Functional boat dynamic scheduling method and system based on gridding sea area intelligent distribution

Abstract

The invention discloses a functional boat dynamic scheduling method and system based on gridding sea area intelligent distribution, and relates to the technical field of ocean engineering and intelligent unmanned system control, and the method comprises the steps: dividing operation grids based on the total area of a sea area and the number of unmanned boats, and distributing functional boats for different operations; task data of the functional boat are collected in real time, a weighting coefficient is set based on the type of the functional boat to calculate an operation load index, residual energy is dynamically predicted by adopting an index relation model, and a short-term load change trend is calculated; on the basis of a load prediction result and an energy state, executing local grid boundary adjustment and responsible boat redistribution, dynamically calculating the priority of the functional boat, and synchronizing the state of the functional boat on the basis of an inter-boat communication link; according to the method, through dynamic division of the operation grids, intelligent distribution of the functional boats and real-time reconstruction of the responsibility area based on the operation load and the energy state, state synchronization of multiple unmanned boats is realized, and the operation load adaptive capability and cluster cooperation stability of the unmanned boats in a complex sea area environment are improved.

Description

Technical Field

[0001] The present invention relates to the field of marine engineering and intelligent unmanned system control technology, and in particular to a method and system for dynamic scheduling of functional boats based on intelligent allocation of gridded sea areas. Background Art

[0002] As the scope of marine operations continues to expand, unmanned boats (UAVs), as intelligent, low-cost, and highly flexible operating platforms, have been widely adopted in a variety of application areas, including marine surveying and mapping, environmental monitoring, communications support, and emergency rescue. Traditional UAV operation models are often based on pre-set routes or fixed-area task allocation, making it difficult to effectively address the dynamic changes in the operating environment, uneven task loads, and the complex demands of multi-functional boat collaboration. Especially in large-scale marine environments, relying solely on static divisions or centralized command and dispatch can easily lead to problems such as uneven load distribution within the operating area, waste of multi-functional boat resources, and local overload or energy depletion.

[0003] In terms of sea area division and unmanned boat operation management, existing technologies generally adopt methods based on geometric rules or simplified area division, lacking a mechanism for real-time adjustment based on the operating status and dynamic changes of the functional boat's load. The perception of the functional boat's operating load usually relies on a single parameter evaluation, failing to integrate multiple factors such as track length, power consumption level, number of tasks and environmental complexity, making it difficult to accurately reflect the actual operating pressure. At the same time, energy management is mostly based on linear estimation or static models, ignoring the nonlinear relationship between operating load and energy consumption, affecting the accuracy of the remaining energy prediction. In addition, during the multi-boat collaboration process, state synchronization and responsibility division often lag behind the actual operating status changes. There is a lack of effective load prediction drive and priority dynamic adjustment mechanism, which is prone to problems such as communication link interruption, operation interruption or resource mismatch in complex sea conditions. These limitations restrict the operating capabilities and collaborative operation efficiency of existing unmanned boat systems in large-scale, long-term and complex environments. 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 unmanned boat operation management method has the problems of static operation grid division, single functional boat operation load perception, low remaining energy prediction accuracy, and low collaborative scheduling response efficiency. It is difficult to cope with the challenges of dynamic changes in unmanned boat operation load and uneven energy consumption in complex sea environments, as well as how to dynamically adjust the grid division based on real-time operation load and energy status, optimize functional boat allocation and achieve synchronization of multiple unmanned boat status.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a dynamic scheduling method for functional boats based on intelligent allocation of gridded sea areas, including dividing the operation grid based on the total area of the sea area and the number of unmanned boats, and allocating functional boats to different operations; real-time collection of functional boat mission data, setting weighted coefficients based on the type of functional boat to calculate the operation load index, using an exponential relationship model to dynamically predict the remaining energy, and inferring the short-term load change trend; based on the load prediction results and energy status, performing local grid boundary adjustment and responsible boat redistribution, dynamically calculating the priority of functional boats, and synchronizing the functional boat status based on the inter-boat communication link.

[0007] As a preferred solution of the method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation described in the present invention, the operation grids are divided based on the total sea area and the number of unmanned boats, including dividing the total sea area by the number of unmanned boats to determine the area of a single grid, and estimating the operation coverage radius of a single boat based on the area of the single grid.

[0008] As a preferred solution of the dynamic scheduling method of functional boats based on gridded sea area intelligent allocation described in the present invention, the allocation of functional boats for different operations includes respectively configuring signal boats, detection boats, maintenance boats and airspace extension boats according to signal coverage requirements, detection operation depth requirements, maintenance operation cycle requirements and airspace monitoring requirements.

[0009] As a preferred solution of the dynamic scheduling method of functional boats based on gridded sea area intelligent allocation described in the present invention, the calculation of the workload index by setting a weighted coefficient based on the functional boat type includes weighting and accumulating the track length, power consumption, number of tasks and environmental complexity of the functional boat according to the weight coefficient corresponding to the functional boat category to generate a real-time workload index.

[0010] As a preferred solution of the dynamic scheduling method of functional boats based on gridded sea area intelligent allocation described in the present invention, the dynamic prediction of residual energy using an exponential relationship model includes estimating the residual energy of the functional boat based on real-time operating load indicators using an exponential relationship, so that the residual energy shows an exponential downward trend with load changes.

[0011] As a preferred solution of the dynamic scheduling method of functional boats based on gridded sea area intelligent allocation described in the present invention, the calculation of short-term load change trends includes setting a sliding time window with a fixed length based on a sliding time window, and predicting future load levels based on the load indicator change rate within the time window.

[0012] As a preferred solution of the method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation described in the present invention, the execution of local grid boundary adjustment and responsible boat redistribution includes dynamically calculating the priority of functional boats based on load prediction results and energy status, and dynamically adjusting the grid and synchronizing the load, energy, and priority status of functional boats in real time through inter-boat communication links.

[0013] Another object of the present invention is to provide a dynamic scheduling system for functional boats based on intelligent allocation of gridded sea areas. It can dynamically adjust the grid boundaries, responsibility boat allocation and functional boat priority synchronization based on real-time workload indicators and energy status, thereby solving the problems of single workload perception, delayed energy change prediction and low efficiency of multi-boat collaborative response in current unmanned boat operation management technology.

[0014] As an optimal solution of the functional boat dynamic scheduling system based on gridded sea area intelligent allocation described in the present invention, it includes: an operation grid division and functional boat configuration module, a load prediction and energy management module, a dynamic grid reconstruction and functional boat collaborative scheduling module; the path planning module is used to divide the operation grid according to the total area of the sea area and the number of unmanned boats, calculate the operation radius of a single boat, and allocate functional boats in combination with communication, detection, maintenance and airspace monitoring needs; the load prediction and energy management module is used to collect the functional boat's track length, power consumption, number of tasks and environmental complexity, calculate the operation load index according to the boat type weight, predict the remaining energy based on the exponential relationship, and calculate the load change trend through a sliding time window; the dynamic grid reconstruction and functional boat collaborative scheduling module is used to dynamically adjust the grid and responsible boat allocation based on the load trend and energy status, calculate the functional boat priority, synchronize the operation status through inter-boat communication, and realize the collaborative operation and responsibility switching of multi-functional boats.

[0015] 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 scheduling method for functional boats based on gridded sea area intelligent allocation.

[0016] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for dynamic scheduling of functional boats based on intelligent allocation of gridded sea areas.

[0017] Beneficial effects of the present invention: The functional boat dynamic scheduling method based on gridded sea area intelligent allocation provided by the present invention dynamically divides the operation grid according to the total sea area and the number of unmanned boats and reasonably allocates functional boats, which can effectively adapt to the problem of uneven local operation density caused by changes in the operating load of functional boats, and improves the dynamic balance of the unmanned boat operation area; the weighted coefficient is set based on the functional boat type to calculate the operation load index, and the remaining energy is dynamically predicted in combination with the exponential relationship model, thereby realizing accurate perception of the unmanned boat operation load status and energy changes, and supporting the real-time dynamic adjustment of grid responsibilities; based on the short-term load change trend calculation, local grid boundary adjustment and responsible boat redistribution are performed, and the functional boat operation status is synchronized in real time through the inter-boat communication link, thereby realizing adaptive balance and efficient coordination of the unmanned boat operation load in a complex sea environment; the present invention has achieved better results in responding to dynamic changes in the operating load of functional boats, improving the operation stability of large-scale unmanned boat clusters, and optimizing the real-time scheduling of sea resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is an overall flow chart of a method for dynamic scheduling of functional boats based on intelligent allocation of gridded sea areas provided by the first embodiment of the present invention.

[0020] Figure 2 This is an overall flow chart of a dynamic scheduling system for functional boats based on intelligent allocation of gridded sea areas, provided as a third embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0022] Example 1, with reference to Figure 1 , which is an embodiment of the present invention, provides a method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation, including:

[0023] S1: Divide the operation grid based on the total sea area and the number of unmanned boats, and assign functional boats to different operations.

[0024] Furthermore, dividing the operation grid based on the total sea area and the number of unmanned boats includes dividing the total sea area by the number of unmanned boats to determine the area of a single grid, and estimating the operation coverage radius of a single boat based on the area of the single grid.

[0025] Furthermore, when calculating the coverage radius of a single boat based on the area of a single grid, the actual boundary shape of the sea area and the distribution of navigation obstacles are taken into consideration to ensure that the operating range of each unmanned boat is continuous and covers each other, with no blind spots or overlaps. A safety buffer factor can be introduced in the calculation process to further optimize the rationality of coverage in the boundary transition area and ensure that the grid divided in a dynamic environment is stable and operable. The coverage radius calculation formula is expressed as:

[0026]

[0027] Among them, r work Indicates the effective coverage radius of a single boat operation; S grid represents the single grid area, which is determined by dividing the total sea area by the number of unmanned boats; α represents the safety redundancy factor, which is used to introduce compensation for coverage overlap and is between 0.05 and 0.20; β represents the environmental adjustment factor, which takes into account the impact of dynamic environments such as wind, waves and current speed on the actual expansion of the operating area; the safety buffer factor is expressed as:

[0028]

[0029] Among them, γ represents the safety buffer coefficient, k1 represents the drift compensation weight coefficient set by the system, k1∈[0.5, 1.0], k2 represents the obstacle compensation weight coefficient, k2∈[0.2, 0.5], v d Indicates the drift speed of the unmanned boat, v c Indicates the stable speed set under normal operating conditions, A o Indicates the proportion of navigation obstacles to the grid area, A grid Represents the area of a single grid.

[0030] It should be noted that allocating functional boats for different operations includes configuring signal boats, detection boats, maintenance boats and airspace extension boats according to signal coverage requirements, detection operation depth requirements, maintenance operation cycle requirements and airspace monitoring requirements.

[0031] It should be noted that according to the signal coverage requirements, detection operation depth requirements, maintenance operation cycle requirements and airspace monitoring requirements, including signal coverage requirements, signal attenuation areas and communication relay requirements, signal boats are configured to build a local communication network; detection operation depth requirements are determined according to changes in sea depth and geological complexity, and detection boats are configured to perform underwater target detection; maintenance operation cycle requirements are set according to the continuity of unmanned boat operations and equipment status, and maintenance boats are configured to perform periodic inspections, energy replenishment or rapid repairs; airspace monitoring requirements indicate the configuration of airspace extension boats equipped with drones or airspace sensors.

[0032] It should be noted that the separate configuration of signal boats, detection boats, maintenance boats and airspace extension boats includes the dynamic priority allocation of specialized unmanned boats with corresponding functions according to the geographical location, environmental characteristics and operation task weights of the grid, ensuring that each operation unit has the ability to complete multiple tasks independently, and the type and quantity of functional boats can be adjusted in time according to real-time changes in operations.

[0033] It should also be noted that by dynamically dividing the operation grid based on the total sea area and the number of unmanned boats and allocating functional boats in a targeted manner, it is beneficial to improve the flexibility of the division of unmanned boat operation areas, the targeted execution of tasks and the overall resource utilization.

[0034] S2: Collect the mission data of functional boats in real time, calculate the workload index by setting the weighted coefficient based on the type of functional boat, use the exponential relationship model to dynamically predict the remaining energy, and calculate the short-term load change trend.

[0035] Furthermore, the calculation of the workload index based on the weighted coefficient set based on the functional boat type includes weighting and accumulating the functional boat's track length, power consumption, number of tasks and environmental complexity according to the weighted coefficient corresponding to the functional boat category to generate a real-time workload index.

[0036] Furthermore, the weighted coefficient of the functional boat type setting is expressed as:

[0037]

[0038] in, represents the real-time workload index of the i-th functional boat, i represents the functional boat category, φ j represents the jth job load factor, ω i,j It represents the weight coefficient of the functional boat type i to the operation factor j, and is expressed by the exponential relationship model as follows:

[0039]

[0040] Among them, E r (t) represents the remaining energy of the functional boat at time t, E0 represents the total energy at the beginning of the operation, βi represents the energy consumption sensitivity coefficient related to the functional boat type, τ represents the operation time, It represents the real-time workload index of the functional boat at time τ.

[0041] It should be noted that the dynamic prediction of the remaining energy using the exponential relationship model includes estimating the remaining energy of the functional boat based on the real-time operating load index using the exponential relationship, so that the remaining energy shows an exponential downward trend as the load changes.

[0042] It should be noted that the exponential relationship factor is dynamically adjusted according to the load index, so that the energy forecast responds to the nonlinear characteristics of the change in operating load. When the operating pressure increases sharply or emergencies occur frequently, the energy warning level and scheduling strategy can be adjusted in time.

[0043] It should also be noted that estimating the short-term load change trend includes setting a sliding time window of fixed length based on a sliding time window, and predicting the future load level based on the load indicator change rate within the time window.

[0044] It should also be noted that by setting weighted load indicators based on functional boat operation data and combining exponential energy prediction with short-term load change calculation, it is beneficial to improve the dynamic accuracy of unmanned boat operation status monitoring, the rationality of energy use and the forward-looking prediction ability of operation risks.

[0045] S3: Based on the load prediction results and energy status, perform local grid boundary adjustment and responsible boat redistribution, dynamically calculate the priority of functional boats, and synchronize the status of functional boats based on the inter-boat communication link.

[0046] Furthermore, performing local grid boundary adjustment and responsible boat redistribution includes dynamically calculating the functional boat priority based on load prediction results and energy status, and dynamically adjusting the grid and synchronizing the functional boat load, energy, and priority status in real time through inter-boat communication links.

[0047] It should be noted that the priority calculation model comprehensively considers the current workload index, remaining energy status and the urgency level of the current workload, and is dynamically updated based on a weighted multi-factor evaluation mechanism to ensure that the functional boats that are most suitable for workload and resource conditions are given priority when allocating responsible boats. At the same time, the status changes of each functional boat are synchronized in real time through the communication link to avoid scheduling failure or resource conflicts due to information lag.

[0048] It should be noted that the dynamic update based on the weighted multi-factor evaluation mechanism includes the integration of the functional boat's real-time workload level, remaining energy status and the urgency level of the current task to construct a comprehensive priority score, which dynamically reflects the comprehensive capability of the functional boat to adapt to the task, expressed as:

[0049]

[0050] in, represents the comprehensive priority score, η1 represents the job load weight coefficient, represents the real-time workload index, η2 represents the remaining energy weight coefficient, E r (i) represents the remaining energy percentage of the functional boat, i represents the functional boat number, η3 represents the weight coefficient of the mission urgency level, ρ i The urgency level of the operational mission currently undertaken by functional boat i.

[0051] It should also be noted that the dynamic redistribution of grid responsibilities and synchronization of functional boat status based on load prediction and energy status is beneficial to improving the real-time responsibility division, scheduling decision consistency and system operation robustness of the unmanned boat group in a dynamic workload change environment.

[0052] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides a dynamic scheduling system for functional boats based on gridded sea area intelligent allocation, including an operation grid division and functional boat configuration module 100, a load prediction and energy management module 200, and a dynamic grid reconstruction and functional boat collaborative scheduling module 300.

[0053] Among them, S4: the operation grid division and functional boat configuration module 100 includes a dynamic grid division submodule 101 and a functional boat allocation submodule 102.

[0054] It should be noted that the dynamic grid division submodule 101 includes dynamically dividing the operation grid based on the total area of the sea area and the number of unmanned boats, calculating the operation coverage radius of a single boat, introducing safety buffer and environmental adjustment factors, and optimizing grid stability; the functional boat allocation submodule 102 includes intelligently allocating signal boats, detection boats, maintenance boats and airspace extension boats based on signal coverage, detection depth, maintenance cycle, and airspace monitoring requirements to ensure that each grid has complete operation functions.

[0055] It should also be noted that the dynamic grid division submodule 101 outputs grid information, which serves as the allocation basis for the functional boat allocation submodule 102 to achieve matching of space resources and boat type resources.

[0056] S5: The load prediction and energy management module 200 includes a multi-factor load modeling submodule 201 and an exponential energy prediction submodule 202.

[0057] It should be noted that the multi-factor load modeling submodule 201 includes real-time collection of the functional boat's track length, power consumption, number of tasks, and environmental complexity, and calculation of the functional boat's operational load index based on a weighted model; the exponential energy prediction submodule 202 includes the use of a load integral exponential relationship model to dynamically estimate the trend of the functional boat's remaining energy as the load accumulates, providing energy status support for scheduling.

[0058] It should also be noted that the multi-factor load modeling submodule 201 provides real-time load data, and the exponential energy prediction submodule 202 calculates the energy state based on load changes to support subsequent dynamic scheduling decisions.

[0059] S6: The dynamic grid reconstruction and functional boat collaborative scheduling module 300 includes a dynamic responsibility area adjustment submodule 301 and a state synchronization and collaboration submodule 302.

[0060] It should be noted that the dynamic responsibility area adjustment submodule 301 includes real-time adjustment of the operation grid boundary and responsibility boat allocation based on load trends and energy status, and dynamic calculation of functional boat priorities; the status synchronization and collaboration submodule 302 includes real-time synchronization of functional boat loads, energy, and priority status through inter-boat communication links to ensure consistency and efficient collaboration of unmanned boat cluster operations.

[0061] It should also be noted that the dynamic responsibility area adjustment submodule 301 completes the responsibility division and priority decision-making, and the status synchronization and collaboration submodule 302 distributes and synchronizes the status of each boat in real time, and updates in a closed loop.

[0062] 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 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments 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, and other media that can store program code.

[0063] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0064] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be 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.

[0065] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc. It should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention and are not intended to be limiting. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced with equivalents without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications should be encompassed by the claims of the present invention.

Claims

1. A method for dynamic scheduling of functional boats based on intelligent allocation of gridded sea areas, characterized in that: include: Divide the operation grid based on the total sea area and the number of unmanned boats, and assign functional boats to different operations; Real-time collection of functional boat mission data, calculation of operational load indicators based on weighted coefficients set based on functional boat types, dynamic prediction of remaining energy using an exponential relationship model, and estimation of short-term load change trends; Based on the load prediction results and energy status, local grid boundary adjustment and responsible boat redistribution are performed, the functional boat priority is dynamically calculated, and the functional boat status is synchronized based on the inter-boat communication link.

2. The method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation according to claim 1, characterized in that: The division of the operation grid based on the total sea area and the number of unmanned boats includes: Divide the total sea area by the number of unmanned boats to determine the area of a single grid, and calculate the single boat operation coverage radius based on the single grid area.

3. The method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation according to claim 1 or 2, characterized in that: The allocation of functional boats for different operations includes: According to the signal coverage requirements, detection operation depth requirements, maintenance operation cycle requirements and airspace monitoring requirements, signal boats, detection boats, maintenance boats and airspace extension boats are configured respectively.

4. The method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation according to claim 3 is characterized by: The calculation of the workload index by setting a weighted coefficient based on the functional boat type includes: The track length, power consumption, number of tasks and environmental complexity of the functional boat are weighted and accumulated according to the weight coefficient corresponding to the functional boat category to generate a real-time workload index.

5. The method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation according to any one of claims 1, 2 or 4, characterized in that: The dynamic prediction of the remaining energy using the exponential relationship model includes: Based on the real-time operational load index, the exponential relationship is applied to estimate the remaining energy of the functional boat, so that the remaining energy shows an exponential downward trend with the load change.

6. The method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation according to claim 5 is characterized by: The estimated short-term load change trend includes: Based on the sliding time window, a sliding time window of fixed length is set, and the future load level is predicted based on the load indicator change rate within the time window.

7. The method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation according to any one of claims 1, 2, 4 or 6, characterized in that: The execution of local grid boundary adjustment and responsible boat reallocation includes: The functional boat priority is dynamically calculated based on the load prediction results and energy status, and the functional boat load, energy and priority status are adjusted and synchronized in real time through the inter-boat communication link.

8. A dynamic scheduling system for functional boats based on intelligent allocation of gridded sea areas, characterized by: It includes an operation grid division and functional boat configuration module (100), a load prediction and energy management module (200), and a dynamic grid reconstruction and functional boat collaborative scheduling module (300); The operation grid division and functional boat configuration module (100) is used to divide the operation grid according to the total sea area and the number of unmanned boats, calculate the operation radius of a single boat, and allocate functional boats in combination with communication, detection, maintenance and airspace monitoring requirements; The load prediction and energy management module (200) is used to collect the track length, power consumption, number of tasks and environmental complexity of the functional boat, calculate the operating load index according to the boat type weight, predict the remaining energy based on the exponential relationship, and calculate the load change trend through a sliding time window; The dynamic grid reconstruction and functional boat collaborative scheduling module (300) is used to dynamically adjust the grid and the allocation of responsible boats based on load trends and energy states, calculate the priorities of functional boats, synchronize the operating states through inter-boat communication, and realize the collaborative operation and responsibility switching of multifunctional boats.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation according to any one of claims 1 to 7 are implemented.

10. 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 method for dynamic scheduling of functional boats based on gridded sea area intelligent allocation according to any one of claims 1 to 7 are implemented.

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