically: S302, constructing a layered underwater acoustic communication protocol stack based on underwater acoustic wave communication technology, modulating the fish resource data, and constructing a fish resource acoustic wave transmission signal; S304, sending the fish resource acoustic wave transmission signal to the water surface control center according to the layered underwater acoustic communication protocol stack, and decoding the fish resource acoustic wave transmission signal to obtain fish resource survey data of the target water layer.

[0033] It should be noted that the layered underwater acoustic communication protocol stack based on underwater acoustic wave communication technology can adapt to the complex underwater environment, effectively resist interference factors such as seawater absorption, scattering, and multipath effects, ensure the stability of fish resource acoustic wave transmission signals during the transmission process, reduce data loss and errors, and ensure that fish resource data is accurately transmitted from underwater to the surface control center. The fish resource data is modulated to construct an acoustic wave transmission signal, and the layered underwater acoustic communication protocol stack is used for sending and decoding, which optimizes the data transmission process and improves the transmission efficiency. The modulation process can convert the data into a form suitable for underwater acoustic wave transmission, and the protocol stack standardizes the data transmission rules, so that the data can quickly reach the surface control center and can be quickly decoded and processed, saving time and cost and meeting real-time requirements.

[0034] Figure 4 A block diagram of an intelligent deep-sea mesopelagic fish resource survey system according to the present invention is shown.

[0035] The second aspect of the present invention further provides a deep-sea mesopelagic fish resource intelligent survey system 4, the system comprising: a memory 41, a processor 42, the memory comprising a deep-sea mesopelagic fish resource intelligent survey method program, the deep-sea mesopelagic fish resource intelligent survey method program when executed by the processor, implements the following steps: Acquire terrain detection signal data of the target water area and fish resource detection signal data of the target water layer based on the multi-beam sonar equipment, and determine the distribution of fish schools in the target water layer according to the fish resource detection signal data; Constructing a three-dimensional terrain model of the target waters according to the terrain detection signal data, and determining a cruising strategy of the underwater robot according to the three-dimensional terrain model and the distribution of the fish school; The underwater robot acquires fish school image data of the target water layer according to the cruising strategy, identifies the fish species and quantity of the target water layer according to the image data, and obtains fish resource data; The fish resource data is transmitted to a surface control center according to underwater acoustic wave communication technology.

[0036] The present invention discloses an intelligent survey system and method for deep-sea mid-layer fish resources. The method obtains terrain detection signal data of the target water area and fish resource detection signal data of the target water layer through a multi-beam sonar device, and determines the distribution of fish schools in the target water layer based on the data. A three-dimensional terrain model of the target water area is constructed using the terrain detection signal data, and the underwater robot cruising strategy is determined in combination with the fish school distribution. The underwater robot obtains fish school image data based on this strategy, identifies the fish species and quantity to obtain fish resource data, and finally transmits the data to the surface control center through underwater acoustic wave communication technology. The present invention realizes the intelligent and efficient survey of deep-sea mid-layer fish resources, provides strong support for the research and development of deep-sea fishery resources, and has broad application prospects and important practical significance.

[0037] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0038] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0039] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0040] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0041] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0042] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent survey method for deep-sea mesopelagic fish resources, characterized in that: The following steps are involved: Acquire terrain detection signal data of the target water area and fish resource detection signal data of the target water layer based on the multi-beam sonar equipment, and determine the distribution of fish schools in the target water layer according to the fish resource detection signal data; Constructing a three-dimensional terrain model of the target waters according to the terrain detection signal data, and determining a cruising strategy of the underwater robot according to the three-dimensional terrain model and the distribution of the fish school; The underwater robot acquires fish school image data of the target water layer according to the cruising strategy, identifies the fish species and quantity of the target water layer according to the image data, and obtains fish resource data; The fish resource data is transmitted to a surface control center according to underwater acoustic wave communication technology.

2. The intelligent survey method for deep-sea mesopelagic fish resources according to claim 1 is characterized in that: The method of obtaining terrain detection signal data of the target water area and fish resource detection signal data of the target water layer based on the multi-beam sonar device, and determining the fish distribution of the target water layer according to the fish resource detection signal data, is specifically as follows: Acquire terrain detection data of target waters and fish resource detection signal data of target water layers in target waters based on multi-beam sonar equipment; Based on the dynamic threshold segmentation algorithm, an adaptive filtering operation is performed on the fish resource detection signal data to obtain the fish resource detection signal data with noise removed, a variational mode decomposition algorithm is introduced, and initial values ​​of the variational mode decomposition algorithm parameters are set; Performing signal decomposition on the fish resource detection signal data after noise removal according to the variational mode decomposition algorithm to obtain k intrinsic mode functions; Performing Hilbert transform on the intrinsic mode function, constructing an analytical signal of each intrinsic mode function, calculating the instantaneous amplitude and instantaneous frequency of each intrinsic mode function according to the analytical signal, and constructing a time-frequency distribution matrix of the instantaneous amplitude and instantaneous frequency; Calculate the energy distribution, amplitude mean, energy sum, and amplitude peak of each frequency band emitted by the multi-beam sonar device according to the time-frequency distribution matrix to obtain a fish detection characteristic spectrum; Acquire the measurement point position information of the fish detection characteristic spectrum according to the fish resource detection signal data, perform position mapping of the fish detection characteristic spectrum of each measurement point according to the measurement point position information, and construct a three-dimensional fish detection characteristic spectrum; The fish density at each measuring point is determined according to the three-dimensional fish detection characteristic spectrum, a fish distribution thermodynamic map and a fish density gradient field of the target water layer are constructed according to the fish density at each measuring point, and the fish distribution situation of the target water layer is determined according to the fish distribution thermodynamic map and the fish density gradient field.

3. The intelligent survey method for deep-sea mesopelagic fish resources according to claim 1 is characterized in that: The three-dimensional terrain model of the target waters is constructed according to the terrain detection signal data, and the cruising strategy of the underwater robot is determined according to the three-dimensional terrain model and the distribution of the fish school, specifically: Performing multi-scale curvature analysis on the terrain detection signal data, constructing a terrain feature tensor through gridding processing, identifying the water bottom type of the echo signal of the terrain detection signal data based on a support vector machine classifier, and generating a three-dimensional terrain model of the target water area including terrain elevation, curvature characteristics and geological type labels; Performing a position mapping operation on the fish distribution situation and the three-dimensional terrain model to construct a three-dimensional spatial distribution model of fish resources, and dividing the three-dimensional spatial distribution model of fish resources according to a preset grid size to construct N subspace areas; According to the fish distribution, fish density information of each subspace area is obtained, and subspace areas with similar fish density in the three-dimensional spatial distribution model of fish resources are clustered based on the k-means clustering algorithm to obtain a clustering result; Determine the distribution probability of fish schools in each subspace area according to the clustering result, obtain the corresponding position information of each subspace area in the target water area, and evaluate the importance of fish school detection for each position of the target water layer in the target water area according to the distribution probability and the corresponding position information to obtain the detection importance score of each position in the target water layer; Determining detection requirement information of each position in the target water layer according to the detection importance score of each position, wherein the detection requirement information includes whether to perform detection and detection accuracy requirement information; Obtaining image acquisition clarity data of the underwater robot for fish schools at different distances, determining image acquisition distance information of the underwater robot for each position of the target water layer according to the image acquisition clarity data and detection requirement information, and determining an image acquisition point set of the underwater robot according to the image acquisition distance information; The cruising strategy of the underwater robot is determined according to the image acquisition point set.

4. The intelligent survey method for deep-sea mesopelagic fish resources according to claim 3 is characterized in that: The cruise strategy of the underwater robot is determined according to the image acquisition point set, specifically: Obtaining the three-dimensional coordinate information of each image acquisition point and the initial position information of the underwater robot, and determining the terrain obstacles in the target waters according to the three-dimensional terrain model; Perform path planning on the initial position information and the three-dimensional coordinate information of each image acquisition point based on the A* algorithm, and use the terrain obstacles as the path planning restriction area to output the shortest initial cruising path of the underwater robot; Acquire in real time the underwater robot's attitude change data during the underwater robot's cruising according to the shortest initial cruising path, and determine the water flow direction and water flow speed at the underwater robot's real-time cruising position according to the attitude change data; Acquiring driving stability data of the underwater robot, wherein the driving stability data includes data on the underwater robot's resistance to water currents of different directions and different speeds; Acquire recognition reaction time data of the underwater robot to the water flow direction and water flow speed, and determine the cruise path deviation correction lag of the underwater robot according to the recognition reaction time data; Analyze the water flow direction and water flow speed at the real-time cruising position of the underwater robot according to the resistance data and the cruising path deviation correction hysteresis, and determine the accumulated amount of the cruising path deviation of the underwater robot within the recognition reaction time; If the accumulated amount of the cruise path deviation is less than a preset value, determining a cruise deviation direction and a cruise deviation distance of the underwater robot according to the shortest initial cruise path according to the accumulated amount of the cruise path deviation, determining a deviation correction direction and a deviation correction distance of the underwater robot according to the cruise deviation direction and the cruise deviation distance, and obtaining deviation correction data; According to the deviation correction data, a deviation correction operation is performed on the shortest initial cruising path of the underwater robot during real-time driving to obtain a first cruising strategy; If the accumulated amount of the cruise path deviation is greater than a preset value, the water flow velocity and water flow direction data of the real-time driving path of the underwater robot are obtained, a water flow change diagram is constructed according to the water flow velocity and water flow direction data of the real-time driving path, and the water flow change trend of the target water layer in the target water area is determined according to the water flow change diagram; An interpolation operation is performed on the water flow change trend based on radial basis function interpolation to determine the water flow information within the preset range of the shortest initial cruising path, and the driving stability of the underwater robot within the preset range of the shortest initial cruising path is determined according to the water flow information and resistance capacity data. According to the driving stability, the shortest initial cruising path section with a cumulative amount of cruise path deviation greater than a preset value is adjusted to obtain an updated cruise path, and the underwater robot performs a cruise operation according to the updated cruise path to obtain a second cruise strategy.

5. The intelligent survey method for deep-sea mesopelagic fish resources according to claim 1 is characterized in that: The underwater robot obtains fish school image data of the target water layer according to the cruising strategy, identifies the fish species and quantity of the target water layer according to the image data, and obtains fish resource data, specifically: Obtain standard image data of different types of fish in the target waters, and label the standard image data with fish names to obtain labeled image data; Constructing a fish recognition model based on a convolutional neural network, and importing the labeled image data into the fish recognition model for training; The underwater robot obtains fish school image data of the target water layer according to the cruising strategy, imports the fish school image data into the trained fish recognition model to identify the fish species, and counts the number of each fish species to obtain fish resource data.

6. The intelligent survey method for deep-sea mesopelagic fish resources according to claim 1 is characterized in that: The fish resource data is transmitted to the surface control center according to the underwater acoustic wave communication technology, specifically: Building a layered underwater acoustic communication protocol stack based on underwater acoustic wave communication technology, modulating the fish resource data, and building a fish resource acoustic wave transmission signal; The fish resource acoustic wave transmission signal is acoustically transmitted to a water surface control center according to the layered underwater acoustic communication protocol stack, and the fish resource acoustic wave transmission signal is decoded to obtain fish resource survey data of the target water layer.

7. An intelligent deep-sea mid-layer fish resource survey system, characterized in that: The deep-sea mesopelagic fish resource intelligent survey system comprises a storage device and a processor, wherein the storage device comprises a deep-sea mesopelagic fish resource intelligent survey method program, and when the deep-sea mesopelagic fish resource intelligent survey method program is executed by the processor, the following steps are implemented: Acquire terrain detection signal data of the target water area and fish resource detection signal data of the target water layer based on the multi-beam sonar equipment, and determine the distribution of fish schools in the target water layer according to the fish resource detection signal data; Constructing a three-dimensional terrain model of the target waters according to the terrain detection signal data, and determining a cruising strategy of the underwater robot according to the three-dimensional terrain model and the distribution of the fish school; The underwater robot acquires fish school image data of the target water layer according to the cruising strategy, identifies the fish species and quantity of the target water layer according to the image data, and obtains fish resource data; The fish resource data is transmitted to a surface control center according to underwater acoustic wave communication technology.

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