A long-term mooring test device and method for marine sensor

Abstract

This application belongs to the field of marine observation and monitoring technology, and relates to a long-term mooring test device and method for marine sensors. The method includes: an energy management step: formulating and implementing a graded energy management strategy based on energy supply and demand balance, marine environmental parameters, and test mission requirements, and initiating data acquisition; an anomaly response step: continuously analyzing the changing trends of marine environmental parameters, automatically switching to emergency operating mode and sending alarm information when an anomaly is detected; real-time monitoring of the optical window transmittance of each sensor, and assessing the degree of sensor fouling based on the optical window transmittance; automatically performing anti-fouling treatment when the degree of sensor fouling reaches a preset anti-fouling trigger threshold; otherwise, determining that the sensor is at an acceptable level of fouling, and continuing to monitor transmittance and assess the degree of fouling.

Description

Technical Field

[0001] This application belongs to the field of marine observation and marine monitoring technology, and in particular relates to a long-term mooring test device and method for marine sensors. Background Technology

[0002] As a crucial foundational equipment for marine environmental observation and monitoring, the long-term stable operation capability of marine sensors directly impacts the continuity, reliability, and comparability of marine observation data. However, constrained by complex marine environmental conditions and limitations of existing testing and operational models, the long-term demonstrative application of marine sensors has been a key factor hindering the development of related technologies. Due to the lack of long-term, continuous, and reliable observation data, as well as the absence of comparative test results with internationally advanced marine sensors in real marine environments, my country's independently developed marine sensors still face significant challenges in performance verification, reliability assessment, and international recognition.

[0003] In the field of marine observation, mooring devices are widely used long-term observation platforms. They typically consist of various observation instruments connected to the seabed or ocean floor via cables, and are maintained at a predetermined depth or surface position using underwater or surface buoyancy devices, while being secured to the seabed by heavy anchors. These mooring devices can carry a variety of marine sensors to acquire long-term observational data of the marine environment. The types of sensors involved include, but are not limited to, temperature, salinity, and depth sensors, chlorophyll sensors, dissolved oxygen sensors, nitrate sensors, pH sensors, irradiance sensors, colored dissolved organic matter (CDOM) sensors, and polycyclic aromatic hydrocarbon sensors.

[0004] Existing marine sensor moored observation devices typically rely on external batteries or battery packs for power. However, the available energy of these batteries is limited after deployment, significantly restricting the system's continuous operating time. To ensure the continuous operation of observation missions, batteries need to be replaced or maintained periodically. Battery replacement often requires dispatching dedicated vessels and personnel to the mooring point, resulting in high overall maintenance costs. To extend battery life, current technologies typically reduce power consumption by decreasing the sensor sampling frequency or the number of data transmissions. However, these methods inevitably reduce the amount of data collected and lower the temporal resolution of the observation data, making it difficult to meet the needs of certain detailed studies or emergency event monitoring.

[0005] Furthermore, in applications requiring real-time or near-real-time observation data, acoustic telemetry or inductive data transmission methods often consume significant amounts of energy. This further accelerates battery depletion, shortens the system's effective operating cycle, and consequently increases the frequency of maintenance and overall cost during long-term operation. While connecting marine sensors to seabed observation networks can achieve long-term continuous power supply and data transmission to some extent, the construction and maintenance costs of seabed observation networks are high, and their deployment often makes it difficult to achieve vertical profile observations of the sensors, thus limiting their application scenarios.

[0006] Therefore, in response to the common problems of limited power supply, high maintenance costs, and limited data continuity and observation capabilities in long-term moored observation of marine sensors, it is still urgent to carry out research on observation devices and test methods for long-term autonomous operation to improve the long-term operation capability and data acquisition quality of marine sensors in complex marine environments.

[0007] In conclusion, how to ensure the stable operation of marine sensors under long-term mooring conditions, the efficient use of energy, and the reliable management of the experimental process while guaranteeing the quality of observation data and observation capabilities has become an urgent technical problem to be solved. Summary of the Invention

[0008] To address the aforementioned technical issues, this application proposes a long-term mooring test device and method for marine sensors. By coordinating the management of energy status, environmental status, and sensor status, the device achieves adaptive control of the sensor's operating status during long-term mooring observation, thereby improving the stability, continuity, and data reliability of long-term marine sensor observations.

[0009] To achieve the above objectives, the first aspect of this application provides a method for long-term mooring testing of marine sensors, comprising the following steps: Energy management steps: Based on the energy supply and demand balance, marine environmental parameters, and experimental mission requirements, formulate and implement a tiered energy management strategy, and initiate data acquisition; Anomaly response steps: Analyze the changing trends of marine environmental parameters in real time, and automatically switch to emergency working mode when an anomaly is detected; Contamination monitoring steps: Real-time monitoring of the light transmittance of the optical window of each sensor, and assessment of the degree of contamination of the sensor based on the light transmittance of the optical window. When the degree of contamination of the sensor reaches the preset anti-contamination trigger threshold, anti-contamination treatment is automatically performed; otherwise, the sensor is determined to be at an allowable level of contamination, and the monitoring of light transmittance and assessment of the degree of contamination continue.

[0010] In some embodiments, the method for formulating the tiered energy management strategy is as follows: Based on the supply and demand matching relationship between the expected renewable energy acquisition and energy consumption demand during the test period, a dynamic balance relationship is constructed to characterize the relationship between energy input and consumption. Based on the calculation results of the dynamic equilibrium relationship, the energy state is divided into four levels: abundant state, normal state, energy-saving state and emergency state, and the operating boundary of each level is determined. For different energy state levels, the corresponding sensor operating frequency, data acquisition method and data processing strategy are determined to ensure observation capability and data quality under conditions of abundant or limited energy. The operating parameters corresponding to each energy status level are summarized and configured, and a complete hierarchical energy management strategy is generated.

[0011] In some embodiments, the method for real-time analysis of the changing trends of marine environmental parameters is as follows: Based on the time scale and response characteristics of each marine environmental parameter, a corresponding sliding time window length is set for each type of marine environmental parameter. Within each sliding time window, the arithmetic mean, standard deviation, linear trend coefficient, and extreme value range of each marine environmental parameter are calculated to obtain statistical characteristic quantities used to describe the changing state of marine environmental parameters. By comparing the statistical characteristic quantities with the seasonal benchmark values ​​for the corresponding time periods, the deviation and trend indicators of marine environmental parameters relative to the seasonal benchmark values ​​are obtained. Based on the deviation and trend of marine environmental parameters from seasonal baseline values, preliminary anomaly assessments are made for these parameters. A marine environmental parameter is marked as a candidate anomaly parameter if any of the following preset anomaly criteria are met: The instantaneous measurement value of a marine environmental parameter exceeds the standard deviation of a preset multiple relative to the arithmetic mean of its sliding time window; The rate of change of marine environmental parameters exceeds a preset multiple of the historical baseline rate of change; The marine environmental parameters showed a continuous monotonic change trend within the corresponding sliding time window; The validity of the marked anomalous candidate parameters is verified, and the actual marine environmental anomalies are confirmed through at least one of the following verification methods: Temporal continuity verification, wherein the same marine environmental parameter is marked as an anomalous candidate parameter in at least two consecutive sliding time windows; Physical correlation verification, wherein at least two marine environmental parameters that have a physical correlation are simultaneously marked as anomalous candidate parameters.

[0012] In some embodiments, the method for real-time monitoring of the transmittance of the optical window is as follows: After each data acquisition cycle, the current light intensity values ​​of the blue and red light bands are obtained respectively, and the seawater temperature around the optical window and the internal temperature of the sensor are recorded. Based on the preset temperature response calibration curve, temperature compensation is performed on the current light intensity value; The light intensity value after temperature compensation is compared with the reference light intensity under the initial clean state of the optical window, and the transmittance retention coefficient of the optical window in each band is calculated. When the transmittance retention coefficient of the optical window in any band is lower than the set threshold, an abnormality flag for that band is triggered; when both the blue and red light bands trigger an abnormality flag simultaneously, it is determined that the optical window has been contaminated.

[0013] In some embodiments, the test method further includes the following steps: Predictive optimization steps: Predict the future energy balance state and pollution evolution rate, and optimize the parameter configuration and pollution trigger threshold setting of the graded energy management strategy accordingly; Performance evaluation steps: Regularly evaluate sensor performance, communication quality, energy efficiency, and anti-fouling effect. If any abnormalities are found, generate maintenance recommendations and send them to the shore-based facility or laboratory; otherwise, continue collecting data until the test ends or a termination command is received.

[0014] In some embodiments, the method for predicting the energy balance state is as follows: Historical energy consumption data and historical environmental observation records were collected, and outliers caused by sensor failures or communication anomalies were removed. For time periods with missing data, a similar day substitution method was used to fill in the gaps. The periodic, trend, and random fluctuation components of energy consumption are extracted through spectrum analysis, and combined with environmental factors during the same period to generate a description of energy consumption characteristics. Based on historical environmental observation records, estimate the amount of renewable energy input in the future time period; By combining the current working mode, sensor enable status and data transmission plan, the energy demand distribution in different time periods in the future is predicted, and the energy consumption forecast value is obtained. By comparing the predicted energy consumption with the input of renewable energy, the dynamic changes in energy supply and demand in future time periods can be obtained.

[0015] In some embodiments, the method for predicting the fouling evolution rate is as follows: The data sequence of the optical window transmittance retention coefficient over time was organized and analyzed. By segmented feature identification and stage division, combined with curve fitting and slope change analysis, the key stages of fouling development were determined. Using marine environmental parameters as input variables and the time change rate of the optical window transmittance retention coefficient as the output index, a multiple regression equation was constructed. Based on the newly acquired marine environmental parameters and the current optical window transmittance retention coefficient, the future downward trend of the optical window transmittance retention coefficient is dynamically predicted using the aforementioned multivariate regression equation, and the time it takes for it to reach a preset trigger threshold is estimated.

[0016] In some embodiments, the parameter configuration and pollution prevention trigger threshold setting of the optimized graded energy management strategy include the following steps: Based on the energy balance prediction results and the pollution evolution prediction results, a comprehensive evaluation objective function is constructed to weigh and constrain energy utilization efficiency, data quality, pollution prevention treatment frequency and equipment wear risk. Under the constraint of ensuring data reliability, a multi-objective optimization method is adopted to iteratively solve the acquisition frequency, communication mode, strategy switching threshold and anti-pollution trigger threshold, and obtain the Pareto optimal solution set of energy utilization efficiency and anti-pollution effectiveness. The Pareto optimal solution set is applied to the current working mode, and the relevant parameters are dynamically corrected based on the deviation between the actual observation results and the predicted values.

[0017] In some embodiments, the method for evaluating sensor performance, communication quality, energy efficiency, and anti-fouling effectiveness is as follows: Based on periodically collected sensor accuracy drift, optical window transmittance retention coefficient, communication link success rate and retransmission records, lithium battery pack remaining capacity and energy recharge data, as well as the recovery range and maintenance duration after each anti-fouling treatment, a dataset organized by category is constructed. By utilizing the characteristics of sensor accuracy drift and transmittance attenuation, the measurement reliability is analyzed, and the sensor performance evaluation results are obtained. Based on statistics of communication link success rate, retransmission count and upload latency, and combined with historical communication records, the working status of the link is judged to obtain communication quality assessment results. Based on the changes in the remaining capacity of the lithium battery pack, energy recharge data, and energy consumption information corresponding to each data acquisition mode, the energy use efficiency is quantitatively analyzed to obtain the energy efficiency assessment results. The cleaning effect is evaluated based on the recovery range of light transmittance, recovery efficiency, and maintenance time after antifouling treatment, and the antifouling effectiveness analysis results are obtained.

[0018] A second aspect of this application provides a long-term mooring test apparatus for marine sensors, used to implement the aforementioned long-term mooring test method for marine sensors, comprising: Ocean buoys, as mooring platforms, are used to maintain the stable floating of equipment in marine environments; Argo buoys, equipped with built-in lithium battery packs, are used to carry ocean sensors and perform vertical profile observation missions; An energy module, installed on the ocean buoy, is used to collect renewable energy at sea; The antifouling module, mounted on the Argo buoy, is used to suppress the impact of marine organisms adhering to the sensor's measurement performance. The intelligent control module, located inside the ocean buoy, is used to automate energy management, anomaly response, and fouling monitoring.

[0019] Compared with the prior art, the advantages and positive effects of this application are as follows: 1) By introducing coordinated control of energy management, anomaly response and fouling monitoring during long-term mooring trials, the marine sensors can continue to operate stably under unattended conditions, effectively overcoming the problems of limited operating cycles and reliance on frequent manual maintenance of existing mooring observation devices, and significantly improving long-term continuous observation capabilities.

[0020] 2) By continuously monitoring and responding to changes in marine environmental parameters, the system can automatically switch operating modes and issue alarm information when abnormal conditions occur, thereby reducing the impact of extreme environments on the operational stability of sensors and test equipment, and improving adaptability and safety under complex sea conditions.

[0021] 3) By real-time monitoring of the transmittance of the sensor's optical window and determination of contamination, protective or treatment measures are taken in a timely manner when the contamination reaches a preset threshold, so as to avoid biological attachment or pollution accumulation from causing continuous interference to the measurement accuracy, thereby ensuring the reliability and consistency of long-term observation data.

[0022] 4) By comprehensively managing energy status, environmental anomalies, and pollution status, unnecessary energy consumption and the need for human intervention can be reduced, thereby lowering ship and labor costs associated with battery replacement, equipment recycling, or maintenance operations, and improving the economy and feasibility of long-term mooring trials. Attached Figure Description

[0023] Figure 1 This is one of the overall structural schematic diagrams of the long-term marine sensor testing device in the embodiments of this application; Figure 2 This is the second schematic diagram of the overall structure of the long-term marine sensor testing device in the embodiments of this application; Figure 3 This is the third schematic diagram of the overall structure of the long-term marine sensor testing device in the embodiments of this application; Figure 4 This is one of the structural schematic diagrams of the buoy and sensor integrated unit in the embodiments of this application; Figure 5 This is a second schematic diagram of the structure of the buoy and sensor integrated unit in the embodiments of this application; Figure 6This is the third schematic diagram of the structure of the buoy and sensor integrated unit in the embodiments of this application; Figure 7 This is the fourth schematic diagram of the structure of the buoy and sensor integrated unit in the embodiments of this application; Figure 8 This is a schematic diagram of the high-pressure water jet cleaning system in the embodiments of this application; Figure 9 This is a flowchart of the long-term mooring test method for marine sensors in the embodiments of this application.

[0024] In the picture: 1. Communication module; 2. Wind power generation module; 3. Photovoltaic composite cable; 4. Solar power generation module; 5. Marine buoy; 6. Wave power generation module; 7. Steel wire rope; 8. Anchor block; 9. Buoy and sensor integrated unit; 10. Winch; 11. High-pressure water jet cleaning unit; 12. Fixed mounting bracket; 13. Linear bearing; 9-1 Argo buoy; 9-2 Ultraviolet light source; 9-3 Cleaning brush; 9-4 Lithium battery pack; 9-5 Chlorophyll sensor; 9-6 pH sensor; 9-7 Nitrate sensor; 9-8 Polycyclic aromatic hydrocarbon sensor; 9-9 CDOM sensor; 9-10 Dissolved oxygen sensor; 9-11 Irradiance sensor; 9-12 Temperature, salinity and depth measuring instrument; 11-1, High-pressure nozzle; 11-2, Pipeline; 11-3, Buoyancy material; 11-4, Solenoid valve; 11-5, Peristaltic pump; 11-6, Cleaning fluid storage tank; 11-7, Pressure regulating valve; 11-8, Pressure gauge; 11-9, High-pressure jet pump. Detailed Implementation

[0025] The present application will now be described in detail through exemplary embodiments. However, it should be understood that, without further description, elements, structures, and features in one embodiment may be advantageously incorporated into other embodiments.

[0026] In a broad embodiment of this application, a method for long-term mooring testing of marine sensors includes the following steps: Energy management steps: Based on the energy supply and demand balance, marine environmental parameters, and experimental mission requirements, formulate and implement a tiered energy management strategy, and initiate data acquisition; Anomaly response steps: Analyze the changing trends of marine environmental parameters in real time, and automatically switch to emergency working mode when an anomaly is detected; Contamination monitoring steps: Real-time monitoring of the light transmittance of the optical window of each sensor, and assessment of the degree of contamination of the sensor based on the light transmittance of the optical window. When the degree of contamination of the sensor reaches the preset anti-contamination trigger threshold, anti-contamination treatment is automatically performed; otherwise, the sensor is determined to be at an allowable level of contamination, and the monitoring of light transmittance and assessment of the degree of contamination continue.

[0027] In some embodiments, the method for formulating the tiered energy management strategy is as follows: Based on the supply and demand matching relationship between the expected renewable energy acquisition and energy consumption demand during the test period, a dynamic balance relationship is constructed to characterize the relationship between energy input and consumption. Based on the calculation results of the dynamic equilibrium relationship, the energy state is divided into four levels: abundant state, normal state, energy-saving state and emergency state, and the operating boundary of each level is determined. For different energy state levels, the corresponding sensor operating frequency, data acquisition method and data processing strategy are determined to ensure observation capability and data quality under conditions of abundant or limited energy. The operating parameters corresponding to each energy status level are summarized and configured, and a complete hierarchical energy management strategy is generated.

[0028] In some embodiments, the method for real-time analysis of the changing trends of marine environmental parameters is as follows: Based on the time scale and response characteristics of each marine environmental parameter, a corresponding sliding time window length is set for each type of marine environmental parameter. Within each sliding time window, the arithmetic mean, standard deviation, linear trend coefficient, and extreme value range of each marine environmental parameter are calculated to obtain statistical characteristic quantities used to describe the changing state of marine environmental parameters. By comparing the statistical characteristic quantities with the seasonal benchmark values ​​for the corresponding time periods, the deviation and trend indicators of marine environmental parameters relative to the seasonal benchmark values ​​are obtained. Based on the deviation and trend of marine environmental parameters from seasonal baseline values, preliminary anomaly assessments are made for these parameters. A marine environmental parameter is marked as a candidate anomaly parameter if any of the following preset anomaly criteria are met: The instantaneous measurement value of a marine environmental parameter exceeds the standard deviation of a preset multiple relative to the arithmetic mean of its sliding time window; The rate of change of marine environmental parameters exceeds a preset multiple of the historical baseline rate of change; The marine environmental parameters showed a continuous monotonic change trend within the corresponding sliding time window; The validity of the marked anomalous candidate parameters is verified, and the actual marine environmental anomalies are confirmed through at least one of the following verification methods: Temporal continuity verification, wherein the same marine environmental parameter is marked as an anomalous candidate parameter in at least two consecutive sliding time windows; Physical correlation verification, wherein at least two marine environmental parameters that have a physical correlation are simultaneously marked as anomalous candidate parameters.

[0029] In some embodiments, the method for real-time monitoring of the transmittance of the optical window is as follows: After each data acquisition cycle, the current light intensity values ​​of the blue and red light bands are obtained respectively, and the seawater temperature around the optical window and the internal temperature of the sensor are recorded. Based on the preset temperature response calibration curve, temperature compensation is performed on the current light intensity value; The light intensity value after temperature compensation is compared with the reference light intensity under the initial clean state of the optical window, and the transmittance retention coefficient of the optical window in each band is calculated. When the transmittance retention coefficient of the optical window in any band is lower than the set threshold, an abnormality flag for that band is triggered; when both the blue and red light bands trigger an abnormality flag simultaneously, it is determined that the optical window has been contaminated.

[0030] In some embodiments, the test method further includes the following steps: Predictive optimization steps: Predict the future energy balance state and pollution evolution rate, and optimize the parameter configuration and pollution trigger threshold setting of the graded energy management strategy accordingly; Performance evaluation steps: Regularly evaluate sensor performance, communication quality, energy efficiency, and anti-fouling effect. If any abnormalities are found, generate maintenance recommendations and send them to the shore-based facility or laboratory; otherwise, continue collecting data until the test ends or a termination command is received.

[0031] In some embodiments, the method for predicting the energy balance state is as follows: Historical energy consumption data and historical environmental observation records were collected, and outliers caused by sensor failures or communication anomalies were removed. For time periods with missing data, a similar day substitution method was used to fill in the gaps. The periodic, trend, and random fluctuation components of energy consumption are extracted through spectrum analysis, and combined with environmental factors during the same period to generate a description of energy consumption characteristics. Based on historical environmental observation records, estimate the amount of renewable energy input in the future time period; By combining the current working mode, sensor enable status and data transmission plan, the energy demand distribution in different time periods in the future is predicted, and the energy consumption forecast value is obtained. By comparing the predicted energy consumption with the input of renewable energy, the dynamic changes in energy supply and demand in future time periods can be obtained.

[0032] In some embodiments, the method for predicting the fouling evolution rate is as follows: The data sequence of the optical window transmittance retention coefficient over time was organized and analyzed. By segmented feature identification and stage division, combined with curve fitting and slope change analysis, the key stages of fouling development were determined. Using marine environmental parameters as input variables and the time change rate of the optical window transmittance retention coefficient as the output index, a multiple regression equation was constructed. Based on the newly acquired marine environmental parameters and the current optical window transmittance retention coefficient, the future downward trend of the optical window transmittance retention coefficient is dynamically predicted using the aforementioned multivariate regression equation, and the time it takes for it to reach a preset trigger threshold is estimated.

[0033] In some embodiments, the parameter configuration and pollution prevention trigger threshold setting of the optimized graded energy management strategy include the following steps: Based on the energy balance prediction results and the pollution evolution prediction results, a comprehensive evaluation objective function is constructed to weigh and constrain energy utilization efficiency, data quality, pollution prevention treatment frequency and equipment wear risk. Under the constraint of ensuring data reliability, a multi-objective optimization method is adopted to iteratively solve the acquisition frequency, communication mode, strategy switching threshold and anti-pollution trigger threshold, and obtain the Pareto optimal solution set of energy utilization efficiency and anti-pollution effectiveness. The Pareto optimal solution set is applied to the current working mode, and the relevant parameters are dynamically corrected based on the deviation between the actual observation results and the predicted values.

[0034] In some embodiments, the method for evaluating sensor performance, communication quality, energy efficiency, and anti-fouling effectiveness is as follows: Based on periodically collected sensor accuracy drift, optical window transmittance retention coefficient, communication link success rate and retransmission records, lithium battery pack remaining capacity and energy recharge data, as well as the recovery range and maintenance duration after each anti-fouling treatment, a dataset organized by category is constructed. By utilizing the characteristics of sensor accuracy drift and transmittance attenuation, the measurement reliability is analyzed, and the sensor performance evaluation results are obtained. Based on statistics of communication link success rate, retransmission count and upload latency, and combined with historical communication records, the working status of the link is judged to obtain communication quality assessment results. Based on the changes in the remaining capacity of the lithium battery pack, energy recharge data, and energy consumption information corresponding to each data acquisition mode, the energy use efficiency is quantitatively analyzed to obtain the energy efficiency assessment results. The cleaning effect is evaluated based on the recovery range of light transmittance, recovery efficiency, and maintenance time after antifouling treatment, and the antifouling effectiveness analysis results are obtained.

[0035] The purpose of this application is also to provide a long-term mooring test apparatus for marine sensors, used to implement the above-mentioned long-term mooring test method for marine sensors, including: Ocean buoys, as mooring platforms, are used to maintain the stable floating of equipment in marine environments; Argo buoys, equipped with built-in lithium battery packs, are used to carry ocean sensors and perform vertical profile observation missions; An energy module, installed on the ocean buoy, is used to collect renewable energy at sea; The antifouling module, mounted on the Argo buoy, is used to suppress the impact of marine organisms adhering to the sensor's measurement performance. The intelligent control module, located inside the ocean buoy, is used to automate energy management, anomaly response, and fouling monitoring.

[0036] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0037] like Figures 1 to 3 As shown, this application discloses a long-term mooring test device for marine sensors, comprising: Ocean Buoy 5, as a mooring platform, is used to maintain the stable floating of the device in the marine environment and to provide structural support for various equipment installed on its upper and lower parts; The buoy and sensor integration unit 9, with a built-in lithium battery pack, serves as an underwater mobile profiling observation platform, used to carry marine sensors and perform vertical profiling observation tasks. The energy module, installed on the ocean buoy 5, includes a wind power generation module 2, a solar power generation module 4, and a wave power generation module 6, which are used to collect renewable energy sources at sea and output electricity to each energy-consuming unit. The optical-electric composite cable 3, with a zero-buoyancy structure, connects the marine buoy 5 and the buoy and sensor integrated unit 9 to realize power transmission and data communication. An antifouling module, mounted on the buoy and sensor integration unit 9, is used to suppress the impact of marine organism attachment on the sensor's measurement performance. Communication module 1, installed on the ocean buoy 5, includes Iridium and Beidou communication devices, used to transmit the collected data back to the shore or laboratory; The intelligent control module, located inside the ocean buoy 5, serves as the control center of the entire test device. It is used to achieve automated control of energy management, anomaly response, fouling monitoring, predictive optimization, and performance evaluation, thereby ensuring the stable operation and data quality of long-term mooring tests.

[0038] Specifically, the ocean buoy 5 is equipped with a winch 10 for winding and releasing the optical fiber composite cable 3; the lower end of the ocean buoy 5 is connected to the anchor block 8 via a steel wire rope 7 to fix the entire test device to the seabed.

[0039] The aforementioned long-term mooring test device for marine sensors, as described in this invention, achieves coordinated operation of long-term stable underwater mooring and vertical profile observation through a multi-energy complementary power supply mooring buoy platform, a liftable sensor integrated unit, and an integrated optoelectronic composite cable connection. The device, combined with an intelligent control module, provides unified scheduling for energy management, fouling suppression, anomaly response, and data transmission, effectively improving autonomous operation capabilities and data reliability in complex marine environments, reducing the frequency of manual maintenance, and making it suitable for long-term continuous testing and observation applications of marine sensors.

[0040] like Figures 4 to 7 As shown, the marine sensors include one or more of the following: chlorophyll sensor 9-5, pH sensor 9-6, nitrate sensor 9-7, polycyclic aromatic hydrocarbon sensor 9-8, CDOM sensor 9-9, dissolved oxygen sensor 9-10, irradiance sensor 9-11, and temperature, salinity, and depth measuring instrument 9-12.

[0041] like Figures 4 to 7 As shown, the anti-fouling module includes an ultraviolet light source 9-2, a cleaning brush 9-3, and a high-pressure water jet cleaning unit 11. The high-pressure water jet cleaning unit 11 is fixedly connected to the buoy and sensor integration unit 9 via a fixed mounting bracket 12. The steel wire cable 7 is threaded inside the fixed mounting bracket 12, and a linear bearing 13 is provided at the connection between the steel wire cable 7 and the fixed mounting bracket 12 to reduce the axial friction of the steel wire cable 7 during reciprocating motion.

[0042] like Figure 8 As shown, the high-pressure water jet cleaning unit 11 includes a high-pressure nozzle 11-1, a pipe 11-2, a solenoid valve 11-4, a peristaltic pump 11-5, a cleaning fluid storage tank 11-6, a pressure regulating valve 11-7, a pressure gauge 11-8, and a high-pressure jet pump 11-9. A buoyancy material 11-3 is installed on the outside of the cleaning fluid storage tank 11-6 to achieve near-zero buoyancy. During operation, the peristaltic pump 11-5 continuously draws cleaning fluid from the cleaning fluid storage tank 11-6 and delivers it to the high-pressure jet pump 11-9 for pressurization via the pipe 11-2. The pressurized liquid's outlet pressure is regulated by the pressure regulating valve 11-7 and monitored and regulated in real time by the pressure gauge 11-8. The solenoid valve 11-4 controls the opening of the cleaning channel, and the high-pressure liquid ultimately forms a high-speed concentrated jet through the high-pressure nozzle 11-1 to perform cleaning operations on the target surface.

[0043] The long-term mooring test device for marine sensors described in this invention achieves long-term, stable, and high-precision observation of water quality elements in complex marine environments through modular integration of multiple types of marine sensors and coordinated configuration of multi-level antifouling modules. Employing a graded antifouling strategy combining ultraviolet suppression, mechanical scrubbing, and high-pressure water jetting, it can adaptively clean according to different degrees of fouling, effectively mitigating the impact of biofouling on sensor performance. The high-pressure water jet cleaning unit adopts a near-zero buoyancy design and reduces the frictional resistance of the mooring wire in reciprocating motion through a linear bearing structure, balancing cleaning efficiency and structural reliability. This significantly reduces the maintenance frequency and operational risks of long-term mooring observations while ensuring measurement accuracy.

[0044] like Figure 9 As shown, a method for long-term mooring testing of marine sensors, applied to the aforementioned long-term mooring testing device for marine sensors, includes the following steps: Energy management steps: Based on the energy supply and demand balance, marine environmental parameters, and experimental mission requirements, formulate and implement a tiered energy management strategy, and initiate data acquisition; Anomaly response steps: Analyze the changing trends of marine environmental parameters in real time, and automatically switch to emergency working mode when an anomaly is detected; Contamination monitoring steps: Real-time monitoring of the light transmittance of the optical window of each sensor, and assessment of the degree of contamination of the sensor based on the light transmittance of the optical window. When the degree of contamination of the sensor reaches the preset anti-contamination trigger threshold, anti-contamination treatment is automatically performed; otherwise, the sensor is determined to be at an allowable level of contamination, and the monitoring of light transmittance and assessment of the degree of contamination continue. Predictive optimization steps: Predict the future energy balance state and pollution evolution rate, and optimize the parameter configuration and pollution trigger threshold setting of the graded energy management strategy accordingly; Performance evaluation steps: Regularly evaluate sensor performance, communication quality, energy efficiency, and anti-fouling effect. If any abnormalities are found, generate maintenance recommendations and send them to the shore-based facility or laboratory; otherwise, continue collecting data until the test ends or a termination command is received.

[0045] The long-term mooring test method for marine sensors described in this invention achieves intelligent control of the entire long-term mooring observation process through the coordinated operation of hierarchical energy management, anomaly self-response, fouling status monitoring, and predictive optimization. This transforms the traditional test mode, which relies on manual inspection and passive maintenance, into an autonomous operation mechanism based on state perception and predictive decision-making. This method can dynamically adjust sensor operation and communication strategies under energy-constrained and sea-state changing conditions, ensuring the continuity and data integrity of key observation tasks. By real-time identification of the degree of optical window fouling and adaptive triggering of anti-fouling measures, it effectively maintains sensor measurement accuracy and extends the stable operating cycle. Simultaneously, combined with performance evaluation and remote maintenance suggestion generation mechanisms, it significantly reduces the frequency of manual intervention and operation and maintenance costs, improving the reliability of long-term marine observation and sensor testing.

[0046] Specifically, the method for formulating the tiered energy management strategy is as follows: Based on the supply and demand matching relationship between the expected renewable energy acquisition and energy consumption demand during the test period, a dynamic balance relationship is constructed to characterize the relationship between energy input and consumption. Based on the calculation results of the dynamic equilibrium relationship, the energy state is divided into four levels: abundant state, normal state, energy-saving state and emergency state, and the operating boundary of each level is determined. For different energy state levels, the corresponding sensor operating frequency, data acquisition method and data processing strategy are determined to ensure observation capability and data quality under conditions of abundant or limited energy. The operating parameters corresponding to each energy status level are summarized and configured, and a complete hierarchical energy management strategy is generated.

[0047] The above-mentioned graded energy management strategy of the present invention realizes refined graded management of energy status by constructing a dynamic balance relationship between renewable energy supply and device energy consumption, and adaptively adjusts the sensor operating frequency and data acquisition strategy according to different energy levels, so as to maintain the necessary observation capabilities and data quality under conditions of abundant or limited energy, thereby improving the energy utilization efficiency and operational reliability of long-term moored operation.

[0048] Specifically, the method for real-time analysis of the changing trends of marine environmental parameters is as follows: Based on the time scale and response characteristics of each marine environmental parameter, a corresponding sliding time window length is set for each type of marine environmental parameter. Within each sliding time window, the arithmetic mean, standard deviation, linear trend coefficient, and extreme value range of each marine environmental parameter are calculated to obtain statistical characteristic quantities used to describe the changing state of marine environmental parameters. By comparing the statistical characteristic quantities with the seasonal benchmark values ​​for the corresponding time periods, the deviation and trend indicators of marine environmental parameters relative to the seasonal benchmark values ​​are obtained. Based on the deviation and trend of marine environmental parameters from seasonal baseline values, preliminary anomaly assessments are made for these parameters. A marine environmental parameter is marked as a candidate anomaly parameter if any of the following preset anomaly criteria are met: The instantaneous measurement value of a marine environmental parameter exceeds the standard deviation of a preset multiple relative to the arithmetic mean of its sliding time window; The rate of change of marine environmental parameters exceeds a preset multiple of the historical baseline rate of change; The marine environmental parameters showed a continuous monotonic change trend within the corresponding sliding time window; The validity of the marked anomalous candidate parameters is verified, and the actual marine environmental anomalies are confirmed through at least one of the following verification methods: Temporal continuity verification, wherein the same marine environmental parameter is marked as an anomalous candidate parameter in at least two consecutive sliding time windows; Physical correlation verification, wherein at least two marine environmental parameters that have a physical correlation are simultaneously marked as anomalous candidate parameters.

[0049] The real-time analysis method for marine environmental parameter variation trends described in this invention achieves continuous perception and automatic anomaly identification of the marine environment through multi-timescale sliding window statistical analysis, seasonal benchmark comparison, and a multi-criteria joint verification mechanism. This transforms traditional environmental judgments relying on human experience and post-event analysis into real-time judgments based on statistical characteristics and physical correlations. This method comprehensively utilizes mean, volatility, rate of change, and trend characteristics to effectively distinguish between normal seasonal fluctuations and anomalous disturbances, significantly reducing the risk of false alarms caused by single-threshold discrimination. By introducing time continuity verification and multi-parameter physical correlation verification, reliable confirmation of real marine environmental anomalies is achieved.

[0050] Specifically, the real-time monitoring method for the transmittance of the optical window is as follows: After each data acquisition cycle, the current light intensity values ​​of the blue and red light bands are obtained respectively, and the seawater temperature around the optical window and the internal temperature of the sensor are recorded. Based on the preset temperature response calibration curve, temperature compensation is performed on the current light intensity value; The light intensity value after temperature compensation is compared with the reference light intensity under the initial clean state of the optical window, and the transmittance retention coefficient of the optical window in each band is calculated. When the transmittance retention coefficient of the optical window in any band is lower than the set threshold, an abnormality flag for that band is triggered; when both the blue and red light bands trigger an abnormality flag simultaneously, it is determined that the optical window has been contaminated.

[0051] Specifically, based on the transmittance retention coefficient and its attenuation characteristics, the degree of soiling is divided into four levels: slight soiling, moderate soiling, severe soiling, and extremely severe soiling. Among them: slight soiling corresponds to a transmittance retention coefficient greater than 0.8 and a daily attenuation rate less than 0.05%, which requires no treatment; moderate soiling corresponds to a transmittance retention coefficient between 0.6 and 0.8 or a daily attenuation rate between 0.05% and 2%, which requires the activation of the ultraviolet light source 9-2 for irradiation; severe soiling corresponds to a transmittance retention coefficient between 0.4 and 0.6 or a daily attenuation rate greater than 2% and less than 5%, which requires the activation of the ultraviolet light source 9-2 and the cleaning brush 9-39-3 for coordinated cleaning; extremely severe soiling corresponds to a transmittance retention coefficient less than 0.4, which requires the activation of the high-pressure water jet cleaning unit 11 for rinsing.

[0052] The real-time monitoring and contamination detection method for optical window transmittance described in this invention achieves continuous sensing and quantitative assessment of the sensor's optical window status through multi-band light intensity acquisition, temperature compensation, and benchmark comparison analysis. This transforms traditional contamination detection methods, which rely on periodic manual inspections, into data-driven automatic judgments. The method utilizes a dual-band criterion of blue and red light to effectively suppress the interference of ambient temperature changes on measurement results and accurately identify the contamination formation process. By classifying the degree of contamination and linking it with differentiated anti-contamination measures, it enables on-demand triggering and precise execution of anti-contamination treatment. This ensures sensor measurement accuracy while reducing unnecessary cleaning operations, extending the stable operating cycle of the equipment, and lowering maintenance costs for long-term moored operation.

[0053] Specifically, the method for predicting the energy balance state is as follows: Historical energy consumption data and historical environmental observation records were collected, and outliers caused by sensor failures or communication anomalies were removed. For time periods with missing data, a similar day substitution method was used to fill in the gaps. The periodic, trend, and random fluctuation components of energy consumption are extracted through spectrum analysis, and combined with environmental factors during the same period to generate a description of energy consumption characteristics. Based on historical environmental observation records, estimate the amount of renewable energy input in the future time period; By combining the current working mode, sensor enable status and data transmission plan, the energy demand distribution in different time periods in the future is predicted, and the energy consumption forecast value is obtained. By comparing the predicted energy consumption with the input of renewable energy, the dynamic changes in energy supply and demand in future time periods can be obtained.

[0054] The energy balance state prediction method of this invention achieves a forward-looking assessment of future energy conditions by cleaning and modeling historical energy consumption and environmental data, extracting periodic features, and coordinating supply and demand prediction. This transforms traditional energy management, which relies on experience-based estimation, into data-driven dynamic regulation. The method comprehensively considers the uncertainty of renewable energy input and changes in system operating demands, constructing energy balance evolution trends across multiple time scales to provide a reliable basis for energy strategy adjustments. Real-time updates of energy management parameters based on the prediction results can improve energy efficiency while ensuring energy security, and ensure the stable and continuous operation of long-term moored test equipment under complex sea conditions.

[0055] Specifically, the method for predicting the fouling evolution rate is as follows: The data sequence of the optical window transmittance retention coefficient over time was organized and analyzed. By segmented feature identification and stage division, combined with curve fitting and slope change analysis, the key stages of fouling development were determined. Using marine environmental parameters as input variables and the time change rate of the optical window transmittance retention coefficient as the output index, a multiple regression equation was constructed. Based on the newly acquired marine environmental parameters and the current optical window transmittance retention coefficient, the future downward trend of the optical window transmittance retention coefficient is dynamically predicted using the aforementioned multivariate regression equation, and the time it takes for it to reach a preset trigger threshold is estimated.

[0056] The aforementioned method for predicting the rate of fouling evolution in this invention, through segmented analysis and stage identification of the temporal characteristics of optical window transmittance, combined with an environmental parameter-driven multiple regression model, achieves quantitative characterization and early prediction of fouling development trends, transforming traditional reactive anti-fouling into proactive intervention based on prediction. This method can dynamically estimate the rate of transmittance decrease and the threshold arrival time, providing a basis for optimizing the triggering of anti-fouling strategies, thereby reducing unnecessary cleaning frequency while ensuring measurement accuracy.

[0057] Specifically, the parameter configuration and pollution prevention trigger threshold setting of the optimized graded energy management strategy include the following steps: Based on the energy balance prediction results and the pollution evolution prediction results, a comprehensive evaluation objective function is constructed to weigh and constrain energy utilization efficiency, data quality, pollution prevention treatment frequency and equipment wear risk. Under the constraint of ensuring data reliability, a multi-objective optimization method is adopted to iteratively solve the acquisition frequency, communication mode, strategy switching threshold and anti-pollution trigger threshold, and obtain the Pareto optimal solution set of energy utilization efficiency and anti-pollution effectiveness. The Pareto optimal solution set is applied to the current working mode, and the relevant parameters are dynamically corrected based on the deviation between the actual observation results and the predicted values.

[0058] The aforementioned hierarchical energy management and anti-fouling parameter optimization method of this invention integrates energy balance prediction and pollution evolution prediction results, and employs a multi-objective optimization mechanism to achieve coordinated configuration of data acquisition strategies and anti-fouling thresholds. This transforms traditional fixed parameter settings into adaptive control based on a comprehensive benefit trade-off. While ensuring data quality and system reliability, this method balances energy utilization efficiency, anti-fouling effectiveness, and equipment wear risk. Furthermore, through dynamic correction of optimized parameters, it ensures the continuous and stable operation of long-term moored observation missions under complex environmental conditions.

[0059] Specifically, the methods for evaluating sensor performance, communication quality, energy efficiency, and anti-fouling effectiveness are as follows: Based on periodically collected sensor accuracy drift, optical window transmittance retention coefficient, communication link success rate and retransmission records, lithium battery pack remaining capacity and energy recharge data, as well as the recovery range and maintenance duration after each anti-fouling treatment, a dataset organized by category is constructed. By utilizing the characteristics of sensor accuracy drift and transmittance attenuation, the measurement reliability is analyzed, and the sensor performance evaluation results are obtained. Based on statistics of communication link success rate, retransmission count and upload latency, and combined with historical communication records, the working status of the link is judged to obtain communication quality assessment results. Based on the changes in the remaining capacity of the lithium battery pack, energy recharge data, and energy consumption information corresponding to each data acquisition mode, the energy use efficiency is quantitatively analyzed to obtain the energy efficiency assessment results. The cleaning effect is evaluated based on the recovery range of light transmittance, recovery efficiency, and maintenance time after antifouling treatment, and the antifouling effectiveness analysis results are obtained.

[0060] The above-mentioned system performance comprehensive evaluation method of the present invention realizes the quantitative evaluation and overall judgment of the operating status of the long-term mooring test device of marine sensors by uniformly collecting and analyzing multi-dimensional operating data such as sensor accuracy, communication link, energy use and anti-fouling effect.

[0061] The above embodiments are used to explain this application, not to limit it. Any modifications and changes made to this application within the spirit and scope of the claims shall fall within the protection scope of this application.

Claims

1. A method for long-term mooring test of marine sensors, characterized in that, Includes the following steps: Energy management steps: Based on the energy supply and demand balance, marine environmental parameters, and experimental mission requirements, formulate and implement a tiered energy management strategy, and initiate data acquisition; Anomaly response steps: Analyze the changing trends of marine environmental parameters in real time, and automatically switch to emergency working mode when an anomaly is detected; Contamination monitoring steps: Real-time monitoring of the light transmittance of the optical window of each sensor, and assessment of the degree of contamination of the sensor based on the light transmittance of the optical window. When the degree of contamination of the sensor reaches the preset anti-contamination trigger threshold, anti-contamination treatment is automatically performed; otherwise, the sensor is determined to be at an allowable level of contamination, and the monitoring of light transmittance and assessment of the degree of contamination continue.

2. The long-term mooring test method for marine sensors as described in claim 1, characterized in that, The method for formulating the tiered energy management strategy is as follows: Based on the supply and demand matching relationship between the expected renewable energy acquisition and energy consumption demand during the test period, a dynamic balance relationship is constructed to characterize the relationship between energy input and consumption. Based on the calculation results of the dynamic equilibrium relationship, the energy state is divided into four levels: abundant state, normal state, energy-saving state and emergency state, and the operating boundary of each level is determined. For different energy state levels, the corresponding sensor operating frequency, data acquisition method and data processing strategy are determined to ensure observation capability and data quality under conditions of abundant or limited energy. The operating parameters corresponding to each energy status level are summarized and configured, and a complete hierarchical energy management strategy is generated.

3. The long-term mooring test method for marine sensors as described in claim 1, characterized in that, The method for real-time analysis of the changing trends of marine environmental parameters is as follows: Based on the time scale and response characteristics of each marine environmental parameter, a corresponding sliding time window length is set for each type of marine environmental parameter. Within each sliding time window, the arithmetic mean, standard deviation, linear trend coefficient, and extreme value range of each marine environmental parameter are calculated to obtain statistical characteristic quantities used to describe the changing state of marine environmental parameters. By comparing the statistical characteristic quantities with the seasonal benchmark values ​​for the corresponding time periods, the deviation and trend indicators of marine environmental parameters relative to the seasonal benchmark values ​​are obtained. Based on the deviation and trend of marine environmental parameters from seasonal baseline values, preliminary anomaly assessments are made for these parameters. A marine environmental parameter is marked as a candidate anomaly parameter if any of the following preset anomaly criteria are met: The instantaneous measurement value of a marine environmental parameter exceeds the standard deviation of a preset multiple relative to the arithmetic mean of its sliding time window; The rate of change of marine environmental parameters exceeds a preset multiple of the historical baseline rate of change; The marine environmental parameters showed a continuous monotonic change trend within the corresponding sliding time window; The validity of the marked anomalous candidate parameters is verified, and the actual marine environmental anomalies are confirmed through at least one of the following verification methods: Temporal continuity verification, wherein the same marine environmental parameter is marked as an anomalous candidate parameter in at least two consecutive sliding time windows; Physical correlation verification, wherein at least two marine environmental parameters that have a physical correlation are simultaneously marked as anomalous candidate parameters.

4. The long-term mooring test method for marine sensors as described in claim 1, characterized in that, The real-time monitoring method for the transmittance of the optical window is as follows: After each data acquisition cycle, the current light intensity values ​​of the blue and red light bands are obtained respectively, and the seawater temperature around the optical window and the internal temperature of the sensor are recorded. Based on the preset temperature response calibration curve, temperature compensation is performed on the current light intensity value; The light intensity value after temperature compensation is compared with the reference light intensity under the initial clean state of the optical window, and the transmittance retention coefficient of the optical window in each band is calculated. When the transmittance retention coefficient of the optical window in any band is lower than the set threshold, an abnormality flag for that band is triggered; when both the blue and red light bands trigger an abnormality flag simultaneously, it is determined that the optical window has been contaminated.

5. The long-term mooring test method for marine sensors as described in claim 4, characterized in that, The test method also includes the following steps: Predictive optimization steps: Predict the future energy balance state and pollution evolution rate, and optimize the parameter configuration and pollution trigger threshold setting of the graded energy management strategy accordingly; Performance evaluation steps: Regularly evaluate sensor performance, communication quality, energy efficiency, and anti-fouling effect. If any abnormalities are found, generate maintenance recommendations and send them to the shore-based facility or laboratory; otherwise, continue collecting data until the test ends or a termination command is received.

6. The long-term mooring test method for marine sensors as described in claim 5, characterized in that, The method for predicting the energy balance state is as follows: Historical energy consumption data and historical environmental observation records were collected, and outliers caused by sensor failures or communication anomalies were removed. For time periods with missing data, a similar day substitution method was used to fill in the gaps. The periodic, trend, and random fluctuation components of energy consumption are extracted through spectrum analysis, and combined with environmental factors during the same period to generate a description of energy consumption characteristics. Based on historical environmental observation records, estimate the amount of renewable energy input in the future time period; By combining the current working mode, sensor enable status and data transmission plan, the energy demand distribution in different time periods in the future is predicted, and the energy consumption forecast value is obtained. By comparing the predicted energy consumption with the input of renewable energy, the dynamic changes in energy supply and demand in future time periods can be obtained.

7. The long-term mooring test method for marine sensors as described in claim 5, characterized in that, The method for predicting the rate of fouling evolution is as follows: The data sequence of the optical window transmittance retention coefficient over time was organized and analyzed. By segmented feature identification and stage division, combined with curve fitting and slope change analysis, the key stages of fouling development were determined. Using marine environmental parameters as input variables and the time change rate of the optical window transmittance retention coefficient as the output index, a multiple regression equation was constructed. Based on the newly acquired marine environmental parameters and the current optical window transmittance retention coefficient, the future downward trend of the optical window transmittance retention coefficient is dynamically predicted using the aforementioned multivariate regression equation, and the time it takes for it to reach a preset trigger threshold is estimated.

8. The long-term mooring test method for marine sensors as described in claim 5, characterized in that, The parameter configuration and pollution prevention trigger threshold setting of the optimized hierarchical energy management strategy include the following steps: Based on the energy balance prediction results and the pollution evolution prediction results, a comprehensive evaluation objective function is constructed to weigh and constrain energy utilization efficiency, data quality, pollution prevention treatment frequency and equipment wear risk. Under the constraint of ensuring data reliability, a multi-objective optimization method is adopted to iteratively solve the acquisition frequency, communication mode, strategy switching threshold and anti-pollution trigger threshold, and obtain the Pareto optimal solution set of energy utilization efficiency and anti-pollution effectiveness. The Pareto optimal solution set is applied to the current working mode, and the relevant parameters are dynamically corrected based on the deviation between the actual observation results and the predicted values.

9. The long-term mooring test method for marine sensors as described in claim 5, characterized in that, The method for evaluating sensor performance, communication quality, energy efficiency, and anti-fouling effectiveness is as follows: Based on periodically collected sensor accuracy drift, optical window transmittance retention coefficient, communication link success rate and retransmission records, lithium battery pack remaining capacity and energy recharge data, as well as the recovery range and maintenance duration after each anti-fouling treatment, a dataset organized by category is constructed. By utilizing the characteristics of sensor accuracy drift and transmittance attenuation, the measurement reliability is analyzed, and the sensor performance evaluation results are obtained. Based on statistics of communication link success rate, retransmission count and upload latency, and combined with historical communication records, the working status of the link is judged to obtain communication quality assessment results. Based on the changes in the remaining capacity of the lithium battery pack, energy recharge data, and energy consumption information corresponding to each data acquisition mode, the energy use efficiency is quantitatively analyzed to obtain the energy efficiency assessment results. The cleaning effect is evaluated based on the recovery range of light transmittance, recovery efficiency, and maintenance time after antifouling treatment, and the antifouling effectiveness analysis results are obtained.

10. A long-term mooring test apparatus for marine sensors, used to implement the long-term mooring test method for marine sensors as described in any one of claims 1-9, characterized in that, include: Ocean buoys, as mooring platforms, are used to maintain the stable floating of equipment in marine environments; Argo buoys, equipped with built-in lithium battery packs, are used to carry ocean sensors and perform vertical profile observation missions; An energy module, installed on the ocean buoy, is used to collect renewable energy at sea; The antifouling module, mounted on the Argo buoy, is used to suppress the impact of marine organisms adhering to the sensor's measurement performance. The intelligent control module, located inside the ocean buoy, is used to automate energy management, anomaly response, and fouling monitoring.

Images