Dynamic energy charging collaboration method, system and equipment under open sea gridding monitoring and medium

Abstract

The invention discloses a dynamic energy charging collaboration method, system, equipment and medium under open-sea gridding monitoring, and relates to the technical field of open-sea monitoring and energy management, and the method comprises the steps: carrying out the grid dynamic division and equipment priority distribution; the illumination receiving efficiency is optimized by adjusting the inclination angle of the photovoltaic film, and a task adjustment and energy management balance mechanism is constructed; and performing anomaly detection and cooperative response based on a multi-sensor fusion technology. According to the method disclosed by the invention, the utilization rate and the monitoring precision of monitoring resources are improved through dynamic grid division and equipment priority allocation and by utilizing a hierarchical quadtree algorithm and real-time data optimization grid boundaries; through a photovoltaic film inclination angle optimization and task energy management balance mechanism, the task completion efficiency is improved while the long-time endurance of the equipment is guaranteed; and on the basis of anomaly detection and collaborative response of a multi-modal sensor fusion technology, complex abnormal events are accurately identified, response tasks are quickly allocated, and the efficiency and the intelligent level of the monitoring system are integrally improved.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore monitoring and energy management, and specifically to a method, system, device and medium for dynamic energy charging coordination under offshore grid monitoring. Background Art

[0002] In recent years, monitoring and management technologies in offshore areas have made significant progress, especially in the fields of marine resource development, ecological protection and security monitoring. The application of intelligent equipment such as unmanned boats and drones has gradually become popular. These devices rely on real-time data collection, automatic navigation and remote control technology to achieve large-scale regional monitoring and data transmission. At the same time, the concept of grid management has been applied in marine monitoring, and the monitoring efficiency has been improved by dividing the monitoring area and deploying distributed equipment. In terms of energy management, the application of solar energy technology in offshore equipment has made it possible for long-term endurance. Some systems are replenished by fixed solar panels or wind power equipment. However, the offshore environment is complex and changeable, including factors such as lighting conditions, wave intensity and frequent abnormal events, which put higher requirements on the continuous and stable operation of existing technologies.

[0003] Although some progress has been made in offshore monitoring and energy management technologies, existing technologies still have significant shortcomings and cannot meet the needs of efficient collaborative monitoring in complex environments. Existing grid monitoring systems are relatively limited in dynamic adjustment capabilities and usually rely on fixed grid division methods. They are unable to optimize grid shape and task allocation based on real-time environmental data, resulting in uneven distribution of monitoring resources. The current energy management strategy for unmanned boats is relatively simple, and the fixed installation method of solar panels is difficult to adjust in real time according to the angle of the sun, resulting in insufficient charging efficiency. When high-priority tasks conflict with charging requirements, the reliability of equipment operation is difficult to guarantee. The fusion analysis capabilities of multimodal sensor data are limited. Most existing systems use single-mode anomaly detection technology, which has low recognition efficiency for complex abnormal events such as illegal ships or marine floating objects, and there are delays in response. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing offshore monitoring and energy management methods have insufficient dynamic adjustment capabilities of the monitoring grid, low equipment charging efficiency, and inaccurate and inefficient anomaly detection and response capabilities, as well as the problem of how to achieve efficient monitoring and collaborative energy management in the complex offshore environment.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a dynamic energy charging coordination method under offshore grid monitoring, including dynamic grid division and equipment priority allocation; optimizing light reception efficiency by adjusting the inclination angle of the photovoltaic film, and building a task adjustment and energy management balance mechanism; performing anomaly detection and coordinated response based on multi-sensor fusion technology.

[0007] As a preferred solution of the dynamic energy charging coordination method under offshore grid monitoring described in the present invention, the dynamic division of the grid includes dynamically adjusting the grid boundaries according to the real-time environmental data, historical risk distribution and monitoring objectives of the offshore area.

[0008] As a preferred solution of the dynamic charging coordination method under the offshore grid monitoring described in the present invention, the dynamic adjustment of the grid boundary includes generating an initial grid based on a predetermined algorithm, adjusting the grid shape after superimposing the grid weight value with the actual needs of the monitoring area; when the real-time monitoring data is updated, the grid boundary is recalculated by the system, and the adjustment result is sent to the device for navigation path adjustment.

[0009] As a preferred solution of the dynamic charging coordination method under offshore grid monitoring described in the present invention, the device priority allocation includes priority calculation based on the remaining power of the device, the distance from the target grid and the importance of the task; the system selects the best device based on the device priority, continuously monitors the device task execution status after assigning the task, and readjusts the priority based on real-time conditions.

[0010] As a preferred scheme of the dynamic energy charging coordination method under the open sea grid monitoring described in the present invention, wherein: the optimization of light receiving efficiency includes: the photovoltaic film is connected to the angle adjustment mechanism through a mechanical bracket, and the adjustment mechanism monitors the incident angle of sunlight through a light sensor; the angle of the photovoltaic film is dynamically adjusted according to the sensor data to keep the photovoltaic film in the best light receiving state; the power generation modules are installed in partitions on the surface of the photovoltaic film, and the inefficient modules are automatically switched by monitoring the performance status of the modules to optimize the charging efficiency; the angle adjustment of the photovoltaic film is coordinated with the navigation path.

[0011] As a preferred scheme of the dynamic energy charging coordination method under the open sea grid monitoring described in the present invention, wherein: the balancing mechanism includes, when the unmanned boat enters the low energy consumption mode, it gives priority to completing the low priority tasks in the grid, and at the same time unfolds the photovoltaic film for charging; when a high priority task is detected, the unmanned boat automatically retracts the photovoltaic film and switches to the task execution state; the task switching threshold is dynamically adjusted through the remaining power monitoring, and the switching mechanism remotely adjusts the strategy through the command system to adapt to environmental changes.

[0012] As a preferred solution of the dynamic charging coordination method under the offshore grid monitoring described in the present invention, the anomaly detection includes obtaining multimodal data of the monitoring area through sea surface visual sensors, sonar detection equipment and underwater multi-beam sensors; the data is fused and processed through a neural network to extract abnormal features and identify floating objects on the sea surface, illegal ships and abnormal marine ecological events; the identification results are uploaded to the command system and the monitoring priority of the relevant grid is automatically adjusted.

[0013] As a preferred scheme of the dynamic charging coordination method under offshore grid monitoring described in the present invention, the coordinated response includes: after the command system detects an anomaly, it reallocates tasks according to the equipment location and remaining power, the equipment closest to the abnormal area responds first, different equipment adjusts paths to support monitoring, the command system adjusts the monitoring range according to equipment feedback, and restores the original task of the equipment after the anomaly ends.

[0014] Another object of the present invention is to provide a dynamic energy charging coordination system under offshore grid monitoring, which can allocate monitoring tasks according to equipment status and task requirements, and adjust task allocation in real time to adapt to environmental changes, solving the problem that current offshore monitoring and energy management technologies contain insufficient dynamic adjustment capabilities of monitoring grids.

[0015] As a preferred solution of the dynamic charging collaborative system under the offshore grid monitoring described in the present invention, it includes: a offshore monitoring area dynamic management module, an energy optimization module, and an abnormal detection response module; the offshore monitoring area dynamic management module includes a grid division module and a priority allocation module, the grid division module is used to dynamically divide the monitoring grid according to the real-time environmental data and the historical monitoring data, the priority allocation module is used to allocate the monitoring tasks according to the equipment status and the task requirements, and adjust the task allocation in real time to adapt to the environmental changes; the energy optimization module includes a charging efficiency optimization module and an energy balance module, the charging efficiency optimization module is used to dynamically adjust the photovoltaic film angle and the power generation module to maximize the light energy utilization efficiency, and the energy balance module is used to dynamically switch the charging and execution task modes according to the task priority and the equipment power status; the abnormal detection response module includes an abnormal detection module and a collaborative response module, the abnormal detection module is used to fuse the multimodal sensor data, identify and classify the abnormal events, and the collaborative response module is used to achieve the collaborative response between the devices through the dynamic task redistribution after the abnormal event occurs.

[0016] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a dynamic charging coordination method under offshore grid monitoring.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a dynamic energy charging coordination method under offshore grid monitoring.

[0018] Beneficial effects of the present invention: The dynamic charging coordination method under offshore grid monitoring provided by the present invention optimizes the grid boundaries by dynamically dividing the grid and allocating equipment priorities, and utilizes the hierarchical quadtree algorithm and real-time data to ensure priority coverage of high-risk areas, thereby improving the utilization rate of monitoring resources and the monitoring accuracy; through the photovoltaic film inclination optimization and task energy management balance mechanism, efficient charging and task switching in complex environments are achieved, and the task completion efficiency is improved while ensuring long-term equipment life; based on the anomaly detection and coordinated response of multimodal sensor fusion technology, complex abnormal events are accurately identified and response tasks are quickly allocated, which shortens the processing delay, enhances the practicality and reliability of the system in the complex environment of the offshore, and improves the efficiency and intelligence level of the monitoring system as a whole. The present invention achieves better results in terms of efficiency, accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 An overall flow chart of a dynamic energy charging coordination method under offshore grid monitoring provided in the first embodiment of the present invention.

[0021] Figure 2 An overall flow chart of a dynamic energy charging coordination system under offshore grid monitoring provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0023] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a dynamic energy charging coordination method under offshore grid monitoring, including:

[0024] S1: Dynamically divide the grid and assign device priorities.

[0025] Furthermore, the grid is dynamically divided, including dynamically adjusting the grid boundaries according to the real-time environmental data, historical risk distribution and monitoring objectives of the offshore area.

[0026] It should be noted that the dynamic adjustment of the grid boundaries includes generating an initial grid based on a predetermined algorithm, adjusting the grid shape after superimposing the grid weight value with the actual needs of the monitoring area; when the real-time monitoring data is updated, the grid boundaries are recalculated by the system, and the adjustment results are sent to the device for navigation path adjustment.

[0027] It should also be noted that a preferred algorithm of the predetermined algorithm includes a regional partitioning algorithm based on a hierarchical quadtree. The quadtree algorithm can divide the region into grids of different levels through recursive segmentation, and make more detailed divisions for high-risk areas, while using coarser-grained grids for low-risk areas, thereby improving the flexibility and efficiency of monitoring resource allocation. The specific steps include inputting the geographical scope of the initial monitoring area and historical risk distribution data, dividing the entire area into four sub-areas with the center point of the monitoring area as the initial segmentation point; calculating the risk weight for each sub-area, and further subdividing if the risk weight of a certain area is greater than a preset threshold; repeating the subdivision steps until The weights of all sub-areas are lower than the threshold, or the subdivision reaches the set maximum level; the hierarchical grid structure is output and the initial grid shape is generated. During the specific adjustment process, the system analyzes the real-time environmental data and historical data to determine whether the current grid boundary meets the monitoring requirements; when it is determined that the grid boundary needs to be adjusted, the shape and distribution of the grid are recalculated through re-optimization. The re-optimization adopts a regional grid refinement strategy to densely divide high-risk areas and sparsely divide low-risk areas; finally, the system distributes new navigation paths and task instructions to the equipment according to the adjusted grid shape to ensure efficient allocation and dynamic adaptability of monitoring resources.

[0028] It should also be noted that device priority allocation includes priority calculation based on the remaining power of the device, the distance from the target grid, and the importance of the task; the system selects the best device based on the device priority, continuously monitors the device task execution status after assigning the task, and readjusts the priority based on real-time conditions.

[0029] It should also be noted that a preferred method for calculating the priority based on the remaining power of the device, the distance to the target grid, and the importance of the task is expressed as:

[0030]

[0031] Among them, P is the task priority score, which indicates the comprehensive priority of the device in executing a task. E is the remaining power of the device, which is used to measure the current endurance of the device. D is the distance between the device and the target grid, which is used to measure the proximity of the device to the task target. The shorter the distance, the higher the priority. R is the task importance, which is used to measure the urgency of the current task and its importance to the overall operation of the system. W1, W2, and W3 are the weight coefficients of power, distance, and task importance, respectively, which are used to adjust the relative importance of the three factors in the priority calculation.

[0032] It should also be noted that the shape of the grid is dynamically adjusted according to real-time data to ensure that high-risk areas are covered first; the system combines historical risk data and current monitoring targets to optimize the grid range in real time; dynamic grid division effectively avoids waste of resources, ensures that the area covered by the equipment is highly matched with the risk distribution, and improves the accuracy of monitoring tasks; adaptive monitoring of complex offshore environments is achieved, improving the efficiency of the monitoring system and the rationality of resource allocation; by calculating the remaining power of the equipment, task importance and distance, the system assigns reasonable tasks to the equipment and prioritizes the equipment in the best state to perform tasks; this allocation mechanism avoids the problem of unbalanced task allocation between devices, ensures efficient use of equipment resources, and reduces the risk of task failure due to insufficient energy or inappropriate location; it improves the equipment's work efficiency and task completion rate, extends the equipment's task duration, and reduces delays caused by task errors.

[0033] S2: Optimize light reception efficiency by adjusting the inclination angle of the photovoltaic film and build a balance mechanism between task adjustment and energy management.

[0034] Furthermore, the efficiency of light reception is optimized, including connecting the photovoltaic film to an angle adjustment mechanism through a mechanical bracket, and the adjustment mechanism monitoring the incident angle of sunlight through a light sensor; dynamically adjusting the angle of the photovoltaic film according to sensor data to keep the photovoltaic film in an optimal light-receiving state; installing power generation modules on the surface of the photovoltaic film in partitions, and automatically switching inefficient modules by monitoring the performance status of the modules to optimize charging efficiency; and coordinating the angle adjustment of the photovoltaic film with the navigation path.

[0035] It should also be noted that a preferred solution for dynamically adjusting the angle of the photovoltaic film based on sensor data includes introducing a wind speed sensor to detect wind speed changes in the environment in real time. When the wind speed exceeds the safety threshold, the adjustment range of the photovoltaic film inclination angle is limited, or the photovoltaic film is automatically folded to avoid equipment damage. When the wind speed is lower than 10 m / s, the angle of the photovoltaic film can be dynamically adjusted to the maximum inclination angle (such as 45 degrees) to obtain optimal lighting; when the wind speed reaches 10-20 m / s, the system limits the inclination adjustment range to within 15 degrees to reduce the pressure of wind resistance on the photovoltaic film structure; when the wind speed exceeds 20 m / s, the photovoltaic film automatically folds to the main body of the equipment and enters safety protection mode.

[0036] It should be noted that the balancing mechanism includes that when the unmanned boat enters the low-energy consumption mode, it gives priority to completing low-priority tasks within the grid, and at the same time unfolds the photovoltaic film for charging; when a high-priority task is detected, the unmanned boat automatically retracts the photovoltaic film and switches to the task execution state; the task switching threshold is dynamically adjusted through the remaining power monitoring, and the switching mechanism remotely adjusts the strategy through the command system to adapt to environmental changes.

[0037] It should also be noted that a preferred solution for dynamically adjusting the task switching threshold through remaining power monitoring includes setting a dynamic switching threshold according to the importance of the task; for example, high-priority tasks can break through the power limit, while low-priority tasks need to strictly follow the switching threshold of the remaining power; normal cruise task (low priority): when the power is less than 60%, stop cruising and switch to charging mode; emergency abnormal response task (high priority): even if the power is as low as 30%, continue to execute the task and start the emergency charging strategy (such as full-power deployment of photovoltaic film); special monitoring task (medium priority): dynamically adjust the threshold according to the current task time requirement to ensure that there is enough power to return to the safe area after the task is completed.

[0038] It should also be noted that the angle of the photovoltaic film is adjusted according to the real-time data of the light sensor to dynamically maintain the optimal incident angle; the partitioned power generation module automatically switches the inefficient module through status monitoring to ensure charging efficiency; this optimization method improves the utilization efficiency of solar energy, and can achieve efficient charging even in environments with insufficient light or poor sea surface reflection conditions; by adjusting the angle in real time, the photovoltaic film can improve the charging efficiency and ensure the energy supply required for the equipment to perform tasks for a long time; enter the low-energy consumption mode in low-priority tasks, and use the photovoltaic film for charging at the same time; when a high-priority task is triggered, the unmanned boat retracts the photovoltaic film and responds to the task immediately; this mechanism establishes an efficient balance between energy supply and task execution, avoiding the situation where task execution is affected by insufficient energy, and at the same time maximizing the equipment's endurance during non-critical tasks; it improves the autonomy and task completion rate of the unmanned boat, and significantly reduces the monitoring interruption caused by energy exhaustion of offshore equipment.

[0039] S3: Anomaly detection and coordinated response based on multi-sensor fusion technology.

[0040] Furthermore, anomaly detection includes acquiring multimodal data of the monitored area through sea surface visual sensors, sonar detection equipment and underwater multi-beam sensors; the data is fused and processed through neural networks to extract abnormal features and identify floating objects on the sea surface, illegal ships and abnormal marine ecological events; the identification results are uploaded to the command system and the monitoring priority of the relevant grid is automatically adjusted.

[0041] It should be noted that a preferred scheme for anomaly detection specifically includes: obtaining optical image data of the monitored area through sea surface visual sensors, the optical image data including the shape, size and color of sea surface objects; obtaining acoustic wave echo signals of underwater targets through sonar detection equipment, the acoustic wave signals including target reflection intensity and distance information; obtaining three-dimensional spatial distribution data of the target area through underwater multi-beam sensors, the three-dimensional distribution data reflecting the position, size and structural characteristics of the underwater targets; denoising the obtained optical image data to eliminate interference caused by sea surface light reflection or weather conditions, and highlighting the edge and color characteristics of the target object through image enhancement technology; performing spectral analysis on the acoustic wave signal data to decompose the original signal into frequency characteristic components, filter out background noise and retain target reflection characteristics; performing three-dimensional reconstruction on the underwater multi-beam data, and restoring the target spatial distribution in the monitored area through an interpolation algorithm; and utilizing the underwater multi-beam data to restore the target spatial distribution in the monitored area. A neural network model is used to extract features from optical images, acoustic signals and three-dimensional distribution data, including extracting target shape and color features from optical images through a convolutional neural network, extracting time series features from acoustic signals through a recurrent neural network, and extracting structural features from three-dimensional spatial data through an autoencoder network. The above features are mapped to a unified multidimensional feature space through a multimodal fusion algorithm to generate a feature vector containing target shape, dynamic changes, reflection characteristics and positional relationships. Abnormal events in the monitoring area are identified by comparing the feature vector with a pre-trained anomaly classification model, including floating objects on the sea surface, illegal vessels and marine ecological anomalies. The identification results and the corresponding feature vectors are uploaded to the command system, which dynamically adjusts the monitoring priority of the relevant grids according to the type and risk level of the abnormal event, and triggers the task allocation and path optimization process to dispatch relevant equipment to respond to the abnormal event.

[0042] It should be noted that coordinated response includes, after the command system detects an anomaly, reallocating tasks based on the equipment location and remaining power, giving priority to equipment closest to the anomaly area, adjusting paths of different equipment to support monitoring, the command system adjusting the monitoring range based on equipment feedback, and restoring the equipment's original task after the anomaly ends.

[0043] It should also be noted that multimodal data is acquired using sea surface visual sensors, sonar equipment and underwater multi-beam sensors, and fusion analysis is performed in combination with neural network models to accurately identify abnormal events (such as illegal ships, floating objects on the sea surface, etc.). Multi-sensor fusion technology improves the system's ability to identify abnormal events, and can achieve rapid response even in complex marine environments, realizing real-time and accurate identification of abnormal events, reducing missed and false alarm rates, and providing a reliable data basis for subsequent responses. The command system dynamically allocates tasks according to the status of the equipment (location, power), and the equipment closest to the abnormal area responds first, and other equipment adjusts its path to assist in monitoring. After the response is completed, the system restores the normal task path of the equipment. The collaborative response mechanism greatly shortens the response time for abnormal event handling. At the same time, the collaborative work of multiple devices improves the overall efficiency of task completion, improves the abnormal handling capability of the offshore monitoring system, and ensures rapid response to abnormal events and optimal resource utilization.

[0044] Example 2 is an embodiment of the present invention, which provides a dynamic charging coordination method under offshore grid monitoring. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0045] First, the experimental area was selected in a coastal sea area with an area of ​​about 100 square kilometers, including high-risk areas (fishing boat-dense areas, ecologically sensitive areas) and low-risk areas (open waters); the experimental equipment included 3 unmanned boats, each equipped with a photovoltaic film charging device, a sea surface vision sensor, a sonar detection device and an underwater multi-beam sensor; the command center used a hierarchical quadtree algorithm for dynamic grid division and priority task allocation; the initial grid division was completed based on historical data and real-time environmental data, and the fishing boat-dense areas and ecologically sensitive areas were finely divided (4-level grids, with a side length of about 0.5 kilometers), and coarse-grained grid division was used for open waters (2-level grids, with a side length of about 2 kilometers); the system dynamically allocated tasks according to the equipment power (70%-100% is high power, 40%-70% is medium power, and <40% is low power), task importance and grid distance ; In the priority calculation, the weights are set as 50% for power, 30% for distance, and 20% for task importance; the system adjusts the inclination angle of the photovoltaic film in real time, and adjusts it to the optimal light receiving angle of 45 degrees at low wind speed (<10 m / s); the inclination angle is adjusted to within 15 degrees at medium wind speed (10-20 m / s); the photovoltaic film is retracted at high wind speed (>20 m / s); when a high-priority task is triggered (such as abnormal event response), the device interrupts charging and switches to task execution mode; monitoring data is collected in real time through sea surface visual sensors, sonar detection equipment and multi-beam sensors; after detecting illegal ships, features are extracted through neural network analysis, and data is uploaded to the command center after confirming the anomaly; the command center assigns response tasks according to the equipment power, location and task status, with the nearest equipment responding first and other equipment monitoring collaboratively; refer to Table 1 to record and analyze the experimental data.

[0046] Table 1 Experimental data record table

[0047]

[0048] Experiments show that under different wind speed conditions, the grid coverage rate always remains above 90%, indicating that the hierarchical quadtree algorithm combined with real-time data can dynamically optimize the grid boundaries to ensure that high-risk areas are covered first; the equipment task completion rate reaches more than 95% under low wind speed conditions, which fully verifies the rationality of the priority allocation mechanism; data show that through the optimization of photovoltaic film angles and dynamic charging mechanism, the charging efficiency is increased to 85% under low wind speed conditions, and even under medium and high wind speed conditions (>10 m / s), the charging efficiency can still be maintained at more than 60%; compared with the traditional fixed-angle charging method, the present invention can adapt to complex environmental changes and extend the battery life of the equipment; in the experiment, the abnormal response delay is reduced to within 10 seconds in the high-priority task triggering scenario, indicating that the collaborative scheduling mechanism of multimodal sensor fusion and the command center can quickly identify anomalies and effectively assign tasks; in addition, through the collaborative work of multiple devices, the efficiency of abnormal event processing is improved, and the task completion rate exceeds 89%, avoiding the delay problem caused by the abnormal processing of a single device.

[0049] Traditional fixed grid division methods are difficult to dynamically adapt to real-time data, while the present invention achieves high efficiency and flexibility in resource allocation by optimizing grid boundaries in real time; fixed photovoltaic film charging methods are inefficient in complex wind speed environments, while the present invention dynamically adjusts the inclination angle of the photovoltaic film to improve charging efficiency; single sensor abnormality detection has a high false alarm rate and low processing efficiency, while the present invention significantly shortens response delays and improves processing accuracy through multimodal data fusion and collaborative response.

[0050] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a dynamic energy charging coordination system under offshore grid monitoring, including an offshore monitoring area dynamic management module 100, an energy optimization module 200, and an abnormality detection response module 300.

[0051] Among them, S4: the dynamic management module 100 of the offshore monitoring area includes a grid division module 101 and a priority allocation module 102. The grid division module 101 is used to dynamically divide the monitoring grid according to the real-time environmental data and historical monitoring data. The priority allocation module 102 is used to allocate monitoring tasks according to the equipment status and task requirements, and adjust the task allocation in real time to adapt to environmental changes.

[0052] It should be noted that after the system is started, the offshore monitoring area dynamic management module 100 obtains real-time environmental data of the offshore area through the grid division module 101, including meteorological conditions, wave height and water flow direction, generates an initial grid based on a predetermined algorithm, and divides the monitoring area. The priority allocation module 102 simultaneously collects equipment status location, power, task load, etc., and assigns tasks to the equipment according to the priority calculation formula to ensure that high-priority tasks are executed first. The grid division module 101 and the priority allocation module 102 in the offshore monitoring area dynamic management module 100 update the monitoring tasks in real time during operation, and pass the update results to the energy optimization module 200 to optimize the equipment's tasks and energy balance.

[0053] S5: The energy optimization module 200 includes a charging efficiency optimization module 201 and an energy balance module 202. The charging efficiency optimization module 201 is used to dynamically adjust the angle of the photovoltaic film and the power generation module to maximize the efficiency of light energy utilization. The energy balance module 202 is used to dynamically switch the charging and task execution modes according to the task priority and the power status of the equipment.

[0054] It should be noted that the charging efficiency optimization module 201 dynamically monitors the lighting conditions, obtains the solar incidence angle data through the light sensor, adjusts the inclination angle of the photovoltaic film in real time, and optimizes the power generation efficiency. If it is detected that the performance of some power generation modules of the photovoltaic film is inefficient, the module will automatically switch to the high-efficiency module to maintain energy output. The energy balance module 202 adjusts the device operation mode according to the task priority and the remaining power status. For example, when the device is executing a low-priority task and the power is insufficient, the energy balance module instructs the device to enter the low-energy consumption mode and start charging the photovoltaic film; if a high-priority task is triggered, the device automatically switches to the task execution mode, turns off the photovoltaic film to focus on the task goal, and during the task execution and charging switching process, the energy optimization module 200 synchronizes the device power status to the abnormal detection response module 300 so that the equipment can be quickly deployed when an abnormal event occurs.

[0055] S6: The anomaly detection response module 300 includes an anomaly detection module 301 and a collaborative response module 302. The anomaly detection module 301 is used to fuse multimodal sensor data, identify and classify abnormal events, and the collaborative response module 302 is used to achieve collaborative response between devices through dynamic task redistribution after an abnormal event occurs.

[0056] It should be noted that the anomaly detection module 301 collects data through multimodal sensors including sea surface visual sensors, sonar detection equipment and underwater multi-beam sensors, and integrates and analyzes abnormal features, such as identifying illegal ships, floating objects or ecological abnormal events. After detecting the anomaly, the module immediately uploads the event information to the command center, and cooperates with the grid division module 101 to dynamically adjust the priority of the relevant grids. The collaborative response module 302 is linked with the priority allocation module 102 according to the urgency and type of the abnormal event, and dynamically adjusts the task allocation. The equipment closest to the abnormal area and with sufficient power will respond first, and other equipment will adjust the path as needed to provide peripheral support. After the response is completed, the system automatically restores the equipment to the original task path and updates the grid priority. The collaborative response module 302 in the anomaly detection response module 300 calls the priority allocation module 102 to dynamically adjust the task when processing the event, and at the same time notifies the grid division module 101 to adjust the monitoring density of the relevant grid area according to the real-time event situation.

[0057] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0058] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0059] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0060] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0061] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A dynamic energy charging coordination method under offshore grid monitoring, characterized in that: include: Perform dynamic grid division and device priority allocation; By adjusting the inclination angle of the photovoltaic film to optimize the light receiving efficiency, a task adjustment and energy management balance mechanism is established; Anomaly detection and coordinated response based on multi-sensor fusion technology.

2. The method for dynamic energy charging coordination under offshore grid monitoring as claimed in claim 1, characterized in that: The grid is dynamically divided, include, The grid boundaries are dynamically adjusted based on real-time environmental data, historical risk distribution and monitoring objectives in offshore areas.

3. The method for dynamic energy charging coordination under offshore grid monitoring as claimed in claim 2, characterized in that: The dynamically adjusting the grid boundary includes: Generate an initial grid based on a predetermined algorithm, and adjust the grid shape after superimposing the grid weight value and the actual needs of the monitoring area; When real-time monitoring data is updated, the grid boundaries are recalculated by the system and the adjustment results are sent to the device for navigation path adjustment.

4. The method for dynamic energy charging coordination under offshore grid monitoring as claimed in claim 1, characterized in that: The device priority allocation includes: Priority calculation is performed based on the remaining power of the device, the distance to the target grid, and the importance of the task; The system selects the best device based on device priority, continuously monitors the device task execution status after assigning tasks, and readjusts priorities based on real-time conditions.

5. The method for dynamic energy charging coordination under offshore grid monitoring as claimed in claim 2 or 4, characterized in that: The optimizing light receiving efficiency comprises: The photovoltaic film is connected to the angle adjustment mechanism through a mechanical bracket, and the adjustment mechanism monitors the incident angle of sunlight through a light sensor; Dynamically adjust the angle of the photovoltaic film according to the sensor data to keep the photovoltaic film in the best light receiving state; The photovoltaic film surface is partitioned to install power generation modules, and the inefficient modules are automatically switched to optimize the charging efficiency by monitoring the module performance status; Photovoltaic film angle adjustment is coordinated with the navigation path.

6. The method for dynamic energy charging coordination under offshore grid monitoring as claimed in claim 1 or 4, characterized in that: The balancing mechanism includes: When the unmanned boat enters low-energy mode, it will give priority to completing low-priority tasks within the grid and at the same time unfold the photovoltaic film for charging; When a high-priority task is detected, the unmanned boat automatically retracts the photovoltaic film and switches to the task execution state; The task switching threshold is dynamically adjusted by monitoring the remaining power, and the switching mechanism remotely adjusts the strategy through the command system to adapt to environmental changes.

7. The method for dynamic energy charging coordination under offshore grid monitoring as claimed in claim 1, characterized in that: The anomaly detection includes: Acquire multimodal data of the monitoring area through sea surface visual sensors, sonar detection equipment and underwater multi-beam sensors; The data is fused and processed through a neural network to extract abnormal features and identify floating objects on the sea surface, illegal vessels, and abnormal marine ecology events; The identification results are uploaded to the command system and the monitoring priority of the relevant grids is automatically adjusted.

8. The method for dynamic energy charging coordination under offshore grid monitoring as claimed in claim 1, 2, 4 or 7, characterized in that: The coordinated response includes: After detecting an anomaly, the command system reallocates tasks based on the device location and remaining power. The device closest to the anomaly area responds first, and different devices adjust their paths to support monitoring. The command system adjusts the monitoring range based on device feedback and restores the device's original task after the anomaly ends.

9. The dynamic energy charging coordination system under offshore grid monitoring is characterized by: It includes a offshore monitoring area dynamic management module (100), an energy optimization module (200), and an abnormality detection response module (300); The offshore monitoring area dynamic management module (100) comprises a grid division module (101) and a priority allocation module (102), wherein the grid division module (101) is used to dynamically divide the monitoring grid according to real-time environmental data and historical monitoring data, and the priority allocation module (102) is used to allocate monitoring tasks according to equipment status and task requirements, and adjust task allocation in real time to adapt to environmental changes; The energy optimization module (200) comprises an energy charging efficiency optimization module (201) and an energy balancing module (202), wherein the energy charging efficiency optimization module (201) is used to dynamically adjust the photovoltaic film angle and the power generation module to maximize the light energy utilization efficiency, and the energy balancing module (202) is used to dynamically switch the charging and task execution modes according to the task priority and the power status of the device; The abnormality detection response module (300) comprises an abnormality detection module (301) and a collaborative response module (302), wherein the abnormality detection module (301) is used to fuse multimodal sensor data and identify and classify abnormal events, and the collaborative response module (302) is used to achieve collaborative response between devices through dynamic task redistribution after an abnormal event occurs.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the dynamic energy charging coordination method under offshore grid monitoring described in any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the dynamic energy charging coordination method under offshore grid monitoring described in any one of claims 1 to 8 are implemented.

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