Marine hydrological monitoring system based on wireless communication

Abstract

The invention 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. Marine hydrological data are comprehensively monitored based on a monitoring module comprising buoy monitoring and submersible monitoring, and a cooperative monitoring task control instruction of buoy monitoring and submersible monitoring is determined through an analysis module and a cooperative control module. The comprehensive performance, accuracy and effectiveness of hydrological data analysis results are improved, energy consumption in the monitoring process is reduced, in addition, a distributed sensing network is established, stable transmission of monitoring data is achieved through the wireless communication technology, and timeliness and reliability of data transmission are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean hydrological monitoring, and particularly to an ocean hydrological monitoring system based on wireless communication. Background Art

[0002] With the in-depth research on marine environmental protection, resource development and climate change, the importance of ocean hydrological monitoring technology has become increasingly prominent. Traditional ocean hydrological monitoring systems usually rely on single monitoring devices to monitor the ocean 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 ocean monitoring devices have been greatly improved. By combining multiple sensors and using wireless communication technology for data synchronization, ocean hydrological monitoring systems can perform multi-point collection at different depths and ranges. However, they also face problems such as optimization of monitoring device energy consumption, difficulty in coordinating monitoring tasks, and unstable monitoring data transmission. Therefore, achieving all-round and intelligent monitoring of the ocean hydrological environment, improving the energy utilization efficiency of the system, and generating optimized collaborative control strategies are still technical bottlenecks in existing ocean monitoring systems. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an ocean hydrological monitoring system based on wireless communication to solve the problems of limited monitoring range of ocean hydrological data, lack of effective coordination among devices, and unstable data transmission at present, improve the comprehensiveness, accuracy and effectiveness of ocean hydrological data monitoring, and improve energy utilization efficiency.

[0005] The present invention discloses an ocean 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 sub-module and a submersible monitoring sub-module; wherein,

[0006] The buoy monitoring sub-module collects hydrological data on the sea surface and different sea water layers by carrying multi-level sensors;

[0007] The submersible monitoring sub-module is used to collect seabed hydrological data;

[0008] The analysis module is used to perform analysis operations on the hydrological data collected by the monitoring module to obtain hydrological data analysis results, and determine monitoring adjustment strategies according to 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 devices in the monitoring module, and determine the collaborative work control instructions for the buoy monitoring sub-module and the submersible monitoring sub-module 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 sub-module and the submersible monitoring sub-module respectively;

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

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

[0012] Furthermore, the hydrological data collected by the submersible monitoring sub-module includes seabed water temperature, water pressure, salinity, flow velocity and direction of water flow, seabed terrain data, sediment thickness and type.

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

[0014] Among them, the multi-level water pressure monitoring unit is respectively arranged at different parts of the submersible for monitoring the water pressure at different depths;

[0015] The multi-angle sonar array unit is arranged at different angles of the submersible, and dynamically adjusts the scanning angle and acoustic wave intensity according to the seabed terrain data obtained in real time through the seabed terrain prediction algorithm.

[0016] Furthermore, the transmission network between the buoy monitoring sub-module and the submersible monitoring sub-module 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 set with a dynamic self-organization mechanism, which automatically adjusts the data transmission path through the dynamic self-organization mechanism according to the faults and performance changes of each monitoring device in the monitoring module, and summarizes the data collected by the monitoring module to the analysis module in real time through multi-hop;

[0018] The wireless near-field transmission technology includes the operation of automatically adjusting the working frequency according to the signal interference situation of the marine environment.

[0019] Furthermore, the system further includes an energy module, which includes a thermoelectric power generation sub-module, a solar power generation sub-module, and a wave energy power generation sub-module; wherein, the thermoelectric power generation sub-module is used to supply power to the submersible monitoring sub-module, and the solar power generation sub-module and the wave energy power generation sub-module are used to supply power to the buoy monitoring sub-module.

[0020] Furthermore, the energy module further includes an energy evaluation sub-module, which evaluates the power generation efficiency of each power generation sub-module through a power generation capacity evaluation algorithm and calculates the available energy of each power generation sub-module.

[0021] Furthermore, the process of the analysis module performing analysis operations on the hydrological data collected by the monitoring module to obtain hydrological data analysis results and determining a monitoring adjustment strategy based on the hydrological data analysis results includes:

[0022] Performing trend modeling on historical hydrological data based on a machine learning-based hydrological trend prediction algorithm to generate a hydrological change trend;

[0023] Identifying hydrological anomaly data using anomaly identification rules;

[0024] According to the hydrological change trend and anomaly data, using a multi-objective optimization algorithm through a weight allocation mechanism, matching the change priority of the hydrological environment with the device monitoring ability to determine the optimal monitoring configuration, and determining the monitoring adjustment strategy according to the optimal monitoring configuration; wherein, the monitoring adjustment strategy includes the determination of the monitoring frequency, monitoring range, and priority monitoring area.

[0025] Furthermore, the collaborative control module is also used to correct the monitoring adjustment strategy determined by the analysis module based on the status and energy consumption of the devices in the monitoring module and the energy evaluation results of the energy module, and determine the collaborative work control instructions for the buoy monitoring sub-module and the submersible monitoring sub-module based on the corrected monitoring 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:

[0027] The present invention sets up an ocean hydrological monitoring system including a monitoring module, an analysis module, a collaborative control module, an energy module, and a wireless communication module. Based on the monitoring module including buoy monitoring and submersible monitoring, it comprehensively monitors ocean hydrological data, and determines the collaborative monitoring task control instructions for buoy monitoring and submersible monitoring through the analysis module and the collaborative control module, improving the comprehensiveness, accuracy, and effectiveness of hydrological data analysis results while reducing the energy consumption during the monitoring process. In addition, it establishes a distributed sensor network and realizes stable transmission of monitoring data through wireless communication technology, ensuring the timeliness and reliability of data transmission. Description of the Drawings

[0028] The accompanying drawings described herein are used to provide a further understanding of the embodiments of the present invention, and constitute a part of this economic application, and do not constitute a limitation on the embodiments of the present invention. In the drawings:

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

[0030] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0031] Embodiment 1

[0032] The present invention discloses a marine hydrological monitoring system based on wireless communication. Please refer to Figure 1 , Figure 1 is a schematic structural diagram 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 sub-module and a submersible monitoring sub-module; wherein,

[0033] The buoy monitoring sub-module collects hydrological data of the sea surface and different sea water layers by carrying multiple levels of sensors;

[0034] The submersible monitoring sub-module is used to collect seabed hydrological data;

[0035] The analysis module is used to perform analysis operations according to the hydrological data collected by the monitoring module to obtain hydrological data analysis results, and determine a monitoring adjustment strategy according to 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 devices in the monitoring module, and determine collaborative work control instructions for the buoy monitoring sub-module and the submersible monitoring sub-module based on the corrected monitoring adjustment strategy; the collaborative work control instructions include monitoring range control instructions, monitoring frequency control instructions, and monitoring priority control instructions for the buoy monitoring sub-module and the submersible monitoring sub-module respectively;

[0037] The wireless communication module is used to enable the monitoring module, the analysis module, and the collaborative control module to communicate in real time.

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

[0039] Specifically, the buoy monitoring module is usually set to float on the sea surface and collect hydrological data of the sea surface and different water layers in real time through multiple sensors. To comprehensively cover the changes in the marine environment, the buoy monitoring sub-module in the embodiments of the present invention is not limited to collecting data on the sea surface, but also collects hydrological data at different water depths through a sensor array. The sensors installed on the surface of the buoy mainly collect parameters related to the sea surface, 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 ocean meteorology, tides, and wave models. And the sensor array connected to the buoy through a rope or an anchoring system is arranged in water layers at different depths, with a sensor node set at a certain distance to measure parameters such as temperature, salinity, flow velocity, flow direction, and water layer turbidity of different water layers, and construct hydrological profiles at different water depths, etc. The multi-level sensor setting can collect a series of hydrological data from the sea surface to different water depths, helping scientists and engineers understand the dynamic changes in the entire water column.

[0040] The submersible monitoring sub-module collects data on the seabed hydrological environment by setting the submersible to dive to the seabed area. Since there are significant differences between the seabed environment and the surface water area, the submersible is mainly responsible for obtaining parameters related to the seabed environment. By setting monitoring devices such as a water pressure sensor, sonar, temperature and salinity sensor, and sediment monitoring sensor, hydrological data such as water temperature, water pressure, salinity, flow velocity and direction of the water flow, seabed terrain data, sediment thickness and type in the seabed area are collected.

[0041] In the embodiments of the present invention, the combined monitoring setting of the buoy and the submersible forms a complete hydrological monitoring chain from the sea surface to the seabed. The buoy sub-module is responsible for large-scale and routine monitoring of the sea surface and water layers, while the submersible sub-module dives deep into the seabed to collect deep-sea data that is difficult to obtain through the buoy, which can provide more comprehensive marine environment information and improve the accuracy and timeliness of monitoring.

[0042] Furthermore, the submersible monitoring sub-module includes a multi-level water pressure monitoring unit and a multi-angle sonar array unit. Among them, the multi-level water pressure monitoring unit is respectively set at different parts of the submersible to monitor the water pressure at different depths.

[0043] The multi-angle sonar array unit is set at different angles of the submersible, and dynamically adjusts the scanning angle and acoustic wave intensity according to the real-time obtained seabed terrain data through the seabed terrain prediction algorithm.

[0044] Sonar technology refers to the detection and ranging through sound waves, which has been widely used in seabed terrain mapping and underwater detection. In a complex seabed terrain environment, traditional fixed-angle sonar systems often suffer from problems such as insufficient echo signals or reflection interference, resulting in a decline in mapping accuracy. In the embodiments of the present invention, a multi-angle sonar array unit is set up. By flexibly adjusting the scanning angle and sound wave intensity, it can be adjusted according to the real-time terrain data of the seabed, optimizing the mapping effect and improving the accuracy of seabed terrain detection.

[0045] Specifically, in the embodiments of the present invention, the multi-angle sonar array unit is composed of multiple sonar transmitters and receivers set at different angles. These transmitters and receivers can simultaneously or sequentially emit sound waves and receive echo signals, and draw a terrain image of the seabed by analyzing the echo signals.

[0046] The multi-angle sonar array unit first conducts a basic scan of the seabed with a default scanning angle and sound wave intensity, obtains preliminary terrain data, and sends this data to the analysis module for real-time processing. Using the preliminary terrain data, a predicted terrain model of the area is generated through a seabed terrain prediction algorithm. Based on terrain features (such as steepness, depression depth), the potential complex terrain structure within the area is speculated. According to the predicted terrain features, it is judged whether the scanning angle of the current sonar array is appropriate, and whether the current sound wave intensity is sufficient by real-time monitoring of the strength of the echo signal. For relatively flat seabed areas, the sonar array can adopt a larger scanning angle to cover a wider area, while for complex terrains such as trenches and faults, the scanning angle is adjusted smaller to focus on key areas to ensure that the terrain details of these areas are detected with high precision; when detecting deep sea areas or sediment bottoms with strong absorption, the sound wave intensity is automatically increased to ensure that the sound wave can penetrate to the target area and generate a strong enough echo signal, while in areas where the echo signal is too strong, the sound wave intensity is reduced to avoid echo interference and ensure the clarity and accuracy of the detection data.

[0047] Furthermore, the transmission network between the buoy monitoring sub-module and the submersible monitoring sub-module 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 sets up a dynamic self-organization mechanism. According to the failures and performance changes of each monitoring device in the monitoring module, the data transmission path is automatically adjusted through the dynamic self-organization mechanism, and the data collected by the monitoring module is summarized to the analysis module in real time through a multi-hop method;

[0049] The wireless near-field transmission technology includes the operation of automatically adjusting the working frequency according to the signal interference situation in the marine environment.

[0050] To ensure stable data transmission between the buoy monitoring sub-module and the submersible monitoring sub-module, the embodiment of the present invention adopts a distributed sensor network as the core structure of the transmission network. This network forms a self-organizing distributed system by interconnecting each sensor node (including various monitoring sensors on the buoy and the submersible).

[0051] In this network, each sensor node uses wireless near-field communication technology for real-time data synchronization. The wireless near-field communication technology ensures real-time data exchange between nodes with a low-power and high-efficiency transmission method, which is particularly suitable for short-distance data transmission in the complex marine environment of the present invention. Each sensor node first performs data synchronization to ensure that each node holds the latest hydrological data. Subsequently, the system aggregates the synchronized data level by level to the main node through multi-hop transmission and finally transmits it to the analysis module for processing.

[0052] The present invention introduces a dynamic self-organization mechanism into the system. The dynamic self-organization mechanism can real-time sense the states of each sensor node in the network, including the failures or performance fluctuations of the monitoring devices. Once a node fails or its transmission performance deteriorates, this mechanism automatically adjusts the data transmission path to ensure the continuity and robustness of data transmission.

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

[0054] Establish an initial connection for all sensor nodes through wireless near-field communication technology. At this time, each node will automatically identify its neighboring other sensor nodes and determine the communication quality between each other through parameters such as signal strength and transmission delay. And a transmission topology map is generated in the initial stage, which contains the positions of each node, information about neighboring nodes, and possible transmission paths. After the initial connection is completed, each node performs the first data synchronization to ensure that the network is in a known state. After the first data synchronization, each sensor node starts to collect hydrological data and transmits the data through wireless near-field communication technology. During this process, the node not only has to send its own data but may also act as a relay node for other nodes, and transmit the data of other nodes to the aggregation node through multi-hop transmission. Among them, multi-hop transmission means that the data is not directly transmitted from the source node to the target node, but is gradually transmitted through a series of relay nodes. By selecting multiple healthy sensor nodes as the relay points of the data, it is ensured that the data can continue to be transmitted outside the faulty node or the signal attenuation area.

[0055] Meanwhile, monitor the working status of each node, transmission quality (such as signal strength, delay, packet loss rate, etc.), remaining battery power of the node, etc., to judge the health status of the network. When the performance of a certain sensor node deteriorates (such as insufficient power, attenuation of the transmission signal, or communication interruption, etc.), the status of this node will be marked as "fault" or "unstable", and then according to the alternative path information in the topology map, select one or more alternative paths for data transmission. When multiple sensor nodes fail, the data transmission path will be automatically adjusted. To ensure the optimization of the transmission path, set to periodically calculate the routing overhead of each path (including signal strength, transmission delay, bandwidth consumption, etc.), and determine the optimal transmission path through the routing optimization algorithm.

[0056] Furthermore, the system also includes an energy module, and the energy module includes a thermoelectric power generation sub-module, a solar power generation sub-module, and a wave energy power generation sub-module; wherein, the thermoelectric power generation sub-module is used to supply power to the submersible monitoring sub-module, and the solar power generation sub-module and the wave energy power generation sub-module are used to supply power to the buoy monitoring sub-module.

[0057] Furthermore, the energy module also includes an energy evaluation sub-module, and the energy evaluation sub-module evaluates the power generation efficiency of each power generation sub-module through a power generation capacity evaluation algorithm, and calculates the available energy amount of each power generation sub-module.

[0058] In the embodiment of the present invention, the purpose of setting the energy evaluation sub-module is to evaluate the power generation efficiency and available energy amount of different power generation sub-modules in the energy module, ensure that the system can intelligently schedule and optimize energy resources, and provide stable power supply for the buoy monitoring sub-module and the submersible monitoring sub-module.

[0059] Specifically, the process of performing power generation capacity evaluation through the power generation capacity evaluation algorithm includes:

[0060] Collect the parameters of the thermoelectric power generation sub-module, including but not limited to the temperature difference between the seabed and the inside of the submersible, the change rate of the temperature gradient, the heat conduction efficiency, etc.; collect the parameters of the solar power generation sub-module, including but not limited to the solar radiation intensity, the light absorption efficiency of the buoy surface, the working temperature and conversion efficiency of the solar cell; collect the parameters of the wave energy power generation sub-module, including but not limited to the height, frequency of the wave, the conversion efficiency of wave kinetic energy, the mechanical rotation state of the wave energy power generation device, etc.

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

[0062]

[0063] wherein, ΔT is the temperature difference, T avg is the average temperature, and ZT is the figure of merit of the thermoelectric material.

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

[0065]

[0066] Wherein, P out is the output power of the solar cell, and P in is the incident light power.

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

[0068]

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

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

[0071] Furthermore, the process by which the analysis module performs an analysis operation based on the hydrological data collected by the monitoring module, obtains the hydrological data analysis result, and determines the monitoring adjustment strategy according to the hydrological data analysis result includes:

[0072] Perform trend modeling on historical hydrological data based on the hydrological trend prediction algorithm based on machine learning to generate a hydrological change trend.

[0073] As a preferred implementation manner of Embodiment 1 of the present invention, in the process of analyzing the hydrological change trend, the present invention combines the time series decomposition technology on the basis of the LSTM algorithm to split the hydrological data X t into a trend T t , a seasonality S t and a residual R t three components, and introduce adaptive coefficients α t , β t , γ t , and perform independent modeling on different components.

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

[0075] X t =α t T t +β t S t +γ t R t

[0076] α t 、β t 、γ tAdaptive weights representing the trend, seasonal, and residual components respectively, varying with time t, are used to adjust the influence of different data characteristics on the prediction of the final hydrological change trend.

[0077] Perform multi-scale decomposition on the trend component T t to extract the short-term trend medium-term trend and long-term trend components The aim is to capture changes on different time scales.

[0078] Independently predict the trend components on different time scales through a multi-scale LSTM model:

[0079]

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

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

[0082]

[0083] 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 environmental response ability of the model, in the implementation of the present invention, it is set to add an environmental response term to the LSTM input. Exemplarily, 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 represent the environmental response coefficients of temperature, salinity, and water depth respectively; T i 、S i 、D i represent the temperature, salinity, and water depth environmental parameters collected by the buoy or submersible monitoring equipment at time t.

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

[0088] In the embodiment of the present invention, the accuracy of the hydrological change trend prediction result can be greatly improved through the above operations.

[0089] Hydrological anomaly data is identified using anomaly identification rules. Among them, the anomaly identification rules are set based on the anomaly patterns detected in historical data, data distribution deviations, and emergencies (such as the impact of floods or heavy rains), and it is judged whether the real-time hydrological data and hydrological change trend data are higher than the preset threshold in the anomaly identification rules. If so, hydrological anomaly data is output.

[0090] According to the hydrological change trend and anomaly data, using a multi-objective optimization algorithm through a weight allocation mechanism, the change priority of the hydrological environment is matched with the device monitoring ability to determine the optimal monitoring configuration, and the monitoring adjustment strategy is determined according to the optimal monitoring configuration; among them, the monitoring adjustment strategy includes the determination of monitoring frequency, monitoring range, and priority monitoring area; the indicators of device monitoring ability include but are not limited to the response speed of the device, data transmission frequency, energy consumption assessment, etc.

[0091] Furthermore, the collaborative control module is also used to correct the monitoring adjustment strategy determined by the analysis module based on the status and energy consumption of the devices in the monitoring module and the energy assessment results of the energy module. For example, to ensure that the monitoring requirements of high-priority areas are preferentially met in the case of insufficient power. And based on the corrected monitoring adjustment strategy, the collaborative work control instructions for the buoy monitoring sub-module and the submersible monitoring sub-module are determined; the collaborative work control instructions also include energy consumption allocation control instructions.

[0092] Finally, it should be noted that: A marine hydrological monitoring system based on wireless communication disclosed in the embodiments of the present invention only discloses the preferred embodiments of the present invention, which are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention 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 recorded in the foregoing embodiments, or perform equivalent replacements on 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 embodiments of the present invention.

Claims

1. An ocean 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 also includes a buoy monitoring submodule and a submersible monitoring submodule; wherein, The buoy monitoring submodule collects hydrological data of the sea surface and different seawater layers by carrying 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 to obtain hydrological data analysis results, and determine the monitoring adjustment strategy based on the hydrological data analysis results; the hydrological data analysis results include hydrological change trends and hydrological anomaly data; The collaborative control module is 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 determine the collaborative work control instructions for the buoy monitoring submodule and the submersible monitoring submodule based on the modified monitoring adjustment strategy; the collaborative work control instructions include the monitoring range control instructions, monitoring frequency control instructions and monitoring priority control instructions of the buoy monitoring submodule and the submersible monitoring submodule respectively; The wireless communication module is used for real-time communication among the monitoring module, the analysis module and the collaborative control module.

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

3. The ocean hydrological monitoring system based on wireless communication according to claim 1, characterized in that: The hydrological data collected by the submersible monitoring submodule include seabed water temperature, water pressure, salinity, flow rate and direction of water flow, seabed topography data, and sediment thickness and type.

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

5. The ocean hydrological monitoring system based on wireless communication according to claim 4, characterized in that: 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 are synchronized in real time through wireless near-field communication technology, and then the synchronized data are summarized in real time to the analysis module.

6. The ocean hydrological monitoring system based on wireless communication according to claim 5, characterized in that: The distributed sensor network is provided with a dynamic self-organizing mechanism, which automatically adjusts the data transmission path according to the failure and performance change of each monitoring device in the monitoring module through the dynamic self-organizing mechanism, and aggregates the data collected by the monitoring module to the analysis module in real time through a multi-hop method; The wireless near-field transmission technology includes an operation of automatically adjusting the operating frequency according to the signal interference situation of the marine environment.

7. The ocean hydrological monitoring system based on wireless communication according to any one of claims 1 to 6, characterized in that: The system also includes an energy module, which includes a thermoelectric submodule, a solar energy submodule and a wave energy submodule; wherein the thermoelectric submodule is used to power the submersible monitoring submodule, and the solar energy submodule and the wave energy submodule are used to power the buoy monitoring submodule.

8. The ocean hydrological monitoring system based on wireless communication according to claim 7, characterized in that: The energy module also includes an energy evaluation submodule, which evaluates the power generation efficiency of each power generation submodule through a power generation capacity evaluation algorithm and calculates the available energy of each power generation submodule.

9. The ocean hydrological monitoring system based on wireless communication according to claim 8, characterized in that: The analysis module performs analysis operations based on the hydrological data collected by the monitoring module to obtain hydrological data analysis results, and determines the monitoring adjustment strategy based on the hydrological data analysis results. The process includes: The hydrological trend prediction algorithm based on machine learning performs trend modeling on historical hydrological data to generate hydrological change trends; Identify hydrological anomaly data using anomaly identification rules; According to the hydrological change trend and abnormal data, a multi-objective optimization algorithm is used through a weight distribution mechanism to match the change priority of the hydrological environment with the equipment monitoring capability, determine the optimal monitoring configuration, and determine the monitoring adjustment strategy based on the optimal monitoring configuration; among them, the monitoring adjustment strategy includes the determination of the monitoring frequency, monitoring range and priority monitoring areas.

10. The ocean hydrological monitoring system based on wireless communication according to claim 9, characterized in that: The collaborative control module is also 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 the energy evaluation results of the energy module, and determine the collaborative work control instructions for the buoy monitoring sub-module and the submersible monitoring sub-module based on the corrected monitoring adjustment strategy; the collaborative work control instructions also include energy consumption allocation control instructions.

Images