Efficient solar charging method and system suitable for marine environment

Abstract

The invention provides an efficient solar charging method and system suitable for an offshore environment. Wherein the angle of the photovoltaic panel is dynamically adjusted to track the sun position and adapt to ocean dynamic conditions, and optimal illumination absorption configuration is obtained; based on the optimal illumination absorption configuration, performing trend analysis on performance data of the energy storage equipment by adopting a particle swarm optimization algorithm and applying a predictive maintenance analysis technology, and generating a preventive maintenance plan; according to the stable and efficient energy storage strategy, a priority scheduling algorithm is applied, the importance and the emergency degree of electric equipment are ranked, reasonable planning processing is conducted on electric energy distribution, and an emergency power supply plan is obtained; and based on the emergency power supply plan, constructing a remote monitoring and maintenance network, integrating a data link of an Internet of Things sensor and a satellite communication technology, realizing comprehensive real-time monitoring and fault early warning of the working state of the offshore solar power station, and obtaining a reliable operation guarantee system. According to the technical scheme provided by the invention, the illumination absorption efficiency is remarkably improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of solar charging technology, and in particular to a high-efficiency solar charging method and system suitable for marine environments. Background Art

[0002] Offshore facilities such as oil platforms, scientific research stations and communication base stations have extremely high requirements for stable and reliable power supply. These facilities are usually located in sea areas far away from land and cannot be covered by traditional power grids, so they rely on renewable energy systems such as solar energy. However, the marine environment is complex and changeable, including factors such as waves, currents, and meteorological changes, which poses a huge challenge to the efficiency and stability of solar energy systems. In order to ensure efficient and stable operation under different conditions, offshore solar energy systems must have the ability to dynamically adjust the angle of photovoltaic panels to maximize light absorption and be able to flexibly respond to dynamic ocean conditions.

[0003] At present, most offshore solar energy systems use photovoltaic panels installed at fixed angles. Although some systems have introduced simple tracking mechanisms, these mechanisms often lack comprehensive consideration of real-time marine environmental data (such as wave height and water flow speed) and meteorological patterns (such as wind speed and cloud cover changes). In addition, the energy distribution strategy of the energy storage system is relatively simple and fails to make full use of intelligent algorithms for optimization and adjustment, resulting in low energy utilization efficiency under different weather conditions. Existing power distribution methods are also mostly based on static priorities and fail to make dynamic adjustments based on the importance and urgency of actual power-consuming equipment, thus affecting the continued operation of key equipment.

[0004] The existing solutions have the following major defects: due to the lack of adaptive adjustment to the dynamic conditions of the ocean, photovoltaic panels cannot always be in the best light absorption position, especially in the complex and changeable marine environment; the energy distribution strategy of the energy storage system is not intelligent enough, and fails to achieve instantaneous power stability and maximize long-term energy storage efficiency, resulting in energy waste and shortened equipment life; the static power distribution method cannot effectively guarantee the continuous operation of key equipment, especially in emergency situations, and power shortages or interruptions are prone to occur. Summary of the invention

[0005] The embodiments of the present application provide a high-efficiency solar energy charging method and system suitable for marine environments, so as to solve the problem of low light absorption efficiency in the prior art.

[0006] In a first aspect, an embodiment of the present application provides an efficient solar charging method suitable for a marine environment, comprising:

[0007] Dynamically adjust the angle of the photovoltaic panels to track the position of the sun and adapt to the dynamic conditions of the ocean. Combined with real-time meteorological data, ocean currents and wave motion models, the angle of the photovoltaic panels on the floating platform is intelligently adjusted to obtain the optimal light absorption configuration;

[0008] Based on the optimal light absorption configuration, the particle swarm optimization algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy. The predictive maintenance analysis technology is used to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and generate a preventive maintenance plan;

[0009] According to the stable and efficient energy storage strategy, a priority scheduling algorithm is applied to rank the importance and urgency of power-consuming equipment, and combined with the remaining power evaluation results, the power distribution is reasonably planned and processed, and the possible fault points are pre-evaluated using the failure mode and effect analysis technology to obtain an emergency power supply plan;

[0010] Based on the emergency power supply plan, a remote monitoring and maintenance network is constructed, integrating the data link of the Internet of Things sensors and satellite communication technology, so as to realize comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning, and obtain a reliable operation guarantee system.

[0011] Optionally, based on the optimal light absorption configuration, a particle swarm optimization algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy, and predictive maintenance analysis technology is used to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and generate a preventive maintenance plan, including:

[0012] By using the optimal light absorption configuration, combined with real-time ocean conditions and historical weather patterns, the energy distribution scheme between the supercapacitor and the lithium-ion battery pack is optimized to obtain a stable and efficient energy storage strategy; based on the stable and efficient energy storage strategy, a particle swarm optimization algorithm is applied to guide the parameter adjustment of the energy storage system through the individual optimal position and the global optimal position in the iteration process to ensure that instantaneous power stability and long-term energy storage efficiency can be maximized under different conditions, and an optimized energy storage configuration scheme is generated; based on the optimized energy storage configuration scheme, predictive maintenance analysis technology is applied to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and obtain preventive maintenance recommendations; using the preventive maintenance recommendations, a detailed preventive maintenance plan is formulated to ensure the high reliability and long-life operation of the energy storage system, and a preventive maintenance plan is generated.

[0013] Optionally, the optimal light absorption configuration is used in combination with real-time ocean conditions and historical weather patterns to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy, including:

[0014] By utilizing the optimal light absorption configuration, real-time ocean condition data and historical weather pattern information are collected, and these data are comprehensively analyzed to obtain a data set for guiding the optimization of the energy storage system; based on the data set for guiding the optimization of the energy storage system, a particle swarm optimization algorithm is applied to simulate the group behavior in nature, and the parameter adjustment of the energy storage system is guided by the individual optimal position and the global optimal position in the iterative process to ensure that instantaneous power stability and long-term energy storage efficiency can be maximized under different conditions, and an optimized energy storage parameter setting is generated; according to the optimized energy storage parameter setting, combined with the characteristics of supercapacitors and lithium-ion battery packs, the energy distribution ratio between the two is accurately adjusted to ensure that efficient and stable energy storage can be maintained under any circumstances, and a stable and efficient energy storage strategy is obtained.

[0015] Optionally, based on the optimized energy storage configuration scheme, predictive maintenance analysis technology is applied to perform trend analysis on energy storage equipment performance data, identify potential failure risks in advance, and obtain preventive maintenance suggestions, including:

[0016] The optimized energy storage configuration scheme is used to collect and preprocess the performance data of the energy storage equipment in real time, remove noise and outliers, and obtain a clean data set; based on the clean data set, predictive maintenance analysis technology is applied to perform trend analysis on the performance data of the energy storage equipment through time series analysis and machine learning algorithms, identify data patterns or abnormal conditions that may indicate potential failures, and generate potential failure warning signals; based on the potential failure warning signals, combined with historical failure records and equipment operating conditions, risk assessment is performed to quantify the risk level and urgency of potential failures and obtain a detailed risk assessment report; using the detailed risk assessment report, a preventive maintenance plan is formulated to plan specific maintenance time and content to ensure that actions are taken before a failure occurs and to generate preventive maintenance recommendations.

[0017] Optionally, according to the stable and efficient energy storage strategy, a priority scheduling algorithm is applied to rank the importance and urgency of the power-consuming equipment, and the power distribution is reasonably planned and processed in combination with the remaining power evaluation result, and the possible fault points are pre-evaluated using the failure mode and effect analysis technology to obtain an emergency power supply plan, including:

[0018] Utilizing the stable and efficient energy storage strategy, applying the priority scheduling algorithm, all electrical equipment is ranked in importance and urgency according to their functions, roles and importance to offshore facilities, and obtaining an equipment priority list; based on the equipment priority list, the remaining power of the energy storage system is monitored in real time, the current available energy is evaluated, and the power distribution is reasonably planned and processed in combination with the equipment priority information to ensure that key equipment can continue to operate and generate an intelligent power distribution plan; wherein, key equipment includes navigation lights and communication base stations; according to the intelligent power distribution plan, the failure mode and effects analysis technology is used to pre-evaluate possible fault points in the system, identify key factors that may cause power supply interruptions, and generate a fault risk assessment report; using the fault risk assessment report, a detailed emergency power supply plan is formulated for the identified potential fault points, including a backup power supply switching mechanism and load reduction measures, to ensure that basic functions can be maintained in the event of a fault, improve the high fault tolerance of the system, and generate an emergency power supply plan.

[0019] Optionally, according to the intelligent power distribution solution, the failure mode and effect analysis technology is used to pre-evaluate possible fault points in the system, identify key factors that may cause power supply interruption, and generate a fault risk assessment report, including:

[0020] Using the intelligent power distribution solution, a comprehensive review of key equipment and power transmission paths within the system is conducted to identify potential fault points that may affect the stability of power supply and obtain a preliminary list of fault points; based on the preliminary list of fault points, failure mode and effect analysis technology is applied to conduct a detailed risk factor analysis of each fault point, taking into account its probability of occurrence, severity and detection difficulty, and generate a risk factor analysis table; based on the risk factor analysis table, the impact of each fault point on the operation of the entire system is evaluated, including the impact on key equipment and functions, and the impact of potential faults is quantified to obtain an impact assessment result; using the impact assessment results, the risk level of all potential fault points is comprehensively analyzed, key factors that may cause power supply interruptions are identified, and a detailed fault risk assessment report is compiled; based on the fault risk assessment report, corresponding preventive measures and emergency plans are formulated for the identified key factors to ensure rapid response when a fault occurs, ensure high reliability and stability of the system, and generate a fault risk management strategy.

[0021] Optionally, based on the emergency power supply plan, a remote monitoring and maintenance network is constructed, integrating data links of IoT sensors and satellite communication technology, realizing comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning, and obtaining a reliable operation guarantee system, including:

[0022] Based on the emergency power supply plan, IoT sensors and satellite communication technology are integrated to build a data link connecting the shore-based control center and the offshore solar power station, ensuring a stable and efficient data transmission channel and obtaining a complete remote data link architecture; using the remote data link architecture, a comprehensive real-time monitoring system is deployed in the offshore solar power station, and the real-time monitoring system collects multi-dimensional data through distributed IoT sensors, and transmits the data to the shore-based control center in real time to generate a detailed working status report; based on the detailed working status report and combined with the preventive measures formulated in the emergency power supply plan, an intelligent fault warning mechanism is developed, which can automatically analyze the real-time monitoring data, identify abnormal patterns or potential faults, issue alarm signals in advance, and generate fault warning information; based on the fault warning information, targeted preventive maintenance operations are implemented, allowing technicians to remotely configure and update system parameters to ensure the continuous and stable operation of the system and obtain a reliable operation guarantee system.

[0023] In a second aspect, an embodiment of the present application provides a high-efficiency solar charging system suitable for a marine environment, comprising:

[0024] The adjustment module is used to dynamically adjust the angle of the photovoltaic panels to track the position of the sun and adapt to the dynamic conditions of the ocean. It combines real-time meteorological data, ocean currents and wave motion models to intelligently adjust the angle of the photovoltaic panels on the floating platform to obtain the optimal light absorption configuration;

[0025] An optimization module is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack based on the optimal light absorption configuration by using a particle swarm optimization algorithm to obtain a stable and efficient energy storage strategy, and to perform trend analysis on the performance data of the energy storage equipment by using a predictive maintenance analysis technology to identify potential failure risks in advance and generate a preventive maintenance plan;

[0026] A planning module is used to apply a priority scheduling algorithm to rank the importance and urgency of power-consuming equipment according to the stable and efficient energy storage strategy, and to reasonably plan the distribution of power in combination with the remaining power evaluation result, and to pre-evaluate possible fault points using failure mode and effect analysis technology to obtain an emergency power supply plan;

[0027] The monitoring module is used to build a remote monitoring and maintenance network based on the emergency power supply plan, integrate the data link of the Internet of Things sensor and satellite communication technology, realize comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning, and obtain a reliable operation guarantee system.

[0028] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an efficient solar charging method suitable for a marine environment as described in the first aspect.

[0029] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, an efficient solar charging method suitable for a marine environment as described in the first aspect is implemented.

[0030] In the embodiment of the present application, the angle of the photovoltaic panel is dynamically adjusted to track the position of the sun and adapt to the dynamic conditions of the ocean. In combination with real-time meteorological data, ocean currents and wave motion models, the angle of the photovoltaic panel on the floating platform is intelligently adjusted to obtain the optimal light absorption configuration; based on the optimal light absorption configuration, a particle swarm optimization algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy, and predictive maintenance analysis technology is used to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and generate a preventive maintenance plan; according to the stable and efficient energy storage strategy, a priority scheduling algorithm is applied to sort the importance and urgency of the electrical equipment, and combined with the remaining power evaluation results, the power distribution is reasonably planned and processed, and the failure mode and effect analysis technology is used to pre-evaluate possible fault points to obtain an emergency power supply plan; based on the emergency power supply plan, a remote monitoring and maintenance network is constructed, and the data link of the Internet of Things sensor and the satellite communication technology is integrated to realize comprehensive real-time monitoring and fault warning of the working status of the offshore solar power station, and a reliable operation guarantee system is obtained.

[0031] The technical solution of this application has the following beneficial effects:

[0032] The angle of the photovoltaic panel is dynamically adjusted to track the position of the sun and adapt to the dynamic conditions of the ocean. The angle of the photovoltaic panel on the floating platform is intelligently adjusted by combining real-time meteorological data, ocean currents and wave motion models. This method ensures that the photovoltaic panel can absorb solar energy to the maximum extent in different seasons and weather conditions, thereby improving the overall energy collection efficiency; based on the optimal light absorption configuration, the particle swarm optimization (PSO) algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy. This not only ensures the stability of instantaneous power, but also maximizes the long-term energy storage efficiency, reduces energy waste, and identifies potential failure risks in advance through predictive maintenance analysis technology, generates preventive maintenance plans, extends equipment life, and reduces maintenance costs; according to the stable and efficient energy storage strategy, the priority scheduling algorithm is applied to sort the importance and urgency of electrical equipment, and the power distribution is reasonably planned in combination with the remaining power evaluation results. The failure mode and effect analysis (FMEA) technology is used to pre-evaluate possible failure points and formulate emergency power supply plans. This approach ensures the continuous operation of key equipment such as navigation lights and communication base stations, improves the reliability and fault tolerance of the system, and provides a smooth service experience even when the network is in poor condition or an emergency occurs; based on the emergency power supply plan, a remote monitoring and maintenance network is built, integrating the data link of IoT sensors and satellite communication technology to achieve comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning. This measure allows technicians to remotely configure and update system parameters, respond to problems quickly, ensure the long-term stable operation of the system, form a reliable operation guarantee system, greatly improve the system's adaptability and automation level, and reduce unplanned downtime.

[0033] Furthermore, this application optimizes the energy distribution scheme between supercapacitors and lithium-ion battery packs by utilizing the optimal light absorption configuration, combining real-time ocean conditions and historical weather patterns, and using a particle swarm optimization algorithm. This not only achieves the stability of instantaneous power and maximizes long-term energy storage efficiency, generates a stable and efficient energy storage strategy, but also uses predictive maintenance analysis technology to perform trend analysis on energy storage equipment performance data, identifies potential failure risks in advance, and generates a detailed preventive maintenance plan. This method significantly improves the reliability and life of the energy storage system, ensures efficient and stable operation in various complex marine environments, and significantly reduces maintenance costs and unplanned downtime, thereby providing more stable and reliable energy support for offshore facilities.

[0034] Furthermore, the present application utilizes a stable and efficient energy storage strategy, applies a priority scheduling algorithm to rank the importance and urgency of power-consuming equipment, and rationally plans and processes the power distribution in combination with the remaining power evaluation results. Specifically, a list of equipment priorities is generated based on the functions and roles of power-consuming equipment and their importance to offshore facilities to ensure that key equipment such as navigation lights and communication base stations can continue to operate. On this basis, the remaining power of the energy storage system is monitored in real time, the current available energy is evaluated, and the power distribution plan is further optimized to form an intelligent power distribution plan. In addition, the failure mode and effect analysis technology is used to pre-evaluate possible fault points in the system, identify key factors that may cause power supply interruptions, generate a fault risk assessment report, and formulate a detailed emergency power supply plan based on this, including a backup power supply switching mechanism and load reduction measures. This method significantly improves the high fault tolerance of the system, ensuring that basic functions can be maintained in the event of a failure, thereby improving the reliability and stability of offshore facilities in complex environments, reducing unplanned downtime and maintenance costs, and providing offshore facilities with a safer and more stable energy guarantee.

[0035] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 A flow chart of an efficient solar charging method suitable for a marine environment provided in an embodiment of the present application;

[0038] Figure 2 A schematic diagram of the structure of a high-efficiency solar charging system suitable for a marine environment provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0041] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be performed in the order in which they appear in this article or may be performed in parallel. In addition, these processes may include more or fewer operations, and these operations may be performed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0042] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0043] Figure 1 A flow chart of an efficient solar charging method suitable for a marine environment is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0044] Dynamically adjust the angle of the photovoltaic panels to track the position of the sun and adapt to the dynamic conditions of the ocean. Combined with real-time meteorological data, ocean currents and wave motion models, the angle of the photovoltaic panels on the floating platform is intelligently adjusted to obtain the optimal light absorption configuration;

[0045] In this step, the dynamic adjustment of the photovoltaic panel angle includes combining real-time meteorological data (such as wind speed, cloud changes), ocean currents and wave motion models to ensure that the photovoltaic panels on the floating platform are always in the best light absorption position. These data are collected by sensors and input into the intelligent adjustment system to adapt to the ever-changing marine environment and maximize the efficiency of solar energy utilization.

[0046] In the embodiment of the present application, by integrating the global positioning system (GPS) with astronomical algorithms to predict the path of sunlight and combining it with real-time ocean conditions, the intelligent control system not only automatically adjusts the angle of the photovoltaic panels, but also responds to changes in the ocean environment, ensuring maximum light absorption while reducing the impact of mechanical stress on the equipment, ultimately obtaining the optimal light absorption configuration.

[0047] Assuming that the offshore floating solar power station is located in a sea area that often encounters strong winds and high waves, the intelligent regulation system adjusts the angle of the photovoltaic panels every hour based on real-time weather forecasts and sea condition monitoring data, so that the photovoltaic panels always remain in the best light receiving position, ensuring high energy collection efficiency even in severe weather conditions.

[0048] Based on the optimal light absorption configuration, the particle swarm optimization algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy. The predictive maintenance analysis technology is used to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and generate a preventive maintenance plan;

[0049] In this step, the particle swarm optimization (PSO) algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack. The PSO algorithm simulates the group behavior of nature and guides the parameter adjustment of the energy storage system through the individual optimal position and the global optimal position in the iterative process to achieve instantaneous power stability and maximize long-term energy storage efficiency. At the same time, predictive maintenance analysis technology is used to perform trend analysis on the performance data of energy storage equipment, identify potential failure risks in advance, and generate preventive maintenance plans.

[0050] In the embodiment of the present application, on the basis of obtaining the optimal light absorption configuration, the PSO algorithm is applied to optimize the parameters of the energy storage system to ensure efficient and stable energy storage under different conditions. In addition, by analyzing the trend of the performance data of the energy storage equipment, possible failure risks can be identified in advance, and a detailed preventive maintenance plan can be formulated, thereby extending the equipment life and improving the system reliability.

[0051] Assume that before a strong storm arrives, the system detects that there will be continuous cloudy days in the next few days. The energy storage system adjusts the energy distribution ratio of supercapacitors and lithium-ion battery packs through the PSO algorithm, giving priority to storing more energy to cope with long periods of no sunlight. At the same time, predictive maintenance analysis technology finds that a certain energy storage unit may have an overheating problem, and the system automatically generates maintenance recommendations, reminding technicians to check and replace related components in advance.

[0052] According to the stable and efficient energy storage strategy, a priority scheduling algorithm is applied to rank the importance and urgency of power-consuming equipment, and combined with the remaining power evaluation results, the power distribution is rationally planned and processed, and the possible fault points are pre-evaluated using the failure mode and effect analysis technology to obtain an emergency power supply plan;

[0053] In this step, the priority scheduling algorithm is applied to sort the importance and urgency of all power-consuming equipment according to their functions, roles and importance to offshore facilities, and obtain a list of equipment priorities. Combined with the remaining power evaluation results of the energy storage system, the power distribution is rationally planned to ensure that key equipment such as navigation lights and communication base stations can continue to operate. Failure mode and effects analysis (FMEA) technology is used to pre-evaluate possible fault points in the system and generate emergency power supply plans.

[0054] In the embodiment of the present application, according to the stable and efficient energy storage strategy, the priority scheduling algorithm is applied to sort the power-consuming equipment, and the power distribution plan is optimized in combination with the remaining power of the energy storage system monitored in real time to ensure the continuous operation of key equipment. FMEA technology helps identify potential fault points, formulate detailed emergency power supply plans, and improve the fault tolerance of the system.

[0055] Assuming that during nighttime operation, the system detects that the remaining power of the energy storage system is low, the priority scheduling algorithm reallocates power according to the importance and urgency of the equipment, giving priority to the power supply of navigation lights and communication base stations, while reducing the power consumption of non-critical loads. FMEA analysis shows that a certain power module may have a risk of failure, and the system automatically generates an emergency power supply plan and activates the backup power switching mechanism to ensure that key equipment is not affected.

[0056] Based on the emergency power supply plan, a remote monitoring and maintenance network is constructed, integrating the data link of the Internet of Things sensors and satellite communication technology, so as to realize comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning, and obtain a reliable operation guarantee system.

[0057] In this step, a remote monitoring and maintenance network is built, including a data link integrating Internet of Things (IoT) sensors and satellite communication technology, which is used to connect the shore-based control center and the offshore solar power plant to achieve comprehensive real-time monitoring and fault warning. The network supports technicians to remotely configure and update system parameters, quickly respond to possible problems, and ensure the long-term stable operation of the system.

[0058] In the embodiment of the present application, based on the emergency power supply plan, a remote monitoring and maintenance network is constructed, and real-time data transmission is realized through IoT sensors and satellite communication technology, providing comprehensive monitoring and fault warning functions for the working status of the offshore solar power station. Technicians can remotely operate and maintain the system to ensure that it can operate stably in various complex marine environments, forming a reliable operation guarantee system.

[0059] If a solar power station on an offshore platform suddenly fails, the remote monitoring system will immediately send an alarm to the shore-based control center and automatically switch to the backup power supply to maintain the operation of key equipment. The technicians can view real-time data through the remote monitoring platform, quickly diagnose the problem, and remotely adjust the system parameters through satellite communication technology to restore the normal operation of the system, avoiding unnecessary on-site dispatch and improving response speed and efficiency.

[0060] In summary, the present invention covers the entire process from dynamic adjustment of photovoltaic panels, energy distribution optimization, power distribution planning to remote monitoring and maintenance, aiming to provide an intelligent and efficient offshore solar power station solution to meet the needs of offshore facilities for stable and reliable power supply, significantly improve the energy utilization efficiency, stability and reliability of the system, reduce maintenance costs and unplanned downtime, and provide offshore facilities with a safer and more stable energy guarantee.

[0061] It should be noted that, for the above-mentioned solar charging method, a corresponding charging device needs to be additionally configured. In one embodiment, the charging device may include a dynamic photovoltaic panel angle adjustment system, an intelligent energy management system, and a priority scheduling system;

[0062] The dynamic photovoltaic panel angle adjustment system includes a solar cell servo mechanism: the mechanism is equipped with a motor and a control system, which can dynamically adjust the angle of the photovoltaic panel according to the real-time sun position data received. It uses GPS, compass and tilt sensors to determine the azimuth and altitude angles to ensure that the photovoltaic panel is always facing the sun.

[0063] The dynamic photovoltaic panel angle adjustment system also includes a current and wave motion compensator. In order to adapt to the dynamic conditions of the ocean, a buoy-type stabilizer is installed at the bottom of the charging device. Combined with the wave prediction model, the horizontality of the platform is automatically adjusted through hydraulic or electric actuators to reduce the impact of waves.

[0064] Furthermore, the intelligent energy management system includes a particle swarm optimization controller (PSO), which is a part of the electronic control unit and is responsible for calculating the optimal energy distribution plan using a particle swarm optimization algorithm. It is connected to supercapacitors and lithium-ion battery packs for efficient energy storage.

[0065] The intelligent energy management system also includes a predictive maintenance analysis module, which contains an embedded computer and data analysis software to monitor the performance of energy storage equipment and identify potential failure risks. It generates a preventive maintenance plan to ensure the long-term reliability of the system.

[0066] Furthermore, the priority dispatching system includes a power equipment manager, which is responsible for sorting all power equipment on board according to importance and urgency. It receives power evaluation information from the intelligent energy management system and makes reasonable power allocation decisions based on it.

[0067] The priority scheduling system also includes a failure mode and effects analysis (FMEA) module, which is integrated into the power equipment manager to pre-evaluate possible failure points and prepare emergency power supply plans.

[0068] In addition, the charging device needs to be equipped with an IoT sensor gateway and a satellite communication terminal. The IoT sensor gateway collects environmental parameters, equipment status and other information from various sensors installed on the entire charging device, and transmits it to the remote server through wireless communication. The satellite communication terminal is used to maintain contact with the onshore control center when far away from land, ensuring that the working status of the power station can be monitored in real time and fault warnings can be issued in time.

[0069] In summary, the charging device not only includes physical hardware such as photovoltaic panels, servo mechanisms, energy storage batteries, etc., but also includes intelligent control systems and remote communication facilities, aiming to provide reliable and efficient clean energy solutions for offshore facilities.

[0070] In order to solve the problems of low energy utilization efficiency and insufficient equipment reliability of energy storage systems in complex marine environments, in some embodiments, based on the optimal light absorption configuration, a particle swarm optimization algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy, and predictive maintenance analysis technology is used to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and generate a preventive maintenance plan, including:

[0071] By using the optimal light absorption configuration, combined with real-time ocean conditions and historical weather patterns, the energy distribution scheme between the supercapacitor and the lithium-ion battery pack is optimized to obtain a stable and efficient energy storage strategy; based on the stable and efficient energy storage strategy, a particle swarm optimization algorithm is applied to guide the parameter adjustment of the energy storage system through the individual optimal position and the global optimal position in the iteration process to ensure that instantaneous power stability and long-term energy storage efficiency can be maximized under different conditions, and an optimized energy storage configuration scheme is generated; based on the optimized energy storage configuration scheme, predictive maintenance analysis technology is applied to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and obtain preventive maintenance recommendations; using the preventive maintenance recommendations, a detailed preventive maintenance plan is formulated to ensure the high reliability and long-life operation of the energy storage system, and a preventive maintenance plan is generated.

[0072] In this embodiment, the particle swarm optimization (PSO) algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack. This includes combining data on real-time ocean conditions (such as wave height, water flow speed, etc.) and historical weather patterns (such as wind speed, cloud cover changes, etc.) to ensure that the parameter adjustment of the energy storage system can adapt to changing environmental conditions and maximize energy utilization efficiency; predictive maintenance analysis technology is used to analyze the trend of energy storage equipment performance data, identify possible failure risks in advance, and generate a detailed preventive maintenance plan.

[0073] In the embodiments of the present application, firstly, the energy distribution of the energy storage system is preliminarily optimized by combining the optimal light absorption configuration and real-time ocean and weather data; secondly, the PSO algorithm is applied to finely adjust the parameters of the energy storage system according to the individual optimal position and the global optimal position in the iteration process to ensure that instantaneous power stability and long-term energy storage efficiency can be maximized under different conditions; thirdly, based on the optimized energy storage configuration plan, the performance data of the energy storage equipment is deeply analyzed using predictive maintenance analysis technology to discover potential failure risks in advance and generate preventive maintenance recommendations; finally, these preventive maintenance recommendations are used to formulate a detailed preventive maintenance plan to ensure the high reliability and long life of the energy storage system.

[0074] Here is a specific example:

[0075] Assume that an offshore solar power station is located in an area that often encounters extreme weather conditions, and the system detects that there will be continuous cloudy days and strong storms in the next few days. First, the system combines the current optimal light absorption configuration and the upcoming severe weather forecast to perform preliminary energy allocation optimization on the energy storage system to ensure that the power supply of key equipment can be maintained during bad weather; secondly, the PSO algorithm is applied to dynamically adjust the energy allocation ratio between supercapacitors and lithium-ion battery packs according to the simulated optimal path and actual operation conditions to cope with unstable energy input; thirdly, based on the optimized energy storage configuration scheme, the system analyzes the historical performance data of the energy storage equipment and finds that a certain energy storage unit may have overheating problems. It generates preventive maintenance suggestions and reminds technicians to check and replace related components in advance; finally, using the above suggestions, the system automatically generates a detailed preventive maintenance plan and arranges technicians to complete the necessary maintenance work in the shortest time to ensure that the energy storage system can maintain efficient and stable operation under extreme weather conditions. Through the above steps, the offshore solar power station not only improves the energy utilization efficiency, but also significantly enhances the reliability and stability of the system and reduces unplanned downtime.

[0076] In order to solve the problems of low energy utilization efficiency and insufficient equipment reliability of energy storage systems in complex marine environments, in some embodiments, the optimal light absorption configuration is used, combined with real-time ocean conditions and historical weather patterns, to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy, including:

[0077] By utilizing the optimal light absorption configuration, real-time ocean condition data and historical weather pattern information are collected, and these data are comprehensively analyzed to obtain a data set for guiding the optimization of the energy storage system; based on the data set for guiding the optimization of the energy storage system, a particle swarm optimization algorithm is applied to simulate the group behavior in nature, and the parameter adjustment of the energy storage system is guided by the individual optimal position and the global optimal position in the iterative process to ensure that instantaneous power stability and long-term energy storage efficiency can be maximized under different conditions, and an optimized energy storage parameter setting is generated; according to the optimized energy storage parameter setting, combined with the characteristics of supercapacitors and lithium-ion battery packs, the energy distribution ratio between the two is accurately adjusted to ensure that efficient and stable energy storage can be maintained under any circumstances, and a stable and efficient energy storage strategy is obtained.

[0078] In this embodiment, firstly, the optimal light absorption configuration is used to collect real-time ocean condition data (such as wave height, water flow speed) and historical weather pattern information (such as wind speed, cloud cover changes), and these data are comprehensively analyzed to generate a data set for guiding the optimization of the energy storage system; secondly, based on the data set for guiding the optimization of the energy storage system, a particle swarm optimization algorithm is applied to simulate the group behavior in nature, and the parameter adjustment of the energy storage system is guided by the individual optimal position and the global optimal position in the iterative process to ensure that instantaneous power stability and long-term energy storage efficiency can be maximized under different conditions; thirdly, according to the optimized energy storage parameter setting, combined with the characteristics of supercapacitors and lithium-ion battery packs, the energy distribution ratio between the two is accurately adjusted to ensure that efficient and stable energy storage can be maintained under any circumstances; finally, through the above adjustments, a stable and efficient energy storage strategy is finally obtained.

[0079] In the embodiment of the present application, first, the system integrates the optimal light absorption configuration, collects and analyzes real-time ocean condition data and historical weather pattern information, and forms a comprehensive data set for subsequent optimization processing; secondly, based on this data set, the particle swarm optimization algorithm is applied to iteratively adjust the parameters of the energy storage system by simulating the group behavior in nature to find the best energy allocation solution; thirdly, according to the optimized parameter setting, the specific characteristics of the supercapacitor and the lithium-ion battery pack are considered to accurately adjust the energy allocation ratio between the two to ensure the efficient operation of the energy storage system; finally, after a series of optimization steps, the system realizes a stable and efficient energy storage strategy that can maintain instantaneous power stability and maximize long-term energy storage efficiency under various conditions.

[0080] Here is a specific example:

[0081] Assume that an offshore solar power station is located in a sea area that frequently encounters extreme weather conditions. The system needs to ensure that it can still operate efficiently and stably in harsh environments. First, the system uses the optimal light absorption configuration to collect real-time ocean condition data (such as wave height, water flow speed) and historical weather pattern information (such as wind speed, cloud cover changes), and conducts a comprehensive analysis to generate a data set for guiding the optimization of the energy storage system; secondly, based on this data set, the system applies the particle swarm optimization algorithm to simulate the group behavior in nature, and through multiple iterations, finds the optimal setting of the energy storage system parameters to ensure that instantaneous power stability and long-term energy storage efficiency can be maximized under different conditions; thirdly, according to the optimized energy storage parameter setting, the system combines the characteristics of supercapacitors and lithium-ion battery packs to accurately adjust the energy distribution ratio of the two, ensuring that the energy storage system can work efficiently and stably regardless of weather changes; finally, through the above steps, the offshore solar power station not only improves energy utilization efficiency, but also significantly enhances the reliability and stability of the system, reduces unplanned downtime, and ensures the continuous operation of key equipment.

[0082] In order to solve the problems of low energy utilization efficiency and insufficient equipment reliability of energy storage systems in complex marine environments, in some embodiments, based on the optimized energy storage configuration scheme, predictive maintenance analysis technology is applied to perform trend analysis on energy storage equipment performance data, identify potential failure risks in advance, and obtain preventive maintenance suggestions, including:

[0083] The optimized energy storage configuration scheme is used to collect and preprocess the performance data of the energy storage equipment in real time, remove noise and outliers, and obtain a clean data set; based on the clean data set, predictive maintenance analysis technology is applied to perform trend analysis on the performance data of the energy storage equipment through time series analysis and machine learning algorithms, identify data patterns or abnormal conditions that may indicate potential failures, and generate potential failure warning signals; based on the potential failure warning signals, combined with historical failure records and equipment operating conditions, risk assessment is performed to quantify the risk level and urgency of potential failures and obtain a detailed risk assessment report; using the detailed risk assessment report, a preventive maintenance plan is formulated to plan specific maintenance time and content to ensure that actions are taken before a failure occurs and to generate preventive maintenance recommendations.

[0084] In this embodiment, first, the performance data (such as voltage, current, temperature, etc.) of the energy storage device is collected and preprocessed in real time using the optimized energy storage configuration scheme to remove noise and outliers to obtain a clean data set; secondly, based on the clean data set, predictive maintenance analysis technology is applied to perform trend analysis on the performance data of the energy storage device through time series analysis and machine learning algorithms, identify data patterns or abnormal conditions that may indicate potential failures, and generate potential failure warning signals; thirdly, based on the potential failure warning signals, combined with historical failure records and equipment operating conditions, risk assessment is performed to quantify the risk level and urgency of potential failures to obtain a detailed risk assessment report; finally, using the detailed risk assessment report, a preventive maintenance plan is formulated to plan specific maintenance time and content to ensure that actions are taken before a failure occurs and to generate preventive maintenance recommendations.

[0085] In the embodiments of the present application, first, the system collects and pre-processes the performance data of the energy storage equipment in real time according to the optimized energy storage configuration scheme to ensure the accuracy and completeness of the data, thereby forming a clean data set; secondly, based on this clean data set, the system applies predictive maintenance analysis technology, uses time series analysis and machine learning algorithms to conduct in-depth data mining, identifies data patterns or abnormal conditions that may indicate potential faults, and generates potential fault warning signals; thirdly, the system conducts a comprehensive risk assessment based on these warning signals, combined with historical fault records and current equipment operating conditions, quantifies the risk level and urgency of potential faults, and generates a detailed risk assessment report; finally, based on the detailed assessment report, the system automatically generates a preventive maintenance plan, clarifies the specific maintenance time and content, and ensures that maintenance measures are taken in time before a fault occurs to avoid unnecessary downtime and losses.

[0086] Here is a specific example:

[0087] Assuming that an offshore solar power station is located in a sea area with a complex and changeable environment, in order to ensure the long-term stable operation of the energy storage system, the system first collects the performance data of the energy storage equipment (such as voltage, current, temperature, etc.) in real time according to the optimized energy storage configuration plan, and removes noise and outliers through data cleaning and standardization to form a clean data set; secondly, the system applies predictive maintenance analysis technology, uses time series analysis and machine learning algorithms to conduct in-depth analysis of the clean data set, identifies data patterns or abnormal conditions that may indicate potential faults, and generates potential fault warning signals; thirdly, based on these warning signals, the system combines historical fault records and current equipment operating conditions to conduct a comprehensive risk assessment, quantify the risk level and urgency of potential faults, and generate a detailed risk assessment report; finally, based on this report, the system formulates a preventive maintenance plan, clearly stipulates the specific maintenance time and content, ensures that effective maintenance measures are taken before the failure occurs, and ensures the high reliability and long life of the energy storage system. Through the above steps, the offshore solar power station not only improves the reliability of the energy storage system, but also significantly reduces maintenance costs and unplanned downtime, providing more stable energy support for offshore facilities.

[0088] This application takes into account that in order to further improve the instantaneous power stability and long-term energy storage efficiency of the energy storage system in a complex marine environment, the following problems exist in the prior art: First, the traditional energy storage parameter adjustment method fails to fully consider the impact of real-time marine environmental data and historical weather patterns; second, the lack of a dynamic evaluation mechanism for the operating status of the energy storage system leads to low energy storage efficiency; finally, the existing optimization algorithm fails to fully utilize the information of the individual optimal position and the global optimal position, affecting the accuracy of parameter adjustment. Therefore, the embodiment of the present invention proposes this optional solution, which aims to iteratively update the parameters of the energy storage system by introducing a particle swarm optimization (PSO) algorithm, combined with real-time marine environmental data and historical weather patterns, to ensure that instantaneous power stability and long-term energy storage efficiency can be maximized under different conditions.

[0089] Optionally, according to the stable and efficient energy storage strategy, a particle swarm optimization algorithm is applied to guide parameter adjustment of the energy storage system through individual optimal positions and global optimal positions in an iterative process, so as to ensure that instantaneous power stability and long-term energy storage efficiency can be maximized under different conditions, and generate an optimized energy storage configuration plan, including:

[0090] In calculating the energy storage system parameter adjustment value P adj (t) Before, the operating status of the energy storage system needs to be evaluated, analyzed in combination with real-time marine environmental data and historical weather patterns, and the energy storage system parameters need to be iteratively updated using a particle swarm optimization algorithm to ensure instantaneous power stability and maximize long-term energy storage efficiency;

[0091] Padj (t) = α·(P indiv_best (t)-P curr (t))+β·(P global_best (t)-P curr (t))+γ·(E avail (t)-E required (t))

[0092] Among them, P adj (t) represents the adjustment amount of the energy storage system parameters at time t; P indiv_best (t) is the parameter value corresponding to the individual optimal position; P curr (t) is the current parameter value; P global_best (t) is the parameter value corresponding to the global optimal position; E avail (t) is the available energy at time t; E required (t) is the energy required at time t; α, β, γ are weight coefficients used to balance the impact of each part on the adjustment amount;

[0093] After calculating the parameter adjustment amount P adj (t), the adjusted energy storage efficiency E is predicted and evaluated by comprehensively considering the current state of the energy storage system, the difference between available energy and stored energy, and the parameter change rate. eff (t) Provide a basis for generating an optimized configuration plan;

[0094]

[0095] Among them, E eff (t) represents the energy storage efficiency at time t; η is the energy storage efficiency constant; P adj (t) is the adjustment amount of the energy storage system parameters at time t; δ is the energy difference adjustment coefficient; E avail (t) is the available energy at time t; E stored (t) is the energy stored at time t; θ is the stability factor; ρ is the adjustment change influence factor; P prev (t-1) is the parameter adjustment amount at the previous moment t-1;

[0096] In order to obtain the energy storage efficiency E eff (t), the operating procedures are further refined, the actual application effects of the adjustment parameters are verified, and all optimization measures are integrated to form a complete energy storage configuration plan to ensure that the energy storage system can maintain efficient and stable operation under any circumstances.

[0097] The formula is designed to dynamically adjust the parameters of the energy storage system to ensure that it can still operate efficiently and stably in a complex and changing environment. This not only improves the response speed and reliability of the system, but also reduces unplanned downtime and ensures the continuous operation of key equipment.

[0098] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0099] P adj (t) = α·(P indiv_best (t)-P curr (t))+β·(P global_best (t)-P curr (t))+γ·(E avail (t)-E required (t))

[0100] The parameter value P corresponding to the individual optimal position indiv_best (t): reflects the current optimal individual parameter configuration to ensure the local optimal solution; the current parameter value P curr (t): represents the current parameter setting, which is used as a benchmark for adjustment; the parameter value P corresponding to the global optimal position global_best (t): reflects the global optimal solution and ensures the overall optimal configuration; available energy E avail (t): Evaluate the energy supply capacity of the current energy storage system; the required energy E required (t): reflects the energy demand of the equipment to ensure that the actual demand is met; weight coefficients α, β, γ: balance the impact of each part on the adjustment amount to ensure the rationality of the adjustment.

[0101] The following is a brief introduction to how to obtain the parameters of the formula:

[0102] The parameter value P corresponding to the individual optimal position indiv_best (t) is obtained through the iteration of the PSO algorithm; the current parameter value P curr (t) is obtained by real-time monitoring of the system; the parameter value P corresponding to the global optimal position global_best (t) obtained by iterative PSO algorithm; available energy E avail (t) obtained by real-time monitoring; the required energy E required (t) is provided or estimated by the equipment manufacturer; the weight coefficients α, β, and γ are optimized through multiple simulation experiments.

[0103] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0104]

[0105] Energy storage efficiency constant η: reflects the inherent efficiency of the energy storage system and ensures the basic accuracy of the calculation; parameter adjustment amount P adj (t): The calculation result based on the above formula reflects the actual effect of parameter adjustment; Energy difference adjustment coefficient δ: amplifies the impact of energy difference to ensure the rationality of adjustment; Available energy E avail(t): Evaluate the energy supply capacity of the current energy storage system; the stored energy E stored (t): reflects the energy reserve of the current energy storage system; stability factor θ: ensures the stability of the system and prevents over-adjustment; adjustment change impact factor ρ: considers the influence of parameter change rate to ensure the smoothness of adjustment; parameter adjustment amount P at the previous moment prev (t-1): As a reference, evaluate the changing trend of parameter adjustment.

[0106] The following is a brief introduction to how to obtain the parameters of the formula:

[0107] The energy storage efficiency constant η is obtained through experimental testing; the parameter adjustment amount P adj (t) is calculated by formula 1; the energy difference adjustment coefficient δ is set through simulation analysis; the available energy E avail (t) Real-time monitoring to obtain the stored energy E stored (t) is obtained by real-time monitoring of the energy storage system; the stability factor θ is set through simulation analysis; the adjustment change influence factor ρ is set through multiple experimental optimization; the parameter adjustment amount P at the previous moment prev (t-1) is automatically recorded by the system.

[0108] Assuming that an offshore solar power station needs to ensure efficient and stable operation of the energy storage system under severe weather conditions, the system first uses the particle swarm optimization algorithm PSA to adjust the weight coefficient according to the stable and efficient energy storage strategy and calculates the energy storage system parameter adjustment amount P adj (t), where P indiv_best (t)=0.95,P curr (t) = 0.85, P global_best (t)=0.98,E avail (t)=600kW,E required (t) = 550kW, substitute into the formula P adj (t) = 0.4 (0.95-0.85) + 0.3 (0.98-0.85) + 0.3 (600-550) ≈ 17.6; then, the system predicts and evaluates the adjusted energy storage efficiency E eff (t), where P adj (t)=17.6,E avail (t)=600kW,E stored (i)=500kW,P prev (i-1)=15, substitute into the formula Finally, the system is based on the energy storage efficiency E eff(i) Refine the operating procedures, verify the actual application effect of the adjustment parameters, and integrate all optimization measures to form a complete energy storage configuration plan to ensure that the energy storage system can maintain efficient and stable operation under any circumstances. Through the above steps, the offshore solar power station not only improves the instantaneous power stability and long-term energy storage efficiency of the energy storage system, but also significantly enhances the reliability and stability of the system, reduces unplanned downtime, and ensures the continuity of power supply.

[0109] Assume that the threshold is set to 0.9. eff (t)≈0.95 is greater than the set threshold, which indicates that the energy storage system has achieved the expected efficient and stable operation state after adjustment, ensuring that it can still operate efficiently and stably in a complex marine environment and providing more reliable energy support.

[0110] In order to solve the reliability and stability problems of power supply for offshore facilities in complex environments and further improve the continuous operation capability of key equipment, in some embodiments, according to the stable and efficient energy storage strategy, a priority scheduling algorithm is applied to rank the importance and urgency of power-consuming equipment, and combined with the remaining power evaluation results, the power distribution is reasonably planned and processed, and the possible fault points are pre-evaluated using the failure mode and effect analysis technology to obtain an emergency power supply plan, including:

[0111] Utilizing the stable and efficient energy storage strategy, applying the priority scheduling algorithm, all electrical equipment is ranked in importance and urgency according to their functions, roles and importance to offshore facilities, and obtaining an equipment priority list; based on the equipment priority list, the remaining power of the energy storage system is monitored in real time, the current available energy is evaluated, and the power distribution is reasonably planned and processed in combination with the equipment priority information to ensure that key equipment can continue to operate and generate an intelligent power distribution plan; wherein, key equipment includes navigation lights and communication base stations; according to the intelligent power distribution plan, the failure mode and effects analysis technology is used to pre-evaluate possible fault points in the system, identify key factors that may cause power supply interruptions, and generate a fault risk assessment report; using the fault risk assessment report, a detailed emergency power supply plan is formulated for the identified potential fault points, including a backup power supply switching mechanism and load reduction measures, to ensure that basic functions can be maintained in the event of a fault, improve the high fault tolerance of the system, and generate an emergency power supply plan.

[0112] In this embodiment, firstly, the stable and efficient energy storage strategy is used, and the priority scheduling algorithm is applied to rank all the electrical equipment according to their functions, roles and importance to offshore facilities, and to generate an equipment priority list; secondly, based on the equipment priority list, the remaining power of the energy storage system is monitored in real time, the current available energy is evaluated, and the power distribution is reasonably planned and processed in combination with the equipment priority information to ensure that key equipment (such as navigation lights and communication base stations) can continue to operate and generate an intelligent power distribution plan; thirdly, according to the intelligent power distribution plan, the failure mode and effects analysis (FMEA) technology is used to pre-evaluate possible fault points in the system, identify key factors that may cause power supply interruption, and generate a fault risk assessment report; finally, using the fault risk assessment report, a detailed emergency power supply plan is formulated for the identified potential fault points, including a backup power supply switching mechanism and load reduction measures, to ensure that basic functions can be maintained in the event of a fault, improve the high fault tolerance of the system, and generate an emergency power supply plan.

[0113] In the embodiment of the present application, firstly, the system applies a priority scheduling algorithm based on a stable and efficient energy storage strategy, sorts all electrical equipment according to their functions, roles and importance to offshore facilities, and forms an equipment priority list; secondly, the system monitors the remaining power of the energy storage system in real time, and optimizes the distribution of electric energy in combination with the equipment priority information to ensure the continuous operation of key equipment such as navigation lights and communication base stations, and generates an intelligent power distribution plan; thirdly, based on the intelligent power distribution plan, the FMEA technology is used to pre-evaluate possible fault points in the system, identify key factors that may cause power supply interruptions, and generate a detailed fault risk assessment report; finally, based on the fault risk assessment report, the system formulates a detailed emergency power supply plan, including a backup power supply switching mechanism and load reduction measures, to ensure that basic functions can be maintained in the event of a fault, thereby improving the high fault tolerance of the system.

[0114] Here is a specific example:

[0115] Assuming that an offshore oil platform is located in a sea area with a complex and changeable environment, in order to ensure the continuous operation of key equipment such as navigation lights and communication base stations, the system first applies a priority scheduling algorithm based on a stable and efficient energy storage strategy to sort all power-consuming equipment according to their functions, roles and their importance to offshore facilities, and generate an equipment priority list; secondly, the system monitors the remaining power of the energy storage system in real time, and combines the equipment priority information to reasonably plan the power distribution to ensure that key equipment can continue to operate and generate an intelligent power distribution plan; thirdly, based on the intelligent power distribution plan, the system uses FMEA technology to pre-evaluate possible fault points, identify key factors that may cause power supply interruptions, and generate a detailed fault risk assessment report; finally, based on this report, the system has formulated a detailed emergency power supply plan, including a backup power supply switching mechanism and load reduction measures, to ensure that basic functions can be maintained in the event of a fault, thereby improving the system's high fault tolerance. Through the above steps, the offshore oil platform not only improves the continuous operation capability of key equipment, but also significantly enhances the reliability and stability of the entire system, reduces unplanned downtime, and ensures the safety and efficiency of offshore operations.

[0116] In order to solve the risk problem of power supply interruption and further improve the reliability and stability of the system, in some embodiments, according to the intelligent power distribution solution, the failure mode and effect analysis technology is used to pre-evaluate the possible fault points in the system, identify the key factors that may cause power supply interruption, and generate a fault risk assessment report, including:

[0117] Using the intelligent power distribution solution, a comprehensive review of key equipment and power transmission paths within the system is conducted to identify potential fault points that may affect the stability of power supply and obtain a preliminary list of fault points; based on the preliminary list of fault points, failure mode and effect analysis technology is applied to conduct a detailed risk factor analysis of each fault point, taking into account its probability of occurrence, severity and detection difficulty, and generate a risk factor analysis table; based on the risk factor analysis table, the impact of each fault point on the operation of the entire system is evaluated, including the impact on key equipment and functions, and the impact of potential faults is quantified to obtain an impact assessment result; using the impact assessment results, the risk level of all potential fault points is comprehensively analyzed, key factors that may cause power supply interruptions are identified, and a detailed fault risk assessment report is compiled; based on the fault risk assessment report, corresponding preventive measures and emergency plans are formulated for the identified key factors to ensure rapid response when a fault occurs, ensure high reliability and stability of the system, and generate a fault risk management strategy.

[0118] In this embodiment, firstly, the intelligent power distribution scheme is used to comprehensively review the key equipment and power transmission paths in the system, identify potential fault points that may affect the stability of power supply, and obtain a preliminary fault point list; secondly, based on the preliminary fault point list, the FMEA technology is applied to conduct a detailed risk factor analysis of each fault point, considering its probability of occurrence, severity and detection difficulty, and generate a risk factor analysis table; thirdly, based on the risk factor analysis table, the impact of each fault point on the operation of the entire system is evaluated, the impact of the potential fault is quantified, and the impact assessment result is obtained; finally, the impact assessment result is used to comprehensively analyze the risk level of all potential fault points, identify the key factors that may cause power supply interruption, and compile a detailed fault risk assessment report. According to the fault risk assessment report, formulate corresponding preventive measures and emergency plans to ensure rapid response when a fault occurs, ensure the high reliability and stability of the system, and generate a fault risk management strategy.

[0119] In the embodiment of the present application, first, the system comprehensively reviews the key equipment and power transmission paths within the system according to the intelligent power distribution solution, identifies potential fault points that may affect the stability of the power supply, and forms a preliminary fault point list; secondly, for each fault point in the preliminary fault point list, the FMEA technology is used for detailed analysis, considering the probability of occurrence, severity and detection difficulty of the fault, and a risk factor analysis table is generated; thirdly, based on the risk factor analysis table, the impact of each fault point on the operation of the entire system is evaluated, especially the impact on key equipment and functions, the impact of potential faults is quantified, and the impact assessment results are obtained; finally, the risk levels of all potential fault points are comprehensively analyzed, the key factors that may cause power supply interruptions are identified, and a detailed fault risk assessment report is compiled, and then preventive measures and emergency plans are formulated to ensure that the system can respond quickly when a fault occurs and ensure high reliability and stability.

[0120] Here is a specific example:

[0121] Assuming that a certain offshore research station is located in a sea area with a complex and changeable environment, in order to ensure the continuous operation of key equipment such as communication base stations and navigation lights, the system first conducts a comprehensive review of all key equipment and power transmission paths in the station according to the intelligent power distribution solution, identifies potential fault points that may affect the stability of power supply, and forms a preliminary list of fault points; secondly, for each fault point in the preliminary list of fault points, the FMEA technology is used for detailed analysis, considering the probability, severity and detection difficulty of the fault, and a risk factor analysis table is generated; thirdly, based on the risk factor analysis table, the system evaluates the impact of each fault point on the operation of the entire research station, especially the impact on key equipment such as communication base stations and navigation lights, quantifies the impact of potential faults, and obtains detailed impact assessment results; finally, the system comprehensively analyzes the risk level of all potential fault points, identifies the key factors that may cause power supply interruption, and compiles a detailed fault risk assessment report. Based on this report, the system has formulated corresponding preventive measures and emergency plans, including backup power switching mechanisms and load reduction measures, to ensure rapid response when a fault occurs and ensure the high reliability and stability of the system. Through the above steps, the offshore research station not only improved the continuous operation capability of key equipment, but also significantly enhanced the reliability and stability of the entire system, reduced unplanned downtime, and ensured the smooth progress of scientific research work.

[0122] This application takes into account that in order to further improve the efficiency and reliability of power distribution for electrical equipment in offshore facilities, the following problems exist in the prior art: first, the static power distribution method cannot effectively cope with the complex and changeable marine environment; second, the lack of dynamic evaluation of the importance and urgency of electrical equipment may result in insufficient power support for key equipment in emergencies; finally, the existing priority scheduling algorithm fails to fully consider factors such as equipment reliability, distance from the energy center, and real-time energy demand differences. Therefore, the embodiment of the present invention proposes this optional solution, which aims to solve the above technical problems by introducing a stable and efficient energy storage strategy and applying a priority scheduling algorithm (PSA), ensuring the continuous operation of key equipment and optimizing the overall power distribution.

[0123] Optionally, the stable and efficient energy storage strategy is used to apply a priority scheduling algorithm to sort all power-consuming equipment by importance and urgency according to their functions, roles and importance to offshore facilities, and obtain a device priority list, including:

[0124] When calculating the priority P of each electrical equipment device (i) Before, the function, importance, reliability and distance from the energy center of the equipment need to be evaluated, combined with the real-time energy demand and available energy difference; the priority scheduling algorithm PSA is applied to adjust the weight coefficient, laying the foundation for the subsequent priority calculation;

[0125]

[0126] Among them, P device (i) represents the priority of the i-th electrical equipment; F function (i) Score the device functionality; importance (i) rate the importance of the equipment to the offshore facility; urgency (i) Rate the urgency of the equipment; R reliability (i) Score the reliability of the equipment; 1 ,ω 2 ,ω 3 ,ω 4 is the weight coefficient of each factor; E required (i) is the energy required by the equipment; E avail is the current available energy; λ is the energy difference impact factor; D distance (i) is the distance between the equipment and the energy center; γ' is the distance influence factor;

[0127] After calculating the specific priority P of the device device (i) After that, we further analyze the priority distribution, consider the priority difference, time criticality difference and operation cost, and generate the comprehensive priority ranking value R sorted (i) Ensure a thorough assessment of the actual importance and urgency of each device;

[0128]

[0129] Among them, R sorted (i) represents the comprehensive priority ranking value of the i-th electrical equipment; P device (i) is the priority of the i-th electrical equipment; μ is the priority difference magnification coefficient; P avg is the average value of all device priorities; τ is the time criticality difference amplification factor; T time_critical(i) Score the time criticality of the device; T avg is the average value of the time criticality of all devices; v is the time criticality weight; C cost (i) is the equipment operating cost; η' is the cost impact factor; ξ is a constant used to prevent the denominator from being zero;

[0130] After obtaining the comprehensive priority ranking value R sorted (i) After that, all electrical equipment are sorted and applied to actual power distribution to verify whether their performance meets expectations; finally, the optimization measures are integrated to form a complete equipment priority list to ensure that the equipment is reasonably sorted according to importance and urgency to achieve optimal power distribution.

[0131] The formula aims to accurately sort the importance and urgency of electrical equipment and generate the optimal power distribution plan based on the current state of the energy storage system. This not only improves the response speed and reliability of the system, but also reduces unplanned downtime, ensuring efficient and stable operation in complex marine environments.

[0132] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0133]

[0134] Equipment function score F function (i): measure the functional importance of the equipment and ensure that key functional equipment has priority power supply; equipment importance score I importance (i): Assess the importance of the equipment to offshore facilities and ensure the continued operation of key facilities; urgency score U urgency (i): reflects the urgency of the equipment and ensures priority power supply in emergencies; equipment reliability score R reliability (i): Consider the reliability and stability of the equipment and reduce the risk of failure; energy difference influence factor λ·E required (i)-E avail |: Adjust the impact caused by differences in energy demand to ensure instantaneous power stability; distance impact factor γ′·D distance (i): Consider the distance between the equipment and the energy center to reduce transmission losses.

[0135] The following is a brief introduction to how to obtain the parameters of the formula:

[0136] Equipment function score F function (i) Obtained through expert evaluation; Equipment Importance Rating I importance (i) Determined based on the critical role of the equipment to the facility; Urgency score U urgency (i) Set according to the purpose and application scenario of the equipment; Equipment reliability score R reliability (i) Derived from historical maintenance records and performance tests; weight coefficient ω 1 ,ω 2 ,ω 3 ,ω 4 Optimize the settings through multiple simulation experiments; the energy required by the equipment E required (i) Provided by the equipment manufacturer; current available energy E avail Real-time monitoring is obtained; the energy difference influencing factor λ is set through simulation analysis; the distance D between the equipment and the energy center distance (i) Measured by Geographic Information System (GIS); the distance influence factor γ′ is adjusted according to the actual transmission loss rate.

[0137] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0138]

[0139] Device priority P device (i): The calculation result based on the above formula reflects the basic priority of the device; the priority difference magnification coefficient μ·|P device (i)-P avg |: Amplify priority differences to ensure that high-priority devices receive more attention; time criticality difference amplification factor τ. |T time_critical (i)-T avg |: Emphasizes the importance of time-sensitive devices; time criticality score T time_critical (i): Identify time-sensitive equipment and ensure timely power supply; cost impact factor η′·C cost (i): Consider the equipment operating cost and optimize the long-term economic benefits; constant ξ: prevent the denominator from being zero and ensure the stability of calculation.

[0140] The following is a brief introduction to how to obtain the parameters of the formula:

[0141] Device priority P device (i) is obtained by calculation; the priority difference magnification coefficient μ is set through simulation analysis; the average priority value of all devices P avg Automatically counted by the system; the time criticality difference magnification factor τ is optimized through multiple experiments; the time criticality score T time_critical (i) Set according to equipment characteristics; time critical average value T avg Automatically counted by the system; time criticality weight v is set through simulation analysis; equipment operation cost C cost (i) Provided or estimated by the equipment manufacturer; the cost impact factor η′ is adjusted according to the actual situation; the constant ξ is set to a fixed value to prevent the denominator from being zero.

[0142] Assuming that a certain offshore scientific research station needs to ensure the continuous operation of navigation lights and communication base stations, the system first uses a stable and efficient energy storage strategy, applies the priority scheduling algorithm PSA to adjust the weight coefficient, and calculates the priority P of each power-consuming device. device (i), where F function (i)=0.85,I importance (i) = 0.90, U urgency (i)=0.75,R reliability (i)=0.95,E required (i)=500kW,E avail =600kW,D distance (i) = 100m, substitute into the formula Subsequently, the system further analyzes the priority distribution and generates a comprehensive priority ranking value R sorted (i), where P device (i)=0.87,P avg =0.85,T time_critical (i)=0.90,T avg =0.88,C cost (i) = 150 yuan / hour, substitute into the formula Finally, the system ranks the values ​​according to the comprehensive priority R sorted (i) Sort all power-consuming equipment and apply them to actual power distribution to verify whether their performance meets expectations; finally, integrate optimization measures to form a complete equipment priority list to ensure that equipment is reasonably sorted by importance and urgency to achieve optimal power distribution. Through the above steps, the offshore research station not only improves the continuous operation capability of key equipment, but also significantly enhances the reliability and stability of the entire system, reduces unplanned downtime, and ensures the smooth progress of scientific research.

[0143] Assume that the threshold is set to 4.0. Since the result R sorted (i)≈4.03 is greater than the set threshold, which indicates that the device is identified as having a higher priority, ensuring that it can still obtain sufficient power support under limited resources, thereby improving the overall reliability and operating efficiency of the system.

[0144] In order to solve the problem of real-time monitoring and fault warning of offshore solar power stations and further improve the reliability and response speed of the system, in some embodiments, based on the emergency power supply plan, a remote monitoring and maintenance network is constructed, and a data link of IoT sensors and satellite communication technology is integrated to achieve comprehensive real-time monitoring and fault warning of the working status of the offshore solar power station, and obtain a reliable operation guarantee system, including:

[0145] Based on the emergency power supply plan, IoT sensors and satellite communication technology are integrated to build a data link connecting the shore-based control center and the offshore solar power station, ensuring a stable and efficient data transmission channel and obtaining a complete remote data link architecture; using the remote data link architecture, a comprehensive real-time monitoring system is deployed in the offshore solar power station, and the real-time monitoring system collects multi-dimensional data through distributed IoT sensors, and transmits the data to the shore-based control center in real time to generate a detailed working status report; based on the detailed working status report and combined with the preventive measures formulated in the emergency power supply plan, an intelligent fault warning mechanism is developed, which can automatically analyze the real-time monitoring data, identify abnormal patterns or potential faults, issue alarm signals in advance, and generate fault warning information; based on the fault warning information, targeted preventive maintenance operations are implemented, allowing technicians to remotely configure and update system parameters to ensure the continuous and stable operation of the system and obtain a reliable operation guarantee system.

[0146] In this embodiment, firstly, based on the emergency power supply plan, IoT sensors and satellite communication technology are integrated to build a data link connecting the shore-based control center and the offshore solar power station, ensuring a stable and efficient data transmission channel and obtaining a complete remote data link architecture; secondly, using the remote data link architecture, a comprehensive real-time monitoring system is deployed in the offshore solar power station. The real-time monitoring system collects multi-dimensional data through distributed IoT sensors and transmits it to the shore-based control center in real time to generate a detailed working status report; thirdly, based on the detailed working status report and in combination with the preventive measures formulated in the emergency power supply plan, an intelligent fault warning mechanism is developed. The intelligent fault warning mechanism can automatically analyze real-time monitoring data, identify abnormal patterns or potential faults, issue alarm signals in advance, and generate fault warning information; finally, based on the fault warning information, targeted preventive maintenance operations are implemented, allowing technicians to remotely configure and update system parameters to ensure the continuous and stable operation of the system and obtain a reliable operation guarantee system.

[0147] In the embodiment of the present application, firstly, the system integrates IoT sensors and satellite communication technology based on the emergency power supply plan, builds a data link connecting the shore-based control center and the offshore solar power station, ensures the stability and efficiency of data transmission, and forms a complete remote data link architecture; secondly, the system uses this remote data link architecture to deploy a comprehensive real-time monitoring system in the offshore solar power station, collects multi-dimensional data including temperature, humidity, wind speed, equipment status, etc. through distributed IoT sensors, and transmits it to the shore-based control center in real time to generate a detailed working status report; thirdly, the system develops an intelligent fault warning mechanism based on these detailed working status reports and the preventive measures formulated in the emergency power supply plan, which can automatically analyze the real-time monitoring data, identify abnormal patterns or potential faults, issue alarm signals in advance, and generate fault warning information; finally, based on the fault warning information, the system implements targeted preventive maintenance operations, allowing technicians to remotely configure and update system parameters to ensure the continuous and stable operation of the system, and ultimately establish a reliable operation guarantee system.

[0148] Here is a specific example:

[0149] Assuming that an offshore wind farm is equipped with a solar power station, in order to ensure its reliable operation in a complex marine environment, the system first integrates IoT sensors and satellite communication technology based on the emergency power supply plan, builds a data link connecting the shore-based control center and the offshore solar power station, ensures the stability and efficiency of data transmission, and forms a complete remote data link architecture; secondly, the system uses this remote data link architecture to deploy a comprehensive real-time monitoring system in the offshore solar power station, collects multi-dimensional data such as temperature, humidity, wind speed, and equipment status through distributed IoT sensors, and transmits it to the shore-based control center in real time to generate a detailed working status report; thirdly, based on these detailed working status reports and the preventive measures formulated in the emergency power supply plan, the system has developed an intelligent fault warning mechanism, which can automatically analyze real-time monitoring data, identify abnormal patterns or potential faults, issue alarm signals in advance, and generate fault warning information; finally, based on these fault warning information, the system implements targeted preventive maintenance operations, allowing technicians to remotely configure and update system parameters to ensure the continuous and stable operation of the system. Through the above steps, the offshore wind farm not only improves the reliability of the solar power station, but also significantly enhances the system's response speed and maintenance efficiency, reduces unplanned downtime, and ensures the efficient operation of the entire wind farm.

[0150] Figure 2 The present application provides a schematic diagram of a high-efficiency solar charging system suitable for marine environments, such as Figure 2 As shown, the device comprises:

[0151] The adjustment module 21 is used to dynamically adjust the angle of the photovoltaic panels to track the position of the sun and adapt to the dynamic conditions of the ocean. It combines real-time meteorological data, ocean currents and wave motion models to intelligently adjust the angle of the photovoltaic panels on the floating platform to obtain the optimal light absorption configuration;

[0152] The optimization module 22 is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack based on the optimal light absorption configuration by using a particle swarm optimization algorithm to obtain a stable and efficient energy storage strategy, and to perform trend analysis on the performance data of the energy storage equipment by using a predictive maintenance analysis technology to identify potential failure risks in advance and generate a preventive maintenance plan;

[0153] The planning module 23 is used to apply the priority scheduling algorithm according to the stable and efficient energy storage strategy, sort the importance and urgency of the power-consuming equipment, and reasonably plan the power distribution in combination with the remaining power evaluation result, and use the failure mode and effect analysis technology to pre-evaluate the possible fault points to obtain the emergency power supply plan;

[0154] The monitoring module 24 is used to build a remote monitoring and maintenance network based on the emergency power supply plan, integrate the data link of the Internet of Things sensor and satellite communication technology, realize comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning, and obtain a reliable operation guarantee system.

[0155] Figure 2 The highly efficient solar charging system suitable for marine environment can be implemented Figure 1 The implementation principle and technical effect of the high-efficiency solar charging method suitable for marine environment described in the embodiment shown are not described in detail. The specific manner in which each module and unit performs operations in the high-efficiency solar charging system suitable for marine environment in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0156] In one possible design, Figure 2 An efficient solar charging system suitable for marine environment of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0157] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0158] The processing component 32 is used to: dynamically adjust the angle of the photovoltaic panel to track the position of the sun and adapt to the dynamic conditions of the ocean, and intelligently adjust the angle of the photovoltaic panel on the floating platform in combination with real-time meteorological data, ocean currents and wave motion models to obtain an optimal light absorption configuration; based on the optimal light absorption configuration, a particle swarm optimization algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy, and predictive maintenance analysis technology is used to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and generate a preventive maintenance plan; according to the stable and efficient energy storage strategy, a priority scheduling algorithm is used to sort the importance and urgency of the electrical equipment, and combined with the remaining power evaluation results, the power distribution is reasonably planned and processed, and the possible fault points are pre-evaluated using the failure mode and effect analysis technology to obtain an emergency power supply plan; based on the emergency power supply plan, a remote monitoring and maintenance network is constructed, and the data link of the Internet of Things sensor and the satellite communication technology is integrated to realize comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning, and obtain a reliable operation guarantee system.

[0159] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0160] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0161] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0162] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0163] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0164] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0165] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a highly efficient solar charging method suitable for use in a marine environment.

[0166] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0167] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0168] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An efficient solar charging method suitable for marine environment, characterized in that: include: Dynamically adjust the angle of the photovoltaic panels to track the position of the sun and adapt to the dynamic conditions of the ocean. Combined with real-time meteorological data, ocean currents and wave motion models, the angle of the photovoltaic panels on the floating platform is intelligently adjusted to obtain the optimal light absorption configuration; Based on the optimal light absorption configuration, the particle swarm optimization algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy. The predictive maintenance analysis technology is used to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and generate a preventive maintenance plan; According to the stable and efficient energy storage strategy, a priority scheduling algorithm is applied to rank the importance and urgency of power-consuming equipment, and combined with the remaining power evaluation results, the power distribution is reasonably planned and processed, and the possible fault points are pre-evaluated using the failure mode and effect analysis technology to obtain an emergency power supply plan; Based on the emergency power supply plan, a remote monitoring and maintenance network is constructed, integrating the data link of the Internet of Things sensors and satellite communication technology, so as to realize comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning, and obtain a reliable operation guarantee system.

2. The method according to claim 1, characterized in that Based on the optimal light absorption configuration, the particle swarm optimization algorithm is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack to obtain a stable and efficient energy storage strategy, and the predictive maintenance analysis technology is used to perform trend analysis on the performance data of the energy storage equipment, identify potential failure risks in advance, and generate a preventive maintenance plan, including: Utilizing the optimal light absorption configuration, combined with real-time ocean conditions and historical weather patterns, the energy distribution scheme between the supercapacitor and the lithium-ion battery pack is optimized to obtain a stable and efficient energy storage strategy; According to the stable and efficient energy storage strategy, a particle swarm optimization algorithm is applied to guide the parameter adjustment of the energy storage system through the individual optimal position and the global optimal position in the iteration process, so as to ensure that the instantaneous power stability and the long-term energy storage efficiency can be maximized under different conditions, and generate an optimized energy storage configuration plan; Based on the optimized energy storage configuration scheme, predictive maintenance analysis technology is applied to perform trend analysis on the performance data of energy storage equipment, identify potential failure risks in advance, and obtain preventive maintenance suggestions; Utilizing the preventive maintenance recommendations, a detailed preventive maintenance plan is developed to ensure high reliability and long life operation of the energy storage system and generate a preventive maintenance plan.

3. The method according to claim 2, characterized in that The optimal light absorption configuration is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack in combination with real-time ocean conditions and historical weather patterns to obtain a stable and efficient energy storage strategy, including: Using the optimal light absorption configuration, real-time ocean condition data and historical weather pattern information are collected, and these data are comprehensively analyzed to obtain a data set for guiding energy storage system optimization; Based on the data set used to guide the optimization of the energy storage system, a particle swarm optimization algorithm is applied to simulate the group behavior in nature, and the parameter adjustment of the energy storage system is guided by the individual optimal position and the global optimal position in the iteration process to ensure that the instantaneous power stability and the long-term energy storage efficiency can be maximized under different conditions, and the optimized energy storage parameter setting is generated; According to the optimized energy storage parameter setting, combined with the characteristics of supercapacitors and lithium-ion battery packs, the energy distribution ratio between the two is accurately adjusted to ensure that efficient and stable energy storage can be maintained under any circumstances, thereby obtaining a stable and efficient energy storage strategy.

4. The method according to claim 2, characterized in that: Based on the optimized energy storage configuration scheme, predictive maintenance analysis technology is applied to perform trend analysis on the performance data of energy storage equipment, identify potential failure risks in advance, and obtain preventive maintenance suggestions, including: Using the optimized energy storage configuration scheme, the performance data of the energy storage device is collected and preprocessed in real time to remove noise and outliers to obtain a clean data set; Based on the clean data set, applying predictive maintenance analysis technology, through time series analysis and machine learning algorithms, to perform trend analysis on the performance data of the energy storage equipment, identify data patterns or abnormal conditions that may indicate potential failures, and generate potential failure warning signals; Based on the potential fault warning signal, combined with historical fault records and equipment operating conditions, risk assessment is performed to quantify the risk level and urgency of the potential fault and obtain a detailed risk assessment report; Utilize the detailed risk assessment report to develop a preventive maintenance plan, plan specific maintenance time and content, ensure action is taken before a failure occurs, and generate preventive maintenance recommendations.

5. The method according to claim 1, characterized in that According to the stable and efficient energy storage strategy, the priority scheduling algorithm is applied to sort the importance and urgency of the power-consuming equipment, and the power distribution is reasonably planned and processed in combination with the remaining power evaluation results. The possible fault points are pre-evaluated using the failure mode and effect analysis technology to obtain the emergency power supply plan, including: By using the stable and efficient energy storage strategy and applying a priority scheduling algorithm, all electrical equipment are sorted by importance and urgency according to their functions, roles and importance to offshore facilities, and a list of equipment priorities is obtained; Based on the equipment priority list, the remaining power of the energy storage system is monitored in real time, the current available energy is evaluated, and the power distribution is reasonably planned and processed in combination with the equipment priority information to ensure that key equipment can continue to operate and generate an intelligent power distribution plan; wherein, key equipment includes navigation lights and communication base stations; According to the intelligent power distribution solution, using failure mode and effect analysis technology, pre-evaluate possible fault points in the system, identify key factors that may cause power supply interruption, and generate a fault risk assessment report; Utilize the fault risk assessment report to develop a detailed emergency power supply plan for the identified potential fault points, including a backup power supply switching mechanism and load reduction measures, to ensure that basic functions can be maintained in the event of a fault, improve the system's high fault tolerance, and generate an emergency power supply plan.

6. The method according to claim 5, characterized in that According to the intelligent power distribution solution, the failure mode and effect analysis technology is used to pre-evaluate possible fault points in the system, identify key factors that may cause power supply interruption, and generate a fault risk assessment report, including: Using the intelligent power distribution solution, a comprehensive review of key equipment and power transmission paths within the system is conducted to identify potential fault points that may affect the stability of power supply and obtain a preliminary list of fault points; Based on the preliminary list of fault points, apply the failure mode and effect analysis technology to conduct a detailed risk factor analysis on each fault point, consider its probability of occurrence, severity and detection difficulty, and generate a risk factor analysis table; Based on the risk factor analysis table, evaluate the impact of each fault point on the operation of the entire system, including the impact on key equipment and functions, quantify the impact of potential faults, and obtain impact assessment results; Using the impact assessment results, comprehensively analyze the risk levels of all potential fault points, identify key factors that may cause power supply interruption, and compile a detailed fault risk assessment report; According to the fault risk assessment report, corresponding preventive measures and emergency plans are formulated for the identified key factors to ensure rapid response when a fault occurs, ensure high reliability and stability of the system, and generate a fault risk management strategy.

7. The method according to claim 1, characterized in that Based on the emergency power supply plan, a remote monitoring and maintenance network is constructed, and the data link of the Internet of Things sensors and satellite communication technology is integrated to realize comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning, and obtain a reliable operation guarantee system, including: Based on the emergency power supply plan, IoT sensors and satellite communication technology are integrated to build a data link connecting the shore-based control center and the offshore solar power station, ensuring a stable and efficient data transmission channel and obtaining a complete remote data link architecture; Using the remote data link architecture, a comprehensive real-time monitoring system is deployed in the offshore solar power plant. The real-time monitoring system collects multi-dimensional data through distributed IoT sensors and transmits it to the shore-based control center in real time to generate detailed working status reports; Based on the detailed working status report and in combination with the preventive measures formulated in the emergency power supply plan, an intelligent fault early warning mechanism is developed, which can automatically analyze the real-time monitoring data, identify abnormal patterns or potential faults, issue alarm signals in advance, and generate fault early warning information; Based on the fault warning information, targeted preventive maintenance operations are implemented, allowing technicians to remotely configure and update system parameters to ensure the continuous and stable operation of the system and obtain a reliable operation guarantee system.

8. An efficient solar charging system suitable for marine environment, characterized in that: include: The adjustment module is used to dynamically adjust the angle of the photovoltaic panels to track the position of the sun and adapt to the dynamic conditions of the ocean. It combines real-time meteorological data, ocean currents and wave motion models to intelligently adjust the angle of the photovoltaic panels on the floating platform to obtain the optimal light absorption configuration; An optimization module is used to optimize the energy distribution scheme between the supercapacitor and the lithium-ion battery pack based on the optimal light absorption configuration by using a particle swarm optimization algorithm to obtain a stable and efficient energy storage strategy, and to perform trend analysis on the performance data of the energy storage equipment by using a predictive maintenance analysis technology to identify potential failure risks in advance and generate a preventive maintenance plan; A planning module is used to apply a priority scheduling algorithm to rank the importance and urgency of power-consuming equipment according to the stable and efficient energy storage strategy, and to reasonably plan the distribution of power in combination with the remaining power evaluation result, and to pre-evaluate possible fault points using failure mode and effect analysis technology to obtain an emergency power supply plan; The monitoring module is used to build a remote monitoring and maintenance network based on the emergency power supply plan, integrate the data link of the Internet of Things sensor and satellite communication technology, realize comprehensive real-time monitoring of the working status of the offshore solar power station and fault warning, and obtain a reliable operation guarantee system.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a high-efficiency solar charging method suitable for a marine environment as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a high-efficiency solar charging method suitable for a marine environment as described in any one of claims 1 to 7 is implemented.

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