Underwater cross-medium monitoring and network collaboration method, equipment and medium

Abstract

The invention discloses an underwater cross-medium monitoring and network cooperation method and device and a medium, and relates to the technical field of underwater monitoring. The method comprises the steps that nodes deployed underwater collect energy in a composite mode and operate automatically, synchronously collect water quality parameters, and judge whether abnormal characteristics exist in the water quality parameters or not; if the water quality parameters have abnormal characteristics, taking the corresponding nodes as initiating nodes, and positioning a target abnormal area; starting cross-medium communication scanning through the initiating node, and evaluating and selecting an optimal communication link to the relay equipment; the initiating node sends a calling instruction to the peripheral nodes according to the abnormal characteristics of the water quality parameters; under the convening of the initiating node, the nodes in the cluster perform synchronous measurement on the abnormal region; and the initiating node fuses and analyzes the gathered measurement data to obtain a positioning judgment conclusion of the abnormal source. According to the method, autonomous operation of the monitoring task is realized, and the finding and positioning efficiency of the abnormal event is improved.

Description

Technical Field

[0001] This application relates to the field of underwater monitoring technology, and in particular to an underwater cross-media monitoring and network collaboration method, device and medium. Background Technology

[0002] Current underwater environmental monitoring mainly relies on fixed, cabled sensor networks or buoy base stations that require regular maintenance. Cabled networks are costly to deploy, have limited coverage, and are prone to cable damage. Buoy base stations, on the other hand, depend on solar or wind power, which is unstable in rough seas or during prolonged cloudy weather, and their surface communication antennas are susceptible to interference or damage, leading to data transmission interruptions. Furthermore, existing nodes are typically single-function and lack the ability to operate sustainably for extended periods in complex underwater environments.

[0003] A more prominent problem is the lack of an effective intelligent collaboration mechanism between nodes in the existing monitoring system. When a pollution incident occurs in a local waterway, the system cannot dynamically organize surrounding nodes for rapid and intensive collaborative sensing and location. It can only rely on the back-end center to process all raw data, resulting in delayed response, enormous pressure on communication bandwidth, and an inability to achieve rapid and accurate source tracing of pollution. At the same time, the node communication links are singular, and once the preset link fails, data is at risk of being lost.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows: Existing underwater monitoring systems suffer from high deployment and maintenance costs, weak energy self-sufficiency, poor communication link reliability, and a lack of rapid intelligent collaborative response capabilities. Summary of the Invention

[0005] This application provides an underwater cross-media monitoring and network collaboration method, device, and medium, which can solve the problems of high deployment and maintenance costs, weak energy self-sufficiency, poor communication link reliability, and lack of rapid intelligent collaborative response capabilities in existing underwater monitoring systems.

[0006] In a first aspect, embodiments of this application provide an underwater cross-medium monitoring and network collaboration method, the method comprising: nodes deployed underwater collecting energy and operating autonomously in a composite manner, synchronously collecting water quality parameters, and determining whether the water quality parameters exhibit abnormal characteristics; if abnormal characteristics are found in the water quality parameters, the corresponding node is designated as the initiating node, and the target abnormal area is located; cross-medium communication scanning is initiated by the initiating node, and the optimal communication link to the relay device is evaluated and selected; based on the abnormal characteristics of the water quality parameters, the initiating node sends a summoning command to surrounding nodes to summon and organize surrounding nodes to form a temporary monitoring cluster, and assigns collaborative measurement tasks to the nodes within the temporary monitoring cluster; under the summoning of the initiating node, the nodes within the cluster synchronously measure the abnormal area and send the measurement data to the initiating node; the initiating node fuses and analyzes the aggregated measurement data to obtain a location determination conclusion of the abnormal source, and reports the location determination conclusion through the optimal communication link.

[0007] In one implementation of this application, the underwater node collects energy and operates autonomously through a composite method, synchronously collects water quality parameters, and determines whether there are any abnormal characteristics in the water quality parameters. Specifically, this includes: monitoring wave energy, solar energy, and thermal energy; selecting the main energy source from wave energy, solar energy, and thermal energy according to preset rules based on current water depth data, light intensity data, and temperature data; and determining the type and level of abnormal characteristics based on a comparison of water quality parameters with preset thresholds.

[0008] In one implementation of this application, the method further includes: applying a microcurrent to the microstructure on the outer shell surface of the node and exciting vibration to suppress attached organisms; detecting the growth data of the attached organisms according to a preset period and adjusting the output frequency and amplitude of the microcurrent, and synchronously adjusting the vibration frequency and amplitude of the vibration.

[0009] In one implementation of this application, a cross-medium communication scan is initiated by the initiating node to evaluate and select the optimal communication link to the relay device. Specifically, this includes: calculating the signal-to-noise ratio (SNR) score and delay score of the acoustic link; detecting the presence and strength of the radio frequency (RF) signal and evaluating the availability score of the surface link; identifying specific beacons and establishing beamforming to evaluate the availability score of the air link; comprehensively comparing the SNR score, delay score, surface link availability score, and air link availability score to select the optimal link and a backup link; and switching to the backup link if the optimal link fails.

[0010] In one implementation of this application, the initiating node sends a summoning command to surrounding nodes based on the abnormal characteristics of water quality parameters, summoning and organizing surrounding nodes to form a temporary monitoring cluster. Specifically, the initiating node calculates the required number and distribution density of nodes based on the type and level of the abnormal characteristics; based on the required number and distribution density of nodes, it sends a network formation signaling message through cross-media broadcast, the network formation signaling message including a time window, encryption key, and measurement parameters; the responding node completes time synchronization and key exchange within the time window, and reports node capability information and location information; the initiating node specifies the data transmission path within the cluster based on the node capability information and location information of the responding node.

[0011] In one implementation of this application, the initiating node fuses and analyzes the converged measurement data to arrive at a judgment on the location of the anomaly source. Specifically, this includes: spatially interpolating the measurement data from nodes within the cluster to generate an anomaly parameter distribution map; calculating the parameter gradient of the anomaly parameter distribution map and tracing the gradient direction to locate the core anomaly area; and correcting the core anomaly area by combining the water flow direction information to obtain the confidence level corresponding to the location and range.

[0012] In one implementation of this application, after the initiating node fuses and analyzes the aggregated measurement data to obtain the location determination conclusion of the anomaly source and reports the location determination conclusion through the optimal communication link, the method further includes: after confirming the successful reporting, the initiating node sends a disbanding instruction to the nodes in the cluster, the disbanding instruction including a sleep sequence; according to the disbanding instruction, after completing local data storage, sequentially enters low-power sleep mode or returns to normal monitoring.

[0013] In one implementation of this application, the method further includes: if a node does not receive a call-up instruction or a disband instruction within a predetermined time, initiating a multi-link scan to attempt to reconnect to the network; if the reconnection is successful, synchronizing the network status; if it remains isolated, entering a low-power local monitoring mode; and during the collaborative measurement process, initiating a node to dynamically adjust the task allocation of nodes within the cluster or wake up dormant nodes.

[0014] Secondly, embodiments of this application also provide an underwater cross-medium monitoring and network collaboration device, the device including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: perform any of the steps of an underwater cross-medium monitoring and network collaboration method.

[0015] Thirdly, embodiments of this application also provide a non-volatile computer storage medium for underwater cross-medium monitoring and network collaboration, storing computer-executable instructions configured to execute any one of the steps of an underwater cross-medium monitoring and network collaboration method.

[0016] This application provides an underwater cross-media monitoring and network collaboration method, device, and medium. By combining a wave energy-solar energy-thermal difference energy three-mode composite power supply system with a biomimetic anti-adhesion design, it enhances the long-term underwater survival and operation capability of individual nodes under harsh conditions such as no light, and reduces maintenance requirements. By allowing nodes to autonomously evaluate and select the optimal communication links underwater, on the surface, and in the air, a robust self-healing data transmission network is formed, ensuring the uninterrupted data return channel. By endowing nodes with collective intelligence, a single node can autonomously initiate and organize a high-density collaborative monitoring cluster when an anomaly is detected, completing data fusion and pollution source location calculation at the edge, and only uploading the conclusions. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating an underwater cross-medium monitoring and network collaboration method provided in this application embodiment; Figure 2 This is a schematic diagram of the internal structure of an underwater cross-medium monitoring and network collaboration device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] This application provides an underwater cross-media monitoring and network collaboration method, device, and medium, which solves the problems of high deployment and maintenance costs, weak energy self-sufficiency, poor communication link reliability, and lack of rapid intelligent collaborative response capabilities in existing underwater monitoring systems.

[0020] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0021] Figure 1 This is a flowchart illustrating an underwater cross-medium monitoring and network collaboration method provided in an embodiment of this application. Figure 1 As shown in the figure, the underwater cross-medium monitoring and network collaboration method provided in this application embodiment specifically includes the following steps: Step 10: The underwater nodes collect energy and operate autonomously through a composite method, synchronously collect water quality parameters, and determine whether there are any abnormal characteristics in the water quality parameters; As an optional embodiment, the underwater node collects energy and operates autonomously in a composite manner, synchronously collects water quality parameters, and determines whether there are abnormal characteristics in the water quality parameters. Specifically, it may include: Step 101: monitoring wave energy, solar energy, and thermal energy.

[0022] In this step, the node generates electricity through an integrated wave energy converter, solar panels, and seawater thermal energy conversion, and continuously monitors and collects energy output from three energy sources: wave kinetic energy, solar radiation energy, and seawater vertical thermal energy conversion.

[0023] Step 102: Based on the current water depth data, light intensity data, and temperature data, select the main energy source from wave energy, solar energy, and thermal energy according to preset rules.

[0024] In this step, the node acquires water depth data, light intensity data, and surrounding water temperature data measured by its own sensors in real time. Based on preset decision rules, it dynamically switches the main energy source: solar energy is used first when there is sufficient light; thermal energy is used when in deep water or in a dark environment and there is a temperature difference between the surface and deep seawater; and wave energy is mainly relied on when near the sea surface and there is significant wave activity.

[0025] Step 103: Based on the comparison of water quality parameters with preset thresholds, determine the type and level of abnormal characteristics.

[0026] In this step, nodes collect water quality parameters such as dissolved oxygen, pH, and nutrient concentration at set intervals. The real-time data is compared with multiple pre-stored abnormal thresholds. When the parameter value exceeds the threshold, not only is the occurrence of the abnormality confirmed, but also the predefined classification criteria are matched according to the type, value, and combination relationship of the parameters exceeding the limit to determine the specific type of abnormality, including hypoxia type, pollution type, etc., and the severity level.

[0027] As an optional embodiment, the method may further include: step 104: applying a microcurrent to the microstructure on the outer shell surface of the node and exciting vibrations to suppress attached organisms.

[0028] In this step, to reduce biofouling, the node controls the biomimetic microstructures on the shell surface to perform an active cleaning process by applying a microampere-level alternating current to the microstructures through a microcircuit; at the same time, the piezoelectric effect or a micro-actuator is used to excite the microstructures to generate high-frequency mechanical vibrations.

[0029] Step 105: Detect the growth data of attached organisms according to the preset cycle, and adjust the output frequency and amplitude of the microcurrent, and synchronously adjust the vibration frequency and amplitude of the vibration.

[0030] In this step, the node periodically assesses the bio-attachment status through sensors or indirectly based on performance parameters. Based on the bio-attachment growth data obtained from the assessment, the node adaptively adjusts the output frequency and amplitude of the microcurrent and simultaneously adjusts the frequency and amplitude of the excited vibration to achieve an optimal balance between anti-attachment effect and energy consumption control.

[0031] Step 20: If abnormal characteristics are found in the water quality parameters, the corresponding node will be used as the initiating node, and the target abnormal area will be located.

[0032] In this step, nodes that identify abnormal characteristics, types, and levels automatically change their running status from regular monitoring nodes to collaborative initiating nodes, generate a unique identifier for this abnormal event, and initialize an event log to record all subsequent collaborative operations and data.

[0033] The initiating node first performs a preliminary spatial estimation of the anomaly area based on the water quality parameter data it collected that triggered the anomaly detection. Specifically, the initiating node determines its own geographic coordinates as the core reference point. Combining the type of anomaly characteristics and historical hydrological data models, the initiating node can estimate a circular or elliptical area centered on its own location with an initial radius, as the initial version of the target anomaly area. For a sudden appearance of high-concentration point source pollution characteristics, the initial area may be a small area with a radius of tens of meters centered on the node; for a large-scale decrease in dissolved oxygen, the initial area may be set to a larger range.

[0034] Step 30: Initiate a cross-media communication scan through the initiating node to evaluate and select the optimal communication link to the relay device; As an optional embodiment, a cross-media communication scan is initiated by the initiating node to evaluate and select the optimal communication link to the relay device, which may specifically include: Step 301: Calculate the signal-to-noise ratio score and delay score of the acoustic link.

[0035] In this step, the initiating node first activates the underwater acoustic communication module and sends a short probe acoustic signal to a surface relay buoy at a known location or a potential relay node in the vicinity. After receiving a response signal from the other party, the node calculates the signal-to-noise ratio (SNR) score and signal propagation delay score of the underwater acoustic link based on the quality of the received signal and the timestamp of the original signal transmission. The SNR score reflects the link's ability to resist underwater acoustic channel noise and multipath interference, while the delay score is directly related to the real-time performance of data transmission.

[0036] Step 302: Detect the presence and strength of the radio frequency signal and evaluate the availability score of the surface link.

[0037] In this step, the initiating node controls the radio frequency module to scan the predetermined operating frequency band and listen for radio frequency beacon signals broadcast from surface relay devices, including buoys, ships, or shore stations. By detecting the presence or absence of these signals, the received signal strength indication value, and the stability of the signal, the node evaluates the availability score of a link based on surface radio transmission, taking into account the link's physical connectivity, signal quality, and potential atmospheric and sea surface reflection interference.

[0038] Step 303: Identify specific beacons and establish beamforming to assess the availability score of the air link.

[0039] In this step, the initiating node activates its directional millimeter-wave or laser communication module to scan the airspace for specific optical or millimeter-wave beacons emitted from aerial relay platforms such as drones or low-Earth orbit satellites. Once the target beacon is identified and locked, the node attempts to establish a narrow-beam directional alignment by adjusting the pointing of the phased array antenna or optical transmitter. Based on the stability of the beacon lock, the time taken to establish the alignment, and the estimated transmission bandwidth, the node evaluates the availability score of the air link.

[0040] Step 304: Compare the signal-to-noise ratio score, latency score, surface link availability score, and air link availability score to select the optimal link and backup link; Step 305: If the optimal link fails, switch to the backup link.

[0041] In this step, the link management algorithm built into the initiating node will assign weights to each scoring item of the three types of links: underwater acoustic, surface radio, and air directional, and perform weighted comprehensive calculation to obtain the overall quality score of each link. The link with the highest total score is designated as the best communication link to be reported at present, and the link with the second highest total score is marked as the primary backup link. The weight settings can be dynamically adjusted based on task priority, including real-time priority, data volume priority, or energy consumption priority.

[0042] Step 40: Based on the abnormal characteristics of the water quality parameters, the initiating node sends a summoning command to the surrounding nodes to summon and organize the surrounding nodes to form a temporary monitoring cluster, and assigns collaborative measurement tasks to the nodes within the temporary monitoring cluster. As an optional implementation, the initiating node sends a summoning command to surrounding nodes based on the abnormal characteristics of water quality parameters, summoning and organizing the surrounding nodes to form a temporary monitoring cluster, which may specifically include: Step 401: The initiating node calculates the required number of nodes and distribution density based on the type and level of the anomaly characteristics.

[0043] In this step, the initiating node, based on the anomaly characteristic type (including chemical pollutant diffusion, sudden drop in dissolved oxygen saturation, and Level II alarm), calls the built-in or pre-set collaborative strategy model. According to the monitoring parameters required by the anomaly type, spatial resolution requirements, and the monitoring urgency and reliability requirements corresponding to the anomaly level, it calculates the total number of nodes required to complete this collaborative monitoring task under ideal conditions, as well as the spatial distribution density that these nodes should meet in the target anomaly area. For point source pollution that requires high spatial resolution mapping, a higher node density and a smaller distribution spacing may be required.

[0044] Step 402: Based on the required number of nodes and distribution density, send network formation signaling via cross-media broadcast. The network formation signaling includes a time window, encryption key, and measurement parameters.

[0045] In this step, the initiating node encodes the calculated number of nodes and distribution density requirements into a specific recruitment instruction and encapsulates it into a network signaling message. This message is then broadcast across media via established or scanned communication links. The signaling message includes at least: a finite time window that requires surrounding nodes to respond within this window; a temporary encryption key dedicated to this collaborative task to ensure the security of all subsequent intra-cluster communications; and a list of measurement parameters that clearly requires nodes to collaboratively collect specific water quality indicators, such as pH value and specific ion concentrations.

[0046] Step 403: The responding node completes time synchronization and key exchange within the time window, and reports node capability information and location information.

[0047] In this step, if the surrounding nodes that receive the network signaling are in a state that allows it, such as having sufficient power and no higher priority tasks, they will first synchronize their clocks with the initiating node within a specified time window to ensure the time consistency of subsequent collaborative actions. Both parties use the key in the signaling or derive a session key through a security protocol to complete the initial key exchange. The responding node reports its node capability information to the initiating node: a list of sensor types it carries, maximum sampling frequency, current remaining power, and real-time location information, which is obtained through the built-in positioning module or estimated based on the signaling signal strength.

[0048] Step 404: The initiating node specifies the intra-cluster data transmission path based on the node capability information and location information of the responding node.

[0049] In this step, after collecting information from all nodes that respond within the specified time window, the initiating node maps them to the geographic framework of the target anomaly area based on the reported location information. Combining the capability information of each node, the initiating node assigns specific collaborative measurement tasks to each cluster member: nodes equipped with high-precision chemical sensors are assigned to be responsible for sampling in the core contaminated area, and nodes with sufficient power are assigned to undertake more data relay tasks. At the same time, the initiating node calculates and specifies an efficient, multi-hop intra-cluster data transmission path based on the location topology of all nodes, and clearly specifies which upstream neighbor node each node will send measurement data to, which is ultimately converged to the initiating node.

[0050] Step 50: Under the call of the initiating node, the nodes in the cluster perform synchronous measurements on the abnormal area and send the measurement data to the initiating node.

[0051] In this step, the initiating node broadcasts a collaborative measurement start command to all cluster members. The command includes a precise absolute start timestamp determined by time synchronization and the sampling period. At the agreed start time, all cluster nodes simultaneously activate their corresponding sensors according to their assigned tasks, measuring the specified water quality parameters to ensure high temporal comparability of the collected data. After measurement, each node sends its encapsulated data packet, containing the measurement value, timestamp, node location, and its own ID, to its upstream node along the designated intra-cluster data transmission path.

[0052] The data transmission process uses the distributed encryption key for encryption to ensure data security; the data packets are relayed through multiple hops and eventually converge to the initiating node; the initiating node is responsible for verifying the integrity and timing of the received data and temporarily storing all data.

[0053] Step 60: The initiating node fuses and analyzes the aggregated measurement data to obtain the location and determination conclusion of the anomaly source, and reports the location and determination conclusion through the optimal communication link.

[0054] As an optional embodiment, the initiating node fuses and analyzes the aggregated measurement data to arrive at a judgment on the location of the anomaly source. Specifically, it may include: Step 601: Spatial interpolation of the measurement data from nodes within the cluster to generate an anomaly parameter distribution map.

[0055] In this step, the initiating node extracts the geographic coordinates and corresponding abnormal parameter values, such as pollutant concentrations, from all measurement data. Using a spatial interpolation algorithm, the parameter distribution in continuous space is estimated based on discrete sampling point data within the geographic range of the target abnormal area. Finally, a digital abnormal parameter distribution map is generated, which intuitively reflects the continuous spatial changes of abnormal parameters in the form of heat maps or contour maps.

[0056] Step 602: Calculate the parameter gradient of the abnormal parameter distribution map and trace the gradient direction to locate the core region of the abnormality.

[0057] In this step, the initiating node performs mathematical analysis on the generated distribution map, calculating the parameter gradient of each point in the map, that is, the direction and magnitude of the largest rate of change; by tracing the vector direction of the gradient field and finding the direction of gradient vector convergence or significant change from low value to high value, the source of parameters or high concentration core area can be identified; the center point or area of ​​the most drastic gradient change or gradient vector field convergence is initially determined as the abnormal core area, that is, the most likely source of pollution or the area with the most serious abnormal phenomenon.

[0058] Step 603: Correct the core area of ​​the anomaly by combining the water flow direction information to obtain the confidence level corresponding to the location and range.

[0059] In this step, considering that pollutants or anomalies are affected by water transport and diffusion in water bodies, the initially located core area may not be the original release point. The initiating node acquires current or historical water flow direction and velocity data. Based on the water flow direction, the initially located anomaly core area is retrospectively extrapolated or corrected for diffusion. For example, the core area is shifted a certain distance in the opposite direction of the water flow to estimate the possible location of the original release point. Taking into account factors such as data density, interpolation uncertainty, and water flow data reliability, the confidence level of the final location result and the range estimate of the anomaly's impact are calculated, generating a judgment conclusion that includes the presumed source location, the boundary of the impact range, and the confidence level assessment.

[0060] As an optional embodiment, after the initiating node fuses and analyzes the aggregated measurement data to obtain the location determination conclusion of the anomaly source and reports the location determination conclusion through the optimal communication link, the method may further include: Step 701: After confirming the successful reporting, the initiating node sends a disbanding instruction to the nodes in the cluster. The disbanding instruction includes a sleep sequence.

[0061] In this step, after receiving successful confirmation of the reported alarm from the relay device or control center, the initiating node determines that the collaborative task has been successfully completed. The initiating node generates and broadcasts a disband command to all member nodes in the current temporary monitoring cluster. The command encodes a hibernation timing scheme, which specifies the order or delay time for different nodes to enter hibernation state, so as to avoid the instantaneous communication pressure on the local network that may be caused by all nodes entering hibernation at the same time, and can prioritize the maintenance of the state of key relay nodes.

[0062] Step 702: According to the disband instruction, after completing local data storage, sequentially switch to low-power sleep mode or return to normal monitoring.

[0063] In this step, after receiving the disband command, each node in the cluster first archives and saves the measurement data and event logs related to this task in its local memory. According to the hibernation sequence contained in the command, each node controls its own hardware modules to shut down in an orderly manner or enter a deep low-power hibernation state at the specified time point or after a delay. Some nodes can also directly exit the collaborative task mode according to their preset roles and return to the periodic, low-intensity routine monitoring state to continue monitoring the environment.

[0064] As an optional embodiment, the method may further include: if a node does not receive a call-up instruction or a disband instruction within a predetermined time, initiating a multi-link scan to attempt to reconnect to the network; if the reconnection is successful, synchronizing the network status; if it remains isolated, entering a low-power local monitoring mode; and during the collaborative measurement process, initiating a node to dynamically adjust the task allocation of nodes within the cluster or wake up dormant nodes.

[0065] In this step, each node continuously monitors the network channel under any operating state, including routine monitoring, collaborative tasks, and timed wake-ups during sleep periods. If any node fails to receive any valid network signaling, such as a call command, or fails to receive the expected synchronization signaling or disbanding command within the collaborative cluster within a predetermined time window, the node autonomously initiates a multi-link scanning procedure to attempt to reconnect to known network neighbors or relay devices. If the reconnection is successful, the node will synchronize the current network topology and task status information with the reconnected network node and restore its corresponding collaborative or monitoring functions based on the synchronized information. If the node remains isolated and cannot rebuild a valid network connection, it will autonomously enter an extremely low-power local monitoring mode. In this mode, it only maintains the most basic environmental parameter acquisition and local storage functions and periodically attempts to scan and reconnect.

[0066] Furthermore, during collaborative measurement, the initiating node can continuously assess the data report quality, link stability, and remaining energy of each node within the cluster. Based on this assessment, the initiating node can dynamically adjust the task allocation among nodes within the cluster, transferring some measurement tasks from nodes with low power to nodes with sufficient power, or, if it determines that the current monitoring density is insufficient, proactively waking up suitable nodes in dormant mode in the vicinity and inviting them to join a temporary monitoring cluster to enhance collaborative monitoring capabilities.

[0067] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide an underwater cross-medium monitoring and network collaboration device, the structure of which is as follows: Figure 2 As shown.

[0068] Figure 2 This is a schematic diagram of the internal structure of an underwater cross-medium monitoring and network collaboration device provided in an embodiment of this application. Figure 2 As shown, the device includes: At least one processor 201; And a memory 202 that is communicatively connected to at least one processor; The memory 202 stores instructions that can be executed by at least one processor, which is executed by at least one processor 201 to enable at least one processor 201 to: perform any of the steps of an underwater cross-medium monitoring and network collaboration method.

[0069] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for underwater cross-medium monitoring and network collaboration stores computer-executable instructions, which are configured to execute any one of the steps of an underwater cross-medium monitoring and network collaboration method.

[0070] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0071] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0072] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0078] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0080] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for underwater cross-medium monitoring and network collaboration, characterized in that, The method includes: The underwater nodes collect energy and operate autonomously through a composite method, synchronously collect water quality parameters, and determine whether the water quality parameters have any abnormal characteristics. If the water quality parameters show abnormal characteristics, the corresponding node will be used as the initiating node, and the target abnormal area will be located. The initiating node initiates a cross-media communication scan to evaluate and select the optimal communication link to the relay device. The initiating node sends a summoning command to surrounding nodes based on the abnormal characteristics of the water quality parameters, so as to summon and organize surrounding nodes to form a temporary monitoring cluster, and assign collaborative measurement tasks to the nodes within the temporary monitoring cluster. Under the initiating node's call, the cluster nodes synchronously measure the abnormal region and send the measurement data to the initiating node; The initiating node fuses and analyzes the aggregated measurement data to obtain a location determination conclusion for the anomaly source, and reports the location determination conclusion through the optimal communication link.

2. The underwater cross-medium monitoring and network collaboration method according to claim 1, characterized in that, The underwater-deployed nodes collect energy and operate autonomously through a composite method, simultaneously collecting water quality parameters and determining whether the water quality parameters exhibit any abnormal characteristics, specifically including: Monitoring wave energy, solar energy, and thermal energy conversion; Based on current water depth data, light intensity data, and temperature data, the primary energy source is selected from wave energy, solar energy, and thermal energy conversion energy according to preset rules. Based on the comparison between the water quality parameters and preset thresholds, the type and level of the abnormal characteristics are determined.

3. The underwater cross-medium monitoring and network collaboration method according to claim 2, characterized in that, The method further includes: A microcurrent is applied to the microstructures on the outer shell surface of the node, and vibrations are excited to inhibit attached organisms; The growth data of the attached organisms are detected according to a preset cycle, and the output frequency and amplitude of the microcurrent are adjusted accordingly, while the vibration frequency and amplitude of the vibration are adjusted synchronously.

4. The underwater cross-medium monitoring and network collaboration method according to claim 1, characterized in that, The step of initiating a cross-media communication scan through the initiating node, evaluating and selecting the optimal communication link to the relay device, specifically includes: Calculate the signal-to-noise ratio score and time delay score of the acoustic link; Detect the presence and intensity of radio frequency signals to assess the availability score of the surface link; Identify specific beacons and establish beamforming to assess the availability score of air links; By comprehensively comparing the signal-to-noise ratio score, latency score, surface link availability score, and air link availability score, the optimal link and backup link are selected. If the optimal link fails, switch to the backup link.

5. The underwater cross-medium monitoring and network collaboration method according to claim 2, characterized in that, The initiating node sends a summoning command to surrounding nodes based on the abnormal characteristics of the water quality parameters, summoning and organizing the surrounding nodes to form a temporary monitoring cluster, specifically including: The initiating node calculates the required number of nodes and their distribution density based on the type and level of the abnormal characteristics. Based on the required number of nodes and their distribution density, network formation signaling is transmitted via cross-media broadcast. The network formation signaling includes a time window, encryption key, and measurement parameters. The responding node completes time synchronization and key exchange within the time window, and reports node capability information and location information; The initiating node specifies the intra-cluster data transmission path based on the node capability information and location information of the responding node.

6. The underwater cross-medium monitoring and network collaboration method according to claim 1, characterized in that, The initiating node fuses and analyzes the aggregated measurement data to arrive at a conclusion regarding the location of the anomaly source, specifically including: Spatial interpolation is performed on the measurement data from the nodes within the cluster to generate an anomaly parameter distribution map; Calculate the parameter gradient of the abnormal parameter distribution map and trace the gradient direction to locate the core region of the anomaly; By combining the water flow direction information, the abnormal core area is corrected to obtain the confidence level corresponding to the location and range.

7. The underwater cross-medium monitoring and network collaboration method according to claim 1, characterized in that, After the initiating node fuses and analyzes the aggregated measurement data to obtain a location determination conclusion for the anomaly source, and reports the location determination conclusion through the optimal communication link, the method further includes: After confirming successful reporting, the initiating node sends a disband command to the nodes within the cluster. The disband command includes a sleep sequence. According to the disband instruction, after completing local data storage, the system sequentially enters low-power sleep mode or returns to normal monitoring.

8. The underwater cross-medium monitoring and network collaboration method according to claim 1, characterized in that, The method further includes: If a node does not receive the summoning or disbanding instruction within the predetermined time, it will initiate a multi-link scan to attempt to reconnect to the network. If the reconnection is successful, synchronize the network status; If the isolation persists, it will enter a low-power local monitoring mode. During the collaborative measurement process, the initiating node dynamically adjusts the task allocation of nodes within the cluster or wakes up dormant nodes.

9. An underwater cross-medium monitoring and network collaboration device, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Perform the steps of the underwater transmedia monitoring and network collaboration method as described in any one of claims 1-8.

10. A non-volatile computer storage medium for underwater cross-media monitoring and network collaboration, storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: Perform the steps of the underwater transmedia monitoring and network collaboration method as described in any one of claims 1-8.

Images