A marine hydrological monitoring system based on wireless communication

Abstract

The application relates to the technical field of marine hydrological monitoring, and discloses a marine hydrological monitoring system based on wireless communication, which comprises a monitoring module, an analysis module, a cooperative control module, an energy module and a wireless communication module. The marine hydrological data is comprehensively monitored based on the monitoring module comprising buoy monitoring and submersible monitoring, the cooperative monitoring task control instruction of the buoy monitoring and the submersible monitoring is determined through the analysis module and the cooperative control module, the energy consumption of the monitoring process is reduced while the comprehensiveness, accuracy and effectiveness of the hydrological data analysis result are improved, in addition, a distributed sensing network is established, and the stable transmission of the monitoring data is realized through wireless communication technology, so that the timeliness and reliability of the data transmission are ensured.

Description

Technical Field

[0001] This invention relates to the field of marine hydrological monitoring technology, and in particular to a marine hydrological monitoring system based on wireless communication. Background Technology

[0002] With the deepening of research on marine environmental protection, resource development, and climate change, the importance of marine hydrological monitoring technology is becoming increasingly prominent. Traditional marine hydrological monitoring systems typically rely on single monitoring devices to monitor the marine environment by collecting limited hydrological data.

[0003] In recent years, with the development of wireless communication technology and new energy power generation technology, data transmission and energy management among marine monitoring equipment have been greatly improved. By combining multiple sensors and using wireless communication technology for data synchronization, marine hydrological monitoring systems can collect data from multiple points at different depths and ranges. However, they also face challenges such as optimizing energy consumption of monitoring equipment, difficulties in coordinating monitoring tasks, and unstable data transmission. Therefore, achieving comprehensive and intelligent monitoring of the marine hydrological environment, improving the system's energy efficiency, and generating optimized collaborative control strategies remain the technical bottlenecks of existing marine monitoring systems. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a marine hydrological monitoring system based on wireless communication to solve the problems of limited monitoring range of marine hydrological data, lack of effective coordination between devices, and unstable data transmission, thereby improving the comprehensiveness, accuracy and effectiveness of marine hydrological data monitoring and improving energy utilization efficiency.

[0005] This invention discloses a marine hydrological monitoring system based on wireless communication. The system includes a monitoring module, an analysis module, a collaborative control module, and a wireless communication module. The monitoring module further includes a buoy monitoring submodule and a submersible monitoring submodule.

[0006] The buoy monitoring submodule collects hydrological data of the sea surface and different sea layers by being equipped with multi-level sensors.

[0007] The submersible monitoring submodule is used to collect seabed hydrological data;

[0008] The analysis module is used to perform analysis operations based on the hydrological data collected by the monitoring module, obtain hydrological data analysis results, and determine monitoring adjustment strategies based on the hydrological data analysis results; the hydrological data analysis results include hydrological change trends and hydrological anomaly data;

[0009] The collaborative control module is used to correct the monitoring adjustment strategy determined by the analysis module based on the status and energy consumption of the equipment in the monitoring module, and to determine the collaborative work control instructions for the buoy monitoring submodule and the submersible monitoring submodule based on the corrected monitoring adjustment strategy; the collaborative work control instructions include the monitoring range control instructions, monitoring frequency control instructions and monitoring priority control instructions for the buoy monitoring submodule and the submersible monitoring submodule respectively.

[0010] The wireless communication module is used for real-time communication between the monitoring module, the analysis module, and the collaborative control module.

[0011] Furthermore, the hydrological data collected by the buoy monitoring submodule includes seawater temperature, salinity, water flow velocity and direction, wave height and period, sea surface height, and water turbidity.

[0012] Furthermore, the hydrological data collected by the submersible monitoring submodule includes seabed temperature, water pressure, salinity, water flow velocity and direction, seabed topography data, and sediment thickness and type.

[0013] Furthermore, the submersible monitoring submodule includes a multi-level water pressure monitoring unit and a multi-angle sonar array unit;

[0014] The multi-level water pressure monitoring units are respectively installed in different parts of the submersible to monitor water pressure at different depths;

[0015] The multi-angle sonar array unit is set at different angles of the submersible, and the sweeping angle and sound wave intensity are dynamically adjusted according to the real-time seabed topography data and the seabed topography prediction algorithm.

[0016] Furthermore, the transmission network between the buoy monitoring submodule and the submersible monitoring submodule is set as a distributed sensor network, and the data between all sensors is first synchronized in real time through wireless near-field communication technology, and then the synchronized data is summarized to the analysis module in real time.

[0017] Furthermore, the distributed sensor network is equipped with a dynamic self-organizing mechanism, which automatically adjusts the data transmission path based on the faults and performance changes of each monitoring device in the monitoring module, and aggregates the data collected by the monitoring module to the analysis module in real time through a multi-hop method.

[0018] The wireless near-field transmission technology includes automatically adjusting the operating frequency based on signal interference in the marine environment.

[0019] Furthermore, the system also includes an energy module, which comprises a thermoelectric generator module, a solar generator module, and a wave generator module; wherein the thermoelectric generator module is used to power the submersible monitoring submodule, and the solar generator module and the wave generator module are used to power the buoy monitoring submodule.

[0020] Furthermore, the energy module also includes an energy assessment submodule, which evaluates the power generation efficiency of each power generation module using a power generation capacity assessment algorithm and calculates the available energy of each power generation module.

[0021] Furthermore, the process by which the analysis module performs analysis operations based on the hydrological data collected by the monitoring module, obtains hydrological data analysis results, and determines the monitoring adjustment strategy based on the hydrological data analysis results includes:

[0022] A hydrological trend prediction algorithm based on machine learning models historical hydrological data to generate hydrological change trends.

[0023] Identify hydrological anomalies using anomaly detection rules;

[0024] Based on hydrological change trends and abnormal data, a multi-objective optimization algorithm is used with a weight allocation mechanism to match the priority of hydrological environment changes with the equipment monitoring capabilities to determine the optimal monitoring configuration. Based on the optimal monitoring configuration, a monitoring adjustment strategy is determined. The monitoring adjustment strategy includes the determination of monitoring frequency, monitoring range, and priority monitoring areas.

[0025] Furthermore, the collaborative control module is also used to correct the monitoring and adjustment strategy determined by the analysis module based on the status and energy consumption of the equipment in the monitoring module and the energy assessment results of the energy module, and to determine the collaborative work control instructions for the buoy monitoring submodule and the submersible monitoring submodule based on the corrected monitoring and adjustment strategy; the collaborative work control instructions also include energy consumption allocation control instructions.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] This invention establishes a marine hydrological monitoring system comprising a monitoring module, an analysis module, a collaborative control module, an energy module, and a wireless communication module. Based on the monitoring module, which includes both buoy and submersible monitoring, it comprehensively monitors marine hydrological data. The analysis and collaborative control modules determine the collaborative monitoring task control commands for buoy and submersible monitoring, improving the comprehensiveness, accuracy, and effectiveness of hydrological data analysis results while reducing energy consumption during the monitoring process. Furthermore, a distributed sensor network is established, and wireless communication technology ensures stable transmission of monitoring data, guaranteeing the timeliness and reliability of data transmission. Attached Figure Description

[0028] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and constitute a part of this application, do not constitute a limitation thereof. In the drawings:

[0029] Figure 1 This is a schematic diagram of a marine hydrological monitoring system based on wireless communication disclosed in an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0031] Example 1

[0032] This invention discloses a marine hydrological monitoring system based on wireless communication. Please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of the structure of a marine hydrological monitoring system based on wireless communication disclosed in an embodiment of the present invention. The system includes a monitoring module, an analysis module, a collaborative control module, and a wireless communication module; the monitoring module further includes a buoy monitoring submodule and a submersible monitoring submodule; wherein,

[0033] The buoy monitoring submodule collects hydrological data on the sea surface and different sea layers by carrying multi-level sensors;

[0034] The submersible monitoring submodule is used to collect seabed hydrological data;

[0035] The analysis module is used to perform analysis operations based on the hydrological data collected by the monitoring module, obtain hydrological data analysis results, and determine monitoring adjustment strategies based on the hydrological data analysis results; the hydrological data analysis results include hydrological change trends and hydrological anomaly data;

[0036] The collaborative control module is used to correct the monitoring adjustment strategy determined by the analysis module based on the status and energy consumption of the equipment in the monitoring module, and to determine the collaborative work control instructions for the buoy monitoring submodule and the submersible monitoring submodule based on the corrected monitoring adjustment strategy; the collaborative work control instructions include the monitoring range control instructions, monitoring frequency control instructions and monitoring priority control instructions for the buoy monitoring submodule and the submersible monitoring submodule respectively.

[0037] The wireless communication module is used for real-time communication between the monitoring module, analysis module, and collaborative control module.

[0038] Optionally, the wireless communication module is also used for real-time communication between the buoy monitoring submodule and the submersible monitoring submodule.

[0039] Specifically, the buoy monitoring module is typically set up to float on the sea surface and collects hydrological data on the sea surface and different water layers in real time through multiple sensors. To comprehensively cover changes in the marine environment, the buoy monitoring submodule in this embodiment of the invention is not limited to surface data collection but also collects hydrological data at different water depths through a sensor array. Sensors installed on the buoy surface primarily collect sea surface parameters, including but not limited to seawater temperature, wave height and period, sea surface height, and wind speed. These data are particularly important for the analysis of marine meteorology, tides, and wave models. The sensor array, connected to the buoy via ropes or an anchoring system, is arranged in water layers at different depths, with a sensor node placed at regular intervals to measure parameters such as temperature, salinity, current velocity, current direction, and turbidity at different water layers, constructing hydrological profiles at different depths. This multi-layered sensor setup can collect a series of hydrological data from the sea surface to different water depths, helping scientists and engineers understand the dynamic changes throughout the water column.

[0040] The submersible monitoring submodule collects seabed hydrological data by deploying the submersible to the seabed. Due to the significant differences between the seabed environment and surface waters, the submersible is primarily responsible for acquiring parameters related to the seabed environment. By using monitoring equipment such as water pressure sensors, sonar, temperature and salinity sensors, and sediment monitoring sensors, it collects hydrological data on the seabed area, including water temperature, water pressure, salinity, water flow velocity and direction, seabed topography, and sediment thickness and type.

[0041] In this embodiment of the invention, the monitoring setup combining buoys and submersibles forms a complete hydrological monitoring chain from the sea surface to the seabed. The buoy submodule is responsible for routine monitoring of a large area, including the sea surface and water layers, while the submersible submodule goes deep into the seabed to collect deep-sea data that is difficult to obtain through buoys. This provides more comprehensive marine environmental information and improves the accuracy and timeliness of monitoring.

[0042] Furthermore, the submersible monitoring submodule includes a multi-level water pressure monitoring unit and a multi-angle sonar array unit. The multi-level water pressure monitoring unit is located in different parts of the submersible to monitor water pressure at different depths.

[0043] The multi-angle sonar array units are set at different angles of the submersible, and the scanning angle and sound wave intensity are dynamically adjusted according to the real-time seabed topography data and the seabed topography prediction algorithm.

[0044] Sonar technology refers to the use of sound waves for detection and ranging, and it has been widely used in seabed topographic mapping and underwater exploration. Traditional fixed-angle sonar systems often suffer from insufficient echo signals or reflection interference in complex seabed environments, leading to decreased mapping accuracy. This invention employs a multi-angle sonar array unit, which, by flexibly adjusting the scanning angle and sound wave intensity, can be adjusted based on real-time seabed topographic data, optimizing mapping results and improving the accuracy of seabed topographic exploration.

[0045] Specifically, in this embodiment of the invention, the multi-angle sonar array unit consists of multiple sonar transmitters and receivers set at different angles. These transmitters and receivers can transmit sound waves simultaneously or sequentially and receive echo signals. By analyzing the echo signals, a topographic image of the seabed can be drawn.

[0046] The multi-angle sonar array unit first performs a basic scan of the seabed using default scanning angles and sound wave intensities to acquire preliminary topographic data. This data is then sent to the analysis module for real-time processing. Using the preliminary topographic data, a predicted topographic model of the area is generated through a seabed topographic prediction algorithm. Based on topographic features (such as steepness and depression depth), the potential complex topographic structures within the area are inferred. The appropriateness of the current sonar array scanning angle is determined based on the predicted topographic features, and the sufficiency of the current sound wave intensity is assessed by real-time monitoring of the echo signal strength. For relatively flat seabed areas, the sonar array can use a larger scanning angle to cover a wider area. For complex topography, such as trenches and faults, the scanning angle is adjusted to a smaller angle, focusing on key areas to ensure high-precision detection of topographic details. When detecting deep-sea areas or highly absorbent silt bottoms, the sound wave intensity is automatically increased to ensure that the sound waves can penetrate the target area and generate a sufficiently strong echo signal. In areas with excessively strong echo signals, the sound wave intensity is reduced to avoid echo interference, ensuring the clarity and accuracy of the detection data.

[0047] Furthermore, the transmission network between the buoy monitoring submodule and the submersible monitoring submodule is set as a distributed sensor network, and the data between all sensors is first synchronized in real time through wireless near-field communication technology, and then the synchronized data is summarized to the analysis module in real time.

[0048] Furthermore, the distributed sensor network is equipped with a dynamic self-organizing mechanism. Based on the faults and performance changes of each monitoring device in the monitoring module, the data transmission path is automatically adjusted through the dynamic self-organizing mechanism, and the data collected by the monitoring module is aggregated to the analysis module in real time through a multi-hop method.

[0049] Wireless near-field transmission technology includes automatically adjusting the operating frequency based on signal interference in the marine environment.

[0050] To ensure stable data transmission between the buoy monitoring submodule and the submersible monitoring submodule, this embodiment of the invention employs a distributed sensor network as the core structure of the transmission network. This network interconnects each sensor node (including various monitoring sensors on the buoy and submersible) to form a self-organizing distributed system.

[0051] In this network, sensor nodes utilize wireless near-field communication (NFC) technology for real-time data synchronization. NFC technology ensures real-time data exchange between nodes through low-power, high-efficiency transmission, making it particularly suitable for short-range data transmission in the complex marine environment described in this invention. Each sensor node first synchronizes its data to ensure that each node possesses the latest hydrological data. Subsequently, the system uses a multi-hop transmission method to progressively aggregate the synchronized data to the master node, and finally transmits it to the analysis module for processing.

[0052] This invention introduces a dynamic self-organizing mechanism into the system. This mechanism can sense the status of each sensor node in the network in real time, including monitoring device faults or performance fluctuations. Once a node fails or its transmission performance degrades, this mechanism automatically adjusts the data transmission path to ensure the continuity and robustness of data transmission.

[0053] As a preferred embodiment of the present invention, the above process specifically includes:

[0054] All sensor nodes establish an initial connection via wireless near-field communication (NFC). At this point, each node automatically identifies its neighboring sensor nodes and determines the communication quality between them using parameters such as signal strength and transmission delay. A transmission topology map is generated in the initial phase, containing the location of each node, information about neighboring nodes, and possible transmission paths. After the initial connection is established, each node performs its first data synchronization to ensure the network is in a known state. After the first data synchronization, each sensor node begins collecting hydrological data and transmitting it via NFC. During this process, nodes not only send their own data but may also act as relay nodes for other nodes, transmitting data to the aggregation node via multi-hop transmission. Multi-hop transmission means that data is not transmitted directly from the source node to the target node but is transmitted step-by-step through a series of relay nodes. By selecting multiple healthy sensor nodes as data relay points, data transmission can continue outside of faulty nodes or areas of signal attenuation.

[0055] Simultaneously, the system monitors the operating status, transmission quality (such as signal strength, latency, packet loss rate, etc.), and remaining battery power of each node to determine the network's health. When a sensor node experiences performance degradation (e.g., insufficient battery power, signal attenuation, or communication interruption), its status is marked as "faulty" or "unstable." Subsequently, based on alternative path information in the topology graph, one or more alternative paths are selected for data transmission. When multiple sensor nodes fail, the data transmission path is automatically adjusted. To ensure optimal transmission paths, the routing cost (including signal strength, transmission latency, bandwidth consumption, etc.) of each path is periodically calculated, and the optimal transmission path is determined using a routing optimization algorithm.

[0056] Furthermore, the system also includes an energy module, which comprises a thermoelectric generator module, a solar generator module, and a wave generator module; wherein the thermoelectric generator module is used to power the submersible monitoring submodule, and the solar generator module and the wave generator module are used to power the buoy monitoring submodule.

[0057] Furthermore, the energy module also includes an energy assessment submodule, which evaluates the power generation efficiency of each power generation module using a power generation capacity assessment algorithm and calculates the available energy of each power generation module.

[0058] In this embodiment of the invention, the purpose of setting up an energy assessment submodule is to assess the power generation efficiency and available energy of different power generation modules in the energy module, so as to ensure that the system can intelligently schedule and optimize energy resources and provide a stable power supply for the buoy monitoring submodule and the submersible monitoring submodule.

[0059] Specifically, the process of assessing power generation capacity using a power generation capacity assessment algorithm includes:

[0060] The system collects parameters from the thermoelectric generator module, including but not limited to the temperature difference between the seabed and the interior of the submersible, the rate of change of the temperature gradient, and the thermal conductivity; it also collects parameters from the solar generator module, including but not limited to the solar radiation intensity, the light absorption efficiency of the buoy surface, and the operating temperature and conversion efficiency of the solar cells; and it collects parameters from the wave generator module, including but not limited to the wave height, frequency, wave kinetic energy conversion efficiency, and the mechanical rotation status of the wave power generation device.

[0061] Preferably, the power generation efficiency of the thermoelectric module is calculated as follows:

[0062]

[0063] Where ΔT is the temperature difference, T avg ZT represents the average temperature, and ZT is the quality factor of the thermoelectric material.

[0064] The power generation efficiency of the solar power module is calculated as follows:

[0065]

[0066] Among them, P out P represents the output power of the solar cell. in This represents the incident light power.

[0067] The power generation efficiency of the wave energy electronic module is calculated as follows:

[0068]

[0069] Where ρ is the density of seawater, g is the gravitational acceleration, H is the wave height, and T is the wave period.

[0070] Calculate the instantaneous power generation of each power generation module and evaluate the cumulative power generation of each power generation module.

[0071] Furthermore, the analysis module performs analysis operations based on the hydrological data collected by the monitoring module to obtain hydrological data analysis results, and the process of determining monitoring adjustment strategies based on the hydrological data analysis results includes:

[0072] A hydrological trend prediction algorithm based on machine learning models historical hydrological data to generate hydrological change trends.

[0073] As a preferred embodiment of the present invention, in the process of analyzing and obtaining hydrological change trends, the present invention combines time series decomposition technology with the LSTM algorithm to process hydrological data X. t Break it down into trend T t Seasonal S t and residual R t Three components, and an adaptive coefficient α is introduced. t β t γ t Different components are modeled independently.

[0074] Specifically, the decomposition formula is constructed as follows:

[0075] X t =α t T t +β t S t +γ t R t

[0076] α t β t γ tThese represent the adaptive weights of the trend, seasonality, and residual components, which change with time t and are used to adjust the impact of different data characteristics on the final hydrological trend prediction.

[0077] For trend component T t Perform multi-scale decomposition to extract short-term trends separately. Medium-term trend and long-term trend components The aim is to capture changes at different time scales.

[0078] Independent prediction of trend components at different time scales is achieved using a multi-scale LSTM model.

[0079]

[0080] The predicted value of the trend component at the future time t+Δt; These are LSTM prediction functions for short-term, medium-term, and long-term trends, respectively; X t-k:t Input historical data from the past k time steps for short-term trend prediction; X t-2k:t Input historical data from the past 2,000 time steps for use in medium-term trend forecasting; X t-3k:t Historical data from the past 3,000 time steps is input for long-term trend prediction.

[0081] For seasonal component S t Establish a periodic model:

[0082]

[0083] φ is the predicted seasonal component value; A is the amplitude, representing the magnitude of the fluctuation; w is the frequency, representing the periodicity of the seasonal component; φ is the phase, used to control the initial position of the seasonal fluctuation.

[0084] Furthermore, to enhance the model's environmental responsiveness, in this embodiment of the invention, an environmental response term is added to the LSTM input. For example, the environmental response term includes temperature, salinity, and water depth, and the adaptive prediction formula is:

[0085]

[0086] Where, η T η S η D These represent the environmental response coefficients for temperature, salinity, and water depth, respectively; T i S i D i This represents the temperature, salinity, and water depth environmental parameters collected by the buoy / submersible monitoring equipment at time t.

[0087] Finally, the predicted values ​​of trend components at each time scale, multi-level seasonal components, and residual components are recombined to generate the final hydrological change trend.

[0088] In this embodiment of the invention, the above operations can significantly improve the accuracy of hydrological change trend prediction results.

[0089] Anomaly detection rules are used to identify hydrological anomalies. These rules are set based on anomaly patterns detected in historical data, data distribution deviations, and sudden events (such as floods or rainstorms). The rules determine whether real-time hydrological data and hydrological trend data exceed the preset thresholds in the anomaly detection rules. If so, the hydrological anomaly data is output.

[0090] Based on hydrological change trends and abnormal data, a multi-objective optimization algorithm is used with a weight allocation mechanism to match the priority of hydrological environment changes with equipment monitoring capabilities to determine the optimal monitoring configuration. Based on the optimal monitoring configuration, a monitoring adjustment strategy is determined. The monitoring adjustment strategy includes the determination of monitoring frequency, monitoring range, and priority monitoring areas. The indicators of equipment monitoring capabilities include, but are not limited to, equipment response speed, data transmission frequency, and energy consumption assessment.

[0091] Furthermore, the collaborative control module is also used to modify the monitoring adjustment strategy determined by the analysis module based on the status and energy consumption of the equipment in the monitoring module and the energy assessment results of the energy module. For example, it ensures that the monitoring needs of high-priority areas are prioritized when power is insufficient. Based on the modified monitoring adjustment strategy, it determines collaborative operation control instructions for the buoy monitoring submodule and the submersible monitoring submodule; the collaborative operation control instructions also include energy consumption allocation control instructions.

[0092] Finally, it should be noted that the marine hydrological monitoring system based on wireless communication disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is used only to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A marine hydrological monitoring system based on wireless communication, characterized in that, The system includes a monitoring module, an analysis module, a collaborative control module, and a wireless communication module; the monitoring module further includes a buoy monitoring submodule and a submersible monitoring submodule; wherein... The buoy monitoring submodule collects hydrological data of the sea surface and different sea layers by being equipped with multi-level sensors. The submersible monitoring submodule is used to collect seabed hydrological data; The analysis module is used to perform analysis operations based on the hydrological data collected by the monitoring module, obtain hydrological change trends and hydrological anomaly data, and use a multi-objective optimization algorithm with a weight allocation mechanism to match the priority of hydrological environment changes with the equipment monitoring capabilities to determine the optimal monitoring configuration. Based on the optimal monitoring configuration, a monitoring adjustment strategy is determined. The monitoring adjustment strategy includes the determination of monitoring frequency, monitoring range, and priority monitoring areas. The collaborative control module is used to correct the monitoring adjustment strategy determined by the analysis module based on the status and energy consumption of the equipment in the monitoring module, and to determine the collaborative work control instructions for the buoy monitoring submodule and the submersible monitoring submodule based on the corrected monitoring adjustment strategy; the collaborative work control instructions include the monitoring range control instructions, monitoring frequency control instructions and monitoring priority control instructions for the buoy monitoring submodule and the submersible monitoring submodule respectively. The wireless communication module is used for real-time communication between the monitoring module, the analysis module, and the collaborative control module. The transmission network between the buoy monitoring submodule and the submersible monitoring submodule is set as a distributed sensor network, and the data between all sensors is first synchronized in real time through wireless near-field communication technology, and then the synchronized data is summarized to the analysis module in real time. The distributed sensor network is equipped with a dynamic self-organizing mechanism. Based on the faults and performance changes of each monitoring device in the monitoring module, the data transmission path is automatically adjusted through the dynamic self-organizing mechanism, and the data collected by the monitoring module is aggregated to the analysis module in real time through a multi-hop method. The wireless near-field communication technology includes automatically adjusting the operating frequency based on signal interference in the marine environment.

2. The marine hydrological monitoring system based on wireless communication according to claim 1, characterized in that, The hydrological data collected by the buoy monitoring submodule includes seawater temperature, salinity, water flow velocity and direction, wave height and period, sea surface height, and water turbidity.

3. The marine hydrological monitoring system based on wireless communication according to claim 1, characterized in that, The hydrological data collected by the submersible monitoring submodule includes seabed temperature, water pressure, salinity, current velocity and direction, seabed topography, and sediment thickness and type.

4. The marine hydrological monitoring system based on wireless communication according to claim 3, characterized in that, The submersible monitoring submodule includes a multi-level water pressure monitoring unit and a multi-angle sonar array unit. The multi-level water pressure monitoring units are respectively installed in different parts of the submersible to monitor water pressure at different depths; The multi-angle sonar array unit is set at different angles of the submersible, and the sweeping angle and sound wave intensity are dynamically adjusted according to the real-time seabed topography data and the seabed topography prediction algorithm.

5. The marine hydrological monitoring system based on wireless communication according to any one of claims 1-4, characterized in that, The system also includes an energy module, which comprises a thermoelectric generator module, a solar generator module, and a wave generator module; wherein the thermoelectric generator module is used to power the submersible monitoring submodule, and the solar generator module and the wave generator module are used to power the buoy monitoring submodule.

6. The marine hydrological monitoring system based on wireless communication according to claim 5, characterized in that, The energy module also includes an energy assessment submodule, which evaluates the power generation efficiency of each power generation module using a power generation capacity assessment algorithm and calculates the available energy of each power generation module.

7. The marine hydrological monitoring system based on wireless communication according to claim 6, characterized in that, The analysis module performs analysis operations based on the hydrological data collected by the monitoring module to obtain hydrological change trends and hydrological anomaly data. The process includes: A hydrological trend prediction algorithm based on machine learning models historical hydrological data to generate hydrological change trends. Anomaly identification rules are used to identify hydrological anomalies.

8. The marine hydrological monitoring system based on wireless communication according to claim 7, characterized in that, The collaborative control module is also used to correct the monitoring and adjustment strategy determined by the analysis module based on the status and energy consumption of the equipment in the monitoring module and the energy assessment results of the energy module, and to determine the collaborative work control instructions for the buoy monitoring submodule and the submersible monitoring submodule based on the corrected monitoring and adjustment strategy; the collaborative work control instructions also include energy consumption allocation control instructions.

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