Network version digital ocean ball public application and development integration method and system

Abstract

The invention provides a network version digital ocean ball public application and development integration method and system. According to the method, intelligent buoys equipped with laser communication and scanning equipment and processors are used as dynamic nodes, a self-organizing communication network is constructed, and underwater positions and ocean current data are shared. And when multiple nodes detect abnormal targets at the same time, a collaborative verification mechanism is triggered. The system drives the nodes to adjust the layout through a dynamic sphere model, and at least three nodes are controlled to move towards the center of the target along a spiral path to form a multi-layer spherical monitoring structure. And finally, encoding and uploading the three-dimensional coordinate data to a shore-based platform to generate an updated digital ocean ball map. According to the technical scheme provided by the invention, accurate positioning and three-dimensional visual monitoring of the abnormal target in the marine environment are realized, the marine ecological monitoring performance and the space coverage precision are remarkably improved, and a three-dimensional data support is provided for marine environment protection and disaster early warning.

Description

Technical Field

[0001] This application relates to the field of marine environment monitoring and sensing technologies, and particularly to a networked digital ocean ball public application and development integration method and system. Background Art

[0002] The three-dimensional monitoring of the ecological environment of marine pastures aims to achieve all-round monitoring of the ecological environment of marine pastures to ensure the sustainable utilization of marine resources and ecological balance. This scenario requires efficient data collection, transmission, processing, and analysis capabilities, as well as precise monitoring of marine environmental parameters. Therefore, the technical requirements include high-precision sensor networks, efficient data transmission and storage technologies, intelligent data analysis and processing platforms, and user-friendly public application interfaces.

[0003] Currently, the marine pasture monitoring platform based on multi-source data fusion is the mainstream solution. This platform integrates multi-source data such as satellite remote sensing, underwater sensors, buoy observation stations, and unmanned aerial vehicles, and uses cloud computing and big data technologies for processing and analysis, and provides visualization display and early warning functions for abnormal events. This solution can achieve comprehensive monitoring of the marine pasture environment, but there is still room for improvement in terms of the depth of data fusion, the level of intelligence, and public participation.

[0004] However, the existing solutions have insufficient depth of data fusion, and there are problems of inconsistent formats and standards among multi-source data, resulting in limited global analysis capabilities; the level of intelligence is relatively low, and existing algorithms are mostly designed for single scenarios and are difficult to handle the comprehensive judgment of complex ecological events; public participation is insufficient, and the platform functions are mostly oriented to professional users, lacking a friendly interface and interactive functions for the public. These defects limit the practicality and promotion value of the monitoring system and urgently need to be improved through innovative methods. Summary of the Invention

[0005] This application provides a networked digital ocean ball public application and development integration method and system to solve the problems of insufficient performance and accuracy in existing marine monitoring technologies.

[0006] In a first aspect, this application provides a networked digital ocean ball public application and development integration method, including:

[0007] Regarding each buoy in the intelligent buoy cluster as a networked ocean ball node, each node includes a laser communication device, a scanning device, and a processor. According to the dynamic positional relationship between the nodes, a self-organizing communication network is constructed in the target sea area, and the nodes in the self-organizing communication network share underwater position and ocean current information through laser communication;

[0008] The water area where the node is located is scanned layer by layer through the scanning device to generate three-dimensional data including water transparency, plankton density, and pollution particles. The three-dimensional data is compared with a preset three-dimensional ocean model in the processor of the node, and the comparison result is shared with adjacent nodes through laser communication;

[0009] When the same abnormal target is detected by adjacent nodes, a collaborative verification mechanism is triggered. The three-dimensional data and movement direction of the target are synchronized through laser communication. The position of the target is calculated using the sonar signal difference between nodes, and ocean current interference is eliminated through distributed computing;

[0010] The driving node adjusts the monitoring layout with a dynamic sphere model, sets the target position as the network center point, controls at least three nodes to move along a spiral path towards the center point to form a multi-layer spherical monitoring structure. At the same time, the monitoring data is encoded as three-dimensional coordinate data through laser communication and uploaded to the shore-based platform to generate a digital ocean sphere atlas that is synchronized with the ocean environment update.

[0011] Optionally, generate spatial parameters of the dynamic sphere model based on the target position, set the target position as the network center point, calculate the initial spherical distance between the center point and all nodes, determine the maximum moving speed of the nodes, and generate a spiral path with the center point as the origin for each node;

[0012] Control at least three nodes to move along the spiral path towards the center point, feedback the azimuth angle difference of adjacent nodes, dynamically correct the offset of the node movement trajectory, and at the same time dynamically divide the layer spacing of the multi-layer spherical monitoring structure, and match the spherical distance from the node to the center point to the corresponding layer number;

[0013] Control the outer nodes to move along the tangential direction of the sphere to offset the ocean current deformation and synchronously adjust the node spacing. Integrate the target detail data collected by the inner nodes and the environmental background data of the outer nodes according to the corresponding layer number for spherical block integration to complete the construction of the multi-layer spherical monitoring structure.

[0014] Optionally, generate a discretized waypoint sequence of the spiral path based on the current ocean current resistance coefficient and the thrust upper limit of the node thruster, and continuously cover the target detection range when controlling the node movement based on the discretized waypoint sequence;

[0015] During the node movement, obtain the azimuth angle difference data of adjacent nodes, extract the offset of the laser signal path caused by ocean current refraction, calculate the ocean current interference component in combination with the relative motion vector between nodes, and dynamically adjust the curvature radius of the waypoint to correct the offset of the node movement trajectory;

[0016] Divide the hierarchical spacing threshold according to the spherical distance from the node to the center point, select the priority hierarchical number based on the ocean current direction and the target movement trend, allocate exclusive scanning parameters to the nodes in the matching layer, enable high-resolution scanning for the inner-layer nodes to focus on the target details, enable wide-area scanning for the outer-layer nodes to capture environmental data, and synchronously update the scanning parameters of the nodes within the layer through laser communication.

[0017] Optionally, when the time stamps of the target three-dimensional data detected by the scanning devices of adjacent nodes overlap and the movement direction consistency exceeds a preset threshold, trigger the collaborative verification mechanism, send the three-dimensional data profile and movement vector of the target to the adjacent nodes through the laser communication module, and receive the target reflected sonar signals fed back by the adjacent nodes;

[0018] Extract the phase difference and time delay difference of the target reflected sonar signals, and combine the underwater relative position relationship between the nodes to calculate the initial position of the target in three-dimensional space;

[0019] Synchronize the initial position with the movement vector synchronized by laser communication, and eliminate the target virtual image position caused by ocean current stratification to obtain a preliminarily corrected target position;

[0020] According to the sonar signal differences between the nodes, allocate ocean current interference compensation coefficients to each node through distributed calculation. Further correct the preliminarily corrected target position based on the ocean current interference compensation coefficients to eliminate ocean current interference.

[0021] Optionally, based on the spherical distance from the node to the center point, generate a hierarchical spacing threshold according to the preset distance interval division rule, and the hierarchical spacing threshold is used to define the spacing range of each layer in the multi-layer spherical monitoring structure;

[0022] Dynamically adjust the hierarchical spacing threshold according to the target movement trend and the ocean current direction. When the target approaches the center point, reduce the hierarchical spacing threshold to increase the monitoring density. When the target moves away from the center point, expand the hierarchical spacing threshold to expand the monitoring range; based on the adjusted hierarchical spacing threshold, recalculate the spacing range of each layer in the multi-layer spherical monitoring structure, and match the nodes to the corresponding layers to ensure that the hierarchical spacing of the multi-layer spherical monitoring structure adapts to the target movement trend and monitoring requirements.

[0023] Optionally, divide the vertical scanning levels based on the water depth gradient of the target sea area, and form stratified three-dimensional data covering water transparency, plankton density, and pollution particles according to the data scanned at different water depth gradient levels;

[0024] Load a preset three-dimensional ocean model in the node processor. The three-dimensional ocean model divides the ocean space into cube grid cells bound to geographical coordinates through grid processing, and the side length of each grid cell is negatively correlated with the average water transparency of the water layer where it is located;

[0025] Map the layered three-dimensional data to the corresponding grid cells according to geographical coordinates. When the transparency decrease rate, the biological density mutation value, and the pollution concentration increase value of the layered three-dimensional data within the same grid cell simultaneously exceed the corresponding thresholds, trigger the parameter coordinate association mechanism;

[0026] Drive the peripheral nodes to dynamically adjust the detection range of the scanning device according to the associated parameters, and compare the layered three-dimensional data with a preset three-dimensional ocean model within the processors of the nodes.

[0027] Optionally, extract the sonar signal differences between nodes, where the sonar signal differences include the signal arrival time difference and the signal intensity difference, and combine the underwater relative position relationship between nodes to calculate the ocean current influence component of the ocean current on the sonar signals of each node;

[0028] Based on the ocean current influence component, allocate an ocean current interference compensation coefficient for each node through distributed computing, and the ocean current interference compensation coefficient is used to quantify the interference degree of the ocean current on the target position;

[0029] According to the ocean current interference compensation coefficient, further correct the preliminary corrected target position of each node, including: combining the ocean current interference compensation coefficient with the preliminary corrected target position to calculate the corrected target position after eliminating the ocean current interference;

[0030] Synchronize the corrected target position to all nodes through the laser communication module to ensure that all nodes reach an agreement on the target position, and use the target position after eliminating the ocean current interference as the input parameter for driving the node to adjust the monitoring layout.

[0031] In a second aspect, the present application provides a networked digital ocean sphere public application and development integration system, including:

[0032] A construction module that uses each buoy in the intelligent buoy cluster as a networked ocean sphere node. Each node includes a laser communication device, a scanning device, and a processor. According to the dynamic position relationship between nodes, an ad-hoc communication network is constructed in the target sea area, and the nodes in the ad-hoc communication network share underwater position and ocean current information through laser communication;

[0033] A scanning module that performs layered scanning on the water area where the node is located through the scanning device to generate three-dimensional data including water transparency, plankton density, and pollution particles. The three-dimensional data is compared with a preset three-dimensional ocean model within the processor of the node, and the comparison result is shared with adjacent nodes through laser communication;

[0034] Verification module: When adjacent nodes detect the same abnormal target, it triggers a collaborative verification mechanism, synchronizes the three-dimensional data and movement direction of the target through laser communication, calculates the target position using the sonar signal differences between nodes, and eliminates ocean current interference through distributed computing.

[0035] Generation module: It drives the nodes to adjust the monitoring layout with a dynamic sphere model, sets the target position as the network center point, controls at least three nodes to move along a spiral path towards the center point to form a multi-layer spherical monitoring structure. Meanwhile, it encodes the monitoring data as three-dimensional coordinate data through laser communication and uploads it to the shore-based platform to generate a digital ocean sphere atlas that is synchronized and updated with the ocean environment.

[0036] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a networked digital ocean sphere public application and development integration method as described in the first aspect above.

[0037] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which when executed by a computer, implements a networked digital ocean sphere public application and development integration method as described in the first aspect.

[0038] In an embodiment of the present application, each buoy in the intelligent buoy cluster is used as a networked ocean sphere node. Each node includes a laser communication device, a scanning device, and a processor. According to the dynamic position relationship between nodes, a self-organizing communication network is constructed in the target sea area. The nodes in the self-organizing communication network share underwater position and ocean current information through laser communication; the scanning device performs layered scanning on the water area where the node is located to generate three-dimensional data including water transparency, plankton density, and pollution particles. The three-dimensional data is compared with a preset three-dimensional ocean model in the processor of the node, and the comparison result is shared with adjacent nodes through laser communication; when adjacent nodes detect the same abnormal target, it triggers a collaborative verification mechanism, synchronizes the three-dimensional data and movement direction of the target through laser communication, calculates the target position using the sonar signal differences between nodes, and eliminates ocean current interference through distributed computing; it drives the nodes to adjust the monitoring layout with a dynamic sphere model, sets the target position as the network center point, controls at least three nodes to move along a spiral path towards the center point to form a multi-layer spherical monitoring structure. Meanwhile, it encodes the monitoring data as three-dimensional coordinate data through laser communication and uploads it to the shore-based platform to generate a digital ocean sphere atlas that is synchronized and updated with the ocean environment.

[0039] The technical solution of the present application has the following beneficial effects:

[0040] This application dynamically constructs a self-organizing communication network based on intelligent buoys, uses laser communication to share node positions and ocean current information, significantly improves the transmission of ocean environmental data and the anti-interference ability of the network, and solves the problems of communication delay and data island in traditional monitoring; secondly, three-dimensional data of water transparency, plankton density, and pollution particles are generated through hierarchical scanning and compared with the grid-based ocean model to achieve accurate mapping of ecological parameters and geographical coordinates, enhancing the spatial resolution and data interpretability of ocean environmental anomaly recognition; at the same time, in multi-node collaborative verification, ocean current interference is eliminated through sonar signal difference analysis and distributed computing, improving the positioning accuracy of abnormal targets and reducing the false detection rate, forming a cross-node data mutual verification mechanism; finally, the buoy is driven to form a multi-layer spherical monitoring structure along a spiral path, focusing on the target area and encrypting the coverage, synchronously encoding the three-dimensional coordinate data into an updated digital ocean sphere atlas, providing high spatio-temporal resolution dynamic visualization decision support for ocean ecological assessment and disaster warning.

[0041] Further, spatial parameters of a dynamic sphere model are generated based on the target position, the target is set as the center point of the network and the initial spherical distance of the nodes is calculated, and a spiral path is generated for each node in combination with the maximum moving speed; at least three nodes are controlled to move along the spiral path towards the center point, the trajectory deviation is corrected by feedback of the azimuth angle difference, the hierarchical spacing is dynamically divided, and the spherical distance is matched to the hierarchical number; the outer nodes are synchronously controlled to move tangentially along the spherical surface to offset the ocean current deformation, and the inner target details and the outer environmental background data are integrated according to the hierarchical number, completing the dynamic construction of the multi-layer spherical monitoring structure. Through spiral path planning, dynamic trajectory correction, and adaptive division of hierarchical spacing, this method realizes the rapid focusing on abnormal targets and high-density monitoring coverage; uses tangential movement along the spherical surface to offset ocean current interference, combines hierarchical block integration of inner and outer layer data, and significantly improves the stability of target tracking and the efficiency of multi-scale data fusion. Finally, the constructed multi-layer spherical structure takes into account the ability to capture local details and global environmental perception, providing a spatial cooperation solution for accurate target monitoring in complex ocean scenarios.

[0042] These aspects or other aspects of this application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 Shows a flowchart of a networked digital ocean sphere public application and development integration method provided by this application;

[0045] Figure 2 Shows a schematic structural diagram of a networked digital ocean ball public application and development integration system provided by the present application;

[0046] Figure 3 Shows a schematic structural diagram of a computing device provided by the present application. Detailed implementation manners

[0047] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

[0048] In some processes described in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are of different types.

[0049] This article aims to be guided by the needs of ocean environmental monitoring. By designing a buoy as an intelligent node equipped with laser communication, scanning equipment and a processor, a self-organizing communication network based on dynamic position relationships is constructed to solve the communication delay problem caused by the isolated operation of traditional buoys; on this basis, a three-dimensional data of water body parameters is generated by using a hierarchical scanning technology, and the ecological parameters are associated with the geographical coordinates through a grid-based three-dimensional ocean model, breaking through the bottleneck that single-dimensional data cannot accurately map environmental anomalies; for the multi-node collaboration scenario, a verification mechanism integrating sonar signal difference analysis and distributed computing is designed to eliminate ocean current interference and improve the reliability of target positioning; finally, a dynamic sphere model is used to drive the nodes to focus on the target area along a spiral path, forming a multi-layer monitoring structure with complementary inner and outer layer data, and synchronously updating the three-dimensional coordinate encoding and the digital ocean ball atlas, realizing a full-link closed loop from data collection to visual presentation, and meeting the core requirements of high-precision, refined and three-dimensional monitoring of the ocean environment.

[0050] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0051] Figure 1 The flowchart of a networked digital ocean ball public application and development integration method provided by an embodiment of the present application is as Figure 1 shown, and the method includes:

[0052] 101. Take each buoy in the intelligent buoy cluster as a networked ocean ball node. Each node includes a laser communication device, a scanning device, and a processor. According to the dynamic position relationship between the nodes, an ad-hoc communication network is constructed in the target sea area. The nodes in the ad-hoc communication network share underwater position and ocean current information through laser communication;

[0053] In this step, the intelligent buoy node refers to an autonomous floating platform equipped with a laser communication module, a multi-spectral scanner, and an edge computing core, and has functions of environmental perception, data processing, and wireless networking.

[0054] The laser communication device refers to a communication device based on a directional laser beam, which realizes high-speed data transmission between nodes by modulating optical signals, and has the characteristics of strong anti-interference and high bandwidth.

[0055] The scanning device refers to a composite module integrating sonar detection and optical sensors, which can collect water physical parameters (such as temperature, salinity) and biochemical indicators (such as chlorophyll concentration).

[0056] The processor refers to a computing unit built with an adaptive networking protocol and a path planning algorithm, which is responsible for coordinating device operations, analyzing data, and making decisions on network topology adjustment.

[0057] The dynamic position relationship refers to the position change data generated by the nodes affected by environmental factors such as ocean currents and winds, including relative distance, azimuth angle, and motion speed vector.

[0058] The ad-hoc communication network refers to a wireless network autonomously constructed by nodes based on signal strength and link stability, which can dynamically adjust the relay path to maintain global data interconnection.

[0059] In the embodiment of the present application, after the intelligent buoy nodes are deployed, the initial coordinates are first determined through satellite positioning and underwater sonar fusion positioning technology. Subsequently, the laser communication module is activated to scan neighboring nodes. The processor dynamically selects the optimal communication path using the ant colony optimization algorithm based on the signal strength and the node movement trend, and preferentially connects nodes with low displacement correlation to enhance the network robustness. When the ocean current causes the node spacing to exceed the laser communication threshold, the processor triggers the topology reconstruction mechanism: electing relay nodes through a distributed negotiation protocol, re-establishing the link, and synchronizing the network-wide status information. The laser communication device uses wavelength division multiplexing technology to increase the channel capacity, ensuring the transmission of coordinates, ocean current vectors, and abnormal event warning data, and finally forming a monitoring network with self-healing capabilities.

[0060] Taking a red tide monitoring in the Yellow Sea as an example, the initially deployed buoy nodes shifted their positions due to the southeast ocean current. After node A detected the communication interruption with node B, the processor immediately analyzed the signal quality of neighboring nodes and selected node C as the relay node. Node A sent its own coordinates, ocean current speed, and the scanned chlorophyll anomaly data to node C through the laser link, and the latter synchronized and integrated the data and forwarded it to the shore-based center. During this process, the network topology underwent three dynamic adjustments, always maintaining the global data interconnection, laying the foundation for subsequent collaborative verification.

[0061] 102. The node's water area is scanned in layers by the scanning device to generate three-dimensional data including water transparency, plankton density, and pollution particles. The three-dimensional data is compared with a preset three-dimensional ocean model in the node's processor, and the comparison results are shared with adjacent nodes through laser communication;

[0062] In this step, layer-by-layer scanning means vertically dividing the water body into multiple detection layers and collecting parameters such as optical transparency, acoustic reflection intensity, and particulate matter concentration layer by layer.

[0063] The three-dimensional data refers to a multi-dimensional data set that integrates depth, geographical location, and ecological parameters, including plankton density gradient, pollutant distribution hot spots, and water turbidity profile.

[0064] The three-dimensional ocean model refers to a virtual ocean environment constructed based on historical observations and satellite remote sensing data, and predicting the benchmark range of ecological parameters for each grid through machine learning algorithms.

[0065] Grid processing means dividing the sea area into spatially continuous cubic units, each unit binding geographical coordinates and storing benchmark data such as the average transparency and biomass threshold.

[0066] In the embodiments of the present application, after a node starts hierarchical scanning, a multibeam sonar emits a fan-shaped sound wave underwater, receives echo signals at different depths to calculate the turbidity profile, and simultaneously enables a laser backscatter analyzer to analyze the particle size distribution of suspended particles. The processor aligns the collected parameters such as transparency and plankton density by depth layer, compares them with the historical mean and variance of the corresponding grid in the three-dimensional ocean model, and shares the comparison results with adjacent nodes through laser communication. If the data of a certain layer deviates from the reference range by more than a preset threshold (for example, the biological density suddenly increases by 200%), the grid is marked as abnormal, and feature vectors (such as concentration gradient, spatial distribution pattern) are extracted to generate an anomaly report. The data comparison process uses a GPU-accelerated convolutional neural network to achieve millisecond-level response; the gridded model fills the parameter estimates of the undetected area through the Kriging interpolation algorithm.

[0067] Continuing with the Yellow Sea scenario, node A discovers an abnormal increase in the turbidity value of the middle layer of water (depth 20 - 30 meters) during hierarchical scanning, and the plankton density reaches three times the reference value. The processor compares the data with the historical data of grid G-2057 in the model, determines it as a sign of the initial stage of a red tide, immediately compresses the abnormal data and sends it to adjacent nodes through a self-organizing network. After receiving the warning, node B and node C directedly enhance the scanning frequency of this grid, confirm that the biological density continues to rise, and trigger a collaborative verification process.

[0068] 103. When adjacent nodes detect the same abnormal target, a collaborative verification mechanism is triggered. The three-dimensional data and movement direction of the target are synchronized through laser communication, the target position is calculated using the difference in sonar signals between nodes, and ocean current interference is eliminated through distributed computing;

[0069] In this step, the collaborative verification mechanism refers to a collaborative process in which multiple nodes perform data cross-verification and joint positioning on the same target, and the spatio-temporal consistency condition needs to be met.

[0070] The difference in sonar signals refers to the differences in characteristics such as the arrival time difference, Doppler frequency shift, and phase distortion of the target reflection signals received by different nodes.

[0071] The elimination of ocean current interference refers to a technique for correcting the target positioning error through the displacement compensation of the node itself and the Kalman filtering algorithm.

[0072] In the embodiments of the present application, when more than three nodes report the same grid anomaly within a time window, the collaborative verification mechanism is automatically activated. Each node first synchronizes the target feature data (such as the time series of sudden increase in biological density) through laser communication and records the accurate arrival timestamp of the sonar signal. The processor constructs a time difference positioning equation based on the node coordinates and uses the least squares iterative method to solve the three-dimensional position of the target; at the same time, the acoustic Doppler current profiler built in each node measures the offset of the ocean current on its own position, and fuses the motion data of multiple nodes through a distributed Kalman filter to dynamically correct the target coordinates. The final output result includes the target center coordinates, the diffusion radius, and the confidence evaluation value, providing input parameters for the monitoring network reorganization.

[0073] In the Yellow Sea scenario, nodes A, B, and C all detected an anomaly in grid G-2057. After the collaborative verification was initiated, the three nodes synchronously emitted sonar pulses and recorded the time difference of the echoes. The processor calculated the initial target coordinates as (longitude X, latitude Y, depth 25 meters). Subsequently, node B reported the northeast displacement data affected by the ocean current, and node C provided the flow velocity information. After being corrected by the Kalman filter, the target coordinates were corrected 120 meters westward and determined to be the core area of the red tide, with the positioning error controlled within 5 meters, significantly superior to the traditional single-node positioning accuracy.

[0074] 104. The driving node adjusts the monitoring layout with a dynamic sphere model, sets the target position as the center point of the network, controls at least three nodes to move along a spiral path towards the center point to form a multi-layer spherical monitoring structure, and at the same time encodes the monitoring data into three-dimensional coordinate data through laser communication and uploads it to the shore-based platform to generate a digital ocean sphere atlas that is synchronized and updated with the ocean environment.

[0075] In this step, the dynamic sphere model refers to a three-dimensional structure in which nodes are deployed in layers according to the monitoring accuracy requirements with the target as the center. The inner layer focuses on high-resolution data acquisition, and the outer layer is responsible for large-scale tracking.

[0076] The spiral path refers to the movement trajectory of the nodes converging towards the target center along a spiral line, taking into account both the coverage efficiency and the collision avoidance requirements.

[0077] The digital ocean sphere atlas refers to a dynamic three-dimensional visualization model generated by fusing heterogeneous data of multiple nodes, supporting concentration field rendering and trend prediction.

[0078] In the embodiments of the present application, the system generates a hierarchical monitoring instruction according to the target coordinates output by collaborative verification. The processor plans the node path based on the improved artificial potential field algorithm: the inner-layer nodes approach the target center along a spiral line at a constant angular velocity, and the outer-layer nodes expand the scanning range according to a logarithmic spiral. The propulsion system dynamically adjusts the thruster power according to the ocean current prediction model to ensure that the nodes move stably along the planned path. After the monitoring data is compressed by Huffman coding, it is uploaded to the shore-based platform through laser communication. The platform uses volume rendering technology to fuse multi-source data and generates a digital ocean sphere atlas containing the red tide concentration gradient and pollution diffusion vector, supporting the playback of the evolution process along the time axis.

[0079] In the case of the Yellow Sea, the system drives nodes A, B, and C to surround the core area of the red tide along a spiral path, and at the same time schedules nodes D and E to form a monitoring barrier on the periphery. Node A uploads high-precision biological density data every 30 seconds. After being integrated by the shore-based platform, a map is generated, showing that the red tide mass spreads northwest at a speed of 1.2 kilometers per hour. The map intuitively presents the concentration distribution through the change of color temperature, providing a decision-making basis for the maritime department to delimit the ship no-go area. The whole process from anomaly detection to map generation only takes 45 minutes.

[0080] In summary, an elastic self-organizing network is constructed through steps 101 to 104 to resist ocean current disturbances and ensure continuous data transmission; by integrating hierarchical scanning and model comparison, accurate identification at the abnormal grid level is achieved; the target positioning accuracy is improved to the meter level by using multi-node collaborative verification and error compensation technology; and a high-refresh-rate digital atlas is generated through dynamic path planning and data fusion. In the Yellow Sea red tide event, the system realizes the full-process autonomous response from anomaly detection, collaborative positioning to diffusion prediction, improves the efficiency compared with traditional monitoring means, reduces the false alarm rate, and provides an innovative solution from "discrete detection" to "global perception" for marine ecological protection.

[0081] To solve the problems of weak anti-interference ability and low data fusion efficiency caused by the difficulty of dynamically adjusting the layout of the monitoring network in a complex marine environment, a multi-layer adaptive monitoring system based on the collaboration of a dynamic sphere model and a spiral trajectory is developed, realizing the self-organizing construction of a multi-layer spherical monitoring structure, and significantly improving the anti-interference ability of marine three-dimensional monitoring and the accuracy of multi-source data fusion. In some embodiments, in step 104, the driving nodes adjust the monitoring layout with a dynamic sphere model, set the target position as the network center point, and control at least three nodes to move along a spiral path towards the center point to form a multi-layer spherical monitoring structure, including:

[0082] 201. Generate the spatial parameters of the dynamic sphere model based on the target position, set the target position as the network center point, calculate the initial spherical distance between the center point and all nodes, determine the maximum moving speed of the nodes, and generate a spiral path with the center point as the origin for each node;

[0083] In step 201, the spatial parameters of the dynamic sphere model refer to the geometric and logical attributes of the virtual monitoring sphere constructed with the target position as the core, including the sphere radius range, the hierarchical division rule, and the node distribution density strategy; the initial spherical distance represents the three-dimensional straight-line distance from each node to the target center point and is used to divide the initial monitoring levels of the nodes; the maximum moving speed is the upper limit of the safe moving speed dynamically calculated based on the node propulsion ability and ocean current interference; the spiral path is the movement trajectory designed for the nodes to spiral and converge from the periphery to the center point, taking into account both path efficiency and collision avoidance requirements.

[0084] In the embodiment of the present application, first, the target position is converted into the origin of the three-dimensional space coordinate system, the initial spherical distances from all nodes to the origin are calculated through the geospatial mapping algorithm, and the initial monitoring layers are divided according to the preset hierarchical density rule. The maximum moving speed is dynamically calculated by fusing ocean current data and node thrust parameters: the ocean current sensor provides the flow velocity and direction, and the built-in controller of the node generates a safety speed threshold according to the thrust upper limit and the energy consumption model. The generation of the spiral path adopts the space trajectory planning algorithm, and a unique spiral convergence path is assigned to each node. The path curvature matches the node moving ability to ensure the spatial coverage and collision-free property during the cooperative movement of multiple nodes. Finally, the system encapsulates the spatial parameters into a control instruction set and sends it to each node through the laser communication network.

[0085] 202. Control at least three nodes to move along the spiral path towards the center point, feedback the azimuth angle difference between adjacent nodes, dynamically correct the offset of the node movement trajectory, and at the same time dynamically divide the hierarchical spacing of the multi-layer spherical monitoring structure, and match the spherical distance from the node to the center point to the corresponding hierarchical number;

[0086] In step 202, the azimuth angle difference refers to the heading deviation angle between the actual movement direction of the node and the theoretical spiral path; the offset correction is to adjust the node propulsion direction through feedback control technology to eliminate the heading deviation; the hierarchical spacing is the radial distance threshold between adjacent monitoring layers that is dynamically adjusted to adapt to the change of the target size; the hierarchical number is the classification identifier that matches the node position to the corresponding monitoring level.

[0087] In the embodiments of the present application, when a node moves along a spiral path, it continuously exchanges azimuth data with neighboring nodes through laser communication. The azimuth difference calculation module uses a heading fusion algorithm to compare the node gyroscope data with the theoretical path direction and generates a heading deviation value. If the deviation exceeds a preset threshold, the node controller triggers an offset correction process: the thruster generates a yaw moment according to the proportional-integral-derivative control algorithm to gradually reduce the heading deviation until it returns to the theoretical path. The dynamic adjustment of the layer spacing is based on the output of the target feature recognition module: if the target is diffuse pollution, the system increases the layer spacing according to the diffusion rate prediction model; if the target is a static object, a fixed spacing strategy is adopted. The layer numbering is achieved by matching the spherical distance with the layer spacing threshold, and the data acquisition priority and communication neighbor list are automatically updated when the node layer switches.

[0088] 203. Control the outer layer nodes to move tangentially along the sphere to offset the ocean current deformation and synchronously adjust the node spacing, and spherically partition and integrate the target detail data collected by the inner layer nodes and the environmental background data of the outer layer nodes according to the corresponding layer numbers to complete the construction of a multi-layer spherical monitoring structure.

[0089] In step 203, the spherical tangential movement refers to the compensation movement of the outer layer nodes along the tangent direction of the sphere surface, which is used to offset the deformation of the monitoring network caused by ocean currents; the ocean current deformation offset is to balance the lateral interference of the ocean current on the node position through the tangential movement; the spherical partition integration is the process of dividing and fusing the data of nodes at different layers according to the spherical space blocks into a complete three-dimensional model.

[0090] In the embodiments of the present application, the outer layer nodes start the ocean current deformation offset mode: the ocean current sensor collects the flow velocity and direction data, and the node controller decomposes the ocean current velocity into radial and tangential components and generates a reverse tangential movement instruction to offset the lateral displacement. The inner layer nodes focus on the collection of target details and obtain the three-dimensional contour and chemical parameters of the target through high-precision optical sensors and sonar arrays. In the data integration stage, the system divides the spherical space into equal blocks according to longitude and latitude, and the data at different layers within the same block are fused through the spherical interpolation algorithm: the inner layer data provides target details, and the outer layer data supplements the environmental background parameters. The fused data generates a seamless three-dimensional dynamic map through volume rendering technology to support multi-dimensional visualization and trend prediction.

[0091] The following is a specific example:

[0092] During a red tide event in the South China Sea, the system generated a dynamic sphere model centered on the core area of the red tide. Nodes were divided into inner, middle, and outer layers according to the initial distance. A node in the middle layer deviated from the spiral path due to strong ocean currents. The system corrected it back to the trajectory through course feedback and thruster adjustment. As the red tide mass spread, the layer spacing expanded dynamically. Outer layer nodes initiated tangential movement to counteract the deformation caused by ocean currents. Inner layer nodes collected peak data on plankton concentration, and outer layer nodes synchronized abnormal information on water temperature and salinity. After all the data were fused according to spherical blocks, a map showing the three-dimensional diffusion gradient of the red tide and its correlation with temperature was generated, guiding the precise operation of governance vessels. The entire process from monitoring to decision-making response took less than an hour.

[0093] In summary, through steps 201 to 203, the intelligent spatial layout of the monitoring network was achieved through a dynamic sphere model, and the stability under complex sea conditions was ensured by combining course correction and deformation cancellation technologies. Finally, a high-precision three-dimensional map was generated through hierarchical data fusion. Its core value lies in: improving the accuracy and traceability of target positioning, enhancing the adaptability of the monitoring network to the dynamic ocean environment, realizing the full-dimensional data integration from microscopic details to macroscopic background, and providing efficient and three-dimensional decision-making support for marine ecological protection and disaster emergency response.

[0094] To solve the problems that it is difficult to compensate for the trajectory deviation of marine monitoring nodes caused by ocean current interference during dynamic adjustment, and the multi-level scanning parameters cannot be dynamically adapted to the detection requirements, a multi-modal collaborative scanning system based on ocean current interference compensation and hierarchical self-adaptation was developed, realizing the precise correction of node trajectories under dynamic ocean current interference suppression and the coordination of multi-level heterogeneous scanning modes, significantly improving the fusion detection efficiency of target details and environmental data in complex ocean environments. In some embodiments, in step 202, controlling at least three nodes to move along the spiral path towards the center point, feeding back the azimuth difference between adjacent nodes, dynamically correcting the offset of the node movement trajectory, and at the same time dynamically dividing the layer spacing of the multi-layer spherical monitoring structure, and matching the spherical distance from the node to the center point to the corresponding layer number, includes:

[0095] 301. Generating a discretized waypoint sequence of the spiral path based on the current ocean current resistance coefficient and the thrust upper limit of the node thruster, and continuously covering the target detection range when controlling the node movement based on the discretized waypoint sequence;

[0096] In step 301, the ocean current resistance coefficient is a quantitative index of the hydrodynamic hindrance effect generated by the ocean current on the movement of the node, which is related to the ocean current speed, water density, and node shape; the upper limit of the thruster thrust is the maximum thrust that the node propulsion system can output under safe operating conditions, which is limited by the energy supply and mechanical structure; the discretized waypoint sequence of the spiral path refers to the set of continuous navigation points obtained by decomposing the theoretical spiral trajectory, and each waypoint contains three-dimensional coordinates and a residence duration parameter; the target detection range coverage refers to the continuous monitoring area formed by the sensor scanning during the movement of the node, and it is necessary to ensure that the scanning areas of adjacent waypoints overlap to avoid data blind spots.

[0097] In the embodiment of the present application, first, the ocean current monitoring data and the performance parameters of the node thruster are integrated through the multi-source data fusion technology to construct a dynamic calculation model of the ocean current resistance coefficient. The upper limit of the thruster thrust is dynamically adjusted by the node energy management system according to the remaining power and heat dissipation conditions to ensure that the propulsion system is in the optimal working range. The discretized waypoint generation of the spiral path adopts an adaptive trajectory segmentation algorithm: according to the detection radius of the node sensor, the threshold of the distance between adjacent waypoints is calculated to ensure that the overlap rate of the scanning area meets the preset requirements; the curvature radius of the waypoint sequence matches the maneuverability of the node to avoid trajectory deviation caused by sharp turns. After the waypoint sequence is generated, the path following controller adjusts the output power of the thruster through the proportional integral derivative algorithm according to the position feedback, drives the node to move precisely along the waypoint sequence, and simultaneously triggers the sensor to perform omnidirectional scanning during the waypoint residence.

[0098] 302. During the movement of the node, obtain the azimuth difference data of adjacent nodes, extract the laser signal path offset caused by ocean current refraction, calculate the ocean current interference component in combination with the relative motion vector between nodes, and dynamically adjust the curvature radius of the waypoint to correct the offset of the node movement trajectory;

[0099] In step 302, the azimuth difference data refers to the signal arrival angle deviation of the laser communication link between adjacent nodes, which is caused by both ocean current refraction and relative node movement; the laser signal path offset refers to the geometric offset distance between the actual transmission path of the laser beam and the theoretical straight line path; the ocean current interference component refers to the velocity and direction correction amount generated by the ocean current on the node movement trajectory; the waypoint curvature radius compensation refers to dynamically adjusting the path bending degree of subsequent waypoints according to the ocean current interference to offset the node position drift.

[0100] In the embodiment of the present application, first, the node periodically transmits a data packet containing its own coordinates and heading angle through a laser communication module. After receiving the data packet, the adjacent node analyzes the signal arrival angle, and calculates the azimuth difference value by combining its own attitude data provided by the inertial navigation system. The laser signal path offset is obtained by inverting the transmission time difference and the node motion vector: a refraction path model of the laser signal in the ocean current disturbance medium is established, and the spatial offset is calculated in combination with the node relative speed. The calculation of the ocean current interference component adopts the Kalman filter fusion algorithm, and the path offset, node motion vector and historical ocean current data are iteratively optimized to output the interference intensity and direction of the current ocean current on the node. Waypoint curvature radius compensation is achieved through dynamic path planning: if the ocean current causes the node to drift eastward, the system increases the curvature of the subsequent waypoint trajectory bending westward, and inserts compensation waypoints to correct the accumulated error, ensuring that the actual motion trajectory of the node converges to the theoretical spiral path.

[0101] 303. Divide the layer spacing threshold according to the spherical distance from the node to the center point, and dynamically adjust the layer spacing of the multi-layer spherical monitoring structure based on the layer spacing threshold to adapt to the target movement trend and monitoring requirements.

[0102] In step 303, the spherical distance refers to the three-dimensional straight-line distance from the node to the center point of the monitoring target, which is calculated by integrating laser ranging and inertial navigation system; the layer spacing threshold refers to the minimum and maximum radial distance intervals between adjacent monitoring layers that are dynamically set according to the target movement speed, diffusion rate and environmental interference intensity; dynamic adjustment refers to the process of the system continuously optimizing the layer spacing based on monitoring data to ensure that the monitoring network always matches the spatial distribution characteristics and movement trends of the target.

[0103] In the embodiment of the present application, the system first calculates the spherical distance from the node to the target center point through the fusion of the laser ranging module and the inertial navigation system, and combines the target motion trend prediction model with the multi-source environmental parameters, and uses the fuzzy control algorithm to dynamically optimize the layer spacing threshold - when the target diffusion speed accelerates, the layer spacing is expanded based on the radial velocity gradient calculation to cover a larger range; when the ocean current interference increases or the water turbidity increases, the layer spacing is compressed through the constraint optimization model to improve data redundancy and scanning accuracy. Subsequently, the system broadcasts the updated layer spacing threshold and layer boundary value to all network nodes through laser communication, triggering the node to autonomously match the layer to which it belongs based on the spherical distance, and jointly adjusts the thruster power and sensor working mode, and finally realizes the dynamic reconstruction of the multi-layer spherical monitoring structure, ensuring that the network layout always maintains adaptive matching with the target motion characteristics and environmental disturbances.

[0104] 304. Select the priority level number based on the ocean current direction and the target movement trend, assign exclusive scanning parameters to the nodes of the matching level, enable high-resolution scanning for the inner-layer nodes to focus on the target details, enable wide-area scanning for the outer-layer nodes to capture environmental data, and synchronously update the scanning parameters of the nodes within the level through laser communication.

[0105] In step 304, the priority level number refers to the monitoring weights assigned to different levels based on the ocean current direction and the target movement trend, and more scanning resources are allocated to the levels with higher priority; the exclusive scanning parameters refer to the sensor working modes configured for the nodes of different levels, including resolution, scanning frequency, and data sampling depth.

[0106] In the embodiment of the present application, first, the hierarchical spacing threshold is dynamically divided through the target movement trend prediction model: if the target is a rapidly spreading pollutant, the hierarchical spacing expands exponentially with the diffusion rate; if the target is a static object, a linear expansion strategy is adopted. The determination of the priority level number is based on the included angle between the ocean current direction and the target movement vector: when the ocean current direction is consistent with the target diffusion direction, the downstream level is set as the highest priority to enhance the scanning density of the nodes in this layer; if the ocean current is in the opposite direction to the target movement direction, the priority of the upstream level is increased. The configuration rule for the exclusive scanning parameters is as follows: the inner-layer nodes enable the high-resolution scanning mode, the optical sensor switches to the macro-focus state, and the sonar array uses narrow-beam high-frequency sampling; the outer-layer nodes enable the wide-area scanning mode, the optical sensor switches to the wide-angle lens, and the sonar array uses wide-beam low-frequency sampling to cover a larger range. All nodes synchronize the level number and scanning parameters through the laser communication network to ensure that there is no conflict in data collection between adjacent levels and the time and space are aligned.

[0107] The following is a specific example:

[0108] In a large-scale crude oil leakage incident caused by the rupture of an undersea oil pipeline in the East China Sea, the oil slick was quickly pushed northwest by the southeast ocean current, forming a pollution belt several kilometers long. The system generated a dynamic sphere model centered on the leakage point. The inner-layer nodes enabled deep-sea cameras and laser Raman spectrometers to conduct millimeter-scale crack scanning and crude oil component analysis on the undersea leakage point. When the middle-layer nodes moved along the spiral path, they encountered an ocean current with a strength of 3 knots, and the laser communication signal showed a significant eastward deviation. The system inversely calculated the ocean current interference component through the refraction path, dynamically inserted compensation waypoints, and corrected the node trajectory 150 meters westward to ensure that the scanning covered the oil slick front area. The outer-layer nodes started the tangential motion mode, continuously adjusted their positions in the opposite direction of the ocean current, and simultaneously collected data on sea surface wind speed, wave height, and oil film thickness. After integrating the data numbered by hierarchy for all nodes, the generated three-dimensional map clearly showed that the undersea leakage point had radial cracks, and the surface oil slick spread northwest at a speed of 1.8 kilometers per hour, and was highly correlated with the wind speed vector. Based on this, the shore-based platform demarcated a three-level emergency response area, directed the adsorption ships to operate preferentially in the area with the thickest oil film, and dispatched drones to deploy oil booms along the diffusion path. Finally, the pollution range was controlled within 12 hours, and the efficiency was improved compared with traditional monitoring methods.

[0109] In summary, through steps 301 to 304, the intelligent generation and adaptive adjustment of the spiral waypoint sequence are realized, ensuring that the nodes move precisely along the preset path and fully cover the target detection area under complex sea conditions, and completely eliminating the monitoring blind spots caused by the traditional straight path. Through the dynamic curvature compensation technology, the node trajectory tracking accuracy is improved to the sub-meter level, breaking through the problem of position drift control in a strong ocean current environment. Relying on the spherical block fusion algorithm and the laser communication clock synchronization technology, the millisecond-level spatio-temporal alignment and seamless splicing of multi-level node data are realized, generating a high-fidelity dynamic map to accurately depict the target evolution process, and finally achieving a triple leap in monitoring efficiency, positioning accuracy, and data dimension, providing core technical support for the transformation of ocean environmental monitoring from "discrete perception" to "global perspective".

[0110] To solve the problems of false image misjudgment and insufficient target positioning accuracy caused by ocean current stratification interference during multi-node collaborative detection in ocean monitoring, a multi-modal perception collaborative verification and distributed ocean current interference elimination system is developed, realizing multi-node collaborative false image suppression and precise target positioning compensation under ocean current interference, providing high-precision input parameters for optimizing the dynamic monitoring layout, and significantly improving the reliability and positioning consistency of abnormal targets in a complex ocean environment. In some embodiments, when adjacent nodes detect the same abnormal target in step 103, a collaborative verification mechanism is triggered, synchronizing the three-dimensional data and movement direction of the target through laser communication, calculating the target position using the sonar signal difference between nodes, and eliminating ocean current interference through distributed calculation, including:

[0111] 401. When the time stamps of the target 3D data detected by the scanning devices of adjacent nodes overlap and the consistency of the motion directions exceeds a preset threshold, a collaborative verification mechanism is triggered. The 3D data profile and motion vector of the target are sent to the adjacent nodes through the laser communication module, and the target reflected sonar signal fed back by the adjacent nodes is received.

[0112] In step 401, the time stamp overlap means that there is an intersection interval between the start and end times of collecting the target data by different nodes, indicating that multiple nodes observe the same target within the same time period. The consistency of the motion directions means that the angle between the target movement directions deduced from the scanning data by different nodes is less than the preset angle threshold, which is used to determine whether the targets are the same entity. The target 3D data profile refers to the set of the target surface geometric shape and spatial distribution characteristics generated by the fusion of multi-spectral scanning and sonar reflection data. The target reflected sonar signal refers to the target echo signal received by the sonar device, which contains physical characteristic parameters such as reflection intensity, phase information, and transmission time delay.

[0113] In the embodiment of the present application, the node scanning device continuously collects the target 3D data, and the processor aligns the data time series of adjacent nodes through the sliding time window algorithm. When it is detected that the time stamps overlap and the included angle of the motion direction vectors is less than the preset threshold, the collaborative verification mechanism is triggered: the node encapsulates the 3D point cloud data and motion vector of the target into an encrypted data packet through the laser communication module and sends it to the preset collaborative node group in a directed manner; the receiving node synchronously activates the sonar array, collects the target reflection signal in a high-frequency pulse mode, extracts the signal features (such as the peak value of the echo intensity, Doppler frequency shift), and feeds them back to the initiating node through the laser link to complete the preliminary cross-verification of multi-modal data.

[0114] 402. Extract the phase difference and time delay difference of the target reflected sonar signal, and combine the underwater relative position relationship between the nodes to calculate the initial position of the target in the three-dimensional space.

[0115] In step 402, the target reflected sonar signal refers to the sonar signal reflected by the target and is used to locate the target position. The phase difference refers to the phase difference of the target reflected sonar signal between different nodes and is used to calculate the target position. The time delay difference refers to the time delay difference of the target reflected sonar signal between different nodes and is used to calculate the target position. The underwater relative position relationship between the nodes refers to the relative positions of different nodes underwater and is used to assist in calculating the target position. The initial position refers to the target position calculated based on the phase difference, time delay difference, and the relative position relationship between the nodes and is the basis for subsequent correction.

[0116] In the embodiments of the present application, first, the phase difference and time delay difference of the target reflected sonar signal are extracted. The extraction of the phase difference and time delay difference usually adopts signal processing techniques, such as calculating the phase and time differences between signals through Fourier transform or cross-correlation analysis. Then, in combination with the underwater relative position relationship between nodes, the initial position of the target in three-dimensional space is calculated. The calculation of the initial position usually adopts triangulation or multi-point positioning methods. By inputting the phase difference, time delay difference, and node position information into the positioning model, the initial position of the target is obtained. Through this step, the system can preliminarily determine the target position and provide basic data for subsequent corrections.

[0117] 403. Synchronize the initial position with the motion vector synchronized by laser communication, and eliminate the target virtual image position caused by ocean current stratification to obtain a preliminarily corrected target position;

[0118] In step 403, the initial position refers to the target position calculated based on the phase difference, time delay difference, and the relative position relationship between nodes, which is the basis for subsequent corrections. The motion vector synchronized by laser communication refers to the motion vector obtained through laser communication synchronization and is used to assist in the correction of the target position. Ocean current stratification refers to the stratification phenomenon of ocean currents at different depths underwater, which may cause the target virtual image position. The target virtual image position refers to the false target position caused by ocean current stratification and needs to be eliminated. The preliminarily corrected target position refers to the target position obtained after synchronizing the initial position with the motion vector synchronized by laser communication and is the basis for further corrections.

[0119] In the embodiments of the present application, first, the initial position is synchronized with the motion vector synchronized by laser communication. The synchronization of the motion vector usually adopts timestamp alignment or motion trajectory matching techniques to ensure the consistency between the target position and the motion vector. Then, the target virtual image position caused by ocean current stratification is eliminated to obtain a preliminarily corrected target position. The elimination of the virtual image position usually adopts signal filtering or anomaly detection techniques, such as analyzing the characteristics of the target reflected sonar signal to identify and eliminate the virtual image position. Through this step, the system can preliminarily correct the target position and improve the positioning accuracy.

[0120] 404. According to the sonar signal differences between nodes, distribute ocean current interference compensation coefficients to each node through distributed computing. Further correct the preliminarily corrected target position based on the ocean current interference compensation coefficients to eliminate ocean current interference.

[0121] In step 404, the sonar signal difference refers to the difference between the target reflected sonar signals at different nodes, which is used to calculate the ocean current interference compensation coefficient. Distributed computing refers to assigning computing tasks to multiple nodes for parallel processing to improve computing efficiency. The ocean current interference compensation coefficient refers to the compensation coefficient calculated based on the sonar signal difference, which is used to eliminate ocean current interference. The preliminary corrected target position refers to the target position obtained after synchronizing the initial position with the motion vector synchronized with the laser communication, which is the basis for further correction. The further corrected target position refers to the target position obtained after correcting the preliminary corrected target position based on the ocean current interference compensation coefficient, which is the final positioning result.

[0122] In an embodiment of the present application, first, an ocean current interference compensation coefficient is allocated to each node through distributed computing based on the sonar signal differences between the nodes. The ocean current interference compensation coefficient is usually calculated using regression analysis or machine learning algorithms. For example, the compensation coefficient is calculated by analyzing the relationship between the sonar signal differences and the ocean current interference. Next, the initially corrected target position is further corrected based on the ocean current interference compensation coefficient to eliminate the ocean current interference. The correction process usually uses a weighted average or optimization algorithm, for example, by applying the compensation coefficient to the target position calculation model to obtain the corrected target position. Through this step, the system can effectively eliminate ocean current interference and further improve positioning accuracy and system stability.

[0123] Here is a specific example:

[0124] In a submarine natural gas pipeline leakage incident in the East China Sea, multiple nodes detected that the bubble plume spread to the sea surface. In step 401, nodes A, B, and C detected that the consistency of the bubble movement direction in the overlapping time window reached 95%, triggering the collaborative verification mechanism to exchange three-dimensional contour data; node B found that the sonar reflection point produced a deep virtual shadow due to the sudden change of the sound speed in the thermocline, and the virtual shadow was eliminated through ray tracing reconstruction and spatial matching; the compensation coefficient of the low-temperature and high-density water layer where node C is located was calculated to be 0.85, and the compensation coefficient of node A in the surface turbulent area was 1.2; the corrected bubble core position was confirmed by PBFT consensus, driving the node group to shrink the monitoring circle to the leakage source. The final generated three-dimensional map shows that the bubble plume has a diameter of 15 meters and a central rising speed of 0.8 meters per second, guiding the engineering ship to accurately block the leakage point and complete the emergency disposal 6 hours earlier than traditional monitoring methods.

[0125] In summary, the problems of target location distortion and false alarm interference in complex marine environments are solved through steps 401 to 404. Based on the collaborative trigger mechanism of timestamp overlap and motion direction consistency, the accuracy of multi-source data association and the reliability of target recognition are significantly improved. The adaptive reorganization of the monitoring network is achieved through closed-loop feedback control, forming a full-link technology system from multi-modal data perception, environmental interference suppression to intelligent decision-making response. This solution provides high-precision and strong-robustness three-dimensional monitoring capabilities for scenarios such as marine geological disaster early warning and pollutant diffusion tracking, significantly superior to traditional single-point monitoring and static networking methods, and realizing the technological leap from "discrete detection" to "collaborative perspective".

[0126] To solve the problems of poor adaptability of scanning parameters to the environment caused by fixed hierarchical spacing in dynamic marine monitoring and data quality degradation caused by cross-hierarchical signal interference, a dynamic hierarchical division and anti-interference collaborative scanning system based on environmental adaptability is developed, which realizes the precise adaptation of multi-level scanning parameters to the dynamic environment and target characteristics, effectively suppresses cross-hierarchical signal interference, and significantly improves the coordination and reliability of target detail capture and environmental data collection in complex underwater scenarios. In some embodiments, step 303 of dividing the hierarchical spacing threshold according to the spherical distance from the node to the center point and dynamically adjusting the hierarchical spacing of the multi-layer spherical monitoring structure to adapt to the target movement trend and monitoring requirements includes:

[0127] 501. Generate a hierarchical spacing threshold according to the spherical distance from the node to the center point according to a preset distance interval division rule, and the hierarchical spacing threshold is used to define the spacing range of each layer in the multi-layer spherical monitoring structure;

[0128] In step 501, the node refers to the monitoring node in the underwater monitoring system, which is used to collect and transmit monitoring data. The center point refers to the central position of the multi-layer spherical monitoring structure, usually the center of the target or key area. The spherical distance refers to the distance from the node to the center point, which is used to dynamically adjust the monitoring structure. The preset distance interval division rule refers to the division rule preset according to the monitoring requirements, which is used to generate the hierarchical spacing threshold. The hierarchical spacing threshold refers to the threshold used to define the spacing range of each layer in the multi-layer spherical monitoring structure, which is used to guide the hierarchical matching of the nodes. The multi-layer spherical monitoring structure refers to a spherical monitoring structure composed of multiple layers, which is used to achieve all-round monitoring of the target.

[0129] In the embodiments of the present application, first, based on the spherical distance from the node to the center point, hierarchical spacing thresholds are generated according to a preset distance interval division rule. The generation of hierarchical spacing thresholds usually adopts equal-distance division or adaptive division methods. For example, for areas closer to the center point, smaller spacings are divided to improve monitoring accuracy; for areas farther from the center point, larger spacings are divided to expand the monitoring range. Then, the hierarchical spacing thresholds are applied to the multi-layer spherical monitoring structure to define the spacing range of each layer. Through this step, the system can initially optimize the hierarchical division of the multi-layer spherical monitoring structure and provide basic support for subsequent dynamic adjustment.

[0130] 502. Dynamically adjust the hierarchical spacing thresholds according to the target movement trend and the ocean current direction. When the target approaches the center point, reduce the hierarchical spacing thresholds to increase the monitoring density. When the target moves away from the center point, expand the hierarchical spacing thresholds to expand the monitoring range. Based on the adjusted hierarchical spacing thresholds, recalculate the spacing range of each layer in the multi-layer spherical monitoring structure and match the nodes to the corresponding levels to ensure that the hierarchical spacing of the multi-layer spherical monitoring structure adapts to the target movement trend and monitoring requirements.

[0131] In step 502, the target movement trend refers to the movement trend of the target underwater and is used to guide the dynamic adjustment of the hierarchical spacing thresholds. The ocean current direction refers to the flowing direction of the ocean current underwater and is used to assist the dynamic adjustment of the hierarchical spacing thresholds. The hierarchical spacing threshold refers to the threshold used to define the spacing range of each layer in the multi-layer spherical monitoring structure and is used to guide the hierarchical matching of nodes. The monitoring density refers to the number of monitoring nodes per unit area or unit volume and is used to measure the monitoring accuracy. The monitoring range refers to the area range covered by the multi-layer spherical monitoring structure and is used to measure the monitoring ability. The multi-layer spherical monitoring structure refers to a spherical monitoring structure composed of multiple levels and is used to achieve all-round monitoring of the target.

[0132] In the embodiments of the present application, first, dynamically adjust the hierarchical spacing thresholds according to the target movement trend and the ocean current direction. When the target approaches the center point, reduce the hierarchical spacing thresholds to increase the monitoring density and improve the monitoring accuracy of the target. When the target moves away from the center point, expand the hierarchical spacing thresholds to expand the monitoring range and ensure continuous monitoring of the target. Then, based on the adjusted hierarchical spacing thresholds, recalculate the spacing range of each layer in the multi-layer spherical monitoring structure and match the nodes to the corresponding levels. Node matching usually adopts the nearest neighbor algorithm or the clustering algorithm. For example, by calculating the distance from the node to each level, the node is matched to the nearest level. Through this step, the system can optimize the hierarchical division of the multi-layer spherical monitoring structure, ensure that the hierarchical spacing adapts to the target movement trend and monitoring requirements, and further improve the monitoring accuracy and system adaptability.

[0133] The following is a specific example:

[0134] In a marine environment monitoring system, taking a deep - sea biological tracking platform as an example, the system first generates a hierarchical spacing threshold based on the spherical distance from the node to the center point according to a preset distance interval division rule. For example, for every 500 - meter increase in the distance from the center point, the hierarchical spacing threshold increases by 100 meters, which is used to define the spacing range of each layer in the multi - layer spherical monitoring structure. Then, the system dynamically adjusts the hierarchical spacing threshold according to the target movement trend and the ocean current direction. When the target approaches the center point, the hierarchical spacing threshold shrinks, for example, from 100 meters to 50 meters, to increase the monitoring density; when the target moves away from the center point, the hierarchical spacing threshold expands, for example, from 100 meters to 150 meters, to expand the monitoring range. Based on the adjusted hierarchical spacing threshold, the system recalculates the spacing range of each layer in the multi - layer spherical monitoring structure and matches the nodes to the corresponding layers. For example, a node 300 meters away from the center point is adjusted to a closer layer to ensure that the hierarchical spacing of the multi - layer spherical monitoring structure adapts to the target movement trend and monitoring requirements. Through this mechanism, the system can efficiently track the activity trajectories of deep - sea organisms and provide accurate data support for marine ecological research.

[0135] In summary, the initial division of the monitoring structure is achieved through steps 501 to 504; further, the hierarchical spacing threshold is dynamically adjusted according to the target movement trend and the ocean current direction. When the target approaches the center point, the hierarchical spacing threshold is reduced to increase the monitoring density, and when the target moves away from the center point, the hierarchical spacing threshold is expanded to expand the monitoring range, ensuring the adaptive adjustment of the monitoring structure to the target movement state; based on the adjusted hierarchical spacing threshold, the spacing range of each layer in the multi - layer spherical monitoring structure is recalculated, and the nodes are matched to the corresponding layers, achieving a high degree of matching between the monitoring structure and the target movement trend and monitoring requirements; this method provides flexible and accurate technical support for target monitoring in complex marine environments through dynamic adjustment of the hierarchical spacing threshold and node matching, significantly improving the monitoring efficiency and adaptability.

[0136] To solve the problems of lagging abnormal area identification and low dynamic response efficiency caused by the disconnection between multi - parameter data and geographical coordinates in marine ecological monitoring, an adaptive marine monitoring system based on a multi - parameter correlation grid model is developed, which realizes the dynamic correlation analysis of multi - dimensional ecological parameters and spatial grids, significantly improving the capture ability of marine pollution diffusion and ecological abnormal events and the adaptive response accuracy of the monitoring network. In some embodiments, in step 103, the water area where the node is located is scanned layer by layer by the scanning device to generate three - dimensional data including water transparency, plankton density, and pollution particles. The three - dimensional data is compared with a preset three - dimensional ocean model in the processor of the node. The three - dimensional ocean model correlates ecological parameters with geographical coordinates through grid processing, including:

[0137] 601. Divide the vertical scanning levels based on the water depth gradient of the target sea area, and form stratified three-dimensional data covering water transparency, plankton density, and pollution particles according to the data scanned at different water depth gradient levels.

[0138] In step 601, the vertical scanning level refers to the vertical detection level divided according to the physical property differences in different depth intervals of the target sea area, such as the surface layer, the middle layer, and the deep layer; the stratified three-dimensional data refers to the multi-dimensional data set containing water transparency, plankton density, and pollution particle concentration independently collected for each vertical level; the water depth gradient refers to the depth interval with significant environmental parameter differences divided by sonar sounding data.

[0139] In the embodiment of the present application, the multi-beam sonar carried by the node performs a vertical profile scan on the target sea area, and combines with a laser backscatterometer to measure the distribution of suspended particles in each water layer. The processor identifies the water depth gradient boundary according to the sudden change point of the sonar echo intensity, and divides the water body into multiple vertical scanning levels: the surface layer focuses on optical parameters (transparency, chromaticity), the middle layer strengthens biological parameters (plankton density), and the deep layer focuses on chemical parameters (pollutant concentration). The three-dimensional data of each level is independently stored according to the depth coordinate, forming a three-dimensional data cube with seamless connection in the vertical dimension.

[0140] 602. Load a preset three-dimensional ocean model in the node processor. The three-dimensional ocean model divides the ocean space into cube grid cells bound to geographical coordinates through grid processing, and the side length of each grid cell is negatively correlated with the average water transparency of the water layer where it is located.

[0141] In step 602, the three-dimensional ocean model refers to a virtual ocean environment constructed based on historical observation data, including the three-dimensional distribution of temperature, salinity, and ecological parameters; grid processing refers to dividing the sea area space into cube grids bound to geographical coordinates; the adaptability of the grid cell side length means that the grid size is dynamically adjusted according to the water layer transparency, and the lower the transparency, the smaller the grid size to improve the resolution.

[0142] In the embodiment of the present application, the processor loads a preset three-dimensional ocean model, and this model maps historical data to a three-dimensional grid through a spatial interpolation algorithm. The grid cell size is dynamically set according to the average transparency of the water layer: large-size grids (such as 100-meter side length) are used in the high-transparency area of the surface layer, and small-size grids (such as 30-meter side length) are used in the low-transparency area of the deep layer. Each grid is bound with longitude and latitude coordinates and stores the benchmark value of ecological parameters (such as the plankton density threshold), forming a dynamically updatable spatial reference framework.

[0143] 603. Map the layered 3D data to the corresponding grid cells according to geographical coordinates. When the transparency decline rate, the biological density mutation value, and the pollution concentration growth value of the layered 3D data in the same grid cell simultaneously exceed the corresponding thresholds, trigger the parameter coordinate association mechanism;

[0144] In step 603, the parameter coordinate association mechanism refers to the data fusion and alarm process triggered when multiple ecological parameters in the same grid are abnormal simultaneously; the synchronous threshold exceeding means that the transparency decline rate, the biological density mutation value, and the pollution concentration growth value all exceed the preset safety range at the same time.

[0145] In the embodiment of the present application, after the layered 3D data is mapped to the grid cells, the system calculates the dynamic change rate of each parameter through time series analysis. If in a certain grid, the following conditions are simultaneously met: the transparency decline rate exceeds the limit (such as a 30% decrease per hour), the plankton density suddenly increases beyond the limit (such as doubling within 3 hours), and the pollution concentration growth rate exceeds the limit (such as the linear growth slope is greater than the threshold), then the parameter coordinate association mechanism is triggered. This mechanism starts the multi-source data fusion process: superimpose the optical, biological, and chemical abnormal data on the same grid, generate a comprehensive abnormal report with weighted scores, and push it to the relevant nodes through the laser communication network.

[0146] 604. Drive the peripheral nodes to dynamically adjust the detection range of the scanning device according to the associated parameters, and compare the layered 3D data with the preset 3D ocean model in the processor of the node.

[0147] In step 604, the dynamic adjustment of the detection range means reconfiguring the coverage area of the node scanning device according to the position of the abnormal grid; the model comparison means analyzing the difference between the data and the historical reference value of the 3D model to evaluate the degree of abnormality.

[0148] In the embodiment of the present application, when the parameter coordinate association mechanism is triggered, the system drives the peripheral nodes to shrink the scanning range towards the abnormal grid area: the inner layer nodes enable narrow beam high-density scanning, and the outer layer nodes expand the scanning angle to cover the associated area. The processor compares the scanned data with the historical reference value of the corresponding grid in the 3D model, calculates the abnormal confidence score (such as the transparency abnormality confidence, the biological density abnormality confidence), and generates a multi-dimensional alarm instruction including the spatial position, the abnormal type, and the emergency level.

[0149] The following is a specific example:

[0150] During a cross - level ecological disaster event in the Yellow Sea, the system detected abnormal sonar echoes in the middle - layer water area. After vertical scanning layer division, the abnormal area was locked in the lower part of the middle depth interval. When loading the three - dimensional ocean model, for this low - transparency water layer, the side length of the grid cells was compressed to a fine scale to improve data analysis ability. The mapping showed that within a certain grid cell, the transparency dropped sharply, the density of plankton increased sharply, and the concentration of heavy - metal pollution continued to rise. When the three parameters exceeded the threshold simultaneously, the correlation mechanism was triggered. The system immediately drove the peripheral nodes to form a double - layer monitoring circle: the inner - layer nodes enabled high - resolution laser scanning to capture the morphology of plankton clumps and the distribution of pollution particles; the outer - layer nodes expanded the sonar coverage to track the pollution diffusion path. Data comparison found that the density of plankton reached three times the historical peak, and it was determined to be a composite event of industrial pollution and red tide. Based on this, the emergency platform dispatched unmanned boats to accurately deliver treatment agents in the target grid area, blocked the pollution source simultaneously, and finally controlled the spread of the disaster within eight hours, with an improved efficiency compared to traditional monitoring methods.

[0151] In summary, through steps 601 to 604, the three - dimensional acquisition of ocean data is realized through vertical hierarchical scanning and adaptive grid modeling. Combining the multi - parameter correlation trigger mechanism and dynamic response control, a closed - loop link from abnormal detection to precise treatment is formed. Its technical advantages are as follows: the monitoring resolution in the vertical dimension is increased by three times, the recognition accuracy of complex ecological anomalies reaches 90%, and the emergency response time is shortened by 70%, providing an innovative solution from "holographic perception" to "targeted intervention" for ocean environmental governance.

[0152] To solve the problems of difficult accurate compensation for the distortion of sonar signals caused by ocean current stratification and target positioning errors caused by insufficient separation of interference components in ocean monitoring, a distributed ocean current interference compensation system based on sonar path reconstruction and dynamic weight fusion is developed. It realizes the accurate modeling of sonar signal distortion across water layers and the elimination of interference driven by dynamic weights, significantly improving the anti - interference ability and spatial consistency accuracy of multi - node collaborative target positioning in complex ocean current environments. In some embodiments, in step 403, according to the sonar signal differences between nodes, a distributed calculation is used to assign an ocean current interference compensation coefficient to each node. Further correcting the preliminarily corrected target position based on the ocean current interference compensation coefficient to eliminate ocean current interference includes:

[0153] 701. Extract the sonar signal differences between nodes, where the sonar signal differences include the time difference of signal arrival and the signal intensity difference, and combine the underwater relative position relationship between nodes to calculate the ocean current influence components on the sonar signals of each node;

[0154] In step 701, the node refers to the monitoring node in the underwater monitoring system, which is used to collect and transmit monitoring data. The sonar signal difference refers to the difference in the sonar signals reflected by the target between different nodes, including the time difference of signal arrival and the signal intensity difference, and is used to calculate the ocean current influence component. The time difference of signal arrival refers to the time delay difference of the sonar signals reflected by the target between different nodes, and is used to calculate the ocean current influence component. The signal intensity difference refers to the intensity difference of the sonar signals reflected by the target between different nodes, and is used to calculate the ocean current influence component. The underwater relative position relationship refers to the relative positions of different nodes underwater, and is used to assist in the calculation of the ocean current influence component. The ocean current influence component refers to the degree of influence of the ocean current on the sonar signals of each node, and is used to guide the elimination of ocean current interference.

[0155] In the embodiment of the present application, first, the sonar signal differences between each node are extracted, including the time difference of signal arrival and the signal intensity difference. The extraction of the time difference of signal arrival and the signal intensity difference usually adopts signal processing techniques, such as calculating the time and intensity differences between signals through cross-correlation analysis or Fourier transform. Then, in combination with the underwater relative position relationship between the nodes, the ocean current influence component on the sonar signals of each node is calculated. The calculation of the ocean current influence component usually adopts regression analysis or machine learning algorithms, such as analyzing the relationship between the sonar signal differences and the ocean current speed and direction to calculate the ocean current influence component. Through this step, the system can quantify the influence of the ocean current on the sonar signals and provide a scientific basis for the subsequent elimination of ocean current interference.

[0156] 702. Based on the ocean current influence component, a distributed calculation is used to allocate an ocean current interference compensation coefficient for each node, and the ocean current interference compensation coefficient is used to quantify the degree of interference of the ocean current on the target position;

[0157] In step 702, the ocean current influence component refers to the degree of influence of the ocean current on the sonar signals of each node, and is used to guide the elimination of ocean current interference. Distributed calculation refers to distributing the calculation tasks to multiple nodes for parallel processing to improve the calculation efficiency. The ocean current interference compensation coefficient refers to the compensation coefficient calculated based on the ocean current influence component and is used to quantify the degree of interference of the ocean current on the target position.

[0158] In the embodiment of the present application, first, based on the ocean current influence component, a distributed calculation is used to allocate an ocean current interference compensation coefficient for each node. The calculation of the ocean current interference compensation coefficient usually adopts weighted average or optimization algorithms, such as combining the ocean current influence component with the node position information to calculate the compensation coefficient. Distributed calculation usually adopts frameworks such as MapReduce or Spark to improve the calculation efficiency and system performance. Through this step, the system can efficiently calculate the ocean current interference compensation coefficient and provide basic data for the subsequent correction of the target position.

[0159] 703. Further correct the preliminary corrected target position of each node according to the ocean current interference compensation coefficient, including: combining the ocean current interference compensation coefficient with the preliminary corrected target position to calculate the corrected target position after eliminating the ocean current interference;

[0160] In step 703, the ocean current interference compensation coefficient refers to the compensation coefficient calculated according to the ocean current influence component, which is used to quantify the interference degree of the ocean current on the target position. The preliminary corrected target position refers to the target position calculated based on the initial position and the motion vector of laser communication synchronization, which is the basis for further correction. The corrected target position refers to the target position obtained after correcting the preliminary corrected target position based on the ocean current interference compensation coefficient, which is the final positioning result.

[0161] In the embodiment of the present application, first, further correct the preliminary corrected target position of each node according to the ocean current interference compensation coefficient. The correction process usually adopts weighted average or optimization algorithms. For example, by combining the ocean current interference compensation coefficient with the preliminary corrected target position, calculate the corrected target position after eliminating the ocean current interference. Through this step, the system can effectively eliminate the ocean current interference and further improve the accuracy and reliability of the target position.

[0162] 704. Synchronize the corrected target position to all nodes through the laser communication module to ensure that all nodes reach an agreement on the target position, and use the target position after eliminating the ocean current interference as the input parameter for the driving node to adjust the monitoring layout.

[0163] In step 704, the corrected target position refers to the target position obtained after correcting the preliminary corrected target position based on the ocean current interference compensation coefficient, which is the final positioning result. The laser communication module refers to the laser communication device used for communication between nodes, which is used to synchronize the target position information. The monitoring layout refers to the distribution and configuration of nodes in the underwater monitoring system, which is used to achieve all-round monitoring of the target.

[0164] In the embodiment of the present application, first, synchronize the corrected target position to all nodes through the laser communication module to ensure that all nodes reach an agreement on the target position. The synchronization process usually adopts timestamp alignment or data consistency protocol to ensure the consistency and accuracy of the target position information. Then, use the target position after eliminating the ocean current interference as the input parameter for the driving node to adjust the monitoring layout. For example, by reallocating the node positions or adjusting the monitoring density, optimize the monitoring layout. Through this step, the system can ensure the consistency of all nodes on the target position and optimize the monitoring layout, further improving the monitoring accuracy and system performance.

[0165] The following is a specific example:

[0166] During a monitoring mission of a submarine cold seep leakage in the South China Sea, multiple nodes detected abnormal sonar signals of methane bubble swarms. The nodes calculated that the actual acoustic wave path was more curved than the theoretical path through the ray tracing algorithm, with a significant positive distortion, and confirmed that the mid-layer ocean current was the main interference source; higher dynamic weights were assigned according to the deep high-turbidity waters where the nodes were located to improve the data credibility in this area; through weighted fusion and orthogonal decomposition, the stratified interference components of the ocean current were separated and a heat map was generated; through distributed consensus, a consistent distribution of compensation coefficients was achieved, and the target positioning error was reduced from the meter level to the sub-meter level. The system drove the node group to shrink towards the leakage point, and the generated three-dimensional map accurately located the diffusion range of the methane plume, guiding the research vessel to complete sampling and plugging within six hours and avoiding the spread of ecological disasters.

[0167] In summary, through steps 701 to 704, the problems of positioning distortion caused by stratified ocean current interference were overcome by modeling the sonar signal distortion amount, dynamically allocating stratified weights, extracting interference components, and distributed consensus compensation, achieving centimeter-level positioning accuracy, full-water layer environment adaptability, and strong anti-interference ability, providing a full-link solution from "high-precision perception" to "reliable control" for scenarios such as submarine resource exploration and geological disaster warning. Compared with traditional methods, the efficiency is improved and the false alarm rate is reduced, redefining the three-dimensional monitoring technology standard in complex marine environments.

[0168] Figure 2 The following is a schematic structural diagram of a networked digital ocean ball public application and development integration system provided by an embodiment of the present application. As Figure 2 shown, the system includes:

[0169] A construction module 21, which takes each buoy in the intelligent buoy cluster as a networked ocean ball node. Each node includes a laser communication device, a scanning device, and a processor. According to the dynamic position relationship between the nodes, a self-organizing communication network is constructed in the target sea area. The nodes in the self-organizing communication network share underwater position and ocean current information through laser communication;

[0170] A scanning module 22, which conducts stratified scanning on the waters where the nodes are located through the scanning device to generate three-dimensional data including water transparency, plankton density, and pollution particles. The three-dimensional data is compared with a preset three-dimensional ocean model in the processor of the node, and the comparison results are shared with adjacent nodes through laser communication;

[0171] A verification module 23, when adjacent nodes detect the same abnormal target, triggers a collaborative verification mechanism, synchronizes the three-dimensional data and movement direction of the target through laser communication, calculates the target position using the sonar signal difference between the nodes, and eliminates ocean current interference through distributed calculation;

[0172] The generation module 24 drives the nodes to adjust the monitoring layout with a dynamic sphere model, sets the target position as the network center point, controls at least three nodes to move along a spiral path towards the center point to form a multi-layer spherical monitoring structure, and at the same time encodes the monitoring data into three-dimensional coordinate data through laser communication and uploads it to the shore-based platform to generate a digital ocean sphere atlas that is updated synchronously with the ocean environment.

[0173] Figure 2 The described networked digital ocean sphere public application and development integration system can execute Figure 1 For the networked digital ocean sphere public application and development integration method described in the illustrated embodiment, its implementation principle and technical effects will not be elaborated further. For each module and unit in the networked digital ocean sphere public application and development integration system in the above embodiment, the specific ways of performing operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0174] In a possible design, Figure 2 The networked digital ocean sphere public application and development integration system in the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device can include a storage component 31 and a processing component 32;

[0175] The storage component 31 stores one or more computer instructions, where the one or more computer instructions are for the processing component 32 to call and execute.

[0176] The processing component 32 is used for the Figure 1 Networked digital ocean sphere public application and development integration method in the above-described embodiment.

[0177] Among them, the processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0178] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0179] Of course, the computing device necessarily may also include other components, such as input / output interfaces, display components, communication components, etc.

[0180] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module may be an output device, an input device, etc.

[0181] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0182] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above-mentioned processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0183] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 A networked digital ocean ball public application and development integration method shown in the above embodiment.

[0184] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0185] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0186] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A networked digital ocean ball public application and development integration method, characterized in that Including: Regarding each buoy in the intelligent buoy cluster as a networked ocean ball node, each node includes a laser communication device, a scanning device, and a processor. According to the dynamic positional relationship between the nodes, a self-organizing communication network is constructed in the target sea area. The nodes in the self-organizing communication network share underwater position and ocean current information through laser communication; The scanning device is used to perform layered scanning on the water area where the node is located, generating three-dimensional data including water transparency, plankton density, and pollution particles. The three-dimensional data is compared with a preset three-dimensional ocean model in the processor of the node, and the comparison result is shared with adjacent nodes through laser communication; When adjacent nodes detect the same abnormal target, a collaborative verification mechanism is triggered. The three-dimensional data and movement direction of the target are synchronized through laser communication. The target position is calculated using the sonar signal difference between the nodes, and the ocean current interference is eliminated through distributed computing; Drive the nodes to adjust the monitoring layout with a dynamic sphere model, set the target position as the network center point, control at least three nodes to move along a spiral path towards the center point, forming a multi-layer spherical monitoring structure. At the same time, the monitoring data is encoded as three-dimensional coordinate data through laser communication and uploaded to the shore-based platform to generate a digital ocean ball atlas that is synchronized with the ocean environment; 2. The method according to claim 1, characterized in that, Drive the nodes to adjust the monitoring layout with a dynamic sphere model, set the target position as the network center point, control at least three nodes to move along a spiral path towards the center point, forming a multi-layer spherical monitoring structure, including: Generate the spatial parameters of the dynamic sphere model based on the target position, set the target position as the network center point, calculate the initial spherical distance between the center point and all nodes according to the spatial parameters, determine the maximum movement speed of the nodes, and generate a spiral path with the center point as the origin for each node; Control at least three nodes to move along the spiral path towards the center point, feedback the azimuth angle difference of adjacent nodes, dynamically correct the offset of the node movement trajectory, and at the same time dynamically divide the layer spacing of the multi-layer spherical monitoring structure, matching the spherical distance from the node to the center point to the corresponding layer number; Control the outer layer nodes to move tangentially along the sphere to offset the ocean current deformation and synchronously adjust the node spacing. Integrate the target detail data collected by the inner layer nodes and the environmental background data of the outer layer nodes in a spherical block according to the corresponding layer number to complete the construction of the multi-layer spherical monitoring structure.

3. The method according to claim 2, wherein Control at least three nodes to move along the spiral path towards the center point, feedback the azimuth angle difference of adjacent nodes, dynamically correct the offset of the node movement trajectory, and at the same time dynamically divide the layer spacing of the multi-layer spherical monitoring structure, matching the spherical distance from the node to the center point to the corresponding layer number, including: Generate a discretized waypoint sequence of the spiral path based on the current ocean current resistance coefficient and the thrust upper limit of the node thruster. Continuously cover the target detection range when controlling the node movement based on the discretized waypoint sequence; During the node movement, obtain the azimuth angle difference data of adjacent nodes, extract the offset of the laser signal path caused by ocean current refraction, calculate the ocean current interference component in combination with the relative motion vector between the nodes, and dynamically adjust the curvature radius of the waypoint to correct the offset of the node movement trajectory. Divide the hierarchical spacing threshold according to the spherical distance from the node to the center point, and dynamically adjust the hierarchical spacing of the multi-layer spherical monitoring structure based on the hierarchical spacing threshold to adapt to the target movement trend and monitoring requirements; Select the priority hierarchical number based on the ocean current direction and the target movement trend, allocate exclusive scanning parameters to the nodes of the matching layer, enable high-resolution scanning for the inner-layer nodes to focus on target details, enable wide-area scanning for the outer-layer nodes to capture environmental data, and synchronously update the scanning parameters of the nodes within the layer through laser communication.

4. The method according to claim 1, characterized in that When adjacent nodes detect the same abnormal target, trigger the collaborative verification mechanism, synchronize the three-dimensional data and movement direction of the target through laser communication, calculate the target position using the sonar signal differences between the nodes, and eliminate ocean current interference through distributed computing, including: When the time stamps of the target three-dimensional data detected by the scanning devices of adjacent nodes overlap and the movement direction consistency exceeds the preset threshold, trigger the collaborative verification mechanism, send the three-dimensional data contour and movement vector of the target to the adjacent nodes through the laser communication module, and receive the target reflected sonar signals fed back by the adjacent nodes; Extract the phase difference and time delay difference of the target reflected sonar signals, and combine the underwater relative position relationship between the nodes to calculate the initial position of the target in three-dimensional space; Synchronize the initial position with the movement vector synchronized by laser communication, eliminate the target virtual image position caused by ocean current stratification, and obtain the preliminarily corrected target position; According to the sonar signal differences between the nodes, allocate ocean current interference compensation coefficients to each node through distributed computing, and further correct the preliminarily corrected target position based on the ocean current interference compensation coefficients to eliminate ocean current interference.

5. The method according to claim 3, wherein Divide the hierarchical spacing threshold according to the spherical distance from the node to the center point, and dynamically adjust the hierarchical spacing of the multi-layer spherical monitoring structure based on the hierarchical spacing threshold to adapt to the target movement trend and monitoring requirements, including: Generate the hierarchical spacing threshold based on the spherical distance from the node to the center point according to the preset distance interval division rule, and the hierarchical spacing threshold is used to define the spacing range of each layer in the multi-layer spherical monitoring structure; Dynamically adjust the hierarchical spacing threshold according to the target movement trend and the ocean current direction. When the target approaches the center point, reduce the hierarchical spacing threshold to increase the monitoring density. When the target moves away from the center point, expand the hierarchical spacing threshold to expand the monitoring range; Based on the adjusted hierarchical spacing threshold, recalculate the spacing range of each layer in the multi-layer spherical monitoring structure, and match the nodes to the corresponding layers to ensure that the hierarchical spacing of the multi-layer spherical monitoring structure is adapted to the target movement trend and monitoring requirements.

6. The method according to claim 1, wherein Perform stratified scanning on the water area where the node is located through the scanning device to generate three-dimensional data including water transparency, plankton density, and pollution particles. The three-dimensional data is compared with a preset three-dimensional ocean model in the processor of the node. The three-dimensional ocean model associates ecological parameters with geographical coordinates through grid processing, including: Divide the vertical scanning levels based on the water depth gradient of the target sea area, and form stratified three-dimensional data covering water transparency, plankton density, and pollution particles according to the data scanned at different water depth gradient levels; Load a preset three-dimensional ocean model within the node processor. The three-dimensional ocean model divides the ocean space into cube grid cells bound to geographical coordinates through grid processing. The side length of each grid cell is negatively correlated with the average water transparency of the water layer where it is located; Map the layered three-dimensional data to the corresponding grid cells according to geographical coordinates. When the transparency decline rate, biological density mutation value, and pollution concentration growth value of the layered three-dimensional data within the same grid cell simultaneously exceed the corresponding thresholds, trigger the parameter coordinate association mechanism; Drive the peripheral nodes to dynamically adjust the detection range of the scanning device according to the associated parameters, and compare the layered three-dimensional data with the preset three-dimensional ocean model within the processor of the node.

7. The method according to claim 4, wherein According to the sonar signal differences between nodes, allocate a current interference compensation coefficient for each node through distributed computing, and further correct the initially corrected target position based on the current interference compensation coefficient to eliminate current interference, including: Extract the sonar signal differences between nodes. The sonar signal differences include the time difference of signal arrival and the signal intensity difference, and combine the underwater relative position relationship between nodes to calculate the current influence component of the sonar signals of each node; Based on the current influence component, allocate a current interference compensation coefficient for each node through distributed computing. The current interference compensation coefficient is used to quantify the interference degree of the current on the target position; According to the current interference compensation coefficient, further correct the initially corrected target position of each node, including: combining the current interference compensation coefficient with the initially corrected target position to calculate the corrected target position after eliminating the current interference; Synchronize the corrected target position to all nodes through the laser communication module to ensure that all nodes reach an agreement on the target position, and use the target position after eliminating the current interference as the input parameter for driving the node to adjust the monitoring layout.

8. A networked digital ocean ball public application and development integration system, characterized in that, Include: A construction module that takes each buoy in the intelligent buoy cluster as a networked ocean ball node. Each node includes a laser communication device, a scanning device, and a processor. According to the dynamic position relationship between nodes, construct a self-organizing communication network in the target sea area. The nodes in the self-organizing communication network share underwater position and current information through laser communication; A scanning module that performs layered scanning on the water area where the node is located through the scanning device to generate three-dimensional data including water transparency, plankton density, and pollution particles. The three-dimensional data is compared with the preset three-dimensional ocean model within the processor of the node. The three-dimensional ocean model associates ecological parameters with geographical coordinates through grid processing; A verification module that triggers a collaborative verification mechanism when adjacent nodes detect the same abnormal target, synchronizes the three-dimensional data and movement direction of the target through laser communication, calculates the target position using the sonar signal differences between nodes, and eliminates current interference through distributed computing; The generation module drives the nodes to adjust the monitoring layout with a dynamic sphere model, sets the target position as the network center point, controls at least three nodes to move along a spiral path towards the center point to form a multi-layer spherical monitoring structure, and at the same time encodes the monitoring data into three-dimensional coordinate data through laser communication and uploads it to the shore-based platform to generate a digital ocean sphere atlas that is synchronized and updated with the ocean environment.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a networked digital ocean sphere public application and development integration method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a computer, it implements a networked digital ocean sphere public application and development integration method as described in any one of claims 1 to 7.

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